The pathophysiology of attention-deficit/hyperactivity disorder (ADHD) is not fully understood, but increasing evidence suggests that gut microbiota may play a role. This study compared the gut microbiota of children with ADHD with that of a control group of healthy children, and examined their dietary and sleep habits. Ten medication-naïve children aged 612 years who had recently been diagnosed with ADHD and ten healthy controls were included. ADHD diagnoses were confirmed using the Schedule for Affective Disorders and Schizophrenia for School-Age ChildrenPresent and Lifetime Version (K-SADS-PL). Sleep and eating habits were assessed using the 2nd Level Sleep Disorder Short Form, the Children's Eating Behaviour Inventory, and a form to collect sociodemographic and clinical information. The gut microbiota were analysed using 16S NGS metagenome analysis. A significant decrease in the Shannon and Simpson diversity index values was observed in the ADHD group compared to the control group. Despite the presence of a percentage difference, no statistically significant differences were observed between the groups with respect to species, genus, family, order, class or phylum. Following evaluation of the sleep and eating habit scale scores, no statistically significant difference between the groups was determined.These findings suggests that children with ADHD may alter gut microbiota diversity.However, the absence of significant taxonomic differences and the small sample size mean that these results should be interpreted with caution. Further, larger, adequately powered studies are needed to validate these findings and clarify the potential role of gut microbiota in the pathophysiology of ADHD.
The integrity of the brain's barrier systems-the blood-brain barrier (BBB) and blood-cerebrospinal fluid barrier (BCSFB)-is crucial for central nervous system homeostasis. Oxytocin (OXT), a neuropeptide with emerging peripheral roles, has been implicated in vascular function. This study investigates the hypothesis that OXT directly modulates the functional properties of the BBB and BCSFB via receptor-mediated mechanisms. Using Transwell monoculture in vitro models employing primary rat brain microvascular endothelial cells (BMECs) and choroid plexus epithelial cells (ChPlECs), we first confirmed biological purity and demonstrated constitutive expression of both OXT receptor (OXTR) and the receptor for advanced glycation end products (RAGE) in both cell types. Notably, OXTR and RAGE expression were significantly higher in choroid plexus cells compared to BMECs. Treatment with 800 nM OXT significantly increased transendothelial/epithelial electrical resistance (TEER) in both models, indicating enhanced barrier tightness, with a more rapid effect observed in the BCSFB model. However, paracellular permeability to Lucifer yellow remained unchanged. OXT treatment induced a transient increase in lactoperoxidase (LPO) levels in the conditioned medium at 24 h, followed by a decline at 48-72 h, coinciding with peak TEER values. These findings establish OXT as a potent modulator of cerebral barrier function, with the BCSFB exhibiting higher sensitivity. This novel role of OXT in barrier regulation extends its physiological repertoire and presents a potential therapeutic avenue for neurodevelopmental and neurodegenerative disorders associated with barrier dysfunction.
Neurotoxicity induced by the repeated anesthetic exposure during the neurodevelopmental period and its potential long-term cognitive consequences remain a matter of concern. This study investigated the neurobiological and histological changes in the hippocampus as well as potential long-term neurobehavioral alterations, following repeated administration of ketamine (KET) and dexmedetomidine (DEX) in neonatal rats. Postnatal Day 7 (PND7) rat pups were randomly assigned to four groups: Control (0.9% NaCl), KET (50 mg/kg), DEX (25 µg/kg), and DEXKET (DEX (25 µg/kg) + KET (50 mg/kg)). Intraperitoneal (i.p.) injections were performed for three consecutive days (PND8-10). Developmental neurotoxicity was assessed by measuring apoptotic markers (caspase-3, Bax, and Bcl-2) and pyroptosis-related proteins (caspase-1, gasdermin D, IL-1β, and IL-18) in hippocampus via ELISA. Western blotting was used to analyze long-term hippocampal caspase-1, brain-derived neurotrophic factor (BDNF), and growth associated protein 43 (GAP43) levels. Long-term cognitive effects, including learning, memory, and attention, were evaluated on PND40 using the Barnes maze and the novel object recognition (NOR) tests. Hippocampal morphology was examined by Nissl staining. Although KET or DEX alone did not alter classical apoptotic pathways, they significantly reduced caspase-1. However, both KET and DEX alone impaired recognition memory and attention in the long term, without altering spatial learning. Notably, the combined administration of KET and DEX enhanced sedation while maintaining caspase-1, BDNF, and GAP43 levels close to control values, preserving recognition memory and spatial learning. These findings indicate that co-administration of KET and DEX during the neonatal period may provide a safer anesthetic strategy by reducing cognitive side effects and modulating neuroinflammatory pathways.
