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Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations.

Md Mahfuzur Rahman^1,2, Vince Calhoun², Sergey Plis^1,2

¹Department of Computer Science, Georgia State University, Atlanta, GA, United States.

Imaging Neuroscience (Cambridge, Mass.)

|March 13, 2026

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Deep learning (DL) models offer powerful biomarker discovery in neuroimaging but face challenges due to their opacity. Improving DL interpretability is crucial for safe clinical integration in mental health.

Area of Science:

Neuroimaging
Artificial Intelligence
Mental Health

Background:

Deep learning (DL) models are increasingly used in neuroimaging for biomarker discovery.
DL models learn directly from raw data, bypassing traditional feature extraction.
The opacity of DL models hinders their clinical adoption in healthcare.

Purpose of the Study:

To review the literature on DL interpretability in neuroimaging.
To highlight challenges and strategies for DL deployment in mental health.
To foster trust and transparency in AI for clinical applications.

Main Methods:

Comprehensive literature review of DL interpretability.
Focus on applications within neuroimaging studies.
Analysis of strategies for enhancing model transparency.

Keywords:

brain dynamics deep learning interpretability neuroimaging psychiatric disorders

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Main Results:

DL shows promise for analyzing complex neuroimaging data.
Model interpretability remains a significant barrier to clinical translation.
Transparency is essential for trust in safety-critical healthcare domains.

Conclusions:

Interpretable DL is vital for integrating AI into clinical practice for mental disorders.
Further research is needed to develop and validate DL interpretability methods.
Bridging the gap between DL capabilities and clinical needs requires a focus on transparency.