Cartographie du cerveau | EPFL Graph Search

La cartographie du cerveau est un ensemble de techniques des neurosciences dédiées à la création de représentations spatiales des quantités ou des propriétés biologiques du cerveau humain ou non-humain. La cartographie du cerveau est en outre définie par la Société pour la cartographie et le traitement du cerveau (Society for Brain Mapping and Therapeutics - SBMT) comme l'étude de l'anatomie et du fonctionnement du cerveau et de la moelle épinière grâce à l'utilisation de l' (intra-opératoire, microscopique, endoscopique et l'imagerie multi-modalité), l’utilisation de l'immunohistochimie, la génétique moléculaire et l’optogénétique, les cellules souches et la biologie cellulaire, l'ingénierie (matériel, électrique et biomédical), la neurophysiologie et les nanotechnologies. Vue d'ensemble Toute peut être considérée comme faisant partie de la cartographie du cerveau. La cartographie du cerveau peut être conçue comme une forme supérieure de la neuro-imagerie, visant à produi

Connectivity Searchlight: A Novel Approach For Mri Information Mapping Using Multivariate Connectivity

Soheil Faridi, Jonas Richiardi, Dimitri Nestor Alice Van De Ville

Brain mapping using magnetic resonance imaging (MRI) is traditionally performed using voxel-wise statistical hypothesis testing. Such mass-univariate approach ignores subtle spatial interactions. The searchlight method, in contrast, uses a multivariate predictive model in each local neighborhood in brain space-named the searchlight. The classification performance is then reported at the center of the searchlight to build an information map. We extend the searchlight technique to take into account additional voxels that can be considered as a meaningful network; i.e., we define a criterion of multivariate connectivity to identify voxels that are statistically dependent on those in searchlight. We coin the term "connectivity searchlight" for the extended searchlight. Using simulated data, we empirically show improved performance for brain regions with low signal-to-noise ratio and recovery of underlying network structures that would otherwise remain hidden. The proposed methodology is general and can be applied to both functional and structural data. We also demonstrate promising results on a well-known fMRI dataset where images of different categories are presented.

Ieee2013

The Neuron Phenotype Ontology: A FAIR Approach to Proposing and Classifying Neuronal Types

Sean Lewis Hill, Mohameth François Sy

The challenge of defining and cataloging the building blocks of the brain requires a standardized approach to naming neurons and organizing knowledge about their properties. The US Brain Initiative Cell Census Network, Human Cell Atlas, Blue Brain Project, and others are generating vast amounts of data and characterizing large numbers of neurons throughout the nervous system. The neuroscientific literature contains many neuron names (e.g. parvalbumin-positive interneuron or layer 5 pyramidal cell) that are commonly used and generally accepted. However, it is often unclear how such common usage types relate to many evidence-based types that are proposed based on the results of new techniques. Further, comparing different types across labs remains a significant challenge. Here, we propose an interoperable knowledge representation, the Neuron Phenotype Ontology (NPO), that provides a standardized and automatable approach for naming cell types and normalizing their constituent phenotypes using identifiers from community ontologies as a common language. The NPO provides a framework for systematically organizing knowledge about cellular properties and enables interoperability with existing neuron naming schemes. We evaluate the NPO by populating a knowledge base with three independent cortical neuron classifications derived from published data sets that describe neurons according to molecular, morphological, electrophysiological, and synaptic properties. Competency queries to this knowledge base demonstrate that the NPO knowledge model enables interoperability between the three test cases and neuron names commonly used in the literature.

HUMANA PRESS INC2022

Automatic detection of generalized paroxysmal fast activity in interictal EEG using time-frequency analysis

Amir Hossein Omidvarnia

Objective: Markup of generalized interictal epileptiform discharges (IEDs) on EEG is an important step in the diagnosis and characterization of epilepsy. However, manual EEG markup is a time-consuming, subjective, and the specialized task where the human reviewer needs to visually inspect a large amount of data to facilitate accurate clinical decisions. In this study, we aimed to develop a framework for automated detection of generalized paroxysmal fast activity (GPFA), a generalized IED seen in scalp EEG recordings of patients with the severe epilepsy of Lennox-Gastaut syndrome (LGS). Methods: We studied 13 children with LGS who had GPFA events in their interictal EEG recordings. Timefrequency information derived from manually marked IEDs across multiple EEG channels was used to automatically detect similar events in each patient's interictal EEG. We validated true positives and false positives of the proposed spike detection approach using both standalone scalp EEG and simultaneous EEG-functional MRI (EEG-fMRI) recordings. Results: GPFA events displayed a consistent low-high frequency arrangement in the time-frequency domain. This 'bimodal' spectral feature was most prominent over frontal EEG channels. Our automatic detection approach using this feature identified EEG events with similar time-frequency properties to the manually marked GPFAs. Brain maps of EEG-fMRI signal change during these automatically detected IEDs were comparable to the EEGfMRI brain maps derived from manual IED markup. Conclusion: GPFA events have a characteristic bimodal time-frequency feature that can be automatically detected from scalp EEG recordings in patients with LGS. The validity of this time-frequency feature is demonstrated by EEG-fMRI analysis of automatically detected events, which recapitulates the brain maps we have previously shown to underlie generalized IEDs in LGS. Significance: This study provides a novel methodology that enables a fast, automated, and objective inspection of generalized IEDs in LGS. The proposed framework may be extendable to a wider range of epilepsy syndromes in which monitoring the burden of epileptic activity can aid clinical decision-making and faster assessment of treatment response and estimation of future seizure risk.

PERGAMON-ELSEVIER SCIENCE LTD2021

Automatic detection of generalized paroxysmal fast activity in interictal EEG using time-frequency analysis

Amir Hossein Omidvarnia

PERGAMON-ELSEVIER SCIENCE LTD2021