Attention Deficit Hyperactivity Disorder Subjects Research Articles

Integrated use of biofeedback and neurofeedback techniques in treating pathological conditions and improving performance: a narrative review.

In recent years, the scientific community has begun tо explore the efficacy оf an integrated neurofeedback + biofeedback approach іn various conditions, both pathological and non-pathological. Although several studies have contributed valuable insights into its potential benefits, this review aims tо further investigate its effectiveness by synthesizing current findings and identifying areas for future research. Our goal іs tо provide a comprehensive overview that may highlight gaps іn the existing literature and propose directions for subsequent studies. The search for articles was conducted on the digital databases PubMed, Scopus, and Web of Science. Studies to have used the integrated neurofeedback + biofeedback approach published between 2014 and 2023 and reviews to have analyzed the efficacy of neurofeedback and biofeedback, separately, related to the same time interval and topics were selected. The search identified five studies compatible with the objectives of the review, related to several conditions: nicotine addiction, sports performance, Autism Spectrum Disorder (ASD), and Attention Deficit Hyperactivity Disorder (ADHD). The integrated neurofeedback + biofeedback approach has been shown to be effective in improving several aspects of these conditions, such as a reduction in the presence of psychiatric symptoms, anxiety, depression, and withdrawal symptoms and an increase in self-esteem in smokers; improvements in communication, imitation, social/cognitive awareness, and social behavior in ASD subjects; improvements in attention, alertness, and reaction time in sports champions; and improvements in attention and inhibitory control in ADHD subjects. Further research, characterized by greater methodological rigor, is therefore needed to determine the effectiveness of this method and the superiority, if any, of this type of training over the single administration of either. This review іs intended tо serve as a catalyst for future research, signaling promising directions for the advancement оf biofeedback and neurofeedback methodologies.

Effects of Gestational Exercise on Nociception, BDNF, and Irisin Levels in an Animal Model of ADHD

Abnormal cognitive and sensorial properties have been reported in patients with psychiatric and neurodevelopmental conditions, such as attention deficit hyperactivity disorder (ADHD). ADHD patients exhibit impaired dopaminergic signaling and plasticity in brain areas related to cognitive and sensory processing. The spontaneous hypertensive rat (SHR), in comparison to the Wistar Kyoto rat (WKY), is the most used genetic animal model to study ADHD. Brain neurotrophic factor (BDNF), critical for midbrain and hippocampal dopaminergic neuron survival and differentiation, is reduced in both ADHD subjects and SHR. Physical exercise (e.g. swimming) promotes neuroplasticity and improves cognition by increasing BDNF and irisin. Here we investigate the effects of gestational swimming on sensorial and behavioral phenotypes, striatal dopaminergic parameters, and hippocampal FNDC5/irisin and BDNF levels observed in WKY and SHR. Gestational swimming improved nociception in SHR rats (p = 0.006) and increased hippocampal BDNF levels (p = 0.02) in a sex-dependent manner in adolescent offspring. Sex differences were observed in hippocampal FNDC5/irisin levels (p = 0.002), with females presenting lower levels than males. Our results contribute to the notion that swimming during pregnancy is a promising alternative to improve ADHD phenotypes in the offspring.

Impact of stimulant treatment on refractive errors and pupil diameter in attention deficit hyperactivity disorder

