Transcriptome-neuroimaging association: bridging the gap between microscale genetic expression and macroscale brain organization

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Background

Brain structure and function can be characterized in vivo by various neuroimaging-derived phenotypes (NIDPs) measured by multimodal neuroimaging techniques such as magnetic resonance imaging. Many of these NIDPs are heritable but their genetic bases are largely unknown. Joint analysis of genetic and brain imaging data presents a new opportunity for understanding the molecular basis of macroscopic neuroimaging phenotypes related to brain development, aging, and diseases. This work involves conventional single-nucleotide polymorphisms heritability analysis that explores the associations between genetic variations and NIDPs variability across subjects, and nascent transcriptome-neuroimaging association analysis that identifies genes with spatial expression profiles that track anatomical variations in the studied NIDPs.
In this Research Topic, we aim to utilize brain-wide gene expression data, such as the most frequently used Allen Human Brain Atlas (AHBA), to bridge the gap between molecular function and macroscopic NIDPs in healthy and clinical conditions. We hope this topic could provide clues for better understanding the physiological significance of NIDPs from the perspective of molecular mechanisms and determine whether NIDPs could act as reliable intermediate phenotypes for assessing the genetic architecture of diseases. We encourage the submission of papers investigating the molecular underpinnings of NIDPs in healthy individuals and NIDPs alterations in disease-affected populations. Researchers are also encouraged to submit original articles on the application of advanced methods for relating gene expression measures to neuroimaging data. Original research articles, reviews, meta-analyses, protocols and commentaries on existing publications are welcomed.
Potential topics include, but are not limited to, the following:
 The molecular mechanisms of specific NIDPs in healthy adults using transcriptome-neuroimaging spatial association analysis
 The genetic substrates of NIDPs changes associated with development and aging
 The transcriptomic correlates of NIDPs alterations in major brain disorders (e.g., depression, schizophrenia, autism, and bipolar disorder)
 Whether and how patterns of correlated gene expression between pairs of brain regions carry information regarding inter-regional structural or functional connectivity
 Transcriptomic signatures of brain connectome topology
 Advanced methodology linking gene expression measures to neuroimaging data (e.g., constructing sophisticated computational models to synthesize high-dimensional and multi-scale datasets of neuroimaging and transcriptome)

Keywords: brain; neuroimaging-derived phenotypes; magnetic resonance imaging; genetics; transcriptome; molecular mechanism; connectome; computational model

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