[Resting state-EEG connectivity and machine learning: towards improved treatment response predictions in psychiatry].
Innovatieve eeg-analyse voor voorspelling van behandelrespons in de psychiatrie.
Journal
Tijdschrift voor psychiatrie
ISSN: 0303-7339
Titre abrégé: Tijdschr Psychiatr
Pays: Netherlands
ID NLM: 0423731
Informations de publication
Date de publication:
2023
2023
Historique:
medline:
4
1
2024
pubmed:
4
1
2024
entrez:
4
1
2024
Statut:
ppublish
Résumé
Innovations in the analysis of resting-state EEG focused on connectivity and network organization, combined with machine learning, offer new opportunities for treatment response predictions in psychiatry. Introduction of analysis methods in this emerging field, description of some promising results, and critical consideration of possibilities and challenges for implementation in clinical practice. Narrative review of the literature. EEG connectivity and network properties may contain predictive information for treatment response to pharmacological interventions, neurostimulation, and psychotherapeutic treatments. However, the results are currently based on studies with small sample sizes and limited validation in independent datasets. Factors such as placebo effects, natural course and treatment adherence during therapy necessitate a cautious interpretation of promising results. Independent replication studies and research on implementation are needed to determine whether developed algorithms that predict treatment outcomes based on EEG recordings are of value in clinical practice.
Sections du résumé
BACKGROUND
BACKGROUND
Innovations in the analysis of resting-state EEG focused on connectivity and network organization, combined with machine learning, offer new opportunities for treatment response predictions in psychiatry.
AIM
OBJECTIVE
Introduction of analysis methods in this emerging field, description of some promising results, and critical consideration of possibilities and challenges for implementation in clinical practice.
METHOD
METHODS
Narrative review of the literature.
RESULTS
RESULTS
EEG connectivity and network properties may contain predictive information for treatment response to pharmacological interventions, neurostimulation, and psychotherapeutic treatments. However, the results are currently based on studies with small sample sizes and limited validation in independent datasets. Factors such as placebo effects, natural course and treatment adherence during therapy necessitate a cautious interpretation of promising results.
CONCLUSION
CONCLUSIONS
Independent replication studies and research on implementation are needed to determine whether developed algorithms that predict treatment outcomes based on EEG recordings are of value in clinical practice.
Types de publication
English Abstract
Journal Article
Langues
dut
Sous-ensembles de citation
IM