IntroductionThe human brain exhibits complex functions that emerge from interactions among spatially distributed neural regions. Electroencephalography (EEG) microstate analysis has been widely adopted to capture transient topographies reflecting large-scale network dynamics; moreover, it has been linked to cognitive functions, intrinsic brain networks, and neuropsychiatric disorders. Building on this framework, we recently proposed a novel approach based on instantaneous frequency (IF), defined as the temporal derivative of the instantaneous phase, which characterizes microstates in a dimension distinct from that of conventional amplitude-based microstates by explicitly capturing the phase leading and lagging. Although IF microstates have shown promise in characterizing the pathology and cognitive decline in Alzheimer's, their relevance to normal aging has not been investigated. This study aimed to identify age-group differences in large-scale EEG-dynamic properties using IF microstates.MethodsWe recorded resting-state EEG with eyes closed from 29 younger and 18 middle-aged healthy adults. IF time series were extracted from sensor-level EEG signals in the theta and alpha bands. The IF microstates were identified using a hidden Markov model to ensure temporal continuity in state segmentation. Subsequently, we evaluated the sensor-level spatial distributions, mean dwell times, occupancy, and transition probabilities of the IF microstates and assessed age-group differences using appropriate statistical tests with false discovery rate correction.ResultsWe identified several IF microstates characterized by frontal IF delay and occipital IF lead, as well as microstates deviating from these patterns. Group comparisons revealed age-group differences in dynamic properties; in the middle-aged group, mean dwell times increased in some states and decreased in others, while occupancy and transition probabilities also exhibited significant changes.DiscussionIF microstate analysis provides a novel and informative perspective on age-group differences in spatiotemporal EEG dynamics. This approach, which is distinct from conventional amplitude-based microstates, may be useful for understanding healthy aging neural mechanisms.

IntroductionThe auditory steady-state response (ASSR) at 40 Hz provides a well-established probe of gamma-band neural synchronization and is thought to reflect excitation–inhibition balance in cortical microcircuits—a mechanism implicated in autism spectrum disorder (ASD). Previous studies focusing on regional ASSR measures have yielded inconsistent findings for ASD, suggesting that system-level approaches may provide complementary information about ASD-related neurophysiological alterations.MethodsWe examined source-level functional brain networks during the 40-Hz ASSR in children with ASD (n = 19) and typically developing (TD) children (n = 34) aged 5–8 years using magnetoencephalography (MEG). Functional connectivity was estimated using the phase lag index (PLI) within the 30–50 Hz band, and graph-theoretical metrics—clustering coefficient (CC), characteristic path length (PL), and small-worldness (SW)—were computed from binary undirected networks. Group differences were assessed using multiple linear regression adjusting for age, sex, and cognitive ability. Associations between network measures and autistic traits, indexed by the Social Responsiveness Scale (SRS), were examined using a regression model including diagnosis and the interaction of diagnosis × graph measure.ResultsThe experimental paradigm elicited ASSR-related cortical activity in both groups. A significant main effect of diagnosis was observed for characteristic PL, with children with ASD exhibiting shorter PL than TD children, indicating altered network topology during 40-Hz ASSR (p = 0.0023). No group differences were found for CC or SW. In the secondary analysis, shorter PL was associated with higher SRS total T-scores in the omnibus model (p = 0.027). The diagnosis × PL interaction was not significant, indicating that this association did not differ detectably between ASD and TD groups.DiscussionThese findings suggest that graph-theoretical analysis of functional brain networks during the 40-Hz ASSR may provide complementary information about ASD-related neural organization beyond conventional ASSR measures. Shorter characteristic PL in ASD, together with its association with autistic-trait severity across the full sample, points to altered large-scale network configuration during externally driven gamma-band synchronization. The results support the value of examining functional brain network topology during the 40-Hz ASSR in ASD.

