	<rdf:RDF xmlns:admin="http://webns.net/mvcb/" xmlns="http://purl.org/rss/1.0/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:prism="http://purl.org/rss/1.0/modules/prism/" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:syn="http://purl.org/rss/1.0/modules/syndication/">
	<channel rdf:about="https://biorxiv.org">
	<admin:errorReportsTo rdf:resource="mailto:biorxiv@cshlpress.edu"/>
	<title>bioRxiv Channel: NeurotechEU</title>
	<link>https://biorxiv.org</link>
	<description>
	This feed contains articles for bioRxiv Channel "NeurotechEU"
	</description>

		<items>
	<rdf:Seq>
		</rdf:Seq>
	</items>
	<prism:eIssn/>
	<prism:publicationName>bioRxiv</prism:publicationName>
	<prism:issn/>

	<image rdf:resource=""/>
	</channel>
	<image rdf:about="">
	<title>bioRxiv</title>
	<url/>
	<link>https://biorxiv.org</link>
	</image>
	<item rdf:about="https://biorxiv.org/cgi/content/short/2021.02.15.431309v1?rss=1">
<title>
<![CDATA[
Audiovisual adaptation is expressed in spatial and decisional codes 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.02.15.431309v1?rss=1"
</link>
<description><![CDATA[
The brain adapts dynamically to the changing sensory statistics of its environment. The neural circuitries and representations that support this cross-sensory plasticity remain unknown. We combined psychophysics and model-based representational fMRI and EEG to characterize how the adult human brain adapts to misaligned audiovisual signals. We show that audiovisual adaptation moulds regional BOLD-responses and fine-scale activity patterns in a widespread network from Heschls gyrus to dorsolateral prefrontal cortices. Crucially, audiovisual recalibration relies on distinct spatial and decisional codes that are expressed with opposite gradients and timecourses across the auditory processing hierarchy. Early activity patterns in auditory cortices encode sounds in a continuous space that flexibly adapts to misaligned visual inputs. Later activity patterns in frontoparietal cortices code decisional uncertainty consistent with these spatial transformations. Our findings demonstrate that regions throughout the auditory processing hierarchy multiplex spatial and decisional codes to adapt flexibly to the changing sensory statistics in the environment.
]]></description>
<dc:creator>Aller, M.</dc:creator>
<dc:creator>Mihalik, A.</dc:creator>
<dc:creator>Noppeney, U.</dc:creator>
<dc:date>2021-02-17</dc:date>
<dc:identifier>doi:10.1101/2021.02.15.431309</dc:identifier>
<dc:title><![CDATA[Audiovisual adaptation is expressed in spatial and decisional codes]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-02-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.05.08.083758v1?rss=1">
<title>
<![CDATA[
Differential functional neural circuitry behind autism subtypes with marked imbalance between social-communicative and restricted repetitive behavior symptom domains 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.05.08.083758v1?rss=1"
</link>
<description><![CDATA[
Social-communication (SC) and restricted repetitive behaviors (RRB) are autism diagnostic symptom domains. SC and RRB severity can markedly differ within and between individuals and may be underpinned by different neural circuitry and genetic mechanisms. Modeling SC-RRB balance could help identify how neural circuitry and genetic mechanisms map onto such phenotypic heterogeneity. Here we developed a phenotypic stratification model that makes highly accurate (97-99%) out-of-sample SC=RRB, SC>RRB, and RRB>SC subtype predictions. Applying this model to resting state fMRI data from the EU-AIMS LEAP dataset (n=509), we find that while the phenotypic subtypes share many commonalities in terms of intrinsic functional connectivity, they also show replicable differences within some networks compared to a typically-developing group (TD). Specifically, the somatomotor network is hypoconnected with perisylvian circuitry in SC>RRB and visual association circuitry in SC=RRB. The SC=RRB subtype show hyperconnectivity between medial motor and anterior salience circuitry. Genes that are highly expressed within these networks show a differential enrichment pattern with known autism-associated genes, indicating that such circuits are affected by differing autism-associated genomic mechanisms. These results suggest that SC-RRB imbalance subtypes share many commonalities, but also express subtle differences in functional neural circuitry and the genomic underpinnings behind such circuitry.
]]></description>
<dc:creator>Bertelsen, N.</dc:creator>
<dc:creator>Landi, I.</dc:creator>
<dc:creator>Bethlehem, R. A. I.</dc:creator>
<dc:creator>Seidlitz, J.</dc:creator>
<dc:creator>Busuoli, E. M.</dc:creator>
<dc:creator>Mandelli, V.</dc:creator>
<dc:creator>Satta, E.</dc:creator>
<dc:creator>Trakoshis, S.</dc:creator>
<dc:creator>Auyeung, B.</dc:creator>
<dc:creator>Kundu, P.</dc:creator>
<dc:creator>Loth, E.</dc:creator>
<dc:creator>Dumas, G.</dc:creator>
<dc:creator>Baumeister, S.</dc:creator>
<dc:creator>Beckmann, C. F.</dc:creator>
<dc:creator>Bolte, S.</dc:creator>
<dc:creator>Bourgeron, T.</dc:creator>
<dc:creator>Charman, T.</dc:creator>
<dc:creator>Durston, S.</dc:creator>
<dc:creator>Ecker, C.</dc:creator>
<dc:creator>Holt, R.</dc:creator>
<dc:creator>Johnson, M. H.</dc:creator>
<dc:creator>Jones, E. J. H.</dc:creator>
<dc:creator>Mason, L.</dc:creator>
<dc:creator>Meyer-Lindenberg, A.</dc:creator>
<dc:creator>Moessnang, C.</dc:creator>
<dc:creator>Oldehinkel, M.</dc:creator>
<dc:creator>Persico, A.</dc:creator>
<dc:creator>Tillmann, J.</dc:creator>
<dc:creator>Williams, S. C. R.</dc:creator>
<dc:creator>Spooren, W.</dc:creator>
<dc:creator>Murphy, D. G. M.</dc:creator>
<dc:creator>Buitelaar, J. K.</dc:creator>
<dc:creator>EU-AIMS LEAP group,</dc:creator>
<dc:creator>Baron-Cohen, S.</dc:creator>
<dc:creator>Lai, M.-C.</dc:creator>
<dc:creator>Lombardo, M. V.</dc:creator>
<dc:date>2020-05-10</dc:date>
<dc:identifier>doi:10.1101/2020.05.08.083758</dc:identifier>
<dc:title><![CDATA[Differential functional neural circuitry behind autism subtypes with marked imbalance between social-communicative and restricted repetitive behavior symptom domains]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-05-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.02.05.429886v1?rss=1">
<title>
<![CDATA[
Reducing the efforts to create reproducible analysis code with FieldTrip 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.02.05.429886v1?rss=1"
</link>
<description><![CDATA[
The analysis of EEG and MEG data typically requires a lengthy and complicated sequence of analysis steps, often requiring large amounts of computations, which are ideally represented in analysis scripts. These scripts are often written by researchers without formal training in computer science, resulting in the quality and readability of these analysis scripts to be highly dependent on individual coding expertise and style. Even though the computational outcomes and interpretation of the results can be correct, the inconsistent style and quality of analysis scripts make reviewing the details of the analysis difficult for other researchers that are either involved in the study or not, and the quality of the scripts might compromise the reproducibility of obtained results. This paper describes the design and implementation of a strategy that allows complete reproduction of MATLAB-based scripts with little extra efforts on behalf of the user, which we have implemented as part of the FieldTrip toolbox. Starting from the researchers idiosyncratic pipeline scripts, this new functionality allows researchers to automatically create and publish analysis pipeline scripts in a standardized format, along with all relevant intermediate data. We demonstrate the functionality and validate its effectiveness by applying it to the analysis of a recently published MEG study.
]]></description>
<dc:creator>van Es, M. W. J.</dc:creator>
<dc:creator>Spaak, E.</dc:creator>
<dc:creator>Schoffelen, J.-M.</dc:creator>
<dc:creator>Oostenveld, R.</dc:creator>
<dc:date>2021-02-08</dc:date>
<dc:identifier>doi:10.1101/2021.02.05.429886</dc:identifier>
<dc:title><![CDATA[Reducing the efforts to create reproducible analysis code with FieldTrip]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-02-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.02.12.430956v1?rss=1">
<title>
<![CDATA[
Rapid processing and quantitative evaluation of multicontrast EPImix scans for adaptive multimodal imaging 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.02.12.430956v1?rss=1"
</link>
<description><![CDATA[
Current neuroimaging acquisition and processing approaches tend to be optimised for quality rather than speed. However, rapid acquisition and processing of neuroimaging data can lead to novel neuroimaging paradigms, such as adaptive acquisition, where rapidly processed data is used to inform subsequent image acquisition steps. Here we first evaluate the impact of several processing steps on the processing time and quality of registration of manually labelled T1-weighted MRI scans. Subsequently, we apply the selected rapid processing pipeline both to rapidly acquired multicontrast EPImix scans of 95 participants (which include T1-FLAIR, T2, T2*, T2-FLAIR, DWI & ADC contrasts, acquired in [~]1 minute), as well as to slower, more standard single-contrast T1-weighted scans of a subset of 66 participants. We quantify the correspondence between EPImix and single-contrast T1-weighted scans, using correlations between voxels and regions of interest across participants, measures of within- and between-participant identifiability as well as regional structural covariance networks. Furthermore, we explore the use of EPImix for the rapid construction of morphometric similarity networks. Finally, we quantify the reliability of EPImix-derived data using test-retest scans of 10 participants. Our results demonstrate that quantitative information can be derived from a neuroimaging scan acquired and processed within minutes, which could further be used to implement adaptive multimodal imaging and tailor neuroimaging examinations to individual patients.



O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=77 SRC="FIGDIR/small/430956v1_ufig1.gif" ALT="Figure 1">
View larger version (33K):
org.highwire.dtl.DTLVardef@3ae0e9org.highwire.dtl.DTLVardef@1840d39org.highwire.dtl.DTLVardef@80339borg.highwire.dtl.DTLVardef@bc16cf_HPS_FORMAT_FIGEXP  M_FIG Graphical abstract.

