Objective and automatic assessment approach for diagnosing attention-deficit/hyperactivity disorder based on skeleton detection and classification analysis in outpatient videos.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is diagnosed in accordance with Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria by using subjective observations and information provided by parents and teachers. However, subjective analysis often leads to overdiagnosis or underdiagnosis. There are two types of motor abnormalities in patients with ADHD. First, hyperactivity with fidgeting and restlessness is the major diagnostic criterium for ADHD. Second, developmental coordination disorder characterized by deficits in the acquisition and execution of coordinated motor skills is not the major criterium for ADHD. In this study, a machine learning-based approach was proposed to evaluate and classify 96 patients into ADHD (48 patients, 26 males and 22 females, with mean age: 7y6m) and non-ADHD (48 patients, 26 males and 22 females, with mean age: 7y8m) objectively and automatically by quantifying their movements and evaluating the restlessness scales. METHODS: This approach is mainly based on movement quantization through analysis of variance in patients' skeletons detected in outpatient videos. The patients' skeleton sequence in the video was detected using OpenPose and then characterized using 11 values of feature descriptors. A classification analysis based on six machine learning classifiers was performed to evaluate and compare the discriminating power of different feature combinations. RESULTS: The results revealed that compared with the non-ADHD group, the ADHD group had significantly larger means in all cases of single feature descriptors. The single feature descriptor "thigh angle", with the values of 157.89 ± 32.81 and 15.37 ± 6.62 in ADHD and non-ADHD groups (p < 0.0001), achieved the best result (optimal cutoff, 42.39; accuracy, 91.03%; sensitivity, 90.25%; specificity, 91.86%; and AUC, 94.00%). CONCLUSIONS: The proposed approach can be used to evaluate and classify patients into ADHD and non-ADHD objectively and automatically and can assist physicians in diagnosing ADHD.

Music and movement therapy improves quality of life and attention and associated electroencephalogram changes in patients with attention-deficit/hyperactivity disorder.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is the most common neurobehavioral disorder. Treatments for ADHD include pharmacological and nonpharmacological therapy. However, pharmacological treatments have side effects such as poor appetite, sleep disturbance, and headache. Moreover, nonpharmacological treatments are not effective in ameliorating core symptoms and are time-consuming. Hence, developing an alternative and effective treatment without (or with fewer) side effects is crucial. Music therapy has long been used to treat numerous neurological diseases. Although listening to music is beneficial for mood and cognitive functions in patients with ADHD, research on the effects of music and movement therapy in children with ADHD is lacking. METHODS: The present study investigated the effects of an 8-week music and movement intervention in 13 children with ADHD. The Pediatric Quality of Life Inventory (PedsQL) was used to evaluate changes in participants' quality of life. Conners' Kiddie Continuous Performance Test (K-CPT 2) and the Swanson, Nolan, and Pelham rating scale (SNAP-IV) were used to assess core symptoms. Electroencephalogram (EEG) recordings were analyzed to determine neurophysiological changes. RESULTS: The results revealed that the participants' quality of life increased significantly after the 8-week intervention. Furthermore, the participants' hit reaction times in the block 1 and block 2 tests of K-CPT 2 decreased significantly after the intervention. EEG analysis demonstrated an increase in alpha power and Higuchi's fractal dimension and a decrease in delta power in certain EEG channels. CONCLUSION: Our music and movement intervention is a potential alternative and effective tool for ADHD treatment and it can significantly improve patients' quality of life and attention.

Predicting the therapeutic response to valproic acid in childhood absence epilepsy through electroencephalogram analysis using machine learning.

