3D-SSF: A bio-inspired approach for dynamic multi-subject clustering of white matter tracts

Chekir, Amira; Hassas, Salima; Descoteaux, Maxime; Ct, M.; Garyfallidis, Eleftherios; Oulebsir-Boumghar, Fatima

doi:10.1016/j.compbiomed.2017.01.016

Cited by 2 publications

(1 citation statement)

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“…However, there are challenges in the application of connectome-style analyses to dMRI [91] including anatomical variability of fiber tract terminations and cortical anatomy [8, 42, 96], spatial distortions [50], and reduced reproducibility due to the non-continuous nature of the matrix representation [56]. In contrast, white-matter-centric fiber analysis approaches can leverage information about the full course of a fiber tract, which is the anatomical basis for fiber tract definition [101], and empirical results indicate that white-matter-centric approaches benefit from low variability across subjects in terms of the WM parcellations that can be defined [109, 64, 19, 37].…”

Section: Introductionmentioning

confidence: 99%

Suprathreshold fiber cluster statistics: Leveraging white matter geometry to enhance tractography statistical analysis

Zhang

Ning

et al. 2018

NeuroImage

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This work presents a suprathreshold fiber cluster (STFC) method that leverages the whole brain fiber geometry to enhance statistical group difference analyses. The proposed method consists of 1) a well-established study-specific data-driven tractography parcellation to obtain white matter tract parcels and 2) a newly proposed nonparametric, permutation-test-based STFC method to identify significant differences between study populations. The basic idea of our method is that a white matter parcel's neighborhood (nearby parcels with similar white matter anatomy) can support the parcel's statistical significance when correcting for multiple comparisons. We propose an adaptive parcel neighborhood strategy to allow suprathreshold fiber cluster formation that is robust to anatomically varying inter-parcel distances. The method is demonstrated by application to a multi-shell diffusion MRI dataset from 59 individuals, including 30 attention deficit hyperactivity disorder patients and 29 healthy controls. Evaluations are conducted using both synthetic and in-vivo data. The results indicate that the STFC method gives greater sensitivity in finding group differences in white matter tract parcels compared to several traditional multiple comparison correction methods.

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