Giancarlo Valente

TL;DR: Recursive Feature Elimination is evaluated in terms of sensitivity of discriminative maps (Receiver Operative Characteristic analysis) and generalization performances and compare it to previously used univariate voxel selection strategies based on activation and discrimination measures.

TL;DR: The results demonstrate that the well-established dichotomy of the human brain has a more nuanced organization than previously thought and engages in different patterns of correlation and anti-correlation during specific sub-phases of a cognitive task.

TL;DR: The mathematical foundations of machine learning applications in fMRI are described, and two methods are focused on, support vector machines and relevance vector machines, respectively suited for the classification and regression of fMRI patterns.

TL;DR: It is shown that it is possible to retrieve the perceptual interpretation of ambiguous phonemes from fMRI measurements of brain activity in auditory areas in the superior temporal cortex, most prominently on the posterior bank of the left Heschl's gyrus and sulcus and in the adjoining left planum temporale.

TL;DR: The task dependency of speaker/vowel classification demonstrates that the informative fMRI response patterns reflect the top-down enhancement of behaviorally relevant sound representations and suggests that successful selection, processing, and retention of task-relevant sound properties relies on the joint encoding of information across early and higher-order regions of the auditory cortex.