Childhood absence epilepsy (CAE) is a common type of idiopathic generalized epilepsy, manifesting as daily multiple absence seizures. Although seizures in most patients can be adequately controlled with first-line antiseizure medication (ASM), approximately 25 % of patients respond poorly to first-line ASM. In addition, an accurate method for predicting first-line medication responsiveness is lacking. We used the quantitative electroencephalogram (QEEG) features of patients with CAE along with machine learning to predict the therapeutic effects of valproic acid in this population. We enrolled 25 patients with CAE from multiple medical centers. Twelve patients who required additional medication for seizure control or who were shifted to another ASM and 13 patients who achieved seizure freedom with valproic acid within 6 months served as the nonresponder and responder groups. Using machine learning, we analyzed the interictal background EEG data without epileptiform discharge before ASM. The following features were analyzed: EEG frequency bands, Hjorth parameters, detrended fluctuation analysis, Higuchi fractal dimension, Lempel-Ziv complexity (LZC), Petrosian fractal dimension, and sample entropy (SE). We applied leave-one-out cross-validation with support vector machine, K-nearest neighbor (KNN), random forest, decision tree, Ada boost, and extreme gradient boosting, and we tested the performance of these models. The responders had significantly higher alpha band power and lower delta band power than the nonresponders. The Hjorth mobility, LZC, and SE values in the temporal, parietal, and occipital lobes were higher in the responders than in the nonresponders. Hjorth complexity was higher in the nonresponders than in the responders in almost all the brain regions, except for the leads FP1 and FP2. Using KNN classification with theta band power in the temporal lobe yielded optimal performance, with sensitivity of 92.31 %, specificity of 76.92 %, accuracy of 84.62 %, and area under the curve of 88.46 %.We used various EEG features along with machine learning to accurately predict whether patients with CAE would respond to valproic acid. Our method could provide valuable assistance for pediatric neurologists in selecting suitable ASM.

Designing Bioinspired Composite Structures via Genetic Algorithm and Conditional Variational Autoencoder.

Designing composite materials with tailored stiffness and toughness is challenging due to the massive number of possible material and geometry combinations. Although various studies have applied machine learning techniques and optimization methods to tackle this problem, we still lack a complete understanding of the material effects at different positions and a systematic experimental procedure to validate the results. Here we study a two-dimensional (2D) binary composite system with an edge crack and grid-like structure using a Genetic Algorithm (GA) and Conditional Variational Autoencoder (CVAE), which can design a composite with desired stiffness and toughness. The fitness of each design is evaluated using the negative mean square error of their predicted stiffness and toughness and the target values. We use finite element simulations to generate a machine-learning dataset and perform tensile tests on 3D-printed specimens to validate our results. We show that adding soft material behind the crack tip, instead of ahead of the tip, tremendously increases the overall toughness of the composite. We also show that while GA generates composite designs with slightly better accuracy (both methods perform well, with errors below 20%), CVAE takes considerably less time (~1/7500) to generate designs. Our findings may provide insights into the effect of adding soft material at different locations of a composite system and may also provide guidelines for conducting experiments and Explainable Artificial Intelligence (XAI) to validate the results.

14-3-3 proteins regulate cullin 7-mediated Eag1 degradation.

BACKGROUND: Mutations in the human gene encoding the neuron-specific Eag1 (KV10.1; KCNH1) potassium channel are linked to congenital neurodevelopmental diseases. Disease-causing mutant Eag1 channels manifest aberrant gating function and defective protein homeostasis. Both the E3 ubiquitin ligase cullin 7 (Cul7) and the small acid protein 14-3-3 serve as binding partners of Eag1. Cul7 mediates proteasomal and lysosomal degradation of Eag1 protein, whereas over-expression of 14-3-3 notably reduces Eag1 channel activity. It remains unclear whether 14-3-3 may also contribute to Eag1 protein homeostasis. RESULTS: In human cell line and native rat neurons, disruptions of endogenous 14-3-3 function with the peptide inhibitor difopein or specific RNA interference up-regulated Eag1 protein level in a transcription-independent manner. Difopein hindered Eag1 protein ubiquitination at the endoplasmic reticulum and the plasma membrane, effectively promoting the stability of both immature and mature Eag1 proteins. Suppression of endogenous 14-3-3 function also reduced excitotoxicity-associated Eag1 degradation in neurons. Difopein diminished Cul7-mediated Eag1 degradation, and Cul7 knock-down abolished the effect of difopein on Eag1. Inhibition of endogenous 14-3-3 function substantially perturbed the interaction of Eag1 with Cul7. Further structural analyses suggested that the intracellular Per-Arnt-Sim (PAS) domain and cyclic nucleotide-binding homology domain (CNBHD) of Eag1 are essential for the regulatory effect of 14-3-3 proteins. Significantly, suppression of endogenous 14-3-3 function reduced Cul7-mediated degradation of disease-associated Eag1 mutant proteins. CONCLUSION: Overall these results highlight a chaperone-like role of endogenous 14-3-3 proteins in regulating Eag1 protein homeostasis, as well as a therapeutic potential of 14-3-3 modulators in correcting defective protein expression of disease-causing Eag1 mutants.

