Time varying effective connectivity for describing brain network changes induced by a memory rehabilitation treatment

J. Toppi, D. Mattia, A. Anzolin, M. Risetti, M. Petti, F. Cincotti, F. Babiloni, L. Astolfi

Research output: Contribution to journalArticle

Abstract

In clinical practice, cognitive impairment is often observed after stroke. The efficacy of rehabilitative interventions is routinely assessed by means of a neuropsychological test battery. Nowadays, more evidences indicate that the neuroplasticity which occurs after stroke can be better understood by investigating changes in brain networks. In this study we applied advanced methodologies for effective connectivity estimation in combination with graph theory approach, to define EEG derived descriptors of brain networks underlying memory tasks. In particular, we proposed such descriptors to identify substrates of efficacy of a Brain-Computer Interface (BCI) controlled neurofeedback intervention to improve cognitive function after stroke. Electroencephalographic (EEG) data were collected from two stroke patients before and after a neurofeedback-based training for memory deficits. We show that the estimated brain connectivity indices were sensitive to different training intervention outcomes, thus suggesting an effective support to the neuropsychological assessment in the evaluation of the changes induced by the BCI-based cognitive rehabilitative intervention.

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Patient rehabilitation
Brain
Brain computer interface
Rehabilitation
Stroke
Neurofeedback
Brain-Computer Interfaces
Data storage equipment
Graph theory
Neuronal Plasticity
Neuropsychological Tests
Memory Disorders
Therapeutics
Cognition
Substrates

ASJC Scopus subject areas

  • Medicine(all)

Cite this

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title = "Time varying effective connectivity for describing brain network changes induced by a memory rehabilitation treatment",
abstract = "In clinical practice, cognitive impairment is often observed after stroke. The efficacy of rehabilitative interventions is routinely assessed by means of a neuropsychological test battery. Nowadays, more evidences indicate that the neuroplasticity which occurs after stroke can be better understood by investigating changes in brain networks. In this study we applied advanced methodologies for effective connectivity estimation in combination with graph theory approach, to define EEG derived descriptors of brain networks underlying memory tasks. In particular, we proposed such descriptors to identify substrates of efficacy of a Brain-Computer Interface (BCI) controlled neurofeedback intervention to improve cognitive function after stroke. Electroencephalographic (EEG) data were collected from two stroke patients before and after a neurofeedback-based training for memory deficits. We show that the estimated brain connectivity indices were sensitive to different training intervention outcomes, thus suggesting an effective support to the neuropsychological assessment in the evaluation of the changes induced by the BCI-based cognitive rehabilitative intervention.",
author = "J. Toppi and D. Mattia and A. Anzolin and M. Risetti and M. Petti and F. Cincotti and F. Babiloni and L. Astolfi",
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AU - Toppi, J.

AU - Mattia, D.

AU - Anzolin, A.

AU - Risetti, M.

AU - Petti, M.

AU - Cincotti, F.

AU - Babiloni, F.

AU - Astolfi, L.

PY - 2014

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AB - In clinical practice, cognitive impairment is often observed after stroke. The efficacy of rehabilitative interventions is routinely assessed by means of a neuropsychological test battery. Nowadays, more evidences indicate that the neuroplasticity which occurs after stroke can be better understood by investigating changes in brain networks. In this study we applied advanced methodologies for effective connectivity estimation in combination with graph theory approach, to define EEG derived descriptors of brain networks underlying memory tasks. In particular, we proposed such descriptors to identify substrates of efficacy of a Brain-Computer Interface (BCI) controlled neurofeedback intervention to improve cognitive function after stroke. Electroencephalographic (EEG) data were collected from two stroke patients before and after a neurofeedback-based training for memory deficits. We show that the estimated brain connectivity indices were sensitive to different training intervention outcomes, thus suggesting an effective support to the neuropsychological assessment in the evaluation of the changes induced by the BCI-based cognitive rehabilitative intervention.

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