C_FIG
]]></description>
<dc:creator>Vasa, F.</dc:creator>
<dc:creator>Hobday, H.</dc:creator>
<dc:creator>Stanyard, R. A.</dc:creator>
<dc:creator>Daws, R. E.</dc:creator>
<dc:creator>Giampietro, V.</dc:creator>
<dc:creator>ODaly, O.</dc:creator>
<dc:creator>Lythgoe, D. J.</dc:creator>
<dc:creator>Seidlitz, J.</dc:creator>
<dc:creator>Skare, S.</dc:creator>
<dc:creator>Williams, S. C. R.</dc:creator>
<dc:creator>Marquand, A. F.</dc:creator>
<dc:creator>Leech, R.</dc:creator>
<dc:creator>Cole, J. H.</dc:creator>
<dc:date>2021-02-16</dc:date>
<dc:identifier>doi:10.1101/2021.02.12.430956</dc:identifier>
<dc:title><![CDATA[Rapid processing and quantitative evaluation of multicontrast EPImix scans for adaptive multimodal imaging]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-02-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/854927v1?rss=1">
<title>
<![CDATA[
Genetic Association Study of Childhood Aggression across raters, instruments and age 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/854927v1?rss=1"
</link>
<description><![CDATA[
Childhood aggressive behavior (AGG) has a substantial heritability of around 50%. Here we present a genome-wide association meta-analysis (GWAMA) of childhood AGG, in which all phenotype measures across childhood ages from multiple assessors were included. We analyzed phenotype assessments for a total of 328 935 observations from 87 485 children aged between 1.5 and 18 years, while accounting for sample overlap. We also meta-analyzed within subsets of the data - i.e. within rater, instrument and age. SNP-heritability for the overall meta-analysis (AGGoverall) was 3.31% (SE=0.0038). We found no genome-wide significant SNPs for AGGoverall. The gene-based analysis returned three significant genes: ST3GAL3 (P=1.6E-06), PCDH7 (P=2.0E-06) and IPO13 (P=2.5E-06). All three genes have previously been associated with educational traits. Polygenic scores based on our GWAMA significantly predicted aggression in a holdout sample of children (variance explained = 0.44%) and in retrospectively assessed childhood aggression (variance explained = 0.20%). Genetic correlations (rg) among rater-specific assessment of AGG ranged from rg =0.46 between self- and teacher-assessment to rg =0.81 between mother- and teacher-assessment. We obtained moderate to strong rgs with selected phenotypes from multiple domains, but hardly with any of the classical biomarkers thought to be associated with AGG. Significant genetic correlations were observed with most psychiatric and psychological traits (range |rg| : 0.19 - 1.00), except for obsessive-compulsive disorder. Aggression had a negative genetic correlation (rg =~ -0.5) with cognitive traits and age at first birth. Aggression was strongly genetically correlated with smoking phenotypes (range |rg| : 0.46 - 0.60). The genetic correlations between aggression and psychiatric disorders were weaker for teacher-reported AGG than for mother- and self-reported AGG. The current GWAMA of childhood aggression provides a powerful tool to interrogate the rater-specific genetic etiology of AGG.
]]></description>
<dc:creator>Ip, H. F.</dc:creator>
<dc:creator>van der Laan, C. M.</dc:creator>
<dc:creator>Brikell, I.</dc:creator>
<dc:creator>Sanchez-Mora, C.</dc:creator>
<dc:creator>Nolte, I. M.</dc:creator>
<dc:creator>St Pourcain, B.</dc:creator>
<dc:creator>Bolhuis, K.</dc:creator>
<dc:creator>Palviainen, T.</dc:creator>
<dc:creator>Zafarmand, H.</dc:creator>
<dc:creator>Colodro-Conde, L.</dc:creator>
<dc:creator>Gordon, S.</dc:creator>
<dc:creator>Zayats, T.</dc:creator>
<dc:creator>Aliev, F.</dc:creator>
<dc:creator>Jiang, C.</dc:creator>
<dc:creator>Wang, C. A.</dc:creator>
<dc:creator>Saunders, G.</dc:creator>
<dc:creator>Karhunen, V.</dc:creator>
<dc:creator>Hammerschlag, A. R.</dc:creator>
<dc:creator>Adkins, D. E.</dc:creator>
<dc:creator>Border, R.</dc:creator>
<dc:creator>Peterson, R. E.</dc:creator>
<dc:creator>Prinz, J. A.</dc:creator>
<dc:creator>Thiering, E.</dc:creator>
<dc:creator>Seppälä, I.</dc:creator>
<dc:creator>Vilor-Tejedor, N.</dc:creator>
<dc:creator>Ahluwalia, T. S.</dc:creator>
<dc:creator>Day, F. R.</dc:creator>
<dc:creator>Hottenga, J.-J.</dc:creator>
<dc:creator>Allegrini, A. G.</dc:creator>
<dc:creator>Krapohl, E. M. L.</dc:creator>
<dc:creator>Rimfeld, K.</dc:creator>
<dc:creator>Chen, Q.</dc:creator>
<dc:creator>Lu, Y.</dc:creator>
<dc:creator>Martin, J.</dc:creator>
<dc:creator>Soler Artigas, M.</dc:creator>
<dc:creator>Rovira, P.</dc:creator>
<dc:creator>Bosch, R.</dc:creator>
<dc:creator>Espanol, G.</dc:creator>
<dc:creator>Ramos Quiroga, J. A.</dc:creator>
<dc:creator>Neumann, A.</dc:creator>
<dc:creator>Ensink, J.</dc:creator>
<dc:creator></dc:creator>
<dc:date>2019-11-29</dc:date>
<dc:identifier>doi:10.1101/854927</dc:identifier>
<dc:title><![CDATA[Genetic Association Study of Childhood Aggression across raters, instruments and age]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-11-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.01.25.428068v1?rss=1">
<title>
<![CDATA[
Induction of peri-implantation stage synthetic embryos using reprogramming paradigms in ESCs 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.01.25.428068v1?rss=1"
</link>
<description><![CDATA[
Blastocyst-derived stem cell lines were shown to self-organize into embryo-like structures in 3D cell culture environments. Here, we provide evidence that synthetic embryo-like structures are generated solely based on transcription factor-mediated molecular reprogramming of embryonic stem cells in a simple 3D co-culture system. ESCs in these cultures self-organize into elongated, compartmentalized synthetic embryo-like structures over the course of reprogramming exhibiting anterior visceral endoderm formation and symmetry breaking. Single-cell RNA-Seq reveals transcriptional profiles resembling epiblast, visceral endoderm, and extraembryonic ectoderm of early murine embryos around E4.5-E5.5. Within the epiblast, compartment marker gene expression supports primordial germ cell specification. After transplantation, synthetic embryo-like structures implant in uteri and initiate the formation of decidual tissues. This system allows for fast and reproducible generation of synthetic embryo-like structures, providing further insights into synthetic embryology.
]]></description>
<dc:creator>Langkabel, J.</dc:creator>
<dc:creator>Horne, A.</dc:creator>
<dc:creator>Bonaguro, L.</dc:creator>
<dc:creator>Hesse, T.</dc:creator>
<dc:creator>Knaus, A.</dc:creator>
<dc:creator>Riedel, Y.</dc:creator>
<dc:creator>Händler, K.</dc:creator>
<dc:creator>Bassler, K.</dc:creator>
<dc:creator>Reusch, N.</dc:creator>
<dc:creator>Yeghiazarian, L. H.</dc:creator>
<dc:creator>Pecht, T.</dc:creator>
<dc:creator>Aschenbrenner, A. C.</dc:creator>
<dc:creator>Kaiser, F.</dc:creator>
<dc:creator>Kubaczka, C.</dc:creator>
<dc:creator>Schultze, J. L.</dc:creator>
<dc:creator>Schorle, H.</dc:creator>
<dc:date>2021-01-25</dc:date>
<dc:identifier>doi:10.1101/2021.01.25.428068</dc:identifier>
<dc:title><![CDATA[Induction of peri-implantation stage synthetic embryos using reprogramming paradigms in ESCs]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-01-25</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.03.10.434856v1?rss=1">
<title>
<![CDATA[
Non-linearity matters: a deep learning solution to the generalization of hidden brain patterns across population cohorts 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.03.10.434856v1?rss=1"
</link>
<description><![CDATA[
Finding an interpretable and compact representation of complex neuroimage data can be extremely useful for understanding brain behavioral mapping and hence for explaining the biological underpinnings of mental disorders. Hand-crafted representations, as well as linear transformations, may not accurately reflect the significant variability across individuals. Here, we applied a data-driven approach to learn interpretable and generalizable latent representations that link cognition with underlying brain systems; we applied a three-dimensional autoencoder to two large-scale datasets to find an interpretable latent representation of high dimensional task fMRI image data. This representation also accounts for demographic characteristics, achieved by solving a joint optimization problem that simultaneously reconstructs the data and predicts clinical or demographic variables. We then applied normative modeling to the latent variables to define summary statistics ( latent indices) to find a multivariate mapping to non-imaging measures. We trained our model with multi-task fMRI data derived from the Human Connectome Project (HCP) that provides whole-brain coverage across a range of cognitive tasks. Next, in a transfer learning setting, we tested the generalization of our latent space on UK Biobank data as an independent dataset. Our model showed high performance in terms of age and predictions and was capable of capturing complex behavioral characteristics and preserving the individualized variabilities using a highly interpretable latent representation.
]]></description>
<dc:creator>Zabihi, M.</dc:creator>
<dc:creator>Kia, S. M.</dc:creator>
<dc:creator>Wolfers, T.</dc:creator>
<dc:creator>Dinga, R.</dc:creator>
<dc:creator>Llera, A.</dc:creator>
<dc:creator>Bzdok, D.</dc:creator>
<dc:creator>Beckmann, C.</dc:creator>
<dc:creator>marquand, A.</dc:creator>
<dc:date>2021-03-14</dc:date>
<dc:identifier>doi:10.1101/2021.03.10.434856</dc:identifier>
<dc:title><![CDATA[Non-linearity matters: a deep learning solution to the generalization of hidden brain patterns across population cohorts]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-03-14</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.02.15.431231v1?rss=1">
<title>
<![CDATA[
Shared genetic influences on resting-state functional networks of the brain 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.02.15.431231v1?rss=1"
</link>
<description><![CDATA[
The amplitude of activation in brain resting state networks (RSNs), measured with resting-state functional MRI, is heritable and genetically correlated across RSNs, indicating pleiotropy. Recent univariate genome-wide association studies (GWAS) explored the genetic underpinnings of individual variation in RSN activity. Yet univariate genomic analyses do not describe the pleiotropic nature of RSNs. In this study we used a novel multivariate method called genomic SEM to model latent factors that capture the shared genomic influence on RSNs and to identify SNPs and genes driving this pleiotropy. Using summary statistics from GWAS of 21 RSNs reported in UK Biobank (N = 31,688), the genomic latent factor analysis was first conducted in a discovery sample (N = 21,081), and then tested in an independent sample from the same cohort (N = 10,607). In the discovery sample, we show that the genetic organization of RSNs can be best explained by two distinct but correlated genetic factors that divide multimodal association networks and sensory networks. Eleven of the 17 factor loadings were replicated in the independent sample. With the multivariate GWAS, we found and replicated nine independent SNPs associated with the joint architecture of RSNs. Further, by combining the discovery and replication samples, we discovered additional SNP and gene associations with the two factors of RSN amplitude. We conclude that modelling the genetic effects on brain function in a multivariate way is a powerful approach to learn more about the biological mechanisms involved in brain function.
]]></description>
<dc:creator>Guimaraes, J. P. O. F. T.</dc:creator>
<dc:creator>Sprooten, E.</dc:creator>
<dc:creator>Beckmann, C. F.</dc:creator>
<dc:creator>Franke, B.</dc:creator>
<dc:creator>Bralten, J.</dc:creator>
<dc:date>2021-02-15</dc:date>
<dc:identifier>doi:10.1101/2021.02.15.431231</dc:identifier>
<dc:title><![CDATA[Shared genetic influences on resting-state functional networks of the brain]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-02-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.03.18.436018v1?rss=1">
<title>
<![CDATA[
Personalized Closed-Loop Brain Stimulation for Effective Neurointervention Across Participants 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.03.18.436018v1?rss=1"
</link>
<description><![CDATA[
Accumulating evidence from human-based research has highlighted that the prevalent one-size-fits-all approach for neural and behavioral interventions is inefficient. This approach can benefit one individual, but be ineffective or even detrimental for another. Studying the efficacy of the large range of different parameters for different individuals is costly, time-consuming and requires a large sample size that makes such research impractical and hinders effective interventions. Here an active machine learning technique is presented across participants--personalized Bayesian optimization (pBO)--that searches available parameter combinations to optimize an intervention as a function of an individuals ability. This novel technique was utilized to identify transcranial alternating current stimulation frequency and current strength combinations most likely to improve arithmetic performance, based on a subjects baseline arithmetic abilities. The pBO was performed across all subjects tested, building a model of subject performance, capable of recommending parameters for future subjects based on their baseline arithmetic ability. pBO successfully searches, learns, and recommends parameters for an effective neurointervention as supported by behavioral, stimulation, and neural data. The application of pBO in human-based research opens up new avenues for personalized and more effective interventions, as well as discoveries of protocols for treatment and translation to other clinical and non-clinical domains.
]]></description>
<dc:creator>van Bueren, N.</dc:creator>
<dc:creator>Reed, T.</dc:creator>
<dc:creator>Nguyen, V.</dc:creator>
<dc:creator>Sheffield, J.</dc:creator>
<dc:creator>van der Ven, S.</dc:creator>
<dc:creator>Osborne, M.</dc:creator>
<dc:creator>Kroesbergen, E.</dc:creator>
<dc:creator>Cohen Kadosh, R.</dc:creator>
<dc:date>2021-03-18</dc:date>
<dc:identifier>doi:10.1101/2021.03.18.436018</dc:identifier>
<dc:title><![CDATA[Personalized Closed-Loop Brain Stimulation for Effective Neurointervention Across Participants]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-03-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.01.29.428809v1?rss=1">
<title>
<![CDATA[
Defensive freezing and its relation to approach-avoidance decision-making under threat 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.01.29.428809v1?rss=1"
</link>
<description><![CDATA[
Successful responding to acutely threatening situations requires adequate approach-avoidance decisions. However, it is unclear how threat-induced states-like freezing-related bradycardia-impact the weighing of the potential outcomes of such value-based decisions. Insight into the underlying computations is essential, not only to improve our models of decision-making but also to improve interventions for maladaptive decisions, for instance in anxiety patients and first-responders who frequently have to make decisions under acute threat. Forty-two participants made passive and active approach-avoidance decisions under threat-of-shock when confronted with mixed outcome-prospects (i.e., varying money and shock amounts). Choice behavior was best predicted by a model including individual action-tendencies and bradycardia, beyond the subjective value of the outcome. Moreover, threat-related bradycardia interacted with subjective value, depending on the action-context (i.e., passive vs. active). Specifically, in action-contexts incongruent with participants intrinsic action-tendencies, strong freezers showed diminished effects of subjective value on choice. These findings illustrate the relevance of testing approach-avoidance decisions in relatively ecologically valid conditions of acute and primarily reinforced threat. These mechanistic insights into approach-avoidance conflict-resolution may inspire biofeedback-related techniques to optimize decision-making under threat. Critically, the findings demonstrate the relevance of incorporating internal psychophysiological states and external action-contexts into models of approach-avoidance decision-making.
]]></description>
<dc:creator>Klaassen, F. H.</dc:creator>
<dc:creator>Held, L.</dc:creator>
<dc:creator>Figner, B.</dc:creator>
<dc:creator>O'Reilly, J. X.</dc:creator>
<dc:creator>Klumpers, F.</dc:creator>
<dc:creator>de Voogd, L. D.</dc:creator>
<dc:creator>Roelofs, K.</dc:creator>
<dc:date>2021-02-01</dc:date>
<dc:identifier>doi:10.1101/2021.01.29.428809</dc:identifier>
<dc:title><![CDATA[Defensive freezing and its relation to approach-avoidance decision-making under threat]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-02-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.02.08.428915v1?rss=1">
<title>
<![CDATA[
Brain age relates to early life factors but not to accelerated brain aging 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.02.08.428915v1?rss=1"
</link>
<description><![CDATA[
Brain age is a widely used index for quantifying individuals brain health as deviation from a normative brain aging trajectory. Higher than expected brain age is thought partially to reflect above-average rate of brain aging. We explicitly tested this assumption in two large datasets and found no association between cross-sectional brain age and steeper brain decline measured longitudinally. Rather, brain age in adulthood was associated with early-life influences indexed by birth weight and polygenic scores. The results call for nuanced interpretations of cross-sectional indices of the aging brain and question their validity as markers of ongoing within-person changes of the aging brain. Longitudinal imaging data should be preferred whenever the goal is to understand individual change trajectories of brain and cognition in aging.
]]></description>
<dc:creator>Vidal-Pineiro, D.</dc:creator>
<dc:creator>Wang, Y.</dc:creator>
<dc:creator>Krogsrud, S. K.</dc:creator>
<dc:creator>Amlien, I. K.</dc:creator>
<dc:creator>Baare, W. F.</dc:creator>
<dc:creator>Bartres-Faz, D.</dc:creator>
<dc:creator>Bertram, L.</dc:creator>
<dc:creator>Brandmaier, A. M.</dc:creator>
<dc:creator>Drevon, C. A.</dc:creator>
<dc:creator>Duzel, S.</dc:creator>
<dc:creator>Ebmeier, K. P.</dc:creator>
<dc:creator>Henson, R. N.</dc:creator>
<dc:creator>Junque, C.</dc:creator>
<dc:creator>Kievit, R.</dc:creator>
<dc:creator>Kuhn, S.</dc:creator>
<dc:creator>Leonardsen, E.</dc:creator>
<dc:creator>Lindenberger, U.</dc:creator>
<dc:creator>Madsen, K. S.</dc:creator>
<dc:creator>Magnussen, F.</dc:creator>
<dc:creator>Mowinckel, A. M.</dc:creator>
<dc:creator>Nyberg, L.</dc:creator>
<dc:creator>Roe, J. M.</dc:creator>
<dc:creator>Segura, B.</dc:creator>
<dc:creator>Sorensen, O.</dc:creator>
<dc:creator>Suri, S.</dc:creator>
<dc:creator>Zsoldos, E.</dc:creator>
<dc:creator>AIBL,</dc:creator>
<dc:creator>Walhovd, K. B.</dc:creator>
<dc:creator>Fjell, A. M.</dc:creator>
<dc:date>2021-02-08</dc:date>
<dc:identifier>doi:10.1101/2021.02.08.428915</dc:identifier>
<dc:title><![CDATA[Brain age relates to early life factors but not to accelerated brain aging]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-02-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.01.22.427803v1?rss=1">
<title>
<![CDATA[
Social prediction modulates activity of macaque superior temporal cortex 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.01.22.427803v1?rss=1"
</link>
<description><![CDATA[
The ability to attribute thoughts to others, also called theory of mind (TOM), has been extensively studied. Computationally, the basis of TOM in humans has been interpreted within the predictive coding framework and associated with activity in the temporo-parietal junction (TPJ). However, the evolutionary origins of these human mindreading abilities have been challenged since the concept was coined. Here we identify a brain region in the Rhesus macaque that shares computational properties with the human TPJ. We revealed, using a non-linguistic task and functional magnetic resonance imaging, that activity in a region of the macaque middle superior temporal cortex was specifically modulated by the predictability of social interactions. As in human TPJ, this region could be distinguished from other temporal regions involved in face processing. Our result suggests the existence of a precursor for the theory of mind ability in the last common ancestor of human and old-world monkeys.
]]></description>
<dc:creator>Roumazeilles, L.</dc:creator>
<dc:creator>Schurz, M.</dc:creator>
<dc:creator>Lojkiewiez, M.</dc:creator>
<dc:creator>Verhagen, L.</dc:creator>
<dc:creator>Schuffelgen, U.</dc:creator>
<dc:creator>Marche, K.</dc:creator>
<dc:creator>Mahmoodi, A.</dc:creator>
<dc:creator>Emberton, A.</dc:creator>
<dc:creator>Simpson, K.</dc:creator>
<dc:creator>Joly, O.</dc:creator>
<dc:creator>Khamassi, M.</dc:creator>
<dc:creator>Rushworth, M. F.</dc:creator>
<dc:creator>Mars, R. B.</dc:creator>
<dc:creator>Sallet, J.</dc:creator>
<dc:date>2021-01-23</dc:date>
<dc:identifier>doi:10.1101/2021.01.22.427803</dc:identifier>
<dc:title><![CDATA[Social prediction modulates activity of macaque superior temporal cortex]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-01-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.02.19.431732v1?rss=1">
<title>
<![CDATA[
Individual variation in brain microstructural-cognition relationships in aging 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.02.19.431732v1?rss=1"
</link>
<description><![CDATA[
The sources of inter- and intra-individual variability in age-related cognitive decline remain poorly understood. We examined the association between 20-year trajectories of cognitive decline and multimodal brain structure and morphology in older age. We used the Whitehall II Study, an extensively characterised cohort with 3T brain magnetic resonance images acquired at older age (mean age = 69.52{+/-} 4.9) and 5 repeated cognitive performance assessments between mid-life (mean age = 53.2 {+/-}4.9 years) and late-life (mean age = 67.7 {+/-}4.9). Using non-negative matrix factorization, we identified 10 brain components integrating cortical thickness, surface area, fractional anisotropy, and mean and radial diffusivities. We observed two latent variables describing distinct brain-cognition associations. The first describes variations in 5 structural components associated with low mid-life performance across multiple cognitive domains, decline in reasoning, but maintenance of fluency abilities. The second describes variations in 6 structural components associated with low mid-life performance in fluency and memory, but retention of multiple abilities. Expression of latent variables predicts future cognition 3.2 years later (mean age = 70.87 {+/-}4.9). This data-driven approach highlights brain-cognition relationships wherein individuals degrees of cognitive decline and maintenance across diverse cognitive functions that are both positively and negatively associated with cortical structure.
]]></description>
<dc:creator>Patel, R.</dc:creator>
<dc:creator>Mackay, C. E.</dc:creator>
<dc:creator>Jansen, M. G.</dc:creator>
<dc:creator>Devenyi, G.</dc:creator>
<dc:creator>O'Donoghue, M. C.</dc:creator>
<dc:creator>Kivimäki, M.</dc:creator>
<dc:creator>Singh-Manoux, A.</dc:creator>
<dc:creator>Zsoldos, E.</dc:creator>
<dc:creator>Ebmeier, K. P.</dc:creator>
<dc:creator>Chakravarty, M.</dc:creator>
<dc:creator>Suri, S.</dc:creator>
<dc:date>2021-02-20</dc:date>
<dc:identifier>doi:10.1101/2021.02.19.431732</dc:identifier>
<dc:title><![CDATA[Individual variation in brain microstructural-cognition relationships in aging]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-02-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.02.23.432416v1?rss=1">
<title>
<![CDATA[
Cross-sectional analysis of the microbiota of the human gut and their direct environment (exposome) in a household cohort in northern Vietnam. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.02.23.432416v1?rss=1"
</link>
<description><![CDATA[
Comprehensive insight into the human gut microbiota and the interaction with their environment in communities with a high background of antibiotic use and antibiotic resistance genes is currently largely lacking. In a cohort (Vietnam), individuals within the same household, also individuals within their geographical cluster share more bacterial taxa than individuals from different households or geographical clusters. The microbial diversity among individuals who used antibiotics in the past four months was significantly lower than those who did not. Fecal microbiota of humans was more diverse than non-human samples, shared a small part of its amplicon sequence variants (ASVs) with feces from animals (7.4%), water (2.2%) and food (3.1%). Sharing of ASVs between humans and companion animals was not associated with household. There is a correlation between an Enterobacteriaceae ASV and the presence of blactx-m-2 in feces from humans and animals, hinting towards an exchange of antimicrobial resistant strains between reservoirs.
]]></description>
<dc:creator>Vu Thi Ngoc, B.</dc:creator>
<dc:creator>Ho Bich, H.</dc:creator>
<dc:creator>Galazzo, G.</dc:creator>
<dc:creator>Vu Tien Viet, D.</dc:creator>
<dc:creator>Oomen, M.</dc:creator>
<dc:creator>Nghiem Nguyen Minh, T.</dc:creator>
<dc:creator>Tran Huy, H.</dc:creator>
<dc:creator>Van Doorn, R.</dc:creator>
<dc:creator>Wertheim, H. F.</dc:creator>
<dc:creator>Penders, J.</dc:creator>
<dc:date>2021-02-23</dc:date>
<dc:identifier>doi:10.1101/2021.02.23.432416</dc:identifier>
<dc:title><![CDATA[Cross-sectional analysis of the microbiota of the human gut and their direct environment (exposome) in a household cohort in northern Vietnam.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-02-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.03.20.436248v1?rss=1">
<title>
<![CDATA[
The within-host evolutionary dynamics of seasonal and pandemic human influenza A viruses in young children 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.03.20.436248v1?rss=1"
</link>
<description><![CDATA[
The evolution of influenza viruses is fundamentally shaped by within-host processes. However, the within-host evolutionary dynamics of influenza viruses remain incompletely understood, in part because most studies have focused on within-host virus diversity of infections in otherwise healthy adults based on single timepoint data. Here, we analysed the within-host evolution of 82 longitudinally-sampled individuals, mostly young children, infected with A/H3N2 or A/H1N1pdm09 viruses between 2007 and 2009. For A/H1N1pdm09 infections during the 2009 pandemic, nonsynonymous changes were common early in infection but decreased or remained constant throughout infection. For A/H3N2 viruses, early infection was dominated by purifying selection. However, as infections progressed, nonsynonymous variants increased in frequencies even though within-host virus titres decreased, leading to the maintenance of virus diversity via mutation-selection balance. Our findings suggest that this maintenance of genetic diversity in these children combined with their longer duration of infection may provide important opportunities for within-host virus evolution.
]]></description>
<dc:creator>Han, A. X.</dc:creator>
<dc:creator>Felix Garza, Z. C.</dc:creator>
<dc:creator>Welkers, M. R. A.</dc:creator>
<dc:creator>Vigeveno, R. M.</dc:creator>
<dc:creator>Tran, N. D.</dc:creator>
<dc:creator>Le, T. Q. M.</dc:creator>
<dc:creator>Pham, Q. T.</dc:creator>
<dc:creator>Tran, T. N. A.</dc:creator>
<dc:creator>Ha, M. T.</dc:creator>
<dc:creator>Nguyen, T. H.</dc:creator>
<dc:creator>Le, Q. T.</dc:creator>
<dc:creator>Le, T. H.</dc:creator>
<dc:creator>Hoang, T. B. N.</dc:creator>
<dc:creator>Chokephaibulkit, K.</dc:creator>
<dc:creator>Puthavathana, P.</dc:creator>
<dc:creator>Nguyen, V. V. C.</dc:creator>
<dc:creator>Nghiem, M. N.</dc:creator>
<dc:creator>Tran, T. H.</dc:creator>
<dc:creator>Wertheim, H. F. L.</dc:creator>
<dc:creator>Horby, P.</dc:creator>
<dc:creator>Fox, A.</dc:creator>
<dc:creator>van Doorn, H. R.</dc:creator>
<dc:creator>Eggink, D.</dc:creator>
<dc:creator>de Jong, M. D.</dc:creator>
<dc:creator>Russell, C. A.</dc:creator>
<dc:date>2021-03-20</dc:date>
<dc:identifier>doi:10.1101/2021.03.20.436248</dc:identifier>
<dc:title><![CDATA[The within-host evolutionary dynamics of seasonal and pandemic human influenza A viruses in young children]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-03-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.03.18.435948v1?rss=1">
<title>
<![CDATA[
Kawasaki Disease patient stratification and pathway analysis based on host transcriptomic and proteomic profiles 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.03.18.435948v1?rss=1"
</link>
<description><![CDATA[
The aetiology of Kawasaki Disease (KD), an acute inflammatory disorder of childhood, remains unknown despite various triggers of KD having been proposed. Host  omic profiles offer insights into the host response to infection and inflammation, with the interrogation of multiple  omic levels in parallel providing a more comprehensive picture. We used differential abundance analysis, pathway analysis, clustering and classification techniques to explore whether the host response in KD is more similar to the response to bacterial or viral infection at the transcriptomic and proteomic levels through comparison of  omic profiles from children with KD to those with bacterial and viral infections. Pathways activated in patients with KD included those involved in anti-viral and anti-bacterial responses. Unsupervised clustering showed that the majority of KD patients clustered with bacterial patients on both  omic levels, whilst application of diagnostic signatures specific for bacterial and viral infections revealed that many transcriptomic KD samples had low probabilities of having bacterial or viral infections, suggesting that KD may be triggered by a different process not typical of either common bacterial or viral infections. Clustering based on the transcriptomic and proteomic responses during KD revealed three clusters of KD patients on both  omic levels, suggesting heterogeneity within the inflammatory response during KD. The observed heterogeneity may reflect differences in the host response to a common trigger, or variation dependent on different triggers of the condition.
]]></description>
<dc:creator>Jackson, H.</dc:creator>
<dc:creator>Menikou, S.</dc:creator>
<dc:creator>Hamilton, S.</dc:creator>
<dc:creator>McArdle, A.</dc:creator>
<dc:creator>Shimizu, C.</dc:creator>
<dc:creator>Galassini, R.</dc:creator>
<dc:creator>Huang, H.</dc:creator>
<dc:creator>Kim, J.</dc:creator>
<dc:creator>Tremoulet, A.</dc:creator>
<dc:creator>de Jonge, M.</dc:creator>
<dc:creator>Kuijpers, T. W.</dc:creator>
<dc:creator>Wright, V.</dc:creator>
<dc:creator>Burns, J.</dc:creator>
<dc:creator>Casals-Pascual, C.</dc:creator>
<dc:creator>Herberg, J.</dc:creator>
<dc:creator>Levin, M.</dc:creator>
<dc:creator>Kaforou, M.</dc:creator>
<dc:creator>The PERFORM Consortium,</dc:creator>
<dc:date>2021-03-19</dc:date>
<dc:identifier>doi:10.1101/2021.03.18.435948</dc:identifier>
<dc:title><![CDATA[Kawasaki Disease patient stratification and pathway analysis based on host transcriptomic and proteomic profiles]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-03-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.08.05.228700v1?rss=1">
<title>
<![CDATA[
Individualized characterization of volumetric development in the preterm brain 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.08.05.228700v1?rss=1"
</link>
<description><![CDATA[
The diverse cerebral consequences of preterm birth create significant challenges for understanding pathogenesis or predicting later outcome. Instead of focusing on describing effects common to the group, comparing individual infants against robust normative data offers a powerful alternative to study brain maturation. Here we used Gaussian process regression to create normative curves characterising brain volumetric development in 274 term-born infants, modelling for age at scan and sex. We then compared 89 preterm infants scanned at termequivalent age to these normative charts, relating individual deviations from typical volumetric development to perinatal risk factors and later neurocognitive scores. To test generalisability, we used a second independent dataset comprising of 253 preterm infants scanned using different acquisition parameters and scanner. We describe rapid, non-uniform brain growth during the neonatal period. In both preterm cohorts, cerebral atypicalities were widespread, often multiple, and varied highly between individuals. Deviations from normative development were associated with respiratory support, nutrition, birth weight, and later neurocognition, demonstrating their clinical relevance. Group-level understanding of the preterm brain disguise a large degree of individual differences. We provide a method and normative dataset that offer a more precise characterisation of the cerebral consequences of preterm birth by profiling the individual neonatal brain.
]]></description>
<dc:creator>Dimitrova, R.</dc:creator>
<dc:creator>Arulkumaran, S.</dc:creator>
<dc:creator>Carney, O.</dc:creator>
<dc:creator>Chew, A.</dc:creator>
<dc:creator>Falconer, S.</dc:creator>
<dc:creator>Ciarrusta, J.</dc:creator>
<dc:creator>Wolfers, T.</dc:creator>
<dc:creator>Batalle, D.</dc:creator>
<dc:creator>Cordero-Grande, L.</dc:creator>
<dc:creator>Price, A. N.</dc:creator>
<dc:creator>Teixeira, R. P.</dc:creator>
<dc:creator>Hughes, E.</dc:creator>
<dc:creator>Egloff, A.</dc:creator>
<dc:creator>Hutter, J.</dc:creator>
<dc:creator>Makropoulos, A.</dc:creator>
<dc:creator>Robinson, E. C.</dc:creator>
<dc:creator>Schuh, A.</dc:creator>
<dc:creator>Vecchiato, K.</dc:creator>
<dc:creator>Steinweg, J. K.</dc:creator>
<dc:creator>Macleod, R.</dc:creator>
<dc:creator>Marquand, A. F.</dc:creator>
<dc:creator>McAlonan, G.</dc:creator>
<dc:creator>Rutherford, M. A.</dc:creator>
<dc:creator>Counsell, S. J.</dc:creator>
<dc:creator>Smith, S. M.</dc:creator>
<dc:creator>Rueckert, D.</dc:creator>
<dc:creator>Hajnal, J. V.</dc:creator>
<dc:creator>O'Muircheartaigh, J.</dc:creator>
<dc:creator>Edwards, A. D.</dc:creator>
<dc:date>2020-08-05</dc:date>
<dc:identifier>doi:10.1101/2020.08.05.228700</dc:identifier>
<dc:title><![CDATA[Individualized characterization of volumetric development in the preterm brain]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-08-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.12.22.423823v1?rss=1">
<title>
<![CDATA[
Cell cycle corruption in a preleukemic ETV6-RUNX1 model exposes RUNX1 addiction as a therapeutic target in acute lymphoblastic leukemia 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.12.22.423823v1?rss=1"
</link>
<description><![CDATA[
The ETV6-RUNX1 onco-fusion arises in utero, initiating a clinically silent pre-leukemic state associated with the development of pediatric B-acute lymphoblastic leukemia (B-ALL). We characterize the ETV6-RUNX1 regulome by integrating chromatin immunoprecipitation- and RNA-sequencing and show that ETV6-RUNX1 functions primarily through competition for RUNX1 binding sites and transcriptional repression. In pre-leukemia, this results in ETV6-RUNX1 antagonization of cell cycle regulation by RUNX1 as evidenced by mass cytometry analysis of B-lineage cells derived from ETV6-RUNX1 knock-in human pluripotent stem cells. In frank leukemia, knockdown of RUNX1 or its co-factor CBF{beta} results in cell death suggesting sustained requirement for RUNX1 activity which is recapitulated by chemical perturbation using an allosteric CBF{beta}-inhibitor. Strikingly, we show that RUNX1 addiction extends to other genetic subtypes of pediatric B-ALL and also adult disease. Importantly, inhibition of RUNX1 activity spares normal hematopoiesis. Our results implicate chemical intervention in the RUNX1 program as an exciting therapeutic opportunity in ALL.
]]></description>
<dc:creator>Wray, J. P.</dc:creator>
<dc:creator>Deltcheva, E. M.</dc:creator>
<dc:creator>Boiers, C.</dc:creator>
<dc:creator>Richardson, S. E.</dc:creator>
<dc:creator>Chettri, J. B.</dc:creator>
<dc:creator>Gagrica, S.</dc:creator>
<dc:creator>Guo, Y.</dc:creator>
<dc:creator>Illendula, A.</dc:creator>
<dc:creator>Martens, J. H.</dc:creator>
<dc:creator>Stunnenberg, H. H.</dc:creator>
<dc:creator>Bushweller, J. H.</dc:creator>
<dc:creator>Nimmo, R.</dc:creator>
<dc:creator>Enver, T.</dc:creator>
<dc:date>2020-12-22</dc:date>
<dc:identifier>doi:10.1101/2020.12.22.423823</dc:identifier>
<dc:title><![CDATA[Cell cycle corruption in a preleukemic ETV6-RUNX1 model exposes RUNX1 addiction as a therapeutic target in acute lymphoblastic leukemia]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-12-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.02.01.429143v1?rss=1">
<title>
<![CDATA[
Cortico-amygdalar connectivity and externalizing/internalizing behavior in children with neurodevelopmental disorders 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.02.01.429143v1?rss=1"
</link>
<description><![CDATA[
BackgroundExternalizing and internalizing behaviors are common and contribute to impairment in children with neurodevelopmental disorders (NDDs). Associations between externalizing or internalizing behaviors and cortico-amygdalar connectivity have been found in children with and without clinically significant internalizing/externalizing behaviors. This study examined whether such associations are present across children with different NDDs.

