POST-DOCTORAL FELLOW


Neuroimaging | Brain connectivity | 
Graph Signal Processing 
Brain Computer Interfaces| Deep Learning

WELCOME!

I am a researcher in signal and image processing applied to neuroscience, with a focus on multi-modal modeling and imaging of brain activity and networks. If during my thesis I concentrated on multivariate causal measures of brain connectivity from EEG recordings, I extended my expertise during my post-doctoral experience to a range of neuroimaging tools (anatomical and functional magnetic resonance imaging and diffusion weighted imaging). My current research focuses on integrating these multimodal neuroimaging data using graph signal processing and unsupervised learning.
E-mail: lioi.giulia@gmail.com
Scholar   -- Twitter -- Github 

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SHORT BIO

Post-Doc IMT Atlantique (FRANCE) on  Graph Signal Processing and Machine Learning for multimodal neuroimaging.

Post-Doc INRIA Rennes (FRANCE) on EEG-fMRI Integration for Neurofeedback

PhD University of Southampton (UK) on multivariate causal estimators of brain connectivity to monitor anaesthesia

Research Internship Santa Lucia European Centre for Brain Research, Rome (ITALY)

Master Degree Biomedical Engineering , La Sapienza University, Rome (ITALY)

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RESEARCH INTERESTS

I am fascinated by the complex architecture and function of the brain and the wide range of methods that can help us understanding it:

  • Neuroimaging  (EEG, fMRI, DWI) 
  • Graph Signal Processing
  • Machine Learning
  • Brain Computer Interface and Neurofeedback
  • Network Neuroscience


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NEWS

1) Sept 2020: NEW PREPRINT!  Gradients of Connectivity as Graph Fourier Bases of Brain Activity (link

2) June 2020: PAPER PUBLISHED in SCIENTIFIC DATA-NATURE (link)

3) May 2020: PAPER PUBLISHED in Annals of Physical and Rehabilitation Medicine (link)

4) May 2020: NEW PREPRINT! The Impact of Neurofeedback on Effective Connectivity Networks in Chronic Stroke Patients (link).

5) Dec 2019: We have released two open access datasets [1] [2] of simultaneous EEG and MRI data during neurofeedback on OpenNeuro.org 

Research

Graph Signal Processing to learn from multimodal neuroimaging

Modern attempts at understanding brain function have leveraged the use of graph theory to grasp complex properties of neuronal networks, giving rise to the field of network neuroscience.

Despite the tremendous progress that has been achieved in network neuroscience, surprisingly relatively few methods such as graph signal processing (GSP) exploit brain connectivity (i.e. take into account the topology of brain network) to characterize brain activity.

In
 this project I use GSP and unsupervised learning to jointly model electrophysiological (EEG), functional (fMRI) and structural (MRI, DWI) properties of the brain.  

I recently published an opinion paper preprint that  discuss the potential of an the GSP framework to reveal a spectral basis of brain activity grounded on connectivity.

Bimodal EEG-fMRI NEUROFEEDBACK

Neurofeedback consists in training self-regulation of a specific brain function by providing a subject real-time information about his own brain activity. It is therefore thought to impact on the related pathological condition and a promising neurorehabilitation technique. 

In the Empenn team (INRIA, Rennes) we aim at making full and enhanced use of the neurofeedback paradigm by integrating EEG and fMRI in a unique experimental platform. We are currently testing the efficacity of bimodal neurofeedback for stroke rehabilitation and treadment of resistant depression.

My presentation for the OHBM Symposium (2019) on  EEG-fMRI Neurofeedback Integration for Stroke Rehabilitation https://www.pathlms.com/ohbm/courses/12238/sections/15843/video_presentations/137855

 The experimental platform for bimodal neurofeedback

University Hospital of Rennes, Neurinfo 
EEG effective connectivity in sleep and anaesthesia

Analysis of brain connectivity is crucial to understand the complex behaviour of the brain . During my thesis I concentrated on multivariate causal measures of brain connectivity from EEG recordings. In particular I was interested in assessing the impact of instantaneous causality on causal measures of  effective connectivity to identify robust measures to apply in the clinical context (for instance to monitor the depth of anaesthesia and sleep).

