Abstract
Functional brain images are rich and noisy data that can capture indirect signatures of neural activity underlying cognition in a given experimental setting. Can data mining leverage them to build models of cognition? Only if it is applied to well-posed questions, crafted to reveal cognitive mechanisms. Here we review how predictive models have been used on neuroimaging data to ask new questions, i.e., to uncover new aspects of cognitive organization. We also give a statistical learning perspective on these progresses and on the remaining gaping holes.
Highlights
Functional neuroimaging has opened the door to quantitative yet non invasive experiments on brain function
This paper presents a subjective view on the work that has been done combining machine learning with functional neuroimaging to advance the understanding of brain function
This review focuses on functional Magnetic Resonance Imaging (fMRI) in humans, that represents most of the accumulated functional neuroimaging data; most of the concepts carry to other imaging modalities
Summary
Functional neuroimaging has opened the door to quantitative yet non invasive experiments on brain function These experiments contribute to bridging the gap between cognitive sciences and neuroscience: the former analyse thought and mind while the latter probes the nervous system at various spatial and temporal scales. The advent of machine learning has brought huge progress to data processing of large datasets These techniques are geared towards well-posed predictive tasks. This paper presents a subjective view on the work that has been done combining machine learning with functional neuroimaging to advance the understanding of brain function. It dwells mostly on modeling considerations: how and what do the predictive models teach us about the brain? The last section reviews the use of unsupervised learning to extract relevant structures in functional images: the interaction structure that underlies brain function, or their natural spatial organization
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