MethodsMulti-modal neuroimaging and behavioral data from the Province of Ontario Neurodevelopmental Disorders (POND) Network were used. POND participants aged 6-18 years with a primary diagnosis of autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD) or obsessive-compulsive disorder (OCD), as well as typically developing children (TDC) with T1-weighted, resting-state fMRI or diffusion weighted imaging and parent-report Child Behavioral Checklist (CBCL) data available, were analyzed (n range=157-346). Associations between externalizing or internalizing behavior and cortico-amygdalar structural and functional connectivity indices were examined using linear regressions, controlling for age, gender, and image-modality specific covariates. Behavior-by-diagnosis interaction effects were also examined.

ResultsNo significant linear associations (or diagnosis-by-behavior interaction effects) were found between CBCL-measured externalizing or internalizing behaviors and any of the connectivity indices examined. Post-hoc bootstrapping analyses indicated stability and reliability of these null results.

ConclusionsThe current study provides evidence in favour of the absence of a shared linear relationship between internalizing or externalizing behaviors and cortico-amygdalar connectivity properties across a transdiagnostic sample of children with various NDDs and TDC. Detecting shared brain-behavior relationships in children with NDDs may benefit from the use of different methodological approaches, including incorporation of multi-dimensional behavioral data (i.e. behavioral assessments, neurocognitive tasks, task-based fMRI) or clustering approaches to delineate whether subgroups of individuals with different brain-behavior profiles are present within heterogeneous cross-disorder samples.
]]></description>
<dc:creator>Nakua, H.</dc:creator>
<dc:creator>Hawco, C.</dc:creator>
<dc:creator>Forde, N. J.</dc:creator>
<dc:creator>Jacobs, G. R.</dc:creator>
<dc:creator>Joseph, M.</dc:creator>
<dc:creator>Voineskos, A.</dc:creator>
<dc:creator>Wheeler, A. L.</dc:creator>
<dc:creator>Lai, M.-C.</dc:creator>
<dc:creator>Szatmari, P.</dc:creator>
<dc:creator>Kelley, E.</dc:creator>
<dc:creator>Liu, X.</dc:creator>
<dc:creator>Georgiades, S.</dc:creator>
<dc:creator>Nicolson, R.</dc:creator>
<dc:creator>Schachar, R.</dc:creator>
<dc:creator>Crosbie, J.</dc:creator>
<dc:creator>Anagnostou, E.</dc:creator>
<dc:creator>Lerch, J. P.</dc:creator>
<dc:creator>Arnold, P. D.</dc:creator>
<dc:creator>Ameis, S. H.</dc:creator>
<dc:date>2021-02-02</dc:date>
<dc:identifier>doi:10.1101/2021.02.01.429143</dc:identifier>
<dc:title><![CDATA[Cortico-amygdalar connectivity and externalizing/internalizing behavior in children with neurodevelopmental disorders]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-02-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.02.22.432227v1?rss=1">
<title>
<![CDATA[
pyControl: Open source, Python based, hardware and software for controlling behavioural neuroscience experiments. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.02.22.432227v1?rss=1"
</link>
<description><![CDATA[
Laboratory behavioural tasks are an essential research tool. As questions asked of behaviour and brain activity become more sophisticated, the ability to specify and run richly structured tasks becomes more important. An increasing focus on reproducibility also necessitates accurate communication of task logic to other researchers. To these ends we developed pyControl, a system of open source hardware and software for controlling behavioural experiments comprising; a simple yet flexible Python-based syntax for specifying tasks as extended state machines, hardware modules for building behavioural setups, and a graphical user interface designed for efficiently running high throughput experiments on many setups in parallel, all with extensive online documentation. These tools make it quicker, easier and cheaper to implement rich behavioural tasks at scale. As important, pyControl facilitates communication and reproducibility of behavioural experiments through a highly readable task definition syntax and self-documenting features.

ResourcesDocumentation: https://pycontrol.readthedocs.io

Repositories: https://github.com/pyControl

User support: https://groups.google.com/g/pycontrol
]]></description>
<dc:creator>Akam, T.</dc:creator>
<dc:creator>Lustig, A.</dc:creator>
<dc:creator>Rowland, J.</dc:creator>
<dc:creator>Kapanaiah, S. K. T.</dc:creator>
<dc:creator>Esteve-Agraz, J.</dc:creator>
<dc:creator>Panniello, M.</dc:creator>
<dc:creator>Marquez, C.</dc:creator>
<dc:creator>Kohl, M.</dc:creator>
<dc:creator>Kätzel, D.</dc:creator>
<dc:creator>Costa, R. M.</dc:creator>
<dc:creator>Walton, M. E.</dc:creator>
<dc:date>2021-02-23</dc:date>
<dc:identifier>doi:10.1101/2021.02.22.432227</dc:identifier>
<dc:title><![CDATA[pyControl: Open source, Python based, hardware and software for controlling behavioural neuroscience experiments.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-02-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.08.13.249813v1?rss=1">
<title>
<![CDATA[
Sex-Dependent Shared and Non-Shared Genetic Architecture Across Mood and Psychotic Disorders 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.08.13.249813v1?rss=1"
</link>
<description><![CDATA[
BACKGROUNDSex differences in incidence and/or presentation of schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BIP) are pervasive. Previous evidence for shared genetic risk and sex differences in brain abnormalities across disorders suggest possible shared sex-dependent genetic risk.

METHODSWe conducted the largest to date genome-wide genotype-by-sex (GxS) interaction of risk for these disorders, using 85,735 cases (33,403 SCZ, 19,924 BIP, 32,408 MDD) and 109,946 controls from the Psychiatric Genomics Consortium (PGC) and iPSYCH.

RESULTSAcross disorders, genome-wide significant SNP-by-sex interaction was detected for a locus encompassing NKAIN2 (rs117780815; p=3.2x10-8), that interacts with sodium/potassium-transporting ATPase enzymes implicating neuronal excitability. Three additional loci showed evidence (p<1x10-6) for cross-disorder GxS interaction (rs7302529, p=1.6x10-7; rs73033497, p=8.8x10-7; rs7914279, p=6.4x10-7) implicating various functions. Gene-based analyses identified GxS interaction across disorders (p=8.97x10-7) with transcriptional inhibitor SLTM. Most significant in SCZ was a MOCOS gene locus (rs11665282; p=1.5x10-7), implicating vascular endothelial cells. Secondary analysis of the PGC-SCZ dataset detected an interaction (rs13265509; p=1.1x10-7) in a locus containing IDO2, a kynurenine pathway enzyme with immunoregulatory functions implicated in SCZ, BIP, and MDD. Pathway enrichment analysis detected significant GxS of genes regulating vascular endothelial growth factor (VEGF) receptor signaling in MDD (pFDR<0.05).

CONCLUSIONSIn the largest genome-wide GxS analysis of mood and psychotic disorders to date, there was substantial genetic overlap between the sexes. However, significant sex-dependent effects were enriched for genes related to neuronal development, immune and vascular functions across and within SCZ, BIP, and MDD at the variant, gene, and pathway enrichment levels.
]]></description>
<dc:creator>Blokland, G. A.</dc:creator>
<dc:creator>Grove, J.</dc:creator>
<dc:creator>Chen, C.-Y.</dc:creator>
<dc:creator>Cotsapas, C.</dc:creator>
<dc:creator>Tobet, S.</dc:creator>
<dc:creator>Handa, R.</dc:creator>
<dc:creator>Schizophrenia Working Group of the Psychiatric Genomics Consortium,</dc:creator>
<dc:creator>St Clair, D.</dc:creator>
<dc:creator>Lencz, T.</dc:creator>
<dc:creator>Mowry, B. J.</dc:creator>
<dc:creator>Periyasamy, S.</dc:creator>
<dc:creator>Cairns, M. J.</dc:creator>
<dc:creator>Tooney, P. A.</dc:creator>
<dc:creator>Wu, J. Q.</dc:creator>
<dc:creator>Kelly, B.</dc:creator>
<dc:creator>Kirov, G.</dc:creator>
<dc:creator>Sullivan, P. F.</dc:creator>
<dc:creator>Corvin, A.</dc:creator>
<dc:creator>Riley, B. P.</dc:creator>
<dc:creator>Esko, T.</dc:creator>
<dc:creator>Milani, L.</dc:creator>
<dc:creator>Jönsson, E. G.</dc:creator>
<dc:creator>Palotie, A.</dc:creator>
<dc:creator>Ehrenreich, H.</dc:creator>
<dc:creator>Begemann, M.</dc:creator>
<dc:creator>Steixner-Kumar, A.</dc:creator>
<dc:creator>Sham, P. C.</dc:creator>
<dc:creator>Iwata, N.</dc:creator>
<dc:creator>Weinberger, D. R.</dc:creator>
<dc:creator>Gejman, P. V.</dc:creator>
<dc:creator>Sanders, A. R.</dc:creator>
<dc:creator>Buxbaum, J. D.</dc:creator>
<dc:creator>Rujescu, D.</dc:creator>
<dc:creator>Giegling, I.</dc:creator>
<dc:creator>Konte, B.</dc:creator>
<dc:creator>Hartmann, A. M.</dc:creator>
<dc:creator>Bramon, E.</dc:creator>
<dc:creator>Murray, R. M.</dc:creator>
<dc:creator>Pato, M. T.</dc:creator>
<dc:creator>Lee,</dc:creator>
<dc:date>2020-08-17</dc:date>
<dc:identifier>doi:10.1101/2020.08.13.249813</dc:identifier>
<dc:title><![CDATA[Sex-Dependent Shared and Non-Shared Genetic Architecture Across Mood and Psychotic Disorders]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-08-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.04.27.441137v1?rss=1">
<title>
<![CDATA[
Germline loss-of-function variants in the base-excision repair gene MBD4 cause a Mendelian recessive syndrome of adenomatous colorectal polyposis and acute myeloid leukaemia 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.04.27.441137v1?rss=1"
</link>
<description><![CDATA[
Inherited defects in base-excision repair (BER) predispose to adenomatous polyposis and colorectal cancer (CRC), yet our understanding of this important DNA repair pathway remains incomplete. By combining detailed clinical, histological and molecular profiling, we reveal biallelic germline loss-of-function (LOF) variants in the BER gene MBD4 to predispose to adenomatous polyposis and -uniquely amongst CRC predisposition syndromes- to myeloid neoplasms. Neoplasms from MBD4-deficient patients almost exclusively accumulate somatic CpG>TpG mutations, resembling mutational signature SBS1. MBD4-deficient adenomas harbour mutations in known CRC driver genes, although AMER1 mutations were more common and KRAS mutations less frequent. We did not find an increased risk for colorectal tumours in individuals with a monoallelic MBD4 LOF variant. We suggest that this condition should be termed MBD4-associated neoplasia syndrome (MANS) and that MBD4 is included in testing for the genetic diagnosis of polyposis and/or early-onset AML.
]]></description>
<dc:creator>Palles, C.</dc:creator>
<dc:creator>Chew, E.</dc:creator>
<dc:creator>Grolleman, J. E.</dc:creator>
<dc:creator>Galavotti, S.</dc:creator>
<dc:creator>Flensburg, C.</dc:creator>
<dc:creator>Jansen, E. A. M.</dc:creator>
<dc:creator>Curley, H.</dc:creator>
<dc:creator>Chegwidden, L.</dc:creator>
<dc:creator>Arbe Barnes, E.</dc:creator>
<dc:creator>Bajel, A.</dc:creator>
<dc:creator>Sherwood, K.</dc:creator>
<dc:creator>Martin, L.</dc:creator>
<dc:creator>Thomas, H.</dc:creator>
<dc:creator>Georgiou, D.</dc:creator>
<dc:creator>Fostira, F.</dc:creator>
<dc:creator>Goldberg, Y.</dc:creator>
<dc:creator>Adams, D.</dc:creator>
<dc:creator>van der Biezen, S.</dc:creator>
<dc:creator>Christie, M.</dc:creator>
<dc:creator>Clendenning, M.</dc:creator>
<dc:creator>Deltas, C.</dc:creator>
<dc:creator>Dimovski, A. J.</dc:creator>
<dc:creator>Dymerska, D.</dc:creator>
<dc:creator>Lubinski, J.</dc:creator>
<dc:creator>Mahmood, K.</dc:creator>
<dc:creator>van der Post, R. S.</dc:creator>
<dc:creator>Sanders, M.</dc:creator>
<dc:creator>Weitz, J.</dc:creator>
<dc:creator>Taylor, J. C.</dc:creator>
<dc:creator>Turnbull, C.</dc:creator>
<dc:creator>Vreede, L.</dc:creator>
<dc:creator>van Wezel, T.</dc:creator>
<dc:creator>Whalley, C.</dc:creator>
<dc:creator>Arnedo Pac, C.</dc:creator>
<dc:creator>Caravagna, G.</dc:creator>
<dc:creator>Cross, W.</dc:creator>
<dc:creator>Chubb, D.</dc:creator>
<dc:creator>Frangou, A.</dc:creator>
<dc:creator>Gruber, A.</dc:creator>
<dc:creator>Kinnersley, B.</dc:creator>
<dc:creator>Noyvert, B.</dc:creator>
<dc:creator>Church, D.</dc:creator>
<dc:creator>Graham, T.</dc:creator>
<dc:creator></dc:creator>
<dc:date>2021-04-28</dc:date>
<dc:identifier>doi:10.1101/2021.04.27.441137</dc:identifier>
<dc:title><![CDATA[Germline loss-of-function variants in the base-excision repair gene MBD4 cause a Mendelian recessive syndrome of adenomatous colorectal polyposis and acute myeloid leukaemia]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-04-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.04.05.438429v1?rss=1">
<title>
<![CDATA[
Warped Bayesian Linear Regression for Normative Modelling of Big Data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.04.05.438429v1?rss=1"
</link>
<description><![CDATA[
Normative modelling is becoming more popular in neuroimaging due to its ability to make predictions of deviation from a normal trajectory at the level of individual participants. It allows the user to model the distribution of several neuroimaging modalities, giving an estimation for the mean and centiles of variation. With the increase in the availability of big data in neuroimaging, there is a need to scale normative modelling to big data sets. However, the scaling of normative models has come with several challenges.

So far, most normative modelling approaches used Gaussian process regression, and although suitable for smaller datasets (up to a few thousand participants) it does not scale well to the large cohorts currently available and being acquired. Furthermore, most neuroimaging modelling methods that are available assume the predictive distribution to be Gaussian in shape. However, deviations from Gaussianity can be frequently found, which may lead to incorrect inferences, particularly in the outer centiles of the distribution. In normative modelling, we use the centiles to give an estimation of the deviation of a particular participant from the  normal trend. Therefore, especially in normative modelling, the correct estimation of the outer centiles is of utmost importance, which is also where data are sparsest.

Here, we present a novel framework based on Bayesian Linear Regression with likelihood warping that allows us to address these problems, that is, to scale normative modelling elegantly to big data cohorts and to correctly model non-Gaussian predictive distributions. In addition, this method provides also likelihood-based statistics, which are useful for model selection.

To evaluate this framework, we use a range of neuroimaging-derived measures from the UK Biobank study, including image-derived phenotypes (IDPs) and whole-brain voxel-wise measures derived from diffusion tensor imaging. We show good computational scaling and improved accuracy of the warped BLR for certain IDPs and voxels if there was a deviation from normality of these parameters in their residuals.

The present results indicate the advantage of a warped BLR in terms of; computational scalability and the flexibility to incorporate non-linearity and non-Gaussianity of the data, giving a wider range of neuroimaging datasets that can be correctly modelled.
]]></description>
<dc:creator>Fraza, C.</dc:creator>
<dc:creator>Dinga, R.</dc:creator>
<dc:creator>Beckmann, C. F.</dc:creator>
<dc:creator>Marquand, A. F.</dc:creator>
<dc:date>2021-04-06</dc:date>
<dc:identifier>doi:10.1101/2021.04.05.438429</dc:identifier>
<dc:title><![CDATA[Warped Bayesian Linear Regression for Normative Modelling of Big Data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-04-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.04.16.440223v1?rss=1">
<title>
<![CDATA[
Post mortem mapping of connectional anatomy for the validation of diffusion MRI 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.04.16.440223v1?rss=1"
</link>
<description><![CDATA[
Despite the impressive advances in diffusion MRI (dMRI) acquisition and analysis that have taken place during the Human Connectome era, dMRI tractography is still an imperfect source of information on the circuitry of the brain. In this review, we discuss methods for post mortem validation of dMRI tractography, fiber orientations, and other microstructural properties of axon bundles that are typically extracted from dMRI data. These methods include anatomic tracer studies, Klinglers dissection, myelin stains, label-free optical imaging techniques, and others. We provide an overview of the basic principles of each technique, its limitations, and what it has taught us so far about the accuracy of different dMRI acquisition and analysis approaches.
]]></description>
<dc:creator>Yendiki, A.</dc:creator>
<dc:creator>Aggarwal, M.</dc:creator>
<dc:creator>Axer, M.</dc:creator>
<dc:creator>Howard, A. F.</dc:creator>
<dc:creator>van Cappellen van Walsum, A.-M.</dc:creator>
<dc:creator>Haber, S. N.</dc:creator>
<dc:date>2021-04-19</dc:date>
<dc:identifier>doi:10.1101/2021.04.16.440223</dc:identifier>
<dc:title><![CDATA[Post mortem mapping of connectional anatomy for the validation of diffusion MRI]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-04-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.05.05.442735v1?rss=1">
<title>
<![CDATA[
Subtly altered topological asymmetry of brain structural covariance networks in autism spectrum disorder across 43 datasets from the ENIGMA consortium 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.05.05.442735v1?rss=1"
</link>
<description><![CDATA[
Small average differences in the left-right asymmetry of cerebral cortical thickness have been reported in individuals with autism spectrum disorder (ASD) compared to typically developing controls. Although these alterations affect multiple and widespread cortical regional asymmetries, the extent to which specific structural networks might be affected remains unknown. Inter-regional morphological covariance analysis can capture network connectivity relations between different cortical areas at the macroscale level. Here, we used cortical thickness data from 1,455 individuals with ASD and 1,560 controls, across 43 independent datasets of the ENIGMA consortiums ASD Working Group, to assess hemispheric asymmetries of intra-individual structural covariance networks, using graph theory-based topological metrics. Compared with typical features of small-world architecture in controls, the ASD sample showed significantly altered asymmetry of hemispheric networks involving the fusiform, rostral middle frontal, and medial orbitofrontal cortex, driven by shifts toward higher randomization of the corresponding right-hemispheric networks in ASD. A network involving the superior frontal cortex showed decreased right-hemisphere randomization. Based on comparisons with meta-analyzed functional neuroimaging data, the altered connectivity asymmetry particularly affected networks that subserve working memory, executive functions, language, reading, and sensorimotor processes. Taken together, these findings provide new insights into how altered brain left-right asymmetry in ASD affects specific structural and functional brain networks. Altered asymmetrical brain development in ASD may be partly propagated among spatially distant regions through structural connectivity.
]]></description>
<dc:creator>Sha, Z.</dc:creator>
<dc:creator>Rooij, D. v.</dc:creator>
<dc:creator>Anagnostou, E.</dc:creator>
<dc:creator>Arango, C.</dc:creator>
<dc:creator>Auzias, G.</dc:creator>
<dc:creator>Behrmann, M.</dc:creator>
<dc:creator>Bernhardt, B.</dc:creator>
<dc:creator>Bolte, S.</dc:creator>
<dc:creator>Busatto, G. F.</dc:creator>
<dc:creator>Calderoni, S.</dc:creator>
<dc:creator>Calvo, R.</dc:creator>
<dc:creator>Daly, E.</dc:creator>
<dc:creator>Deruelle, C.</dc:creator>
<dc:creator>Duan, M.</dc:creator>
<dc:creator>Duran, F. L. S.</dc:creator>
<dc:creator>Durston, S.</dc:creator>
<dc:creator>Ecker, C.</dc:creator>
<dc:creator>Ehrlich, S.</dc:creator>
<dc:creator>Fair, D.</dc:creator>
<dc:creator>Fedor, J.</dc:creator>
<dc:creator>Fitzgerald, J.</dc:creator>
<dc:creator>Floris, D. L.</dc:creator>
<dc:creator>Franke, B.</dc:creator>
<dc:creator>Freitag, C. M.</dc:creator>
<dc:creator>Gallagher, L.</dc:creator>
<dc:creator>Glahn, D. C.</dc:creator>
<dc:creator>Haar, S.</dc:creator>
<dc:creator>Hoekstra, L.</dc:creator>
<dc:creator>Jahanshad, N.</dc:creator>
<dc:creator>Jalbrzikowski, M.</dc:creator>
<dc:creator>Janssen, J.</dc:creator>
<dc:creator>King, J. A.</dc:creator>
<dc:creator>Lazaro, L.</dc:creator>
<dc:creator>Luna, B.</dc:creator>
<dc:creator>McGrath, J.</dc:creator>
<dc:creator>Medland, S. E.</dc:creator>
<dc:creator>Molloy, C.</dc:creator>
<dc:creator>Muratori, F.</dc:creator>
<dc:creator>Murphy, D. G. M.</dc:creator>
<dc:creator>Neufeld, J.</dc:creator>
<dc:creator>O'Hearn, K.</dc:creator>
<dc:creator>Oranje, B.</dc:creator>
<dc:creator>Parellada, M.</dc:creator>
<dc:creator>Pariente, J.</dc:creator>
<dc:creator>Postema, M.</dc:creator>
<dc:date>2021-05-06</dc:date>
<dc:identifier>doi:10.1101/2021.05.05.442735</dc:identifier>
<dc:title><![CDATA[Subtly altered topological asymmetry of brain structural covariance networks in autism spectrum disorder across 43 datasets from the ENIGMA consortium]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-05-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.05.12.443095v1?rss=1">
<title>
<![CDATA[
Clone expansion of mutation-driven clonal hematopoiesis is associated with aging and metabolic dysfunction in individuals with obesity 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.05.12.443095v1?rss=1"
</link>
<description><![CDATA[
AimsHaematopoietic clones caused by somatic mutations with [&ge;]2% variant allele frequency (VAF), known as clonal haematopoiesis of indeterminate potential (CHIP), increase with age and have been linked to risk of haematological malignancies and cardiovascular disease. Recent observations suggest that smaller clones are also associated with adverse clinical outcomes. Our aims were to determine the prevalence of clonal haematopoiesis driven by clones of variable sizes, and to examine the development of clones over time in relation to age and metabolic dysregulation over up to 20 years in individuals with obesity.