AbstractPurposeThe relationship between attention deficit hyperactivity disorder (ADHD) and visual impairment remains poorly understood, and the impact of visual impairment on the development of ADHD is uncertain. The aim of this study was to investigate the refractive profile and ocular biometric characteristics in patients diagnosed with ADHD and compare them with a control group. Additionally, we aimed to explore the potential influence of sex and medication intake.MethodsA cohort of 100 participants, including 50 individuals with ADHD and 50 age‐ and sex‐matched control subjects, was included in this study. Ocular biometric parameters were measured, and refractive error was assessed using cycloplegic and non‐cycloplegic autorefraction. Subgroup analyses were performed within the ADHD group based on sex, medication intake and age to investigate potential associations with the ocular findings.ResultsWe observed no statistically significant differences in axial length, corneal topography parameters or anterior chamber characteristics between ADHD and control subjects. However, subgroup analysis within the ADHD group revealed that the prevalence of ametropia under cycloplegia was significantly higher in unmedicated (69.6%) compared to medicated (37.5%) (X2(2) = 7.320, p = 0.026) participants. Pupil diameter was significantly larger in medicated (3.91 mm) compared to unmedicated (3.58 mm; p = 0.017) individuals. Males had flatter (p = 0.004) and thicker (p = 0.008) corneas than females. Older ADHD participants had higher refractive error (p = 0.008 for non‐cycloplegic and p = 0.0.003 for cycloplegic), axial length (p = 0.002) and corneal astigmatism (p = 0.049).ConclusionsOur study provides compelling evidence that individuals diagnosed with ADHD exhibit a similar incidence of refractive errors and ocular parameters compared to normal subjects. Nonetheless, the prevalence of refractive errors appears to be higher in unmedicated ADHD patients, suggesting the potential benefit of stimulant treatment. Additionally, stimulant use is associated with an increase in pupil diameter.

Rhythmic Attention and ADHD: A Narrative and Systematic Review.

In recent decades, a growing body of evidence has confirmed the existence of rhythmic fluctuations in attention, but the effect of inter-individual variations in these attentional rhythms has yet to be investigated. The aim of this review is to identify trends in the attention deficit/hyperactivity disorder (ADHD) literature that could be indicative of between-subject differences in rhythmic attention. A narrative review of the rhythmic attention and electrophysiological ADHD research literature was conducted, and the commonly-reported difference in slow-wave power between ADHD subjects and controls was found to have the most relevance to an understanding of rhythmic attention. A systematic review of the literature examining electrophysiological power differences in ADHD was then conducted to identify studies with conditions similar to those utilised in the rhythmic attention research literature. Fifteen relevant studies were identified and reviewed. The most consistent finding in the studies reviewed was for no spectral power differences between ADHD subjects and controls. However, the strongest trend in the studies reporting power differences was for higher power in the delta and theta frequency bands and lower power in the alpha band. In the context of rhythmic attention, this trend is suggestive of a slowing in the frequency and/or increase in the amplitude of the attentional oscillation in a subgroup of ADHD subjects. It is suggested that this characteristic electrophysiological modulation could be indicative of a global slowing of the attentional rhythm and/or an increase in the rhythmic recruitment of neurons in frontal attention networks in individuals with ADHD.

Functional connectivity in ADHD children doing Go/No-Go tasks: An fMRI systematic review and meta-analysis.

Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders diagnosed in childhood. Two common features of ADHD are impaired behavioural inhibition and sustained attention. The Go/No-Go experimental paradigm with concurrent functional magnetic resonance imaging (fMRI) scanning has previously revealed important neurobiological correlates of ADHD such as the supplementary motor area and the prefrontal cortex. The coordinate-based meta-analysis combined with quantitative techniques, such as activation likelihood estimate (ALE) generation, provides an unbiased and objective method of summarising these data to understand the brain network architecture and connectivity in ADHD children. Go/No-Go task-based fMRI studies involving children and adolescent subjects were selected. Coordinates indicating foci of activation were collected to generate ALEs using threshold values (voxel-level: p < 0.001; cluster-level: p < 0.05). ALEs were matched to one of seven canonical brain networks based on the cortical parcellation scheme derived from the Human Connectome Project. Fourteen studies involving 457 children met the eligibility criteria. No significant convergence of Go/No-Go related brain activation was found for ADHD groups. Three significant ALE clusters were detected for brain activation relating to controls or ADHD < controls. Significant clusters were related to specific areas of the default mode network (DMN). Network-based analysis revealed less extensive DMN, dorsal attention network, and limbic network activation in ADHD children compared to controls. The presence of significant ALE clusters may be due to reduced homogeneity in the selected sample demographic and experimental paradigm. Further investigations regarding hemispheric asymmetry in ADHD subjects would be beneficial.

