Diffusion MRI connectometry automatically reveals affected fiber pathways in individuals with chronic stroke

Fang-Cheng Yeh,Pei-Fang Tang,Wen-Yih Isaac Tseng

doi:10.1016/j.nicl.2013.06.014

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.

Highlights

Cerebral connectivity is believed to play an important role in the function of the human brain and could aid in the discovery of disease biomarkers (Akil et al, 2011)
We developed an automatic analytical method referred to as diffusion MRI connectometry to identify fiber pathways with deviant connectivity in each individual subject
We demonstrated its performance in patients with chronic stroke

Summary

Introduction

Cerebral connectivity is believed to play an important role in the function of the human brain and could aid in the discovery of disease biomarkers (Akil et al, 2011). Measuring the structural connectivity of the human brain in vivo is a major challenge in the field of neuroscience. By modeling diffusion MRI data with a diffusion ellipsoid, diffusion tensor imaging (DTI) (Basser et al, 1994) can characterize the structural integrity of axonal fibers via fractional anisotropy (FA) and apparent diffusion coefficient (ADC) analyses (Basser and Pierpaoli, 1996; Pierpaoli and Basser, 1996; Pierpaoli et al, 1996). The principle orientation of the diffusion tensor can facilitate fiber tracking to reveal structural connectivity (Basser et al, 2000; Conturo et al, 1999; Mori et al, 1999).

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Conclusion