Transmodal Learning of Functional Networks for Alzheimer's Disease Prediction

Abstract : Functional connectivity describes neural activity from resting-state functional magnetic resonance imaging (rs-fMRI). This noninvasive modality is a promising imaging biomarker of neurodegenerative diseases, such as Alzheimer's disease (AD), where the connectome can be an indicator to assess and to understand the pathology. However, it only provides noisy measurements of brain activity. As a consequence, it has shown fairly limited discrimination power on clinical groups. So far, the reference functional marker of AD is the fluorodeoxyglucose positron emission tomography (FDG-PET). It gives a reliable quantification of metabolic activity, but it is costly and invasive. Here, our goal is to analyze AD populations solely based on rs-fMRI, as functional connectivity is correlated to metabolism. We introduce transmodal learning: leveraging a prior from one modality to improve results of another modality on different subjects. A metabolic prior is learned from an independent FDG-PET dataset to improve functional connectivity-based prediction of AD. The prior acts as a regularization of connectivity learning and improves the estimation of discriminative patterns from distinct rs-fMRI datasets. Our approach is a two-stage classification strategy that combines several seed-based connectivity maps to cover a large number of functional networks that identify AD physiopathology. Experimental results show that our transmodal approach increases classification accuracy compared to pure rs-fMRI approaches, without resorting to additional invasive acquisitions. The method successfully recovers brain regions known to be impacted by the disease.
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IEEE Journal of Selected Topics in Signal Processing, IEEE, 2016, 10 (7), pp.1204 - 1213. 〈10.1109/JSTSP.2016.2600400〉
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Mehdi Rahim, Bertrand Thirion, Claude Comtat, Gaël Varoquaux. Transmodal Learning of Functional Networks for Alzheimer's Disease Prediction. IEEE Journal of Selected Topics in Signal Processing, IEEE, 2016, 10 (7), pp.1204 - 1213. 〈10.1109/JSTSP.2016.2600400〉. 〈hal-01353728〉

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