Deep learning for small and big data in psychiatry.

Area of Science:

• Neuroscience
• Psychiatry
• Machine Learning

Background:

• Psychiatric disorders exhibit complex heterogeneity, challenging traditional statistical methods with small sample sizes.
• Effective, personalized treatments require a deeper understanding of underlying pathophysiological mechanisms.

Purpose of the Study:

• To provide a comprehensive overview of using deep learning models for prediction in psychiatry.
• To compare machine learning approaches with traditional statistical methods.
• To address the challenge of small sample sizes in psychiatric research for predictive modeling.

Main Methods:

• Review of machine learning (ML) and deep learning (DL) algorithms in psychiatric neuroscience.
• Comparison of ML/DL with traditional statistical hypothesis-driven approaches.
• Discussion on the relationship between model complexity and sample size requirements.

Main Results:

• Deep learning excels at complex predictor-response mappings but necessitates large training datasets.
• Small sample sizes in psychiatric research (n < 10,000) pose a significant challenge for DL model parameter inference.
• Strategies for optimal utilization of powerful ML/DL techniques in psychiatric neuroscience with limited data are explored.

Conclusions:

• Despite sample size limitations, deep learning holds promise for advancing predictive modeling in psychiatry.
• Optimizing ML/DL application requires careful consideration of model complexity and data availability.
• Further research is needed to effectively bridge the gap between DL capabilities and psychiatric data constraints for personalized treatment prediction.