Urgent challenges in quantification and interpretation of brain grey matter atrophy in individual MS patients using MRI

Abstract

Atrophy of the brain grey matter (GM) is an accepted and important feature of multiple sclerosis (MS). However, its accurate measurement is hampered by various technical, pathological and physiological factors. As a consequence, it is challenging to investigate the role of GM atrophy in the disease process as well as the effect of treatments that aim to reduce neurodegeneration. In this paper we discuss the most important challenges currently hampering the measurement and interpretation of GM atrophy in MS. The focus is on measurements that are obtained in individual patients rather than on group analysis methods, because of their importance in clinical trials and ultimately in clinical care. We discuss the sources and possible solutions of the current challenges, and provide recommendations to achieve reliable measurement and interpretation of brain GM atrophy in MS.

Keywords: BET, brain extraction tool; Brain atrophy; CNS, central nervous system; CTh, cortical thickness; DGM, deep grey matter; DTI, diffusion tensor imaging; FA, fractional anisotropy; GM, grey matter; Grey matter; MRI, magnetic resonance imaging; MS, multiple sclerosis; Magnetic resonance imaging; Multiple sclerosis; TE, echo time; TI, inversion time; TR, repetition time; VBM, voxel-based morphometry; WM, white matter.

Fig. 1

The effect of WM lesions on the segmentation using FSL-FAST software. Left column: The native (non-filled) T1-weighted image and its WM segmentation. Right column: T1-weighted image after lesion-filling and its WM segmentation. Arrows point to obvious WM lesions. Color bar indicates the WM partial volume estimate.

Fig. 2

An example of image misregistration due to atrophy and a possible method for its improvement based on multi-channel mapping. Warped contours of lateral ventricles and surrounding regions are superimposed on T1-weighted image as color overlays. (a) The red outline indicates the contour of the lateral ventricle warped to the target image space by default parameters FNIRT. This registration compared to the actual location of the lateral ventricle (white dotted line) is poor. (b) Taking into account the large deformations necessary to co-register images in the presence of severe atrophy (method proposed by Djamanakova et al. (2013)) slightly improved the registration. (c) Using a dual-channel approach incorporating a coarse ventricle segmentation in the target image space improved the lateral ventricle registration substantially.

Fig. 3

The effect of grey/white contrast on cortical thickness measurement using FreeSurfer. Statistical p maps thresholded at p < 10⁻² superimposed on a template brain's semi-inflated surface showing the results from GLMs testing the difference between Alzheimer's disease (AD) and controls (Con). Warm colors denote areas with significantly thinner cortex in AD compared to controls. Adjusting for grey/white tissue contrast (bottom row) increases sensitivity to the AD-control differences in cortical thickness over large portions of the brain compared to results obtained when not adjusting for this contrast (top row).

Fig. 4

MPRAGE T1-weighted images of a healthy volunteer to compare the image contrast and signal intensity by modifying only one imaging parameter, i.e. the inversion time (TI). (a) TI = 450 ms and (b) TI = 400 ms.