Phd Position F/m Machine Learning for Efficient

il y a 4 semaines

Rennes, France Inria Temps plein

Le descriptif de l’offre ci-dessous est en Anglais_

**Type de contrat **:CDD

**Niveau de diplôme exigé **:Bac + 5 ou équivalent

**Fonction **:Doctorant

**A propos du centre ou de la direction fonctionnelle**:
The Inria Centre at Rennes University is one of Inria's nine centres and has more than thirty research teams. The Inria Centre is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc.

**Contexte et atouts du poste**:
The project lies at the interface of signal and image processing, behavioural neuroscience and neurofeedback. Neurofeedback approaches (NF) (see [1] for a complete and actual introduction), also known as restorative brain-computer interface (restorative BCI), consist in providing real-time feedback to a patient about his or her own brain activity in order to learn how self-regulate specific brain regions and help him or her perform a given task. The estimation of neurofeedback scores is done through online brain functional feature extraction relying for the majority on electroencephalography (EEG) or functional magnetic resonance imaging (fMRI). Recent studies [2, 3, 4] have shown the high potential of combining EEG and fMRI in a bi-modal NF training (i.e., NF scores are estimated in real-time from features of both modalities) to achieve an improved self-regulation, by providing a more specific estimation of the underlying neural activity. NF is a very promising brain rehabilitation technique for psychiatric disorders, stroke and other neurological pathologies.

Measures of brain activity through fMRI or EEG are ground solutions in the context of NF for brain rehabilitation protocols and EEG is currently the only modality used by NF clinical practitioners. EEG, which directly measures changes in electrical potential occurring in the brain in real time, has an excellent temporal resolution (hundreds of milliseconds), but has a limited spatial resolution (around centimeter) due to cortical currents volume conduction through head tissue. On the other hand, fMRI offers a better spatial resolution (few millimeters) but has slow dynamics (one or two seconds) as it measures neuro-vascular (i.e. changes in the blood oxygenation level) activities, which occurs in general, a few seconds after a neural event [5, 6]. Moreover, using a MRI scanner is costly, exhausting for patients (since staying perfectly still when suffering is challenging), and time-consuming.

Although exceptional progress has been obtained during the past decades to explore the human brain, researches based on different neuro-imaging modalities are crucial to shed light on the variety of human brains, as well as understanding the complex link between anatomical and functional properties of the brain [7, 8].

**References**:
[1] Sitaram, R., Ros, T., Stoeckel, L., Haller, S., Scharnowski, F., Lewis-Peacock, J., Weiskopf, N., Blefari, M. L., Rana, M., Oblak, E., Birbaumer, N., and Sulzer, J. Closed-loop brain training: the science of neurofeedback. Nature Reviews Neuroscience 18, 2 (Feb. 2017), 86-100.

[2] Zotev, V., Mayeli, A., Misaki, M., and Bodurka, J. Emotion self-regulation training in major depressive disorder using simultaneous real-time fMRI and EEG neurofeedback. NeuroImage: Clinical 27 (2020), 102331.

[3] Zotev, V., Phillips, R., Yuan, H., Misaki, M., and Bodurka, J. Self-regulation of human brain activity using simultaneous real-time fMRI and EEG neurofeedback. NeuroImage 85 (Jan. 2014), 985-995.

[4] Perronnet, L., Lécuyer, A., Mano, M., Bannier, E., Lotte, F., Clerc, M., and Barillot, C. Unimodal Versus Bimodal EEG-fMRI Neurofeedback of a Motor Imagery Task. Frontiers in Human Neuroscience 11 (Apr. 2017).

[5] Friston, K. J., Jezzard, P., and Turner, R. Analysis of functional MRI time-series. Human Brain Mapping 1, 2 (1994), 153-171.

[6] Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., and Oeltermann, A. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 6843 (July 2001), 150-157.

[7] Sui, J., Adali, T., Yu, Q., Chen, J., and Calhoun, V. D. A review of multivariate methods for multimodal fusion of brain imaging data. Journal of Neuroscience Methods 204, 1 (Feb. 2012), 68-81.

[8] Sui, J., Jiang, R., Bustillo, J., and Calhoun, V. Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises. Biological Psychiatry 88, 11 (Dec. 2020), 818-828.

[9] Pinte, C., Fleury, M., Maurel, P. Deep learning-based localization of EEG electrodes within MRI acquisitions. Frontiers in Neurology, 2021.

**Mission confiée**:
Relying on this context, the goal of this thesis is to investigate and propose methods to provide more specific and efficient NF training to participants. This t

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