Predicting functional cortical ROIs via DTI-Derived Fiber Shape Models

Tuo Zhang, Lei Guo, Kaiming Li, Changfeng Jing, Yan Yin, Dajiang Zhu, Guangbin Cui, Lingjiang Li, Tianming Liu

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82 引用 (Scopus)

摘要

Studying structural and functional connectivities of human cerebral cortex has drawn significant interest and effort recently. A fundamental and challenging problem arises when attempting to measure the structural and/or functional connectivities of specific cortical networks: how to identify and localize the best possible regions of interests (ROIs) on the cortex? In our view, the major challenges come from uncertainties in ROI boundary definition, the remarkable structural and functional variability across individuals and high nonlinearities within and around ROIs. In this paper, we present a novel ROI prediction framework that localizes ROIs in individual brains based on their learned fiber shape models from multimodal task-based functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data. In the training stage, shape models of white matter fibers are learnt from those emanating from the functional ROIs, which are activated brain regions detected from task-based fMRI data. In the prediction stage, functional ROIs are predicted in individual brains based only on DTI data. Our experiment results show that the average ROI prediction error is around 3.94 mm, in comparison with benchmark data provided by working memory and visual task-based fMRI. Our work demonstrated that fiber bundle shape models derived from DTI data are good predictors of functional cortical ROIs.

源语言英语
页(从-至)854-864
页数11
期刊Cerebral Cortex
22
4
DOI
出版状态已出版 - 1 4月 2012

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