1. Antonyan L, Shaheen SM, Burton CL, Baldwin G, Neill R, Easter P, MacMaster F, Hanna GL, Rosenberg D, Arnold PD. Association of Brain Structural Measurements and Polygenic Risk Scores with Obsessive-Compulsive Symptoms in Adolescents Diagnosed with Obsessive-Compulsive Disorder, Attention-Deficit/Hyperactivity Disorder, Anxiety, Depression, Autism and Tic Disorders. medRxiv. 2025.

Obsessive-compulsive symptoms, characterized by intrusive thoughts and repetitive behaviors, are prevalent among youth. These symptoms are known to be moderately heritable and linked to structural brain changes involved in their pathophysiology. This study investigates the connections between structural brain alterations (cortical thickness, surface area and subcortical volume), genetic variation, and childhood obsessive-compulsive symptom scores within 143 samples of healthy control participants and cases diagnosed with obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, anxiety disorder, autism spectrum disorders and/or tic disorders. We hypothesize that the effect of genetic variants on standardized scores of obsessive-compulsive symptoms is mediated by imaging endophenotypes. To do so we test for associations between polygenic risk scores and structural imaging phenotypes within cortico-striato-thalamo-cortical circuitry and perform mendelian randomization analyses to identify potential causal pathways linking polygenic risk scores of structural brain alterations and obsessive-compulsive symptoms assessed with the Obsessive-Compulsive Subscale of the Child Behavior Checklist. We observed that changes in cortical thickness of rostral middle frontal cortex and surface area of orbitofrontal cortex, along with other four regions have a significant genetic contribution in OCS adolescent samples. Additionally, surface area of inferior parietal lobule may act as a causal mediator between high-risk variants and obsessive-compulsive symptoms. The mentioned three regions are part of cortico-striato-thalamo-cortical circuitry that have various regulatory effects on obsessive-compulsive symptoms. If these findings replicated in larger samples, they could offer valuable insights into the neurobiology of obsessive-compulsive traits and related structural alterations in specific brain regions.

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2. González García E, Arciniegas Villanueva A, Ortiz Muñoz MJ, Pinzón Martínez J, Guinovart Julian A, Cuesta Herráiz L, Ordoño Saiz MV. Study of alterations in P300 evoked potentials in patients with attention-deficit/hyperactivity disorder. An Pediatr (Engl Ed). 2026: 504052.

INTRODUCTION: Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent developmental problems in school age. Its diagnosis is clinical, through an adequate exploration of symptoms, psychological and performance assessments and the use of specific questionnaires and scales. P300 wave cognitive evoked potentials provide an objective measure that serves as an indicator of the cognitive processes of attention, which is therefore considered an important aid to the diagnosis of these patients. PATIENTS AND METHOD: We studied the P300 wave results obtained through auditory stimuli in 26 pediatric patients: 13 with a recent clinical diagnosis of ADHD and naïve to pharmacological treatment and 13 healthy controls. We measured both the latency and amplitude of the P300 wave, comparing the values in each group, as well as the percentage of errors in the identification of the stimulus in each subject. RESULTS: We obtained reproducible responses in all study participants, with a mean (SD) latency of 354.25 (41) ms and a mean (SD) amplitude of 11.98 (5.6) µV in the ADHD group compared to 313.59 (21) ms and 13.24 (5.9) µV, respectively, in the control group. There was a higher percentage of errors in the ADHD group. We only found significant differences between groups in the latency of the P300 wave. CONCLUSION: Pediatric patients with ADHD exhibited responses with a higher mean latency compared to peers without the disorder, so the assessment of P300 component cognitive evoked potentials could be used as an objective measure to support the diagnosis of ADHD.

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3. Lv GG, Shan YD, Shao JJ, Ma CY, Yu ZF, Liu JZ, Zhang LM, Zhang W. 3-Methyladenine administration alleviates cognitive and memory dysfunction in attention-deficit/hyperactivity disorder by modulating autophagy. Psychopharmacology (Berl). 2026.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD), a highly prevalent neurodevelopmental disorder among children, is directly associated with impairments in cognition and memory. In the nervous system, autophagy is essential for the development of neurons, the formation and remodeling of synapses, and the transmission of neurotransmitters. The 3-methyladenine (3-MA), an autophagy inhibitor, mitigates cognitive and memory impairment in neurological disorders. This study aimed to investigate the potential role of 3-MA in ADHD. METHODS: An ADHD model was established in offspring mice by intraperitoneal injection of S-ketamine during mid-to-late gestation. Postnatal day 14 offspring received intraperitoneal 3-MA (15 mg/kg/day) or vehicle for 7 consecutive days. To assess behavioral, electrophysiological, and pathological changes in mice, several tests were employed, including the open field test (OFT), novel object recognition (NOR) test, fear conditioning (FC), local field potential recording, western blot, transmission electron microscopy and immunofluorescence assays. RESULTS: Compared to controls, ADHD model mice exhibited: Increased total distance in OFT, Decreased recognition index in NOR, Reduced context- and cue-related freezing time in FC and Attenuated theta oscillation power in the prefrontal cortex. RNA sequencing revealed significant enrichment of the PI3KC3 pathway and autophagy-related genes. ADHD model mice showed upregulated autophagy-related protein expression, elevated LC3II/I ratio, increased autophagosomes, and accumulated abnormal organelles in the mPFC. TH-positive neurities and PSD95-positive puncta were significantly reduced in the mPFC. 3-MA treatment partially reversed these alterations. CONCLUSION: Cognitive and memory impairments in the mPFC due to ADHD are correlated with autophagy, and these impairments might be alleviated by 3-MA.

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4. Meng H, Li S, Xing X, Fu R, Li Y, Liu Q, Wang X. Machine learning-guided feature selection and predictive model construction for attention-deficit/hyperactivity disorder. Front Psychiatry. 2025; 16: 1724359.

BACKGROUND: Attention Deficit/Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental disorder, but its diagnosis remains constrained. This study aimed to identify potential candidate indicators and construct an interpretable machine learning model for the identification of ADHD. METHODS: A total of 8,598 children were enrolled and classified into three groups: ADHD (n=3,678), subthreshold ADHD (s-ADHD) (n=1,495), and healthy controls (HC) (n=3,425). Data collection covered 40 variables, including demographics, routine blood counts, serum biochemical parameters, body composition and systemic inflammation markers. Analysis of Variance (ANOVA) compared differences among the three groups, and key predictors were selected via Least Absolute Shrinkage and Selection Operator (LASSO) regression. Five machine learning models (Decision Tree, Random Forest, Multilayer Perceptron, Extreme Gradient Boosting, and Light Gradient Boosting Machine [LightGBM]) were developed for three clinically relevant binary classification tasks. SHapley Additive exPlanations (SHAP) values were applied to interpret the optimal model. RESULTS: ANOVA indicated significant differences (P < 0.05) in most parameters among the three groups. However, post-hoc Least Significant Difference (LSD) tests showed that compared with HC, the ADHD group showed elevated inflammatory markers (NLR, PLR, SII), glucose, body mass index(BMI), and body fat percentage, but reduced albumin, total cholesterol, and lymphocyte counts. Similar alterations were observed in the s-ADHD group, showing a pattern consistent with that of the ADHD group. LASSO regression (λ.1se=0.038) selected 11 core predictors, with age, RDW-SD, sex, calcium, glucose, and albumin among the most contributing variables. Among the models, LightGBM demonstrated the best performance when distinguishing ADHD from HC (AUC = 0.924 with 36 features vs. AUC = 0.885 with 11 features). However, the model failed to effectively distinguish between ADHD and s-ADHD. CONCLUSIONS: This study reveals potential candidate indicators of ADHD and establishes an interpretable, low-cost machine learning model based on routine clinical data, offering a promising tool for early screening and clinical decision support.

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5. Reindl V, Fischer NL, Konrad K, Leong V. Applying interpersonal neuroscience for understanding classroom learning in students with ADHD. Front Psychol. 2025; 16: 1690093.

Interpersonal neuroscience has gained importance in complementing single-person approaches to understand the neural underpinnings of learning in social contexts, mainly in neurotypical adults and children. This Perspective explores how such methods could be leveraged to improve our understanding of classroom learning in children with diverse educational needs, in particular with Attention Deficit/Hyperactivity Disorder, taking into account their unique symptomatology and the challenges they face in social and educational settings.

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6. Văidean T, Dobrean A, Predescu E, Yap MBH, Georgescu RD. Meta-Analysis: Parenting Children and Adolescents with Attention-Deficit/Hyperactivity Disorder Compared to Healthy Controls. J Am Acad Child Adolesc Psychiatry. 2025.

