Increased neural variability in adolescents with ADHD symptomatology: Evidence from a single-trial EEG study

Einziger, Tzlil; Devor, Tali; Ben‐Shachar, Mattan S.; Arazi, Ayelet; Dinstein, Ilan; Klein, Christoph; Auerbach, Judith G.; Berger, Andrea

doi:10.1016/j.cortex.2023.06.007

Cited by 4 publications

(1 citation statement)

References 74 publications

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“…Intraindividual variability can be manifested as inconsistent or erratic RTs, where an individual may respond quickly on some trials and much more slowly on others, even when the stimuli and task demands remain constant. This measurement has been correlated to cognitive control deficits in people with ADHD (Bella-Fernández et al, 2023;Einziger et al, 2023;Kofler et al, 2013). On the other hand, we define that the interindividual variability in RT highlights the differences in RT performance between different individuals with ADHD, thus illustrating the heterogeneity of the disorder in a group of individuals with ADHD.…”

Section: Introductionmentioning

confidence: 80%

ADHD Adults Show Lower Interindividual Similarity in Ex-Gaussian Reaction Time Vectors for Congruent Stimuli Compared to Control Peers

DeLuna-Castruita,

Lizarraga-Cortes,

Flores

et al. 2023

J Atten Disord

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Objective: Interindividual similarity refers to how similarly individuals respond when receiving the same stimulus or intervention. In this study, we aimed to examine interindividual similarity in adults with ADHD. Method: We used the cosine similarity index of ex-Gaussian reaction time (RT) vectors of mu, sigma, and tau parameters during a Stroop task. Results: Our results demonstrate that the ADHD group exhibits a reduced interindividual similarity index in their ex-Gaussian RT vectors for congruent stimuli compared to the healthy control group. Importantly, we did not find significant differences in the interindividual similarity index to incongruent stimuli between both groups, thus suggesting that this reduced index was selective for congruent stimuli. Conclusion: Our findings highlight that ADHD adults exhibit more significant interindividual differences in cognitive functioning when processing congruent stimuli than healthy controls. These results provide new insights into the selective mechanisms underlying ADHD and may contribute to developing new targeted interventions for this disorder.

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Section: Introductionmentioning

confidence: 80%

ADHD Adults Show Lower Interindividual Similarity in Ex-Gaussian Reaction Time Vectors for Congruent Stimuli Compared to Control Peers

DeLuna-Castruita,

Lizarraga-Cortes,

Flores

et al. 2023

J Atten Disord

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Topological feature search method for multichannel EEG: Application in ADHD classification

Cai,

Zhao,

Zang

et al. 2025

Biomedical Signal Processing and Control

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A novel brain network analysis method for pediatric ADHD using RFE-GA feature selection strategy

Gu,

Dang,

Shi

et al. 2024

Biomed. Phys. Eng. Express

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Attention Deficit Hyperactivity Disorder (ADHD) is a highly prevalent childhood disorder, and related research has been increasing in recent years. However, it remains a challenging issue to accurately identify individuals with ADHD. The research proposes a method for ADHD detection using Recursive Feature Elimination-Genetic Algorithm (RFE-GA) for the feature selection of EEG data. Firstly, this study employed Transfer Entropy (TE) to construct brain networks from the EEG data of the ADHD and Normal groups, conducting an analysis of effective connectivity to unveil causal relationships in the brain's information exchange activities. Subsequently, a dual-layer feature selection method combining Recursive Feature Elimination (RFE) and Genetic Algorithm (GA) was proposed. Using the global search capability of GA and the feature selection ability of RFE, the performance of each feature subset is evaluated to find the optimal feature subset. Finally, a Support Vector Machine (SVM) classifier was employed to classify the ultimate feature set. The results revealed the control group exhibited lower connectivity strength in the left temporal alpha and beta bands, but higher frontal connectivity strength compared to the ADHD group. Additionally, in the gamma frequency band, the control group had higher top lobe connectivity strength than the ADHD group. Through the RFE-GA feature selection method, the optimized feature set was more concise, achieving classification accuracies of 91.3%, 94.1%, and 90.7% for the alpha, beta, and gamma frequency bands, respectively. The proposed RFE-GA feature selection method significantly reduced the number of features, thereby improving classification accuracy.

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Increased neural variability in adolescents with ADHD symptomatology: Evidence from a single-trial EEG study

Cited by 4 publications

References 74 publications

ADHD Adults Show Lower Interindividual Similarity in Ex-Gaussian Reaction Time Vectors for Congruent Stimuli Compared to Control Peers

ADHD Adults Show Lower Interindividual Similarity in Ex-Gaussian Reaction Time Vectors for Congruent Stimuli Compared to Control Peers

Topological feature search method for multichannel EEG: Application in ADHD classification

A novel brain network analysis method for pediatric ADHD using RFE-GA feature selection strategy

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