Developmental Neurobiology最新文献

{"title":"Neurodevelopmental Consequences Induced by Early-Life Lead Exposure Through Disruption of Neurotransmitter Pathways and Molecular Mechanisms: A Systematic Review.","authors":"P Harshitha, Aluru Parithathvi, Kamalesh Dattaram Mumbrekar, Herman Sunil Dsouza","doi":"10.1002/dneu.70043","DOIUrl":"10.1002/dneu.70043","url":null,"abstract":"<p><p>Lead is a toxic heavy metal with significant health risks, as maternal lead exposure during pregnancy disrupts fetal neural development through placental transfer, leading to persistent neurological, developmental, and long-term health consequences in the progeny. Lead exposure typically affects the central nervous system, which is especially harmful during fetal development and early infancy. This review emphasizes the impact of early developmental exposure leading to disruption of neurotransmitter mechanisms. Mechanistically, lead disrupts neurodevelopment through various pathways, including alterations in myelin proteins, synaptic function, and neuroinflammatory responses, by interfering with the regulatory action of calcium, as lead mimics and replaces calcium. Alterations caused by lead, particularly in the dopaminergic, cholinergic, glutamatergic, and GABAergic pathways, increase the risk of neurodevelopmental disorders such as attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and cognitive disabilities in children. Lead is known to induce cellular, molecular, and epigenetic changes that impair neuronal connectivity, synaptic plasticity, and network functionality, ultimately leading to the disruption of neurodevelopmental pathways. The cumulative evidence highlights various aspects of lead neurotoxicity and emphasizes its role in the etiology of neurodevelopmental impairments. Understanding these pathways is critical for early intervention strategies to mitigate the long-term cognitive and behavioral consequences of lead exposure during sensitive developmental periods.</p>","PeriodicalId":11300,"journal":{"name":"Developmental Neurobiology","volume":"86 3","pages":"e70043"},"PeriodicalIF":2.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148041570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Expanding the Neurodevelopmental Spectrum of Xia-Gibbs Syndrome: New Clinical Entities and Novel Variants From a Turkish Cohort.","authors":"Irem Kalay, Kadri Karaer, Sumeyra Oguz, Enise Avci Durmusalioglu, Elvin Kazancıoğlu Yıldız, Fayize Maden Bedel, Lamiya Mardan, Tahir Atik","doi":"10.1002/dneu.70036","DOIUrl":"10.1002/dneu.70036","url":null,"abstract":"<p><p>Xia-Gibbs syndrome (XGS) is a rare neurodevelopmental disorder caused by heterozygous variants in the AHDC1 gene. While the core phenotype includes developmental delay, hypotonia, and expressive language impairment, the syndrome displays considerable clinical and genetic heterogeneity. Data from non-European populations remain scarce. We report the clinical and molecular findings of five unrelated Turkish patients with XGS diagnosed via whole-exome sequencing (WES). This is a multicenter study, and clinical data were collected from participating centers using standardized forms. All patients carried heterozygous, de novo AHDC1 variants (three frameshift, two nonsense), all of which were classified as pathogenic or likely pathogenic. Three of these, p.(Ala1432Profs13), p.(Val900Glyfs2), and p.(Val704Profs*26), are novel and predicted to cause protein truncation. Nonsense mutations are p.(Arg108*) and p.(Trp638*), which were previously reported as disease-causing. One patient had dual diagnoses with AHDC1 and CDK13 variants, explaining ocular and cardiac anomalies, leading to a particularly severe neurodevelopmental phenotype. This is the largest study reported to date in Turkey on XGS, a rare genetic disorder, further expanding the disease's genotypic and phenotypic spectrum. Recognition of dual diagnoses in a single patient underscores the value of WES in detecting complex genotypes and avoiding misattributed genotype-phenotype associations. As a recently described syndrome, careful evaluation of patients with XGS for atypical phenotypes may provide important contributions to the clinical presentation described in the literature.</p>","PeriodicalId":11300,"journal":{"name":"Developmental Neurobiology","volume":"86 3","pages":"e70036"},"PeriodicalIF":2.