Review: Understanding Neuroimaging–Genetic Intersections in the Human Brain

Neuroimaging–genetics is a fast-growing subfield of clinical neuroscience. It explores relationships between genetics and the structural and functional connectivity in normal human brain. It also provides insight into mechanisms of risk for psychiatric and neurologic diseases.

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Writing in Current Opinion in Neurology, Ibai Diez, PhD, an instructor in the Sepulcre Lab at the Gordon Center for Medical Imaging at Massachusetts General Hospital, and Jorge Sepulcre, PhD, DMSc, MD, director of the lab, recently reviewed the methodological approaches that have been used to combine neuroimaging findings with genetic data.

Brain Transcriptome Atlases

Brain transcriptome atlases provide valuable insights into gene expression patterns in different brain areas. Publicly available datasets are allowing unprecedented progress in understanding associations between in vivo neuroimaging (usually MRI) and its underlying molecular basis.

Integration of image-derived phenotypes with gene expression atlases allows the study of their spatial intersections throughout the brain. One of the most popular datasets is the Allen Human Brain Atlas, which covers 20,737 genes across the whole brain. It was built by collecting postmortem brain samples from six healthy donors. Other atlases:

• Are restricted to certain brain regions but include hundreds of donors
• Are limited to donors with certain neurodegenerative disorders
• Provide complementary information such as temporal information throughout the lifespan or information from prenatal brain development
• Provide genome-wide expression profiles at the single-cell level

Spatial Association Analyses

Patients with Alzheimer's disease (AD), Parkinson's disease (PD) and other neurological disorders display neuroimaging phenotypes—cortical and subcortical spatial signatures—that have been consistently replicated over decades of research.

These phenotypes have made neurodegenerative disorders an ideal framework for investigating associations between neuroimages and genetic information. The steps are to:

1. Match the imaging phenotypes with gene expression data using one of several spatial association approaches:

• Use the phenotype-related imaging values from the exact locations where the brain tissue samples were extracted for genetic assessments
• Use a brain atlas to define broad regions of anatomical or functional hallmarks, then use it to compute the average gene expression values and average imaging phenotype–related values corresponding to the atlas regions
• Perform a voxel-based interpolation within the spatial domain of the genetic data to cover all imaging phenotype space and match them

2. Compute the spatial similarity between image phenotypes and spatial expression of genes in the brain

3. Select for further study the top-ranked genes that are highly similar to the imaging phenotype of interest. Gene set enrichment analysis is performed on the top-ranked list to find functional annotations and to find biological meanings to neuroimaging findings.

Connectomic Analysis

The pathogenesis of neurodegenerative diseases such as AD and PD is described as a spreading phenomenon across cerebral tissue (e.g., Lewy bodies spread throughout the brain in PD). There is intense interest in understanding which specific brain networks are genetically vulnerable to being targeted.

Adding connectomics—the study of the brain's structural and functional connections—to neuroimaging–genetic research can produce a greater understanding of this pathologic propagation. For example, in Nature Medicine, Dr. Diaz, Dr. Sepulcre, and colleagues previously described distinct genetic profiles that may confer vulnerability to tau and amyloid propagation in AD.

Future Directions

New technical developments will yield new transcriptome atlases that, when combined with new methodological approaches, will allow for a deeper understanding of brain organization. Along with advances in computational methods, this new data may eventually make it possible to create disease-modifying treatments for neurodegenerative disorders.

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