Estimated connectivity networks outperform observed connectivity networks when classifying people with multiple sclerosis into disability groups
Authors: Tozlu Ceren , Jamison Keith , Gu Zijin , Gauthier Susan A. , Kuceyeski Amy 
Published: June 2021

Background

Multiple Sclerosis (MS), a neurodegenerative and neuroinflammatory disease, causing lesions that disrupt the brain’s anatomical and physiological connectivity networks, resulting in cognitive, visual and/or motor disabilities. Advanced imaging techniques like diffusion and functional MRI allow measurement of the brain’s structural connectivity (SC) and functional connectivity (FC) networks, and can enable a better understanding of how their disruptions cause disability in people with MS (pwMS). However, advanced MRI techniques are used mainly for research purposes as they are expensive, time-consuming and require high-level expertise to acquire and process. As an alternative, the Network Modification (NeMo) Tool can be used to estimate SC and FC using lesion masks derived from pwMS and a reference set of controls’ connectivity networks.

Objective

Here, we test the hypothesis that estimated SC and FC (eSC and eFC) from the NeMo Tool, based only on an individual’s lesion masks, can be used to classify pwMS into disability categories just as well as SC and FC extracted from advanced MRI directly in pwMS. We also aim to find the connections most important for differentiating between no disability vs evidence of disability groups.

Materials and Methods

One hundred pwMS (age:45.5 ± 11.4 years, 66% female, disease duration: 12.97 8.07 years) were included in this study. Expanded Disability Status Scale (EDSS) was used to assess disability, 67 pwMS had no disability (EDSS < 2). Observed SC and FC were extracted from diffusion and functional MRI directly in pwMS, respectively. The NeMo Tool was used to estimate the remaining structural connectome (eSC), by removing streamlines in a reference set of tractograms that intersected the lesion mask. The NeMo Tool’s eSC was used then as input to a deep neural network to estimate the corresponding FC (eFC). Logistic regression with ridge regularization was used to classify pwMS into disability categories (no disability vs evidence of disability), based on demographics/clinical information (sex, age, race, disease duration, clinical phenotype, and spinal lesion burden) and either pairwise entries or regional summaries from one of the following matrices: SC, FC, eSC, and eFC. The area under the ROC curve (AUC) was used to assess the classification performance. Both univariate statistics and parameter coefficients from the classification models were used to identify features important to differentiating between the groups.

Results

The regional eSC and eFC models outperformed their observed FC and SC counterparts (p-value<0.05), while the pairwise eSC and SC performed similarly (p=0.10). Regional eSC and eFC models had higher AUC (0.66-0.68) than the pairwise models (0.60-0.65), with regional eFC having highest classification accuracy across all models. Ridge regression coefficients for the regional eFC and regional observed FC models were significantly correlated (Pearson’s r = 0.52, p-value < 10e-7). Decreased estimated SC node strength in default mode and ventral attention networks and increased eFC node strength in visual networks was associated with evidence of disability.

Discussion

Here, for the first time, we use clinically-acquired lesion masks to estimate both structural and functional connectomes in patient populations to better understand brain lesion-dysfunction mapping in pwMS. Models based on the NeMo Tool’s estimates of SC and FC better classified pwMS by disability level than SC and FC observed directly in the individual using advanced MRI. This work provides a viable alternative to performing high-cost, advanced MRI in patient populations, bringing the connectome one step closer to the clinic.

Highlights

We compared the accuracy of models based on observed functional connectivity (FC) and structural connectivity (SC) networks extracted from advanced MRI and estimated FC and SC networks derived using only lesion masks from conventional MRI in classifying people with multiple sclerosis (pwMS) into disability groups.

Estimated SC and FC generally outperformed observed SC and FC in classifying pwMS into no disability vs evidence of disability groups, with regional estimated SC and FC having the best performance.

Increased estimated FC node strength of regions in the visual network was associated with disability.

Decreased estimated SC node strength of regions in the default mode and ventral attention networks was associated with disability.

Despite their varied sources of origin, feature weights for the regional estimated FC and the regional observed FC classification models was significantly correlated (Pearson’s r = 0.52, p-value < 10e-7).