Microstructural Characterization of Network-Based Neurodegeneration in Multiple Sclerosis

1594 

Abstract Submission 

Florence Chiang¹, Eva Krijnen¹, Hansol Lee¹, Hong-Hsi Lee¹, Peter Fox², Eric Klawiter¹, Susie Huang¹

¹Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, ²Research Imaging Institute, The University of Texas Health Science Center at San Antonio, San Antonio, TX

Florence Chiang, MD PhD  
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA

Eva Krijnen, MD  
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA

Hansol Lee, PhD  
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA

Hong-Hsi Lee, MD PhD  
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA

Peter Fox, MD  
Research Imaging Institute, The University of Texas Health Science Center at San Antonio
San Antonio, TX

Eric Klawiter, MD MSc  
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA

Susie Huang, MD PhD  
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA

Neurodegeneration is a key component of clinical disability in multiple sclerosis (MS). However, the underlying mechanism of localized gray matter (GM) atrophy in MS remains unknown. More recently, a network-based etiology has been postulated, which may be associated with clinical progression (Schoonheim, Broeders, and Geurts 2022; Chard and Miller 2016; Chiang et al. 2021). The goal of this study was to assess network behavior of microstructural alteration in atrophy-prone GM (Chiang et al. 2021; 2019). We leveraged high gradient diffusion MRI (dMRI) to probe GM at the mesoscopic scale by using the SANDI (Soma and Neurite Density Imaging) method, a novel biophysical modeling approach (Palombo et al. 2020). Our hypothesis was that cell body density of atrophy-prone GM will be decreased in MS which will correlate with disease severity and that these regions will exhibit microstructural covariation. Findings of this study would clarify the microstructural substrate of network-based GM atrophy and improve current understanding of network concepts in MS.

Whole-brain dMRI was obtained for all participants on the 3T Connectome MRI scanner with 300 mT/m maximum gradient strength (MAGNETOM Connectom, Siemens Healthineers)(Huang et al. 2020). dMRI was acquired with a multi-shell diffusion protocol using a diffusion time ∆ = 19 ms, 8 b-values (b = 50-350-800-1500 s/mm2 in 32 directions, and b = 2400-3450-4750-6000 s/mm2 in 64 directions), and an isotropic resolution of 2 mm. A short diffusion time was used to minimize the potential confound of intercompartmental exchange (Jelescu et al. 2022). After all imaging data were preprocessed using an established pipeline (Tian et al. 2022), SANDI model fitting was performed using AMICO (Daducci et al. 2015). SANDI metrics were computed including the intra-soma signal fraction (fis), which reflects cell body density. Nodes in the Atrophy-based Functional Network (AFN) model was used to define regions-of-interest (ROIs; Figure) (Chiang et al. 2021; 2019). ROIs were binarized and transformed from standard (Montreal Neurological Institute) to the diffusion space of each participant with nonlinear registration. ROIs were then used to sample the fis map of each participant. Statistical analyses included group-wise comparisons of the average fis of each ROI and for the nodal aggregate using independent samples t-tests with FDR-correction. Association of AFN nodal aggregate fis with the Expanded Disability Status Scale (EDSS) score and disease duration were assessed using Spearman's rank-order correlation. Pearson's correlation coefficients were computed for all ROIs pairs to generate a covariance matrix.

Participants included 38 MS (M/F: 11/27; age 44 ± 11 years; EDSS 2.8 ± 1.7, 1 - 7.5; disease duration 9.5 ± 6.6 years) and 35 age-matched healthy controls (HC; M/F: 15/20; age 39 ± 15 years; p = 0.13). fis was decreased between MS and HC for the aggregate average of all AFN atrophy nodes. fis of both cortical and subcortical nodes including in the basal ganglia, thalamus, and precentral gyrus remained significantly decreased at the individual nodal level (Table). The aggregate nodal fis demonstrated a strong association with the EDSS score (ρ = -0.543, p < 0.001) and a relatively weaker correlation with disease duration (ρ = -0.317, p = 0.056). Correlations between all nodes were computed and displayed in a heatmap, which demonstrated presence of medium to large effect sizes (Figure).

In conclusion, decreased cell body density was observed in atrophy-prone GM of MS which correlated with clinical disability. Further, covariance of localized GM microstructural alteration suggests that neuronal loss may relate in part to network-based effects. Network-based microstructural measures may provide the foundation for future development of quantitative non-invasive methods to help achieve more sensitive monitoring of disease progression in MS, which would enable prompt clinical intervention.

Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) ²

Connectivity (eg. functional, effective, structural)

Diffusion MRI Modeling and Analysis ¹

Multivariate Approaches

Diffusion MRI

Computational Neuroscience

Degenerative Disease

Demyelinating

Meta- Analysis

Neurological

Other - Multiple sclerosis, gray matter atrophy, connectomics, microstructure, high gradient MRI