Objective diagnosis of ADHD through movement analysis by using a smart chair with piezoelectric material.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is the most common neuropsychiatric disorder in schoolchildren. ADHD diagnoses are generally made based on criteria from the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. The diagnosis is made clinically based on observation and information provided by parents and teachers, which is highly subjective and can lead to disparate results. Considering that hyperactivity is one of the main symptoms of ADHD, the inaccuracy of ADHD diagnosis based on subjective criteria necessitates the identification of a method to objectively diagnose ADHD. METHODS: In this study, a medical chair containing a piezoelectric material was applied to objectively analyze movements of patients with ADHD, which were compared with those of patients without ADHD. This study enrolled 62 patients-31 patients with ADHD and 31 patients without ADHD. During the clinical evaluation, participants' movements were recorded by the piezoelectric material for analysis. The variance, zero-crossing rate, and high energy rate of movements were subsequently analyzed. RESULTS: The results revealed that the variance, zero-crossing rate, and high energy rate were significantly higher in patients with ADHD than in those without ADHD. Classification performance was excellent in both groups, with the area under the curve as high as 98.00%. CONCLUSION: Our findings suggest that the use of a smart chair equipped with piezoelectric material is an objective and potentially useful method for supporting the diagnosis of ADHD.

Prediction of seizure recurrence using electroencephalogram analysis with multiscale deep neural networks before withdrawal of antiepileptic drugs.

BACKGROUND: The decision to continue or discontinue antiepileptic drug (AED) treatment in patients who are seizure free for a prolonged time is critical. Studies have used certain risk factors or electroencephalogram (EEG) findings to predict seizure recurrence after the withdrawal of AEDs. However, applicable biomarkers to guide the withdrawal of AEDs are lacking. METHODS: In this study, we used EEG analysis based on multiscale deep neural networks (MSDNN) to establish a method for predicting seizure recurrence after the withdrawal of AEDs. A total of 60 patients with epilepsy were divided into two groups (30 in the recurrence group and 30 in the non-recurrence group). All patients were seizure free for at least 2 years. Before AED withdrawal, an EEG was performed for each patient, which showed no epileptiform discharges. These EEG recordings were classified using MSDNN. RESULTS: We found that the performance indices of classification between recurrence and non-recurrence groups had a mean sensitivity, mean specificity, mean accuracy, and mean area under the receiver operating characteristic curve of 74.23%, 75.83%, 74.66%, and 82.66%, respectively. CONCLUSION: Our proposed method is a promising tool to help physicians to predict seizure recurrence after AED withdrawal among seizure-free patients.

Early and Objective Evaluation of the Therapeutic Effects of ADHD Medication through Movement Analysis Using Video Recording Pixel Subtraction.