Glutamate receptor genetic variants affected peripheral glutamatergic transmission and treatment induced improvement of Indian ADHD probands

Attention deficit hyperactivity disorder (ADHD), a childhood-onset neurobehavioral disorder, often perturbs scholastic achievement and peer-relationship. The pivotal role of glutamate (Glu) in learning and memory indicated an influence of Glu in ADHD, leading to the exploration of Glu in different brain regions of ADHD subjects. We for the first time analyzed GluR genetic variations, Glu levels, as well as expression of Glu receptors (GluR) in the peripheral blood of eastern Indian ADHD probands to find out the relevance of Glu in ADHD prognosis. After obtaining informed written consent for participation, peripheral blood was collected for analyzing the genetic variants, Glu level, and expression of target genes. Since ADHD probands are often treated with methylphenidate or atomoxetine for providing symptomatic remediation, we have also tested post-therapeutic improvement in the ADHD trait scores in the presence of different GluR genotypes. Two variants, GRM7 rs3749380 “T” and GRIA1 rs2195450 “C”, exhibited associations with ADHD (P ≤ 0.05). A few GluR genetic variants showed significant association with higher trait severity, low IQ, lower plasma Glu level, down-regulated GluR mRNA expression, and poor response to medications. This indicates that down-regulated glutamatergic system may have an effect on ADHD etiology and treatment efficacy warranting further in-depth investigation.

Pupillary dilations in a Target/Distractor visual task paradigm and attention deficit hyperactivity disorder (ADHD)

ADHD is a neurocognitive disorder characterized by attention difficulties, hyperactivity, and impulsivity, often persisting into adulthood with substantial personal and societal consequences. Despite the importance of neurophysiological assessment and treatment monitoring tests, their availability outside of research settings remains limited. Cognitive neuroscience investigations have identified distinct components associated with ADHD, including deficits in sustained attention, inefficient enhancement of attended Targets, and altered suppression of ignored Distractors. In this study, we examined pupil activity in control and ADHD subjects during a sustained visual attention task specifically designed to evaluate the mechanisms underlying Target enhancement and Distractor suppression. Our findings revealed some distinguishing factors between the two groups which we discuss in light of their neurobiological implications.

Neuropsychological Assessment of the Relationship of Working Memory with K-BIT Matrices and Vocabulary in Normal Development and ADHD Children and Adolescents.

The present report tries to understand the possible relationship between working memory (WM) and intelligence measurements, using the direct scores of the Working Memory Test Battery for Children (WMTBC) and Kaufman's Brief Intelligence Test (K-BIT), in normal development (ND) and diagnosed attention deficit hyperactivity disorder (ADHD) children and adolescents. Partial correlations, discounting the effect of age, showed a significant correlation in ND subjects between the central executive (CE) component of WM and the WM visuospatial sketchpad (VSS) component and the WM phonological loop (PL); also, significant correlations were obtained for the WM VSS with the K-BIT Matrices scores, the WM PL with the K-BIT Vocabulary, and the K-BIT Matrices scores with the K-BIT Vocabulary. For ADHD subjects, there were significant correlations between WM VSS and WM CE, and WM VSS and K-BIT Matrices. We tested the robustness of these correlations by selecting a small number of subjects through permutations; a robust correlation between WM CE and WM PL in ND, and between WM VSS and WM CE and WM VSS and K-BIT Matrices scores was obtained. These results were also supported by mediation analysis. There is a relationship during development between WM as measured with WMTBC and general intelligence as measured with K-BIT in ND and ADHD subjects. The dysexecutive character of ADHD has been shown, given that by controlling for intelligence, the differences in WM performance between ND and ADHD disappear, except for WM CE. The results suggest that in ADHD subjects, the WM VSS component presents a more pivotal role during cognitive processing compared to ND subjects.

Baseline ImPACT Composite Scores in Student-Athletes With Attention-Deficit/Hyperactivity Disorder.

