Network-based cognitive training

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Petr75
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Network-based cognitive training

Post by Petr75 »

2021 Jun
Department of Neuroscience, University of Sheffield, Sheffield, UK
A network-based cognitive training induces cognitive improvements and neuroplastic changes in patients with relapsing-remitting multiple sclerosis: an exploratory case-control study
https://pubmed.ncbi.nlm.nih.gov/33269758/

Abstract

Cognitive impairments are commonly observed in patients with multiple sclerosis and are associated with lower levels of quality of life. No consensus has been reached on how to tackle effectively cognitive decline in this clinical population non-pharmacologically. This exploratory case-control study aims to investigate the effectiveness of a hypothesis-based cognitive training designed to target multiple domains by promoting the synchronous co-activation of different brain areas and thereby improve cognition and induce changes in functional connectivity in patients with relapsing-remitting multiple sclerosis. Forty-five patients (36 females and 9 males, mean age 44.62 ± 8.80 years) with clinically stable relapsing-remitting multiple sclerosis were assigned to either a standard cognitive training or to control groups (sham training and non-active control). The standard training included twenty sessions of computerized exercises involving various cognitive functions supported by distinct brain networks. The sham training was a modified version of the standard training that comprised the same exercises and number of sessions but with increased processing speed load. The non-active control group received no cognitive training. All patients underwent comprehensive neuropsychological and magnetic resonance imaging assessments at baseline and after 5 weeks. Cognitive and resting-state magnetic resonance imaging data were analyzed using repeated measures models. At reassessment, the standard training group showed significant cognitive improvements compared to both control groups in memory tasks not specifically targeted by the training: the Buschke Selective Reminding Test and the Semantic Fluency test. The standard training group showed reductions in functional connectivity of the salience network, in the anterior cingulate cortex, associated with improvements on the Buschke Selective Reminding Test. No changes were observed in the sham training group. These findings suggest that multi-domain training that stimulates multiple brain areas synchronously may improve cognition in people with relapsing-remitting multiple sclerosis if sufficient time to process training material is allowed. The associated reduction in functional connectivity of the salience network suggests that training-induced neuroplastic functional reorganization may be the mechanism supporting performance gains. This study was approved by the Regional Ethics Committee of Yorkshire and Humber (approval No. 12/YH/0474) on November 20, 2013.
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Petr75
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Posts: 1615
Joined: Sat Oct 19, 2013 10:17 am
Location: Czech Republic
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Re: Network-based cognitive training

Post by Petr75 »

2020 Dec 9
Department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherland
Functional brain network organization measured with magnetoencephalography predicts cognitive decline in multiple sclerosis
https://pubmed.ncbi.nlm.nih.gov/33295249/

Abstract

Background: Cognitive decline remains difficult to predict as structural brain damage cannot fully explain the extensive heterogeneity found between MS patients.

Objective: To investigate whether functional brain network organization measured with magnetoencephalography (MEG) predicts cognitive decline in MS patients after 5 years and to explore its value beyond structural pathology.

Methods: Resting-state MEG recordings, structural MRI, and neuropsychological assessments were analyzed of 146 MS patients, and 100 patients had a 5-year follow-up neuropsychological assessment. Network properties of the minimum spanning tree (i.e. backbone of the functional brain network) indicating network integration and overload were related to baseline and longitudinal cognition, correcting for structural damage.

Results: A more integrated beta band network (i.e. smaller diameter) and a less integrated delta band network (i.e. lower leaf fraction) predicted cognitive decline after 5 years (R2adj=15%), independent of structural damage. Cross-sectional analyses showed that a less integrated network (e.g. lower tree hierarchy) related to worse cognition, independent of frequency band.

Conclusions: The level of functional brain network integration was an independent predictive marker of cognitive decline, in addition to the severity of structural damage. This work thereby indicates the promise of MEG-derived network measures in predicting disease progression in MS.
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