ArticleCyborg and bionic systems (Washington, D.C.)2025
Enhancing Attention Network Spatiotemporal Dynamics for Motor Rehabilitation in Parkinson's Disease.
Article in Cyborg and bionic systems (Washington, D.C.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Neurophysiological effects of transcranial alternating current stimulation combined with multidisciplinary rehabilitation in Parkinson's disease.Frontiers in psychiatry · 2026Trial
- Time-Varying Effective Network of Rehabilitation in Parkinson's Disease With Disease Laterality: A TMS-EEG Study.CNS neuroscience & therapeutics · 2026Article
- Multimodal Electrophysiological Signals for Machine Learning-Aided Parkinson's Disease Diagnosis.Biosensors · 2026Article
- Transcriptional correlates of structure-function coupling plasticity in trigeminal neuralgia: unveiling the synaptic and metabolic associations.The journal of headache and pain · 2026Article
- Cell-type and spatiotemporal transcriptional signatures of white matter morphometric similarity network alterations in major depressive disorder.Psychological medicine · 2026Article
- EEG biomarkers can predict early-stage Alzheimer's disease and correlate with intracerebral pathology: a multimodal machine learning study.Alzheimer's research & therapy · 2026Article
- Simulated depression risk classification from Parkinson's voice features using a self-attention-enhanced MLP architecture.Scientific reports · 2026Article
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Optimizing resource allocation for Parkinson's disease (PD) motor rehabilitation necessitates identifying biomarkers of responsiveness and dynamic neuroplasticity signatures underlying efficacy. A cohort study of 52 early-stage PD patients undergoing 2-week multidisciplinary intensive rehabilitation therapy (MIRT) was conducted, which stratified participants into responders and nonresponders. A multimodal analysis of resting-state electroencephalography (EEG) microstates and functional magnetic resonance imaging (fMRI) coactivation patterns was performed to characterize MIRT-induced spatiotemporal network reorganization. Responders demonstrated clinically meaningful improvement in motor symptoms, exceeding the minimal clinically important difference threshold of 3.25 on the Unified PD Rating Scale part III, alongside significant reductions in bradykinesia and a significant enhancement in quality-of-life scores at the 3-month follow-up. Resting-state EEG in responders showed a significant attenuation in microstate C and a significant enhancement in microstate D occurrences, along with significantly increased transitions from microstate A/B to D, which significantly correlated with motor function, especially in bradykinesia gains. Concurrently, fMRI analyses identified a prolonged dwell time of the dorsal attention network coactivation/ventral attention network deactivation pattern, which was significantly inversely associated with microstate C occurrence and significantly linked to motor improvement. The identified brain spatiotemporal neural markers were validated using machine learning models to assess the efficacy of MIRT in motor rehabilitation for PD patients, achieving an average accuracy rate of 86%. These findings suggest that MIRT may facilitate a shift in neural networks from sensory processing to higher-order cognitive control, with the dynamic reallocation of attentional resources. This preliminary study validates the necessity of integrating cognitive-motor strategies for the motor rehabilitation of PD and identifies novel neural markers for assessing treatment efficacy.
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Registered trials
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