Methods and ResultsWe used an ultrasensitive single-molecule molecular inversion probe sequencing assay to identify clonal haematopoiesis driver mutations (CHDMs) in blood samples from individuals with obesity from the Swedish Obese Subjects study. In a single-timepoint dataset with samples from 1050 individuals, we identified 273 candidate CHDMs in 216 individuals, with VAF ranging from 0.01% to 31.15% and CHDM prevalence and clone sizes increasing with age. Longitudinal analysis over 20 years in CHDM-positive samples from 40 individuals showed that small clones can grow over time and become CHIP. VAF increased on average by 7% (range -4% to 27%) per year. Rate of clone growth was positively associated with insulin resistance (R=0.40, P=0.025) and low circulating levels of high-density lipoprotein-cholesterol (HDL-C) (R=-0.68, P=1.74E-05).

ConclusionOur results show that haematopoietic clones can be detected and monitored before they become CHIP and indicate that insulin resistance and low HDL-C, well-established cardiovascular risk factors, are associated with clonal expansion in individuals with obesity.

Translational perspectivesClonal haematopoiesis-driver mutations are somatic mutations in haematopoietic stem cells that lead to clones detectable in peripheral blood. Haematopoietic clones with a variant allele frequency (VAF) [&ge;]2%, known as clonal haematopoiesis of indeterminate potential (CHIP), are recognized as an independent cardiovascular risk factor. Here, we show that smaller clones are prevalent, and also correlate with age. Our longitudinal observations in individuals with obesity over 20 years showed that more than half of all clone-positive individuals show growing clones and clones with VAF <2% can grow and become CHIP. Importantly, clone growth was accelerated in individuals with insulin resistance and low high-density lipoprotein-cholesterol (HDL-C).

Translational outlook 1: Haematopoietic clones can be detected and monitored before they become CHIP.

Translational outlook 2: The association between insulin resistance and low HDL-C with growth of haematopoietic clones opens the possibility that treatments improving metabolism, such as weight loss, may reduce growth of clones and thereby cardiovascular risk.

One Sentence SummaryIn obesity, the growth rate of mutation-driven haematopoietic clones increased with insulin resistance and low HDL-C, both known risk factors for cardiovascular disease.
]]></description>
<dc:creator>van Deuren, R. C.</dc:creator>
<dc:creator>Andersson-Assarsson, J. C.</dc:creator>
<dc:creator>Kristensson, F. M.</dc:creator>
<dc:creator>Steehouwer, M.</dc:creator>
<dc:creator>Sjöholm, K.</dc:creator>
<dc:creator>Svensson, P.-A.</dc:creator>
<dc:creator>Peterse, M.</dc:creator>
<dc:creator>Gilissen, C.</dc:creator>
<dc:creator>Taube, M.</dc:creator>
<dc:creator>Jacobson, P.</dc:creator>
<dc:creator>Perkins, R.</dc:creator>
<dc:creator>Brunner, H. G.</dc:creator>
<dc:creator>Netea, M. G.</dc:creator>
<dc:creator>Peltonen, M.</dc:creator>
<dc:creator>Carlsson, B.</dc:creator>
<dc:creator>Hoischen, A.</dc:creator>
<dc:creator>Carlsson, L. M.</dc:creator>
<dc:date>2021-05-13</dc:date>
<dc:identifier>doi:10.1101/2021.05.12.443095</dc:identifier>
<dc:title><![CDATA[Clone expansion of mutation-driven clonal hematopoiesis is associated with aging and metabolic dysfunction in individuals with obesity]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-05-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.04.30.442229v1?rss=1">
<title>
<![CDATA[
The Immunological Factors Predisposing To Severe COVID-19 Are Already Present In Healthy Elderly And Men 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.04.30.442229v1?rss=1"
</link>
<description><![CDATA[
BackgroundMale sex and old age are risk factors for COVID-19 severity, but the underlying causes are unknown. A possible explanation for this might be the differences in immunological profiles in males and the elderly before the infection. Given the seasonal profile of COVID-19, the seasonal response against SARS-CoV-2 could also be different in these groups.

MethodsThe abundance of circulating proteins and immune populations associated with severe COVID-19 was analyzed in 2 healthy cohorts. PBMCs of female, male, young, and old subjects in different seasons of the year were stimulated with heat-inactivated SARS-CoV-2.

ResultSeveral T cell subsets, which are known to be depleted in severe COVID-19 patients, were intrinsically less abundant in men and older individuals. Plasma proteins increasing with disease severity, including HGF, IL-8, and MCP-1, were more abundant in the elderly and males. The elderly produced significantly more IL-1RA and had a dysregulated IFN{gamma} response with lower production in the summer compared with young individuals.

ConclusionsThe immune characteristics of severe COVID-19, described by a differential abundance of immune cells and circulating inflammatory proteins, are intrinsically present in healthy men and the elderly. This might explain the susceptibility of men and the elderly to SARS-CoV-2 infection.

SummaryImmunological profile of severe COVID-19, characterized by altered immune cell populations and inflammatory plasma proteins is intrinsically present in healthy men and the elderly. Different age and sex groups show distinct seasonal responses to SARS-CoV-2.
]]></description>
<dc:creator>Kilic, G.</dc:creator>
<dc:creator>Bulut, O.</dc:creator>
<dc:creator>Jaeger, M.</dc:creator>
<dc:creator>ter Horst, R.</dc:creator>
<dc:creator>Koeken, V.</dc:creator>
<dc:creator>Moorlag, S. J. C. F. M.</dc:creator>
<dc:creator>Mourits, V.</dc:creator>
<dc:creator>de Bree, C.</dc:creator>
<dc:creator>Dominguez-Andres, J.</dc:creator>
<dc:creator>Joosten, L. A. B.</dc:creator>
<dc:creator>Netea, M.</dc:creator>
<dc:date>2021-05-04</dc:date>
<dc:identifier>doi:10.1101/2021.04.30.442229</dc:identifier>
<dc:title><![CDATA[The Immunological Factors Predisposing To Severe COVID-19 Are Already Present In Healthy Elderly And Men]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-05-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.04.30.442117v1?rss=1">
<title>
<![CDATA[
Topographic divergence of atypical cortical asymmetry and regional atrophy patterns in temporal lobe epilepsy: a worldwide enigma study 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.04.30.442117v1?rss=1"
</link>
<description><![CDATA[
AO_SCPLOWBSTRACTC_SCPLOWTemporal lobe epilepsy (TLE), a common drug-resistant epilepsy in adults, is primarily a limbic network disorder associated with predominant unilateral hippocampal pathology. Structural MRI has provided an in vivo window into whole-brain grey matter pathology in TLE relative to controls, by either mapping (i) atypical inter-hemispheric asymmetry or (ii) regional atrophy. However, similarities and differences of both atypical asymmetry and regional atrophy measures have not been systematically investigated. Here, we addressed this gap using the multi-site ENIGMA-Epilepsy dataset comprising MRI brain morphological measures in 732 TLE patients and 1,418 healthy controls. We compared spatial distributions of grey matter asymmetry and atrophy in TLE, contextualized their topographies relative to spatial gradients in cortical microstructure and functional connectivity, and examined clinical associations using machine learning. We identified a marked divergence in the spatial distribution of atypical inter-hemispheric asymmetry and regional atrophy mapping. The former revealed a temporo-limbic disease signature while the latter showed diffuse and bilateral patterns. Our findings were robust across individual sites and patients. Cortical atrophy was significantly correlated with disease duration and age at seizure onset, while degrees of asymmetry did not show a significant relationship to these clinical variables. Our findings highlight that the mapping of atypical inter-hemispheric asymmetry and regional atrophy tap into two complementary aspects of TLE-related pathology, with the former revealing primary substrates in ipsilateral limbic circuits and the latter capturing bilateral disease effects. These findings refine our notion of the neuropathology of TLE and may inform future discovery and validation of complementary MRI biomarkers in TLE.
]]></description>
<dc:creator>Park, B.-y.</dc:creator>
<dc:creator>Lariviere, S.</dc:creator>
<dc:creator>Rodriguez-Cruces, R.</dc:creator>
<dc:creator>Royer, J.</dc:creator>
<dc:creator>Tavakol, S.</dc:creator>
<dc:creator>Wang, Y.</dc:creator>
<dc:creator>Caciagli, L.</dc:creator>
<dc:creator>Caligiuri, M. E.</dc:creator>
<dc:creator>Gambardella, A.</dc:creator>
<dc:creator>Concha, L.</dc:creator>
<dc:creator>Keller, S. S.</dc:creator>
<dc:creator>Cendes, F.</dc:creator>
<dc:creator>Alvim, M. K.</dc:creator>
<dc:creator>Yasuda, C.</dc:creator>
<dc:creator>Bonilha, L.</dc:creator>
<dc:creator>Gleichgerrcht, E.</dc:creator>
<dc:creator>Focke, N. K.</dc:creator>
<dc:creator>Kreilkamp, B. A.</dc:creator>
<dc:creator>Domin, M.</dc:creator>
<dc:creator>von Podewils, F.</dc:creator>
<dc:creator>Langner, S.</dc:creator>
<dc:creator>Rummel, C.</dc:creator>
<dc:creator>Rebsamen, M.</dc:creator>
<dc:creator>Wiest, R.</dc:creator>
<dc:creator>Martin, P.</dc:creator>
<dc:creator>Kotikalapudi, R.</dc:creator>
<dc:creator>Bender, B.</dc:creator>
<dc:creator>O'Brien, T. J.</dc:creator>
<dc:creator>Law, M.</dc:creator>
<dc:creator>Sinclair, B.</dc:creator>
<dc:creator>Vivash, L.</dc:creator>
<dc:creator>Desmond, P. M.</dc:creator>
<dc:creator>Malpas, C. B.</dc:creator>
<dc:creator>Lui, E.</dc:creator>
<dc:creator>Alhusaini, S.</dc:creator>
<dc:creator>Doherty, C. P.</dc:creator>
<dc:creator>Cavalleri, G. L.</dc:creator>
<dc:creator>Delanty, N.</dc:creator>
<dc:creator>Kalviainen, R.</dc:creator>
<dc:creator>Jackson, G. D.</dc:creator>
<dc:creator>Kowalczyk, M.</dc:creator>
<dc:creator>Mascalchi, M.</dc:creator>
<dc:creator>Se</dc:creator>
<dc:date>2021-04-30</dc:date>
<dc:identifier>doi:10.1101/2021.04.30.442117</dc:identifier>
<dc:title><![CDATA[Topographic divergence of atypical cortical asymmetry and regional atrophy patterns in temporal lobe epilepsy: a worldwide enigma study]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-04-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.09.24.311654v1?rss=1">
<title>
<![CDATA[
Associations between ADHD symptom remission and white matter microstructure: a longitudinal analysis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.09.24.311654v1?rss=1"
</link>
<description><![CDATA[
BackgroundAttention-deficit hyperactivity disorder (ADHD) is associated with white matter (WM) microstructure. Our objective was to investigate how WM microstructure is longitudinally related to symptom remission in adolescents and young adults with ADHD.

MethodsWe obtained diffusion-weighted imaging (DWI) data from 99 participants at two time points (mean age baseline: 16.91 years, mean age follow-up: 20.57 years). We used voxel-wise Tract-Based Spatial Statistics (TBSS) with permutation-based inference to investigate associations of inattention (IA) and hyperactivity-impulsivity (HI) symptom change with fractional anisotropy (FA) at baseline, follow-up, and change between time points.

ResultsRemission of combined HI and IA symptoms was significantly associated with reduced FA at follow-up in the left superior longitudinal fasciculus and the left corticospinal tract (CST) (PFWE=0.038 and PFWE=0.044, respectively), mainly driven by an association between HI remission and follow-up CST FA (PFWE=0.049). There was no significant association of combined symptom decrease with FA at baseline or with changes in FA between the two assessments.

ConclusionsIn this longitudinal DWI study of ADHD using dimensional symptom scores, we show that greater symptom decrease is associated with lower follow-up FA in specific WM tracts. Altered FA thus may appear to follow, rather than precede, changes in symptom remission. Our findings indicate divergent WM developmental trajectories between individuals with persistent and remittent ADHD, and support the role of prefrontal and sensorimotor tracts in the remission of ADHD.
]]></description>
<dc:creator>Damatac, C. G.</dc:creator>
<dc:creator>Leenders, A. E. M.</dc:creator>
<dc:creator>Soheili-Nezhad, S.</dc:creator>
<dc:creator>Chauvin, R. J. M.</dc:creator>
<dc:creator>Mennes, M. J. J.</dc:creator>
<dc:creator>Zwiers, M. P.</dc:creator>
<dc:creator>van Rooij, D.</dc:creator>
<dc:creator>Akkermans, S. E. A.</dc:creator>
<dc:creator>Naaijen, J.</dc:creator>
<dc:creator>Franke, B.</dc:creator>
<dc:creator>Buitelaar, J. K.</dc:creator>
<dc:creator>Beckmann, C. F.</dc:creator>
<dc:creator>Sprooten, E.</dc:creator>
<dc:date>2020-09-25</dc:date>
<dc:identifier>doi:10.1101/2020.09.24.311654</dc:identifier>
<dc:title><![CDATA[Associations between ADHD symptom remission and white matter microstructure: a longitudinal analysis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-09-25</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.05.31.445808v1?rss=1">
<title>
<![CDATA[
Scaling principles of white matter brain connectivity 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.05.31.445808v1?rss=1"
</link>
<description><![CDATA[
Brains come in many shapes and sizes. Nature has endowed big-brained primate species like humans with a proportionally large cerebral cortex. White matter connectivity - the brains infrastructure for long-range communication - might not always scale at the same pace as the cortex. We investigated the consequences of this allometric scaling for white matter brain network connectivity. Structural T1 and diffusion MRI data were collated across fourteen primate species, describing a comprehensive 350-fold range in brain volume. We report volumetric scaling relationships that point towards a restriction in macroscale connectivity in larger brains. Building on previous findings, we show cortical surface to outpace white matter volume and the corpus callosum, suggesting the emergence of a white matter  bottleneck of lower levels of connectedness through the corpus callosum in larger brains. At the network level, we find a potential consequence of this bottleneck in shaping connectivity patterns, with homologous regions in the left and right hemisphere showing more divergent connectivity in larger brains. Our findings show conserved scaling relationships of major brain components and their consequence for macroscale brain circuitry, providing a comparative framework for expected connectivity architecture in larger brains such as the human brain.
]]></description>
<dc:creator>Ardesch, D. J.</dc:creator>
<dc:creator>Scholtens, L. H.</dc:creator>
<dc:creator>de Lange, S. C.</dc:creator>
<dc:creator>Roumazeilles, L.</dc:creator>
<dc:creator>Khrapitchev, A. A.</dc:creator>
<dc:creator>Preuss, T. M.</dc:creator>
<dc:creator>Rilling, J. K.</dc:creator>
<dc:creator>Mars, R. B.</dc:creator>
<dc:creator>van den Heuvel, M. P.</dc:creator>
<dc:date>2021-05-31</dc:date>
<dc:identifier>doi:10.1101/2021.05.31.445808</dc:identifier>
<dc:title><![CDATA[Scaling principles of white matter brain connectivity]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-05-31</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.05.28.446120v1?rss=1">
<title>
<![CDATA[
Federated Multi-Site Normative Modeling using Hierarchical Bayesian Regression 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.05.28.446120v1?rss=1"
</link>
<description><![CDATA[
AO_SCPLOWBSTRACTC_SCPLOWClinical neuroimaging data availability has grown substantially in the last decade, providing the potential for studying heterogeneity in clinical cohorts on a previously unprecedented scale. Normative modeling is an emerging statistical tool for dissecting heterogeneity in complex brain disorders. However, its application remains technically challenging due to medical data privacy issues and difficulties in dealing with nuisance variation, such as the variability in the image acquisition process. Here, we introduce a federated probabilistic framework using hierarchical Bayesian regression (HBR) for multi-site normative modeling. The proposed method completes the life-cycle of normative modeling by providing the possibilities to learn, update, and adapt the model parameters on decentralized neuroimaging data. Our experimental results confirm the superiority of HBR in deriving more accurate normative ranges on large multi-site neuroimaging datasets compared to the current standard methods. In addition, our approach provides the possibility to recalibrate and reuse the learned model on local datasets and even on datasets with very small sample sizes. The proposed federated framework closes the technical loop for applying normative modeling across multiple sites in a decentralized manner. This will facilitate applications of normative modeling as a medical tool for screening the biological deviations in individuals affected by complex illnesses such as mental disorders.
]]></description>
<dc:creator>Kia, S. M.</dc:creator>
<dc:creator>Huijsdens, H.</dc:creator>
<dc:creator>Rutherford, S.</dc:creator>
<dc:creator>Dinga, R.</dc:creator>
<dc:creator>Wolfers, T.</dc:creator>
<dc:creator>Mennes, M.</dc:creator>
<dc:creator>Andreassen, O.</dc:creator>
<dc:creator>Westlye, L. T.</dc:creator>
<dc:creator>Beckmann, C. F.</dc:creator>
<dc:creator>Marquand, A. F.</dc:creator>
<dc:date>2021-05-30</dc:date>
<dc:identifier>doi:10.1101/2021.05.28.446120</dc:identifier>
<dc:title><![CDATA[Federated Multi-Site Normative Modeling using Hierarchical Bayesian Regression]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-05-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.05.20.444828v1?rss=1">
<title>
<![CDATA[
Rare copy number variants (CNVs) and breast cancer risk 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.05.20.444828v1?rss=1"
</link>
<description><![CDATA[
BackgroundCopy number variants (CNVs) are pervasive in the human genome but potential disease associations with rare CNVs have not been comprehensively assessed in large datasets. We analysed rare CNVs in genes and non-coding regions for 86,788 breast cancer cases and 76,122 controls of European ancestry with genome-wide array data.

ResultsGene burden tests detected the strongest association for deletions in BRCA1 (P= 3.7E-18). Nine other genes were associated with a p-value < 0.01 including known susceptibility genes CHEK2 (P= 0.0008), ATM (P= 0.002) and BRCA2 (P= 0.008). Outside the known genes we detected associations with p-values < 0.001 for either overall or subtype-specific breast cancer at nine deletion regions and four duplication regions. Three of the deletion regions were in established common susceptibility loci.

