Memory decline is common in multiple sclerosis (MS), although pathophysiological mechanisms are not fully understood.
Objective:
The objective was to investigate the relationship of changes in structural and functional neuroimaging markers to memory decline over 3-year follow-up.
Methods:
Participants with MS underwent cognitive evaluation and structural, diffusion, and functional 3T magnetic resonance imaging (MRI) scans at baseline and 3-year follow-up. Changes in neuroimaging metrics from baseline to follow-up were compared between memory stable and memory decline groups. Our hypothesis that structural and functional connectivity changes mediate the association of atrophy to memory decline was tested.
Results:
A total of 249 MS patients completed baseline visit; 169 (67.8%) returned at 3-year follow-up. Based on ⩾10% decline, memory decline was observed in 44.4% (n = 75). Those with memory decline showed marginally greater whole-brain volume loss over time compared with those with stable memory performance (p = 0.08). In those with memory decline, changes in white matter tract integrity were related to regional cortical thinning (p < 0.01). Exploratory mediation analysis revealed structural and functional connectivity to mediate the relationship of atrophy to verbal and visual memory decline.
Conclusion:
Further work is needed to characterize complex interrelationships of atrophy and structural/functional connectivity changes to memory decline in MS.
AttfieldKEJensenLTKaufmannM, et al. The immunology of multiple sclerosis. Nat Rev Immunol2022; 22: 734–750.
2.
PortaccioEAmatoMP. Cognitive impairment in multiple sclerosis: An update on assessment and Management. Neurosci2022; 3: 667–676.
3.
EshaghiAMarinescuRVYoungAL, et al. Progression of regional grey matter atrophy in multiple sclerosis. Brain2018; 141: 1665–1677.
4.
BenedictRHAmatoMPDeLucaJ, et al. Cognitive impairment in multiple sclerosis: Clinical management, MRI, and therapeutic avenues. Lancet Neurol2020; 19: 860–871.
5.
GlanzBHollandCGauthierS, et al. Cognitive dysfunction in patients with clinically isolated syndromes or newly diagnosed multiple sclerosis. Mult Scler J2007; 13: 1004–1010.
6.
MenascuSSternMAloniR, et al. Assessing cognitive performance in radiologically isolated syndrome. Mult Scler Relat Disorder2019; 32: 70–73.
7.
RoccaMAAmatoMPDe StefanoN, et al. Clinical and imaging assessment of cognitive dysfunction in multiple sclerosis. Lancet Neurol2015; 14: 302–317.
8.
KletenikICohenALGlanzBI, et al. Multiple sclerosis lesions that impair memory map to a connected memory circuit. J Neurol2023; 270: 5211–5222.
9.
RoccaMABarkhofFDe LucaJ, et al. The hippocampus in multiple sclerosis. Lancet Neurol2018; 17: 918–926.
10.
SumowskiJFLeavittVMRoccaMA, et al. Mesial temporal lobe and subcortical grey matter volumes differentially predict memory across stages of multiple sclerosis. Mult Scler J2018; 24: 675–678.
11.
DamjanovicDValsasinaPRoccaM, et al. Hippocampal and deep gray matter nuclei atrophy is relevant for explaining cognitive impairment in MS: A multicenter study. Am J Neuroradiol2017; 38: 18–24.
12.
EijlersAJMeijerKAvan GeestQ, et al. Determinants of cognitive impairment in patients with multiple sclerosis with and without atrophy. Radiology2018; 288: 544–551.
13.
BuyukturkogluKZengDBharadwajS, et al. Classifying multiple sclerosis patients on the basis of SDMT performance using machine learning. Mult Scler2021; 27: 107–116.
14.
Van Den HeuvelMPPolHEH. Exploring the brain network: A review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol2010; 20: 519–534.
15.
SjøgårdMWensVVan SchependomJ, et al. Brain dysconnectivity relates to disability and cognitive impairment in multiple sclerosis. Hum Brain Map2021; 42: 626–643.
16.
LeavittVMPaxtonJSumowskiJF. Default network connectivity is linked to memory status in multiple sclerosis. J Int Neuropsychol Soc2014; 20: 937–944.
17.
Andrews-HannaJRReidlerJSSepulcreJ, et al. Functional-anatomic fractionation of the brain’s default network. Neuron2010; 65: 550–562.
18.
MengDWeltonTElsarrajA, et al. Dorsolateral prefrontal circuit effective connectivity mediates the relationship between white matter structure and PASAT-3 performance in multiple sclerosis. Hum Brain Map2021; 42: 495–509.
19.
SchoonheimMMMeijerKAGeurtsJJ. Network collapse and cognitive impairment in multiple sclerosis. Front Neurol2015; 6: 82.
20.
SilemekACHFischerLPöttgenJ, et al. Functional and structural connectivity substrates of cognitive performance in relapsing remitting multiple sclerosis with mild disability. Neuroimage: Clin2020; 25: 102177.
21.
BizzoBCArruda-SanchezTTobyneSM, et al. Anterior insular resting-state functional connectivity is related to cognitive reserve in multiple sclerosis. J Neuroimag2021; 31: 98–102.
22.
