MIND Networks: Robust Estimation of Structural Similarity from Brain MRI

Sebenius, I.; Seidlitz, J.; Warrier, V.; Bethlehem, R. A. I.; Alexander-Bloch, A.; Mallard, T.; Romero Garcia, R.; Bullmore, E. T.; Morgan, S. E.

doi:10.1101/2022.10.12.511922

bioRxiv · 10.1101/2022.10.12.511922

MIND Networks: Robust Estimation of Structural Similarity from Brain MRI

Sebenius, I.·Seidlitz, J.·Warrier, V.·Bethlehem, R. A. I.·Alexander-Bloch, A.·Mallard, T.·Romero Garcia, R.·Bullmore, E. T.·Morgan, S. E.

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Abstract

Structural similarity networks are a central focus of magnetic resonance imaging (MRI) research into human brain connectomes in health and disease. We present Morphometric INverse Divergence (MIND), a robust method to estimate within-subject structural similarity between cortical areas based on the Kullback-Leibler divergence between the multivariate distributions of their structural features. Compared to the prior approach of morphometric similarity networks (MSNs) on N>10,000 data from the ABCD cohort, MIND networks were more consistent with known cortical symmetry, cytoarchitecture, and (in N=19 macaques) gold-standard tract-tracing connectivity, and were more invariant to cortical parcellation. Importantly, MIND networks were remarkably coupled with cortical gene co-expression, providing fresh evidence for the unified architecture of brain structure and transcription. Using kinship (N=1282) and genetic data (N=4085), we characterized the heritability of MIND phenotypes, identifying stronger genetic influence on the relationship between structurally divergent regions compared to structurally similar regions. Overall, MIND presents a biologically-validated lens for analyzing the structural organization of the cortex using readily-available MRI measurements.

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Sebenius, I., Seidlitz, J., Warrier, V., Bethlehem, R. A. I., Alexander-Bloch, A., Mallard, T., Romero Garcia, R., Bullmore, E. T., Morgan, S. E.. 2022-10-17. MIND Networks: Robust Estimation of Structural Similarity from Brain MRI. https://doi.org/10.1101/2022.10.12.511922

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