Transcriptional signatures of connectomic subregions of the human striatum

Abstract

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.

A practical guide to linking brain-wide gene expression and neuroimaging data

“…Negi and Guda (2017) PC Select the probe with the highest loading onto the first principal component of probes. Parkes et al (2017) DS Select the probe with most consistent pattern of regional variationscross the six donor brains, as quantified using a measure called Differential Stability (DS). Hawrylycz et al (2015), Kirsch and Chechik (2016) Connectivity variance/intensity Select the probe with the highest average correlation to other probes a for >2 probes; select the probe with maximum variance (connectivity variance) or maximum intensity (connectivity intensity) when only 2 probes available.…”

“…The distance between sample and region is commonly estimated as the Euclidean distance in 3D space. This sample-region distance has been computed in different ways, including representing a region in space by its centroid coordinate , McColgan et al, 2018, or taking the minimum distance between the sample and any voxel in the region (French and Paus, 2015, Parkes et al, 2017, Romme et al, 2017. The latter approach is more accurate, given that regions in any given parcellation vary in size and folded geometry (e.g., Figure 5A).…”

“…In other words, while the data normalization procedures applied by the Allen Institute prior to data release removed batch effects and artefactual inter-individual differences, a considerable degree of intrinsic donor-specific variance remains and must be accounted for in order to perform valid data aggregation. One approach for addressing donor-specific effects is to perform a leave-one-out analysis, where the analysis is repeated six times, excluding one of the brains at each iteration (Parkes et al, 2017, McColgan et al, 2018. This approach can ensure that the results are not driven by single brain.…”

“…This unprecedented capacity to link molecular function to macroscale brain organization has given rise to the nascent field of imaging transcriptomics, which has begun to yield new insights into how regional variations in gene expression relate to functional connectivity within: canonical resting-state networks (Richiardi et al, 2015, Forest et al, 2017; fiber tract connectivity between brain regions (Goel et al, 2014); temporal and topological properties of large-scale brain functional networks (Cioli et al, 2014; the specialization of cortical and subcrotical areas (Krienen et al, 2016, Parkes et al, 2017, Anderson et al, 2018; regional maturation during embryonic and adolescent brain development (Kirsch andChechik, 2016, Whitaker et al, 2016); and pathological changes in brain disorders , Romme et al, 2017, McColgan et al, 2018, Romero-Garcia et al, 2018a. Software toolboxes to facilitate the integration of brain-wide transcriptomic and imaging data have also been developed (French and Paus, 2015, Gorgolewski et al, 2015, Rizzo et al, 2016, Rittman et al, 2017.…”

Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain

“…MRI data were downloaded from the Human Connectome Project (HCP) [52]. We selected the HCP 100 unrelated participants dataset (54 males, 46 females) for detailed analysis [52], as in previous work [76]. All participants were healthy and aged between 22-35 years and provided written informed consent; ethics was approved by the Institutional Review Board of Washington University.…”