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Marie-Noële Magnié-Mauro

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titre
When fNIRS meets fMRI to complement cerebellar exploration
auteur
Giulia Rocco, Stephen Ramanoël, Christophe N Habas, Angelo Arleo, Olivier Meste, Marie-Noële Magnié-Mauro, Jerome Lebrun
article
IEEE International Symposium on Biomedical Imaging (ISBI 2022), IEEE Signal Processing Society (SPS); IEEE Engineering in Medicine and Biology Society (EMBS), Mar 2022, Kolkata, India. ⟨10.13140/RG.2.2.14254.18245⟩
annee_publi
2022
resume
Towards an fMRI validation of our previous fNIRS-based explorations of cerebellar activity [Rocco et al. EMBC 2021], we introduce here a joint sequential fNIRS/fMRI study based on finger tapping that provides both finer time and space granularities for the assessment of BOLD effect in the cerebellum.
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https://hal.archives-ouvertes.fr/hal-03630639/file/ISBI2022_eposter_final.pdf BibTex
titre
Interactive Human Interface for ERP Component Extraction from Gifted Children
auteur
Kawther Benharrath, Amine Ben Slama, Balkine Khadoumi, Mounir Sayadi, Olivier Meste, Sophie Guetat, Jerome Lebrun, Marie-Noële Magnié-Mauro
article
Intelligent Automation and Soft Computing, Tech Science Press, 2022, Intelligent Automation & Soft Computing, 33 (2), pp.1063-1080. ⟨10.32604/iasc.2022.023446⟩
annee_publi
2022
resume
In the last century, scientists started to give importance to gifted children (GC) and to understand their behavior. Since then, research has pursued the various categories of these children and their early diagnosis in order to find the best control of their skills. Therefore, most researchers focus on recent advances in electroencephalogram (EEG) and cognitive events. The event-related brain potentials (ERPs) technique is generally used in the cognitive neuroscience process. However, it is still a challenge to extract these potentials from a few trials of electroencephalogram (EEG) data. The N400 ERP component is an important part of the studies of cerebral science and clinical neuropsychology. In this ongoing study, a new experimentation protocol and human tablet interactive equipment were assigned to analyze the brain activity. A combination of two techniques the Integral Shape Averaging (ISA) and Integral Shape Averaging applied on belated window (ISA-BW) was built to extract the semantic component from a single trial and to enhance the signal-to-noise ratio (S/N). The results obtained were compared with the most used method in the medical field Grand Average (GA). In addition, a statistical study was performed on a database for accurate characterization of children using feature reduction. The experimental results show the efficiency of the suggested approach which manifests the discriminant statistical feature extraction (J = 2.032) from ERP component dataset that can contribute to the recognition of GC. The proposed method is reinforced by a pilot device processed by an electrical engineer to improve the protocol simulation. The experimental procedure proves that the present approach is very interesting and helpful for improving the identification of such gifted children.
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Article dans une revue
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https://hal.archives-ouvertes.fr/hal-03587593/file/Interactive_Human_Interface_for_ERP_Comp.pdf BibTex
titre
Que sait-on en 2021 des particularités neurophysiologiques des HPI et de leurs corrélats ?
auteur
Marie-Noële Magnié-Mauro
article
L’ANPEIP, 50 ans d’actions pour les enfants à haut potentiel intellectuel (HPI), Nov 2021, Nice, France
annee_publi
2021
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titre
Présentation du Prix Nobel de Médecine 2021
auteur
Marie-Noële Magnié-Mauro
article
7ème Journée Nobel de Nice, Nov 2021, Nice, France
annee_publi
2021
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titre
Single-Trial Detection of Event-Related Potentials with Integral Shape Averaging: An Application to the Elusive N400
auteur
Giulia Rocco, Hervé Rix, Jerome Lebrun, Sophie Guetat, Lucile Chanquoy, Olivier Meste, Marie-Noële Magnié-Mauro
article
43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC 2021), IEEE, Nov 2021, Mexico (virtual), Mexico. pp.1022-1025, ⟨10.1109/EMBC46164.2021.9630271⟩
annee_publi
2021
resume
The estimation of Event-Related Potentials (ERPs) from the ambient EEG is a difficult task, usually achieved through the synchronous averaging of an extensive series of trials. However, this technique has some caveats: the ERPs have to be strictly time-locked with similar shape, i.e. emitted with the same latency and the same profile, with minor fluctuations of their amplitudes. Also, the method requires a huge number of valid trials (~100) to efficiently raise the ERPs from the EEG trials. In the case of cognitive ERPs, as with the N400, the delivered stimulus has to be different for each trial, the latencies are varying, and the number of available trials is usually low. In this paper, an alternative method, coined Integral Shape Averaging (ISA) and its derivatives are detailed. ISA is robust to varying latencies and affine transforms of shape. Furthermore, a new method coined ISAD can be derived to extract ERPs even from a single trial experiment. The aim here is to illustrate the potential of ISAD for N400 component extraction on real EEG data, with emphasis on its general applicability for ERPs computation and its major assets like reduced experimental protocol. Some insights are also given on its potential use to study ERP variability, through shape and latency.Clinical Relevance— The proposed algorithm aims to be a helpful tool in clinical practice to analyze and interpret evoked responses in real experimental settings, especially for particularities in neurology.
