Discovering prominent differences in structural and functional connectomes using a multinomial stochastic block model

Understanding the differences between functional and structural human brain connectivity has been a focus of an extensive amount of neuroscience research. We employ a novel approach using the multinomial stochastic block model (MSBM) to explicitly extract components that characterize prominent differences across graphs. We analyze structural and functional connectomes derived from high-resolution diffusion-weighted MRI and fMRI scans of 250 Human Connectome Project subjects, analyzed at group connectivity level across 50 subjects.

The inferred brain partitions revealed consistent, spatially homogeneous clustering patterns across inferred resolutions demonstrating the MSBM’s reliability in identifying brain areas with prominent structure-function differences. Prominent differences in low-resolution brain maps (K = {3,  4} clusters) were attributed to weak functional connectivity in the bilateral anterior temporal lobes, while higher resolution results (K ≥ 25) revealed stronger interhemispheric functional than structural connectivity. Our findings emphasize significant differences in high-resolution functional and structural connectomes, revealing challenges in extracting meaningful connectivity measurements from both modalities, including tracking fibers through the corpus callosum and attenuated functional connectivity in anterior temporal lobe fMRI data, which we attribute to increased noise levels. The MSBM emerges as a valuable tool for understanding differences across graphs, with potential future applications and avenues beyond the current focus on characterizing modality-specific distinctions in connectomics data.

We present a probabilistic framework, the multinomial stochastic block model (MSBM), to discover prominent differences between structural and functional high-resolution networks derived from diffusion-weighted MRI and fMRI scans from 250 Human Connectome Project subjects. By varying the number of clusters, the MSBM reveals at different levels of resolution the most prominent differences in how brain regions are functionally and structurally connected. Synthetic experiments and consistent brain map patterns extracted in real high-resolution connectomes highlight the MSBM’s ability to identify brain areas with prominent structure-function differences. This includes prominent differences in the connectivity of the anterior temporal lobes as well as interhemispheric connectivity, which we attribute mainly to the specific technical and methodological limitations of the functional and structural modality, respectively.