Smith-Magenis syndrome (SMS) is a rare multisystem genetic disorder caused by a 17p11.2 microdeletion or pathogenic variants in the retinoic acid-induced 1 (RAI1) gene. It is characterized by developmental delay, distinctive craniofacial features, behavioral dysregulation, and inverted sleep-wake rhythm. Because early clinical findings are often nonspecific, diagnosis is frequently delayed, and patients may initially present to child psychiatry services with behavioral complaints. We report a 5-year-old girl referred for hyperactivity, severe circadian sleep disturbance with recurrent nocturnal awakenings, self-injurious behaviors, sensory-seeking behaviors, and developmental delay. Comprehensive psychiatric, developmental, neurological, physical, and genetic evaluations revealed a 17p11.2 microdeletion involving both RAI1 and folliculin (FLCN). The diagnosis of SMS was confirmed, and the involvement of FLCN indicated additional potential long-term medical risks. This case underscores the importance of considering genetic etiologies in children presenting with severe behavioral dysregulation and sleep problems and highlights the critical role of comprehensive genetic assessment in guiding diagnosis, management, and long-term follow-up.
The current study compares the novel serum inflammatory biomarkers of adolescents with disruptive mood dysregulation disorder (DMDD) and bipolar spectrum disorder (BPSD) between the ages of 12 and 18. This study included 24 adolescents with DMDD, 24 adolescents with BPSD, and 24 healthy controls. All adolescents in the study were assessed for novel serum inflammatory biomarkers, including neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), aggregate index of systemic inflammation (AISI), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI). The Affective Reactivity Index was used to assess the degree of irritability. All novel serum inflammatory biomarkers showed significant differences between the groups, with healthy adolescents scoring statistically significantly lower. NLR and PLR were positively correlated with the degree of irritability in all groups. The findings of this study may support that inflammation may play a role in mood disorders.
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social communication deficits and repetitive behaviors. Prenatal exposure to valproic acid (VPA), a teratogenic antiepileptic agent, is known to induce ASD-like symptoms in animal models. This study aimed to assess the therapeutic effects of Spirulina platensis (SP) and Risperidone (Ris) in a VPA-induced autism rat model. Pregnant Wistar rats received a single intraperitoneal dose of VPA (600 mg/kg) on gestational day 12.5. Male offspring received SP (200 mg/kg, p.o.), and/or Ris (1 mg/kg, p.o.), and combination group(Ris+SP) via oral gavage from postnatal day (PND) 23 to PND 52. Behavioral evaluations were performed on PND 52, and biochemical, molecular, and histological analyses were conducted on PND 61, following decapitation. Oxidative stress markers (malondialdehyde [MDA], superoxide dismutase [SOD]), inflammatory cytokines (IL-1β, tumor necrosis factor-alpha [TNF-α]), and extracellular signal-regulated kinase (ERK) protein expression were measured. Nissl staining was used to assess neuronal loss in the prefrontal cortex (PFC). VPA exposure induced anxiety-like behaviors, repetitive actions, impaired social interactions, increased MDA levels, decreased SOD activity, upregulated ERK signaling, elevated IL-1β and TNF-α levels, and neuronal loss in the PFC. Treatment with SP and/or Ris alleviated these deficits, improving behavioral abnormalities, oxidative stress, inflammatory markers, and histological alterations. Combined SP and Ris treatment provided neuroprotective and behavioral improvements in the VPA-induced ASD model, suggesting potential therapeutic roles for ASD interventions.