Athletes who are diagnosed with attention-deficit/hyperactivity disorder (ADHD) are at increased risk of concussion compared to other athletes. Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) is a widely used concussion tool, but it relies on pre-injury baseline testing that can be affected by psychiatric conditions. This research aims to determine if there are differences in pre-injury testing composite scores in student-athletes with ADHD compared to those without ADHD diagnosis. We obtained 11,563 pre-season ImPACT assessments of 7,454 student-athletes (ages: 12-22) from 2009 to 2019. After exclusions, there were 6,920 control and 276 ADHD subjects. Multivariable linear regression analyses compared the independent effect of ADHD on the six ImPACT composite score metrics with Bonferroni correction for multiple comparisons with a = 0.008. Univariate analyses indicated ADHD is associated with more symptoms as measured by the Post-Concussion Symptom Scale (PCSS) (β = 2.67, 95% CI: 1.47-3.87, p < .0001) and worse Impulse Control scores (β = 0.93, 95% CI: 0.33-1.53, p = .002). In multivariate analysis, this association was the same for symptom score (β = 2.48, 95% CI: 1.22-3.74, p < .0001), but Impulse Control was not significantly different after multiple comparison adjustment (β = 0.87, 95% CI: 0.22-1.15, p = .009). The ADHD subjects reported worse symptoms at baseline and had worse Impulse Control in univariate analysis, but not multivariate analysis. These results can further guide clinicians in concussion diagnosis and test interpretations for student-athletes with ADHD, considering the symptom burden at baseline.

Regional Contribution in Electrophysiological-Based Classifications of Attention Deficit Hyperactive Disorder (ADHD) Using Machine Learning

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental condition in children and is characterized by challenges in maintaining attention, hyperactivity, and impulsive behaviors. Despite ongoing research, we still do not fully understand what causes ADHD. Electroencephalography (EEG) has emerged as a valuable tool for investigating ADHD-related neural patterns due to its high temporal resolution and non-invasiveness. This study aims to contribute to diagnostic accuracy by leveraging EEG data to classify children with ADHD and healthy controls. We used a dataset containing EEG recordings from 60 children with ADHD and 60 healthy controls. The EEG data were captured during cognitive tasks and comprised signals from 19 channels across the scalp. Our primary objective was to develop a machine learning model capable of distinguishing ADHD subjects from controls using EEG data as discriminatory features. We employed several well-known classifiers, including a support vector machine, random forest, decision tree, AdaBoost, Naive Bayes, and linear discriminant analysis, to discern distinctive EEG patterns. To further enhance classification accuracy, we explored the impact of regional data on the classification outcomes. We arranged the EEG data according to the brain regions from which they were derived (namely frontal, temporal, central, parietal, and occipital) and examined their collective effects on the accuracy of our classifications. Notably, we considered combinations of three regions at a time and found that certain combinations led to enhanced accuracy. Our findings underscore the potential of EEG-based classification in distinguishing children with ADHD from healthy controls. The Naive Bayes classifier yielded the highest accuracy (84%) when applied to specific region combinations. Moreover, we evaluated the classification performance based on hemisphere-specific EEG data and found promising results, particularly when using the right hemisphere region channels.

Expression alteration of Neuroligin family gene in attention deficit and hyperactivity disorder and autism spectrum disorder

