Imaging Transcriptomics of Brain Disorders

Abstract

Noninvasive neuroimaging is a powerful tool for quantifying diverse aspects of brain structure and function in vivo, and it has been used extensively to map the neural changes associated with various brain disorders. However, most neuroimaging techniques offer only indirect measures of underlying pathological mechanisms. The recent development of anatomically comprehensive gene expression atlases has opened new opportunities for studying the transcriptional correlates of noninvasively measured neural phenotypes, offering a rich framework for evaluating pathophysiological hypotheses and putative mechanisms. Here, we provide an overview of some fundamental methods in imaging transcriptomics and outline their application to understanding brain disorders of neurodevelopment, adulthood, and neurodegeneration. Converging evidence indicates that spatial variations in gene expression are linked to normative changes in brain structure during age-related maturation and neurodegeneration that are in part associated with cell-specific gene expression markers of gene expression. Transcriptional correlates of disorder-related neuroimaging phenotypes are also linked to transcriptionally dysregulated genes identified in ex vivo analyses of patient brains. Modeling studies demonstrate that spatial patterns of gene expression are involved in regional vulnerability to neurodegeneration and the spread of disease across the brain. This growing body of work supports the utility of transcriptional atlases in testing hypotheses about the molecular mechanism driving disease-related changes in macroscopic neuroimaging phenotypes.

Keywords: Brain imaging; Connectome; Gene expression; Neurodegeneration; Neurodevelopment; Psychiatric disorders.

Figure 1

Approaches for relating gene expression to neuroimaging data. (A) Transcriptional atlas data can be collated into a region × gene matrix, from which different estimates of gene expression can be quantified. In this example, the regional expression profile of any given gene, indicating the spatial patterning of the gene’s transcriptional activity, corresponds to a single column of the matrix. A given region’s expression profile across genes corresponds to a row of the matrix. Correlated gene expression is therefore calculated by correlating pairs of rows (resulting in region × region similarity matrix), while gene coexpression is calculated by correlating pairs of columns (resulting in gene × gene similarity matrix). (B) When applied to brain disorders, spatial maps of a given IDP measure in cases and controls are compared to yield some kind of difference map. This difference map is then correlated (C) with the spatial maps of each gene (D) in a hypothesis- or data-driven method. Genes associated with high spatial correlations to the difference map represent transcriptional correlates of the IDP. IDP, imaging-derived phenotype.

Figure 2

Transcriptional correlates of neurodevelopment and neurodevelopmental disorders. (A) The relationship between cortical thinning profile (i.e., the degree of cortical thinning per year) throughout life and gene expression in each of 9 cell types. Both in early and late life, significant associations (indicated by circles) were identified for astrocyte, microglia, and CA1 pyramidal cell markers. (B) Regional morphometric similarity change observed in Turner syndrome (characterized by the loss of the X chromosome) and chromosome ranking from a PLS analysis, based on the median rank of gene loadings on a PLS-derived latent variable linking gene expression with regional morphometric similarity change. The chromosomal gene set for the X chromosome shows the lowest rank, which is consistent with the chromosomal deletion known to cause the disorder. (C) Correspondence between brain changes in ADHD and gene expression. Left: differences in cortical thickness, surface area, and subcortical volumes in ADHD as identified by the ENIGMA Consortium, where larger positive Cohen’s d corresponds to decreases in a particular measure in patients with ADHD vs. control subjects. Right: the relationship between regional brain volumes and the expression of genes involved in apoptosis, autophagy, neurodevelopment, neurotransmission, and oxidative stress-related genes. Apoptosis, autophagy, and neurodevelopment-related genes demonstrate significant negative associations such that higher expression of those genes is associated with reductions in brain volume in patients with ADHD. Panel (A) adapted with permission from (81). Panel (B) adapted with permission from (39). Panel (C) adapted with permission from (85). ADHD, attention-deficit/hyperactivity disorder; CA, cornu ammonis; CNV, copy number variant; ENIGMA, Enhancing Neuro Imaging Genetics through Meta Analysis; LCBC, Center for Lifespan Changes in Brain and Cognition; PLS, partial least squares.

Figure 3

Transcriptional correlates of brain changes in neuropsychiatric disorders. (A) Gene expression profiles are associated with morphometric similarity differences between psychosis cases and controls. Cortical maps represent regional PLS1 scores (right) and case-control differences in morphometric similarity. (B) Associations between the cortical gene expression of SST interneurons, CORT, and NPY and reduced GBC in depression. The scatter plot represents the relationship for SST genes; regression lines represent relationships for all 3 gene markers. Panel (A) adapted with permission from (102). Panel (B) adapted with permission from (105). CORT, cortistatin; GBC, global brain connectivity; NPY, neuropeptide Y; PC1, first principal component; PLS, partial least squares; SST, somatostatin.

Figure 4

Gene expression is related to neurodegeneration and neurodegenerative disease spread. (A) Cell-specific marker association with cortical thinning in AD. Correlation distributions quantifying the relationship between cell-specific gene expression profiles for CA1 pyramidal, microglia, and astrocyte cell types and cortical thickness reductions in AD (patients with AD vs. HCs; AD vs. MCI; MCI vs. HC). Vertical axis denotes estimated probability density for the correlation coefficients. Vertical black line denotes the average correlation coefficient across all genes; shaded gray box indicates the 95% limits of the empirical null distribution. (B) Gene expression is associated with cortical iron decomposition, quantified using QSM, in Parkinson’s disease. Cortical maps represent QSM scores and regional linearly weighted sum of gene expression scores defined by PLS2. (C) Schematic representation of pathology spread across the brain: misfolded α-synuclein propagates via structural connections; simulated neuronal loss (atrophy) is compared against empirical atrophy, estimated from patients with Parkinson’s disease. Simulated and empirical atrophy patterns show a high level of spatial correspondence (Spearman correlation, r = 0.63, p = 2 × 10⁻⁵). Panel (A) adapted with permission from (81). Panel (B) adapted with permission from (121). Panel (C) adapted with permission from (132). α-syn, α-synuclein; AD, Alzheimer’s disease; CA, cornu ammonis; DBM, deformation-based morphometry; HC, healthy control; MCI, mild cognitive impairment; PLS, partial least squares; QSM, quantitative susceptibility mapping.