Generative Embedding for Model-Based Classification of fMRI Data

Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in ‘hidden’ physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups

On the Use of an Iterative Estimation of Continuous Probabilistic Transforms for Voice Conversion

ISBN : 978-1-4244-5996-4International audienceVoice conversion is a technique that modifies a source speaker's speech to be perceived as if a target speaker had spoken it. Among the algorithms of conversion published in the literature, the techniques using GMM are nowadays the reference. In this paper, we focus on a new technique for estimating parameters of the conversion function. We show that it is possible to determinate these parameters without statistical estimation using the classical Expectation Minimization (EM) algorithm. We propose a new method for determining the conversion function parameters. The technique proposed is based on an iterative statistical refinement algorithm working directly from data. The consequence of this strategy is that the estimation of the conversion function is very fast

Poursuite de non stationnarités par filtrage multirésolution adaptatif

On propose dans cet article un schéma d'identification adaptative des systèmes non stationnaires. Ce schéma est fondé sur une "décomposition multirésolution" des signaux d'entrée et de sortie du système à identifier. Grâce à une approche de filtrage linéaire des opérateurs de décomposition en ondelettes, on établit que la méthode proposée permet d'améliorer la capacité de poursuite de non stationnarité par rapport au schéma d'identification classique. La non stationnarité considérée est modélisée par une promenade aléatoire