Multivariate analyses of neural patterns in the human brain

In the last two decades, neuroscientists have tried to establish the way in which anatomically connected groups of neurons, despite displaying non synchronized neural activity, can work together according to a specific functional architecture. From a methodological perspective, the analysis of such neural organization requires the possibility to measure and integrate the information extracted from large portions of cortex. To this end, recent methodological advancements have prompted the emergence of a new approach, namely multi-voxel pattern analysis (MVPA). Most recent MVPA has also been bred with complex machine learning techniques, which allow to identify whether information is represented in a region (e.g., decoding), and how such information is coded in specific patterns of neural activity (e.g., encoding). Here, we discuss four MVPA algorithms successfully applied in three different functional Magnetic Resonance Imaging (fMRI) studies. In the first experiment, brain activity of the left fronto-temporal cortex was analyzed using a rank-based multi-class decoding algorithm to identify which brain regions were able to discriminate the seven Italian vowels during their listening, imagery and utterance. Moreover, by means of a canonical correlation analysis, we linearly reconstructed an acoustic, frequency-based model of vowels, using the neural information extracted from the left superior temporal sulcus and the left inferior frontal gyrus. In the second experiment, four models, based on either perceptual or semantic features, were tested to predict brain activity of the left parietal cortex employing a representational similarity encoding algorithm. Finally, in the third fMRI experiment, using a multivariate technique, we were able to recognize at the individual subject level memories of real autobiographical events, highlighting both the time frame at which the recollection occurred and the brain networks involved in such process. Overall, these studies tackle the role of machine learning algorithms applied to multivariate patterns of brain activity, and emphasize how the combination of these methods allows an assessment where the information is encoded, spread and organized in the human brain