OBJECTIVE: This systematic review and meta-analysis aimed to quantify differences in parenting practices between families of children with attention-deficit/hyperactivity disorder (ADHD) and typically developing controls. METHOD: We conducted a systematic review and meta-analysis of studies comparing parenting practices among children and adolescents (≤18 years) with a specialist-confirmed ADHD diagnosis and non-clinical controls. PubMed, PsycINFO, Web of Science, and Scopus were searched from inception through August 12, 2025, without language restrictions. Studies were eligible if they used validated instruments to assess at least one parenting dimension and reported comparative data. Sixty-two studies were included (N = 13,105; ADHD = 6,543; controls = 6,562) from 19 countries. We computed pooled standardized mean differences (Hedges’ g) using random-effects models and evaluated heterogeneity (Q and I(2)), 95% prediction intervals, and risk of bias (Newcastle-Ottawa Scale). Meta-regression and subgroup analyses examined potential moderators. RESULTS: Parents of children with ADHD showed significantly lower levels of positive parenting (g = -0.37, 95% CI -0.46 to -0.27) and higher levels of negative parenting (g = 0.47, 95% CI 0.39 to 0.56) than controls. Strongest effects were found for authoritative parenting (g = -0.56), warmth (g = -0.45), abuse (g = 0.60), family conflict (g = 0.52), and hostility (g = 0.51). Heterogeneity was high (I(2) ≥ 90%), and no consistent moderators or small-study effects were identified. CONCLUSION: Parenting differs significantly between families of children with and without ADHD. Interventions should promote structured, warm, and non-coercive parenting. High heterogeneity and cross-sectional designs limit causal inference.

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7. Wang L, Peng X, Dong H, Ma C, Liang G, Zhao W, Jia F, Du L. The effect of Ecological Executive Skills training on emotional problems in children with attention-deficit hyperactivity disorder. BMC Psychiatry. 2026.

BACKGROUND: Emotional problems represents a prevalent clinical manifestation in pediatric populations with attention-deficit/hyperactivity disorder (ADHD), exacerbating functional impairments and complicating treatment outcomes. While pharmacological and behavioral interventions target core ADHD symptoms, structured approaches addressing emotional dysregulation through executive function training remain underexplored. Ecological executive skills training (EEST) has emerged as a promising intervention. This study systematically evaluates the multidimensional impact of EEST by integrating behavioral, cognitive, and emotional metrics to establish an evidence-based framework. METHODS: A total of 65 primary school children diagnosed with ADHD for the first time were recruited for this study. Children with ADHD were evaluated by Achenbach Child Behavior Checklist (CBCL), Strengths and Difficulties Questionnaire(SDQ), Conner’s Parent Symptom Questionnaire (PSQ), Vanderbilt ADHD Diagnostic Parent Rating Scale (VADPRS), Behavior Rating Inventory of Executive Function(BRIEF) and Weiss Functional Impairment Rating Scales-Parent Report (WFIRSP). Statistical analysis was conducted using SPSS software (version 19.0). Categorical variables were presented as counts (n) and percentages (%). Improvements in Inattention were analyzed using Generalized Estimating Equations (GEE) with robust standard error estimators. RESULTS: 1.Efficacy of Mood Regulation: After three months of treatment, the experimental group exhibited significantly greater improvement than the control group in anxiety and depression subscales of the CBCL, SDQ teacher emotional symptom ratings, PSQ (Anxiety), VADPRS and VADTRS anxiety/depression items, and the BRIEF Emotion Regulation Index. 2. Core Symptom Severity: The experimental group demonstrated significantly greater reductions in inattention and hyperactivity/impulsivity symptoms compared to controls, as measured by the VADPRS and VADTRS. 3.Executive Functioning Improvements: The experimental group showed significantly greater enhancements (P < 0.05) across all BRIEF executive function domains, with particularly significant improvements observed in working memory. 4. EEST can improve Social Functioning Outcomes. CONCLUSIONS: EEST significantly alleviates emotional probloms in children with ADHD, including symptoms of anxiety, depression, and impaired emotional control. Furthermore, this intervention demonstrates measurable improvements in core ADHD symptoms, executive functioning, and social competency. CLINICAL TRIAL REGISTRY: The Chinese Clinical Trial Registry (ChiCTR). TRIAL REGISTRATION NUMBER: 2,200,062,513.

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