AWARDS & GRANTS


  • June 2020 -- L’Oreal Unesco Talent for Women in Science (Ranked in the top 110 over 700 applications

  • April 2016 -- Best Student Paper: Second Prize (Mediterranean Conference on Medical and Biological Engineering and Computing)

  • 2015-2016 -- PhD mobility grant: Fundação de Amparo à Pesquisa (FAPEMIG)(UFMG, Belo Horizonte, Brazil)

  • 2006-2007 -- Merit based grant: Master Biomedical Engineering (Regional organization for the right to study and the promotion of knowledge - DISCO)

TEACHING

2020 - ongoing

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IMT Atlantique Engineering SChool

  • Algorithmics and Discrete Mathematics (PyRat) (24 h, TP classe invers\'ee) 1st common year (IMT Atlantique)
  • Le Patient Numerique (4h30 TP) TAF Health (IMT Atlantique) 


ESIR Engineering School

Medical Imaging (4 h, CM), Master 2 Information Technology (3rd year) - slides

January 2020

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October 2017

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University of Southampton (UK)

Biomedical Signal Processing (12 h, CM+TD), Master 2 in Biomedical Engineering

University of Southampton (UK)


  • Mechanical Science (30 h, TP+TD), Engineering preparatory year
  •  Electrophysiology of Evoked responses (9 h, TD), Master 2 in Audiology


2015-2016

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BIMODAL EEG-fMRI NEUROFEEDBACK TRAINING

24 -25 February 2020,  CHU Pontchaillou Rennes

PUBLICATIONS

Reviewed International Journals

1. Lioi G, Cury C, Perronnet L., Mano M., L ́ecuyer A, Barillot C, Bonan I.SimultaneousEEG-fMRI during a neurofeedback task, a brain imaging dataset for multimodal data integrationScientific Data (2020) volume 7, 173 https://www.nature.com/articles/s41597-020-0498-32 

2. Mathis F, Lioi G, Barillot C, L ́ecuyer A.A Survey on the Use of Haptic Feedback for Brain-Computer Interfaces and Neurofeedback. Front. Neurosci. (2020) 14 :528. https://doi.org/10.3389/fnins.2020.005283 

3. Butet S*, Lioi G, Fleury M, Bannier E, Lecuyer A, Barillot C, Bonan I. Alternative bimodal unimodal neurofeedback training to induce cerebral reorganization after chronic stroke : a proof-of-concept case report. Annals of Physical and Rehabilitation Medicine (2020) 11 ;S1877-0657(20)30114-7 https://pubmed.ncbi.nlm.nih.gov/32535167/

4. Lioi G, Butet S, Fleury M, Bannier E, L ́ecuyer A, Bonan I, Barillot C.A multi-target motorimagery training using bimodal EEG-fMRI 1 Neurofeedback : a pilot study on chronic strokepatientsFrontiers in Human Neuroscience (2020) 14 (February), 1–13. https://doi.org/10.3389/fnhum.2020.000375.

5. Lioi G, Bell SL, Smith DC, Simpson DM.Measuring depth of anaesthesia using changes indirectional connectivity : a comparison with auditory middle latency response and estimatedbispectral index during propofol anaesthesia. Anaesthesia (2018) pp : anae.14535 https://doi.org/10.1111/anae.145356.

6. Lioi G, Bell SL, Smith DC, Simpson DM.Directional Connectivity in the EEG is able todiscriminate wakefulness from NREM sleep. Physiol. Meas. 38 (2017) 1802 1820https://iopscience.iop.org/article/10.1088/1361-6579/aa81b57.

7. Perronnet L, Anatole L, Mano M, Fleury M, Lioi G, Cury C, ... Barillot C.Learning 2-in-1 : Towards Integrated EEG-fMRI Neurofeedback. BioRxiv (2020) 1–30.(Under Revision in Frontiers in Neuroscience)

8. Lioi G, Gripon V, Basset A, Rousseau F, Farrugia N., Gradients of Connectivity as Graph Fourier Bases of Brain Activity2020 (Submitted to Network Neuroscience)  https://arxiv.org/abs/2009.12567 

