Deep neural networks in psychiatry.

Area of Science:

• Computational neuroscience
• Psychiatric research
• Artificial intelligence in medicine

Background:

• Machine learning (ML) and deep learning (DL) are increasingly impactful in data analysis, prediction, and classification.
• These artificial intelligence (AI) methods are finding significant applications in medical research.
• Deep and recurrent neural networks are key components of modern AI.

Purpose of the Study:

• To provide an overview of ML methods, focusing on deep and recurrent neural networks.
• To review current and potential applications of neural networks in psychiatry.
• To explore the integration of interpretable computational models with statistical machine learning for deeper insights.

Main Methods:

• Overview of machine learning principles, emphasizing neural networks.
• Review of existing literature on AI applications in psychiatry.
• Discussion of embedding computational models within statistical machine learning frameworks.

Main Results:

• Machine and deep learning are powerful for large-scale data analysis in data-rich environments.
• Neural networks show significant potential for applications in psychiatry.
• Integrating computational models with statistical inference can yield insights beyond prediction.

Conclusions:

• Machine and deep learning offer substantial potential for advancing psychiatric research and applications.
• Future directions include leveraging neural networks for enhanced prediction and classification in psychiatry.
• A novel approach combining computational models with statistical inference may uncover mechanisms of psychiatric dysfunction and inform treatment development.