Robust estimation of cortical similarity networks from brain MRI

Abstract

Structural similarity is a growing focus for magnetic resonance imaging (MRI) of connectomes. Here we propose Morphometric INverse Divergence (MIND), a new method to estimate within-subject similarity between cortical areas based on the divergence between their multivariate distributions of multiple MRI features. Compared to the prior approach of morphometric similarity networks (MSNs) on n > 11,000 scans spanning three human datasets and one macaque dataset, MIND networks were more reliable, more consistent with cortical cytoarchitectonics and symmetry and more correlated with tract-tracing measures of axonal connectivity. MIND networks derived from human T1-weighted MRI were more sensitive to age-related changes than MSNs or networks derived by tractography of diffusion-weighted MRI. Gene co-expression between cortical areas was more strongly coupled to MIND networks than to MSNs or tractography. MIND network phenotypes were also more heritable, especially edges between structurally differentiated areas. MIND network analysis provides a biologically validated lens for cortical connectomics using readily available MRI data.

MRI-based structural covariance network in early human brain development

“…Individual-level morphological networks offer a novel perspective on understanding brain development and neuropsychiatric conditions (Cai et al, 2023). The individual SCNs assess inter-regional morphological similarity in single subjects mainly by estimating the similarity/divergence of regional feature distributions (Li et al, 2021;Sebenius et al, 2023) or by evaluating the correlation of regional feature vectors (Li et al, 2017;Seidlitz et al, 2018). This approach enables the investigation of individual topological brain changes in both healthy and diseased states.…”

“…at the population level (He et al, 2007;Alexander-Bloch et al, 2013;Gilmore et al, 2018), which has been widely applied in adult and adolescent populations. In recent years, some studies have proposed SCNs constructed at the individual level (Kong et al, 2015;Meng et al, 2015;Li et al, 2017Li et al, , 2021Seidlitz et al, 2018;Yu et al, 2018;Sebenius et al, 2023), which can capture individual difference information. However, current studies of early brain development based on cortical morphology mainly focus on statistical analysis of morphological features, while exploration of the SCN is relatively limited, especially at the individual level.…”

Heterogeneity of morphometric similarity networks in health and schizophrenia

“…Morphometric similarity is a recently developed neuroimaging phenotype of cortical inter-regional connectivity by quantifying the similarity of a region to all other regions based on multiple MRI parameters assessed at each region (Seidlitz et al, 2018; Sebenius et al, 2023). That is, each brain region is represented as a vector of several MRI features, such as cortical thickness and volume, and based on the pairwise correlation between the regional feature vectors, morphometric similarity can be estimated.…”

Distinct gradients of cortical architecture capture visual representations and behavior across the lifespan

“…However, these maps cannot be built for individual brains in vivo to capture individual differences, or the functional, behavioral or developmental relevance of these larger scale organizational principles. MRI technological advances have made it possible to map architectural correlates in human cortex in a noninvasive, and importantly individual-specific way, to test if the individual variation in functional organization across brains is reflected in the variation of architectural features of cortex [9][10][11] . In the case of visual cortex, general trend along the cardinal axis have been observed in architectural features such as myelination in adults 12 and infants 13 and cortical thinning 14 , as well as functional properties of neurons such as receptive field size 15,16 and temporal sensitivity 17,18 .…”