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

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

doi:10.1016/j.nicl.2020.102550

Cited by 23 publications

(44 citation statements)

References 57 publications

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“…The observed GM volume changes in this study are in line with previous studies reporting early-stage cerebellar atrophy [19] and they are unsurprising as the cerebellum has been shown to play an essential role in both sensorimotor and cognitive dysfunction in MS [77–80].…”

Section: Discussionsupporting

confidence: 91%

“…This discrepancy may be explained by the changes within the latter regions being less localised which may have left them undetected by VBM. The early GM changes observed here are partly in line with previous studies showing prominent early changes in the occipital [19], temporal [20, 73, 86], parietal (posterior cingulum) [14] and frontal lobe [86]. However, these observations differ from the current results in that they do not report on GM changes in all lobes simultaneously.…”

Section: Discussionsupporting

confidence: 84%

“…Previous studies have shown that neurodegeneration also plays a prominent role in the disease evolution of RRMS and is importantly already present in the early stages of the disease [13–18]. Early-stage grey matter (GM) atrophy has been observed in specific brain areas, including the cingulate gyrus, precuneus, thalamus, basal ganglia, brainstem and cerebellum [14, 19–21]. Less regional detail appears to be known about WM atrophy in RRMS, but it also occurs in early stages and has been suggested to occur independently of WML development [13, 22, 23].…”

Section: Introductionmentioning

confidence: 99%

“…To our knowledge, current literature does not provide detailed insights into regional WM volume loss in very early-stage RRMS, and only a limited number of studies have examined specific GM volume in early stages of the disease [14, 19–21]. The current study uses data from FutureMS [42], a large multicentre cohort of recently-diagnosed RRMS patients in Scotland, which acquired extensive MR imaging [43] at baseline and one-year follow-up.…”

Section: Introductionmentioning

confidence: 99%

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Patterns of brain degeneration in early-stage relapsing-remitting multiple sclerosis

Meijboom

York

Kampaite

et al. 2021

Preprint

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Recurrent neuroinflammation in relapsing-remitting MS (RRMS) is thought to lead to neurodegeneration, resulting in progressive disability. Repeated magnetic resonance imaging (MRI) of the brain provides non-invasive measures of atrophy over time, a key marker of neurodegeneration. This study investigates regional neurodegeneration of the brain in early-stage RRMS using volumetry and voxel-based morphometry (VBM).RRMS patients (N=354) underwent 3T structural MRI at diagnosis and 1-year follow-up, as part of the Scottish multicentre ‘FutureMS’ study. MRI data were processed using FreeSurfer to derive volumetrics, and FSL for VBM (grey matter (GM) only), to establish patterns of change in GM and normal-appearing white matter (NAWM) over time throughout the cerebrum, cerebellum and brainstem.Volumetric analyses showed a decrease over time (q<0.05) in bilateral cortical GM and NAWM, multiple subcortical structures, cerebellar GM and the brainstem. Additionally, NAWM and GM volume decreased respectively in the following cortical regions, frontal: 14 out of 28 regions and 17/28; temporal: 18/18 and 15/18; parietal: 14/14 and 11/14; occipital: 7/8 and 8/8. Left GM and NAWM asymmetry was observed in the frontal lobe. GM VBM analysis showed three major clusters of decrease over time: 1) temporal lobe and subcortical areas, 2) cerebellum, 3) anterior cingulum and supplementary motor cortex; and four smaller clusters within the occipital lobe.Widespread neurodegeneration was observed in early-stage RRMS; particularly in the brainstem, cerebellar GM, and subcortical and occipital-temporal regions. Volumetric and VBM results emphasise different as well as overlapping patterns of longitudinal change, and provide potential response markers for existing therapies and trials of neuroprotective agents.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionsupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Patterns of brain degeneration in early-stage relapsing-remitting multiple sclerosis

Meijboom

York

Kampaite

et al. 2021

Preprint

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“…One model archetype that has found wide success in the context of neurodegenerative diseases [11][12][13][14] and AD specifically [15] is the event-based model (EBM) [13]. It is a data-driven probabilistic generative model that characterises the progression of a disease in the form of a single sequence of events which describes the relative order of measured markers turning from a normal state to a diseased state (i.e., abnormal state).…”

Section: Introductionmentioning

confidence: 99%

Comparison and aggregation of event sequences across ten cohorts to describe the consensus biomarker evolution in Alzheimer’s disease

Khatami

Salimi

Hofmann-Apitius

et al. 2021

Preprint

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BackgroundPrevious models of Alzheimer’s disease (AD) progression were primarily hypothetical or based on data originating from single cohort studies. However, cohort datasets are subject to specific inclusion and exclusion criteria that influence the signals observed in their collected data. Furthermore, each study measures only a subset of AD relevant variables. To gain a comprehensive understanding of AD progression, the heterogeneity and robustness of estimated progression patterns must be understood, and complementary information contained in cohort datasets be leveraged.MethodsWe compared ten event-based models that we fit to ten independent AD cohort datasets. Additionally, we designed and applied a novel rank aggregation algorithm that combines partially overlapping, individual event sequences into a meta-sequence containing the complementary information from each cohort.ResultsWe observed overall consistency across the ten event-based model sequences (Kendall’s tau correlation coefficient of 0.78±0.13), despite variance in the positioning of mainly imaging variables. The changes described in the aggregated meta-sequence are broadly consistent with current understanding of AD progression, starting with cerebrospinal fluid amyloid beta, followed by memory impairment, tauopathy, FDG-PET, and ultimately brain deterioration and impairment of visual memory.ConclusionOverall, the event-based models demonstrated similar and robust disease cascades across independent AD cohorts. Aggregation of data-driven results can combine complementary strengths and information of patient-level datasets. Accordingly, the derived meta-sequence draws a more complete picture of AD pathology compared to models relying on single cohorts.

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Computer-Aided Diagnosis and Prediction in Brain Disorders

Venkatraghavan

Voort

Bos

et al. 2023

Machine Learning for Brain Disorders

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Computer-aided methods have shown added value for diagnosing and predicting brain disorders and can thus support decision making in clinical care and treatment planning. This chapter will provide insight into the type of methods, their working, their input data –such as cognitive tests, imaging, and genetic data– and the types of output they provide. We will focus on specific use cases for diagnosis, i.e., estimating the current “condition” of the patient, such as early detection and diagnosis of dementia, differential diagnosis of brain tumors, and decision making in stroke. Regarding prediction, i.e., estimation of the future “condition” of the patient, we will zoom in on use cases such as predicting the disease course in multiple sclerosis and predicting patient outcomes after treatment in brain cancer. Furthermore, based on these use cases, we will assess the current state-of-the-art methodology and highlight current efforts on benchmarking of these methods and the importance of open science therein. Finally, we assess the current clinical impact of computer-aided methods and discuss the required next steps to increase clinical impact.

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

Cited by 23 publications

References 57 publications

Patterns of brain degeneration in early-stage relapsing-remitting multiple sclerosis

Patterns of brain degeneration in early-stage relapsing-remitting multiple sclerosis

Comparison and aggregation of event sequences across ten cohorts to describe the consensus biomarker evolution in Alzheimer’s disease

Computer-Aided Diagnosis and Prediction in Brain Disorders

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