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147940715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association Analysis Between HEI-2020 Index and Maternal Pregnancy Behaviors on ADHD in Adolescent Populations: A NHANES Cross-Sectional Study","authors":"Min Peng,&nbsp;Run Li,&nbsp;Chengwei Yu","doi":"10.1002/dneu.70046","DOIUrl":"10.1002/dneu.70046","url":null,"abstract":"<div>\u0000 \u0000 <p>Attention-deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in adolescents, with maternal pregnancy behaviors and adolescent dietary quality considered risk factors for its onset and progression. This study aimed to analyze associations of maternal pregnancy behaviors and the Healthy Eating Index (HEI)-2020 with adolescent ADHD. Data from 3859 adolescents were extracted from NHANES 2013–2018. Dietary quality was assessed using two-day dietary recall data and quantified as HEI-2020 scores. Multivariate logistic regression assessed associations of HEI-2020 and maternal pregnancy behaviors with ADHD likelihood. Restricted cubic spline regression models were utilized to describe nonlinear relationships, and subgroup analyses were conducted. HEI-2020 index was linked to a lower adolescent ADHD reporting rate (OR = 0.977, 95% CI: 0.954–1.000, <i>p</i> = 0.048). Subgroup analysis showed that higher HEI-2020 was significantly linked to lower ADHD reporting rates when maternal age of a mother at pregnancy was &lt; 25 or ≥ 35 years or when the child's birth weight was not overweight (OR &lt; 1, <i>p</i> &lt; 0.05), with no nonlinear relationship detected. Joint association analysis revealed that higher HEI-2020 score was significantly associated with lower ADHD reporting rates in women with maternal age at pregnancy&lt; 25 (OR = 0.515, 95% CI: 0.310–0.851, <i>p</i> = 0.012). Across all HEI-2020 levels, adolescents born to non-smoking mothers had significantly lower ADHD rates than those born to smoking mothers (OR &lt; 1, <i>p</i> &lt; 0.05). A higher HEI-2020 diet was associated with lower ADHD reporting rates, whereas maternal smoking during pregnancy correlated with higher rates. Improving adolescent diet and promoting healthy pregnancy behaviors are recommended for better prevention.</p>\u0000 </div>","PeriodicalId":11300,"journal":{"name":"Developmental Neurobiology","volume":"86 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148224119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Effective LRSF-DLNN-Based Autism Spectrum Disorder Prediction Using EEG and fMRI","authors":"Sahunthala S.,&nbsp;Malathi S.,&nbsp;TamilThendral M.,&nbsp;Sudhahar T. N.","doi":"10.1002/dneu.70047","DOIUrl":"10.1002/dneu.70047","url":null,"abstract":"<div>\u0000 \u0000 <p>In today's society, autism spectrum disorder (ASD) is a common neurological disorder that affects a person's behavior and communication. Hence, an early ASD prediction is essential for improving the lifecycle of ASD patients. Recently, many works have been deployed for diagnosing ASD using the electroencephalogram (EEG) and magnetic resonance imaging (MRI). But they are inefficient owing to the irrelevant noises and misclassification outcomes. Hence, a well-ordered logistic regression with scaled function-based deep learning neural network (LRSF-DLNN)-based ASD prediction using EEG and functional MRI (fMRI) is proposed. Initially, the inputs, like the EEG signal and fMRI image of the eye, are gathered and then subjected to the preprocessing phase. Here, for performing noise removal of EEG, the cosine-based Butterworth filter (CBF) approach is established. Similarly, the fMRI undergoes slice time correction and a GF filter-based smoothing process. Then, the preprocessed EEG is decomposed by utilizing weighted penalty factor-centric variational mode decomposition (WPFVMD), and the process of alpha and theta band estimation is performed on the basis of single-scale time dependent–based event-related spectral perturbation (SSTD-ERSP). Likewise, the features are extracted from both the decomposed EEG and smoothed fMRI. Next, the extracted features are given for feature selection (FS) utilizing distance functional green anaconda optimization (DFGAO). Lastly, the optimal features are given to the LRSF-DLNN classifier, which efficiently predicts ASD. Hence, the evaluation outcomes exposed that the proposed technique achieved better performance with a higher accuracy (98.8%) than other prevailing models.