Applied Neuropsychology: Child最新文献

The present scoping review was conducted to map interventions to promote executive functions (EF) in a school context with children and adolescents. A systematic literature search was performed on Web of Science, Scopus, SciELO, PubMed/Medline, PsycINFO, ERIC, Lilacs, and Embase. One hundred and twenty-two studies were reviewed. Within these 122 studies, 133 interventions were identified, among these, were 108 distinct interventions, with 14 interventions being repeated across 25 studies. Most of the interventions analyzed are aimed at samples of children and adolescents with typical development. Regarding the application, most of it was individual with playful components and computerized. The most frequent interventions were: Cogmed (7.4%), Working Memory Training (3.3%), Carribean Quest Game (2.4%) and Captain's Log (2.4%). Considering the 108 interventions analyzed, the majority focused on stimulating a single component of EF and the component most stimulated was working memory. These findings indicated paths and changes in the way interventions are applied to promote EF in the school context. EF interventions in school contexts can be an important strategy for promoting skills and preventing difficulties, it has the potential to take advantage of the wide window of opportunity for the development of EF and self-regulation throughout childhood and adolescence.

Information processing impairments are associated with the social and sensory symptoms of autism spectrum disorder (ASD). Functional connectivity forms the basis of information exchange and integration. Previous research has shown atypical functional connectivity patterns in school-aged children, adolescents, and adults with ASD. However, connectivity alterations in children with ASD in preschool years, a critical period for brain and cognition function development, are yet to be explored. Here, we compared resting-state EEG functional connectivity between high-functioning preschool boys with ASD and typically developing (TD) peers and investigated the relationships between functional connectivity and symptom severity of ASD as measured by the Childhood Autism Rating Scale (CARS). Network-based statistic (NBS) identified five intrahemispheric (left prefrontal-left central, left frontal-left central, left central-left occipital, right central-right parietal, and right temporal-right parietal) and four interhemispheric (left prefrontal-right central, left central-right parietal, left occipital-right parietal, and left parietal-right temporal) decreased long-range connections in the delta (1-4 Hz) band in ASD. Cliff's delta analysis indicated all of the alterations were of large effect sizes. Correlation analysis further revealed significant relationships between dysconnectivity and symptom severity of ASD. Findings remained significant after Benjamini-Hochberg correction. Specifically, decreased left prefrontal-right central connectivity was correlated with worse "relationship to people" and better "activity level," impaired right central-right parietal and left frontal-left central connections were individually associated with severe "listening response," and reduced left central-left occipital connectivity was related to better "taste-smell-touch response and use." In conclusion, this study suggests a trend of weakened long-range functional connectivity in high-functioning preschool boys with ASD and highlights its influence on the symptoms of ASD. Larger-scale studies are required to verify its potential as a biomarker for early screening, diagnosis, and neurophysiological intervention strategy planning of ASD.

Objective: This study aimed to examine the comorbidity of Cognitive Disengagement Syndrome (CDS) in children with Cerebral Palsy (CP) and to explore neuropsychological differences between clinical subgroups.

Materials and methods: The study included 24 children with CP and 18 typically developing (TD) peers, aged 6-17 years, who were evaluated using a sociodemographic form and a neuropsychological battery.

Results: Children with both CP and CDS showed significantly reduced cognitive flexibility and processing speed compared to TD children and those with CP only (p < .001). No significant difference in processing speed was observed between the CP-only and TD groups. Additionally, BCAS scores were moderately and negatively correlated with processing speed, executive functions, and attention (p < .05).

Conclusion: CDS symptoms are frequently seen in children with CP and are closely linked to executive dysfunctions. These findings underscore that CDS symptoms are not merely comorbid features but constitute a core dimension that substantially shapes and differentiates the neuropsychological profile of children with CP.

Objective: This study was designed to evaluate the utility of the short form of the Boston Naming Test (BNT-15) as an index of English proficiency and its relationship with the Common European Framework of Reference for Languages (CEFR).

Method: The BNT-15 was administered to a sample of 51 Cuban university students (native speakers of Spanish) with various levels of English proficiency as part of a battery of neuropsychological tests administered in both Spanish and English.

Results: The BNT-15 and CEFR were positively correlated with each other and relative language proficiency. A linear relationship emerged between levels of English proficiency operationalized using BNT-15 scores or CEFR ratings for tests with high verbal mediation. However, English proficiency was unrelated to performance on tests with low verbal mediation. Item-level responses on the BNT-15 suggest culture- and language-specific influences independent of overall level of English proficiency.

Conclusions: The BNT-15 and CEFR provide valid measures of English proficiency, although both leave a high percentage of variance in test performance unexplained. Large-scale replications are needed to further explore the utility of the BNT-15 as an index of English proficiency.

This study aimed to explore the implementation of an AI-enhanced motor and cognitive intervention for a 7-year-old child with developmental delay. A case study design was employed using an A-B framework (pre-test, intervention, post-test) over 12 weeks. The intervention incorporated AI-based tools such as Lumosity, Just Dance, and Cogmed for tailored motor and cognitive activities. The Bruininks-Oseretsky Test of Motor Proficiency-2 Brief Form (BOT-2 BF) and the Dynamic Occupational Therapy Cognitive Assessment for Children (DOTCA-Ch) were used to evaluate outcomes. Post-intervention, significant improvements were observed in BOT-2 BF and DOTCA-Ch scores, indicating enhanced motor coordination, and cognitive abilities. AI-enhanced interventions demonstrated the potential to address developmental delays by providing adaptive, engaging, and effective therapeutic activities. The findings highlight the feasibility of integrating AI tools into therapy, with implications for broader adoption in addressing developmental challenges. Further research is recommended to explore generalizability and long-term effects.

Objective: This study examined neurocognitive deficits in Cognitive Flexibility (CF) and Visual-Motor Search (VMS) among children with Frontal Lobe Epilepsy (FLE), Temporal Lobe Epilepsy (TLE), and Non-Epileptic (NE) controls to inform subtype-specific interventions.

Methods: We assessed 90 children (30 FLE, 30 TLE, 30 NE; ages 7-12), matched on key demographics, using the Wisconsin Card Sorting Test (WCST) to assess CF and Trail Making Tests (TMT-A/B) to evaluate VMS. Group differences were analyzed using MANOVA with Bonferroni post-hoc comparisons.

Results: MANOVA revealed significant group differences with a large effect size (F (12, 164) = 28.43, p < .001, ηp² = .68). Post-hoc tests confirmed a hierarchy of impairment: children with FLE performed worse than those with TLE on both CF and VMS measures, and the TLE group scored lower than NE controls on all tasks. Significant differences were found on all TMT and WCST sub-measures.

Discussion: The findings indicate hierarchical CF and VMS impairments in pediatric epilepsy, likely linked to frontal-parietal and hippocampal-temporal dysfunction. This suggests a need for tailored interventions, such as metacognitive training for FLE and contextual cueing for TLE. Future research should use neuroimaging and more diverse populations.

Objectives: The aim was to investigate the impact of mindfulness-based intervention on repetitive and stereotypical behaviors in children with autism spectrum disorder (ASD).

Methods: This study is a quasi-experimental pretest-post-test design with a control group, in which the effect of a mindfulness-based intervention program on the level of stereotypical behaviors of children with ASD is examined. Children with ASD aged 7 to 12 who were receiving education and services in special centers for children with ASD in Jeddah in 2024-2025. To select the sample group, two of the autism centers in Jeddah were referred, and 60 children from the center were selected using purposive sampling and randomly divided into two groups of 30 experimental and 30 control subjects. In the study, covariance analysis (ANCOVA) was used.

Results: The results showed that the mindfulness-based intervention program was effective on all subscales of stereotypical behaviors of children with ASD.

Conclusions: These findings suggest great promise for the use of mindfulness-based intervention with students with ASD to improve their stereotypical behaviors.

