title: Predicting functional cortical ROIs via DTI-derived fiber shape models.
abstract: 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.
authors.affiliation: School of Automation, Northwestern Polytechnic University, Xi'an 710071, China.
authors.forename: Tuo
authors.initials: T
authors.lastname: Zhang
authors.forename: Lei
authors.initials: L
authors.lastname: Guo
authors.forename: Kaiming
authors.initials: K
authors.lastname: Li
authors.forename: Changfeng
authors.initials: C
authors.lastname: Jing
authors.forename: Yan
authors.initials: Y
authors.lastname: Yin
authors.forename: Dajiang
authors.initials: D
authors.lastname: Zhu
authors.forename: Guangbin
authors.initials: G
authors.lastname: Cui
authors.forename: Lingjiang
authors.initials: L
authors.lastname: Li
authors.forename: Tianming
authors.initials: T
authors.lastname: Liu
mesh_terms: D000293:Adolescent; D000465:Algorithms; D001931:Brain Mapping; D002540:Cerebral Cortex / Q000098:blood supply* / Q000502:physiology*; D002648:Child; D003198:Computer Simulation; D056324:Diffusion Tensor Imaging; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007858:Learning; D008279:Magnetic Resonance Imaging; D008297:Male; D008570:Memory, Short-Term / Q000502:physiology; D008962:Models, Theoretical; D009413:Nerve Fibers, Myelinated / Q000502:physiology; D010100:Oxygen / Q000097:blood; D010775:Photic Stimulation; D011237:Predictive Value of Tests; D025341:Principal Component Analysis; D013334:Students; D014495:Universities; D055815:Young Adult
chemical_list: D010100:Oxygen