Contributions From the Analysis of Graphs for Identification of Neural Cliques
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 21
- 试验地点
- 2
- 主要终点
- Three main criteria are to be considered to validate the tool for measuring the similarity between the graphs obtained
研究概览
简要总结
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).
详细描述
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Other
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •18 years and older
- •Right-handed ;
- •French native speaker
- •Having given written informed consent
排除标准
- •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
结局指标
主要结局
Three main criteria are to be considered to validate the tool for measuring the similarity between the graphs obtained
时间窗: 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
时间窗: 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.
次要结局
- 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)