Modified connectivity of vulnerable brain nodes in multiple sclerosis, their impact on cognition and their discriminative value

Elisabeth Solana,Sara Llufriu,Jordi Casas-Roma,Elisabet Lopez-Soley,Laura Calvet,Maria Sepulveda,Ferran Prados,Irene Pulido-Valdeolivas,Carmen Montejo,Eloy Martinez-Heras,Albert Saiz,Magi Andorra,Nuria Sola-Valls,Yolanda Blanco

doi:10.1038/s41598-019-56806-z

Abstract

Brain structural network modifications in multiple sclerosis (MS) seem to be clinically relevant. The discriminative ability of those changes to identify MS patients or their cognitive status remains unknown. Therefore, this study aimed to investigate connectivity changes in MS patients related to their cognitive status, and to define an automatic classification method to classify subjects as patients and healthy volunteers (HV) or as cognitively preserved (CP) and impaired (CI) patients. We analysed structural brain connectivity in 45 HV and 188 MS patients (104 CP and 84 CI). A support vector machine with k-fold cross-validation was built using the graph metrics features that best differentiate the groups (p < 0.05). Local efficiency (LE) and node strength (NS) network properties showed the largest differences: 100% and 69.7% of nodes had reduced LE and NS in CP patients compared to HV. Moreover, 55.3% and 57.9% of nodes had decreased LE and NS in CI compared to CP patients, in associative multimodal areas. The classification method achieved an accuracy of 74.8–77.2% to differentiate patients from HV, and 59.9–60.8% to discriminate CI from CP patients. Structural network integrity is widely reduced and worsens as cognitive function declines. Central network properties of vulnerable nodes can be useful to classify MS patients.

Highlights

Brain structural network modifications in multiple sclerosis (MS) seem to be clinically relevant
As such, using a supervised learning model we set out to provide an automatic classification method based on this information that is capable of distinguishing MS patients from healthy individuals, and of distinguishing cognitively impaired (CI) and cognitively preserved (CP) MS patients
Local efficiency was reduced in CP patients relative to the healthy volunteers (HV) in all the nodes studied, while in CP compared to CI patients local efficiency was decreased in 42 (55.3%) of the studied nodes

Summary

Introduction

Brain structural network modifications in multiple sclerosis (MS) seem to be clinically relevant. Graph theory analyses of the structural brain network suggests that MS is associated with an imbalance in the integration and segregation of the network components[3], which deteriorates the information flow among brain regions[1,2], and affects their function[3,4] In this sense, cognitive impairment has been related to a decrease in network efficiency, and changes in nodes and connections involving the insula, deep grey matter and regions of the frontoparietal network[1,2]. Measures of small-worldness and network segregation, such as local efficiency or clustering coefficient, can provide regional information by characterising the interactions of an individual node with its immediate neighbours[5] Such network parameters may be modified by the disease and lead to cognitive dysfunction when the network collapses[6]. As such, using a supervised learning model we set out to provide an automatic classification method based on this information that is capable of distinguishing MS patients from healthy individuals, and of distinguishing cognitively impaired (CI) and cognitively preserved (CP) MS patients

Objectives

Methods

Results

Conclusion