Published: December 2025 | Author: Maryam Rahimi Movassagh
The human brain is one of the most complex biological systems we know. For decades, neuroscience has studied it from two largely separate perspectives: brain structure and function, measured through imaging techniques such as MRI and EEG, and molecular biology, studied through gene expression and transcriptomics. Imaging transcriptomics aims to bring these worlds together.
At its core, imaging transcriptomics asks a powerful question: How do patterns of gene expression shape large-scale brain organization, activity, and vulnerability to disease? By linking molecular data with brain imaging, we begin to understand not only where changes occur in the brain, but why they occur.
Imaging transcriptomics is a multi-modal approach that integrates spatial gene-expression data with neuroimaging features. Gene-expression maps—often derived from postmortem brain atlases or spatial transcriptomics—are aligned with imaging-derived measures such as cortical thickness, functional connectivity, or electrophysiological activity.
The result is a framework that allows researchers to associate molecular programs with brain regions, networks, and functional states. Instead of treating imaging patterns as abstract signals, imaging transcriptomics grounds them in underlying biological mechanisms.
Many neurological and neurodegenerative diseases show distinct spatial patterns in the brain. Certain regions degenerate earlier, exhibit abnormal activity, or display altered connectivity. Imaging transcriptomics helps explain these patterns by revealing which genes and molecular pathways are enriched in vulnerable regions.
For example, differences in gene-expression gradients across the cortex may help explain why specific brain networks are more susceptible to disorders such as Alzheimer’s disease, epilepsy, or ALS. This molecular context provides insight beyond what imaging alone can offer.
Modern brain imaging generates detailed maps of structure, function, and dynamics. However, interpreting these maps remains challenging. Imaging transcriptomics adds a biological layer that helps translate imaging features into interpretable mechanisms.
By associating imaging-derived patterns with gene sets, cell types, and molecular pathways, researchers can move from descriptive observations toward mechanistic understanding. This shift is essential for building clinically meaningful models of brain disease.
Integrating imaging and transcriptomic data is computationally demanding. These datasets differ in scale, resolution, and structure. Artificial intelligence plays a critical role in aligning, modeling, and interpreting these complex data sources.
Graph-based models, representation learning, and interpretable AI enable the brain to be modeled as an interconnected system— one that links molecular signals to functional networks. Importantly, transparency and interpretability are key, especially when insights are intended for biomedical discovery.
Imaging transcriptomics represents a shift in how we study the brain. It moves neuroscience toward a truly integrative view, where genes, cells, circuits, and behavior are studied together. As datasets grow and methods mature, this approach has the potential to transform how we understand brain development, aging, and disease.
By grounding brain imaging in molecular biology, imaging transcriptomics brings us closer to a unified, biologically interpretable model of the brain— one that connects the smallest genetic signals to the most complex human behaviors.
From: Maryam Rahimi Movassagh
Ph.D. Student, University of Central Florida (UCF)