From Evaluation to Prediction: Behavioral Effects and Biological Markers of Cognitive Control Intervention

Although the intervention effectiveness of cognitive control is disputed, some methods, such as single-task training, integrated training, meditation, aerobic exercise, and transcranial stimulation, have been reported to improve cognitive control. This review of recent advances from evaluation to pr...

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Main Author: Bin Xuan
Format: Article
Language:English
Published: Hindawi Limited 2020-01-01
Series:Neural Plasticity
Online Access:http://dx.doi.org/10.1155/2020/1869459
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spelling doaj-6ee39aa464da426bb4a4a0a26c202faf2020-11-25T02:29:25ZengHindawi LimitedNeural Plasticity2090-59041687-54432020-01-01202010.1155/2020/18694591869459From Evaluation to Prediction: Behavioral Effects and Biological Markers of Cognitive Control InterventionBin Xuan0School of Educational Science, Anhui Normal University, Wuhu 241000, ChinaAlthough the intervention effectiveness of cognitive control is disputed, some methods, such as single-task training, integrated training, meditation, aerobic exercise, and transcranial stimulation, have been reported to improve cognitive control. This review of recent advances from evaluation to prediction of cognitive control interventions suggests that brain modularity may be an important candidate marker for informing clinical decisions regarding suitable interventions. The intervention effect of cognitive control has been evaluated by behavioral performance, transfer effect, brain structure and function, and brain networks. Brain modularity can predict the benefits of cognitive control interventions based on individual differences and is independent of intervention method, group, age, initial cognitive ability, and education level. The prediction of cognitive control intervention based on brain modularity should extend to task states, combine function and structure networks, and assign different weights to subnetwork modularity.http://dx.doi.org/10.1155/2020/1869459
collection DOAJ
language English
format Article
sources DOAJ
author Bin Xuan
spellingShingle Bin Xuan
From Evaluation to Prediction: Behavioral Effects and Biological Markers of Cognitive Control Intervention
Neural Plasticity
author_facet Bin Xuan
author_sort Bin Xuan
title From Evaluation to Prediction: Behavioral Effects and Biological Markers of Cognitive Control Intervention
title_short From Evaluation to Prediction: Behavioral Effects and Biological Markers of Cognitive Control Intervention
title_full From Evaluation to Prediction: Behavioral Effects and Biological Markers of Cognitive Control Intervention
title_fullStr From Evaluation to Prediction: Behavioral Effects and Biological Markers of Cognitive Control Intervention
title_full_unstemmed From Evaluation to Prediction: Behavioral Effects and Biological Markers of Cognitive Control Intervention
title_sort from evaluation to prediction: behavioral effects and biological markers of cognitive control intervention
publisher Hindawi Limited
series Neural Plasticity
issn 2090-5904
1687-5443
publishDate 2020-01-01
description Although the intervention effectiveness of cognitive control is disputed, some methods, such as single-task training, integrated training, meditation, aerobic exercise, and transcranial stimulation, have been reported to improve cognitive control. This review of recent advances from evaluation to prediction of cognitive control interventions suggests that brain modularity may be an important candidate marker for informing clinical decisions regarding suitable interventions. The intervention effect of cognitive control has been evaluated by behavioral performance, transfer effect, brain structure and function, and brain networks. Brain modularity can predict the benefits of cognitive control interventions based on individual differences and is independent of intervention method, group, age, initial cognitive ability, and education level. The prediction of cognitive control intervention based on brain modularity should extend to task states, combine function and structure networks, and assign different weights to subnetwork modularity.
url http://dx.doi.org/10.1155/2020/1869459
work_keys_str_mv AT binxuan fromevaluationtopredictionbehavioraleffectsandbiologicalmarkersofcognitivecontrolintervention
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