Association between increased EEG signal complexity and cannabis dependence

Laprévote, Vincent; Bon, Laura; Krieg, Julien; Schwitzer, Thomas; Bourion-Bédès, Stéphanie; Maillard, Louis; Schwan, Raymund

doi:10.1016/j.euroneuro.2017.10.038

Cited by 11 publications

(7 citation statements)

References 26 publications

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“…Recent related work has focused on reducing the gap between the fast heuristic learning approach (the heuristic algorithm is used relative to the optimization algorithm. Heuristic algorithm is an intuition-based or experienced algorithm that can search for the best solution within the acceptable computational cost, such as computing time, and occupied space, but does not guarantee finding a feasible or optimal solution; in most cases, the approximation of the optimal solution cannot be explained) and the slow but following certain principles or spectral methods [ 14 ].…”

Section: Application Methods Of Gnn In Dfd Based On Eeg Signalsmentioning

confidence: 99%

Application of Graph Neural Network in Driving Fatigue Detection Based on EEG Signals

Jin

Yin

et al. 2022

Computational Intelligence and Neuroscience

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The objective of this article is to solve the current social phenomenon of a large number of fatigue driving, so that social safety becomes more stable in the future, and the detection and application of driving fatigue are more meaningful. This article aims to study the application of graph neural network (GNN) in driving fatigue detection (this article is abbreviated as DFD) based on EEG signals. This article uses a pattern classification method based on a multilayer perceptual overlimit learning machine to find the hidden information of the signal through an unsupervised learning self-encoding structure, which achieves the optimization purpose and has a better classification effect than traditional classifiers. An improved soft threshold (the soft threshold can be used to solve the optimization problem, and the optimization problem solved is similar to the base pursuit noise reduction problem, but it is not the same, and it should be noted that the soft threshold cannot solve the base pursuit noise reduction problem) denoising algorithm is selected, and the collected EEG (a technique for capturing brain activity using electrophysiological markers is the electroencephalogram). The sum of the postsynaptic potentials produced simultaneously by a large number of neurons occurs when the brain is active. It records the process of brain activity in the cerebral cortex or scalp surface) signals are preprocessed, so that the feature extraction efficiency of extracting EEG signals is improved. The final experimental data show that the traditional support vector machine, SVM algorithm, and the KNN convolutional neural (the K-nearest neighbor method, often known as KNN, was first put forth by Cover and Hart in 1968. It is one of the most straightforward machine learning algorithms and a theoretically sound approach) algorithms has a recognition rate of 79% and 81% for fatigue. The improved algorithm in this article has an average recognition rate of 87.5% for driver fatigue, which is greatly improved.

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Section: Application Methods Of Gnn In Dfd Based On Eeg Signalsmentioning

confidence: 99%

Application of Graph Neural Network in Driving Fatigue Detection Based on EEG Signals

Jin

Yin

et al. 2022

Computational Intelligence and Neuroscience

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“…In the analysis of brain behavior in response to external stimuli, Crane NA et al [ 11 ] indicated that the Can group did not exhibit any appreciable change in the delta and theta bands. Lapervote V et al [ 24 ] demonstrated increased brain complexity in acute Can users. Raising the high-frequency band’s power spectrum can result in increasing the complexity of the brain.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Drug Abuse on Brain Function using Power Spectrum Analysis of Electroencephalogram Signals in Methamphetamine, Opioid, Cannabis, and Multi-Drug Abuser Groups

