Combined structural and functional patterns discriminating upper limb motor disability in multiple sclerosis using multivariate approaches

Abstract

A structural or functional pattern of neuroplasticity that could systematically discriminate between people with impaired and preserved motor performance could help us to understand the brain networks contributing to preservation or compensation of behavior in multiple sclerosis (MS). This study aimed to (1) investigate whether a machine learning-based technique could accurately classify MS participants into groups defined by upper extremity function (i.e. motor function preserved (MP) vs. motor function impaired (MI)) based on their regional grey matter measures (GMM, cortical thickness and deep grey matter volume) and inter-regional functional connection (FC), (2) investigate which features (GMM, FC, or GMM + FC) could classify groups more accurately, and (3) identify the multivariate patterns of GMM and FCs that are most discriminative between MP and MI participants, and between each of these groups and the healthy controls (HCs). With 26 MP, 25 MI, and 21 HCs (age and sex matched) underwent T1-weighted and resting-state functional MRI at 3 T, we applied support vector machine (SVM) based classification to learn discriminant functions indicating regions in which GMM or between which FCs were most discriminative between groups. This study demonstrates that there exist structural and FC patterns sufficient for correct classification of upper limb motor ability of people with MS. The classifier with GMM + FC features yielded the highest accuracy of 85.61 % (p < 0.001) to distinguish between the MS groups using leave-one-out cross-validation. It suggests that a machine-learning approach combining structural and functional features is useful for identifying the specific neural substrates that are necessary and sufficient to preserve motor function among people with MS.

Functional Connectivity in Multiple Sclerosis: Recent Findings and Future Directions

“…The second strategy associates FC values of MS patients with clinical data, such as cognitive ( 50 ) and motor performance ( 67 ), fatigue ( 40 , 68 , 69 ), depression ( 70 ), or sleep disturbance ( 71 ). Here, either the respective symptom severity is used as a regressor of the FC data or the group of MS patients is subdivided into subgroups of preserved vs. impaired function.…”

“…Generally, MS patients with impaired function often present with reduced FC in widespread brain regions as compared to patients with preserved function, who are often similar to the HC group [e.g., cognitive functions ( 90 , 105 ), motor functions ( 67 ), sleep problems ( 71 ), and depression ( 70 , 106 , 107 )]. Interestingly, such reductions in FC are often observed between brain regions that are classically linked to the function that is impaired in the MS group.…”

“…Moreover, lower-limb spasticity could be improved with transcranial magnetic stimulation using intermittent theta burst stimulation, which increased the integrity (assessed with graph theory) of the contralateral primary motor cortex ( 114 ). Furthermore, motor-impaired MS patients have also been distinguished from motor-preserved MS patients based on reductions of FC in the thalamus ( 115 ) and in occipital regions ( 39 , 67 ).…”

“…For example, increased SMN-related connectivity (the network associated with motor function) was associated with cognitive deficits via increased FC between the thalamus and sensorimotor/occipital regions ( 117 ), between the thalamus and the cerebellum ( 36 ), or increased ICA-coherence in the thalamic RSN ( 105 ) and the medial visual network ( 85 ). Conversely, increased FC between cognition -related brain regions, such as between the left pallidum and the left anterior cingulate was instead associated with motor deficits ( 67 ).…”

Characterization of relapsing-remitting multiple sclerosis patients using support vector machine classifications of functional and diffusion MRI data

“…Machine leaning approaches have been developed to differentiate MS from other pathologies ( Eshaghi et al, 2016 ; Muthuraman et al, 2016 ), as well as to distinguish sub-categories within the disease ( Bendfeldt et al, 2012 ; Kocevar et al, 2016 ). Some of these approaches have applied DTI (e.g., Stamile et al, 2015 ) and rsfMRI in patients with MS (e.g., Richiardi et al, 2012 ; Zhong et al, 2017 ). One of these studies (e.g., Richiardi et al, 2012 ) successfully classified MS patients and healthy controls with sensitivities and specificities above 80%.…”