Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations.
Overview
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University Atlanta, Atlanta, GA, United States
Abstract
Deep learning (DL) models have experienced a surge in popularity due to their capacity to directly learn from raw data in an end-to-end paradigm without relying on a separate feature extraction process that may be based on restrictive assumptions. The neuroimaging community has enthusiastically embraced DL as it strives to learn biomarkers from complex, multivariate, multimodal datasets. However, a broad replacement of human intelligence with DL in clinical environments is yet far from realization. One of the major obstacles to this transition is the opacity of DL models. A deep understanding of models is essential for their effective deployment in safety-critical domains such as healthcare, where transparency and trust hold substantial significance. We provide a comprehensive review of the interpretability literature, specifically focusing on the current status of DL interpretability in neuroimaging studies. Ultimately, we highlight strategies and insights necessary for successfully integrating DL technology in characterizing and addressing mental disorders.
Reproduced under the paper's license (CC BY), from the paper cited above.
Code
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The paper's code and data availability statement is in the Data section.
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Data
Datasets cited
- kaggle.com/
datasets/ , at Kaggle; found in the referencessartajbhuvaji
Data and Code Availability
The paper is the data: no new data or code was generated for the research described in the article.
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 1, 30 September 2026: the first record
Recorded: type, language, journal, volume, pages, dates, 3 authors, 5 keywords, 4 funders, 599 references.
Cite
This paper
Rahman, M. M., Calhoun, V., & Plis, S. (2026). Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1129. https://
BibTeX
@article{rahman2026deep,
author = {Rahman, Md Mahfuzur and Calhoun, Vince and Plis, Sergey},
title = {{Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = mar,
volume = {4},
pages = {IMAG.a.1129},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/
url = {https://
pmid = {41821831},
pmcid = {PMC12977092}
}
RIS
TY - JOUR
AU - Rahman, Md Mahfuzur
AU - Calhoun, Vince
AU - Plis, Sergey
TI - Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/
VL - 4
SP - IMAG.a.1129
SN - 2837-6056
PB - MIT Press
DO - 10.1162/
UR - https://
LA - en
ER -
CSL-JSON
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