Attention-deficit/hyperactivity disorder (ADHD) affects approximately 5−7% of school-age children. ADHD is usually marked by an ongoing pattern of inattention or hyperactivity−impulsivity, leading to functioning or developmental problems. A common ADHD assessment tool is the Swanson, Nolan, and Pelham (SNAP) questionnaire. However, such scales provide only a subjective perspective, and most of them are used to evaluate therapeutic effects at least 3−12 months after medication initiation. Therefore, we employed an objective assessment method to provide more accurate evaluations of therapeutic effects in 25 children with ADHD (23 boys and 2 girls). To evaluate the participants' improvement and treatment's effectiveness, the pixel subtraction technique was used in video analysis. We compared the efficacy of 1-month Ritalin or Concerta treatment by evaluating the movement in each video within 3 h of medication administration. The movement value was defined as the result of a calculation when using the pixel subtraction technique. Based on behavior observation and SNAP scores, both parent- and teacher-reported scores decreased after 1 month of medication (reduction rates: 19.61% and 16.38%, respectively). Specifically, the parent-reported hyperactivity subscale and teacher-reported oppositional subscale decreased more significantly. By contrast, the reduction rate was 39.27%, as evaluated using the average movement value (AMV). Considering symptomatic improvement as a >25% reduction in scores, the result revealed that the AMV decreased in 18 patients (72%) compared with only 44% and 56% of patients based on parent- and teacher-reported hyperactivity subscale scores. In conclusion, the pixel subtraction method can serve as an objective and reliable evaluation of the therapeutic effects of ADHD medication in the early stage.

Psychophysiological Effects of Lactobacillus plantarum PS128 in Patients with Major Depressive Disorder: A Preliminary 8-Week Open Trial.

Recent studies have suggested that gut-brain axis may be one of the mechanisms of major depression disorder (MDD). The current study aimed to investigate the effects of Lactobacillus plantarum PS128 (PS128) on psychophysiology in patients with MDD. We recruited 11 patients with MDD and gave them PS128 for 8 weeks. We compared depression symptoms, serum markers of inflammation and gut permeability, and gut microbiota before and after 8-week intervention and also explored the correlations among symptoms, biomarkers, and gut microbiota. After 8-week PS128 intervention, scores of Hamilton Depression Rating Scale-17 and Depression and Somatic symptoms Scale significantly decreased. Serum levels of high sensitivity c-reactive protein, interluekin-6, and tumor necrosis factor-α, zonulin and intestinal fatty acid binding protein, and the composition of gut microbiota did not significantly change after 8-week PS128 intervention. However, we found changes of some genera were correlated with changes of symptoms and biomarkers. In conclusion, this is an open trial with small sample size and has several limitations. The results need to be verified by randomized, double-blind, placebo-controlled trial with larger sample size.

Evaluating Therapeutic Effects of ADHD Medication Objectively by Movement Quantification with a Video-Based Skeleton Analysis.

Attention-deficit/hyperactivity disorder (ADHD) is the most common neuropsychiatric disorder in children. Several scales are available to evaluate ADHD therapeutic effects, including the Swanson, Nolan, and Pelham (SNAP) questionnaire, the Vanderbilt ADHD Diagnostic Rating Scale, and the visual analog scale. However, these scales are subjective. In the present study, we proposed an objective and automatic approach for evaluating the therapeutic effects of medication in patients with (ADHD). The approach involved using movement quantification of patients' skeletons detected automatically with OpenPose in outpatient videos. Eleven skeleton parameter series were calculated from the detected skeleton sequence, and the corresponding 33 features were extracted using autocorrelation and variance analysis. This study enrolled 25 patients with ADHD. The outpatient videos were recorded before and after medication treatment. Statistical analysis indicated that four features corresponding to the first autocorrelation coefficients of the original series of four skeleton parameters and 11 features each corresponding to the first autocorrelation coefficients of the differenced series and the averaged variances of the original series of 11 skeleton parameters significantly decreased after the use of methylphenidate, an ADHD medication. The results revealed that the proposed approach can support physicians as an objective and automatic tool for evaluating the therapeutic effects of medication on patients with ADHD.