Cytokines and immune imbalance in generalized anxiety disorder (GAD) have attracted greater interest of all researchers. Studies indicate the involvement of pro-inflammatory cytokines in the pathogenesis of GAD. Therefore, the present study investigated the serum concentrations of interleukin (IL)-12 and carbon-reactive protein (CRP) in GAD patients to explore their role in the pathophysiology of diseases. This study included 52 GAD patients and 30 healthy controls (HCs) matched by age and sex. A psychiatrist diagnosed GAD patients and evaluated HCs according to the DSM-5 criteria. The severity of GAD cases was assessed using a standardized and validated clinical rating scale (GAD-7). ELISA kits were utilized to measure the serum concentrations of IL-12 and CRP in blood. GAD patients revealed significantly elevated concentrations of IL-12 (7.60 ± 1.53 pg/mL) than HCs (3.73 ± 0.42 pg/mL). These increased levels were positively correlated with GAD-7 scores (r = 0.377, p = 0.006). This IL-12 also demonstrated strong predictive ability in the receiver operating characteristic (ROC) investigation, with an area under the curve (AUC) of 0.828 (p < 0.001), indicating 81.6% sensitivity and 77.8% specificity at a threshold of 3.56 pg/mL. In contrast, the level of CRP was not changed between the groups (2.59 ± 0.13 vs. 2.55 ± 0.16 mg/L); furthermore, no association was detected between CRP levels and GAD-7 scores (r = 0.021, p = 0.881). Our investigation indicates that IL-12 in blood levels, but not CRP, may be associated with the pathophysiology and development of GAD. The altered serum IL-12 levels in GAD patients might be the result of disease pathogenesis. However, we recommend more advanced investigation with greater sample sizes to verify the probable diagnostic utility of IL-12.
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 (n = 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) (p < 0.05). Adverse event rates were low and comparable between groups (3.75% vs. 1.22%, p > 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. https://www.chictr.org.cn/index.html.
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, p = 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 < 25 or ≥ 35 years or when the child's birth weight was not overweight (OR < 1, p < 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< 25 (OR = 0.515, 95% CI: 0.310-0.851, p = 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 < 1, p < 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.
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.
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.
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.
Wnt signaling has increasingly drawn attention in discussions of ADHD, yet the peripheral status of its upstream regulators remains largely uncharted. In this study, we examined circulating levels of four key modulators of this pathway-Dkk-1, PORCN, Notum, and Tiki-1-in a sample of children and adolescents with an ADHD diagnosis. A total of 158 children and adolescents, comprising 82 with ADHD and 76 healthy controls aged 8-18 years, were enrolled. ADHD diagnoses were established according to DSM-5 criteria utilizing the K-SADS-PL questionnaire. The severity of ADHD symptoms was assessed using scores from the Turgay DSM-IV-Based Screening and Evaluation Scale for Attention Deficit and Disruptive Behavior Disorders-Parent Form (T-DSM-IV-S). Serum levels of Dkk-1, PORCN, Notum, and Tiki-1 were quantified using ELISA kits. Children with ADHD demonstrated markedly increased serum concentrations of Dkk-1 and Notum relative to controls (p < 0.01). Elevated PORCN levels were also observed; however, this finding should be interpreted with caution (p < 0.01). Tiki-1 levels showed no significant difference between the groups. No substantial relationships were detected between serum concentrations of the studied analytes and the severity of ADHD symptoms. The significance of these findings was maintained after adjustment for age and sex. This study extends the literature by examining peripheral concentrations of Dkk-1, PORCN, Notum, and Tiki-1 in ADHD. The elevated levels of Dkk-1 and Notum may reflect alterations in Wnt-related signaling pathways, while the interpretation of PORCN findings remains limited. Taken together, these findings indicate peripheral molecular differences associated with ADHD and provide a basis for future research investigating Wnt-related signaling pathways in ADHD. However, these results should be interpreted as exploratory findings requiring further validation using complementary analytical approaches.
