Functional connectivity outperforms scale-free brain dynamics as fMRI predictive feature of perceptual learning underwent in MEG

Mehdi Rahim (1, 2) , Philippe Ciuciu (1, 2) , Salma Bougacha (1)

1 NEUROSPIN - Service NEUROSPIN
2 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data

Perceptual learning sculpts ongoing brain activity [1]. This finding has been observed by statistically comparing the functional connectivity (FC) patterns computed from resting-state functional MRI (rs-fMRI) data recorded before and after intensive training to a visual attention task. Hence, functional connectivity serves a dynamic role in brain function, supporting the consolidation of previous experience. Following this line of research, we trained three groups of individuals to a visual discrimination task during a magneto-encephalography (MEG) experiment [2]. The same individuals were then scanned in rs-fMRI. Here, in a supervised classification framework, we demonstrate that FC metrics computed on rs-fMRI data are able to predict the type of training the participants received. On top of that, we show that the prediction accuracies based on tangent embedding FC measure outperform those based on our recently developed multivariate wavelet-based Hurst exponent estimator [3], which captures low frequency fluctuations in ongoing brain activity too.

Domaines: Imagerie; Imagerie médicale; Traitement du signal et de l'image [eess.SP]; Sciences cognitives

Mehdi Rahim, Philippe Ciuciu, Salma Bougacha. Functional connectivity outperforms scale-free brain dynamics as fMRI predictive feature of perceptual learning underwent in MEG. 2016. ⟨hal-01297845⟩