Individualized Cortical Parcellation Based on Diffusion MRI Tractography

Abstract

The spatial topological properties of cortical regions vary across individuals. Connectivity-based functional and anatomical cortical mapping in individuals will facilitate research on structure–function relationships. However, individual-specific cortical topographic properties derived from anatomical connectivity are less explored than those based on functional connectivity. We aimed to develop a novel individualized anatomical connectivity-based parcellation framework and investigate individual differences in spatial topographic features of cortical regions using diffusion magnetic resonance imaging (dMRI) tractography. Using a high-quality, repeated-session dMRI dataset (42 subjects, 2 sessions per subject), cortical parcels were derived through in vivo anatomical connectivity-based parcellation. These individual-specific parcels demonstrated good within-individual reproducibility and reflected interindividual differences in anatomical brain organization. Connectivity in these individual-specific parcels was significantly more homogeneous than that based on the group atlas. We found that the position, size, and topography of these anatomical parcels were highly variable across individuals and demonstrated nonredundant information about individual differences. Finally, we found that intersubject variability in anatomical connectivity was correlated with the diversity of anatomical connectivity patterns. Overall, we identified cortical parcels that show homogeneous anatomical connectivity patterns. These parcels displayed marked intersubject spatial variability, which may be used in future functional studies to reveal structure–function relationships in the human brain.

Individualized Thalamic Parcellation Reveals Alterations in Shape and Microstructure of Thalamic Nuclei in Patients with Disorder of Consciousness

“…Recently, Han et al. found that the individual-specific parcels derived from WM tractography at the individual level generated more homogeneous connectivity pattern than the population atlas ( Han et al. 2019 ), suggesting the advantage of the personalized parcellation in characterizing the individual differences.…”

“…Since individualized segmentation would better characterize individual diversities in development, cognition, and behavior ( Mueller et al. 2013 ; Han et al. 2019 ; Kong et al.…”

BAI-Net: Individualized Anatomical Cerebral Cartography using Graph Neural Network

“…The iterative clustering method can be seen in Supplementary Methods). The original method with the anatomical connectivity was applied on image volume space [30], we adjusted it on individual cortical surface space, details of the method can be seen in Supplementary Methods.…”

“…Many studies adopted cortical fingerprint-based (e.g., anatomical and functional connectivity) mappings for individualizing the population atlas to reveal individualspecific variability of cortical areas [13,[25][26][27][28][29][30][31][32][33]. Population atlas priors provide a blueprint of functional and anatomical architectures of the human brain in these mappings to guide individual-specific topography presented by connection fingerprints [13,23,26,[32][33][34][35][36][37].…”

“…Population priors should be integrated reliably into the mapping to handle the variations of the connectivity fingerprint as well as to avoid obvious outliers in the individual parcellations. Previous methods have made lots of efforts on the utilization of population priors, such as providing rough spatial locations of cortical areas or the distribution of cortical fingerprints on these locations, or iterating group connection fingerprints [13,26,27,30,37]. For example, Han et al presented well delineation of individualized cerebral cartography by iteratively updating reference anatomical connectivities for each area, and matching them with vertex-level anatomical connectivities to identify individual cortical areas (denoted as iterative clustering (IC) methods) [26,30], but it might present areal outliers far from its group locations when faced with fingerprint noises or population biases on certain small areas.…”