Method for determining initial movement time and brain region excitability

An excitatory and regional technology, applied in the direction of radiological diagnostic instruments, neural learning methods, applications, etc., can solve the problems that cannot be fully automatically utilized

Pending Publication Date: 2022-07-08
UNIV DAIX MARSEILLE +1
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] Known methods for modeling activity in entire brain networks (rather than just explored subnetworks) require some manual adjustments to model settings, often based on epileptogenic zone assumptions specified by clinical experts, and thus cannot Fully automatic use

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  • Method for determining initial movement time and brain region excitability
  • Method for determining initial movement time and brain region excitability
  • Method for determining initial movement time and brain region excitability

Examples

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example 1

[0070] Example 1: Test data

[0071] For testing purposes, synthetic data was generated by the same model used for inference. A connectome matrix from 10 subjects was used, using Desikan-Killiany segmentation, with 84 nodes. Two sets of 10 episodes were generated from these 10 linkers, all with three different sets of activation function parameters: one for uncoupling (q = (-5.0, -5.0, -3.0, -3.0)) , one for weak coupling (q = (-6.5, -3.0, -3.5, 12.0)), and one for strong coupling (q = (-11.2, 5.3, -6.2, 69.3)). Finally, among the 84 regions in the segmentation, the number of observed regions was set to 21, 42 and 63. A total of (2 groups) x (3 coupling strengths) x (3 number of observed areas) = ​​18 groups of 10 seizures were obtained. For all cases, excitability c was randomly drawn from a standard normal distribution, and observation nodes were randomly selected. Inferences were made separately for each of the 18 groups.

example 2

[0072] Example 2: Results

[0073] image 3 Exemplary results for extrapolation of a single episode are shown in part, while Figure 4 , 5 , 6A, 6B, 6C, 7A, 7B and 7C show cumulative results from all tests.

[0074] exist image 3 In , examples of inferred results are shown in part for a single episode with strong coupling and 21 observed regions. Left partial panels show true (dots) and inferred (plots) excitability for 84 brain regions. Solid dots mark observed areas, while hollow dots mark hidden areas. The light grey areas are those that have seizures, and the dark grey areas are those that do not. The numbers in the left column are Diagnostic value. The numbers in the right column are the inferred probability p(c>1), which is equivalent to the probability that the node is an epileptogenic node. The right panel shows the real (light / dark grey dots) and inferred (grey dots) region onset times. The rules for solid / hollow and light / dark grey dots are the same as in...

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Abstract

The present invention relates to a method for determining the time of initial movement and excitability of a region of the brain not observed as recruitment or non-recruitment in a seizure activity of the brain of an epileptic patient. The method comprises the following steps: providing a dynamic model of epileptic seizure propagation in a brain network; providing a statistical model defining a probability that the dynamic model generates a set of observations of a state of the brain network; training a dynamic model of propagation of the epileptic seizure using the statistical model and the data set of observations of the training queue; and inverting the trained dynamic model and inferring the initial time and excitability of the third region from the initial time observed for the first and second regions using a statistical model.

Description

technical field [0001] The present invention relates to a method for determining the excitability and onset time of brain regions that are not observed as recruited or not recruited in the seizure activity of the epileptic patient's brain. Background technique [0002] One possible treatment for patients with medically intractable epilepsy is surgical intervention aimed at removing one or more suspected epileptogenic regions, the regions of the brain that are responsible for seizures. However, the success rate of these surgical interventions is only 60-70%. [0003] However, using the method disclosed in document WO2018 / 015778A1 can improve such success rate. This document discloses a method of modulating epileptogenicity in the brain of an epilepsy patient, comprising the steps of: providing a virtual brain; providing models of epileptogenic regions and propagation regions, and loading the models into the virtual brain to create a virtual epileptic brain collecting data o...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/50G16H50/20G16H20/40G06N3/08
CPCG16H50/50G16H50/20G16H20/40G06N3/08A61B5/055A61B6/5217
Inventor V·吉萨V·西普
Owner UNIV DAIX MARSEILLE
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