Adolescent Cannabis Use, Comorbid Attention-Deficit/Hyperactivity Disorder, and Other Internalizing and Externalizing Disorders

Molinero, Karla; Hinckley, Jesse D.

doi:10.1016/j.chc.2022.07.003

Cited by 3 publications

(1 citation statement)

References 51 publications

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“…Attention deficit hyperactivity disorder (ADHD) is a behavioral disorder primarily observed in children [1,2]. This disorder can be identified by parents or educators who notice atypical behaviors such as restlessness, challenges in sustaining concentration and attention, and heightened impulsivity, commonly associated with attention deficit disorder (ADD), particularly in educational settings where children may display unusual behavior or face difficulties with certain academic subjects.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Phase Locking Value to Determine Neurofeedback Treatment Responsiveness in Attention Deficit Hyperactivity Disorder

Yousefi,

Khanahmadi,

Dehghani

2024

J. Integr. Neurosci.

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Background: Neurofeedback is a non-invasive brain training technique used to enhance and treat hyperactivity disorder by altering the patterns of brain activity. Nonetheless, the extent of enhancement by neurofeedback varies among individuals/patients and many of them are irresponsive to this treatment technique. Therefore, several studies have been conducted to predict the effectiveness of neurofeedback training including the theta/beta protocol with a specific emphasize on slow cortical potential (SCP) before initiating treatment, as well as examining SCP criteria according to age and sex criteria in diverse populations. While some of these studies failed to make accurate predictions, others have demonstrated low success rates. This study explores functional connections within various brain lobes across different frequency bands of electroencephalogram (EEG) signals and the value of phase locking is used to predict the potential effectiveness of neurofeedback treatment before its initiation. Methods: This study utilized EEG data from the Mendelian database. In this database, EEG signals were recorded during neurofeedback sessions involving 60 hyperactive students aged 7–14 years, irrespective of sex. These students were categorized into treatable and non-treatable. The proposed method includes a five-step algorithm. Initially, the data underwent preprocessing to reduce noise using a multi-stage filtering process. The second step involved extracting alpha and beta frequency bands from the preprocessed EEG signals, with a particular emphasis on the EEG recorded from sessions 10 to 20 of neurofeedback therapy. In the third step, the method assessed the disparity in brain signals between the two groups by evaluating functional relationships in different brain lobes using the phase lock value, a crucial data characteristic. The fourth step focused on reducing the feature space and identifying the most effective and optimal electrodes for neurofeedback treatment. Two methods, the probability index (p-value) via a t-test and the genetic algorithm, were employed. These methods showed that the optimal electrodes were in the frontal lobe and central cerebral cortex, notably channels C3, FZ, F4, CZ, C4, and F3, as they exhibited significant differences between the two groups. Finally, in the fifth step, machine learning classifiers were applied, and the results were combined to generate treatable and non-treatable labels for each dataset. Results: Among the classifiers, the support vector machine and the boosting method demonstrated the highest accuracy when combined. Consequently, the proposed algorithm successfully predicted the treatability of individuals with hyperactivity in a short time and with limited data, achieving an accuracy of 90.6% in the neurofeedback method. Additionally, it effectively identified key electrodes in neurofeedback treatment, reducing their number from 32 to 6. Conclusions: This study introduces an algorithm with a 90.6% accuracy for predicting neurofeedback treatment outcomes in hyperactivity disorder, significantly enhancing treatment efficiency by identifying optimal electrodes and reducing their number from 32 to 6. The proposed method enables the prediction of patient responsiveness to neurofeedback therapy without the need for numerous sessions, thus conserving time and financial resources.

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Section: Introductionmentioning

confidence: 99%

Utilizing Phase Locking Value to Determine Neurofeedback Treatment Responsiveness in Attention Deficit Hyperactivity Disorder

Yousefi,

Khanahmadi,

Dehghani

2024

J. Integr. Neurosci.

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The prevalence of cannabis use disorder in attention-deficit hyperactivity disorder: A clinical epidemiological meta-analysis

Froude,

Fawcett,

Coles

et al. 2024

Journal of Psychiatric Research

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Impactos Do Uso De Cannabis Em Indivíduos Com Tdah: Efeitos Cognitivos, Decisões E Possíveis Benefícios Terapêuticos

Lemos Amorim,

Alexandre Bezerra Júnior,

Ribeiro Bubula

et al. 2024

RECIMA21

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O Transtorno de Déficit de Atenção e Hiperatividade (TDAH) é um dos distúrbios neuropsiquiátricos mais comuns na infância e adolescência, caracterizado por sintomas persistentes de desatenção, hiperatividade e impulsividade. Estudos recentes indicam que indivíduos com TDAH têm uma propensão significativamente maior para o uso de cannabis em comparação com a população geral. Objetivo: explorar os impactos do uso de cannabis em indivíduos com TDAH. Materiais e métodos: trata-se de uma revisão integrativa, em que a questão norteadora foi “Qual é o impacto do uso de cannabis em indivíduos com TDAH em comparação com aqueles que não usam cannabis, no que diz respeito a efeitos cognitivos, tomada de decisões e sintomas de TDAH?”. A busca pelos artigos ocorreu no PubMed a partir dos termos “attention deficit disorder” e “cannabis”, combinados entre si por operadores booleanos. Resultados e discussão: Os efeitos cognitivos do uso ocasional e crônico de cannabis incluem aprendizado verbal, memória e atenção prejudicados, com redução psicomotora na intoxicação aguda. É importante ressaltar que vários estudos descobriram que a administração aguda de THC prejudicou a tomada de decisões, aumentando a assunção de riscos em usuários recreativos e regulares de cannabis em comparação com não usuários. Conclusão: Mais estudos de usuários de cannabis com histórico de uso comparável e TDAH concomitante são necessários para elucidar melhor os efeitos da cannabis na cognição nesta população, especialmente considerando crenças gerais sobre os benefícios terapêuticos dos produtos derivados da cannabis.

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Adolescent Cannabis Use, Comorbid Attention-Deficit/Hyperactivity Disorder, and Other Internalizing and Externalizing Disorders

Cited by 3 publications

References 51 publications

Utilizing Phase Locking Value to Determine Neurofeedback Treatment Responsiveness in Attention Deficit Hyperactivity Disorder

Utilizing Phase Locking Value to Determine Neurofeedback Treatment Responsiveness in Attention Deficit Hyperactivity Disorder

The prevalence of cannabis use disorder in attention-deficit hyperactivity disorder: A clinical epidemiological meta-analysis

Impactos Do Uso De Cannabis Em Indivíduos Com Tdah: Efeitos Cognitivos, Decisões E Possíveis Benefícios Terapêuticos

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