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A review of studies on constructing classification models to identify mental illness using brain effective
Fangfang Huang1, Yuan Huang1, Siying Guo1
1Department of Preventive Medicine, College of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang 471000, China.
Psychiatry Research. Neuroimaging
|December 3, 2024
Summary
This review synthesizes studies on brain effective connectivity (EC) for diagnosing mental illnesses. Effective connectivity models show promise for identifying psychiatric disorders, guiding future research.
Area of Science:
- Neuroscience
- Medical Informatics
- Psychiatry
Background:
- Brain effective connectivity (EC) quantifies causal influences and network topology in neural activity.
- Growing interest exists in using EC for classifying mental illnesses against healthy controls.
- A comprehensive review synthesizing these classification efforts is lacking.
Purpose of the Study:
- To systematically review and synthesize existing literature on constructing diagnostic models for mental illnesses using brain EC.
- To provide a reference for future research in mental illness identification based on EC.
Main Methods:
- Systematic literature search identifying 35 relevant studies.
- Summarization of EC estimation techniques, classification/validation methods, and model accuracies.
- Discussion of current research limitations and future challenges.
Main Results:
- The review covers various approaches to EC estimation and classification strategies.
- Accuracies of different diagnostic models are reported.
- Key findings from included studies are summarized.
Conclusions:
- Brain EC holds potential for developing diagnostic models for mental illnesses.
- Further research is needed to address current limitations and challenges in the field.