</p>\u0000 </div>","PeriodicalId":11300,"journal":{"name":"Developmental Neurobiology","volume":"86 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148224148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disrupted Vestibular Nuclei Neuron Development in a Chick Model for Congenital Vestibular Disorders","authors":"Elizabeth B. Bogin,&nbsp;Rossella Conti,&nbsp;Kathleen Gallagher,&nbsp;Kaya Oguz,&nbsp;Anastas Popratiloff,&nbsp;Kenna D. Peusner","doi":"10.1002/dneu.70045","DOIUrl":"10.1002/dneu.70045","url":null,"abstract":"<p>Children with syndromic, congenital vestibular disorders (CVDs) form a sac-like inner ear with missing or truncated semicircular canals and experience delayed motor development with lifelong challenges to maintain posture and balance. How the abnormal inner ear affects downstream central vestibular neural circuitry has not been investigated. We hypothesize that inner ear pathology leads to hyperexcitability and dendritic changes in excitatory vestibular nuclei neurons, as reported for excitatory neurons in other neurodevelopmental disorders. To determine whether neurons in CVDs follow a similar trajectory, we recorded from excitatory vestibular nuclei neurons, the principal cells (PCs) of chick tangential nucleus (TN), in the ARO (anterior–posterior rotated otocyst) chick model for CVDs. In ARO chicks, the otocyst is rotated surgically on one side to produce a sac-like inner ear. Whole-cell patch-clamp recordings on brain slices from 16-day-old (E16) ARO and normal chick embryos showed that spontaneous excitatory currents (sEPSCs) increased significantly in PCs on both sides of ARO chicks compared to PCs from normal chicks. After tetrodotoxin exposure, EPSC frequency decreased about 70% in PCs on both sides of ARO chicks, but only 37% in PCs from normal chicks, suggesting the increased sEPSCs were due to action potential-dependent events. In confocal images, biocytin-injected and recorded PCs had fewer dendritic branches on the rotated side compared to PCs in normal chicks and decreased dendritic volume in PCs on the contralateral side compared to PCs on the rotated side of ARO chicks, indicating failure for the normal dendritic pattern to emerge before birth. Altogether, PCs in ARO chicks were hyperexcitable due to significantly increased action potential-dependent sEPSCs with dendritic developmental defects that collectively represent hallmarks of neurodevelopmental disorders.</p>","PeriodicalId":11300,"journal":{"name":"Developmental Neurobiology","volume":"86 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/dneu.70045","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148224079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Enhanced Machine Learning–Based Multimodal Framework for Seizure Detection Using EEG and MRI Data","authors":"V. Dharani,&nbsp;L. Lakshmanan","doi":"10.1002/dneu.70049","DOIUrl":"10.1002/dneu.70049","url":null,"abstract":"<div>\u0000 \u0000 <p>One of the most common neurological disorders that immediately alters a person's way of life is an epileptic seizure. Accurate seizure detection remains a major challenge in neurological research due to the nonstationary and complex nature of electroencephalogram (EEG) signals. Most of the current seizure detection methods use machine learning (ML) and deep learning (DL) models, which highly depend on EEG signals due to their real-time assessment nature. However, these methods still suffer from several limitations: (1) class imbalance problem and (2) discriminative feature extraction. These challenges lead to poor diagnostic accuracy and reliability. This research proposes an innovative unified framework for automated epileptic seizure detection (ESD), integrating EEG and magnetic resonance imaging (MRI) data through advanced DL techniques. In this work, preprocessing is carried out via two distinct streams; that is, initially, high-frequency noise in the EEG signals is removed by applying a Butterworth filter. Then, a data standardization technique is used to normalize the signal, and then signal conversion is carried out using synchrosqueezing wavelet transform (SWT) to represent the signal in a time–frequency domain. Once the EEG is preprocessed, MRI data are preprocessed using