Evidence map›Paper›PMID 40538965›Full record

ArticleCyborg and bionic systems (Washington, D.C.)2025

Enhancing Attention Network Spatiotemporal Dynamics for Motor Rehabilitation in Parkinson's Disease.

Guangying Pei, Mengxuan Hu, Jian Ouyang, Zhaohui Jin, Kexin Wang, Detao Meng, Yixuan Wang, Keke Chen, Li Wang, Li-Zhi Cao and 3 more

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Guangying PeiSchool of Medical Technology, Beijing Institute of Technology, Beijing, China.
Mengxuan HuSchool of Medical Technology, Beijing Institute of Technology, Beijing, China.
Jian OuyangSchool of Medical Technology, Beijing Institute of Technology, Beijing, China.
Zhaohui JinParkinson Medical Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
Kexin WangSchool of Medical Technology, Beijing Institute of Technology, Beijing, China.
Detao MengParkinson Medical Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
Yixuan WangParkinson Medical Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
Keke ChenParkinson Medical Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
Li WangSchool of Medical Technology, Beijing Institute of Technology, Beijing, China.
Li-Zhi CaoSchool of Medical Technology, Beijing Institute of Technology, Beijing, China.
Shintaro FunahashiAdvanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, China.
Tianyi YanSchool of Medical Technology, Beijing Institute of Technology, Beijing, China.
Boyan FangParkinson Medical Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID40538965
PMCPMC12178139

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.