Gene networks show associations with seed region connectivity

Abstract

Primary patterns in adult brain connectivity are established during development by coordinated networks of transiently expressed genes; however, neural networks remain malleable throughout life. The present study hypothesizes that structural connectivity from key seed regions may induce effects on their connected targets, which are reflected in gene expression at those targeted regions. To test this hypothesis, analyses were performed on data from two brains from the Allen Human Brain Atlas, for which both gene expression and DW-MRI were available. Structural connectivity was estimated from the DW-MRI data and an approach motivated by network topology, that is, weighted gene coexpression network analysis (WGCNA), was used to cluster genes with similar patterns of expression across the brain. Group exponential lasso models were then used to predict gene cluster expression summaries as a function of seed region structural connectivity patterns. In several gene clusters, brain regions located in the brain stem, diencephalon, and hippocampal formation were identified that have significant predictive power for these expression summaries. These connectivity-associated clusters are enriched in genes associated with synaptic signaling and brain plasticity. Furthermore, using seed region based connectivity provides a novel perspective in understanding relationships between gene expression and connectivity. Hum Brain Mapp 38:3126-3140, 2017. © 2017 Wiley Periodicals, Inc.

Functional network dynamics in a neurodevelopmental disorder of known genetic origin

“…Whilst there is a strong tradition of examining the impact of genetic effects on static functional connectivity networks through either heritability (Colclough et al, 2017;Demuru et al, 2017;Fu et al, 2015;Posthuma et al, 2005) or associations between gene expression and amplitude envelope coordination in resting-state activity (Gordon, Devaney, Bean, & Vaidya, 2015;Jamadar et al, 2013;Wang et al, 2015), there remains a scarcity of work examining genetic effects on the fast transient dynamics of functional connectivity networks. Investigating network dynamics is particularly compelling to understand how gene expression perturbs communication within large scale brain networks, and downstream impacts on cognitive abilities, due to the profound impact of genetics on synaptic signalling (e.g., Forest et al, 2017;Meda et al, 2014;Richiardi et al, 2015;Whitaker et al, 2016;Willemsen et al, 2013). For the first time we were able to demonstrate proof-of-concept that dynamic connectivity profiles of individuals with a gene mutation are significantly altered and, crucially, that the extent of this alteration is strongly associated with the expression profile of the gene.…”

A practical guide to linking brain-wide gene expression and neuroimaging data

“…This unprecedented capacity to link molecular function to macroscale brain organization has given rise to the nascent field of imaging transcriptomics, which has begun to yield new insights into how regional variations in gene expression relate to functional connectivity within: canonical resting-state networks (Richiardi et al, 2015, Forest et al, 2017; fiber tract connectivity between brain regions (Goel et al, 2014); temporal and topological properties of large-scale brain functional networks (Cioli et al, 2014; the specialization of cortical and subcrotical areas (Krienen et al, 2016, Parkes et al, 2017, Anderson et al, 2018; regional maturation during embryonic and adolescent brain development (Kirsch andChechik, 2016, Whitaker et al, 2016); and pathological changes in brain disorders , Romme et al, 2017, McColgan et al, 2018, Romero-Garcia et al, 2018a. Software toolboxes to facilitate the integration of brain-wide transcriptomic and imaging data have also been developed (French and Paus, 2015, Gorgolewski et al, 2015, Rizzo et al, 2016, Rittman et al, 2017.…”

Developmental lead and/or prenatal stress exposures followed by different types of behavioral experience result in the divergence of brain epigenetic profiles in a sex, brain region, and time-dependent manner: Implications for neurotoxicology

“…Albeit potentially limited by current costs, it will ultimately be important to assess epigenetic profiles more broadly and correlatively across brain regions, recognizing that modularity and site specific changes are only a component of integrated brain function [67, 75]. Looking with both breadth and depth at integration as well as modularity may assist in finding more generalized markers important to specific environmental events or behavioral domains.…”

“…Thus, the networked brain must somehow be linked to patterns of localized epigenetic changes. Correspondingly, studies in both human and mouse/rat brain demonstrate that the functional connectivity of brain regions can actually be predicted by clustered gene expression data, particularly of synaptic activity-related genes [67, 68]. Data sciences could begin to assist in determining whether this linkage relies on patterns of specific epigenetic changes or corresponding emergent global epigenetic consequences.…”

“…As current studies have tended to focus on a single or perhaps two brain regions in assessment of epigenetic profiles, a more holistic networked-based epigenetic characterization has not yet been approached and may indefinitely remain cost prohibitive, even though a sizeable literature documents the fact that brain function is characterized by overlapping large scale functional networks (the connectome, [61, 62]), and that network connectivity can actually be predicted by local clustered gene expression, corresponding to the interactive nature of genetic underpinnings of brain function [67, 75]. …”