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Aurina Arnatkeviciute

Bio: Aurina Arnatkeviciute is an academic researcher from Monash University. The author has contributed to research in topics: Connectome & Human Connectome. The author has an hindex of 7, co-authored 24 publications receiving 289 citations. Previous affiliations of Aurina Arnatkeviciute include Monash University, Clayton campus.

Papers
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Journal ArticleDOI
TL;DR: It is suggested that studies using the AHBA should work towards a unified data processing pipeline to ensure consistent and reproducible results in this burgeoning field of brain structure and function.

359 citations

Posted ContentDOI
09 Jul 2021-bioRxiv
TL;DR: Abagen as discussed by the authors is an open-access software package for working with transcriptomic data, and use it to examine how methodological variability influences the outcomes of research using the Allen Human Brain Atlas.
Abstract: Gene expression fundamentally shapes the structural and functional architecture of the human brain. Open-access transcriptomic datasets like the Allen Human Brain Atlas provide an unprecedented ability to examine these mechanisms in vivo; however, a lack of standardization across research groups has given rise to myriad processing pipelines for using these data. Here, we develop the abagen toolbox, an open-access software package for working with transcriptomic data, and use it to examine how methodological variability influences the outcomes of research using the Allen Human Brain Atlas. Applying three prototypical analyses to the outputs of 750,000 unique processing pipelines, we find that choice of pipeline has a large impact on research findings, with parameters commonly varied in the literature influencing correlations between derived gene expression and other imaging phenotypes by as much as {rho} [≥] 1.0. Our results further reveal an ordering of parameter importance, with processing steps that influence gene normalization yielding the greatest impact on downstream statistical inferences and conclusions. The presented work and the development of the abagen toolbox lay the foundation for more standardized and systematic research in imaging transcriptomics, and will help to advance future understanding of the influence of gene expression in the human brain.

67 citations

Journal ArticleDOI
TL;DR: Using diffusion-weighted magnetic resonance imaging in twins, the authors identify a major role for genes, showing that they preferentially influence connectivity strength between network hubs of the human connectome.
Abstract: Brain network hubs are both highly connected and highly inter-connected, forming a critical communication backbone for coherent neural dynamics. The mechanisms driving this organization are poorly understood. Using diffusion-weighted magnetic resonance imaging in twins, we identify a major role for genes, showing that they preferentially influence connectivity strength between network hubs of the human connectome. Using transcriptomic atlas data, we show that connected hubs demonstrate tight coupling of transcriptional activity related to metabolic and cytoarchitectonic similarity. Finally, comparing over thirteen generative models of network growth, we show that purely stochastic processes cannot explain the precise wiring patterns of hubs, and that model performance can be improved by incorporating genetic constraints. Our findings indicate that genes play a strong and preferential role in shaping the functionally valuable, metabolically costly connections between connectome hubs. How genes sculpt the complex architecture of the human connectome remains unclear. Here, the authors show that genes preferentially influence the strength of connectivity between functionally valuable, metabolically costly connections between brain network hubs.

58 citations

Posted ContentDOI
22 Jun 2020-bioRxiv
TL;DR: Comparing over thirteen generative models of network growth, it is shown that purely stochastic processes cannot explain the precise wiring patterns of hubs, and that model performance can be improved by incorporating genetic constraints.
Abstract: Brain network hubs are both highly connected and highly inter-connected, forming a critical communication backbone for coherent neural dynamics The mechanisms driving this organization are poorly understood Using diffusion-weighted imaging in twins, we identify a major role for genes in shaping hub connectivity of the human connectome, showing that genes preferentially influence connectivity strength between network hubs In two independent samples, we show that DNA variants preferentially related to hub connectivity are expression quantitative trait loci for genes that overlap with those implicated in intelligence, schizophrenia, and metabolism Using transcriptomic atlas data, we show that connected hubs demonstrate tight coupling of transcriptional activity related to metabolic and cytoarchitectonic similarity Finally, comparing thirteen generative models of network growth, we show that stochastic processes cannot explain the spatial distribution, and thus the precise wiring pattern, of hub connectivity Together, our findings indicate that genetic influences on brain connectivity are not uniformly distributed throughout the brain, but are instead concentrated on the functionally valuable, metabolically costly connections between connectome hubs

