Biology subjects

Mano, M.

Publications and source records attributed to Mano, M..

3 recordsLinked to original sources

bioRxiv2019

Simultaneous MRI-EEG during a motor imagery neurofeedback task: an open access brain imaging dataset for multi-modal data integration.

Combining EEG and fMRI allows for integration of fine spatial and accurate temporal resolution yet presents numerous challenges, noticeably if performed in real-time to implement a Neurofeedback (NF) loop. Here we describe a multimodal dataset of EEG and fMRI acquired simultaneously during a motor imagery NF task, supplemented with MRI structural data. The study involved 30 healthy volunteers undergoing five training sessions. We showed the potential and merit of simultaneous EEG-fMRI NF in previous work. Here we illustrate the type of information that can be extracted from this dataset and show its potential use. Our group is the second in the world to have integrated EEG and fMRI for NF, therefore this dataset is unique of its kind. We believe that it will be a valuable tool to O_LIAdvance and test methodologies to integrate complementary neuroimaging techniques (design and validation of methods of multi-modal data integration at various scales) C_LIO_LIImprove the quality of Neurofeedback provided C_LIO_LIImprove methodologies for de-noising EEG acquired under MRI C_LIO_LIInvestigate the neuromarkers of motor-imagery using multi-modal information C_LI

neuroscience↗

bioRxiv2018

Learning 2-in-1: towards integrated EEG-fMRI-neurofeedback

Neurofeedback (NF) allows to exert self-regulation over specific aspects of ones own brain activity by returning information extracted in real-time from brain activity measures. These measures are usually acquired from a single modality, most commonly electroencephalography (EEG) or functional magnetic resonance imaging (fMRI). EEG-fMRI-neurofeedback (EEG-fMRI-NF) is a new approach that consists in providing a NF based simultaneously on EEG and fMRI signals. By exploiting the complementarity of these two modalities, EEG-fMRI-NF opens a new spectrum of possibilities for defining bimodal NF targets that could be more robust, flexible and effective than unimodal ones. Since EEG-fMRI-NF allows for a richer amount of information to be fed back, the question arises of how to represent the EEG and fMRI features simultaneously in order to allow the subject to achieve better self-regulation. In this work, we propose to represent EEG and fMRI features in a single bimodal feedback (integrated feedback). We introduce two integrated feedback strategies for EEG-fMRI-NF and compare their early effects on a motor imagery task with a between-group design. The BiDim group (n=10) was shown a two-dimensional (2D) feedback in which each dimension depicted the information from one modality. The UniDim group (n=10) was shown a one-dimensional (1D) feedback that integrated both types of information even further by merging them into one. Online fMRI activations were significantly higher in the UniDim group than in the BiDim group, which suggests that the 1D feedback is easier to control than the 2D feedback. However subjects from the BiDim group produced more specific BOLD activations with a notably stronger activation in the right superior parietal lobe (BiDim > UniDim, p < 0.001, uncorrected). These results suggest that the 2D feedback encourages subjects to explore their strategies to recruit more specific brain patterns. To summarize, our study shows that 1D and 2D integrated feedbacks are effective but also appear to be complementary and could therefore be used in a bimodal NF training program. Altogether, our study paves the way to novel integrated feedback strategies for the development of flexible and effective bimodal NF paradigms that fully exploits bimodal information and are adapted to clinical applications.

bioengineering↗

bioRxiv2018

Automated Electrodes Detection during simultaneous EEG/fMRI

1The coupling of Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) enables the measure of brain activity at high spatial and temporal resolution. The localisation of EEG sources depends on several parameters including the knowledge of the position of the electrodes on the scalp. An accurate knowledge about this information is important for source reconstruction. Currently, when acquiring EEG and fMRI together, the position of the electrodes is generally estimated according to fiducial points by using a template. In the context of simultaneous EEG/fMRI acquisition, a natural idea is to use magnetic resonance (MR) images to localise EEG electrodes. However, most MR compatible electrodes are built to be almost invisible on MR Images. Taking advantage of a recently proposed Ultra short Echo Time (UTE) sequence, we introduce a fully automatic method to detect and label those electrodes in MR images. Our method was tested on 8 subjects wearing a 64-channel EEG cap. This automated method showed an average detection accuracy of 94% and the average position error was 3.1 mm. These results suggest that the proposed method has potential for determining the position of the electrodes during simultaneous EEG/fMRI acquisition with a very light cost procedure.

neuroscience↗