Aymé-Gripp syndrome is an ultra-rare autosomal dominant multisystem disorder caused by pathogenic variants in the MAF gene, typically affecting the N-terminal transactivation domain. It is characterized by craniofacial dysmorphism, early-onset cataracts, sensorineural hearing loss, developmental delay or intellectual disability, and variable neurological or skeletal anomalies. Here, we report two unrelated Turkish patients harboring heterozygous MAF variants within the glycogen synthase kinase 3 recognition motif, evaluated using clinical, neuroimaging, and molecular approaches. Targeted next-generation sequencing (NGS) and parental segregation analyses by NGS and Sanger sequencing were performed, and a literature review of cases published between January 2015 and April 2026 was conducted. Both patients presented with craniofacial and neurodevelopmental features. However, one patient showed no clinically detectable ocular abnormalities or hearing impairment at the time of evaluation. The detected variant in this patient was inherited from his asymptomatic father with low-level mosaicism (16% variant allele frequency in blood and 22% in buccal mucosa), representing the first reported case suggestive of paternal germline mosaicism in Aymé-Gripp syndrome. Literature review (n = 38) revealed consistent findings of sensorineural hearing loss (94.5%), cataracts (78.3%), developmental delay/intellectual disability (100%), epilepsy (68.5%), skeletal anomalies (72.7%), and cardiac involvement (55.1%). Additional features, including non-cataract ocular abnormalities, renal involvement, dermatologic findings, and hematological manifestations, have also been reported. All variants clustered within residues 54-69 of the transactivation domain. These findings provide clinically and molecularly relevant insights into Aymé-Gripp syndrome and highlight the importance of molecular diagnosis and the detection of parental mosaicism for accurate recurrence risk assessment and genetic counseling.
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.
The cortico-basal ganglia pathways that mediate vocal learning in zebra finches (Taeniopygia guttata) are localized in parallel circuits formed by CORE and SHELL subregions. These circuits traverse a specialized region of the basal ganglia essential for vocal learning (Area X), which includes intermixed striatal and pallidal neurons. The pallidal neurons within Area X exhibit analogs of mammalian direct and indirect pathways that may have opposing effects and thereby increase or inhibit thalamic activity, respectively. Direct pallidal neurons of Area X send projections to the medial portion of the dorsolateral anterior thalamic nucleus (DLM), whereas indirect pallidal neurons form intrinsic connections onto DLM-projecting neurons. Expression of the transcription factor FoxP2 in the basal ganglia is necessary for normal vocal learning and production in both humans and songbirds. We used tract-tracing techniques to label direct pallidal Area X→DLM projection neurons and immunohistochemical techniques to label neurons expressing the transcription factor FoxP2 in adult and juvenile male zebra finches. Our results showed that DLM-projecting neurons did not express FoxP2 in either adults or juveniles. Measurements of nuclear sizes revealed a population of large neurons that expressed FoxP2 but were not retrogradely labeled from DLM. A putative marker of striatal neurons (DARPP-32) did not co-localize with FoxP2 in many of these large neurons, suggesting that they form a class of indirect pallidal neurons. These findings offer FoxP2 as a possible marker for indirect pallidal neurons and support the existence of different subpopulations of neurons that correspond to direct and indirect pathways within Area X.
The classification of autism spectrum disorder (ASD) has reached a new stage of development that includes the former machine learning (ML) designs and image analysis designs. The study introduces a new framework that uses discrete wavelet transformation with Gaussian filter kernel (DWT-GFK) to achieve the robust preprocessing of the image, including noise removal and preserving edges without affecting the quality of the image. The VGG16 deep learning model is used to extract features, elaborate visual patterns, and data features that are pertinent to the identification of ASD. The classification stage uses a family of Elevated Learning-based Boosting Network with Hybrid Learning with Neural Classifier Logic (ELBN-HLNCL) that is optimized with the help of Walrus Optimization Algorithm (WaOA) to derive optimal model settings. Experimental testing of the proposed methods using the autism image dataset (AID), ASD screening dataset, and ABIDE dataset has shown the superiority of the proposed methodology with 99.95% accuracy, 99.9% recall, 99.8% precision, and 99.85% F1-score. The results demonstrate strong competitive performance compared with existing ASD classification approaches across multiple datasets. The findings offer promising applications in early ASD detection, facilitating improved diagnostic tools and aiding healthcare professionals in accurate assessments.
