Magnétoencéphalographie

Résumé

La magnétoencéphalographie (MEG) est une technique de mesure des champs magnétiques induits par l'activité électrique des neurones du cerveau. Cette technique est employée avec une visée clinique en neurologie (notamment pour l'étude de l'épilepsie) mais aussi en cardiologie, ainsi que dans la recherche en neurosciences cognitives. Principe et fonctionnement de la MEG Les champs magnétiques mesurés étant extrêmement faibles (de l'ordre de quelques femtoteslas), la MEG utilise un appareillage basé sur des magnétomètres à SQUID placé dans une pièce isolée magnétiquement par du mu-métal. En 2006, les magnétoencéphalographes peuvent mesurer le champ magnétique via environ 300 canaux (chaque canal étant associé à un SQUID et donc point de mesure autour de la tête). Les champs magnétiques induits par l'activité de neurones individuels étant bien plus faibles que le seuil de détection de la MEG, pour qu'un signal soit mesuré, il est nécessaire que l'activité électrique de p

Source officielle

https://fr.wikipedia.org/wiki/Magn%C3%A9toenc%C3%A9phalographie

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Clinical connectome fingerprints of cognitive decline

Enrico Amico

Brain connectome fingerprinting is rapidly rising as a novel influential field in brain network analysis. Yet, it is still unclear whether connectivity fingerprints could be effectively used for mapping and predicting disease progression from human brain data. We hypothesize that dysregulation of brain activity in disease would reflect in worse subject identification. We propose a novel framework, Clinical Connectome Fingerprinting, to detect individual connectome features from clinical populations. We show that “clinical fingerprints” can map individual variations between elderly healthy subjects and patients with mild cognitive impairment in functional connectomes extracted from magnetoencephalography data. We find that identifiability is reduced in patients as compared to controls, and show that these connectivity features are predictive of the individual Mini-Mental State Examination (MMSE) score in patients. We hope that the proposed methodology can help in bridging the gap between connectivity features and biomarkers of brain dysfunction in large-scale brain networks.

2021

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The progressive loss of brain network fingerprints in Amyotrophic Lateral Sclerosis predicts clinical impairment

Enrico Amico

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterised by functional connectivity alterations in both motor and extra-motor brain regions. Within the framework of network analysis, fingerprinting represents a reliable approach to assess subject-specific connectivity features within a given population (healthy or diseased). Here, we applied the Clinical Connectome Fingerprint (CCF) analysis to source-reconstructed magnetoencephalography (MEG) signals in a cohort of seventy-eight subjects: thirty-nine ALS patients and thirty-nine healthy controls. We set out to develop an identifiability matrix to assess the extent to which each patient was recognisable based on his/her connectome, as compared to healthy controls. The analysis was performed in the five canonical frequency bands. Then, we built a multilinear regression model to test the ability of the "clinical fingerprint" to predict the clinical evolution of the disease, as assessed by the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-r), the King's disease staging system, and the Milano-Torino Staging (MiToS) disease staging system. We found a drop in the identifiability of patients in the alpha band compared to the healthy controls. Furthermore, the "clinical fingerprint" was predictive of the ALSFRS-r (p = 0.0397; beta = 32.8), the King's (p = 0.0001; beta = -7.40), and the MiToS (p = 0.0025; beta = -4.9) scores. Accordingly, it negatively correlated with the King's (Spearman's rho = -0.6041, p = 0.0003) and MiToS scales (Spearman's rho = -0.4953, p = 0.0040). Our results demonstrated the ability of the CCF approach to predict the individual motor impairment in patients affected by ALS. Given the subject-specificity of our approach, we hope to further exploit it to improve disease management.

ELSEVIER SCI LTD2022

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The chronology of events in time-space is naturally available to the senses, and the spatial and temporal dimensions of events entangle in episodic memory when navigating the real world. The mapping of time-space during navigation in both animals and humans implicates the hippocampal formation. Yet, one arguably unique human trait is the capacity to imagine mental chronologies that have not been experienced but may involve real events-the foundation of causal reasoning. Herein, we asked whether the hippocampal formation is involved in mental navigation in time (and space), which requires internal manipulations of events in time and space from an egocentric perspective. To address this question, we reanalyzed a magnetoencephalography data set collected while participants self-projected in time or in space and ordered historical events as occurring before/after or west/east of the mental self [Gauthier, B., Pestke, K., & van Wassenhove, V. Building the arrow of time horizontal ellipsis Over time: A sequence of brain activity mapping imagined events in time and space.Cerebral Cortex,29, 4398-4414, 2019]. Because of the limitations of source reconstruction algorithms in the previous study, the implication of hippocampus proper could not be explored. Here, we used a source reconstruction method accounting explicitly for the hippocampal volume to characterize the involvement of deep structures belonging to the hippocampal formation (bilateral hippocampi [hippocampi proper], entorhinal cortices, and parahippocampal cortex). We found selective involvement of the medial temporal lobes (MTLs) with a notable lateralization of the main effects: Whereas temporal ordinality engaged mostly the left MTL, spatial ordinality engaged mostly the right MTL. We discuss the possibility of a top-down control of activity in the human hippocampal formation during mental time (and space) travels.

MIT PRESS2020

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