Functional Connectivity and Structural Disruption in the Default-Mode Network Predicts Cognitive Rehabilitation Outcomes in Multiple Sclerosis.
Cognitive rehabilitation
functional MRI
multiple sclerosis
network
prediction
Journal
Journal of neuroimaging : official journal of the American Society of Neuroimaging
ISSN: 1552-6569
Titre abrégé: J Neuroimaging
Pays: United States
ID NLM: 9102705
Informations de publication
Date de publication:
07 2020
07 2020
Historique:
received:
18
03
2020
revised:
16
04
2020
accepted:
20
04
2020
pubmed:
12
5
2020
medline:
16
2
2021
entrez:
12
5
2020
Statut:
ppublish
Résumé
Efficacy of restorative cognitive rehabilitation can be predicted from baseline patient factors. In addition, patient profiles of functional connectivity are associated with cognitive reserve and moderate the structure-cognition relationship in people with multiple sclerosis (PwMS). Such interactions may help predict which PwMS will benefit most from cognitive rehabilitation. Our objective was to determine whether patient response to restorative cognitive rehabilitation is predictable from baseline structural network disruption and whether this relationship is moderated by functional connectivity. For this single-arm repeated measures study, we recruited 25 PwMS for a 12-week program. Following magnetic resonance imaging, participants were tested using the Symbol Digit Modalities Test (SDMT) pre- and postrehabilitation. Baseline patterns of structural and functional connectivity were characterized relative to healthy controls. Lower white matter tract disruption in a network of region-pairs centered on the precuneus and posterior cingulate (default-mode network regions) predicted greater postrehabilitation SDMT improvement (P = .048). This relationship was moderated by profiles of functional connectivity within the network (R Patient response to restorative cognitive rehabilitation is predictable from the interaction between structural network disruption and functional connectivity in the default-mode network. This effect may be related to cognitive reserve.
Sections du résumé
BACKGROUND AND PURPOSE
Efficacy of restorative cognitive rehabilitation can be predicted from baseline patient factors. In addition, patient profiles of functional connectivity are associated with cognitive reserve and moderate the structure-cognition relationship in people with multiple sclerosis (PwMS). Such interactions may help predict which PwMS will benefit most from cognitive rehabilitation. Our objective was to determine whether patient response to restorative cognitive rehabilitation is predictable from baseline structural network disruption and whether this relationship is moderated by functional connectivity.
METHODS
For this single-arm repeated measures study, we recruited 25 PwMS for a 12-week program. Following magnetic resonance imaging, participants were tested using the Symbol Digit Modalities Test (SDMT) pre- and postrehabilitation. Baseline patterns of structural and functional connectivity were characterized relative to healthy controls.
RESULTS
Lower white matter tract disruption in a network of region-pairs centered on the precuneus and posterior cingulate (default-mode network regions) predicted greater postrehabilitation SDMT improvement (P = .048). This relationship was moderated by profiles of functional connectivity within the network (R
CONCLUSION
Patient response to restorative cognitive rehabilitation is predictable from the interaction between structural network disruption and functional connectivity in the default-mode network. This effect may be related to cognitive reserve.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
523-530Informations de copyright
© 2020 American Society of Neuroimaging.
Références
Charvet LE, Yang J, Shaw MT, et al. Cognitive function in multiple sclerosis improves with telerehabilitation: results from a randomized controlled trial. PLoS ONE 2017;12:e0177177.
Goverover Y, Chiaravalloti ND, Brien ARO, et al. Evidenced-based cognitive rehabilitation for persons with multiple sclerosis: an updated review of the literature from 2007 to 2016. Arch Phys Med Rehabil 2018;99:390-407.
Fuchs TA, Ziccardi S, Dwyer MG, et al. Response heterogeneity to home-based restorative cognitive rehabilitation in multiple sclerosis: an exploratory study. Mult Scler Relat Disord 2019;34:103-11.
Chiaravalloti ND, Moore NB, Nikelshpur OM, et al. An RCT to treat learning impairment in multiple sclerosis: the MEMREHAB trial. Neurology 2013;81:2066-72.
Fornito A, Zalesky A, Breakspear M. The connectomics of brain disorders. Nat Rev Neurosci 2015;16:159-72.
Schoonheim MM, Meijer KA, Geurts JJG. Network collapse and cognitive impairment in multiple sclerosis. Front Neurol 2015;6:82.
Fuchs TA, Benedict RHB, Bartnik A, et al. Preserved network functional connectivity underlies cognitive reserve in multiple sclerosis. Hum Brain Mapp 2019;40:5231-41.
Jakimovski D, Gandhi S, Paunkoski I, et al. Hypertension and heart disease are associated with development of brain atrophy in multiple sclerosis: a 5-year longitudinal study. Eur J Neurol 2019;26:87-93.
