title: Diffusion MRI connectometry automatically reveals affected fiber pathways in individuals with chronic stroke.
abstract: Building a human connectome database has recently attracted the attention of many researchers, although its application to individual subjects has yet to be explored. In this study, we acquired diffusion spectrum imaging of 90 subjects and showed that this dataset can be used as a norm to examine pathways with deviant connectivity in individuals. This analytical approach, termed diffusion MRI connectometry, was realized by reconstructing patient data to a common stereotaxic space and calculating the percentile rank of the diffusion quantities with respect to those of the norm. The affected tracks were constructed with deterministic tractography using the local tract orientations with substantially low percentile ranks as seeds. To demonstrate the performance of the connectometry, we applied it to 7 patients with chronic stroke and compared the results with lesions shown on T2-weighted images, apparent diffusion coefficient (ADC) maps, and fractional anisotropy (FA) maps, as well as clinical manifestations. The results showed that the affected tracks revealed by the connectometry corresponded well with the stroke lesions shown on T2-weighted images. Moreover, while the T2-weighted images, as well as the ADC and FA maps, showed only the stroke lesions, connectometry revealed entire affected tracks, a feature that is potentially useful for diagnostic or prognostic evaluation. This unique capability may provide personalized information regarding the structural connectivity underlying brain development, plasticity, or disease in each individual subject.
authors.affiliation: Department of Biomedical Engineering, Carnegie Mellon University, PA, USA.
authors.forename: Fang-Cheng
authors.initials: FC
authors.lastname: Yeh
authors.forename: Pei-Fang
authors.initials: PF
authors.lastname: Tang
authors.forename: Wen-Yih Isaac
authors.initials: WY
authors.lastname: Tseng
keywords: Connectometry; Diffusion MRI; Diffusion spectrum imaging; Spin distribution function; Structural connectivity