AIMS:To characterize cross-sectional and one‑year longitudinal associations between sleep disturbance and attention-deficit hyperactivity disorder (ADHD) symptom severity in Japanese children diagnosed with ADHD. METHODS:Forty-five children (six to 13-years-old) with clinically diagnosed ADHD, who were unmedicated and attending regular classrooms, were recruited online. At baseline, parents completed the Conners 3 (Japanese parent form), the Social Responsiveness Scale, Second Edition (SRS-2), and the Japanese Sleep Questionnaire for Elementary Schoolers (JSQ‑ES). Thirty-eight participants completed identical assessments at one‑year follow‑up. Pearson's correlation coefficients examined relationships between JSQ‑ES total T‑scores and Conners 3 Global Index (CGI) and its six factor scales, cross‑sectionally and in change scores (follow‑up minus baseline). Hierarchical multiple regression analyses assessing which ADHD dimensions were predicted by sleep problems after controlling for demographic and autism spectrum disorder (ASD) variables were conducted. RESULTS:Cross‑sectionally, JSQ‑ES total T‑scores correlated significantly with CGI (r = 0.376, p = 0.010) and with the inattention (IN), hyperactivity/impulsivity (HY), learning problems (LP), and aggression (AG) subscales (all p < 0.05). Longitudinally, only HY changes correlated with JSQ‑ES change scores (r = 0.394, p = 0.014). Regression models confirmed that sleep problems independently predicted overall ADHD severity and AG at baseline, in addition to predicting HY over time. CONCLUSIONS:Sleep disturbances are closely linked to ADHD symptom severity, particularly longitudinal hyperactive/impulsive behaviors, in children, even those without clinical sleep disorder diagnoses. These findings highlight the importance of assessing sleep quality in all children with ADHD, even without major sleep disorders. To draw definitive conclusions about whether improving sleep hygiene reduces HY, further intervention studies are warranted.

Abstract Excessive screen use is associated with childhood behavioral problems, but whether associations differ between typically developing (TD) children and those with autism spectrum disorder (ASD) is unclear. Our cross-sectional study included 108 children aged 5–9 years (61 TD, 47 ASD). ASD was diagnosed using standardized clinical instruments. Measures included parent-reported screen time (excluding TV/DVD), cognitive ability (K-ABC), and behavioral problems (Vineland-II). Screen time and externalizing problems were associated in the TD group (Spearman’s ρ = 0.361, p < 0.01), but not in the ASD group. In the regression model, screen time (β = 0.40, t = 2.60, p < 0.05), ASD status (β = 0.70, t = 8.30, p < 0.001), and their interaction (β = −0.34, t = −2.06, p < 0.05) significantly predicted externalizing problems. Considering the diversity within the autism spectrum, future studies with larger sample sizes should consider individual heterogeneity when examining the association between behavioral outcomes and screen time.

[This corrects the article DOI: 10.3389/fnagi.2026.1707228.].

BACKGROUND:Participation in arts-related activities has been shown to positively influence the well-being and social connectedness of young people. However, few studies have explored the neuroendocrinological changes that might contribute to these benefits. In this exploratory study, we assessed oxytocin (OT) and cortisol (CORT) responses in children experiencing facilitated drum circle activities for the first time. These activities involve imitation and coordinated movement, which are known to increase OT levels. Additionally, OT levels are associated with behavioral synchrony and affiliative behaviors in close preexisting relationships, with higher baseline levels observed in individuals who exhibit more affectionate and coordinated interactions. We hypothesized that children participating in facilitated drum circles with their friends would show higher levels of OT than those participating with strangers. METHODS:Elementary school girls were assigned to either a friends group (F-group) or a strangers group (S-group). Salivary samples were collected before and after the facilitated drum circle activities. RESULTS:Salivary OT increased after the activity in the F-group but not in the S-group. Salivary CORT showed no statistical difference between or within groups. CONCLUSION:Our current data suggest that participation in facilitated drum circles with friends may lead to an increase in OT levels in children, and that preexisting bonds may influence the neuroendocrinological response.

Loneliness is a key risk factor for youth mental health; however, existing scales often fail to capture its multidimensional and relational nature. This study developed the [blinded] University Loneliness Scale (KULoS) for students in Grades 1–9 and evaluated its psychometric properties using cross-sectional and longitudinal survey data collected in Ishikawa, Japan (Time 1: n = 856; Time 2: n = 709). Exploratory factor analyses conducted separately for grades 1–4 and grades 5–9 supported a two-factor structure primarily reflecting item wording (direct vs. indirect items). The four-item direct subscale (KULoS-D) demonstrated good internal consistency (ω = 0.801–0.862), whereas the indirect subscale (KULoS-I) showed lower reliability (ω = 0.662–0.666). Confirmatory factor analyses at both time points indicated a good fit for the two-factor model. KULoS-D showed strong associations with single-item loneliness and depressive symptoms (rs ≈ 0.50) and moderate correlations with aggressive and prosocial behavior in the expected directions, whereas KULoS-I demonstrated weaker convergent and criterion-related validity. Cross-lagged panel analyses revealed that KULoS-D at Time 1 significantly predicted depressive symptoms at Time 2 after controlling for baseline depressive symptoms, whereas KULoS-I did not predict depressive symptoms. Overall, these findings support the KULoS-D as a brief and psychometrically robust measure of loneliness in children and early adolescents, with potential utility for large-scale research and early screening.

Optically pumped magnetometers (OPM) can replace superconducting quantum interference devices (SQUID) as the magnetic sensors in magnetoencephalography (MEG) systems to achieve high spatiotemporal resolution for measuring neuronal activity. OPMs are compact and lightweight, enabling wearable OPM-MEG devices. However, they are susceptible to residual magnetic field gradients within a magnetically shielded room as the sensors are not fixed to the room. Herein, we examined whether a 30Hz steady-state visual evoked field elicited by the hemifield pattern-reversal stimulation procedure could be measured using 18 channel wearable OPM-MEG system without noise cancelling in six healthy adults. Steady-state responses were observed in channels near the visual cortex on the hemifield opposite the visual field where a checkerboard pattern was presented. In the same environment, we successfully verified the cortical oscillatory response using electroencephalography (EEG). Visual evoked field components (e.g. P75m), which follow stimulus onset and are clearly recorded by EEG, were not observed with OPM-MEG. These components were masked by artifacts at 3-5Hz, likely generated by slight body movements. Given the motion artifacts in the low-frequency bandwidth, optimal experimental designs for wearable OPM-MEG measurements without noise canceling need to consider targeting cortical oscillation in the frequency band, which is unaffected by artifacts.

Peak alpha frequency (PAF) is a neurophysiological marker of cortical maturation and cognitive function. We aimed to examine PAF reactivity to a visually engaging eyes-open (EO) condition, during which children watched a muted preferred video, compared to a dark-room (DR) resting state without sound, in children with ASD and their TD peers. We analyzed magnetoencephalography data from 68 cortical sources in children aged 5-10 (ASD: n=22; TD: n=29), calculating PAF during a resting-state DR condition and an EO condition involving silent video viewing. Linear mixed-effects models were used to assess the effects of diagnosis, condition, and their interaction on PAF, controlling for age and sex. The results indicated a significant interaction between diagnosis and condition in the right temporal region, where TD children consistently showed a higher PAF in the EO condition relative to the DR condition, whereas children with ASD did not. Furthermore, in TD children, greater PAF reduction in the right temporal region correlated with lower social responsiveness scores, suggesting a link between PAF reactivity and social functioning. These findings suggest that atypical PAF modulation in response to sensory input may reflect altered neural mechanisms underlying social information processing in ASD. Understanding PAF reactivity patterns can inform the development of ASD biomarkers.

INTRODUCTION:In past research, we have examined the characteristics of brain activity caused by prefrontal task completion in healthy older adults in a simplified manner using a near-infrared spectroscopy device, with the aim of identifying methods of early intervention to prevent their cognitive decline. Previously, we reported that prefrontal oxygenation during pre-task preparation was greater in young adults than older adults, and that this greater activation was associated with better task performances in both groups. To extend this research, in the present study, we examined previous findings with task repetitions, as older adults take more time to become familiar new tasks. METHODS:We modified the working memory task with a clear task-set instruction and examined the change in the task-set and task-induced activation in 63 cognitively healthy older adults. RESULTS:Task-set activation did not increase even after three repetitions, and the task-induced activation was greater than task-set activation in most channels. The difference in degree between task-induced activation and task-set activation showed a reduction with task repetitions. Significant inverse correlations were observed between the prefrontal activation due to the task itself in the third session, i.e., after task repetitions and the reaction time of the Trail Making Test, which represents attentional function. DISCUSSION:These results indicate that continued activation by the task itself, which persists even after older adults become familiar with the task, may be associated with executive function decline.

There has been a growing recognition of the benefits of participating in art practices for promoting well-being and social connection. Despite this, only a limited number of studies have assessed the neuroendocrinological changes that might contribute to these benefits. In this exploratory study, we focused on a creative activity related to music composition using digital tools. The emergence of computer software to create music (CSCM) has lowered the barriers to musical technical skills and theory, making music composition more accessible. We examined whether incorporating CSCM into a music-making workshop would affect the levels of two hormones, oxytocin and cortisol, among healthy adults. These two hormones were chosen, because oxytocin is involved in prosocial behavior and bonding, while cortisol plays a role in the stress response. Considering the time it takes to learn and adapt to a typical customized CSCM, we simplified its use to allow participants to experience music-making within a short timeframe and set up two distinct workshops. One was individual music creation with the support of a facilitator (Dyad) and the other was music creation in a group (Group). Participants in the Dyad workshops showed increased oxytocin levels, whereas those in the Group workshops did not. Cortisol levels remained unchanged during the Dyad workshops, but decreased in the Group ones. These results suggest that neuroendocrinological changes may occur during music-making activities using computer software. This work highlights the potential value of CSCM-incorporated music-making activities, although further controlled studies are required to confirm these findings.

