The sequence of structural, functional and cognitive changes in multiple sclerosis

Iris Dekker,Menno M Schoonheim,Vikram Venkatraghavan,Anand J.C Eijlers,Iman Brouwer,Esther E Bron,Stefan Klein,Mike P Wattjes,Alle Meije Wink,Jeroen J.G Geurts,Bernard M.J Uitdehaag,Neil P Oxtoby,Daniel C Alexander,Hugo Vrenken,Joep Killestein,Frederik Barkhof,Viktor Wottschel

doi:10.1016/j.nicl.2020.102550

Abstract

BackgroundAs disease progression remains poorly understood in multiple sclerosis (MS), we aim to investigate the sequence in which different disease milestones occur using a novel data-driven approach. MethodsWe analysed a cohort of 295 relapse-onset MS patients and 96 healthy controls, and considered 28 features, capturing information on T2-lesion load, regional brain and spinal cord volumes, resting-state functional centrality (“hubness”), microstructural tissue integrity of major white matter (WM) tracts and performance on multiple cognitive tests. We used a discriminative event-based model to estimate the sequence of biomarker abnormality in MS progression in general, as well as specific models for worsening physical disability and cognitive impairment. ResultsWe demonstrated that grey matter (GM) atrophy of the cerebellum, thalamus, and changes in corticospinal tracts are early events in MS pathology, whereas other WM tracts as well as the cognitive domains of working memory, attention, and executive function are consistently late events. The models for disability and cognition show early functional changes of the default-mode network and earlier changes in spinal cord volume compared to the general MS population. Overall, GM atrophy seems crucial due to its early involvement in the disease course, whereas WM tract integrity appears to be affected relatively late despite the early onset of WM lesions. ConclusionData-driven modelling revealed the relative occurrence of both imaging and non-imaging events as MS progresses, providing insights into disease propagation mechanisms, and allowing fine-grained staging of patients for monitoring purposes

Highlights

Multiple sclerosis (MS) is a chronic inflammatory, demyelinating and neurodegenerative disease of the central nervous system (CNS) (Longo et al, 2018) frequently leading to physical disability and cognitive decline (Compston and Coles, 2008)
At the time of data acquisition, 243 of relapse-onset MS (ROMS) patients were diagnosed with relapsing-remitting multiple sclerosis (MS) (RRMS) and 52 patients with secondary progressive MS (SPMS)
Seventy-five patients were cognitively impaired (CI), 52 patients were classified as mildly cognitively impaired (MCI) and 168 patients as cognitively preserved (CP) (Fig. 1)

Summary

Introduction

Multiple sclerosis (MS) is a chronic inflammatory, demyelinating and neurodegenerative disease of the central nervous system (CNS) (Longo et al, 2018) frequently leading to physical disability and cognitive decline (Compston and Coles, 2008). Event-based modelling (EBM) is a probabilistic data-driven approach to study disease progression that uses cross-sectional data to estimate the temporal sequence of events and subsequently stage patients within this sequence (Fonteijn et al, 2012; Young et al, 2014). This type of model has been applied in Alzheimer’s disease (Fonteijn et al, 2012; Young et al, 2014; Oxtoby et al, 2018), Huntington’s disease (Fonteijn et al, 2012; Wijeratne et al, 2018), and a recent EBM study in MS pa tients provided insights into the sequence of GM atrophy, but did not include features derived from other modalities (Eshaghi et al, 2018). Conclusion: Data-driven modelling revealed the relative occurrence of both imaging and non-imaging events as MS progresses, providing insights into disease propagation mechanisms, and allowing fine-grained staging of patients for monitoring purposes

Objectives

Methods

Results

Discussion

Conclusion