Individual brains vary greatly in morphology, connectivity and organization. Individualized brain parcellation is capable of precisely localizing subject-specific functional regions. However, most individualization approaches have examined single modalities of data and have not generalized to nonhuman primates. The present study proposed a novel multimodal connectivity-based individual parcellation (MCIP) method, which optimizes within-region homogeneity, spatial continuity and similarity to a reference atlas with the fusion of personal functional and anatomical connectivity. Comprehensive evaluation demonstrated that MCIP outperformed state-of-the-art multimodal individualization methods in terms of functional and anatomical homogeneity, predictability of cognitive measures, heritability, reproducibility and generalizability across species. Comparative investigation showed a higher topographic variability in humans than that in macaques. Therefore, MCIP provides improved accurate and reliable mapping of brain functional regions over existing methods at an individual level across species, and could facilitate comparative and translational neuroscience research.

中文翻译：

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基于多模态连接的人类和 Rh 猴个体分离和分析


个体大脑在形态、连接性和组织方面差异很大。个体化大脑包裹能够精确定位受试者特定的功能区域。然而，大多数个体化方法都检查了数据的单一模态，并没有推广到非人类灵长类动物。本研究提出了一种新颖的基于多模态连接的单独包裹 （MCIP） 方法，该方法融合了个人功能和解剖学连接性，优化了区域内的均匀性、空间连续性和与参考图谱的相似性。综合评估表明，MCIP 在功能和解剖学同质性、认知测量的可预测性、遗传力、可重复性和跨物种泛化性方面优于最先进的多模态个体化方法。比较调查显示，人类的地形变异性高于猕猴。因此，MCIP 在跨物种的个体水平上为现有方法提供了改进的准确和可靠的大脑功能区域映射，并且可以促进比较和转化神经科学研究。