IntroductionPrivate speech has been shown to serve as a self-regulatory and planning tool for children during task-solving activities. While both typically developing children and those with neurodevelopmental disorders—such as attention-deficit/hyperactivity disorder and high-functioning autism spectrum disorder (ASD)—have been found to use private speech, the frequency and developmental trajectories vary across groups. However, little is known about private speech in children with intellectual disabilities.MethodsThis study examined private speech in children with intellectual disabilities (n = 20) during a selective attention task. Verbalizations were recorded and categorized based on their relevance to the task.ResultsSeventy percent of the participants used private speech during the task, and 60% produced task-relevant utterances that appeared to assist in problem-solving. Notably, children with stronger ASD characteristics exhibited more frequent use of private speech than those with milder traits.DiscussionThese findings suggest that children with intellectual disabilities engage in private speech during cognitive tasks, and that this speech may function as a tool for behavioral regulation. The increased use among those with pronounced ASD tendencies may reflect differences in cognitive or social processing, underscoring the importance of considering individual variability in educational support.

The spatial distribution of electroencephalography (EEG) oscillatory power and its temporal transitions are widely recognized as indicators of cognitive processes and pathological conditions, termed as microstates. These microstates reflect whole-brain neural network dynamics, including deep brain regions, and are closely associated with large-scale networks such as the default mode network. The conventional approach to microstate analysis relies on the envelope of EEG oscillations, which corresponds to the instantaneous amplitude. In this study, we aimed to extend conventional microstate analysis by integrating the instantaneous amplitude (power component) and instantaneous frequency data derived from the Hilbert transform. While our previous studies demonstrated that instantaneous frequency also reflects brain activity, this study highlights that integrating both features enables a more comprehensive assessment of aging effects. This integration allows the identification of brain states that cannot be detected using conventional power-based microstate analysis. Our findings suggest that this approach expands and enhances traditional microstate analysis and offers a novel index for detecting brain states. This method has the potential to provide new insights into neural network dynamics and can be applied to the study of cognitive processes and pathological conditions.

We explore an innovative approach to sleep stage analysis by incorporating complexity features into sleep scoring methods for mice. Traditional sleep scoring relies on the power spectral features of electroencephalogram (EEG) and the electromyogram (EMG) amplitude. We introduced a novel methodology for sleep stage classification based on two types of complexity analysis, namely multiscale entropy and detrended fluctuation analysis. Our analysis revealed significant variances in these complexities, not only within the specific theta and delta bands but across a wide frequency spectrum. Based on these findings, we developed a sleep stage scoring model, termed Sleep Analyzer Complex (SAC), a convolutional neural network model that integrates these complexity features with conventional EEG spectrum and EMG amplitude analysis. This integrated model significantly enhances the accuracy of sleep stage identification, achieving an accuracy of 97.4-98.1% for novel wild-type mice, on par with the agreement level among human scorers (97.3-97.8%). The efficacy of SAC was validated through tests conducted on wild-type mice, and it demonstrated remarkable success in identifying sleep architecture abnormalities in narcoleptic mice as well. This approach not only facilitates automated scoring of sleep/wakefulness states but also holds the potential to uncover detailed physiological insights, thereby advancing EEG-based sleep research.

Aim:We investigated orthostatic dysregulation using wearable sensors capable of monitoring electrocardiograms (ECGs) and motion via accelerometers. Methods:Study 1: Thirty-three healthy students participated in the study. Wearable sensors recorded continuous 24-h ECGs, and a triaxial accelerometer was fixed to the participants' waist for three consecutive weekdays. Clinical psychiatric ratings involved assessments using the internet addiction test, the depression self-rating scale, the state-trait anxiety inventory, and the autism spectrum quotient. In Study 2, 19 patients diagnosed with orthostatic dysregulation were compared with healthy controls. Results:No significant correlation was found between depressive symptoms and heart rate variability. A standing test revealed a strong correlation between heart rate changes immediately after changing the position and average 24-h heart rate variability. The orthostatic dysregulation group exhibited a significantly lower high-frequency ratio than the control group. This group also had longer sleep durations, lower sleep efficiency, and lower energy consumption. Conclusion:Wearable sensors proved valuable in evaluating autonomic function. Significant differences in accelerometer-based sleep ratings were observed between normal subjects and patients with orthostatic dysregulation.