Working memory is widely acknowledged as a core and essential executive functions. The aim was to examine the mediating role of self concept in hierarchical relationship between working memory and executive functions. This cross-sectional descriptive-analytical study was conducted. A sample of 120 intellectually gifted children (mean age = 10.77 years, SD = 0.89, 80 females; 66% and 60 males, 44%) participated in the study. A mediation model, was examined, proposing the existence of interrelations between executive functions, working memory capacity and academic performance of highly able students, and a mediated path of self-concept. Structural equation modeling (SEM), with full information maximum likelihood estimation (FIML) was used to examine bivariate correlations and the mediation model. Multiple fit indices in addition to the chi-square test statistic were used to assess model fit. The results of the full mediation structural model fit indicated that the model fit the data well, with Chi-square = 733.041, p = 0.000, Relative Chi-Sq = 1.773; GFI = 0.825, CFI = 0.917, IFI = 0.910, RMSEA = 0.052. The Goodness of-fit indices of the structural model indicated that the GFI, CFI, and IFI approached or exceeded the cutoff value of 0.90.

There is a pressing need for well-defined phonological assessment protocols and the development of valid, reliable, and clinician-friendly tools tailored to the unique linguistic contexts of each country. This study reports the initial phase of establishing normative data on phonological development in Persian-speaking children aged 24-84 months. The objective was to develop a robust set of single words varying in length and syllabic structure for use in speech sound assessment. Using a content analysis approach, 108 words were selected, ranging from one to six syllables (35 monosyllabic, 26 disyllabic, and 47 polysyllabic). Word selection was guided by psycholinguistic principles, including phoneme frequency, phonotactic diversity, syllabic complexity, and lexical familiarity. Criteria ensured cultural appropriateness, absence of repeated phonemes, diverse syllable structures, and inclusion of all consonants across positions. The stimuli are designed to probe underlying speech motor planning and phonological encoding processes in children. These words were organized into 18 semantically related image sets, each containing six words. Monosyllabic and disyllabic items included at least two Persian consonants in both initial and final syllable positions, and each consonant was represented by at least one polysyllabic word. Eight clinically-experienced adults evaluated name-image agreement, while 10 children aged 30-54 months demonstrated over 50% spontaneous naming and over 70% familiarity with the words. The final stimulus set includes a substantial number of polysyllabic words and appears suitable for analyzing phonetic and phonemic inventories, and phonological error patterns in Persian-speaking children.

Objective: Mental health (MH) and attention deficit hyperactivity disorder (ADHD) are inextricably linked, having the same symptoms and complications. The goal of this research is to pinpoint the precise brain areas that cause ADHD in children and to make it possible to diagnose the disorder early. The study intends to provide a trustworthy diagnosis framework that enables prompt intervention using cutting-edge electroencephalogram (EEG) data analysis and machine learning (ML) approaches.

Method: This study uses EEG decomposition for improved ADHD detection. Decomposition techniques, such as the discrete cosine transform (DCT), short-time Fourier transform (STFT), and empirical mode decomposition (EMD), are used to break down EEG signals into sub-bases. As STFT demonstrated the highest accuracy, in further studies ML algorithms use STFT sub-bands on various combinations of brain regions as feed-ins to detect ADHD.

Result: The results demonstrate that STFT methods outperform DCT and EMD. The trial outcomes revealed that, when utilizing a combination of 19 electrode sites, the STFT approach achieved the best accuracies, specifically 96% with light gradient-boosting machine (LightGBM) models. However, when utilizing STFT with LightGBM, the combination of Fp1F3C3C4P4 (5 electrode placements) yields 91% accuracy and 93% on Fp1F3C3C4P4 as well as Fp1F3C3C4F8.

Novelty: While our previous research has separately investigated the efficacy of EMD and STFT/DCT, this presents the first comprehensive, head-to-head comparison of all three techniques within a unified framework. We conclusively demonstrate that STFT-based features, when paired with a LightGBM classifier, achieve a new state-of-the-art accuracy of 96%. Building on this superior model, we conduct a novel and granular electrode-reduction analysis to identify a minimal 5-channel configuration that maintains over 91% accuracy, directly addressing the need for scalable and cost-effective diagnostic systems and establishing a clear pathway for their development.