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titre
A Chiral fNIRS Spotlight on Cerebellar Activation in a Finger Tapping Task
auteur
Giulia Rocco, Jerome Lebrun, Olivier Meste, Marie-Noële Magnié-Mauro
article
43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC 2021), IEEE, Nov 2021, Mexico (virtual), Mexico. pp.1018-1021, ⟨10.1109/EMBC46164.2021.9629565⟩
annee_publi
2021
resume
Functional Magnetic Resonance Imaging (fMRI) has been so far the golden standard to study the functional aspects of the cerebellum. In this paper, a low-cost alternative imaging, i.e. functional Near-Infrared Spectroscopy (fNIRS) is demonstrated to achieve successful measurements of the cerebellar hemodynamics towards the challenging observation of motor and cognitive processes at the cerebellar level. The excitation and reception optodes need to be properly placed to circumvent a major hindering from the shielding by the neck muscles. A simple experimental protocol, i.e. finger tapping task, was implemented to observe the subject’s engagement and the presence of functional asymmetries. Marked differences among subjects with different levels of lateralization were clearly noticed in terms of activation and latencies, together with peaks in the hemodynamic response following neural activation. These preliminary results suggest also differences in the hemodynamic behavior between the brain and the cerebellum and encourage future and extended analysis in this direction.Clinical Relevance—This establishes the possibility to use a novel technique (fNIRS) to study cerebellar hemodynamics instead of fMRI.
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https://hal.archives-ouvertes.fr/hal-03484193/file/Rocco_Chiral_fNIRS_psyarxiv_HAL.pdf BibTex
titre
Asymmetries in Cerebellar Activation during Finger Movements: A Functional Near-Infrared Spectroscopy Study
auteur
Giulia Rocco, Jerome Lebrun, Olivier Meste, Marie-Noële Magnie-Mauro
article
Acta Physiologica: 4th Congress of Physiology and Integrative Biology, 88th Congress of French Physiological Society, Faculté de Médecine, Nice, France, 2‐4 September, 2021, French Physiological Society, Sep 2021, Nice, France. pp.9-10, ⟨10.1111/apha.13745⟩
annee_publi
2021
resume
* Introduction: Research about cerebellar functions has become a trendy field of study, especially regarding the cerebellum involvement in sensorimotor control. The heterogeneity of the findings encourages further investigations in cerebellar activation, hemisphere specificity, but also calls for new techniques to allow easier, cheaper routine assessments. In this study, we investigated hemispheric cerebellar activation during finger movements of the dominant and subdominant hand using functional near-infrared spectroscopy (fNIRS). * Methods: One healthy right-handed subject performed a finger-tapping task consisting of six repetitive blocks (task + rest), respectively, for the left and right hands. The task was repeated twice for each hand changing the activity periods: first, 10 seconds of tapping followed by 30 seconds of rest; then, 20 seconds of tapping followed by 30 seconds of rest. Cerebellar responses for each repetition were time-locked averaged. * Results: Similar haemodynamic responses were observed ipsilaterally and contralaterally for both tasks (10 vs 20 seconds activity). The dominant hemisphere (right-handed subject) proved to be involved even during subdominant hand movements. Higher synchronization of the right hemisphere for left-hand movement was observed and validated using frequency domain analysis. * Conclusion: fNIRS proved to be a good technique to capture cerebellar haemodynamics in a non-clinical setting. Furthermore, the observed asymmetries in cerebellar activation fully agree with similar fMRI studies that suggest the existence of different layers of controls from the cerebellum in the two hemispheres.
