Progress on uncovering the link between stimuli and neural response revealed neurons tuned to richer and higher-level descriptions of the stimulus, such as receptive fields specific to complex shapes, but also a richer description of neural responses, in particular coding distributed across a population of neurons. The seminal work of Hubel and Wiesel showed that neurons in the primary visual cortex have receptive fields tuned to a variety of image features, from simple cells sensitive to local orientation in an image, to more complex cells capturing in addition, motion and length of local image features. In contrast, the study of neural coding, lead historically via intra-cellular recordings of neural activity, has opened the door to breaking down many cognitive functions into atomic steps implemented by ensembles of neurons. Indeed the tests for differences between experimental conditions are statistically well-posed, but not very expressive to refine cognitive models. With regards to brain function, this statistical analysis answers naturally a “where” question, but to a lesser extent a “how” question. The art of fMRI experiment design and analysis then consists in crafting the succession of conditions so that, when properly contrasted, they reveal the neural support corresponding to the cognitive function of interest. A statistical test is performed at each voxel to delineate regions recruited differently by the various conditions. It consists of modeling the brain response evoked via an experimental paradigm as the linear combination of different experimental conditions. The keystone to the use of fMRI in cognitive neuroscience is the standard mass-univariate analysis framework. 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. The first two sections of this paper discuss supervised learning, used first to model brain activity from the stimuli, then to predict the task performed from the evoked activity. FMRI provides images of the brain at the mm scale, however it is only sensitive to the metabolic counterpart of neural activity and suffers from a poor temporal resolution. This review focuses on fMRI in humans, that represents most of the accumulated functional neuroimaging data however, most of the concepts carry to other imaging modalities. It dwells mostly on modeling considerations: how and what do the predictive models teach us about the brain? But it also touches upon machine learning and statistical issues. 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. The key question is then how can they be leveraged to push forward understanding of the brain, beyond merely predicting a numerical signal?
But these techniques are geared towards well-posed predictive tasks. In parallel, the advent of machine learning has brought huge progress to data processing of large datasets. The concurrent progress of scanners and experimental paradigms has made it possible to accumulate very rich imaging data that quantify specific correlates of brain function in an uncountable variety of cognitive tasks and processes. To study high-level aspects of human cognition, the two modalities of choice are functional Magnetic Resonance Imaging (fMRI) and electro-and magneto-encephalography (EEG/MEG), both can be used to observe brain activity with good spatial resolution for fMRI and temporal temporal resolution for EEG/MEG. 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.
Functional neuroimaging has opened the door to quantitative yet non invasive experiments on brain function.
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