45 citations

Journal ArticleDOI
TL;DR: In this article, the authors investigate the consequences of regional heterogeneity using a biophysical model of whole-brain functional magnetic resonance imaging (MRI) dynamics in humans and identify a key role for E:I heterogeneity in generating complex neuronal dynamics and demonstrate the viability of using transcriptomic data to constrain models of large-scale brain function.
Abstract: Brain regions vary in their molecular and cellular composition, but how this heterogeneity shapes neuronal dynamics is unclear. Here, we investigate the dynamical consequences of regional heterogeneity using a biophysical model of whole-brain functional magnetic resonance imaging (MRI) dynamics in humans. We show that models in which transcriptional variations in excitatory and inhibitory receptor (E:I) gene expression constrain regional heterogeneity more accurately reproduce the spatiotemporal structure of empirical functional connectivity estimates than do models constrained by global gene expression profiles or MRI-derived estimates of myeloarchitecture. We further show that regional transcriptional heterogeneity is essential for yielding both ignition-like dynamics, which are thought to support conscious processing, and a wide variance of regional-activity time scales, which supports a broad dynamical range. We thus identify a key role for E:I heterogeneity in generating complex neuronal dynamics and demonstrate the viability of using transcriptomic data to constrain models of large-scale brain function.

44 citations


Cited by
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Journal ArticleDOI
TL;DR: These analyses have revealed that spatial patterning of gene expression and neuronal connectivity are closely linked, following broad spatial gradients that track regional variations in microcircuitry, inter-regional connectivity, and functional specialisation.

209 citations

Journal ArticleDOI
TL;DR: A generative null model is presented, provided as an open-access software platform, that generates surrogate maps with spatial autocorrelation matched to SA of a target brain map that can simulate surrogate brain maps that preserve the SA of cortical, subcortical, parcellated, and dense brain maps.

187 citations

Journal ArticleDOI
TL;DR: It is proposed that this combined analysis of neuroimaging and transcriptional data provides insight into how previously implicated genes and proteins as well as a number of unreported genes in their topological vicinity on the protein interaction network may drive structural brain network changes mediating the genetic risk of schizophrenia.
Abstract: Schizophrenia has been conceived as a disorder of brain connectivity, but it is unclear how this network phenotype is related to the underlying genetics. We used morphometric similarity analysis of MRI data as a marker of interareal cortical connectivity in three prior case–control studies of psychosis: in total, n = 185 cases and n = 227 controls. Psychosis was associated with globally reduced morphometric similarity in all three studies. There was also a replicable pattern of case–control differences in regional morphometric similarity, which was significantly reduced in patients in frontal and temporal cortical areas but increased in parietal cortex. Using prior brain-wide gene expression data, we found that the cortical map of case–control differences in morphometric similarity was spatially correlated with cortical expression of a weighted combination of genes enriched for neurobiologically relevant ontology terms and pathways. In addition, genes that were normally overexpressed in cortical areas with reduced morphometric similarity were significantly up-regulated in three prior post mortem studies of schizophrenia. We propose that this combined analysis of neuroimaging and transcriptional data provides insight into how previously implicated genes and proteins as well as a number of unreported genes in their topological vicinity on the protein interaction network may drive structural brain network changes mediating the genetic risk of schizophrenia.

159 citations

16 Jun 2015
TL;DR: A genome-wide association study in CBD cases and controls shows that CBD and PSP share a genetic risk factor other than MAPT at 3p22 MOBP (myelin-associated oligodendrocyte basic protein).
Abstract: Corticobasal degeneration (CBD) is a neurodegenerative disorder affecting movement and cognition, definitively diagnosed only at autopsy. Here, we conduct a genome-wide association study (GWAS) in CBD cases (n=152) and 3,311 controls, and 67 CBD cases and 439 controls in a replication stage. Associations with meta-analysis were 17q21 at MAPT (P=1.42 × 10(-12)), 8p12 at lnc-KIF13B-1, a long non-coding RNA (rs643472; P=3.41 × 10(-8)), and 2p22 at SOS1 (rs963731; P=1.76 × 10(-7)). Testing for association of CBD with top progressive supranuclear palsy (PSP) GWAS single-nucleotide polymorphisms (SNPs) identified associations at MOBP (3p22; rs1768208; P=2.07 × 10(-7)) and MAPT H1c (17q21; rs242557; P=7.91 × 10(-6)). We previously reported SNP/transcript level associations with rs8070723/MAPT, rs242557/MAPT, and rs1768208/MOBP and herein identified association with rs963731/SOS1. We identify new CBD susceptibility loci and show that CBD and PSP share a genetic risk factor other than MAPT at 3p22 MOBP (myelin-associated oligodendrocyte basic protein).

131 citations

Journal ArticleDOI
TL;DR: In this paper, the authors comprehensively assess the performance of ten published null frameworks in statistical analyses of neuroimaging data and find that naive null models do not preserve spatial autocorrelation consistently yield elevated false positive rates and unrealistically liberal statistical estimates.

107 citations