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

ABSTRACT: 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 p

SUBMITTER: Kohoutova L 

PROVIDER: S-EPMC9533325 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature