Search Results for « ADHD »

Attention-deficit/hyperactivity disorder (ADHD) is characterized by executive dysfunction and difficulties in processing emotional facial expressions, yet the large-scale neural dynamics underlying these impairments remain insufficiently understood. This study applied network-based EEG source analysis to examine oscillatory cortical activity during cognitive and emotional Go/NoGo tasks in individuals with ADHD. EEG data from 272 participants (ADHD n equals 102, controls n equals 170, age range 6 to 60 years) were analyzed using exact low-resolution brain electromagnetic tomography combined with functional independent component analysis, yielding ten frequency-resolved cortical networks. Mixed-effects ANCOVAs were conducted on independent component loadings with Group, Task, and Condition as factors and age and sex as covariates. ADHD participants showed statistically significant but small increases in activation across several networks, including a gamma-dominant inferior temporal component showing a Group effect and a Group by Condition interaction with stronger NoGo-related activation in ADHD. Two additional components showed similar but weaker NoGo-selective patterns. A main effect of Task emerged only for one temporal delta component, with higher activation during the VCPT than the ECPT. No Group by Task interactions were observed. Behavioral results replicated the established ADHD performance profile, with slower responses, greater variability, and higher error rates, particularly during the emotional ECPT. Overall, the findings reveal subtle alterations in oscillatory brain networks during inhibitory processing in ADHD, with modest effect sizes embedded within substantial within-group variability. These results support a dimensional view of ADHD neurobiology and highlight the limited discriminative power of network-level EEG markers.

Attention Deficit Hyperactivity Disorder (ADHD) causes significant impairment in various domains. Early diagnosis of ADHD and treatment could significantly improve the quality of life and functioning. Recently, machine learning methods have improved the accuracy and efficiency of the ADHD diagnosis process. However, the cost of the equipment and trained staff required by the existing methods are generally huge. Therefore, we introduce the video-based frame-level action recognition network to ADHD diagnosis for the first time. We also record a real multi-modal ADHD dataset and extract three action classes from the video modality for ADHD diagnosis. The whole process data have been reported to CNTW-NHS Foundation Trust, which would be reviewed by medical consultants/professionals and will be made public in due course.

Differences in emotion-related processes and experiences impact ADHD people’s wellbeing, but have historically been framed through a deficit-based lens, without sufficient recognition of interpersonal and environmental influences. We explored positive and challenging aspects of ADHD emotions, how these emotions were perceived by others and their impacts on relationships, eliciting perspectives from 15 ADHD people and 15 non-ADHD people (with ADHD loved ones) through semi-structured interviews and the Grid Elaboration Method respectively. Data were analysed using interpretative phenomenological analysis and Reflexive Thematic Analysis respectively. Both groups reported that ADHD emotions co-existed as disabling and sustaining, strength and weakness. Societal pressures appeared to shape both how ADHD people experienced and expressed their emotions, which added to dysregulation; and how they were perceived by non-ADHD people, contributing to frustration. Emotion-related differences in ADHD impact the lives of ADHD and non-ADHD people, and are influenced by societal and systemic inequalities, though the extent to which this is the case is not yet fully understood.

Attention deficit and hyperactivity disorder (ADHD) have two main characteristics: inattention and impulsivity.It has many obstacles to the normal development of children and is very common among children.As an intervention, Serious games for ADHD(SGADs) have shown great potential and are very effective for these ADHD patients.Although many serious games have been developed for ADHD patients, but a review paper that summarizes and generalizes the topic of video games has not yet appeared.In this article, we first classified serious games about ADHD according to different platforms developed by video games, and then we conducted a systematic review of video games that can help children with ADHD diagnose and treat. Finally, we discussed and made suggestions based on the current development of SGADs.

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurobehavioral disorder worldwide. While extensive research has focused on machine learning methods for ADHD diagnosis, most research relies on high-cost equipment, e.g., MRI machine and EEG patch. Therefore, low-cost diagnostic methods based on the action characteristics of ADHD are desired. Skeleton-based action recognition has gained attention due to the action-focused nature and robustness. In this work, we propose a novel ADHD diagnosis system with a skeleton-based action recognition framework, utilizing a real multi-modal ADHD dataset and state-of-the-art detection algorithms. Compared to conventional methods, the proposed method shows cost-efficiency and significant performance improvement, making it more accessible for a broad range of initial ADHD diagnoses. Through the experiment results, the proposed method outperforms the conventional methods in accuracy and AUC. Meanwhile, our method is widely applicable for mass screening.

Attention Deficit Hyperactivity Disorder (ADHD), characterized by inattention, hyperactivity, and impulsivity, is prevalent in the adult population. Long perceived and treated as a childhood condition, ADHD and its characteristics nonetheless impact a significant portion of adults today. In contrast to children with ADHD, adults with ADHD face unique challenges in the workplace and in higher education. In this work-in-progress paper, we present a scoping review as a foundation to understand and explore existing technology-based approaches to support adults with ADHD. In total, our search returned 3,538 papers upon which we selected, based on PRISMA-ScR, a total of 46 papers for in-depth analysis. Our initial findings highlight that most papers take on a therapeutic or intervention perspective instead of a more positive support perspective. Our analysis also found a tremendous increase in recent papers on the topic, which highlights that more and more researchers are becoming aware of the need to address ADHD with adults. For the future, we aim to further analyze the corpus and identify research gaps and potentials for further development of ADHD assistive technologies.