Figure - available from: Scientific Reports
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Scatterplots of the longitudinal relationship between the baseline CGT parameters of risk adjustment, delay aversion, and reflection time (inhibitory control) and parent-reported social problems at follow-up. Delay aversion was the only predictor of these CGT parameters that predicted significantly greater social problems at follow-up.
Source publication
Over half of children with Attention-Deficit/Hyperactivity Disorder (ADHD) display interpersonal and social problems. Several lines of research suggest that suboptimal decision making, the ability to adjust choices to different risk-varying options, influences poorer choices made in social interventions. We thus measured decision making and its pre...
Citations
This paper introduces an innovative approach to Attention-deficit/hyperactivity disorder (ADHD) diagnosis by employing deep learning (DL) techniques on electroencephalography (EEG) signals. This method addresses the limitations of current behavior-based diagnostic methods, which often lead to misdiagnosis and gender bias. By utilizing a publicly available EEG dataset and converting the signals into spectrograms, a Resnet-18 convolutional neural network (CNN) architecture was used to extract features for ADHD classification. The model achieved a high precision, recall, and an overall F1 score of 0.9. Feature extraction highlighted significant brain regions (frontopolar, parietal, and occipital lobes) associated with ADHD. These insights guided the creation of a three-part digital diagnostic system, facilitating cost-effective and accessible ADHD screening, especially in school environments. This system enables earlier and more accurate identification of students at risk for ADHD, providing timely support to enhance their developmental outcomes. This study showcases the potential of integrating EEG analysis with DL to enhance ADHD diagnostics, presenting a viable alternative to traditional methods.