2023

J Biomed Phys Eng

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Background:The effect of different types of substances on brain function is still challenging; however, many studies have shown the functional and structural damage to the brain under influence of substance abuse.Objective: This study aimed to quantitatively compare the effect of opioid (Op), methamphetamine (Meth), cannabis (Can), and simultaneous methamphetamine and opioid (Multi-Drug (MD)) abuse on brain function. Furthermore, the impacts of pure Op and Meth abuse were considered with simultaneous substance abuse. Material and Methods:In this descriptive study, the electroencephalogram (EEG) signal was recorded from 52 participants in the Meth, Op, Can, and MD abusers, and the Healthy Control (HC) groups at rest state. EEG data were analyzed on the frequency domain with electrode-based, cortex-based, and hemisphere-based approaches.Results: However, the power spectrum in the delta band in the Op group, the gamma band in the Can group, and the gamma and beta bands in the MD group more significantly increased compared to the HC group, the power spectrum values in the Meth group reduced in the alpha, beta, and gamma bands. Moreover, the power spectrum values in the MD group more significantly higher than the Meth and Op groups in the beta and gamma bands. Conclusion:Since substance abuse in different types caused various changes in frequency components, the different power spectrum bands analysis in abusers can be reasonable to apply as a biomarker to detect the drug types.

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“…Analysis of EEG has been considered a powerful approach to reveal the psychobiological changes in response to drug abuse [31, 32]. EEG waves are derived from cortical networks reflecting the dynamic integration between excitatory and inhibitory synaptic potentials [33, 34].…”

Section: Discussionmentioning

confidence: 99%

Differential role of dose and environment in initiating and intensifying neurotoxicity caused by MDMA in rats

Shokry

Shields

Callanan

et al. 2019

BMC Pharmacol Toxicol

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Background MDMA causes serotonin (5-HT) syndrome immediately after administration and serotonergic injury in a few days or weeks. However, a serotonin syndrome is not always followed by serotonergic injury, indicating different mechanisms responsible for two adverse effects. The goal of present study was to determine causes for two adverse events and further test that dose and environment have a differential role in initiating and intensifying MDMA neurotoxicity. Methods Initiation and intensification were examined by comparing neurotoxic effects of a high-dose (10 mg/kg × 3 at 2 h intervals) with a low-dose (2 mg/kg × 3) under controlled-environmental conditions. Initiation of a serotonin syndrome was estimated by measuring extracellular 5-HT, body-core temperature, electroencephalogram and MDMA concentrations in the cerebrospinal fluid, while intensification determined in rats examined under modified environment. Initiation and intensification of the serotonergic injury were assessed in rats by measuring tissue 5-HT content, SERT density and functional integrity of serotonergic retrograde transportation. Results Both low- and high-dose could cause increases in extracellular 5-HT to elicit a serotonin syndrome at the same intensity. Modification of environmental conditions, which had no impact on MDMA-elicited increases in 5-HT levels, markedly intensified the syndrome intensity. Although either dose would cause the severe syndrome under modified environments, only the high-dose that resulted in high MDMA concentrations in the brain could cause serotonergic injury. Conclusion Our results reveal that extracellular 5-HT is the cause of a syndrome and activity of postsynaptic receptors critical for the course of syndrome intensification. Although the high-dose has the potential to initiate serotonergic injury due to high MDMA concentrations present in the brain, whether an injury is observed depends upon the drug environment via the levels of reactive oxygen species generated. This suggests that brain MDMA concentration is the determinant in the injury initiation while reactive oxygen species generation associated with the injury intensification. It is concluded that the two adverse events utilize distinctly different mediating molecules during the toxic initiation and intensification. Electronic supplementary material The online version of this article (10.1186/s40360-019-0326-6) contains supplementary material, which is available to authorized users.

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Association between increased EEG signal complexity and cannabis dependence

Cited by 11 publications

References 26 publications

Application of Graph Neural Network in Driving Fatigue Detection Based on EEG Signals

Application of Graph Neural Network in Driving Fatigue Detection Based on EEG Signals

Evaluation of Drug Abuse on Brain Function using Power Spectrum Analysis of Electroencephalogram Signals in Methamphetamine, Opioid, Cannabis, and Multi-Drug Abuser Groups

Differential role of dose and environment in initiating and intensifying neurotoxicity caused by MDMA in rats

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