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Machine Learning for Interpretable Probabilistic Structure of Brain Function - Dimitri Samaras

Speaker : Dimitris Samaras
Associate Professor, Stony Brook University, New York
Holder of the SuBSAmPLE Digiteo Chair

Title :
Machine Learning for Interpretable Probabilistic Structure of Brain Function.

Abstract :
Multivariate analysis of functional Magnetic Resonance Imaging (fMRI) data is being widely used to acquire insights to brain function and dysfunction. In this talk I will present classification of brain function as a first step to answer basic questions on mental state and clinical evaluation of the subject. However fMRI data allows us to explore brain function further and discover probabilistic networks that reveal the mechanisms underlying mental states or clinical conditions. Learning such networks would require prohibitive amounts of fMRI data to train general algorithms, hence we need to impose the appropriate prior constraints both on feature detection and on network structure. Apart from accuracy an efficiency to other important constraints pertain to the learning of functional networks : The stability of the learned network across subjects as well as the interpretability of the results. I will present a number of such constraints and discuss their applicability to a number of fMRI datasets and show results for populations of intense interest such as Autism and Drug Addiction

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