Evaluating EEG complexity metrics as biomarkers for depression.
Psychophysiology
; 60(8): e14274, 2023 08.
Article
en En
| MEDLINE
| ID: mdl-36811526
ABSTRACT
Nonlinear EEG analysis offers the potential for both increased diagnostic accuracy and deeper mechanistic understanding of psychopathology. EEG complexity measures have previously been shown to positively correlate with clinical depression. In this study, resting state EEG recordings were taken across multiple sessions and days with both eyes open and eyes closed conditions from a total of 306 subjects, 62 of which were in a current depressive episode, and 81 of which had a history of diagnosed depression but were not currently depressed. Three different EEG montages (mastoids, average, and Laplacian) were also computed. Higuchi fractal dimension (HFD) and sample entropy (SampEn) were calculated for each unique condition. The complexity metrics showed high internal consistency within session and high stability across days. Higher complexity was found in open-eye recordings compared to closed eyes. The predicted correlation between complexity and depression was not found. However, an unexpected sex effect was observed, in which males and females exhibited different topographic patterns of complexity.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Trastorno Depresivo Mayor
/
Electroencefalografía
Tipo de estudio:
Prognostic_studies
Límite:
Female
/
Humans
/
Male
Idioma:
En
Revista:
Psychophysiology
Año:
2023
Tipo del documento:
Article
País de afiliación:
Estados Unidos