Supra-Threshold Fiber Cluster Statistics for Data-Driven Whole Brain Tractography Analysis

Citation:

Zhang Fan, Wu Weining, Ning Lipeng, McAnulty Gloria, Waber Deborah, Borjan Gagoski, Sarill Kiera, Hamoda Hesham M, Song Yang, Cai Weidong, Yogesh Rathi, and Lauren J O'Donnell. 9/2017. “Supra-Threshold Fiber Cluster Statistics for Data-Driven Whole Brain Tractography Analysis.” Int Conf Med Image Comput Comput Assist Interv. 20 (Pt1), Pp. 556-65.
Zhang-MICCAI2017.pdf2.36 MB

Abstract:

This work presents a supra-threshold fiber cluster (STFC) analysis that leverages the whole brain fiber geometry to enhance sta- tistical group difference analysis. The proposed method consists of (1) a study-specific data-driven tractography parcellation to obtain white matter (WM) tract parcels according to the WM anatomy and (2) a nonparametric permutation-based STFC test to identify significant dif- ferences between study populations (e.g. disease and healthy). The basic idea of our method is that a WM parcel’s neighborhood (parcels with similar WM anatomy) can support the parcel’s statistical significance when correcting for multiple comparisons. The method is demonstrated by application to a multi-shell diffusion MRI dataset from 59 individuals, including 30 attention deficit hyperactivity disorder (ADHD) patients and 29 healthy controls (HCs). Evaluations are conducted using both synthetic and real data. The results indicate that our STFC method gives greater sensitivity in finding group differences in WM tract parcels compared to several traditional multiple comparison correction methods.
Last updated on 10/11/2017