Bridging the Gap between Connectome and Transcriptome

Fornito, Alex; Arnatkevičiūtė, Aurina; Fulcher, Ben D.

doi:10.1016/j.tics.2018.10.005

Cited by 305 publications

(232 citation statements)

References 120 publications

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“…Organizational properties that are shared across scales and species [71] are strong candidates for being under evolutionary selection pressures for serving an important functional advantage. These range from the properties of networks abstracted from the brain's physical structure-like rich club connectivity and modularitythrough to hierarchical gradients [35,36] and the patterning of gene expression with structural connectivity [72][73][74].…”

Section: Discussionmentioning

confidence: 99%

Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain

Fallon

Ward

Parkes

et al. 2019

Preprint

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2 FALLON ET AL. motifs. Our findings demonstrate a conserved property of mouse and human brain organization, in which a brain region's spontaneous activity fluctuations are closely related to their surrounding structural scaffold. K E Y W O R D S structure-function relationship, time-series analysis, structural connectivity, resting-state fMRI, interspecies comparison 1 | INTRODUCTION The brain's complex spatiotemporal dynamics unfold on an intricate web of axonal connections: the connectome [1, 2]. These pathways facilitate information transfer between brain regions, manifesting in a complex relationship between connectome structure and neural dynamics. Reflecting the pairwise nature of structural connectivity (regionregion), existing studies have overwhelmingly compared pairwise measurements of anatomical connectivity to pairwise statistical relationships between neural activity time series, or functional connectivity, often using simulations of network dynamics to better understand how the observed relationships may arise [3-18]. Structural connectivity is highly informative of functional connectivity, consistent with the connectome as a physical substrate constraining inter-regional communication dynamics.Our understanding of pairwise structure-function relationships in the brain remains disconnected from our understanding of how a brain area's structural connectivity properties shape its activity dynamics. Indeed, the structural connectivity profile of a region's incoming and outgoing axonal connections is thought to define its function [19]. Furthermore, the activity dynamics of brain areas follow a functional hierarchy, with rapid dynamics in 'lower' sensory regions, and slower fluctuations in 'higher' regions associated with integrative processes [20][21][22][23][24]. The spatial variation of intrinsic timescales has been measured using ECOG [21], MEG [25,26], TMS-EEG [27], and fMRI [24,[28][29][30][31][32][33], and may a function variation in temporal receptive windows: timescales over which new information can be actively integrated with recently received information [20,33,34]. Spatial variation in intrinsic activity fluctuations may form a key basis for the brain's functional hierarchical organization, shaped by structural variation in the brain's microcircuitry [35][36][37]. This organization is thought to be important for behavior and cognition [20,[38][39][40], and its disruption has clinical implications: e.g., differences in intrinsic timescales are associated with symptom severity in autism [33]. While much is known about the structure-function relationship at the level of brain-region pairs, and how structural and functional connectivity architecture shape cognitive function and are affected in disease [41][42][43][44], relatively little is known about how it affects the information processing dynamics of individual brain areas. In particular, we do not yet understand the role structural connections play in the cortical organization of intrinsic timescales.Recent work has provided statistical e...

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Section: Discussionmentioning

confidence: 99%

Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain

Fallon

Ward

Parkes

et al. 2019

Preprint

Self Cite

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“…Recent technological advancements in neuroimaging, large-scale connectomics, and high-throughput transcriptomics have facilitated the discovery of conserved principles of brain organization (Huntenburg et al, 2018;Burt et al, 2018;Fornito et al, 2019). Studies of spatial representations of brain features -i.e., 5 brain maps -have revealed large-scale gradients of microscale and macroscale features (Wagstyl et al, 2015;Margulies et al, 2016;Burt et al, 2018;Preller et al, 2018;Vázquez-Rodríguez et al, 2019;Fulcher et al, 2019;Royer et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Generative modeling of brain maps with spatial autocorrelation

Burt

Helmer

Shinn

et al. 2020

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Studies of large-scale brain organization have revealed interesting relationships between spatial gradients in brain maps across multiple modalities. Evaluating the significance of these findings requires establishing statistical expectations under a null hypothesis of interest. Through generative modeling of synthetic data that instantiate a specific null hypothesis, quantitative benchmarks can be derived for arbitrarily complex statistical measures. Here, we present a generative null model, provided as an open-access software platform, that generates surrogate maps with spatial autocorrelation (SA) matched to SA of a target brain map. SA is a prominent and ubiquitous property of brain maps that violates assumptions of independence in conventional statistical tests. Our method can simulate surrogate brain maps, constrained by empirical data, that preserve the SA of cortical, subcortical, parcellated, and dense brain maps. We characterize how SA impacts p-values in pairwise brain map comparisons. Furthermore, we demonstrate how SA-preserving surrogate maps can be used in gene ontology enrichment analyses to test hypotheses of interest related to brain map topography. Our findings demonstrate the utility of SA-preserving surrogate maps for hypothesis testing in complex statistical analyses, and underscore the need to disambiguate meaningful relationships from chance associations in studies of large-scale brain organization.

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“…More recently, gene expression in the human brain has also been linked to regional differences in cortical scaling (Reardon et al, 2018), and anatomical hierarchy (Burt et al, 2018), using similar spatial correlation approaches. For a current review on linking neuroimaging and transcriptome, see (Fornito, Arnatkeviciute, & Fulcher, 2019).…”

Section: Introductionmentioning

confidence: 99%

Gene Expression Correlates of the Cortical Network Underlying Sentence Processing

Kong

Tzourio‐Mazoyer

Joliot

et al. 2018

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A pivotal question in modern neuroscience is which genes regulate brain circuits that underlie cognitive functions. However, the field is still in its infancy. Here we report an integrated investigation of the high-level language network (i.e., sentence processing network) in the human cerebral cortex, combining regional gene expression profiles, task fMRI, large-scale neuroimaging meta-analysis, and resting-state functional network approaches. We revealed reliable gene expression-functional network correlations using three different network definition strategies, and identified a consensus set of genes related to connectivity within the sentence-processing network. The genes involved showed enrichment for neural development and actin-related functions, as well as association signals with autism, which can involve disrupted language functioning. Our findings help elucidate the molecular basis of the brain's infrastructure for language. The integrative approach described here will be useful to study other complex cognitive traits.

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Bridging the Gap between Connectome and Transcriptome

Cited by 305 publications

References 120 publications

Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain

Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain

Generative modeling of brain maps with spatial autocorrelation

Gene Expression Correlates of the Cortical Network Underlying Sentence Processing

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