data augmentation techniques that improve the diversity of the dataset. After preprocessing, feature learning is performed separately for both EEG and MRI via the improved activation function-based depthwise convolutional neural network (IADCNN). After that, cross-modal attention fusion (CMAF) is utilized to capture more important features obtained from the MRI and EEG, that is, fused data. After that, the seizure detection is carried out by utilizing the custom loss based on the Extreme Gradient Boosting (CLXGBoost) model. Finally, the confidence calibration strategy is applied to predict the reliability of the proposed framework. Unlike existing hybrid models, the proposed framework integrates feature learning, cross-modal fusion, imbalance-aware classification, and confidence calibration within a single unified learning pipeline. Evaluation is conducted on publicly available CHB-MIT and Neuroimaging Tools and Resources Collaboratory (NITRC) datasets. The system achieved an accuracy of 99.56% under the experimental conditions, illustrating improved performance compared to selective baseline approaches. The outcomes demonstrated that the proposed multimodal approach can improve seizure detection performance effectively under experimental settings considered in this study.</p>\u0000 </div>","PeriodicalId":11300,"journal":{"name":"Developmental Neurobiology","volume":"86 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148216873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Randomized Controlled Trial of Yizhi Kaiqiao Formula Combined With Repetitive Transcranial Magnetic Stimulation on Neurocognitive and Social Outcomes in Preschool Children With Autism Spectrum Disorder","authors":"Dongling Yin,&nbsp;Linlin Fan,&nbsp;Ke Huang,&nbsp;Cheng Deng,&nbsp;Shurong Huang,&nbsp;Qiling Li","doi":"10.1002/dneu.70048","DOIUrl":"10.1002/dneu.70048","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>This paper aimed to unravel the effects of Yizhi Kaiqiao formula combined with repetitive transcranial magnetic stimulation (rTMS) on neurocognitive function and social outcomes in preschool children with autism spectrum disorder (ASD). In this randomized controlled trial, 178 preschool children with ASD recruited between January 2022 and February 2024 were randomly assigned to a combined group (Yizhi Kaiqiao formula + rTMS) or an rTMS-only group (<i>n</i> = 89 each). Both groups received a 3-month intervention. Pre- and post-intervention, TCM and Western medicine symptom severity (TCM syndrome scores, ADOS-2, CARS), inflammatory indicators (IL-6, TNF-α), neurocognitive function (MMN amplitude and latency, DCCS), social ability (SRS), and quality of life (PedsQL 4.0) were compared. Adverse event incidence was also recorded. Nine children in the combined group and seven in the rTMS-only group dropped out, leaving 80 and 82 completers, respectively. Post-intervention, the combined group showed greater improvements than the rTMS-only group in symptoms (TCM, ADOS-2, CARS), neuroinflammation/oxidative stress (IL-6, TNF-α), neurocognition (MMN, DCCS), social ability (SRS), and quality of life (PedsQL 4.0) (<i>p</i> &lt; 0.05). Adverse event rates were low and comparable between groups (3.75% vs. 1.22%, <i>p</i> &gt; 0.05). On the basis of conventional treatment, compared with rTMS adjuvant therapy alone, Yizhi Kaiqiao formula combined with rTMS is a relatively effective strategy for enhancing behavioral rehabilitation adjuvant therapy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <p>https://www.chictr.org.cn/index.html</p>\u0000 </section>\u0000 </div>","PeriodicalId":11300,"journal":{"name":"Developmental Neurobiology","volume":"86 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148224122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":11300,"journal":{"name":"Developmental Neurobiology","volume":"86 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148095841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":11300,"journal":{"name":"Developmental Neurobiology","volume":"86 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148095840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":11300,"journal":{"name":"Developmental Neurobiology","volume":"86 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148093690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}