ConclusionsThis is the first genome-wide analysis of rare CNVs in a large breast cancer case-control dataset. We detected associations with exonic deletions in established breast cancer susceptibility genes. We also detected suggestive associations with non-coding CNVs in known and novel loci with large effects sizes. Larger sample sizes will be required to reach robust levels of statistical significance.
]]></description>
<dc:creator>Dennis, J.</dc:creator>
<dc:creator>Tyrer, J. P.</dc:creator>
<dc:creator>Walker, L. C.</dc:creator>
<dc:creator>Michailidou, K.</dc:creator>
<dc:creator>Dorling, L.</dc:creator>
<dc:creator>Bolla, M. K.</dc:creator>
<dc:creator>Wang, Q.</dc:creator>
<dc:creator>Ahearn, T. U.</dc:creator>
<dc:creator>Andrulis, I. L.</dc:creator>
<dc:creator>Anton-Culver, H.</dc:creator>
<dc:creator>Antonenkova, N. N.</dc:creator>
<dc:creator>Arndt, V.</dc:creator>
<dc:creator>Aronson, K. J.</dc:creator>
<dc:creator>Beane Freeman, L. E.</dc:creator>
<dc:creator>Beckmann, M. W.</dc:creator>
<dc:creator>Behrens, S.</dc:creator>
<dc:creator>Benitez, J.</dc:creator>
<dc:creator>Bermisheva, M.</dc:creator>
<dc:creator>Bogdanova, N. V.</dc:creator>
<dc:creator>Bojesen, S. E.</dc:creator>
<dc:creator>Brenner, H.</dc:creator>
<dc:creator>Castelao, J. E.</dc:creator>
<dc:creator>Chang-Claude, J.</dc:creator>
<dc:creator>Chenevix-Trench, G.</dc:creator>
<dc:creator>Clarke, C. L.</dc:creator>
<dc:creator>Collaborators, N.</dc:creator>
<dc:creator>Collee, J. M.</dc:creator>
<dc:creator>Consortium, C.</dc:creator>
<dc:creator>Couch, F. J.</dc:creator>
<dc:creator>Cox, A.</dc:creator>
<dc:creator>Cross, S. S.</dc:creator>
<dc:creator>Czene, K.</dc:creator>
<dc:creator>Devilee, P.</dc:creator>
<dc:creator>Dork, T.</dc:creator>
<dc:creator>Dossus, L.</dc:creator>
<dc:creator>Eliassen, A. H.</dc:creator>
<dc:creator>Eriksson, M.</dc:creator>
<dc:creator>Evans, D. G.</dc:creator>
<dc:creator>Fasching, P. A.</dc:creator>
<dc:creator>Figueroa, J.</dc:creator>
<dc:creator></dc:creator>
<dc:date>2021-05-21</dc:date>
<dc:identifier>doi:10.1101/2021.05.20.444828</dc:identifier>
<dc:title><![CDATA[Rare copy number variants (CNVs) and breast cancer risk]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-05-21</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.05.29.445691v1?rss=1">
<title>
<![CDATA[
Synthetic Antigen Presenting cells reveal the diversity and functional specialization of extracellular vesicles composing the fourth signal of T cell immunological synapses. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.05.29.445691v1?rss=1"
</link>
<description><![CDATA[
The T cell Immunological Synapse (IS) is a pivotal hub for the regulation of adaptive immunity by endowing the exchange of information between cells engaged in physical contacts. Beyond the integration of antigen (signal one), co-stimulation (signal two), and cytokines (signal three), the IS facilitates the delivery of T-cell effector assemblies including supramolecular attack particles (SMAPs) and extracellular vesicles (EVs). How these particulate outputs differ among T -cell subsets and how subcellular compartments and signals exchanged at the synapse contribute to their composition is not fully understood. Here we harnessed bead-supported lipid bilayers (BSLBs) as a tailorable and versatile technology for the study of synaptic particle biogenesis and composition in different T-cell subsets, including CART. These synthetic antigen-presenting cells (APCs) facilitated the characterisation of trans-synaptic vesicles (tSV) as a heterogeneous population of EVs comprising among others PM-derived synaptic ectosomes and CD63+ exosomes. We harnessed BSLB to unveil the factors influencing the vesicular release of CD40L, as a model effector, identifying CD40 trans presentation, T-cell activation, ESCRT upregulation/recruitment, antigen density/potency, co-repression by PD-1 ligands, and its processing by ADAM10 as major determinants. Further, BSLB made possible the comparison of microRNA (miR) species associated with tSV and steadily released EVs. Altogether, our data provide evidence for a higher specialisation of tSV which are enriched not only in effector immune receptors but also in miR and RNA-binding proteins. Considering the molecular uniqueness and functional complexity of the tSV output, which is also accompanied by SMAPs, we propose their classification as signal four.

Graphical abstract

O_FIG O_LINKSMALLFIG WIDTH=198 HEIGHT=200 SRC="FIGDIR/small/445691v3_ufig1.gif" ALT="Figure 1">
View larger version (67K):
org.highwire.dtl.DTLVardef@100f3aorg.highwire.dtl.DTLVardef@57adfforg.highwire.dtl.DTLVardef@6052fcorg.highwire.dtl.DTLVardef@1e85c21_HPS_FORMAT_FIGEXP  M_FIG C_FIG HighlightsO_LIBead Supported Lipid Bilayers (BSLB) reconstituting antigen-presenting cells support synapse assembly by T cells and the release of effector particles.
C_LIO_LIBSLB facilitate the dissection of the cellular machineries and synapse composition shaping the released tSV.
C_LIO_LItSV and their steadily released counterparts have a different composition. TSV show a higher enrichment of effectors including immune receptors, miR, RNA- and other nucleic acid-binding proteins, than EVs.
C_LI
]]></description>
<dc:creator>Cespedes, P. F.</dc:creator>
<dc:creator>Jainarayanan, A. K.</dc:creator>
<dc:creator>Fernandez-Messina, L.</dc:creator>
<dc:creator>Valvo, S.</dc:creator>
<dc:creator>Saliba, D. G.</dc:creator>
<dc:creator>Kvalvaag, A.</dc:creator>
<dc:creator>Kurz, E.</dc:creator>
<dc:creator>Colin-York, H.</dc:creator>
<dc:creator>Fritzsche, M.</dc:creator>
<dc:creator>Yanchun, P.</dc:creator>
<dc:creator>Dong, T.</dc:creator>
<dc:creator>Siller-Farfan, J. A.</dc:creator>
<dc:creator>Dushek, O.</dc:creator>
<dc:creator>Maj, M.</dc:creator>
<dc:creator>Sezgin, E.</dc:creator>
<dc:creator>Peacock, B.</dc:creator>
<dc:creator>Law, A.</dc:creator>
<dc:creator>Aubert, D.</dc:creator>
<dc:creator>Engledow, S.</dc:creator>
<dc:creator>Attar, M.</dc:creator>
<dc:creator>Sanchez-Madrid, F.</dc:creator>
<dc:creator>Hester, S.</dc:creator>
<dc:creator>Fisher, R.</dc:creator>
<dc:creator>Dustin, M. L.</dc:creator>
<dc:date>2021-05-29</dc:date>
<dc:identifier>doi:10.1101/2021.05.29.445691</dc:identifier>
<dc:title><![CDATA[Synthetic Antigen Presenting cells reveal the diversity and functional specialization of extracellular vesicles composing the fourth signal of T cell immunological synapses.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-05-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.05.25.445571v1?rss=1">
<title>
<![CDATA[
Diffusion and interaction dynamics of the cytosolic peroxisomal import receptor PEX5 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.05.25.445571v1?rss=1"
</link>
<description><![CDATA[
Measuring diffusion dynamics in living cells is essential for the understanding of molecular interactions. While various techniques have been used to explore such characteristics in the plasma membrane, this is less developed for measurements inside the cytosol. An example of cytosolic action is the import of proteins into peroxisomes, via the peroxisomal import receptor PEX5. Here, we combined advanced microscopy and spectroscopy techniques such as fluorescence correlation spectroscopy (FCS) and super-resolution STED microscopy to present a detailed characterization of the diffusion and interaction dynamics of PEX5. Among other features, we disclose a slow diffusion of PEX5, independent of aggregation or target binding, but associated with cytosolic interaction partners via its N-terminal domain. This sheds new light on the functionality of the receptor in the cytosol. Besides specific insights, our study highlights the potential of using complementary microscopy tools to decipher molecular interactions in the cytosol via studying their diffusion dynamics.

SummaryThe peroxisomal import receptor PEX5 transports newly synthesized proteins from the cytosol to the peroxisomal matrix. Here the cytosolic diffusion and interaction dynamics of PEX5 are characterized by advanced microscopic spectroscopy methods, revealing a so far unknown interaction partner.
]]></description>
<dc:creator>Galiani, S.</dc:creator>
<dc:creator>Reglinski, K.</dc:creator>
<dc:creator>Carravilla, P.</dc:creator>
<dc:creator>Barbotin, A.</dc:creator>
<dc:creator>Urbancic, I.</dc:creator>
<dc:creator>Ott, J.</dc:creator>
<dc:creator>Sehr, J.</dc:creator>
<dc:creator>Sezgin, E.</dc:creator>
<dc:creator>Schneider, F.</dc:creator>
<dc:creator>Waithe, D.</dc:creator>
<dc:creator>Hublitz, P.</dc:creator>
<dc:creator>Schliebs, W.</dc:creator>
<dc:creator>Erdmann, R.</dc:creator>
<dc:creator>Eggeling, C.</dc:creator>
<dc:date>2021-05-26</dc:date>
<dc:identifier>doi:10.1101/2021.05.25.445571</dc:identifier>
<dc:title><![CDATA[Diffusion and interaction dynamics of the cytosolic peroxisomal import receptor PEX5]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-05-26</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.06.16.448390v1?rss=1">
<title>
<![CDATA[
PET-BIDS, an extension to the brain imaging data structure for positron emission tomography 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.06.16.448390v1?rss=1"
</link>
<description><![CDATA[
The Brain Imaging Data Structure (BIDS) is a standard for organizing and describing neuroimaging datasets. It serves not only to facilitate the process of data sharing and aggregation, but also to simplify the application and development of new methods and software for working with neuroimaging data. Here, we present an extension of BIDS to include positron emission tomography (PET) data (PET-BIDS). We describe the PET-BIDS standard in detail and share several open-access datasets curated following PET-BIDS. Additionally, we highlight several tools which are already available for converting, validating and analyzing PET-BIDS datasets.
]]></description>
<dc:creator>Norgaard, M.</dc:creator>
<dc:creator>Matheson, G. J.</dc:creator>
<dc:creator>Hansen, H. D.</dc:creator>
<dc:creator>Thomas, A. G.</dc:creator>
<dc:creator>Searle, G.</dc:creator>
<dc:creator>Rizzo, G.</dc:creator>
<dc:creator>Veronese, M.</dc:creator>
<dc:creator>Giacomel, A.</dc:creator>
<dc:creator>Yaqub, M.</dc:creator>
<dc:creator>Tonietto, M.</dc:creator>
<dc:creator>Funck, T.</dc:creator>
<dc:creator>Gillman, A.</dc:creator>
<dc:creator>Boniface, H.</dc:creator>
<dc:creator>Routier, A.</dc:creator>
<dc:creator>Dalenberg, J. R.</dc:creator>
<dc:creator>Betthauser, T.</dc:creator>
<dc:creator>Feingold, F.</dc:creator>
<dc:creator>Markiewicz, C. J.</dc:creator>
<dc:creator>Gorgolewski, K. J.</dc:creator>
<dc:creator>Blair, R. W.</dc:creator>
<dc:creator>Appelhoff, S.</dc:creator>
<dc:creator>Gau, R.</dc:creator>
<dc:creator>Salo, T.</dc:creator>
<dc:creator>Niso, G.</dc:creator>
<dc:creator>Pernet, C.</dc:creator>
<dc:creator>Phillips, C.</dc:creator>
<dc:creator>Oostenveld, R.</dc:creator>
<dc:creator>Carson, R. E.</dc:creator>
<dc:creator>Gallezot, J.-D.</dc:creator>
<dc:creator>Knudsen, G. M.</dc:creator>
<dc:creator>Innis, R. B.</dc:creator>
<dc:creator>Ganz, M.</dc:creator>
<dc:date>2021-06-17</dc:date>
<dc:identifier>doi:10.1101/2021.06.16.448390</dc:identifier>
<dc:title><![CDATA[PET-BIDS, an extension to the brain imaging data structure for positron emission tomography]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-06-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.06.21.449154v1?rss=1">
<title>
<![CDATA[
The Digital Brain Bank: an open access platform for post-mortem datasets 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.06.21.449154v1?rss=1"
</link>
<description><![CDATA[
Post-mortem MRI provides the opportunity to acquire high-resolution datasets to investigate neuroanatomy, and validate the origins of image contrast through microscopy comparisons. We introduce the Digital Brain Bank (open.win.ox.ac.uk/DigitalBrainBank), a data release platform providing open access to curated, multimodal post-mortem neuroimaging datasets. Datasets span three themes - Digital Neuroanatomist: datasets for detailed neuroanatomical investigations; Digital Brain Zoo: datasets for comparative neuroanatomy; Digital Pathologist: datasets for neuropathology investigations. The first Digital Brain Bank release includes twenty one distinctive whole-brain diffusion MRI datasets for structural connectivity investigations, alongside microscopy and complementary MRI modalities. This includes one of the highest-resolution whole-brain human diffusion MRI datasets ever acquired, whole-brain diffusion MRI in fourteen non-human primate species, and one of the largest post-mortem whole-brain cohort imaging studies in neurodegeneration. The Digital Brain Bank is the culmination of our labs investment into post-mortem MRI methodology and MRI-microscopy analysis techniques. This manuscript provides a detailed overview of our work with post-mortem imaging to date, including the development of diffusion MRI methods to image large post-mortem samples, including whole, human brains. Taken together, the Digital Brain Bank provides crossscale, cross-species datasets facilitating the incorporation of post-mortem data into neuroimaging studies.
]]></description>
<dc:creator>Tendler, B. C.</dc:creator>
<dc:creator>Hanayik, T.</dc:creator>
<dc:creator>Ansorge, O.</dc:creator>
<dc:creator>Bangerter-Christensen, S.</dc:creator>
<dc:creator>Berns, G. S.</dc:creator>
<dc:creator>Bertelsen, M. F.</dc:creator>
<dc:creator>Bryant, K. L.</dc:creator>
<dc:creator>Foxley, S.</dc:creator>
<dc:creator>Howard, A. F. D.</dc:creator>
<dc:creator>Huszar, I.</dc:creator>
<dc:creator>Khrapitchev, A. A.</dc:creator>
<dc:creator>Leonte, A.</dc:creator>
<dc:creator>Manger, P. R.</dc:creator>
<dc:creator>Menke, R. A. L.</dc:creator>
<dc:creator>Mollink, J.</dc:creator>
<dc:creator>Mortimer, D.</dc:creator>
<dc:creator>Pallebage-Gamarallage, M.</dc:creator>
<dc:creator>Roumazeilles, L.</dc:creator>
<dc:creator>Sallet, J.</dc:creator>
<dc:creator>Scott, C.</dc:creator>
<dc:creator>Smart, A.</dc:creator>
<dc:creator>Turner, M. R.</dc:creator>
<dc:creator>Wang, C.</dc:creator>
<dc:creator>Jbabdi, S.</dc:creator>
<dc:creator>Mars, R. B.</dc:creator>
<dc:creator>Miller, K. L.</dc:creator>
<dc:date>2021-06-22</dc:date>
<dc:identifier>doi:10.1101/2021.06.21.449154</dc:identifier>
<dc:title><![CDATA[The Digital Brain Bank: an open access platform for post-mortem datasets]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-06-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.12.15.422826v1?rss=1">
<title>
<![CDATA[
Heterogeneous relationships between white matter and behaviour 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.12.15.422826v1?rss=1"
</link>
<description><![CDATA[
Several studies have established specific relationships between White Matter (WM) and behaviour. However, these studies have typically focussed on fractional anisotropy (FA), a neuroimaging metric that is sensitive to multiple tissue properties, making it difficult to identify what biological aspects of WM may drive such relationships. Here, we carry out a pre-registered assessment of WM-behaviour relationships in 50 healthy individuals across multiple behavioural and anatomical domains, and complementing FA with myelin-sensitive quantitative MR modalities (MT, R1, R2*).

Surprisingly, we only find support for predicted relationships between FA and behaviour in one of three pre-registered tests. For one behavioural domain, where we failed to detect an FA-behaviour correlation, we instead find evidence for a correlation between behaviour and R1. This hints that multimodal approaches are able to identify a wider range of WM-behaviour relationships than focusing on FA alone.

To test whether a common biological substrate such as myelin underlies WM-behaviour relationships, we then ran joint multimodal analyses, combining across all MRI parameters considered. No significant multimodal signatures were found and power analyses suggested that sample sizes of 40 to 200 may be required to detect such joint multimodal effects, depending on the task being considered.

These results demonstrate that FA-behaviour relationships from the literature can be replicated, but may not be easily generalisable across domains. Instead, multimodal microstructural imaging may be best placed to detect a wider range of WM-behaviour relationships, as different MRI modalities provide distinct biological sensitivities. Our findings highlight a broad heterogeneity in WMs relationship with behaviour, suggesting that variable biological effects may be shaping their interaction.

