Trial reportNeurorehabilitation and neural repair2026
Brain Network Connectivity During Resting-State and a Visuospatial Task as a Biomarker for Spatial Neglect in Stroke Patients.
Trial report in Neurorehabilitation and neural repair, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04187131 (An Augmented Reality Based System for Neglect Detection, Assessment, and Rehabilitation), which is not on this map. Cited by 1 paper.
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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.
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.
An Augmented Reality Based System for Neglect Detection, Assessment, and Rehabilitation
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BackgroundSpatial neglect (SN) is a common visual attention deficit affecting stroke patients due to large-scale disruptions within brain networks. Most studies have focused only on resting-state, but effective rehabilitation requires a clearer understanding of how brain networks change during visuospatial tasks.ObjectiveThis study aims to identify network disruptions associated with neglect by comparing resting-state and task-based electroencephalography (EEG) connectivity patterns in stroke patients with and without neglect.MethodsWe recorded EEG data from 28 stroke patients using the augmented reality (AR)-based EEG-guided neglect detection system (AREEN) during resting-state and a visuospatial task. Connectivity was measured using coherence in delta, theta, alpha, and beta bands for both conditions, with gamma-band coherence assessed only during the task. Graph-based metrics were applied to model network-level disruptions. Classification models evaluated the significance of connectivity features to find patterns predictive of neglect.ResultsThe neglect group showed reduced connectivity in frontal and right parieto-occipital (ParOcc) regions, primarily in beta and theta bands, during both conditions, with additional gamma-band connectivity differences in the task condition, compared to the non-neglect group. Conversely, connectivity was greater in central and midline regions, which may indicate a maladaptive shift in network organization. Classification models accurately classified patients into neglect and non-neglect groups (resting-state: 87.0% ± 0.7%; task: 80.9% ± 16.0%). Feature importance analysis identified eigenvector and closeness centrality within frontal, right ParOcc, and central regions as key predictors.ConclusionsNetwork disruptions can effectively identify SN and provide potential targets for connectivity-based rehabilitation. Future studies should investigate whether these interventions improve attention and recovery in stroke patients.This study was registered at ClinicalTrials.gov under ID NCT04187131.
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