Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality and Emotion

By Ru Kong, Jingwei Li, C. Orban, Mert Rory Sabuncu, Hesheng Liu, Alexander Schaefer, Nanbo Sun, Xi-Nian Zuo, Avram J. Holmes, S. Eickhoff and 1 more. bioRxiv, 2017

589 citations recorded by the source corpus.

Resting-state functional magnetic resonance imaging (rs-fMRI) offers the opportunity to non-invasively study individual-specific brain networks. We propose a multi-session hierarchical Bayesian model (MS-HBM) that differentiates between intra-subject (within-subject) and inter-subject (between-subject) network variability. Across datasets, sensory-motor networks exhibited lower inter-subject, but higher intra-subject variability than association networks. Therefore, by ignoring intra-subject variability, previous individual-specific network mappings might confuse intra-subject variability for inter-subject differences. Compared with other approaches, MS-HBM cortical parcellations generalized better to new rs-fMRI and task-fMRI data from the same subjects. Importantly, MS-HBM parcellations from a single rs-fMRI session (10 min) were comparable to a recent state-of-the-art algorithm using five sessions (50 min). Individual-specific MS-HBM parcellations were highly reproducible, yet captured inter-subject differences. While other studies have already established that individual-specific networks exhibit features not observed in population-average networks, here we demonstrate that these features are behaviorally meaningful. Using kernel regression, individual differences in the spatial arrangement of cortical networks could be used to predict cognition, personality and emotion. Thus, individuals with more spatially similar parcellations exhibited more similar behavior. Overall, our results suggest that individual-specific cortical network topography might serve as a fingerprint of human behavior, orthogonal to previously proposed functional connectivity fingerprints.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/2a2c643f30782b74c9a5c74408f628229b81f3c7

Citations in the corpus

• Neurodevelopment of the association cortices: Patterns, mechanisms, and implications for psychopathology (2021, unknown): “Higher-order systems exhibit distributed long-range connections, non-canonical circuit organization, and substantial spatial variability across individuals (Buckner and Krienen, 2013; Cui et al., 2020; Kong et al., 2019; Mueller et al., 2013; Oligschl€ager et al., 2017; Sepulcre et al., 2010).”
• Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks (2019, unknown): “…studies have suggested that the spatial arrangement (e.g. topography) and size of individ-ual-specific networks can be predictive of demographics (e.g., sex) and behavior (Bijsterbosch et al. 2018; Salehi et al. 2018; Cui et al. 2019; Kong et al. 2019; Li et al. 2019a, b; Seitzman et al. 2019).”