HighlightsO_LIPre-registered testing of microstructural imaging across modalities (FA, MT, R1, R2*) to test WM-behaviour relationships.
C_LIO_LIPartial support for FA-behaviour relationships hypothesised based on previous literature.
C_LIO_LIMultimodal approaches can help detect WM-behaviour relationships that are not detected with FA alone.
C_LIO_LISample sizes of 40 to 200 may be needed to detect myelin-behaviour relationships in joint multimodal analyses.
C_LIO_LIVariable biological effects may be shaping WM-behaviour relationships.
C_LI
]]></description>
<dc:creator>Lazari, A.</dc:creator>
<dc:creator>Salvan, P.</dc:creator>
<dc:creator>Cottaar, M.</dc:creator>
<dc:creator>Papp, D.</dc:creator>
<dc:creator>van der Werf, O. J.</dc:creator>
<dc:creator>Johnstone, A.</dc:creator>
<dc:creator>Sanders, Z.-B.</dc:creator>
<dc:creator>Sampaio-Baptista, C.</dc:creator>
<dc:creator>Eichert, N.</dc:creator>
<dc:creator>Miyamoto, K.</dc:creator>
<dc:creator>Winkler, A.</dc:creator>
<dc:creator>Callaghan, M. F.</dc:creator>
<dc:creator>Nichols, T. E.</dc:creator>
<dc:creator>Stagg, C. J.</dc:creator>
<dc:creator>Rushworth, M.</dc:creator>
<dc:creator>Verhagen, L.</dc:creator>
<dc:creator>Johansen-Berg, H.</dc:creator>
<dc:date>2020-12-15</dc:date>
<dc:identifier>doi:10.1101/2020.12.15.422826</dc:identifier>
<dc:title><![CDATA[Heterogeneous relationships between white matter and behaviour]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-12-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.06.25.449953v1?rss=1">
<title>
<![CDATA[
Metabolic resilience is encoded in genome plasticity 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.06.25.449953v1?rss=1"
</link>
<description><![CDATA[
Metabolism plays a central role in evolution, as resource conservation is a selective pressure for fitness and survival. Resource-driven adaptations offer a good model to study evolutionary innovation more broadly. It remains unknown how resource-driven optimization of genome function integrates chromatin architecture with transcriptional phase transitions. Here we show that tuning of genome architecture and heterotypic transcriptional condensates mediate resilience to nutrient limitation. Network genomic integration of phenotypic, structural, and functional relationships reveals that fat tissue promotes organismal adaptations through metabolic acceleration chromatin domains and heterotypic PGC1A condensates. We find evolutionary adaptations in several dimensions; low conservation of amino acid residues within protein disorder regions, nonrandom chromatin location of metabolic acceleration domains, condensate-chromatin stability through cis-regulatory anchoring and encoding of genome plasticity in radial chromatin organization. We show that environmental tuning of these adaptations leads to fasting endurance, through efficient nuclear compartmentalization of lipid metabolic regions, and, locally, human-specific burst kinetics of lipid cycling genes. This process reduces oxidative stress, and fatty-acid mediated cellular acidification, enabling endurance of condensate chromatin conformations. Comparative genomics of genetic and diet perturbations reveal mammalian convergence of phenotype and structural relationships, along with loss of transcriptional control by diet-induced obesity. Further, we find that radial transcriptional organization is encoded in functional divergence of metabolic disease variant-hubs, heterotypic condensate composition, and protein residues sensing metabolic variation. During fuel restriction, these features license the formation of large heterotypic condensates that buffer proton excess, and shift viscoelasticity for condensate endurance. This mechanism maintains physiological pH, reduces pH-resilient inflammatory gene programs, and enables genome plasticity through transcriptionally driven cell-specific chromatin contacts. In vivo manipulation of this circuit promotes fasting-like adaptations with heterotypic nuclear compartments, metabolic and cell-specific homeostasis. In sum, we uncover here a general principle by which transcription uses environmental fluctuations for genome function, and demonstrate how resource conservation optimizes transcriptional self-organization through robust feedback integrators, highlighting obesity as an inhibitor of genome plasticity relevant for many diseases.
]]></description>
<dc:creator>Agudelo, L. Z.</dc:creator>
<dc:creator>Tuyeras, R. V.</dc:creator>
<dc:creator>Llinares, C.</dc:creator>
<dc:creator>Morcuende, A.</dc:creator>
<dc:creator>Park, Y.</dc:creator>
<dc:creator>Sun, N.</dc:creator>
<dc:creator>Kuosmanen, S. M.</dc:creator>
<dc:creator>Atabaki-Pasdar, N.</dc:creator>
<dc:creator>Ho, L.-L.</dc:creator>
<dc:creator>Galani, K.</dc:creator>
<dc:creator>Franks, P. W.</dc:creator>
<dc:creator>Kutlu, B.</dc:creator>
<dc:creator>Grove, K.</dc:creator>
<dc:creator>Femenia, T.</dc:creator>
<dc:creator>Kellis, M.</dc:creator>
<dc:date>2021-06-27</dc:date>
<dc:identifier>doi:10.1101/2021.06.25.449953</dc:identifier>
<dc:title><![CDATA[Metabolic resilience is encoded in genome plasticity]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-06-27</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.06.05.446432v1?rss=1">
<title>
<![CDATA[
STED super-resolution imaging of membrane packing and dynamics by exchangeable polarity-sensitive dyes 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.06.05.446432v1?rss=1"
</link>
<description><![CDATA[
Understanding the plasma membrane nano-scale organisation and dynamics in living cells requires microscopy techniques with high spatial and temporal resolution, permitting for long acquisition times, and that allow for the quantification of membrane biophysical properties such as lipid ordering. Among the most popular super-resolution techniques, stimulated emission depletion (STED) microscopy offers one of the highest temporal resolutions, ultimately defined by the scanning speed. However, monitoring live processes using STED microscopy is significantly limited by photobleaching, which recently has been circumvented by exchangeable membrane dyes that only temporarily reside in the membrane. Here, we show that NR4A, a polarity-sensitive exchangeable plasma membrane probe based on Nile Red, permits the super-resolved quantification of membrane biophysical parameters in real time with high temporal and spatial resolution as well as long acquisition times. The potential of this polarity-sensitive exchangeable dye is showcased by live-cell real-time 3D-STED recordings of bleb formation and lipid exchange during membrane fusion, as well as by STED-fluorescence correlation spectroscopy (STED-FCS) experiments for the simultaneous quantification of membrane dynamics and lipid packing, which correlate in model and live-cell membranes.
]]></description>
<dc:creator>Carravilla, P.</dc:creator>
<dc:creator>Dasgupta, A.</dc:creator>
<dc:creator>Zhurgenbayeva, G.</dc:creator>
<dc:creator>Danylchuk, D. I.</dc:creator>
<dc:creator>Klymchenko, A. S.</dc:creator>
<dc:creator>Sezgin, E.</dc:creator>
<dc:creator>Eggeling, C.</dc:creator>
<dc:date>2021-06-05</dc:date>
<dc:identifier>doi:10.1101/2021.06.05.446432</dc:identifier>
<dc:title><![CDATA[STED super-resolution imaging of membrane packing and dynamics by exchangeable polarity-sensitive dyes]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-06-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.06.09.447672v1?rss=1">
<title>
<![CDATA[
Spatiotemporally flexible subnetworks reveal the quasi-cyclic nature of integration and segregation in the human brain. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.06.09.447672v1?rss=1"
</link>
<description><![CDATA[
Though the organization of functional brain networks is modular at its core, modularity does not capture the full range of dynamic interactions between individual brain areas nor at the level of subnetworks. In this paper we present a hierarchical model that represents both flexible and modular aspects of intrinsic brain organization across time by constructing spatiotemporally flexible subnetworks. We also demonstrate that segregation and integration are complementary and simultaneous events. The method is based on combining the instantaneous phase synchrony analysis (IPSA) framework with community detection to identify a small, yet representative set of subnetwork components at the finest level of spatial granularity. At the next level, subnetwork components are combined into spatiotemporally flexibly subnetworks where temporal lag in the recruitment of areas within subnetworks is captured. Since individual brain areas are permitted to be part of multiple interleaved subnetworks, both modularity as well as more flexible tendencies of connectivity are accommodated for in the model. Importantly, we show that assignment of subnetworks to the same community (integration) corresponds to positive phase coherence within and between subnetworks, while assignment to different communities (segregation) corresponds to negative phase coherence or orthogonality. Together with disintegration, i.e. the breakdown of internal coupling within subnetwork components, orthogonality facilitates reorganization between subnetworks. In addition, we show that the duration of periods of integration is a function of the coupling strength within subnetworks and subnetwork components which indicates an underlying metastable dynamical regime. Based on the main tendencies for either integration or segregation, subnetworks are further clustered into larger meta-networks that are shown to correspond to combinations of core resting-state networks. We also demonstrate that subnetworks and meta-networks are coarse graining strategies that captures the quasi-cyclic recurrence of global patterns of integration and segregation in the brain. Finally, the method allows us to estimate in broad terms the spectrum of flexible and/or modular tendencies for individual brain areas.
]]></description>
<dc:creator>Strindberg, M.</dc:creator>
<dc:creator>Fransson, P.</dc:creator>
<dc:creator>Cabral, J.</dc:creator>
<dc:creator>Aden, U.</dc:creator>
<dc:date>2021-06-09</dc:date>
<dc:identifier>doi:10.1101/2021.06.09.447672</dc:identifier>
<dc:title><![CDATA[Spatiotemporally flexible subnetworks reveal the quasi-cyclic nature of integration and segregation in the human brain.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-06-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.06.08.447489v1?rss=1">
<title>
<![CDATA[
Brain charts for the human lifespan 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.06.08.447489v1?rss=1"
</link>
<description><![CDATA[
Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here, we built an interactive resource to benchmark brain morphology, www.brainchart.io, derived from any current or future sample of magnetic resonance imaging (MRI) data. With the goal of basing these reference charts on the largest and most inclusive dataset available, we aggregated 123,984 MRI scans from 101,457 participants aged from 115 days post-conception through 100 postnatal years, across more than 100 primary research studies. Cerebrum tissue volumes and other global or regional MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3; showed high stability of individual centile scores over longitudinal assessments; and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared to non-centiled MRI phenotypes, and provided a standardised measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In sum, brain charts are an essential first step towards robust quantification of individual deviations from normative trajectories in multiple, commonly-used neuroimaging phenotypes. Our collaborative study proves the principle that brain charts are achievable on a global scale over the entire lifespan, and applicable to analysis of diverse developmental and clinical effects on human brain structure. Furthermore, we provide open resources to support future advances towards adoption of brain charts as standards for quantitative benchmarking of typical or atypical brain MRI scans.
]]></description>
<dc:creator>Bethlehem, R. A. I.</dc:creator>
<dc:creator>Seidlitz, J.</dc:creator>
<dc:creator>White, S. R.</dc:creator>
<dc:creator>Vogel, J. W.</dc:creator>
<dc:creator>Anderson, K. M.</dc:creator>
<dc:creator>Adamson, C.</dc:creator>
<dc:creator>Adler, S.</dc:creator>
<dc:creator>Alexopoulos, G. S.</dc:creator>
<dc:creator>Anagnostou, E.</dc:creator>
<dc:creator>Areces-Gonzalez, A.</dc:creator>
<dc:creator>Astle, D. E.</dc:creator>
<dc:creator>Auyeung, B.</dc:creator>
<dc:creator>Ayub, M.</dc:creator>
<dc:creator>Ball, G.</dc:creator>
<dc:creator>Baron-Cohen, S.</dc:creator>
<dc:creator>Beare, R.</dc:creator>
<dc:creator>Bedford, S. A.</dc:creator>
<dc:creator>Benegal, V.</dc:creator>
<dc:creator>Beyer, F.</dc:creator>
<dc:creator>Bin Bae, J.</dc:creator>
<dc:creator>Blangero, J.</dc:creator>
<dc:creator>Blesa Cabez, M.</dc:creator>
<dc:creator>Boardman, J. P.</dc:creator>
<dc:creator>Borzage, M.</dc:creator>
<dc:creator>Bosch-Bayard, J. F.</dc:creator>
<dc:creator>Bourke, N.</dc:creator>
<dc:creator>Calhoun, V. D.</dc:creator>
<dc:creator>Chakravarty, M. M.</dc:creator>
<dc:creator>Chen, C.</dc:creator>
<dc:creator>Chertavian, C.</dc:creator>
<dc:creator>Chetelat, G.</dc:creator>
<dc:creator>Chong, Y. S.</dc:creator>
<dc:creator>Cole, J. H.</dc:creator>
<dc:creator>Corvin, A.</dc:creator>
<dc:creator>Courchesne, E.</dc:creator>
<dc:creator>Crivello, F.</dc:creator>
<dc:creator>Cropley, V. L.</dc:creator>
<dc:creator>Crosbie, J.</dc:creator>
<dc:creator>Crossley, N.</dc:creator>
<dc:creator>Delarue, M.</dc:creator>
<dc:creator>Desrivieres, S.</dc:creator>
<dc:creator></dc:creator>
<dc:date>2021-06-10</dc:date>
<dc:identifier>doi:10.1101/2021.06.08.447489</dc:identifier>
<dc:title><![CDATA[Brain charts for the human lifespan]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-06-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.08.08.455487v1?rss=1">
<title>
<![CDATA[
Charting Brain Growth and Aging at High Spatial Precision 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.08.08.455487v1?rss=1"
</link>
<description><![CDATA[
Defining reference models for population variation, and the ability to study individual deviations is essential for understanding inter-individual variability and its relation to the onset and progression of medical conditions. In this work, we assembled a reference cohort of neuroimaging data from 82 sites (N=58,836; ages 2-100) and use normative modeling to characterize lifespan trajectories of cortical thickness and subcortical volume. Models are validated against a manually quality checked subset (N=24,354) and we provide an interface for transferring to new data sources. We showcase the clinical value by applying the models to a transdiagnostic psychiatric sample (N=1,985), showing they can be used to quantify variability underlying multiple disorders whilst also refining case-control inferences. These models will be augmented with additional samples and imaging modalities as they become available. This provides a common reference platform to bind results from different studies and ultimately paves the way for personalized clinical decision making.
]]></description>
<dc:creator>Rutherford, S.</dc:creator>
<dc:creator>Fraza, C.</dc:creator>
<dc:creator>Dinga, R.</dc:creator>
<dc:creator>Kia, S. M.</dc:creator>
<dc:creator>Wolfers, T.</dc:creator>
<dc:creator>Zabihi, M.</dc:creator>
<dc:creator>Berthet, P.</dc:creator>
<dc:creator>Worker, A.</dc:creator>
<dc:creator>Verdi, S.</dc:creator>
<dc:creator>Andrews, D.</dc:creator>
<dc:creator>Han, L.</dc:creator>
<dc:creator>Bayer, J.</dc:creator>
<dc:creator>Dazzan, P.</dc:creator>
<dc:creator>McGuire, P.</dc:creator>
<dc:creator>Mocking, R. T.</dc:creator>
<dc:creator>Schene, A.</dc:creator>
<dc:creator>Sripada, C.</dc:creator>
<dc:creator>Tso, I. F.</dc:creator>
<dc:creator>Duval, E. R.</dc:creator>
<dc:creator>Chang, S.-E.</dc:creator>
<dc:creator>Pennix, B. W.</dc:creator>
<dc:creator>Heitzeg, M. M.</dc:creator>
<dc:creator>Burt, S. A.</dc:creator>
<dc:creator>Hyde, L.</dc:creator>
<dc:creator>Amaral, D.</dc:creator>
<dc:creator>Nordahl, C. W.</dc:creator>
<dc:creator>Andreasssen, O. A.</dc:creator>
<dc:creator>Westlye, L. T.</dc:creator>
<dc:creator>Zahn, R.</dc:creator>
<dc:creator>Ruhe, H. G.</dc:creator>
<dc:creator>Beckmann, C.</dc:creator>
<dc:creator>Marquand, A. F.</dc:creator>
<dc:date>2021-08-08</dc:date>
<dc:identifier>doi:10.1101/2021.08.08.455487</dc:identifier>
<dc:title><![CDATA[Charting Brain Growth and Aging at High Spatial Precision]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-08-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.08.08.455583v1?rss=1">
<title>
<![CDATA[
The Normative Modeling Framework for Computational Psychiatry 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.08.08.455583v1?rss=1"
</link>
<description><![CDATA[
Normative modeling is an emerging and innovative framework for mapping individual differences at the level of a single subject or observation in relation to a reference model. It involves charting centiles of variation across a population in terms of mappings between biology and behavior which can then be used to make statistical inferences at the level of the individual. The fields of computational psychiatry and clinical neuroscience have been slow to transition away from patient versus "healthy" control analytic approaches, likely due to a lack of tools designed to properly model biological heterogeneity of mental disorders. Normative modeling provides a solution to address this issue and moves analysis away from case-control comparisons that rely on potentially noisy clinical labels. In this article, we define a standardized protocol to guide users through, from start to finish, normative modeling analysis using the Predictive Clinical Neuroscience toolkit (PCNtoolkit). We describe the input data selection process, provide intuition behind the various modeling choices, and conclude by demonstrating several examples of down-stream analyses the normative model results may facilitate, such as stratification of high-risk individuals, subtyping, and behavioral predictive modeling. The protocol takes approximately 1-3 hours to complete.
]]></description>
<dc:creator>Rutherford, S.</dc:creator>
<dc:creator>Kia, S. M.</dc:creator>
<dc:creator>Wolfers, T.</dc:creator>
<dc:creator>Fraza, C.</dc:creator>
<dc:creator>Zabihi, M.</dc:creator>
<dc:creator>Dinga, R.</dc:creator>
<dc:creator>Berthet, P.</dc:creator>
<dc:creator>Worker, A.</dc:creator>
<dc:creator>Verdi, S.</dc:creator>
<dc:creator>Ruhe, H. G.</dc:creator>
<dc:creator>Beckmann, C. F.</dc:creator>
<dc:creator>Marquand, A. F.</dc:creator>
<dc:date>2021-08-10</dc:date>
<dc:identifier>doi:10.1101/2021.08.08.455583</dc:identifier>
<dc:title><![CDATA[The Normative Modeling Framework for Computational Psychiatry]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-08-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.07.27.453965v1?rss=1">
<title>
<![CDATA[
Can fMRI functional connectivity index dynamic neural communication? 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.07.27.453965v1?rss=1"
</link>
<description><![CDATA[
In order to continuously respond to a changing environment and support self-generating cognition and behaviour, neural communication must be highly flexible and dynamic at the same time than hierarchically organized. While whole-brain fMRI measures have revealed robust yet changing patterns of statistical dependencies between regions, it is not clear whether these statistical patterns --referred to as functional connectivity-- can reflect dynamic large-scale communication in a way that is relevant to human cognition. For functional connectivity to reflect cognition, and therefore actual communication, we propose three necessary conditions: it must span sufficient temporal complexity to support the needs of cognition while still being highly organized so that the system behaves reliably; it must be able to adapt to the current behavioural context; it must exhibit fluctuations at timescales that are compatible with the timescales of cognition. To obtain reliable estimations of time-varying functional connectivity, we developed principal components of connectivity analysis (PCCA), an approach based on applying principal component analysis on multiple runs of a time-varying functional connectivity model. We use PCCA to show that functional connectivity follows low-yet multi-dimensional trajectories that can be reliably measured, and that these trajectories meet the aforementioned criteria. These analyses suggest that these trajectories might index certain aspects of communication between neural populations and support moment-to-moment cognition.

