Applied Neuropsychology: Child最新文献

Objective: This study emphasizes the importance of using proper combinations of brain area, extraction of features, and machine learning (ML) techniques for electroencephalogram (EEG)-based attention deficit hyperactivity disorder (ADHD) identification. The effectiveness of EEG-based solutions is determined by the feature extraction method, selection of brain regions, and ML algorithms used.

Method: Empirical mode decomposition (EMD) was employed to identify and analyze ADHD-related abnormalities in EEG waveforms. An analysis of nonstationary and nonlinear time series data using EMD reduces an EEG waveform to a collection of intrinsic mode functions (IMFs). Random forest (RF), AdaBoost (AB), Naive Bayes (NB), support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), linear discriminant analysis (LDA), and deep belief networks (DBNs) classification techniques were utilized.

Result: Our research showed that RF, EMD based NB, and DBN constantly outperformed on all combinations of brain regions. The EMD-based NB classifier obtained the best score at 87% accuracy on the Frontal Pole (FPO) of the brain as well as on frontal region; second, the RF achieved accuracy at 84% on the Fronto-Central Pole (FCP) of the brain and 83% on the frontal region. The precision, accuracy, and recall of the EMD-based DBN and NB algorithms outperformed those of the other models.

Significance: Our technique provides interpretable insights by focusing on particular regions of brain that makes it more applicable and relevant clinically.

Attention-Deficit/Hyperactivity Disorder (ADHD) is the most common child-onset neurodevelopmental disorder. Reading challenges are common in ADHD, regardless of whether or not they meet full criteria for a Specific Learning Disorder. When a child also has a binocular vision deficit, reading can be even more challenging. The purpose of this pilot study was assess the impact of binocular deficits on reading and verbal comprehension in children with ADHD. We hypothesized that children with binocular deficits would perform significantly lower. This study utilized a retrospective chart review. Means for children with and without binocular deficits were compared using Mann Whitney U tests. In contrast to our hypotheses, children with binocular deficits performed significantly better on tasks of reading and verbal comprehension. In conclusion, children with ADHD and known binocular dysfunction did not perform significantly worse on tasks of reading and verbal reasoning. By contrast, a qualitative review of the collective data shows a trend toward worse performance on tasks that were visually-mediated, which warrants further exploration. Thus, a more broad-based exploration of the potential relationship between binocular dysfunction and all aspects of neuropsychological functioning is warranted to fully understand the impact of this relatively common visual finding on neurocognitive functioning in children.

Executive functions, which are closely associated with the functioning of the frontal lobes, encompass a set of cognitive processes that enable individuals to better adapt to their environment. It is known that individuals with Attention Deficit Hyperactivity Disorder (ADHD) have deficiencies in executive functions. To the best of our knowledge, no study has used bibliometric analysis to investigate the issue of executive functions in children with ADHD. This study employed the bibliometric analysis software VOS viewer and the statistical computing platform RStudio to examine 905 studies. This study reveals a notable increase in international research on executive functions in children with ADHD over the years. The highest number of publications on executive functions in children with ADHD are the Journal of Attention Disorders, Child Neuropsychology, and Archives of Clinical Neuropsychology. In the early years of this research field, there was a greater emphasis on brain imaging studies, including evoked potentials, transcranial magnetic stimulation, and evoked-related potentials. As time progressed, greater emphasis was placed on variables within the living environment. This research provides a comprehensive examination of global studies on executive functions in children diagnosed with ADHD, considering a multitude of variables. The findings presented here will inform forthcoming studies in this area, which will contribute to the accumulation of knowledge in this field.

Objective: This study explored NIHTB-CB's utility in assessing cognitive recovery in adolescents with SRC. We hypothesized the NIHTB-CB is sensitive to detecting weaknesses in fluid, but not crystalized, cognition, and adolescents recovering from SRC exhibit greater weaknesses than orthopedically injured (OI) adolescents.

Method: Thirty-six participants diagnosed with SRC or OI were enrolled in a pilot study and completed the NIHTB-CB at the time of enrollment and 4-6 weeks post-injury clearance. A total of 14 participants were excluded from analyses due to attrition (n = 4) and PVT failure (n = 10), with 22 participants included in final analyses (SRC n = 10; OI n = 12).

Results: Overall and fluid cognition composite ANOVAs revealed significant effects of time (p <.01), but not group (p >.22). Working memory (p = .04; d = 0.49) and processing speed (p <.01; d = 0.67) models indicated significant time effects. Models analyzing individual neurocognitive domains did not reveal significant group effects (p >.05). Models examining crystalized cognition did not reveal significant effects of group (p >.10), time (p >.12), or interaction (p >.35).

Conclusions: These results did not indicate unique weaknesses in fluid cognition, concerning for NIHTB-CB's sensitivity in detecting post-injury changes using an OI control group. The NIHTB-CB captured preserved crystallized cognition throughout recovery as hypothesized.

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that affects cognitive functions such as attention, impulse control, and executive functioning. Electroencephalography (EEG) has been widely explored as a noninvasive method for identifying abnormal brain activity patterns associated with ADHD. This study proposes an Attention Module-Based Fused Deep Convolutional Neural Network (AM-FDCNN) to enhance the accuracy of ADHD detection using EEG signals. The model integrates Channel Attention Module (CAM), Spatial Attention Module (SAM), and Position Attention Module (PAM) to selectively focus on critical EEG features, improving classification performance. The dataset, sourced from IEEE DataPort, includes EEG recordings from children diagnosed with ADHD and a control group. The proposed model achieves 97.60% accuracy with an 80-20 training split and 95.12% accuracy with 10-fold cross-validation, outperforming existing machine learning models such as CatBoost, SVM, Random Forest, and Deep CNN. The results indicate that the AM-FDCNN model significantly enhances ADHD detection accuracy, making it a promising tool for clinical and educational applications.