BackgroundAutism spectrum disorder (ASD) is a complex neurodevelopment disorder with social and communicational deficiency, language impairment, and ritualistic behaviors. Attention deficit hyperactivity disorder (ADHD) is a pediatric psychiatric disorder with symptoms, including attention deficit, hyperactivity, and impulsiveness. ADHD is a childhood-onset disorder that can persist into adult life. Neuroligins are post-synaptic cell-adhesion molecules that connect neurons and have an essential role in the mediation of trans-synaptic signaling and shaping the synapse and circuits and neural network functioning. AimsPresent study aimed to shed light on the role of the Neuroligin gene family in ASD and ADHD. Methods and proceduresmRNA levels of the Neuroligin gene family (NLGN1, NLGN2, NLGN3, and NLGN4X) were studied in the peripheral blood of 450 unrelated ASD patients, 450 unrelated ADHD patients, and the normal group included 490 unrelated non-psychiatric children by quantitative PCR. Also, clinical situations were considered. Outcomes and resultsResults showed that mRNA levels of NLGN1, NLGN2, and NLGN3 were significantly down-regulated in the ASD group vs. control subjects. In ADHD, a significant reduction of NLGN2 and NLGN3 was detected in comparison with normal children. A comparison of ASD and ADHD subjects revealed that NLGN2 was significantly down-regulated in ASD subjects. ConclusionsThe Neuroligin family gene may play an essential role in the etiology of ASD and ADHD and thus be a source for a better understanding of neurodevelopment disorders. ImplicationsSimilar patterns of deficiency of Neuroligin family genes in ASDs and ADHDs may indicate the role of these genes in functions that have been affected in both disorders.

Improvement of Classification Performance in High-Dimension Low-Sample-Size Modeling by Sparse Functional Connectivity States in Subjects with Attention Deficit-Hyperactivity Disorder and Healthy Controls

Background: The precise identification of attention deficit-hyperactivity disorder (ADHD) is one of the challenging clinical processes. Disorganizations in functional neural networks revealed via functional magnetic resonance imaging have recently been contributing. Machine learning approaches, particularly classification methods, have commonly been employed as a framework for diverse data analysis, indicating promising medical diagnosis results. However, as the neuroimaging data are high-dimensional with a low sample size (the current dataset), this study aimed to evaluate the classification performance of the models by considering the specific contribution of the sparsity of data matrices. Methods: This cross-sectional study analyzed the preprocessed data from the 2011 ADHD-200 Global Competition. A total of 768 and 171 data items were considered training and test, respectively. The diagnosis status was used as a response variable. Age, gender, hand dominance, and activity relationship between 116 brain regions derived from inverse covariance matrix and inverse sparse covariance matrix were used as predictive variables. Accordingly, this study compared the performance of three models, namely support vector machine (SVM), distance-weighted discrimination (DWD), and data maximum dispersion classifier (DMDC) for ADHD categorization. Results: The highest value for the total accuracy was reported for the SVM model on the sparse covariance matrix. Moreover, the highest values for the balanced classification rate (BCR) (59%) and sensitivity (64%) were reported for DMDC on the sparse covariance matrix. The best level of specificity (99%) was obtained from DWD using the sparse covariance matrix. The highest levels of the values (i.e., total accuracy and BCR) were achieved through the model fitting on the sparse matrices. Among the six models, the DMDC model on sparse covariance matrix was the most optimal algorithm due to the superiority of the two indices (i.e., accuracy: 60% and BCR: 60%) and the favorable balance between sensitivity and specificity values. Conclusions: Among the current studied three models, DMDC performance, applying the sparse data, remarkably improved the results of classification processes. Based on the present findings, the neuronal connectivity among subcortical structures comprising parts of the basal ganglia and cerebellum provides a distinction between ADHD subjects and healthy controls.

Identification of attention deficit hyperactivity disorder with deep learning model.

This article explores the detection of Attention Deficit Hyperactivity Disorder, a neurobehavioral disorder, from electroencephalography signals. Due to the unstable behavior of electroencephalography signals caused by complex neuronal activity in the brain, frequency analysis methods are required to extract the hidden patterns. In this study, the feature extraction was performed with the Multitaper and Multivariate Variational Mode Decomposition methods. Then, these features were analyzed with the neighborhood component analysis and the features that contribute effectively to the classification were selected. The deep learning model including the convolution, pooling, and bidirectional long short term cell and fully connected layer was trained with the selected features. The trained model could effectively classify the subjects with Attention Deficit Hyperactivity Disorder with a deep learning model, support vector machines and linear discriminant analysis. The experiments were validated with an Attention Deficit Hyperactivity Disorder open access dataset ( https://doi.org/10.21227/rzfh-zn36 ). In validation, the deep learning model was able to classify 1210 test samples (600 subjects in the control group as Normal and 610 subjects in the ADHD group as ADHD) in 0.1s with an accuracy of 95.54%. This accuracy rate is quite high compared to the Linear Discriminant Analysis (76.38%) and Support Vector Machines (81.69%). Experimental results showed that the proposed approach can innovatively classify Attention Deficit Hyperactivity Disorder subjects from the Control group effectively.

