Epilepsy
Conditions
Brief summary
The aim of the study is to demonstrate that our semantic knowledge (elements of our long-term memory and the process we use them) respond to a graphic organisation and gather together following accurate patterns called cliques (neural networks).
Detailed description
Electroencephalography (EEG) with very High spatial Resolution (HR) (EEG-HR, 256 electrodes) allows for a better understanding of the global and local activity of the cerebral neocortex. In 2012, following publications by Claude Berrou and Vincent Gripon's Internet, introducing new principles of coding information based on graphical representations in connectionist networks, we approached this team to test biological plausibility of this theory in vivo with EEG. The central concept is the mental information, defined as all elements of knowledge acquired by the long-term memory on which the reason can build to try to respond to new problems. According to this new theory, these elements of knowledge called qualia or features should be connected within cliques networks. However, we currently do not have graphs comparing methods to measure a good index of both spatial and topological similarity between graphs with high resolution electroencephalography. For this new study, we propose to combine the strengths of several existing methods of graph comparison which, on top of this, will be especially adapted to the specific context of the analysis of the graphs in the cerebral cortex. The skills used are diverse: information theory, mathematics, graph theory, computer science, neuropsychology, signal processing and neurology.
Interventions
DEVICEElectroencephalography
DEVICEMRI
Sponsors
Rennes University Hospital
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
OTHER
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* 18 years and older * Right-handed ; * French native speaker * Having given written informed consent
Exclusion criteria
* Presence of any psychiatric, neuropsychological and developmental disorder * Any uncorrected visual impairment * Any trouble or delay in learning to read / speak french * Fully bilingual or multilingual * Medication, treatment and / or substances that may alter or modify brain functions * Pregnancy, breast feeding * Persons under major legal protection and/or deprived of liberty MRI-related criterions * Cardiac pacemaker or implanted defibrillator * Iron-magnetic surgical clips * Cochlear implant * Intra-ocular or brain foreign bodies * Less than 4 weeks-old stents, less than 6 weeks-old osteosynthesis materials * Claustrophobia
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Three main criteria are to be considered to validate the tool for measuring the similarity between the graphs obtained | 2 years | Despite the intra-individual variability, the same object or the same sound repeated several times should generate the most similar connectivity graphs Despite the inter-individual variability, analysis of connectivity graphs must also report high similarity indices between individuals on the same stimuli or stimuli sharing the same semantic properties even if subjects are different. At the stage of conceptual analysis of stimuli, or from 175 ms after the presentation of the image or sound, the analysis of connectivity graphs should reveal strong similarity indices for several different images of the same object (independence to the visual representation); for picture and sound representing the same object (independence to the sensory modality) or two objects belonging to the same semantic category (conceptual similarity, eg: orange, lemon). Indeed, these objects share common characteristics / semantic dimensions (eg mobile vs. stationary or living vs. non-living etc.). |
| Estimate the plausibility of the results obtained with our method directly from the graphs | 2 years | The density (ie: the ratio between the number of links in a given graph on the total possible number of links), the diameter (ie: the longest path in a graph), the average degree (ie: the average number of links connected to each node), the clustering (ie: the density of connections to a group of nodes with the rest of the network) and other parameters will be compared in terms of standard values available in the literature but also with tools that help to calculate the semantic distance between words such as WordNet and many others. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Judgement of the quality of the measurement on simulated data in the laboratory test our method for measuring the similarity between the graphs | 2 years | A computer model of neural network populations in which the experimenter knows in advance where the sources are allows him to test the reliability of his methods while reconstructing graphs and judging their similarity. \[8\] Finally, the quality of the results about the identification of neural cliques (or complete graphs) could be compared to the artificial neural network model we develop elsewhere \[1, 9 and 10\]. This model indicates that the distribution of neural cliques (in response to density and efficiency problems) follows a simple principle that we should find in the brain. For example, it is reasonable to think there are many cliques (or complete graphs) at the scale of a small brain region while those cliques are rare when considering spatially distant sources. It is an organization called small-worlds and which is classical for the neural networks in the cerebral cortex |
Countries
France
Outcome results
None listed