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

Institution:
1Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA.
2College of Computer Science and Technology, Harbin Engineering University, Harbin, China.
3Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA.
4School of Information Technologies, The University of Sydney, Sydney, Australia.
Publisher:
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2017
Publication Date:
Sep-2017
Volume Number:
20
Issue Number:
Pt1
Pages:
556-65
Citation:
Int Conf Med Image Comput Comput Assist Interv. 2017 Sep;20(Pt1):556-65.
Appears in Collections:
NAC, SPL
Sponsors:
P41 EB015902/EB/NIBIB NIH HHS/United States
R01 MH074794/MH/NIMH NIH HHS/United States
R01 MH097979/MH/NIMH NIH HHS/United States
U01 CA199459/CA/NCI NIH HHS/United States
Generated Citation:
Zhang F., Wu W., Ning L., McAnulty G., Waber D., Gagoski B., Sarill K., Hamoda H.M., Song Y., Cai W., Rathi Y., O’Donnell L.J. Supra-Threshold Fiber Cluster Statistics for Data-Driven Whole Brain Tractography Analysis. Int Conf Med Image Comput Comput Assist Interv. 2017 Sep;20(Pt1):556-65.
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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.

Additional Material
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Zhang-MICCAI2017-fig1.jpg (46.195kB)