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System and method to measure and monitor neurodegeneration

A neurodegeneration, subject technology, applied in the field of systems and methods for measuring and monitoring neurodegeneration, can solve problems such as the uncertainty of the effectiveness of Alzheimer's disease, and achieve the effect of avoiding human bias

Pending Publication Date: 2021-10-19
大脑和嵴髓研究所 +4
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the validity of EEG properties as neural markers for the assessment of preclinical Alzheimer's disease has not been established, as most studies have focused on EEG neural markers in late stages of disease after symptom onset

Method used

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  • System and method to measure and monitor neurodegeneration
  • System and method to measure and monitor neurodegeneration
  • System and method to measure and monitor neurodegeneration

Examples

Experimental program
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Effect test

example 1

[0123] Materials and methods

[0124] Observational Learning Design and Participants

[0125] Based on low in AD-marked area 18 F-FDG PET metabolism, combined with 18 Subthreshold to very high amyloid burden as measured by F-florbetapir PET, 20 individuals with severe neurodegeneration were selected to target subjects at highest risk for future cognitive decline. Based on the high in the queue 18 F-FDG PET metabolism, combined with a low amyloid standardized uptake value ratio (SUVR), selected a control group of 20 neurodegeneration-negative subjects to target future conversion to Alzheimer's disease and dementia at very low risk. subjects with cognitive decline despite their subjective memory complaints. use 18 F-florbetapir PET SUVR was used as a continuous variable to assess beta-amyloid burden because of the potentially continuous non-linear relationship between amyloid burden and EEG measures. It is hypothesized that preclinical Alzheimer's disease subjects will pre...

example 2

[0180] Observational study design and participants

[0181] This example is based on a cohort of 70 to 85 year olds with subjective memory complaints and unimpaired cognition [Mini Mental State Examination (MMSE) score 527 and Clinical Dementia Scale score 0], no episodic memory deficits Evidence [Free and Cue-Selective Reminder Test (FCSRT) Total Recall Score 541] Baseline data from 314 cognitively normal individuals. Demographic, cognitive, functional, biological, genetic, genomic, imaging including structural and functional MRI of the brain, 18 F-FDG PET and 18 F-Florbetapir PET electrophysiology and other assessments. EEG is performed every 12 months.

[0182] To assess whether changes in EEG measures were the result of neurodegeneration, amyloid burden, or a combination of both, subjects were assessed according to their amyloid status (determined by 18 F-florbetapir PET confirmed) and neurodegenerative status (by 18 F-FDG PET revealed) the entire cohort was divided i...

example 3

[0207] In this example, machine learning analysis was used to assess the performance of EEG biomarkers at the individual level to identify amyloid status (A+ vs. A-) and neurodegenerative status (N+ vs. N-).

[0208] EEG is of particular interest among the different measures that can be used to distinguish N+ participants from N- participants at the individual level ( Figure 11 ).

[0209] The reduction in the number of electrodes is only possible when only 2 electrodes are used ( Figure 12 ) affects the diagnostic performance, and then the sensitivity remains good at 74%. at the expense of specificity. A set of 4 electrodes (2 frontal and 2 parietal) had good results in the diagnosis of Alzheimer's neurodegeneration at this preclinical stage, with a sensitivity of 64% and a specificity of 61%.

[0210] This example also shows that the strongest predictor of amyloid status is first the ApoE4 genotype, followed by the demographic parameters of age, sex, education level and...

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Abstract

A system to measure and monitor neurodegeneration of a subject, in particular preclinical Alzheimer's disease, comprises an acquisition module configured to acquire electroencephalographic signals with multiple EEC channels from a subject perceptually isolated (101); a calculation module configured to extract at least one EEC metric representative of neurodegeneration (102); and an evaluation module configured to evaluate the at least one EEC metric and extract a neurodegeneration index (103). The EEC metric is selected from the group of: weighted symbolic mutual information in at least one frequency band, power spectral density calculated in at least one frequency band, median spectral frequency, spectral entropy, algorithmic complexity.

Description

technical field [0001] The present invention relates to the field of measuring and monitoring neurodegeneration by assessing changes in neural markers. In particular, the present invention relates to the use of EEG measurements to monitor changes in specific neural markers in preclinical Alzheimer's disease subjects. Background technique [0002] Alzheimer's disease (AD) is the most common form of dementia, accounting for an estimated 60-80% of cases. The pathophysiological process of Alzheimer's disease begins many years before symptoms appear. Diagnosing Alzheimer's disease as early as possible is imperative because patients are more likely to benefit from disease-modifying treatments if treated early in the course of the disease, before severe brain damage occurs. Therefore, it is important to develop neural markers that are sensitive to the early "preclinical" stages of Alzheimer's disease, even before mild cognitive impairment (MCI) occurs. In the preclinical stage, ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/00A61B5/369A61B5/374
CPCA61B5/4088A61B5/7235A61B5/316A61B5/374A61B5/291A61B5/7264G16H50/20G16H50/30
Inventor 史蒂芬·埃佩尔鲍姆辛妮德·戈贝尔费德里科·雷蒙多雅科布·D·西特莱昂内尔·纳卡凯
Owner 大脑和嵴髓研究所
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