9. Lioi G, Veliz A, Coloigner J, Duche Q, Butet S, Fleury M, Leveque-Le Bars E, BannierE, Lecuyer A, Barillot C, Bonan IThe Impact of Neurofeedback on Effective ConnectivityNetworks in Chronic Stroke Patients2020   (Submitted to  Neuroimage Clinical)  https://www.medrxiv.org/content/10.1101/2020.05.04.20087163v1

Reviewed International Conferences With Proceedings

1. Cury C, Lioi G, Perronnet L., Lécuyer A, Maurel P, Barillot C. Impact of 1D and 2D visualisation on EEG-fMRI Neurofeedback training during a motor imagery task, IEEE International Symposium on Biomedical Imaging, 2020 (Accepted for publication)

2. Lioi G, Fleury M, Butet S, Lécuyer A, Barillot C, Bonan I. Bimodal EEG-fMRI Neurofeedback for Stroke Rehabilitation: a Case Report ISPRM, Paris, France. Annals of Physical and Rehabilitation Medicine Volume 61, Supplement, July 2018, Pages e482-e483 https://doi.org/10.1016/j.rehab.2018.05.1127

3. Lioi G, Bell S L and Simpson D M 2016. Changes in Functional Brain Connectivity in the Transition from Wakefulness to Sleep in different EEG bands. In: XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. IFMBE Proceedings vol 57,pp 3–8 https://doi.org/10.1007/978-3-319-32703-7_1 (Best Student Paper Competition: Second Prize)

4. Lioi G, Bell S L, Smith D and Simpson D M 2016. The use of the middle latency response as an indicator of anesthetic depth: an investigation using slow induction of propofol anesthesia. XXV International Evoked Response Audimetry Study Group (IERASG) Biennal Sympo-sium, Warsaw, Poland, May 2017. http://dx.doi.org/10.13140/RG.2.2.19480.14081

5. Lioi G, Butet S, Fleury M, Lécuyer A, Bonan I, Barillot C Efficacy of EEG-fMRI Neurofeedback in stroke in relation to the DTI structural damage: a pilot study. Organization for Human Brain Mapping (OHBM), Rome, Italy, June 2019 https://hal.inria.fr/hal-02265495v1

6. Butet S, Lioi G, Fleury M, Lécuyer A, Barillot C, Bonan I. A multi-target motor imagery training using EEG-fMRI Neurofeedback: an exploratory study on stroke. Organization for Human Brain Mapping (OHBM), Rome, Italy, June 2019 https://hal.inria.fr/hal-02265496v1

7. Cury C, Maurel P, Lioi G, Gribonval R., Barillot C. Learning bi-modal EEG-fMRI neurofeedback to improve neurofeedback in EEG only. Real-Time functional Imaging and Neurofeedback (rtFIN), Maastricht, Netherlands, December 2019 https://hal.inria.fr/inserm-02368720v1

8. Lioi G, Butet S, Fleury M, Cury C, Elise B, et al. Bimodal EEG-fMRI Neurofeedback for upper motor limb rehabilitation: a pilot study on chronic patients. rtFIN 2019 - Real Time Functional Imaging and Neurofeedback, Dec 2019, Maastricht, Netherlands. pp.1-rtFIN 2019 - Real Time Functional Imaging and Neurofeedback, Dec 2019, Maastricht, Netherlands http://hal.archives-ouvertes.fr/hal-02383532

9. Fleury M, Lioi G, Barillot C, Anatole L. The use of haptic feedback in Brain-Computer Interfaces and Neurofeedback. rtFIN 2019 - Real Time Functional Imaging and Neurofeedback, Dec 2019, Maastricht, Netherlands https://hal.archives-ouvertes.fr/hal-02387400  

Public Dataset Release

1. Lioi G, Cury C, Perronnet L., Mano M., Lécuyer A, Barillot C.A multi-modal human neuroimaging dataset for data integration: simultaneous EEG and MRI acquisition during a motor imagery neurofeedback task: XP1. OpenNeuro dataset Repository. https://doi.org/10.18112/openneuro.ds002338.v1.0.1 

2. Lioi G, Cury C, Perronnet L., Mano M., Lécuyer A, Barillot C.A multi-modal human neuroimaging dataset for data integration: simultaneous EEG and MRI acquisition during a motor imagery neurofeedback task: XP2. OpenNeuro dataset Repository. https://doi.org/10.18112/openneuro.ds002336.v1.0.1

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