Significance StatementfMRI functional connectivity is one of the most widely used metrics in neuroimaging research in both theoretical research and clinical applications. However, this work suffers from a lack of context because we still do not fully understand what fMRI functional connectivity can or cannot reflect biologically and behaviourally. In particular, can it reflect between-region neuronal communication? We develop methods to reliably quantify temporal trajectories of functional connectivity and investigate the nature of these trajectories across different experimental conditions. Using these methods, we demonstrate that functional connectivity exhibits reliable changes that are context-dependent, reflect cognitive complexity, and bear a relationship with cognitive abilities. These conditions show that fMRI functional connectivity could reflect changes in between-region communication above and beyond non-neural factors.
]]></description>
<dc:creator>Alonso Martinez, S.</dc:creator>
<dc:creator>Llera Arenas, A.</dc:creator>
<dc:creator>Ter Horst, G. T.</dc:creator>
<dc:creator>Vidaurre, D.</dc:creator>
<dc:date>2021-07-27</dc:date>
<dc:identifier>doi:10.1101/2021.07.27.453965</dc:identifier>
<dc:title><![CDATA[Can fMRI functional connectivity index dynamic neural communication?]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-07-27</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.08.15.456225v1?rss=1">
<title>
<![CDATA[
Arg1+ microglia are critical for shaping cognition in female mice 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.08.15.456225v1?rss=1"
</link>
<description><![CDATA[
Diversity within microglia, the resident brain immune cells, is reported. Whether microglial subsets constitute different subtypes with intrinsic properties and unique functions has not been fully elucidated. Here, we describe a microglial subtype characterized by the expression of the enzyme Arginase-1, i.e. Arg1 +microglia, which is found predominantly in the cholinergic neuron-rich forebrain region during early postnatal development. Arg1+ microglia are frequently observed in close apposition to neurons and exhibit a distinctive molecular signature reflecting a reactive profile. Arg1 deficiency in microglia results in impaired dendritic spine maturation in the hippocampus where cholinergic neurons project, and cognitive behavioural deficiencies in female mice. Our results expand on microglia diversity and provide insights into distinctive spatiotemporal functions exerted by microglial subtypes.
]]></description>
<dc:creator>Stratoulias, V.</dc:creator>
<dc:creator>Ruiz, R.</dc:creator>
<dc:creator>Kanatani, S.</dc:creator>
<dc:creator>Osman, A. M.</dc:creator>
<dc:creator>Armengol, J. A.</dc:creator>
<dc:creator>Rodriguez-Moreno, A.</dc:creator>
<dc:creator>Murgoci, A.-N.</dc:creator>
<dc:creator>Garcia-Dominguez, I.</dc:creator>
<dc:creator>Keane, L.</dc:creator>
<dc:creator>Vazquez-Carbera, G.</dc:creator>
<dc:creator>Alonso-Bellido, I.</dc:creator>
<dc:creator>Vernoux, N.</dc:creator>
<dc:creator>Tejera, D.</dc:creator>
<dc:creator>Grabert, K.</dc:creator>
<dc:creator>Cheray, M.</dc:creator>
<dc:creator>Gonzalez-Rodriguez, P.</dc:creator>
<dc:creator>Perez-Villegas, E. M.</dc:creator>
<dc:creator>Martinez-Gallego, I.</dc:creator>
<dc:creator>Brodin, D.</dc:creator>
<dc:creator>Avila-Carino, J.</dc:creator>
<dc:creator>Airavaara, M.</dc:creator>
<dc:creator>Uhlen, P.</dc:creator>
<dc:creator>Heneka, M. T.</dc:creator>
<dc:creator>Tremblay, M.-E.</dc:creator>
<dc:creator>Blomgren, K.</dc:creator>
<dc:creator>Venero, J. L.</dc:creator>
<dc:creator>Joseph, B.</dc:creator>
<dc:date>2021-08-16</dc:date>
<dc:identifier>doi:10.1101/2021.08.15.456225</dc:identifier>
<dc:title><![CDATA[Arg1+ microglia are critical for shaping cognition in female mice]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-08-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.07.12.452018v1?rss=1">
<title>
<![CDATA[
The spatial landscape of clonal somatic mutations in benign and malignant tissue 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.07.12.452018v1?rss=1"
</link>
<description><![CDATA[
Defining the transition from benign to malignant tissue is fundamental to improve early diagnosis of cancer. Here, we provide an unsupervised approach to study spatial genome integrity in situ to gain molecular insight into clonal relationships. We employed spatially resolved transcriptomics to infer spatial copy number variations in >120 000 regions across multiple organs, in benign and malignant tissues. We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in cancer evolution. We highlight the power of an unsupervised approach to capture the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy.
]]></description>
<dc:creator>Erickson, A.</dc:creator>
<dc:creator>Berglund, E.</dc:creator>
<dc:creator>He, M.</dc:creator>
<dc:creator>Marklund, M.</dc:creator>
<dc:creator>Mirzazadeh, R.</dc:creator>
<dc:creator>Schultz, N.</dc:creator>
<dc:creator>Bergenstrahle, L.</dc:creator>
<dc:creator>Kvastad, L.</dc:creator>
<dc:creator>Andersson, A.</dc:creator>
<dc:creator>Bergenstrahle, J.</dc:creator>
<dc:creator>Larsson, L.</dc:creator>
<dc:creator>Shamikh, A.</dc:creator>
<dc:creator>Basmaci, E.</dc:creator>
<dc:creator>de Stahl, T. D.</dc:creator>
<dc:creator>Rajakumar, T.</dc:creator>
<dc:creator>Thrane, K.</dc:creator>
<dc:creator>Ji, A. L.</dc:creator>
<dc:creator>Khavari, P. A.</dc:creator>
<dc:creator>Tarish, F.</dc:creator>
<dc:creator>Tanoglidi, A.</dc:creator>
<dc:creator>Maaskola, J.</dc:creator>
<dc:creator>Colling, R.</dc:creator>
<dc:creator>Mirtti, T.</dc:creator>
<dc:creator>Hamdy, F. C.</dc:creator>
<dc:creator>Woodcock, D. J.</dc:creator>
<dc:creator>Helleday, T.</dc:creator>
<dc:creator>Mills, I. G.</dc:creator>
<dc:creator>Lamb, A. D.</dc:creator>
<dc:creator>Lundeberg, J.</dc:creator>
<dc:date>2021-07-12</dc:date>
<dc:identifier>doi:10.1101/2021.07.12.452018</dc:identifier>
<dc:title><![CDATA[The spatial landscape of clonal somatic mutations in benign and malignant tissue]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-07-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.08.24.457538v1?rss=1">
<title>
<![CDATA[
Global network structure and local transcriptomic vulnerability shape atrophy in sporadic and genetic behavioral variant frontotemporal dementia 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.08.24.457538v1?rss=1"
</link>
<description><![CDATA[
Connections among brain regions allow pathological perturbations to spread from a single source region to multiple regions. Patterns of neurodegeneration in multiple diseases, including behavioral variant of frontotemporal dementia (bvFTD), resemble the large-scale functional systems, but how bvFTD-related atrophy patterns relate to structural network organization remains unknown. Here we investigate whether neurodegeneration patterns in sporadic and genetic bvFTD are conditioned by connectome architecture. Regional atrophy patterns were estimated in both genetic bvFTD (75 patients, 247 controls) and sporadic bvFTD (70 patients, 123 controls). We first identify distributed atrophy patterns in bvFTD, mainly targeting areas associated with the limbic intrinsic network and insular cytoarchitectonic class. Regional atrophy was significantly correlated with atrophy of structurally- and functionally-connected neighbors, demonstrating that network structure shapes atrophy patterns. The anterior insula was identified as the predominant group epicenter of brain atrophy using data-driven and simulation-based methods, with some secondary regions in frontal ventromedial and anteromedial temporal areas. Finally, we find that FTD-related genes, namely C9orf72 and TARDBP, confer local transcriptomic vulnerability to the disease, effectively modulating the propagation of pathology through the connectome. Collectively, our results demonstrate that atrophy patterns in sporadic and genetic bvFTD are jointly shaped by global connectome architecture and local transcriptomic vulnerability.
]]></description>
<dc:creator>Shafiei, G.</dc:creator>
<dc:creator>Bazinet, V.</dc:creator>
<dc:creator>Dadar, M.</dc:creator>
<dc:creator>Manera, A. L.</dc:creator>
<dc:creator>Collins, D. L.</dc:creator>
<dc:creator>Dagher, A.</dc:creator>
<dc:creator>Borroni, B.</dc:creator>
<dc:creator>Sanchez-Valle, R.</dc:creator>
<dc:creator>Moreno, F.</dc:creator>
<dc:creator>Laforce, R.</dc:creator>
<dc:creator>Graff, C.</dc:creator>
<dc:creator>Synofzik, M.</dc:creator>
<dc:creator>Galimberti, D.</dc:creator>
<dc:creator>Rowe, J. B.</dc:creator>
<dc:creator>Masellis, M.</dc:creator>
<dc:creator>Tartaglia, M. C.</dc:creator>
<dc:creator>Finger, E.</dc:creator>
<dc:creator>Vandenberghe, R.</dc:creator>
<dc:creator>de Mendonca, A.</dc:creator>
<dc:creator>Tagliavini, F.</dc:creator>
<dc:creator>Santana, I.</dc:creator>
<dc:creator>Butler, C.</dc:creator>
<dc:creator>Gerhard, A.</dc:creator>
<dc:creator>Danek, A.</dc:creator>
<dc:creator>Levin, J.</dc:creator>
<dc:creator>Otto, M.</dc:creator>
<dc:creator>Sorbi, S.</dc:creator>
<dc:creator>Jiskoot, L. C.</dc:creator>
<dc:creator>Seelaar, H.</dc:creator>
<dc:creator>van Swieten, J. C.</dc:creator>
<dc:creator>Rohrer, J. D.</dc:creator>
<dc:creator>Misic, B.</dc:creator>
<dc:creator>Ducharme, S.</dc:creator>
<dc:creator>Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI),</dc:creator>
<dc:creator>GENetic Frontotemporal dementia Initiative (GENFI),</dc:creator>
<dc:date>2021-08-26</dc:date>
<dc:identifier>doi:10.1101/2021.08.24.457538</dc:identifier>
<dc:title><![CDATA[Global network structure and local transcriptomic vulnerability shape atrophy in sporadic and genetic behavioral variant frontotemporal dementia]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-08-26</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.08.08.455573v1?rss=1">
<title>
<![CDATA[
Segregation of neural crest specific lineage trajectories from a heterogeneous neural plate border territory only emerges at neurulation 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.08.08.455573v1?rss=1"
</link>
<description><![CDATA[
The epiblast of vertebrate embryos is comprised of neural and non-neural ectoderm, with the border territory at their intersection harbouring neural crest and cranial placode progenitors. Here we profile avian epiblast cells as a function of time using single-cell RNA-seq to define transcriptional changes in the emerging  neural plate border. The results reveal gradual establishment of heterogeneous neural plate border signatures, including novel genes that we validate by fluorescent in situ hybridisation. Developmental trajectory analysis shows that segregation of neural plate border lineages only commences at early neurulation, rather than at gastrulation as previously predicted. We find that cells expressing the prospective neural crest marker Pax7 contribute to multiple lineages, and a subset of premigratory neural crest cells shares a transcriptional signature with their border precursors. Together, our results suggest that cells at the neural plate border remain heterogeneous until early neurulation, at which time progenitors become progressively allocated toward defined lineages.
]]></description>
<dc:creator>Williams, R.</dc:creator>
<dc:creator>Lukoseviciute, M.</dc:creator>
<dc:creator>Sauka-Spengler, T.</dc:creator>
<dc:creator>Bronner, M. E.</dc:creator>
<dc:date>2021-08-08</dc:date>
<dc:identifier>doi:10.1101/2021.08.08.455573</dc:identifier>
<dc:title><![CDATA[Segregation of neural crest specific lineage trajectories from a heterogeneous neural plate border territory only emerges at neurulation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-08-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.02.10.430513v1?rss=1">
<title>
<![CDATA[
Neuromesodermal progenitor origin of trunk neural crest in vivo 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.02.10.430513v1?rss=1"
</link>
<description><![CDATA[
Neural crest (NC) is a vertebrate-specific population of multipotent embryonic cells predisposed to diverse derivatives along the anteroposterior (A-P) axis. Only cranial NC progenitors give rise to ectomesenchymal cell types, whereas trunk NC is biased for neuronal cell fates. By integrating multimodal single-cell analysis, we provide evidence for divergent embryonic origins of cranial vs. trunk NC that explain this dichotomy. We show that the NC regulator foxd3 is heterogeneously expressed across the A-P axis and identify its specific cranial and trunk autoregulatory enhancers. Whereas cranial-specific enhancer is active in the bona fide NC, the trunk foxd3 autoregulatory element surprisingly marked bipotent tailbud neuromesodermal progenitors (NMps). We integrated NMp single cell epigemomics and trasncriptomics data and for the first time reconstructed anamniote NMp gene regulatory network. Moreover, using pseudotime and developmental trajectory analyses of NMps and NC during normal development and in foxd3 mutants, we demonstrate an active role for foxd3 in balancing non-cranial NC and NMp fates during early embryonic development. Strikingly, we show that a portion of posterior NC in the developing zebrafish embryo is derived from the pro-neural NMps. This suggests a common embryonic origin of trunk NC and NM progenitors that is distinct from cranial NC anlage, and elucidates pro-neural bias of trunk NC.
]]></description>
<dc:creator>Lukoseviciute, M.</dc:creator>
<dc:creator>Mayes, S.</dc:creator>
<dc:creator>Sauka-Spengler, T.</dc:creator>
<dc:date>2021-02-10</dc:date>
<dc:identifier>doi:10.1101/2021.02.10.430513</dc:identifier>
<dc:title><![CDATA[Neuromesodermal progenitor origin of trunk neural crest in vivo]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-02-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.08.09.454869v1?rss=1">
<title>
<![CDATA[
Integrative annotation of genomic features reveal functional elements and epigenetic landscapes in the developing zebrafish 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.08.09.454869v1?rss=1"
</link>
<description><![CDATA[
Zebrafish, a popular model for embryonic development and for modelling human diseases, has so far lacked a systematic functional annotation programme akin to those in other animal models. To address this, we formed the international DANIO-CODE consortium and created the first central repository to store and process zebrafish developmental functional genomic data. Our Data Coordination Center (https://danio-code.zfin.org) combines a total of 1,802 sets of unpublished and reanalysed published genomics data, which we used to improve existing annotations and show its utility in experimental design. We identified over 140,000 cis-regulatory elements in development, including novel classes with distinct features dependent on their activity in time and space. We delineated the distinction between regulatory elements active during zygotic genome activation and those active during organogenesis, identifying new aspects of how they relate to each other. Finally, we matched regulatory elements and epigenomic landscapes between zebrafish and mouse and predict functional relationships between them beyond sequence similarity, extending the utility of zebrafish developmental genomics to mammals.
]]></description>
<dc:creator>Baranasic, D.</dc:creator>
<dc:creator>Hoertenhuber, M.</dc:creator>
<dc:creator>Balwierz, P.</dc:creator>
<dc:creator>Zehnder, T.</dc:creator>
<dc:creator>Mukarram, A. K.</dc:creator>
<dc:creator>Nepal, C.</dc:creator>
<dc:creator>Varnai, C.</dc:creator>
<dc:creator>Hadzhiev, Y.</dc:creator>
<dc:creator>Jimenez-Gonzalez, A.</dc:creator>
<dc:creator>Li, N.</dc:creator>
<dc:creator>Wragg, J. W.</dc:creator>
<dc:creator>D'Orazio, F.</dc:creator>
<dc:creator>Diaz, N.</dc:creator>
<dc:creator>Hernandez-Rodriguez, B.</dc:creator>
<dc:creator>Chen, Z.</dc:creator>
<dc:creator>Stoiber, M.</dc:creator>
<dc:creator>Dong, M.</dc:creator>
<dc:creator>Stevens, I.</dc:creator>
<dc:creator>Ross, S. E.</dc:creator>
<dc:creator>Eagle, A.</dc:creator>
<dc:creator>Martin, R.</dc:creator>
<dc:creator>Obasaju, P.</dc:creator>
<dc:creator>Rastegar, S.</dc:creator>
<dc:creator>McGarvey, A. C.</dc:creator>
<dc:creator>Kopp, W.</dc:creator>
<dc:creator>Chambers, E.</dc:creator>
<dc:creator>Wang, D.</dc:creator>
<dc:creator>Kim, H. K.</dc:creator>
<dc:creator>Acemel, R. D.</dc:creator>
<dc:creator>Naranjo, S.</dc:creator>
<dc:creator>Lapinski, M.</dc:creator>
<dc:creator>Chong-Morrison, V.</dc:creator>
<dc:creator>Mathavan, S.</dc:creator>
<dc:creator>Peers, B.</dc:creator>
<dc:creator>Sauka-Spengler, T.</dc:creator>
<dc:creator>Vingron, M.</dc:creator>
<dc:creator>Carninci, P.</dc:creator>
<dc:creator>Ohler, U.</dc:creator>
<dc:creator>Lacadie, S. A.</dc:creator>
<dc:creator>Burgess, S.</dc:creator>
<dc:creator>Winata, C.</dc:creator>
<dc:creator>van Eeden, F.</dc:creator>
<dc:creator>Vaquerizas, J. M.</dc:creator>
<dc:creator>G</dc:creator>
<dc:date>2021-08-09</dc:date>
<dc:identifier>doi:10.1101/2021.08.09.454869</dc:identifier>
<dc:title><![CDATA[Integrative annotation of genomic features reveal functional elements and epigenetic landscapes in the developing zebrafish]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-08-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.09.23.461002v1?rss=1">
<title>
<![CDATA[
Fluctuations in Neural Complexity During Wakefulness Relate To Conscious Level and Cognition 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.09.23.461002v1?rss=1"
</link>
<description><![CDATA[
There has been considerable recent progress in measuring conscious level using neural complexity measures. For instance, such measures can reliably distinguish healthy awake from asleep subjects and vegetative state patients. However, this line of research has never explored the dynamics of conscious level during normal wakefulness. Being able to capture meaningful differences in conscious level during wakefulness may provide a vital new insight into the nature of consciousness, by demonstrating what biological, behavioural and cognitive factors relate to such differences. Here we take advantage of a large MEG and fMRI dataset of healthy adults, to examine within-subject conscious level fluctuations during resting state and tasks, by using a range of complexity measures. We first establish the validity of this approach in both neuroimaging domains by relating neural complexity measures to pre-existing techniques for capturing transitions of consciousness from full wakefulness into drowsiness and the earliest stages of sleep, finding decreased complexity as participants become increasingly drowsy. We further demonstrate that neural complexity measures in both MEG and fMRI change both within and between tasks, and relate to performance on an executive task, with higher complexity associated with better performance and faster reaction times. This approach provides a powerful new route to further explore the cognitive and neural underpinnings of consciousness.
]]></description>
<dc:creator>Mediano, P.</dc:creator>
<dc:creator>Ikkala, A.</dc:creator>
<dc:creator>Kievit, R. A.</dc:creator>
<dc:creator>Jagannathan, S. R.</dc:creator>
<dc:creator>Varley, T. F.</dc:creator>
<dc:creator>Stamatakis, E. A.</dc:creator>
<dc:creator>Bekinschtein, T. A.</dc:creator>
<dc:creator>Bor, D.</dc:creator>
<dc:date>2021-09-23</dc:date>
<dc:identifier>doi:10.1101/2021.09.23.461002</dc:identifier>
<dc:title><![CDATA[Fluctuations in Neural Complexity During Wakefulness Relate To Conscious Level and Cognition]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-09-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.09.16.458157v1?rss=1">
<title>
<![CDATA[
Global trade-offs in tree functional traits 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.09.16.458157v1?rss=1"
</link>
<description><![CDATA[
AO_SCPLOWBSTRACTC_SCPLOWDue to massive energetic investments in woody support structures, trees are subject to unique physiological, mechanical, and ecological pressures not experienced by herbaceous plants. When considering trait relationships across the entire plant kingdom, plant trait frameworks typically must omit traits unique to large woody species, thereby limiting our understanding of how these distinct ecological pressures shape trait relationships in trees. Here, by considering 18 functional traits--reflecting leaf economics, wood structure, tree size, reproduction, and below-ground allocation--we quantify the major axes of variation governing trait expression of trees worldwide. We show that trait variation within and across angiosperms and gymnosperms is captured by two independent processes: one reflecting tree size and competition for light, the other reflecting leaf photosynthetic capacity and nutrient economies. By exploring multidimensional relationships across clusters of traits, we further identify a representative set of seven traits which captures the majority of variation in form and function in trees: maximum tree height, stem conduit diameter, specific leaf area, seed mass, bark thickness, root depth, and wood density. Collectively, this work informs future trait-based research into the functional biogeography of trees, and contributes to our fundamental understanding of the ecological and evolutionary controls on forest biodiversity and productivity worldwide.
]]></description>
<dc:creator>Maynard, D. S.</dc:creator>
<dc:creator>Bialic-Murphy, L.</dc:creator>
<dc:creator>Zohner, C. M.</dc:creator>
<dc:creator>Averill, C.</dc:creator>
<dc:creator>van den Hoogen, J.</dc:creator>
<dc:creator>Ma, H.</dc:creator>
<dc:creator>Mo, L.</dc:creator>
<dc:creator>Smith, G. R.</dc:creator>
<dc:creator>Aubin, I.</dc:creator>
<dc:creator>Berenguer, E.</dc:creator>
<dc:creator>Boonman, C. C. F.</dc:creator>
<dc:creator>Catford, J.</dc:creator>
<dc:creator>Cerabolini, B. E. L.</dc:creator>
<dc:creator>Dias, A.</dc:creator>
<dc:creator>Gonzalez-Melo, A.</dc:creator>
<dc:creator>Heitz, P.</dc:creator>
<dc:creator>Lusk, C. H.</dc:creator>
<dc:creator>Mori, A. S.</dc:creator>
<dc:creator>Niinemets, U.</dc:creator>
<dc:creator>Pillar, V. D.</dc:creator>
<dc:creator>Rosell, J. A.</dc:creator>
<dc:creator>Schurr, F. M.</dc:creator>
<dc:creator>Sheremetev, S. N.</dc:creator>
<dc:creator>da Silva, A. C.</dc:creator>
<dc:creator>Sosinski, E.</dc:creator>
<dc:creator>van Bodegom, P. M.</dc:creator>
<dc:creator>Weiher, E.</dc:creator>
<dc:creator>Bönisch, G.</dc:creator>
<dc:creator>Kattge, J.</dc:creator>
<dc:creator>Crowther, T. W.</dc:creator>
<dc:date>2021-09-17</dc:date>
<dc:identifier>doi:10.1101/2021.09.16.458157</dc:identifier>
<dc:title><![CDATA[Global trade-offs in tree functional traits]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-09-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.09.03.458926v1?rss=1">
<title>
<![CDATA[
Supervised Phenotype Discovery from Multimodal Brain Imaging 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.09.03.458926v1?rss=1"
</link>
<description><![CDATA[
Data-driven discovery of image-derived phenotypes (IDPs) from large-scale multimodal brain imaging data has enormous potential for neuroscientific and clinical research by linking IDPs to subjects demographic, behavioural, clinical and cognitive measures (i.e., non-imaging derived phenotypes or nIDPs). However, current approaches are primarily based on unsupervised approaches, without the use of information in nIDPs. In this paper, we proposed a semi-supervised, multimodal, and multi-task fusion approach, termed SuperBigFLICA, for IDP discovery, which simultaneously integrates information from multiple imaging modalities as well as multiple nIDPs. SuperBigFLICA is computationally efficient and largely avoids the need for parameter tuning. Using the UK Biobank brain imaging dataset with around 40,000 subjects and 47 modalities, along with more than 17,000 nIDPs, we showed that SuperBigFLICA enhances the prediction power of nIDPs, benchmarked against IDPs derived by conventional expert-knowledge and unsupervised-learning approaches (with average nIDP prediction accuracy improvements of up to 46%). It also enables the learning of generic imaging features that can predict new nIDPs. Further empirical analysis of the SuperBigFLICA algorithm demonstrates its robustness in different prediction tasks and the ability to derive biologically meaningful IDPs in predicting health outcomes and cognitive nIDPs, such as fluid intelligence and hypertension.
]]></description>
<dc:creator>Gong, W.</dc:creator>
<dc:creator>Bai, S.</dc:creator>
<dc:creator>Zheng, Y.-Q.</dc:creator>
<dc:creator>Beckmann, C.</dc:creator>
<dc:creator>Smith, S. M.</dc:creator>
<dc:date>2021-09-06</dc:date>
<dc:identifier>doi:10.1101/2021.09.03.458926</dc:identifier>
<dc:title><![CDATA[Supervised Phenotype Discovery from Multimodal Brain Imaging]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-09-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.09.02.458694v1?rss=1">
<title>
<![CDATA[
Diffusion MRI Anisotropy in the Cerebral Cortex is Determined by Unmyelinated Tissue Features 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.09.02.458694v1?rss=1"
</link>
<description><![CDATA[
Diffusion magnetic resonance imaging (dMRI) is commonly used to assess the tissue and cellular substructure of the human brain. In the white matter, myelinated axons are the principal neural elements that shape dMRI through the restriction of water diffusion; however, in the gray matter the relative contributions of myelinated axons and other tissue features to dMRI are poorly understood. Here we investigate the determinants of diffusion in the cerebral cortex. Specifically, we ask whether myelinated axons significantly shape dMRI fractional anisotropy (dMRI-FA), a measure commonly used to characterize tissue properties in humans. We compared ultra-high resolution ex vivo dMRI data from the brain of a marmoset monkey with both myelin- and Nissl-stained histological sections obtained from the same brain after scanning. We found that cortical diffusion was only minimally related to the density and arrangement of myelinated fibers. Instead, the spatial pattern of dMRI-FA in the cortex was more closely related to anisotropy of tissue features indicated in the Nissl stained sections. Our results suggest that unmyelinated neurites such as large caliber apical dendrites are the primary features shaping dMRI measures in the cerebral cortex.
]]></description>
<dc:creator>Reveley, C.</dc:creator>
<dc:creator>Ye, F. Q.</dc:creator>
<dc:creator>Mars, R. B.</dc:creator>
<dc:creator>Leopold, D. A.</dc:creator>
<dc:date>2021-09-02</dc:date>
<dc:identifier>doi:10.1101/2021.09.02.458694</dc:identifier>
<dc:title><![CDATA[Diffusion MRI Anisotropy in the Cerebral Cortex is Determined by Unmyelinated Tissue Features]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-09-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.11.18.469069v1?rss=1">
<title>
<![CDATA[
Sharing individualised template MRI data for MEG source reconstruction: a solution for open data while keeping subject confidentiality 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.11.18.469069v1?rss=1"
</link>
<description><![CDATA[
The increasing requirements for adoption of FAIR data management and sharing original research data from neuroimaging studies can be at odds with protecting the anonymity of the research participants due to the person-identifiable anatomical features in the data. We propose a solution to this dilemma for anatomical MRIs used in MEG source analysis. In MEG analysis, the channel-level data is reconstructed to the source-level using models derived from anatomical MRIs. Sharing data, therefore, requires sharing the anatomical MRI to replicate the analysis. The suggested solution is to replace the individual anatomical MRIs with individualised warped templates that can be used to carry out the MEG source analysis and that provide sufficient geometrical similarity to the original participants MRIs.

First, we demonstrate how the individualised template warping can be implemented with one of the leading open-source neuroimaging analysis toolboxes. Second, we compare results from four different MEG source reconstruction methods performed with an individualised warped template to those using the participants original MRI. While the source reconstruction results are not numerically identical, there is a high similarity between the results for single dipole fits, dynamic imaging of coherent sources beamforming, and atlas-based virtual channel beamforming. There is a moderate similarity between minimum-norm estimates, as anticipated due to this method being anatomically constrained and dependent on the exact morphological features of the cortical sheet.

We also compared the morphological features of the warped template to those of the original MRI. These showed a high similarity in grey matter volume and surface area, but a low similarity in the average cortical thickness and the mean folding index within cortical parcels.

