Projects per year
Functionally distinct regions of the brain are thought to possess a characteristic connectional fingerprint - a profile of incoming and outgoing connections that defines the function of that area. This observation has motivated efforts to subdivide brain areas using their connectivity patterns. However, it remains unclear whether these connectomically-defined subregions can be distinguished at the molecular level. Here, we combine high-resolution diffusion-weighted magnetic resonance imaging with transcriptomic data to show that connectomically-defined subregions of the striatum carry distinct transcriptional signatures. Using data-driven clustering of diffusion tractography, seeded from the striatum in 100 healthy individuals, we identify a tripartite organization of the caudate and putamen that comprises ventral, dorsal and caudal subregions. We then use microarray data of gene expression levels in 19 343 genes, taken from 98 tissue samples distributed throughout the striatum, to accurately discriminate the three connectomically-defined subregions with 80-90% classification accuracy using linear support vector machines. This classification accuracy was robust at the group and individual level and was superior for our parcellation of the striatum when compared with parcellations based on anatomical boundaries or other criteria. Genes contributing strongly to classification were enriched for gene ontology categories including dopamine signaling, glutamate secretion, response to amphetamine and metabolic pathways, and were implicated in risk for disorders such as schizophrenia, autism and Parkinson's disease. Our findings highlight a close link between regional variations in transcriptional activity and inter-regional connectivity in the brain, and suggest that there may be a strong genomic signature of connectomically-defined subregions of the brain.
- Connectivity-based parcellation
1/01/12 → 31/12/16