Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that pervasively interferes with the lives of individuals starting in childhood. Objective. To address the subjectivity of current diagnostic approaches, many studies have been dedicated to efforts to identify the differences between ADHD and neurotypical (NT) individuals using EEG and continuous performance tests (CPT). Approach. In this study, we proposed EEG-based long short-term memory (LSTM) networks that utilize deep learning techniques with learning the cognitive state transition to discriminate between ADHD and NT children via EEG signal processing. A total of thirty neurotypical children and thirty ADHD children participated in CPT tests while being monitored with EEG. Several architectures of deep and machine learning were applied to three EEG data segments including resting state, cognitive execution, and a period containing a fusion of those. Main results. The experimental results indicated that EEG-based LSTM networks produced the best performance with an average accuracy of 90.50 ± 0.81 % in comparison with the deep neural networks, the convolutional neural networks, and the support vector machines with learning the cognitive state transition of EEG data. Novel observations of individual neural markers showed that the beta power activity of the O1 and O2 sites contributed the most to the classifications, subjects exhibited decreased beta power in the ADHD group, and had larger decreases during cognitive execution. Significance. These findings showed that the proposed EEG-based LSTM networks are capable of extracting the varied temporal characteristics of high-resolution electrophysiological signals to differentiate between ADHD and NT children, and brought a new insight to facilitate the diagnosis of ADHD. The registration numbers of the institutional review boards are 16MMHIS021 and EC1070401-F.

This study surveyed and described the pattern of medication use in nursing home residents from admission to the end of life. Results can be used to reinforce clinician and nursing staff awareness of prescription frequency, amounts of medication, and change over time for elderly residents under their care. In addition to safer prescribing practices for the older people, nonpharmacological strategies (e.g., lifestyle modification and physiotherapy for function training) may be used to address common symptoms and complaints during chronic care.

Aim The study investigated the electroencephalography (EEG) functional connectivity (FC) profiles during rest and tasks of young children with attention deficit hyperactivity disorder (ADHD) and typical development (TD). Methods In total, 78 children (aged 5–7 years) were enrolled in this study; 43 of them were diagnosed with ADHD and 35 exhibited TD. Four FC metrics, coherence, phase‐locking value (PLV), pairwise phase consistency, and phase lag index, were computed for feature selection to discriminate ADHD from TD. Results The support vector machine classifier trained by phase‐locking value (PLV) features yielded the best performance to differentiate the ADHD from the TD group and was used for further analysis. In comparing PLVs with the TD group at rest, the ADHD group exhibited significantly lower values on left intrahemispheric long interelectrode lower‐alpha and beta as well as frontal interhemispheric beta frequency bands. However, the ADHD group showed higher values of central interhemispheric PLVs on the theta, higher‐alpha, and beta bands. Regarding PLV alterations within resting and task conditions, left intrahemispheric long interelectrode beta PLVs declined from rest to task in the TD group, but the alterations did not differ in the ADHD group. Negative correlations were observed between frontal interhemispheric beta PLVs and the Disruptive Behavior Disorder Rating Scale as rated by teachers. Conclusions These results, which complement the findings of other sparse studies that have investigated task‐related brain FC dynamics, particularly in young children with ADHD, can provide clinicians with significant and interpretable neural biomarkers for facilitating the diagnosis of ADHD.

This study used a wireless EEG system to investigate neural dynamics in preschoolers with ADHD who exhibited varying cognitive proficiency pertaining to working memory and processing speed abilities. Preschoolers with ADHD exhibiting high cognitive proficiency (ADHD-H, n = 24), those with ADHD exhibiting low cognitive proficiency (ADHD-L, n = 18), and preschoolers with typical development (TD, n = 31) underwent the Conners’ Kiddie Continuous Performance Test and wireless EEG recording under different conditions (rest, slow-rate, and fast-rate task). In the slow-rate task condition, compared with the TD group, the ADHD-H group manifested higher delta and lower beta power in the central region, while the ADHD-L group manifested higher parietal delta power. In the fast-rate task condition, in the parietal region, ADHD-L manifested higher delta power than those in the other two groups (ADHD-H and TD); additionally, ADHD-L manifested higher theta as well as lower alpha and beta power than those with ADHD-H. Unlike those in the TD group, the delta power of both ADHD groups was enhanced in shifting from rest to task conditions. These findings suggest that task-rate-related neural dynamics contain specific neural biomarkers to assist clinical planning for ADHD in preschoolers with heterogeneous cognitive proficiency. The novel wireless EEG system used was convenient and highly suitable for clinical application.

BACKGROUND The present study aimed to characterize children at risk of attention-deficit/hyperactivity disorder (ADHD) at preschool age and provide early intervention. The continuous performance test (CPT) and quantitative electroencephalography (QEEG) can supplement valuable information to facilitate diagnosis. OBJECTIVE This study measured brain dynamics at varying task rates in the CPT using a wireless wearable EEG and identified correlations between the QEEG and CPT data in preschool children with ADHD. METHODS Forty-nine preschool children participated in this study, of which 29 were diagnosed with ADHD and 20 exhibited typical development (TD). Conners Kiddie Continuous Performance Test (K-CPT) and wireless wearable EEG recordings were employed. RESULTS Significant differences were observed between the groups with ADHD and TD in task-related QEEG spectral powers (central delta, as well as posterior delta and beta , P < .01), which were distinct only in the slow-rate condition. A from resting to the CPT task condition induced a central posterior alpha power decrease in the ADHD group. In both resting and task conditions, the delta and theta power were positively correlated with the CPT perseveration scores, whereas the alpha and beta powers were negatively correlated with specific CPT scores mainly on perseveration and omission (P < . 01). CONCLUSIONS These results, which complement the findings of several other studies that have investigated within-task-related brain dynamics in preschoolers, can help specialists working on early intervention to plan training and educational programs for preschoolers with ADHD.