Taken together, this demonstrates that the results obtained by MEG source reconstruction can be preserved with the warped templates, whereas the anatomical and morphological fingerprint is sufficiently altered to protect the anonymity of research participants. In cases where participants consent to sharing anatomical MRI data, it remains preferable to share the original defaced data with an appropriate data use agreement. In cases where participants did not consent to share their MRIs, the individualised warped MRI template offers a good compromise in sharing data for reuse while retaining anonymity for those research participants.
]]></description>
<dc:creator>Vinding, M. C.</dc:creator>
<dc:creator>Oostenveld, R.</dc:creator>
<dc:date>2021-11-19</dc:date>
<dc:identifier>doi:10.1101/2021.11.18.469069</dc:identifier>
<dc:title><![CDATA[Sharing individualised template MRI data for MEG source reconstruction: a solution for open data while keeping subject confidentiality]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-11-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.04.24.031138v1?rss=1">
<title>
<![CDATA[
Dynamics of Brain Structure and its Genetic Architecture over the Lifespan 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.04.24.031138v1?rss=1"
</link>
<description><![CDATA[
Human brain structure changes throughout our lives. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental, and neurodegenerative diseases. Here, we identified common genetic variants that affect rates of brain growth or atrophy, in the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal MRI data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene-set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and ageing.
]]></description>
<dc:creator>Brouwer, R. M.</dc:creator>
<dc:creator>Klein, M.</dc:creator>
<dc:creator>Grasby, K. L.</dc:creator>
<dc:creator>Schnack, H. G.</dc:creator>
<dc:creator>Jahanshad, N.</dc:creator>
<dc:creator>Teeuw, J.</dc:creator>
<dc:creator>Thomopoulos, S. I.</dc:creator>
<dc:creator>Sprooten, E.</dc:creator>
<dc:creator>Franz, C. E.</dc:creator>
<dc:creator>Gogtay, N.</dc:creator>
<dc:creator>Kremen, W.</dc:creator>
<dc:creator>Panizzon, M. S.</dc:creator>
<dc:creator>Olde Loohuis, L. M.</dc:creator>
<dc:creator>Whelan, C. D.</dc:creator>
<dc:creator>Aghajani, M.</dc:creator>
<dc:creator>Alloza, C.</dc:creator>
<dc:creator>Alnaes, D.</dc:creator>
<dc:creator>Artiges, E.</dc:creator>
<dc:creator>Ayesa-Arriola, R.</dc:creator>
<dc:creator>Barker, G. J.</dc:creator>
<dc:creator>Blok, E.</dc:creator>
<dc:creator>Boen, E.</dc:creator>
<dc:creator>Breukelaar, I. A.</dc:creator>
<dc:creator>Bright, J. K.</dc:creator>
<dc:creator>Buimer, E. E.</dc:creator>
<dc:creator>Bülow, R.</dc:creator>
<dc:creator>Cannon, D. M.</dc:creator>
<dc:creator>Ciufolini, S.</dc:creator>
<dc:creator>Crossley, N. A.</dc:creator>
<dc:creator>Damatac, C. G.</dc:creator>
<dc:creator>Dazzan, P.</dc:creator>
<dc:creator>de Mol, C. L.</dc:creator>
<dc:creator>de Zwarte, S. M.</dc:creator>
<dc:creator>Desrivieres, S.</dc:creator>
<dc:creator>Diaz-Caneja, C. M.</dc:creator>
<dc:creator>Doan, N. T.</dc:creator>
<dc:creator>Dohm, K.</dc:creator>
<dc:creator>Fröhner, J. H.</dc:creator>
<dc:creator>Goltermann, J.</dc:creator>
<dc:creator>Grigis, A.</dc:creator>
<dc:creator>Grotegerd, D</dc:creator>
<dc:date>2020-04-27</dc:date>
<dc:identifier>doi:10.1101/2020.04.24.031138</dc:identifier>
<dc:title><![CDATA[Dynamics of Brain Structure and its Genetic Architecture over the Lifespan]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-04-27</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.11.04.466897v1?rss=1">
<title>
<![CDATA[
Genome-wide association analyses of individual differences in quantitatively assessed reading- and language-related skills in up to 34,000 people 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.11.04.466897v1?rss=1"
</link>
<description><![CDATA[
The use of spoken and written language is a capacity that is unique to humans. Individual differences in reading- and language-related skills are influenced by genetic variation, with twin-based heritability estimates of 30-80%, depending on the trait. The relevant genetic architecture is complex, heterogeneous, and multifactorial, and yet to be investigated with well-powered studies. Here, we present a multicohort genome-wide association study (GWAS) of five traits assessed individually using psychometric measures: word reading, nonword reading, spelling, phoneme awareness, and nonword repetition, with total sample sizes ranging from 13,633 to 33,959 participants aged 5-26 years (12,411 to 27,180 for those with European ancestry, defined by principal component analyses). We identified a genome-wide significant association with word reading (rs11208009, p=1.098 x 10-8) independent of known loci associated with intelligence or educational attainment. All five reading-/language-related traits had robust SNP-heritability estimates (0.13-0.26), and genetic correlations between them were modest to high. Using genomic structural equation modelling, we found evidence for a shared genetic factor explaining the majority of variation in word and nonword reading, spelling, and phoneme awareness, which only partially overlapped with genetic variation contributing to nonword repetition, intelligence and educational attainment. A multivariate GWAS was performed to jointly analyse word and nonword reading, spelling, and phoneme awareness, maximizing power for follow-up investigation. Genetic correlation analysis of multivariate GWAS results with neuroimaging traits identified association with cortical surface area of the banks of the left superior temporal sulcus, a brain region with known links to processing of spoken and written language. Analysis of evolutionary annotations on the lineage that led to modern humans showed enriched heritability in regions depleted of Neanderthal variants. Together, these results provide new avenues for deciphering the biological underpinnings of these uniquely human traits.
]]></description>
<dc:creator>Eising, E.</dc:creator>
<dc:creator>Mirza-Schreiber, N.</dc:creator>
<dc:creator>de Zeeuw, E. L.</dc:creator>
<dc:creator>Wang, C. A.</dc:creator>
<dc:creator>Truong, D. T.</dc:creator>
<dc:creator>Allegrini, A. G.</dc:creator>
<dc:creator>Shapland, C. Y.</dc:creator>
<dc:creator>Zhu, G.</dc:creator>
<dc:creator>Wigg, K. G.</dc:creator>
<dc:creator>Gerritse, M.</dc:creator>
<dc:creator>Molz, B.</dc:creator>
<dc:creator>Alagoz, G.</dc:creator>
<dc:creator>Gialluisi, A.</dc:creator>
<dc:creator>Abbondanza, F.</dc:creator>
<dc:creator>Rimfeld, K.</dc:creator>
<dc:creator>Van Donkelaar, M. M.</dc:creator>
<dc:creator>Liao, Z.</dc:creator>
<dc:creator>Jansen, P. R.</dc:creator>
<dc:creator>Andlauer, T. F. M.</dc:creator>
<dc:creator>Bates, T. C.</dc:creator>
<dc:creator>Bernard, M.</dc:creator>
<dc:creator>Blokland, K.</dc:creator>
<dc:creator>Borglum, A. D.</dc:creator>
<dc:creator>Bourgeron, T.</dc:creator>
<dc:creator>Brandeis, D.</dc:creator>
<dc:creator>Ceroni, F.</dc:creator>
<dc:creator>Dale, P. S.</dc:creator>
<dc:creator>de Jong, P. F.</dc:creator>
<dc:creator>DeFries, J. C.</dc:creator>
<dc:creator>Demontis, D.</dc:creator>
<dc:creator>Feng, Y.</dc:creator>
<dc:creator>Gordon, S. D.</dc:creator>
<dc:creator>Guger, S. L.</dc:creator>
<dc:creator>Hayiou-Thomas, M. E.</dc:creator>
<dc:creator>Hernandez-Cabrera, J. A.</dc:creator>
<dc:creator>Hottenga, J.- J.</dc:creator>
<dc:creator>Hulme, C.</dc:creator>
<dc:creator>Kerr, E. N.</dc:creator>
<dc:creator>Koomar, T.</dc:creator>
<dc:creator>Landerl, K.</dc:creator>
<dc:creator>Lovett,</dc:creator>
<dc:date>2021-11-04</dc:date>
<dc:identifier>doi:10.1101/2021.11.04.466897</dc:identifier>
<dc:title><![CDATA[Genome-wide association analyses of individual differences in quantitatively assessed reading- and language-related skills in up to 34,000 people]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-11-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.11.02.466974v1?rss=1">
<title>
<![CDATA[
N4BP1 is dimerization-dependent linear ubiquitin reader regulating TNFR1 signalling through linear ubiquitin binding and Caspase-8-mediated processing 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.11.02.466974v1?rss=1"
</link>
<description><![CDATA[
Signalling through TNFR1 modulates proinflammatory gene transcription and programmed cell death, and its impairment causes autoimmune diseases and cancer. NEDD4-binding protein 1 (N4BP1) was recently identified as a critical suppressor of proinflammatory cytokine production1, whose mode of action remained unknown. Here, we show that N4BP1 is a novel linear ubiquitin reader that negatively regulates NF{kappa}B signalling by its unique dimerizationdependent ubiquitin-binding module that we named LUBIN. Dimeric N4BP1 strategically positions two non-selective ubiquitin-binding domains to ensure exclusive recognition of linear ubiquitin. Under proinflammatory conditions, N4BP1 is recruited to the nascent TNFR1 signalling complex, where it regulates duration of proinflammatory signalling in LUBIN-dependent manner. N4BP1 deficiency accelerates TNF-induced cell death by increasing complex II assembly. Under proapoptotic conditions, Caspase-8 mediates proteolytic processing of N4BP1 and the resulting cleavage fragment of N4BP1, which retains the ability to bind linear ubiquitin, is rapidly degraded by the 26S proteasome, accelerating apoptosis. In summary, our findings demonstrate that N4BP1 dimerization creates a unique linear ubiquitin reader that ensures timely and coordinated regulation of TNFR1-mediated inflammation and cell death.
]]></description>
<dc:creator>Kliza, K. W.</dc:creator>
<dc:creator>Song, W.</dc:creator>
<dc:creator>Pinzuti, I.</dc:creator>
<dc:creator>Schaubeck, S.</dc:creator>
<dc:creator>Kunzelmann, S.</dc:creator>
<dc:creator>Kuntin, D.</dc:creator>
<dc:creator>Fornili, A.</dc:creator>
<dc:creator>Pandini, A.</dc:creator>
<dc:creator>Hofmann, K.</dc:creator>
<dc:creator>Garnett, J.</dc:creator>
<dc:creator>Stieglitz, B.</dc:creator>
<dc:creator>Husnjak, K.</dc:creator>
<dc:date>2021-11-02</dc:date>
<dc:identifier>doi:10.1101/2021.11.02.466974</dc:identifier>
<dc:title><![CDATA[N4BP1 is dimerization-dependent linear ubiquitin reader regulating TNFR1 signalling through linear ubiquitin binding and Caspase-8-mediated processing]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-11-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.11.15.468619v1?rss=1">
<title>
<![CDATA[
The influence of the extracellular domains on the dynamic behavior of membrane proteins 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.11.15.468619v1?rss=1"
</link>
<description><![CDATA[
The dynamic behavior of plasma membrane proteins mediates various cellular processes such as cellular motility, communication, and signaling. It is widely accepted that the dynamics of the membrane proteins is determined either by the interactions of the transmembrane domain with the surrounding lipids or by the interactions of the intracellular domain with cytosolic components such as cortical actin. Although initiation of different cellular signaling events at the plasma membrane has been attributed to the extracellular domain (ECD) properties recently, the impact of ECDs on the dynamic behavior of membrane proteins is rather unexplored. Here, we investigate how the ECD properties influence protein dynamics in the lipid bilayer by reconstituting ECDs of different sizes or glycosylation in model membrane systems and analyzing ECD-driven protein sorting in lipid domains as well as protein mobility. Our data shows that increasing the ECD mass or glycosylation leads to a decrease in ordered domain partitioning and diffusivity. Our data reconciles different mechanisms proposed for the initiation of cellular signaling by linking the ECD size of membrane proteins with their localization and diffusion dynamics in the plasma membrane.

SIGNIFICANCE STATEMENTWe studied how the size and glycosylation of the proteins influences their dynamic behavior in a lipid bilayer by reconstituting the ECDs of different sizes or glycosylation in model membrane systems and analyzing their sorting into lipid domains as well as their mobility. We observe that increasing the ECD apparent mass leads to a decrease in membrane ordered domain partitioning and diffusivity. Our data reconciles multiple mechanisms proposed for the initiation of cellular signaling by linking the ECD properties of membrane proteins with their localization and diffusion dynamics in the plasma membrane.
]]></description>
<dc:creator>Wedemann, L.</dc:creator>
<dc:creator>Gurdap, C. O.</dc:creator>
<dc:creator>Sych, T.</dc:creator>
<dc:creator>Sezgin, E.</dc:creator>
<dc:date>2021-11-16</dc:date>
<dc:identifier>doi:10.1101/2021.11.15.468619</dc:identifier>
<dc:title><![CDATA[The influence of the extracellular domains on the dynamic behavior of membrane proteins]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-11-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.11.12.468429v1?rss=1">
<title>
<![CDATA[
Cerebellar and subcortical atrophy contribute to psychiatric symptoms in frontotemporal dementia 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.11.12.468429v1?rss=1"
</link>
<description><![CDATA[
Recent studies have suggested that cerebellar and subcortical structures are impacted early in the disease progression of genetic frontotemporal dementia (FTD) due to microtubule-associated protein tau (MAPT), progranulin (GRN) and chromosome 9 open reading frame 72 (C9orf72). However, the clinical contribution of the structures involved in the cerebello-subcortical circuitry has been understudied in FTD given their potentially central role in cognition and behaviour processes. The present study aims to investigate whether there is an association between the atrophy of the cerebellar and subcortical structures, and neuropsychiatric symptoms (using the revised version of the Cambridge Behavioral Inventory, CBI-R) across genetic mutations and whether this association starts during the preclinical phase of the disease. Our study included 983 participants from the Genetic Frontotemporal dementia Initiative (GENFI) including mutation carriers (n=608) and non-carrier first-degree relatives of known symptomatic carriers (n= 375). Voxel-wise analysis of the thalamus, striatum, globus pallidus, amygdala, and the cerebellum was performed using deformation based morphometry (DBM) and partial least squares analyses (PLS) were used to link morphometry and behavioural symptoms. Our univariate results suggest that in this group of primarily presymptomatic subjects, volume loss in subcortical and cerebellar structure was primarily a function of aging, with only the C9orf72 group showing more pronounced volume loss in the thalamus compared to the non-carrier individuals. PLS analyses demonstrated that the cerebello-subcortical circuitry is related to all neuropsychiatric symptoms from the CBI-R, with significant overlap in brain/behaviour patterns, but also specificity for each genetic group. The biggest differences were in the extent of the cerebellar involvement (larger extent in C9orf72 group) and more prominent amygdalar contribution in the MAPT group. Finally, our findings demonstrated that C9orf72 and MAPT brain scores were related to estimated years before the age of symptom onset (EYO) in a second order relationship highlighting a steeper brain score decline 20 years before expected symptom onset, while GRN brain scores were related to age and not EYO. Overall, these results demonstrated the important role of the subcortical structures and especially of the cerebellum in genetic FTD symptom expression.
]]></description>
<dc:creator>Bussy, A.</dc:creator>
<dc:creator>Levy, J.</dc:creator>
<dc:creator>Patel, R.</dc:creator>
<dc:creator>Cupo, L.</dc:creator>
<dc:creator>Best, T.</dc:creator>
<dc:creator>Van Langenhove, T.</dc:creator>
<dc:creator>Nielsen, J.</dc:creator>
<dc:creator>Pijnenburg, Y.</dc:creator>
<dc:creator>Landqvist Waldö, M.</dc:creator>
<dc:creator>Remes, A.</dc:creator>
<dc:creator>Schroeter, M. L.</dc:creator>
<dc:creator>Santana, I.</dc:creator>
<dc:creator>Pasquier, F.</dc:creator>
<dc:creator>Otto, M.</dc:creator>
<dc:creator>Danek, A.</dc:creator>
<dc:creator>Levin, J.</dc:creator>
<dc:creator>Le Ber, I.</dc:creator>
<dc:creator>Vandenberghe, R.</dc:creator>
<dc:creator>Synofzik, M.</dc:creator>
<dc:creator>Moreno, F.</dc:creator>
<dc:creator>de Mendonca, A.</dc:creator>
<dc:creator>Sanchez-Valle, R.</dc:creator>
<dc:creator>Laforce, R.</dc:creator>
<dc:creator>Langheinrich, T.</dc:creator>
<dc:creator>Gerhard, A.</dc:creator>
<dc:creator>Graff, C.</dc:creator>
<dc:creator>Butler, C. R.</dc:creator>
<dc:creator>Sorbi, S.</dc:creator>
<dc:creator>Jiskoot, L.</dc:creator>
<dc:creator>Seelaar, H.</dc:creator>
<dc:creator>van Swieten, J. C.</dc:creator>
<dc:creator>Finger, E.</dc:creator>
<dc:creator>Tartaglia, M. C.</dc:creator>
<dc:creator>Masellis, M.</dc:creator>
<dc:creator>Tiraboschi, P.</dc:creator>
<dc:creator>Galimberti, D.</dc:creator>
<dc:creator>Borroni, B.</dc:creator>
<dc:creator>Rowe, J.</dc:creator>
<dc:creator>Bocchetta, M.</dc:creator>
<dc:creator>Rohrer, J. D.</dc:creator>
<dc:creator>Devenyi, G. A.</dc:creator>
<dc:creator>Chakravarty, M. M.</dc:creator>
<dc:creator>Ducha</dc:creator>
<dc:date>2021-11-16</dc:date>
<dc:identifier>doi:10.1101/2021.11.12.468429</dc:identifier>
<dc:title><![CDATA[Cerebellar and subcortical atrophy contribute to psychiatric symptoms in frontotemporal dementia]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-11-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.10.19.462578v1?rss=1">
<title>
<![CDATA[
Uncovering the Genetic Architecture of Broad Antisocial Behavior through a Genome-Wide Association Study Meta-analysis. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.10.19.462578v1?rss=1"
</link>
<description><![CDATA[
Despite the substantial heritability of antisocial behavior (ASB), specific genetic variants robustly associated with the trait have not been identified. The present study by the Broad Antisocial Behavior Consortium (BroadABC) meta-analyzed data from 28 discovery samples (N = 85,359) and five independent replication samples (N = 8,058) with genotypic data and broad measures of ASB. We identified the first significant genetic associations with broad ASB, involving common intronic variants in the forkhead box protein P2 (FOXP2) gene (lead SNP rs12536335, P = 6.32 x 10-10). Furthermore, we observed intronic variation in Foxp2 and one of its targets (Cntnap2) distinguishing a mouse model of pathological aggression (BALB/cJ strain) from controls (BALB/cByJ strain). The SNP-based heritability of ASB was 8.4% (s.e.= 1.2%). Polygenic-risk-score (PRS) analyses in independent samples revealed that the genetic risk for ASB was associated with several antisocial outcomes across the lifespan, including diagnosis of conduct disorder, official criminal convictions, and trajectories of antisocial development. We found substantial genetic correlations of ASB with mental health (depression rg{square}={square}0.63, insomnia rg = 0.47), physical health (overweight rg = 0.19, waist-to-hip ratio rg = 0.32), smoking (rg{square}={square}0.54), cognitive ability (intelligence rg= -0.40), educational attainment (years of schooling rg = -0.46) and reproductive traits (age at first birth rg={square}- 0.58, fathers age at death rg= -0.54). Our findings provide a starting point towards identifying critical biosocial risk mechanisms for the development of ASB.
]]></description>
<dc:creator>Tielbeek, J. J.</dc:creator>
<dc:creator>Uffelmann, E.</dc:creator>
<dc:creator>Williams, B. S.</dc:creator>
<dc:creator>Colodro-Conde, L.</dc:creator>
<dc:creator>Gagnon, E.</dc:creator>
<dc:creator>Mallard, T. T.</dc:creator>
<dc:creator>Levitt, B. E.</dc:creator>
<dc:creator>Jansen, P. R.</dc:creator>
<dc:creator>Johansson, A.</dc:creator>
<dc:creator>Sallis, H. M.</dc:creator>
<dc:creator>Pistis, G.</dc:creator>
<dc:creator>Saunders, G. R.</dc:creator>
<dc:creator>Allegrini, A. G.</dc:creator>
<dc:creator>Rimfeld, K.</dc:creator>
<dc:creator>Konte, B.</dc:creator>
<dc:creator>Klein, M.</dc:creator>
<dc:creator>Hartmann, A. M.</dc:creator>
<dc:creator>Salvatore, J. E.</dc:creator>
<dc:creator>Nolte, I. M.</dc:creator>
<dc:creator>Demontis, D.</dc:creator>
<dc:creator>Malmberg, A.</dc:creator>
<dc:creator>Burt, S. A.</dc:creator>
<dc:creator>Savage, J. E.</dc:creator>
<dc:creator>Sugden, K.</dc:creator>
<dc:creator>Poulton, R.</dc:creator>
<dc:creator>Harris, K. M.</dc:creator>
<dc:creator>Vrieze, S.</dc:creator>
<dc:creator>McGue, M.</dc:creator>
<dc:creator>Iacono, W. G.</dc:creator>
<dc:creator>Mota, N. R.</dc:creator>
<dc:creator>Mill, J.</dc:creator>
<dc:creator>Viana, J. F.</dc:creator>
<dc:creator>Mitchell, B. L.</dc:creator>
<dc:creator>Morosoli, J. J.</dc:creator>
<dc:creator>Andlauer, T. F. M.</dc:creator>
<dc:creator>Ouellet-Morin, I.</dc:creator>
<dc:creator>Tremblay, R. E.</dc:creator>
<dc:creator>Cote, S. M.</dc:creator>
<dc:creator>Gouin, J.-P.</dc:creator>
<dc:creator>Brendgen, M. R.</dc:creator>
<dc:creator>Dionne,</dc:creator>
<dc:date>2021-10-20</dc:date>
<dc:identifier>doi:10.1101/2021.10.19.462578</dc:identifier>
<dc:title><![CDATA[Uncovering the Genetic Architecture of Broad Antisocial Behavior through a Genome-Wide Association Study Meta-analysis.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-10-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.10.29.339317v1?rss=1">
<title>
<![CDATA[
COVID Moonshot: Open Science Discovery of SARS-CoV-2 Main Protease Inhibitors by Combining Crowdsourcing, High-Throughput Experiments, Computational Simulations, and Machine Learning 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.10.29.339317v1?rss=1"
</link>
<description><![CDATA[
We report the results of the COVID Moonshot, a fully open-science, crowd sourced, structure-enabled drug discovery campaign targeting the SARS-CoV-2 main protease. We discovered a non-covalent, non-peptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>840 ligand-bound X-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2,400 compounds) for this campaign were shared rapidly and openly, creating a rich open and IP-free knowledgebase for future anti-coronavirus drug discovery.
]]></description>
<dc:creator>The COVID Moonshot Consortium,</dc:creator>
<dc:creator>Achdout, H.</dc:creator>
<dc:creator>Aimon, A.</dc:creator>
<dc:creator>Bar-David, E.</dc:creator>
<dc:creator>Barr, H.</dc:creator>
<dc:creator>Ben-Shmuel, A.</dc:creator>
<dc:creator>Bennett, J.</dc:creator>
<dc:creator>Bobby, M. L.</dc:creator>
<dc:creator>Brun, J.</dc:creator>
<dc:creator>BVNBS, S.</dc:creator>
<dc:creator>Calmiano, M.</dc:creator>
<dc:creator>Carbery, A.</dc:creator>
<dc:creator>Cattermole, E.</dc:creator>
<dc:creator>Chodera, J. D.</dc:creator>
<dc:creator>Clyde, A.</dc:creator>
<dc:creator>Coffland, J. E.</dc:creator>
<dc:creator>Cohen, G.</dc:creator>
<dc:creator>Cole, J.</dc:creator>
<dc:creator>Contini, A.</dc:creator>
<dc:creator>Cox, L.</dc:creator>
<dc:creator>Cvitkovic, M.</dc:creator>
<dc:creator>Dias, A.</dc:creator>
<dc:creator>Douangamath, A.</dc:creator>
<dc:creator>Duberstein, S.</dc:creator>
<dc:creator>Dudgeon, T.</dc:creator>
<dc:creator>Dunnett, L.</dc:creator>
<dc:creator>Eastman, P. K.</dc:creator>
<dc:creator>Erez, N.</dc:creator>
<dc:creator>Fairhead, M.</dc:creator>
<dc:creator>Fearon, D.</dc:creator>
<dc:creator>Fedorov, O.</dc:creator>
<dc:creator>Ferla, M.</dc:creator>
<dc:creator>Foster, H.</dc:creator>
<dc:creator>Foster, R.</dc:creator>
<dc:creator>Gabizon, R.</dc:creator>
<dc:creator>Gehrtz, P.</dc:creator>
<dc:creator>Gileadi, C.</dc:creator>
<dc:creator>Giroud, C.</dc:creator>
<dc:creator>Glass, W. G.</dc:creator>
<dc:creator>Glen, R.</dc:creator>
<dc:creator>Glinert, I.</dc:creator>
<dc:creator>Gorichko, M.</dc:creator>
<dc:creator>Gorrie-Stone, T.</dc:creator>
<dc:creator>Griffen, E. J.</dc:creator>
<dc:creator>Heer</dc:creator>
<dc:date>2020-10-30</dc:date>
<dc:identifier>doi:10.1101/2020.10.29.339317</dc:identifier>
<dc:title><![CDATA[COVID Moonshot: Open Science Discovery of SARS-CoV-2 Main Protease Inhibitors by Combining Crowdsourcing, High-Throughput Experiments, Computational Simulations, and Machine Learning]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-10-30</prism:publicationDate>
<prism:section></prism:section>
</item>
</rdf:RDF>
