Participating TeamsVisages U746

HybridPanama

EA 4712 (associated)Athena (associated)

HEMISFERHybrid Eeg-MrI and Simultaneous neuro-FEedback

for brain Rehabilitation

Start date: 01-2014

General objectives of HEMISFER

• Make full use of neurofeedback (NF) paradigm forbrain self-regulation/stimulation in:• Rehabilitation (ADHD, Strokes, …)• Psychiatric disorders (anxiety, schizophrenia,

resistant mood disorders, ….)• Main Challenges:

• Learn a coupling model associating functionaland metabolic information from simultaneousMagnetic Resonance Imaging (fMRI) andElectro-encephalography (EEG)

• Enhance the NF paradigm from the couplingmodel

Context

From Imaging Biomarkers to Therapeutic Concept

EEG

Neurovascular

coupling

ASL

BOLD

fMRI

Neuronal activity

Cerebral blood flow and volume (CBF, CBV)

BOLD Signal

Energy needs of brain tissues

Consumption of glucose (CMR_Glc)

& O2 (CMR_O2)

Blood concentration in dHb

Local magnetic perturbation due to dHb

Example :Perfusion abnormalities in chronic resistant depression[Batail JM et al. 2014]

Group Analysis

Right Hippocampus + Amygdala

(p<0,02 FWE)

Resistant Group > Control Group (n=7)

R+L Hippocampus, R median Cingulum

(p<0,05 cluster FWE)

Resistive Patient 16 > control group

(n=30)

Resistive Patient 01 > control group

(n=30)

Hippocampus + Amygdala(p<0,05 cluster FWE)

Individual Analysis

Data Processing

• Pretreatments (artifacts, …)

• Real time data analysis

• Extraction of patterns of interest

Production of

« feedback »

HEMISFER

Project

Current Status:• fMRI with Siemens MR 3T scanner• EEG with Brain Products 64-channel system• Real-time EEG and fMRI data acquisition,

processing, NF calculation and visualization infull synchronization.

Future Challenges:• Online NF with joint modeling approaches• Improve performance with GPU based real-time

parallel processing

Experimental Platform

Major Challenges

• Develop new NF paradigms able to profit from simultaneous EEG/fMRI/fASL

recordings

• We expect these novel paradigms to be able to concentrate the brain

metabolism on specific regions of the brain

• Learn models at the signal level able to explain the coupling of EEG and fMRI

signals under simple and more advanced brain stimuli (e.g. BOLD fMRI, fASL,

basal ASL)

• Learn both the domain in which brain activity is sparse (e.g., dictionary

learning), and adjust parametric models of the acquisition processes

• Achieve super-resolution in the spatial and frequency domain by

expressing the problem as a linear inverse problems regularized with the

learned coupled model

• Use brain connectivity models as prior information (later stage)

• Use the learned coupling models in order to “enhance” the EEG signal while

performing the same stimuli and NF tasks outside MRI

Coupling Model

Introduction of a new linear EEG/BOLD coupling modelusing sparsity-based regularization term:

Participating People : Elise Bannier, Christian Barillot, Jean-Marie Batail, Isabelle Bonan, Maureen Clerc, Isabelle Corouge, Dominique Drapier, Remi Gribonval, Anatole Lecuyer, Jussi Lindgren, Marsel Mano, Saman Noorzadeh, Pierre Maurel, Thomas Oberlin, Lorraine Perronnet

Simultaneous EEG-fMRI data acquisition Off-MRI EEG data acquisition

Learned EEG-MRI coupling model

BCI program

Visual Stimuli

Data Processing

Patient

BCI program

Visual Stimuli

Patient

Data Processing

Model Update

Coupling Model

Paradigm

EEG signal leadfield mixing matrix fMRI signal Hemodynamic model

Results

In the first study:• We compared EEG-NF, fMRI-NF and EEG-fMRI-NF

on a motor imagery task with 10 subjects• We propose an integrated feedback metaphor for

EEG-fMRI-NFSparse regularization

fMRI Subsystem

NFB Unit

EEG object

fMRI object

RecorderRecView

Buffer

RDA

RDA

Visualize

NFB Control Joint NFB

RDARDA

USB

Parallel

HDMI

USB

EEG Subsystem64 Ch. EEG Cap

2 x 32 Ch. Amplifiers

USB adapter

Brain Activity

Brain Activity

Trigger Unit

MR Head Coil

MR Console

HDD

Buffer

Cues/NFB

Display

SyncBox

*Recorder and RecView are commercial software, the rest of the NFB UNIT modules are build in-house (Matlab/C++)

sync &control

Patient

Acquisition of brain functions (MRI/EEG)

• Motor imagery-related fMRI activations were stronger during EEG-fMRI-NF than EEG-NF

• During EEG-fMRI-NF participants regulated more the modality that was harder to control

In the second study, we compared 1D EEG-fMRI-NF and 2D EEG-fMRI-NF

localization based on EEG localization based on fMRI

localization based on EEG-fMRI three-layer head model