Integration of AI and Neuroscience: New Methods in Studying the Causes of Mental Disorders

Abstract

Objective

This review systematizes knowledge about the use of artificial intelligence (AI) in neurobiological research of mental disorders and assesses its potential in identifying their causes.

Methods

A qualitative synthesis of scientific literature from the Scopus and Web of Science databases for 2020-2024 was conducted. A total of 50 sources were identified, including papers describing the use of AI in the analysis of neuroimaging, biomarkers, cognitive impairment, and genetics data. A thematic encoding was used to analyze methods, accuracy, and limitations.

Results

Machine learning algorithms have accelerated the processing of large amounts of data, including magnetic resonance imaging, electroencephalogram, and genomic profiles, which has revealed new biomarkers and neural patterns associated with depression and schizophrenia. However, AI technologies face several limitations: low specificity, high computational complexity, and problems with reproducibility of results.

Conclusions

The integration of AI with neuroscience has significantly advanced the understanding of the etiology of mental disorders, revealing the complex relationships between genetic, neural, and behavioral factors. The practical significance of the research lies in the potential of AI to create personalized approaches to the diagnosis and treatment of mental disorders. This can improve the quality of life of patients and reduce the burden on healthcare systems.

Keywords

neuroimaging biomarkers machine learning cognitive impairment genetic data behaviour

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