Speech signal analysis as an aid to clinical diagnosis and assessment of mental health disorders

ObjectiveIn this study, we estimate speech features from different Verbal Fluency Tests (VFT) conditions to distinguish comorbid Bipolar Disorder (BD) in adults suffering from Attention Deficit Hyperactivity Disorder (ADHD) and to identify ADHD subtypes such as the inattentive (ADHD-I) from the combined one (ADHD-C). MethodsProsodic and spectral features in five conditions of VFTs were extracted and selected for the classification performed with machine learning methods. Specifically, a Support Vector Machine exploiting Recursive Features Elimination (SVM-RFE) has been trained with clinical scores and exploiting the within subject variability of speech features across VFT conditions. The final classification was optimized by combining the marginal classification outcomes obtained from the different VFTs using a voting scheme. ResultsOur results show that we successfully classify the ADHD+BD comorbidity and the ADHD subtypes according to clinician diagnosis. The results are discussed in the light of possible benefits of developing such approach within clinical research. ConclusionSignificant information is carried out by speech audio features acquired with VFTs, allowing to classify ADHD subtypes and comorbid patterns. This work clearly shows that the audio analysis of speech, along with properly designed speech tasks, is a candidate for the development of clinical decision support systems in psychiatry. SignificanceThis work represents a major contribution to the applications of speech analysis in ADHD subjects and could support clinicians by identifying objective biomarkers.

Meta-analysis reveals a reduced surface area of the amygdala in individuals with attention deficit/hyperactivity disorder.

Despite the reported lack of structural alterations in the amygdala of individuals with attention deficit/hyperactivity disorder (ADHD) in previous meta-analyses, subsequent observational studies produced conflicting results. Through incorporating the updated data from observational studies on structural features of the amygdala in ADHD, the primary goal of this study was to examine the anatomical differences in amygdala between subjects with ADHD and their neurotypical controls. Using the appropriate keyword strings, we searched the PubMed, Embase, and Web of Science databases for English articles from inception to February 2022. Eligibility criteria included observational studies comparing the structure of the amygdala between ADHD subjects and their comparators using magnetic resonance imaging (MRI). Subgroup analyses were conducted focusing on the amygdala side, as well as the use of different scanners and approach to segmentation. The effects of other continuous variables, such as age, intelligence quotient, and male percentage, on amygdala size were also investigated. Of the 5703 participants in 16 eligible studies, 2928 were diagnosed with ADHD. Compared with neurotypical controls, subjects with ADHD had a smaller amygdala surface area (particularly in the left hemisphere) but without a significant difference in volume between the two groups. Subgroup analysis of MRI scanners and different approaches to segmentation showed no statistically significant difference. There was no significant correlation between continuous variables and amygdala size. Our results showed consistent surface morphological alterations of the amygdala, in particular on the left side, in subjects with ADHD. However, the preliminary findings based on the limited data available for analysis warrant future studies for verification.

STARS Adjunct Trial: Evidence for the Effectiveness of a Digital Therapeutic as Adjunct to Treatment With Medication in Pediatric ADHD

