The human brain exhibits high degree of individual variability in both its structure and function, which underlies inter-subject differences in cognition and behavior. It was previously shown that functional connectivity is more variable in the hetero-modal association cortex but less variable in the unimodal cortices. Structural connectivity is the anatomical substrate of functional connectivity, but the spatial and temporal patterns of individual variability in structural connectivity (IVSC) remain largely unknown. In the present study, we discovered a detailed and robust chart of IVSC obtained by applying diffusion MRI and tractography techniques to 1724 adults (770 males and 954 females) from multiple imaging datasets. Our results showed that the structural connectivity exhibited the highest and lowest variability in the limbic regions and the unimodal sensorimotor regions, respectively. With increased age, higher IVSC was observed across most brain regions. Moreover, the specific spatial distribution of IVSC is related to the cortical laminar differentiation and myelination content. Finally, we proposed a modified ridge regression model to predict individual cognition and generated idiographic brain mapping, which was significantly correlated with the spatial pattern of IVSC. Overall, our findings further contribute to the understanding of the mechanisms of individual variability in brain structural connectivity and link to the prediction of individual cognitive function in adult subjects.Significance Statement White matter connectivity between grey matter regions plays an important role in integrating information from distributed regions when individuals performing complex cognitive functions. Unique white matter connectivity is the neuroanatomical identity of each individual. The authors systematically explored the spatio-temporal pattern of individual variability in structural connectivity, and the results show higher variability in structural connectivity relates to higher neuroplasticity. In addition, this study reveals that structural connectivity involved in executive function and attention task is different among individuals and highlights the importance of individualized statistical methods for mapping neural pathway of complex cognition.
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