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朝着大脑形态的个体化连接组学迈进。

Toward individualized connectomes of brain morphology.

机构信息

Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; Center for Studies of Psychological Application, South China Normal University, Guangzhou 510631, China.

IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.

出版信息

Trends Neurosci. 2024 Feb;47(2):106-119. doi: 10.1016/j.tins.2023.11.011. Epub 2023 Dec 22.

Abstract

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.

摘要

形态脑连接组(MBC)描绘了跨脑区的局部形态特征(如皮质厚度)的协调模式。虽然经典地使用基于人群的方法构建,但个体化建模的趋势日益增长。目前,个体化 MBC 的方法多种多样,这给方法选择和跨研究比较带来了挑战。在这里,我们总结了通过低阶方法(基于相关、发散、距离和偏差的方法)和高阶方法(捕捉这些低阶关系中的相似性)来构建个体化 MBC 的方法。我们讨论了不同方法的优缺点,并在稳健性、可重复性和可靠性的背景下对它们进行了检验。我们强调了阐明个体化连接组背后的细胞和分子机制以及建立规范基准来评估发育、衰老和神经精神障碍个体差异的重要性。

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