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https://hal.archives-ouvertes.fr/hal-03529215/file/SPF_poster_final_Rocco%281%29.pdf BibTex
titre
Exploring the Cerebellum with Functional Near-Infrared Imaging: A Preliminary Study
auteur
Giulia Rocco, Jerome Lebrun, Olivier Meste, Marie-Noële Magnié-Mauro
article
IEEE 18th International Symposium on Biomedical Imaging (ISBI 2021), IEEE, Apr 2021, Nice (virtuel), France. ⟨10.13140/RG.2.2.30022.14400⟩
annee_publi
2021
resume
This study details the possibility to use functional Near-Infrared Spectroscopy (fNIRS) as a novel technique to unravel the cerebellar role in motor and cognitive processes. A simple motor task highlighted differences in the cerebellar hemodynamics between baseline and experimental session due to the subject's engagement.
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https://hal.archives-ouvertes.fr/hal-03264733/file/ISBI2021_eposter_01.pdf BibTex
titre
A new approach to detect N400 event-related potentials in giftedness children
auteur
Kawther Benharrath, Balkine Khaddoumi, Mounir Sayadi, Herve Rix, Olivier Meste, Jerome Lebrun, Sophie Guetat, Marie-Noële Magnié-Mauro
article
2020 IEEE 4th International Conference on Advanced Systems and Emergent Technologies (IC_ASET), Dec 2020, Hammamet, Tunisia. pp.75-78, ⟨10.1109/IC_ASET49463.2020.9318262⟩
annee_publi
2020
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titre
P300 event-related potentials classification from EEG data through interval feature extraction and recurrent neural networks
auteur
Giulia Rocco, Jerome Lebrun, Marie-Noële Magnié-Mauro, Olivier Meste
article
SophIA Summit 2020, Nov 2020, Sophia Antipolis, France. ⟨10.13140/RG.2.2.34911.12960⟩
annee_publi
2020
resume
Event-related potentials (ERPs) are reproducible electrophysiological responses after the administration of an external stimulus, e.g. visual, auditory. They appear as weak signals with low amplitude and signal- to-noise ratio, typically masked by noise and spontaneous EEG activity. Thus, the classical experimental protocol used to measure ERPs consists in multiple presentations of the same kind of stimulus: epochs, that is the EEG time series after the stimulus onset, are time-locked averaged so as to cancel out noise and observe the desired waveform. An example of an ERP component is the P300, i.e. a positive deflection appearing around 300 ms after the stimulus presentation, which is typically elicited by the oddball paradigm: sequences of repetitive stimuli (non-target) are infrequently interrupted by a deviant stimulus (target). The P300 component concerns the evaluation and categorization of a stimulus, allowing to discriminate the subject’s brain states. Thus, the different neural responses can be used to investigate dysfunctions in sensory and cognitive processing, as well as being a suitable tool for brain- computer interface. Hence, novel signal processing and machine learning algorithms are gaining ground to achieve robust automatic ERPs classification, and consequently a more extensive and practical use of EEG. In the current study, we examined the performance of a LSTM network to classify P300 component between target and non-target stimuli in an auditory oddball paradigm. EEG recordings of 19 channels of two subjects were acquired during an auditory oddball paradigm with the 80% of the tones at a low frequency pitch, and the remaining 20% at a high frequency pitch. The signals were re-referenced with respect to the mastoids and band-passed filtered between 0.1 and 30 Hz. After extracting the epochs and removing the baseline, visual inspection and rejection was performed to end up with a total of 324 trials (200 from S1, 124 from S2). A different classification problem was considered for each electrode. Interval features derived from signal segments of various length were extracted. For each time i of the interval, segments with length varying as the power of two are formed, by keeping always i as the starting point and including the rest of the time series until the length of the interval exceeds the epoch. Then, for each interval the average amplitude and the standard deviation were calculated and used as features to feed the network. A single layer Long Short-Term Memory (LSTM) network was defined, followed by fully connected layers and a softmax activation function. Two approaches were tested: a subject-dependent one, by using 75% of S1 samples as training set and the other 25% as test; a subject- independent one, by using 200 samples (100 S1 + 100 S2) as training set and the remaining ones as test. The mean classification accuracy over all the electrodes for the first approach was 81.16%, while for the second approach 75.55%. These results are in line with the previous literature studies, in which recurrent neural network outperformed other common algorithms, such as SVM. In particular, F3, F4, Fp1 and Fp2 presents globally the best accuracies. Indeed, P300 is typically stronger on the frontal lobes. Also, compared to other feature extraction methods, the interval feature extraction is intuitive and easy- to-implement, and does not rely on any other parameter. The results encourage the use of this classifier, but further analyses need to be done. For example, it would be interesting to collect data with a more balanced number of events – target occur always less than non-targets. Moreover, the interval feature extraction could be combined to other features, e.g. in the frequency domain, and fed to a LSTM bidirectional layer.
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Poster de conférence
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https://hal.archives-ouvertes.fr/hal-03446272/file/Poster_Sophia_summit_2020_final.pdf BibTex
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