Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA.
Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA.
Cell Rep. 2023 Dec 26;42(12):113487. doi: 10.1016/j.celrep.2023.113487. Epub 2023 Nov 22.
During adolescence, the brain undergoes extensive changes in white matter structure that support cognition. Data-driven approaches applied to cortical surface properties have led the field to understand brain development as a spatially and temporally coordinated mechanism that follows hierarchically organized gradients of change. Although white matter development also appears asynchronous, previous studies have relied largely on anatomical tract-based atlases, precluding a direct assessment of how white matter structure is spatially and temporally coordinated. Harnessing advances in diffusion modeling and machine learning, we identified 14 data-driven patterns of covarying white matter structure in a large sample of youth. Fiber covariance networks aligned with known major tracts, while also capturing distinct patterns of spatial covariance across distributed white matter locations. Most networks showed age-related increases in fiber network properties, which were also related to developmental changes in executive function. This study delineates data-driven patterns of white matter development that support cognition.
在青春期，大脑的白质结构发生了广泛的变化，这些变化支持认知。应用于皮质表面特性的基于数据的方法使该领域能够将大脑发育理解为一个空间和时间上协调一致的机制，该机制遵循层次组织的变化梯度。尽管白质发育似乎也是异步的，但以前的研究主要依赖于基于解剖结构的图谱，这排除了对白质结构在空间和时间上如何协调的直接评估。利用扩散建模和机器学习的进展，我们在一个大样本的年轻人中确定了 14 个随时间变化的白质结构的驱动数据模式。纤维协方差网络与已知的主要束一致，同时也捕捉到了分布在白质位置上的不同空间协方差模式。大多数网络显示出与纤维网络特性相关的年龄相关增加，这些特性也与执行功能的发育变化有关。这项研究描绘了支持认知的白质发育的驱动数据模式。