Benedict RH, DeLuca J, Phillips G, et al. Validity of the symbol digit modalities test as a cognition performance outcome measure for multiple sclerosis. Mult Scler 2017;23:721-33.
Kappus N, Weinstock-Guttman B, Hagemeier J, et al. Cardiovascular risk factors are associated with increased lesion burden and brain atrophy in multiple sclerosis. J Neurol Neurosurg Psychiatry 2016;87:181-7.
Smith A. Symbol Digit Modalities Test (SDMT): Manual. Revised. Los Angeles, CA: Western Psychological Services;1982.
Strober L, DeLuca J, Benedict RH, et al. Symbol digit modalities test: a valid clinical trial endpoint for measuring cognition in multiple sclerosis. Mult Scler 2018;25:1781-90.
Smith SM, Zhang Y, Jenkinson M, et al. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage 2002;17:479-89.
Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. I. segmentation and surface reconstruction. Neuroimage 1999;9:179-94.
Zivadinov R, Heininen-Brown M, Schirda C V., et al. Abnormal subcortical deep-gray matter susceptibility-weighted imaging filtered phase measurements in patients with multiple sclerosis: a case-control study. Neuroimage 2012;59:331-9.
Avants BB, Tustison NJ, Song G, et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 2011;54:2033-44.
Kuceyeski A, Maruta J, Relkin N, et al. The Network Modification (NeMo) Tool: elucidating the effect of white matter integrity changes on cortical and subcortical structural connectivity. Brain Connect 2013;3:451-63.
Fuchs TA, Dwyer MG, Kuceyeski A, et al. White matter tract network disruption explains reduced conscientiousness in multiple sclerosis. Hum Brain Mapp 2018;39:3682-90.
Griffanti L, Douaud G, Bijsterbosch J, et al. Hand classification of fMRI ICA noise components. Neuroimage 2017;154:188-205.
Abraham A, Pedregosa F, Eickenberg M, et al. Machine learning for neuroimaging with scikit-learn. Front Neurosci 2014;8:1-10.
Zalesky A, Fornito A, Bullmore ET. Network-based statistic: identifying differences in brain networks. Neuroimage 2010;53:1197-207.
Smith SM, Fox PT, Miller KL, et al. Correspondence of the brain's functional architecture during activation and rest. Proc Natl Acad Sci USA 2009;106:13040-5.
Nickerson LD, Smith SM, Ongur D, et al. Using dual regression to investigate network shape and amplitude in functional connectivity analyses. Front Neurosci 2017;11:115.
Benedict RHB, Duquin JA, Jurgensen S, et al. Repeated assessment of neuropsychological deficits in multiple sclerosis using the Symbol Digit Modalities Test and the MS Neuropsychological Screening Questionnaire. Mult Scler 2008;14:940-6.
Savini G, Pardini M, Castellazzi G, et al. Default mode network structural integrity and cerebellar connectivity predict information processing speed deficit in multiple sclerosis. Front Cell Neurosci 2019;13:21.
Eijlers AJC, Meijer KA, Wassenaar TM, et al. Increased default-mode network centrality in cognitively impaired multiple sclerosis patients. Neurology 2017;88:952-60.
Rocca MA, Valsasina P, Absinta M, et al. Default-mode network dysfunction and cognitive impairment in progressive MS. Neurology 2010;74:1252-9.
van Geest Q, Douw L, van ‘t Klooster S, et al. Information processing speed in multiple sclerosis: relevance of default mode network dynamics. Neuroimage Clin 2018;19:507-15.
Duncan J, Owen AM.Common regions of the human frontal lobe recruited by diverse cognitive demands. Trends Neurosci 2000;23:475-83.
Benedict RHB, Bakshi R, Simon JH, et al. Frontal cortex atrophy predicts cognitive impairment in multiple sclerosis. J Neuropsychiatry Clin Neurosci 2002;14:44-51.
Hawellek DJ, Hipp JF, Lewis CM, et al. Increased functional connectivity indicated the severity of cognitive impairment in multiple sclerosis. Proc Natl Acad Sci USA 2011;108:19066-71.
Dobryakova E, Rocca MA, Filippi M. Cerebral reorganization and cognition in multiple sclerosis. In: DeLuca J, Sandroff B, eds. Cognition and Behavior in Multiple Sclerosis. Washington DC: American Psychological Association, 2018:67-87.
Schoonheim MM. Functional reorganization is a maladaptive response to injury-commentary. Mult Scler 2017;23:194-6.
Meijer KA, van Geest Q, Eijlers AJC, et al. Is impaired information processing speed a matter of structural or functional damage in MS?Neuroimage Clin 2018;20:844-50.
Rocca MA, Filippi M.Functional reorganization is a maladaptive response to injury-yes. Mult Scler 2017;23:191-4.
Schoonheim MM, Geurts JJG, Barkhof F. The limits of functional reorganization in multiple sclerosis. Neurology 2010;74:1246-7.