The role of high-density EEG in diagnosis and prognosis of neurological diseases: A systematic review

脑电图 医学 重症监护医学 精神科
作者
Caterina Formica,Elvira Gjonaj,Lilla Bonanno,Angelica Quercia,Emanuele Cartella,Laura Romeo,Angelo Quartarone,Silvia Marino,Simona De Salvo
出处
期刊:Clinical Neurophysiology [Elsevier BV]
卷期号:174: 37-47 被引量:11
标识
DOI:10.1016/j.clinph.2025.03.026
摘要

• HD-EEG in neurological field increased due to analysis of brain connectivity and study cortical reorganization. • HD-EEG contributed to investigate motor and cognitive networks dynamics, identify markers of cognitive decline. • HD-EEG is effective to detect biomarkers for the diagnosis in neurological disorders, contributing to avoid misdiagnosis. Objective: The use of High-Density Electroencephalography (HD-EEG) increased in neurological disorders, due to analysis of brain connectivity. This method is able to create a detailed brain mapping. The aim is to investigate studies that employed HD-EEG in neurological and neurodegenerative filed. Methods: This systematic review was conducted and reported in accordance with the PRISMA. A research terms was conducted for: (1) dementia, (2) Multiple Sclerosis (MS), (3) Parkinson Disease (PD), (4) stroke, (5) epilepsy. Results: The study included a total of 89 articles: 22 dementia; 33 epilepsy; 5 MS; 24 PD; 5 S. Articles were discussed for each neurological disorder and for different types of EEG analysis: analysis of event-related potentials, specific EEG features at resting state, spectral and connectivity analysis, time–frequency analysis and EEG recordings combined with other types of intervention. Discussion: HD-EEG recordings provide evidence about the evaluation of early markers of the disease onset, mapping of cortical activity distribution of neurological disorders. Significance: HD-EEG demonstrated it effectiveness in detection of biomarkers for the diagnosis and prognosis. In dementia contributed to misdiagnosis between different subtype and identifying markers of cognitive decline, investigating motor and cognitive networks dynamics in stroke, PD and MS, and to detect task-specific network reorganization.
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