RoccaMAValsasinaPCeccarelliA, et al. Structural and functional MRI correlates of Stroop control in benign MS. Hum Brain Map2009; 30: 276–290.
23.
ChanMYAlhazmiFHParkDC, et al. Resting-state network topology differentiates task signals across the adult life span. J Neurosci2017; 37: 2734–2745.
24.
CabezaRAlbertMBellevilleS, et al. Maintenance, reserve and compensation: The cognitive neuroscience of healthy ageing. Nat Rev Neurosci2018; 19: 701–710.
25.
LeavittVMDworkinJDBuyukturkogluK, et al. Summary metrics of memory subnetwork functional connectivity alterations in multiple sclerosis. Mult Scler J2022; 28: 1963–1972.
26.
LeavittVMBrandstadterRFabianM, et al. Dissociable cognitive patterns related to depression and anxiety in multiple sclerosis. Mult Scler2020; 26: 1247–1255.
27.
BeattyWWWilbanksSLBlancoCR, et al. Memory disturbance in multiple sclerosis: Reconsideration of patterns of performance on the selective reminding test. J Clin Exp Neuropsychol1996; 18: 56–62.
28.
BenedictRHSchretlenDGroningerL, et al. Revision of the Brief Visuospatial Memory Test: Studies of normal performance, reliability, and validity. Psychol Assess1996; 8: 145.
29.
AndersonJRFitzgeraldKCMurroughJW, et al. Depression symptoms and cognition in multiple sclerosis: Longitudinal evidence of a specific link to executive control. Mult Scler2023; 29: 1632–1645.
30.
LindvallEAbzhandadzeTQuinnTJ, et al. Is the difference real, is the difference relevant: The minimal detectable and clinically important changes in the Montreal Cognitive Assessment. Cereb Circ Cogn Behav2024; 6: 100222.
31.
SkorveELundervoldAJTorkildsenØ, et al. A two-year longitudinal follow-up of cognitive performance assessed by BICAMS in newly diagnosed patients with MS. Mult Scler Relat Disord2020; 46: 102577.
32.
NyáriAKokasZSzamosiS, et al. The 7-year follow-up of the Hungarian BICAMS validation cohort implies that cognitive performance may improve in multiple sclerosis patients. Neurol Sci2024; 45: 3369–3378.
33.
SternYAlbertMBarnesCA, et al. A framework for concepts of reserve and resilience in aging. Neurobiol Aging2023; 124: 100–103.
34.
Has SilemekACNolteGPöttgenJ, et al. Topological reorganization of brain network might contribute to the resilience of cognitive functioning in mildly disabled relapsing remitting multiple sclerosis. J Neurosci Res2023; 101: 143–161.
35.
TahedlMLevineSMGreenleeMW, et al. Functional connectivity in multiple sclerosis: Recent findings and future directions. Front Neurol2018; 9: 828.
36.
ZhangJCorteseRDe StefanoN, et al. Structural and functional connectivity substrates of cognitive impairment in multiple sclerosis. Front Neurol2021; 12: 671894.
37.
RoosendaalSDSchoonheimMMHulstHE, et al. Resting state networks change in clinically isolated syndrome. Brain2010; 133: 1612–1621.
38.
BonavitaSGalloASaccoR, et al. Distributed changes in default-mode resting-state connectivity in multiple sclerosis. Mult Scler J2011; 17: 411–422.
39.
PetraccaMPontilloGMocciaM, et al. Neuroimaging correlates of cognitive dysfunction in adults with multiple sclerosis. Brain Sci2021; 11: 346.
40.
EijlersAJvan GeestQDekkerI, et al. Predicting cognitive decline in multiple sclerosis: A 5-year follow-up study. Brain2018; 141: 2605–2618.
41.
AzevedoCJCenSYKhadkaS, et al. Thalamic atrophy in multiple sclerosis: A magnetic resonance imaging marker of neurodegeneration throughout disease. Annal Neurol2018; 83: 223–234.
42.
RojasJIMurphyGSanchezF, et al. Thalamus volume change and cognitive impairment in early relapsing–remitting multiple sclerosis patients. Neuroradiol J2018; 31: 350–355.
43.
DongXXuGWangJ, et al. Clinical and MRI predictors of cognitive decline in patients with relapsing-remitting multiple sclerosis: A 2-year longitudinal study. Mult Scler Relat Disorder2022; 65: 103838.
44.
Di FilippoMPortaccioEManciniA, et al. Multiple sclerosis and cognition: Synaptic failure and network dysfunction. Nat Rev Neurosci2018; 19: 599–609.
45.
MandolesiGGentileAMusellaA, et al. Synaptopathy connects inflammation and neurodegeneration in multiple sclerosis. Nat Rev Neurol2015; 11: 711–724.
46.
BellingacciLCanonichesiJManciniA, et al. Cytokines, synaptic plasticity and network dynamics: A matter of balance. Neural Regen Res2023; 18: 2569–2572.
47.
LandmeyerNCBürknerP-CWiendlH, et al. Disease-modifying treatments and cognition in relapsing-remitting multiple sclerosis: A meta-analysis. Neurology2020; 94: e2373–e2383.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
