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Title

White Matter Integrity in Neurological and Neuropsychiatric Diseases with Diffusion-weighted MRI

Introduction

A variety of neurological and neuropsychiatric diseases, such as Alzheimer’s disease (AD) and autism, are related to white matter degeneration over the entire brain. In previous studies, Previous studies usually only calculated either some summary statistics over regions of interest (ROI analysis) or some statistics along mean skeleton lines (Tract Based Spatial Statistic [TBSS]), so they cannot quantify subtle local white matter alterations along major tracts.

Methods

We propose an automated Multi-Atlas Tract Extraction (autoMATE) algorithm. We can reliably extract 18 major white matter tracts from whole brain tractography in a population with autoMATE. We then extract multiple DWI-derived parameters of WM integrity (e.g. fractional anisotropy [FA]/mean diffusivity [MD]/axial diffusivity [AxD]/radial diffusivity [RD]) along the white matter tracts across all subjects. To conduct a population study, we apply a fiber matching scheme to establish fiber correspondence for each tract across the population. A novel statistical functional analysis method-FADTTS (Functional Analysis for Diffusion Tensor Tract Statistics) was applied to quantify degenerative patterns along WM tracts across different stages of the disease. We performed the algorithm on an AD study of 200 subjects: 49 healthy elderly normal controls (NC), 110 with mild cognitive impairment (MCI), and 41 AD patients.

Findings

Figures 1(a-d) show the 3D local profiles of the 4 parameters of the 18 WM tracts in the group comparisons for MCI versus NC (NC-baseline), AD versus MCI (MCI-baseline), and AD versus NC (NC-baseline), respectively. The –log10 p-values correspond to the color bars. Redder colors indicate greater differences. We corrected for multiple comparisons across all points on each tract by using the false discovery rate (FDR). Group differences were considered statistically significant at points with –log values>1.3 (FDR corrected p=0.05). We marked the significant areas with positive change in the parameter with the “+” sign and negative change with the “–” sign.

References

Y. Jin, Y. Shi, L. Zhan, B.A. Gutman, G.I. de Zubicaray, K.L. McMahon, M.J. Wright, A.W. Toga, and P.M. Thompson, “Automatic clustering of white matter fibers in brain diffusion MRI with an application to genetics,” NeuroImage 100, 75-90, 2014.

Y. Jin, C. Huang, M. Daianu, L. Zhan, E.L. Dennis, R.I. Reid, C.R. Jack, Jr, H. Zhu, P.M. Thomposn, and the Alzheimer’s Disease Neuroimaging Initiative, “3D tract-specific local and global analysis of white matter integrity in Alzheimer’s disease,” Human Brain Mapping, in press, 2016.