Advances in perinatal care have improved survival rates of preterm infants; however, concerns regarding their neurodevelopmental outcomes persist. This study investigates the relationship between intraindividual variability (IIV) in sleep duration and crystallized intelligence in very low birth weight (VLBW) children at the preschool stage. This study had 38 participants, including 18 VLBW children and 20 full-term children aged 5 to 6 years. Sleep duration was assessed using actigraphy and sleep diaries, while crystallized intelligence was evaluated using the Achievement Scale (ACH) of the Kaufman Assessment Battery for Children (K-ABC). Statistical analyses, including correlation and multiple regression analyses, were conducted to examine the effect of preterm birth on the relationship between sleep variability and learning outcomes. The results revealed that sleep duration IIV adversely affected ACH scores of preterm children but had no impact on those of full-term children. In multiple regression analysis, the interaction term between sleep variability and preterm birth significantly predicted ACH scores (p = 0.032), suggesting that early childhood learning in preterm children is more vulnerable to the effects of sleep instability. Additionally, among VLBW children, older age was associated with lower ACH scores, implying that learning difficulties may become more pronounced with age. These results show the potential role of sleep regulatory mechanisms in cognitive development. The findings show the importance of monitoring and supporting healthy sleep habits in preterm children to mitigate potential learning difficulties. Early interventions targeting sleep stability may play a critical role in improving academic performance in this high-risk population.

Background Neuroleptic malignant syndrome (NMS) is a rare but life-threatening emergency precipitated by the use or withdrawal of dopamine-receptor antagonists or dopaminergic agents. It classically presents with lead-pipe rigidity, hyperthermia, autonomic dysregulation, and altered mental status. Delayed recognition still carries considerable mortality. Alcohol-withdrawal syndrome (AWS) ranges from mild tremor to delirium tremens and overlaps with NMS in fever, agitation, and autonomic instability, making early distinction difficult. Case We describe a middle-aged man with alcohol-use disorder who developed NMS during treatment of AWS after intramuscular haloperidol. Given significant hepatic dysfunction, dantrolene was avoided; ICU-level supportive care with continuous intravenous midazolam was provided. He improved clinically and biochemically; lorazepam was tapered off and he was discharged. Conclusion Overlapping pathophysiology—particularly reduced central dopaminergic tone in withdrawal states—may lower the threshold for NMS. A high index of suspicion for NMS is warranted in patients with AWS after antipsychotics administration. In the presence of hepatic impairment, drug choice and dosing should be tailored to liver function.

Children with Autism Spectrum Disorder (ASD) often have more sleep disturbances than typically developing children. These sleep disturbances have been suggested to be associated with atypical sensory features in children with ASD. Sleep habits have also been linked to intelligence and cognitive function in children. However, it remains unclear whether sleep disturbances in children with ASD are related to intelligence or sensory features. This study examined whether sleep disturbances in children can be explained by the presence or absence of ASD characteristics, sensory features, and cognitive skills. Sleep disturbances and atypical sensory features were determined using the Japanese Sleep Questionnaire for Preschoolers and the Caregiver Sensory Profile, as reported by their caregivers. Cognitive skills were assessed using the Japanese translation of the Kaufman Assessment Battery for Children. Consequently, children with below-average cognitive scores demonstrated that higher sensory scores were associated with poorer sleep quality; children with above-average cognitive scores showed no such patterns. These findings may aid in the development of support for sleep disturbances in various subtypes of ASD.

Preterm birth, defined as delivery before 37 weeks of gestation, is associated with alterations in brain development and an increased likelihood of motor difficulties, with effects that often persist across childhood. However, the underlying neurophysiological mechanisms remain insufficiently understood. Oscillatory activity in the primary motor cortex is closely linked to motor execution and reflects the dynamic properties of cortical function, yet motor-related oscillations have rarely been examined directly in children born preterm. In this study, we investigated motor-induced gamma oscillations in school-aged children born preterm using magnetoencephalography (MEG). Eighteen children born preterm and nineteen age- and IQ-matched children born at full term (5 to 7 years old) completed a child-friendly dominant-hand finger movement task during recording with a child-customised MEG system. The preterm group exhibited significantly slower response times and approximately twenty-two per cent weaker contralateral gamma power compared with their full-term peers. They also showed reduced hemispheric lateralisation, indicating greater bilateral cortical engagement. Motor-related gamma power and lateralisation indices were positively associated with perinatal factors and motor performance scores. These findings suggest that attenuated gamma activity and altered lateralisation patterns in children born preterm may relate to delayed maturation of motor cortical circuits and variations in callosal development, potentially affecting interhemispheric communication and motor processing efficiency. This study provides new insight into the neurophysiological basis of motor development following preterm birth. ### Competing Interest Statement The authors have declared no competing interest. Center of Innovation Program, Japan Science and Technology Agency University of Birmingham, https://ror.org/03angcq70