Toward individualized connectomes of brain morphology

• Published in: Trends in neurosciences (Regular ed.) Vol. 47; no. 2; pp. 106 - 119
• Main Authors: Wang, Jinhui, He, Yong
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.02.2024
• Subjects: Aging; Brain - anatomy & histology; brain morphology; brain network; Connectome - methods; connectomics; graph theory; Humans; Magnetic Resonance Imaging - methods; MRI; Reproducibility of Results; brain morphology; connectomics; graph theory; MRI; brain network
• ISSN: 0166-2236; 1878-108X; 1878-108X
• DOI: 10.1016/j.tins.2023.11.011

The morphological brain connectome (MBC) can be mapped at an individual level from a single structural MRI scan.Individualized MBCs can be modeled using both low-order and high-order methods, each of which has its own advantages and disadvantages.Individualized MBCs exhibit nontrivial topological properties such as small-worldness, modular organization, and hubs with high robustness, reproducibility, and reliability.Future work is needed to elucidate the cellular and molecular basis of individualized MBCs and to establish normative benchmarks to better understand individual variations in healthy and diseased conditions. The morphological brain connectome (MBC) delineates the coordinated patterns of local morphological features (such as cortical thickness) across brain regions. While classically constructed using population-based approaches, there is a growing trend toward individualized modeling. Currently, the methods for individualized MBCs are varied, posing challenges for method selection and cross-study comparisons. Here, we summarize how individualized MBCs are modeled through low-order methods (correlation-, divergence-, distance-, and deviation-based methods) describing relations in brain morphology, as well as high-order methods capturing similarities in these low-order relations. We discuss the merits and limitations of different methods, examining them in the context of robustness, reproducibility, and reliability. We highlight the importance of elucidating the cellular and molecular mechanisms underlying the individualized connectome, and establishing normative benchmarks to assess individual variation in development, aging, and neuropsychiatric disorders. The morphological brain connectome (MBC) delineates the coordinated patterns of local morphological features (such as cortical thickness) across brain regions. While classically constructed using population-based approaches, there is a growing trend toward individualized modeling. Currently, the methods for individualized MBCs are varied, posing challenges for method selection and cross-study comparisons. Here, we summarize how individualized MBCs are modeled through low-order methods (correlation-, divergence-, distance-, and deviation-based methods) describing relations in brain morphology, as well as high-order methods capturing similarities in these low-order relations. We discuss the merits and limitations of different methods, examining them in the context of robustness, reproducibility, and reliability. We highlight the importance of elucidating the cellular and molecular mechanisms underlying the individualized connectome, and establishing normative benchmarks to assess individual variation in development, aging, and neuropsychiatric disorders.