Epileptic seizure (ES) detection from electroencephalography (EEG) signals is difficult because of noise and the intricate, patient-specific nature of brain activity. Traditional methods often suffer from low accuracy, high computational costs, and poor generalization. To tackle these challenges, an improved hybrid local binary structural pattern shallow graph deep convolutional attention neural networks with synergistic fibroblast optimization (IHLBSPSGDCAN2Nets + SFO) is proposed for automated ES detection as well as diagnosis in EEG signals. First, the input EEG signals obtained in the Bonn and Children's Hospital Boston and Massachusetts Institute of Technology (CHB-MIT) datasets are subjected to preprocessing using the subaperture keystone transform matched filtering (SAKTMF) approach, which allows reducing noise and artifact effects. Subsequently, feature extraction is performed using the second-order synchroextracting transform combined with empirical wavelet transform (SOSTC-EWT), capturing both time-frequency as well as spectral characteristics of EEG signals with high precision. For classification, the improved hybrid local binary structural pattern shallow graph deep convolutional attention neural network (IHLBSPSGDCAN2Nets) architecture is employed, which integrates the improved local binary pattern shallow deep convolutional neural network (ILBPSDCNN) with a hybrid structural graph attention network (HSGAN). Synergistic fibroblast optimization (SFO) is also employed to optimize hyperparameters and achieve optimal model performance, with accelerated convergence, fewer classification errors, and minimal computational cost. Results from experiments demonstrate that IHLBSPSGDCAN2Nets + SFO delivers an outstanding classification accuracy of 99.9, by far exceeding that of conventional methods. The proposed method effectively handles noise, extracts precise EEG features, optimizes model performance, enhances convergence, and significantly improves classification reliability, robustness, and efficiency, outperforming traditional methods in automated ES detection.
Although much is known about the encoding of experience, how the brain organizes neural circuits capable of learning and memory formation is largely unstudied. Canonical critical periods emerge from a convergence of maturation- and experience-dependent processes. Notably, they rely on experience itself to close, resulting in permanent traces of early life. Mechanisms of perceptual critical periods have been well studied, but cognitive critical periods are rare and poorly understood. One well-established critical period for learning is in the juvenile male zebra finch songbird. Juvenile females also perform sensory song learning at ages that overlap with those of the male critical period, though females may learn outside of its age boundaries. The examination of critical period closure thus allows for mechanistic evaluation of the convergence of maturation and experience, plus it provides a unique opportunity to identify neural factors that promote or limit the ability to learn. Because regulated transcription is necessary for the organization and function of cells and circuits, we used a discovery tool (ChIP-seq) for H3K27ac, a strong epigenetic marker of active promoters and enhancers. We examined male and female auditory forebrain, a region necessary for sensory song learning, at two ages and under rearing conditions that affect male critical period closing. We report specific transcription factors, genes, and biological processes that may promote and limit the ability to learn in males, evidence for fluctuations in female developmental plasticity, and evidence that males and females successfully perform sensory song learning using distinct mechanisms.
Fragile X syndrome (FXS) is the most common inherited cause of intellectual disability and is associated with attention deficits, hyperactivity, anxiety, impulsivity, and repetitive behaviors. The disorder results from transcriptional silencing of the FMR1 gene, leading to loss of fragile X messenger ribonucleoprotein (FMRP), an RNA-binding protein that regulates local dendritic translation by repressing ribosomal activity. To examine how impaired local protein synthesis affects dendritic organization, we used Golgi-Cox staining to analyze hippocampal CA1 principal neurons across four developmental stages (P14-21, P30-40, P60-80, and P120-150) in an FXS mouse model. We identified a progressive reduction in dendritic complexity, reflected by decreased Sholl intersections and reduced dendritic branch number and length. In contrast, spine density was increased in both apical and basal dendrites during early development but normalized to wild-type levels in adulthood. Collectively, these structural alterations are likely to disrupt neural circuit development, with downstream consequences for cognition and behavior characteristic of FXS.