AbstractBackgroundTreatment of attention deficit hyperactivity disorder (ADHD) includes pharmacological and non-pharmacological interventions, both of which have demonstrated short-term efficacy. While efficacious, there are limitations to both modalities of treatment. Due partly to these limitations, there has been considerable interest in additional approaches to augmenting ADHD management. Digital therapeutics may offer improved access, minimal side effects, and low potential for abuse, while providing targeted treatment options for improving cognitive functions such as attention. AKL-T01 (EndeavorRx®) is the first and only FDA-cleared nonpharmacological prescription digital therapeutic delivered through a video game interface for the treatment of ADHD.ObjectiveThe objective is to summarize the data from a clinical trial in support of FDA clearance using AKL-T01 adjunctively in children currently taking stimulant medication for ADHD.MethodsThe STARS-Adjunct Trial was a multicenter, 12-week, open-label study of AKL-T01 in 206 children aged 8 to 14 years with a confirmed diagnosis of primarily inattentive or combined-type ADHD. The study included two cohorts: (1) subjects currently treated with ADHD medication (n=130) and (2) subjects not on any ADHD medication (n=76). Subjects had an ADHD Impairment Rating Scale (IRS) score ≥3 at baseline, and both cohorts used AKL-T01 for approximately 25 minutes per day, 5 days per week, over two 4-week treatment periods separated by a 4-week treatment pause.ResultsAKL-T01 significantly improved (lowered) ADHD-related impairment as measured with the IRS (clinician rated) after the first 4-week treatment in both cohorts (P&lt; 0.001). Results show that effects persist during a 4-week treatment pause and further improve with a second 4-week treatment period. A majority of parents and children indicated a perceived improvement in ability to pay attention after the trial. Most common device-related adverse events were decreased frustration tolerance, headache, and irritability which ranged from mild to moderate. No serious adverse events were reported.ConclusionsThis study adds to and extends the clinical evidence base for AKL-T01, a video game-based treatment for improving attentional functioning in 8–12-year-old children with ADHD. Continued evaluation of the effects of AKL-T01 on other important aspects of functioning, like academic and social functioning, health utilization, and health outcomes, would continue to add to the evidence base that the effects observed in this and previous studies have substantial clinical and functional impact.FundingAkili Interactive

Cerebral blood flow characteristics of drug-naïve attention-deficit/hyperactivity disorder with social impairment: Evidence for region-symptom specificity.

Social deficits are among the most important functional impairments in attention-deficit/hyperactivity disorder (ADHD). However, the relationship between social impairment and ADHD core symptoms as well as the underlying cerebral blood flow (CBF) characteristics remain unclear. A total of 62 ADHD subjects with social deficits (ADHD + SD), 100 ADHD subjects without social deficits (ADHD-SD) and 81 age-matched typically developing controls (TDC) were enrolled. We first examined the correlation between the Social Responsiveness Scale (SRS-1) and ADHD core symptoms (inattention, hyperactivity, and impulsion) and then explored categorical and dimensional ADHD-related regional CBF by arterial spin labeling (ASL). For the categorical analysis, a voxel-based comparison of CBF maps between the ADHD + SD, ADHD-SD, and TDC groups was performed. For the dimensional analysis, the whole-brain voxel-wise correlation between CBF and ADHD symptoms (inattention, hyperactivity/impulsivity, and total scores) was evaluated in three groups. Finally, correlations between the SRS-1 and ADHD-related regional CBF were investigated. We applied Gaussian random field (GRF) for the correction of multiple comparisons in imaging results (voxel-level P < 0.01, and cluster-level P < 0.05). The clinical characteristics analysis showed that social deficits positively correlated with ADHD core symptoms, especially in social communication and autistic mannerisms domains. In the categorical analysis, we found that CBF in the left middle/inferior temporal gyrus in ADHD groups was higher than TDCs and was negatively correlated with the social motivation scores. Moreover, in dimensional analysis, we found that CBF in the left middle frontal gyrus was negatively correlated with the inattention scores, SRS total scores and autistic mannerisms scores in ADHD + SD subjects. The present study shows that inattention, hyperactivity, and impulsivity may be responsible for the occurrence of social deficits in ADHD, with autistic traits being another significant contributing factor. Additionally, CBF in the left middle/inferior temporal gyrus and the left middle frontal gyrus might represent the corresponding physiological mechanisms underlying social deficits in ADHD.

