ArticleScientific reports2023
Prediction of cognitive decline in Parkinson's disease (PD) patients with electroencephalography (EEG) connectivity characterized by time-between-phase-crossing (TBPC).
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 of them syntheses that pooled it.
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3 citing papers in PubMed, 2 syntheses or guidelines pooled it, 9 citations in OpenAlex.
- Resting-state EEG changes associated with cognitive decline in Parkinson's disease: a systematic review.Journal of neural transmission (Vienna, Austria : 1996) · 2026Pooled it
- Machine learning methods for the detection and prediction of cognitive impairment in Parkinson's disease: a systematic review and meta-analysis.Frontiers in aging neuroscience · 2025Pooled it
- Network Hyperexcitability in Early-Stage Alzheimer's Disease: Evaluation of Functional Connectivity Biomarkers in a Computational Disease Model.Journal of Alzheimer's disease : JAD · 2024Article
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Authors and funding
4 authors at 2 institutions in 1 country.
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Abstract
The aim of the study is to identify the dynamic change pattern of EEG to predict cognitive decline in patients with Parkinson's disease. Here we demonstrate that the quantification of synchrony-pattern changes across the scalp, measured using electroencephalography (EEG), offers an alternative approach of observing an individual's functional brain organization. This method, called "Time-Between-Phase-Crossing" (TBPC), is based on the same phenomenon as the phase-lag-index (PLI); it also considers intermittent changes in the signals of phase differences between pairs of EEG signals, but additionally analyzes dynamic connectivity changes. We used data from 75 non-demented Parkinson's disease patients and 72 healthy controls, who were followed over a period of 3 years. Statistics were calculated using connectome-based modeling (CPM) and receiver operating characteristic (ROC). We show that TBPC profiles, via the use of intermittent changes in signals of analytic phase differences of pairs of EEG signals, can be used to predict cognitive decline in Parkinson's disease (p < 0.05).
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