Cognitive research in autism has often approached mentalizing abilities in global terms, and ultimately judged them to be impaired in Autism Spectrum Disorder (ASD). However, recent studies have demonstrated that these social-cognitive abilities constitute neurocognitive constructs, encompassing both cognitive and affective domains, which are selectively found to be lacking in clinical categories. This study investigates the variation in autistic symptoms in relation to the graduation in gravity of cognitive and affective domains of mentalizing in children with ASD, aged 8 to 12 years. Mentalizing domains are assessed by the false-belief paradigms and by the Reading the Mind in the Eyes tasks in a group of 50 children with ASD. The findings showed that the severity of the children's autistic symptoms is inversely and strongly associated with their performance in first-(r = -0.762, p < .001) and second-order cognitive mentalizing (r = -0.674, p < .001), as well as affective mentalizing (r = -0.653, p < .001). However, the capacity for affective mentalizing was noticeably more impaired compared to those of cognitive mentalizing. The evidence confirms that the lower performance of children with ASD in mentalizing abilities is relatively dependent on the variation in their autistic symptoms and that the cognitive and affective domains of the latter are interrelated constructs.

This study aimed to investigate the effect of mindfulness training on improving attention and executive function in children with mild to borderline intellectual functioning. Two groups of children participated in the study. The first group consisted of 30 children with borderline intellectual functioning, ages ranging from 7 years to 9 years. A group of 30 children, ages ranging from 7 years to 9 years, formed a comparison group. The effect of mindfulness training on improving attention and executive function in children with borderline intellectual functioning and mild intellectual disability were assessed using pre- and post test design. The mindfulness group received 24 sessions of mindfulness training, while the control group received no training at all. The results showed that the post-test scores of inhibitory control, working memory, cognitive flexibility and the total score of children in mindfulness group are significantly higher than those in control group.

This study intends to investigate the mediating role of executive function in the relationship between central auditory processing and problem behaviors (PBs) in preschool children with ADHD. This study proposes a mediation model to test whether executive function plays a mediating role between central auditory processing and children's PBs. A total of 120 participants took part in the study. Structural equation modeling was used to test whether executive function plays a mediating role between central auditory processing and children's PBs. The results of this study show that executive function has a significant mediating effect between central auditory processing and PBs, and the mediating effect accounts for 30% of the total effect. Therefore, central auditory processing can not only directly affect children's PB but also affect PBs by affecting executive function.

This study aimed to differentiate "pure" cognitive disengagement syndrome (CDS) from attention-deficit/hyperactivity disorder-restrictive inattentive presentation (ADHD-RI) by examining their neurocognitive profiles and associations with autistic traits (ATs) and depressive symptoms. A cross-sectional study was conducted involving three groups: pure CDS (n = 24), ADHD-RI (n = 32), and controls (n = 31). Participants underwent neuropsychological assessments using Computerized Neurocognitive Assessment Software (CNS) Vital Signs, alongside evaluations for ATs with the Autism Spectrum Screening Questionnaire, depressive symptoms with the Children's Depression Inventory, and CDS symptoms with the Barkley Child Attention Scale. A semi-structured interview was also conducted with all participants and their parents to ensure the diagnostic validity of the groups. Findings indicated no dimensional symptomatological distinctions between CDS and ADHD-RI, except for CDS symptoms, and demonstrated no significant differences in neurocognitive test profiles between CDS and ADHD-RI, except for the neurocognition index and reaction time. ATs and depressive symptoms did not significantly differ between the pure CDS and ADHD-RI groups but were significantly elevated in both groups compared to controls. The ADHD-RI group exhibited significantly worse performance than CDS concerning the neurocognition index and reaction time, and worse than controls regarding the neurocognition index, reaction time, psychomotor speed, and complex attention. This study elucidates that ADHD-RI and "pure" CDS have substantially overlapping neurocognitive and phenotypic profiles despite certain minor differences, which is detrimental to subjects with ADHD-RI in terms of overall neurocognition and reaction time. It can be argued that, compared to "pure" CDS, executive dysfunction might be slightly more specific to ADHD-RI, while depressive symptoms and ATs are common in both psychological constructs.

Learning disabilities (LD) affect a significant portion of the global population, presenting tremendous emotional, social, and academic challenges. The multifaceted nature of LD necessitates early and objective identification focusing on educational and cognitive factors. This survey systematically reviews pen-paper and technology-based approaches for identifying LD, employing the PRISMA protocol to ensure a comprehensive and rigorous meta-analysis. A total of 3,783 papers were initially identified during the search process, of which 160 articles met the inclusion criteria. The study explores the various aspects of the identification and intervention of LD, intending to reduce long-term impacts. The essential skills required to recognize different types of LD were identified and classified, underscoring the importance of a set of fundamental skills for accurate differentiation. In addition, this research underscores the importance of objective assessments by integrating emerging technologies such as virtual reality, augmented reality and artificial intelligence, which improves the precision and efficiency of LD identification. The paper also discusses the critical role of early LD identification and addresses associated challenges. It advocates for a comprehensive approach through interdisciplinary collaboration and systemic reforms, ultimately promoting greater inclusivity and equity in education and society.