Owner-rated hyperactivity/impulsivity is associated with sleep efficiency in family dogs: a non-invasive EEG study

Subjective sleep disturbances are reported by humans with attention-deficit/hyperactivity disorder (ADHD). However, no consistent objective findings related to sleep disturbances led to the removal of sleep problems from ADHD diagnostic criteria. Dogs have been used as a model for human ADHD with questionnaires validated for this purpose. Also, their sleep physiology can be measured by non-invasive methods similarly to humans. In the current study, we recorded spontaneous sleep EEG in family dogs during a laboratory session. We analyzed the association of sleep macrostructure and deep sleep (NREM) slow-wave activity (SWA) with a validated owner-rated ADHD questionnaire, assessing inattention (IA), hyperactivity/impulsivity (H/I) and total (T) scores. Higher H/I and T were associated with lower sleep efficiency and longer time awake after initial drowsiness and NREM. IA showed no associations with sleep variables. Further, no association was found between ADHD scores and SWA. Our results are in line with human studies in which poor sleep quality reported by ADHD subjects is associated with some objective EEG macrostructural parameters. This suggests that natural variation in dogs’ H/I is useful to gain a deeper insight of ADHD neural mechanisms.

Surface electroencephalographic neurofeedback improves sustained attention in ADHD: a meta-analysis of randomized controlled trials

BackgroundThe efficacy of surface electroencephalographic neurofeedback (EEG-NF) for improving attentional performance assessed by laboratory measures in patients with attention-deficit/hyperactivity disorder (ADHD) remains unclear.MethodsFollowing the PRISMA guidelines, the PubMed, Embase, ClinicalKey, Cochrane CENTRAL, ScienceDirect, Web of Science, and ClinicalTrials.gov databases were systematically searched for randomized controlled trials on the efficacy of surface EEG-NF against ADHD focusing on attentional performance evaluated by laboratory measures from inception to January 2022.ResultsFourteen eligible studies were analyzed. Of the 718 participants involved, 429 diagnosed with ADHD received EEG-NF treatment. Significant improvement in attentional performance in ADHD subjects receiving EEG-NF was noted compared to their comparators (p < 0.01). Besides, there was a significant EEG-NF-associated beneficial effect on sustained attention (Hedges’ g = 0.32, p < 0.01), whereas the impact on selective attention (p = 0.57) and working memory (p = 0.59) was limited. Moreover, protocol including beta wave enhancement was superior to that only focusing on reducing theta/beta ratio or modulation of slow cortical potential. Subgroup analyses showed that three sessions per week of EEG-NF produced the best effect, while the efficacy of surface EEG-NF was much poorer (Hedges’ g = 0.05) when only studies that blinded their participants from knowledge of treatment allocation were included. No significant difference was noted in the improvement of attentional performance 6–12 months after EEG-NF intervention (n = 3, p = 0.42).ConclusionsOur results demonstrated the satisfactory effectiveness of surface EEG-NF for improving sustained attention, especially when beta wave enhancement was included, despite its failure to sustain a long-term effect. Further large-scale trials are warranted to support our findings.

Dense attentive GAN-based one-class model for detection of autism and ADHD

We investigate two neuro-developmental disorders in children– Autism Spectrum Disorder (ASD) and Attention-deficit/hyperactivity disorder (ADHD). Most works in literature have examined these disorders separately, e.g., ASD or ADHD subjects vs healthy subjects. We base our framework on the approach adopted by a paediatrician. We propose a one-class model for characterizing healthy subjects. Any subject with ASD/ADHD is considered an outlier by this one-class model. We adopt a Dense GAN architecture with self-attention modules as our one-class model. Our system uses T1-weighted longitudinal structural magnetic resonance images (sMRI) as input modalities. Further, we train our framework using longitudinal data (two scans per subject over time) only, instead of the traditional approaches using cross-sectional data (one scan per subject). Our approach is similar to paediatricians diagnosing the subject over multiple sessions to confirm the disorder. Comprehensive experiments show that our proposed approach performs better than competing ASD and ADHD works.