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Related Experiment Video

Updated: Jan 18, 2026

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients
09:42

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Published on: September 1, 2023

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Altered brain dynamics in post-stroke cognitive and motor dysfunction.

Xiaoying Liu1, Guihua Song2, Xiaoyun Zhuang1

  • 1Department of Rehabilitation Medicine, 900th Hospital of PLA Joint Logistic Support Force, Fuzhou, Fujian, China.

Frontiers in Aging Neuroscience
|September 11, 2025
PubMed
Summary
This summary is machine-generated.

Post-stroke cognitive and motor dysfunction (PSCMD) involves abnormal dynamic brain reorganization. This study used HMM on fMRI data to reveal altered brain states in PSCMD patients, suggesting potential for optimized neuromodulation strategies.

Keywords:
cognitive and motor dysfunctiondynamic functional connectivityhidden markov modelmotor dysfunctionstroke

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Medical Imaging

Background:

  • Stroke often leads to single dysfunctions, but combined cognitive and motor deficits (PSCMD) and their brain dynamics are poorly understood.
  • Understanding PSCMD brain dynamics is crucial for effective rehabilitation strategies.
  • Current research lacks focus on the dynamic brain network changes associated with PSCMD.

Purpose of the Study:

  • To explore the dynamic brain network characteristics of post-stroke cognitive and motor dysfunction (PSCMD).
  • To identify repetitive brain activity states and assess their temporal properties in PSCMD.
  • To evaluate the correlation between dynamic brain network characteristics and clinical outcomes.

Main Methods:

  • Collected resting-state functional MRI (rs-fMRI) data from 75 patients with post-stroke motor dysfunction (PSMD), 33 with PSCMD, and 35 healthy controls (HCs).
  • Applied Hidden Markov Model (HMM) to rs-fMRI data to identify dynamic brain states.
  • Analyzed spatiotemporal attributes (fractional occupancy, Lifetime, transition probability) and their correlation with clinical scores (e.g., MoCa).

Main Results:

  • Identified five distinct HMM states, with significant dynamic spatiotemporal changes in PSMD and PSCMD groups compared to HCs.
  • PSCMD patients showed altered fractional occupancy in State 3, characterized by reduced sensorimotor/cognitive network activation and increased right prefrontal-parietal network activation.
  • Fractional occupancy in State 3 negatively correlated with Montreal Cognitive Assessment (MoCa) scores in PSCMD patients.

Conclusions:

  • PSCMD is associated with abnormal dynamic brain reorganization patterns.
  • HMM-derived brain states offer insights into the neural underpinnings of PSCMD.
  • These findings may inform the optimization of neuromodulation strategies for stroke rehabilitation.