Functional Connectivity and Structural Disruption in the Default-Mode Network Predicts Cognitive Rehabilitation Outcomes in Multiple Sclerosis.
J Neuroimaging
; 30(4): 523-530, 2020 07.
Article
em En
| MEDLINE
| ID: mdl-32391981
ABSTRACT
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 (R2 = .385, P = .017, Interaction ß = -.415).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.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Encéfalo
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Cognição
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Substância Branca
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Rede de Modo Padrão
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Esclerose Múltipla
Tipo de estudo:
Prognostic_studies
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Risk_factors_studies
Limite:
Adult
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Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article