Toward a unified framework for interpreting machine-learning models in neuroimaging

• Published in: Nature protocols Vol. 15; no. 4; pp. 1399 - 1435
• Main Authors: Kohoutová, Lada, Heo, Juyeon, Cha, Sungmin, Lee, Sungwoo, Moon, Taesup, Wager, Tor D., Woo, Choong-Wan
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.04.2020; Nature Publishing Group
• Subjects: 631/114/1305; 631/1647/245/1628; 631/1647/794; 631/378/116; 692/53; Adolescent; Adult; Analytical Chemistry; Biological Techniques; Biomedical and Life Sciences; Brain; Brain - diagnostic imaging; Brain mapping; Computational Biology/Bioinformatics; Computational neuroscience; Databases, Factual; Diagnostic imaging; Female; Functional magnetic resonance imaging; Humans; Image Interpretation, Computer-Assisted - methods; Learning algorithms; Life Sciences; Machine Learning; Magnetic resonance imaging; Male; Medical imaging; Methods; Microarrays; Nervous system; Neuroimaging; Neuroimaging - methods; Neurosciences; Organic Chemistry; Prediction models; Programming languages; Protocol; Software; Young Adult; South Korea
• ISSN: 1754-2189; 1750-2799; 1750-2799
• DOI: 10.1038/s41596-019-0289-5

Machine learning is a powerful tool for creating computational models relating brain function to behavior, and its use is becoming widespread in neuroscience. However, these models are complex and often hard to interpret, making it difficult to evaluate their neuroscientific validity and contribution to understanding the brain. For neuroimaging-based machine-learning models to be interpretable, they should (i) be comprehensible to humans, (ii) provide useful information about what mental or behavioral constructs are represented in particular brain pathways or regions, and (iii) demonstrate that they are based on relevant neurobiological signal, not artifacts or confounds. In this protocol, we introduce a unified framework that consists of model-, feature- and biology-level assessments to provide complementary results that support the understanding of how and why a model works. Although the framework can be applied to different types of models and data, this protocol provides practical tools and examples of selected analysis methods for a functional MRI dataset and multivariate pattern-based predictive models. A user of the protocol should be familiar with basic programming in MATLAB or Python. This protocol will help build more interpretable neuroimaging-based machine-learning models, contributing to the cumulative understanding of brain mechanisms and brain health. Although the analyses provided here constitute a limited set of tests and take a few hours to days to complete, depending on the size of data and available computational resources, we envision the process of annotating and interpreting models as an open-ended process, involving collaborative efforts across multiple studies and laboratories. Neuroimaging-based machine-learning models should be interpretable to neuroscientists and users in applied settings. This protocol describes how to assess the interpretability of models based on fMRI.