Frontiers in Integrative Neuroscience

Individuals with autism spectrum disorder (ASD) often exhibit atypical auditory processing, yet it remains unclear whether and how the integration of simple acoustic features and contextual information is impacted in ASD. One real-world example of this integration is the auditory looming bias, the prioritized processing and perception of approaching auditory stimuli. We designed a paradigm that presents intensity-rising (looming) and intensity-falling (receding) auditory stimuli to 3-4-year-old children with ASD (n = 21), children with sensory processing concerns who do not have ASD (SPC; n = 16) and children with typical development (TD; n = 30). We recorded neural responses using electroencephalography (EEG) and found evidence of looming bias in the SPC and TD groups, as indexed by greater P1 peak amplitude during the looming than receding stimuli (TD: t(64) = 6.87, p < .001; SPC: t(64) = 4.07, p < .001). But this finding was not present in the ASD group (p = .194). Additionally, the ASD group showed reduced differentiation between looming and receding stimuli, as indicated by significantly lower Rise-Fall Difference Score (RFDS) in comparison to the TD group (Z = -3.00, padj = .008). These findings suggested altered context-dependent modulation of sensory input in ASD. Lay SummaryMany children with autism show differences in how they process sounds. Using sound patterns in which loudness gradually increased and decreased over time, like many real-world sounds, we found that children with autism showed less neural differentiation between increasing and decreasing sounds. This finding suggested that the brain may process changes in sound differently in autism, particularly in how it adjusts to sounds as they change over time, which could contribute to the sensory challenges many children with autism experience in daily life.

Differences in looking to and processing of audiovisual speech have been theorized to contribute to heterogeneity in language ability in autistic children. Differential audiovisual speech processing has been indexed by event-related potentials (ERPs), specifically via amplitude suppression in response to audiovisual versus auditory-only speech, and linked with vocabulary in school-aged children. This study used an intact-group comparison and concurrent correlational design in infant siblings of autistic children (Sibs-Autism) and non-autistic children (Sibs-NA) to determine whether amplitude suppression is (a) present in infancy, (b) different in Sibs-Autism versus Sibs-NA, and (c) related to looking to audiovisual speech and language abilities. We collected EEG data from 54 infants aged 12-18 months (29 Sibs-Autism; 25 Sibs-NA) while they viewed videos of audiovisual and auditory-only speech, as well as eye tracking and language data. We found significant amplitude differences at the N2 ERP component in response to audiovisual versus auditory-only speech but no significant group differences in ERP amplitudes. Associations between looking to audiovisual speech, amplitude effects, and language were moderated by group, chronological age, and biological sex. Our findings suggest that differential audiovisual speech processing is present in 12-18-month-olds and may explain heterogeneity in looking to audiovisual speech and emerging language ability.

Background and Objectives In ADHD, a heterogeneous neurodevelopmental condition, behavioral and motor manifestations may reflect multiple inefficient or perturbed inhibitory systems. To evaluate Transcranial Magnetic Stimulation (TMS) evoked cortical silent period (CSP) duration, an indicator of GABA(B) receptor-mediated inhibition in motor cortex, as a potential biomarker of Attention-Deficit/Hyperactivity Disorder (ADHD) in children. Method We retrospectively analyzed TMS data, obtained using both round and figure-of-8 coils, from three cross-sectional studies conducted in 8- to 12-year-old children with ADHD (n=79; 10.7 +/- 1.5 years old) and age-and-sex-matched typically developing controls (n=96; 10.5 +/- 1.4 years old). Results Median CSP was 32% shorter in ADHD (p=0.02). Regression analysis demonstrated a relationship between shorter CSP and both lower active motor thresholds (p < 0.0001) and more severe hyperactivity symptom rating (p = 0.026). Test-retest CSP measures in 83 children showed moderate reliability (intraclass correlation 0.77 [ADHD], 0.75 [controls]). Conclusion TMS-evoked CSP may be a useful biomarker in future investigations of ADHD subtypes, domains of impaired function, or treatment outcomes.

BackgroundAutism Spectrum Disorder (ASD) is marked by pronounced biological heterogeneity, yet most neurochemical studies have relied on single-analyte comparisons that cannot capture coordinated variation across neurotransmitter systems. Whether ASD blood neurotransmitter profiles reflect discrete subtypes, a continuous landscape, or something in between remains unresolved. MethodsWe applied NeuroCLAD, a structured multivariate analytical framework, to peripheral blood neurotransmitter profiles from 261 children with ASD (mean age 6.98 {+/-} 3.13 years; 78.5% male). The pipeline incorporated z-score normalisation, natural cubic spline residualisation for age and sex, principal component analysis, k-means clustering, consensus stability assessment, Gaussian mixture modelling, Cohens d enrichment analysis, and clinical symptom mapping. Cross-compartment consistency was explored using urine neurotransmitter profiles from the same cohort. ResultsTwelve reproducible biochemical cluster patterns were identified, each characterised by distinct pathway-level fingerprints spanning trace amines, monoamines, catecholamine turnover, histamine signalling, and excitatory-inhibitory amino acid balance. Cluster stability was confirmed across 200 bootstrap iterations. Gaussian mixture modelling showed that most individuals were assigned with high confidence, while a subset occupied transitional positions between clusters, consistent with stable biochemical modes embedded within a continuous landscape. Descriptive behavioral mapping revealed graded symptom tendencies across biochemical modes, particularly for aggressiveness, self-injurious behaviour, and picky eating. LimitationsThe findings are based on peripheral blood measurements, which indirectly reflect central neurochemical activity. The study is cross-sectional, lacks a neurotypical comparison group, and behavioural associations are exploratory given cluster sizes. External replication in an independent cohort has not yet been performed. ConclusionsBlood neurotransmitter biology in ASD is neither uniform nor discretely partitioned, but organised into reproducible biochemical modes within a continuous multivariate landscape. These findings support a dimensional view of ASD neurochemistry and provide a foundation for pathway-informed, individualised approaches to biological characterisation.

Although HIV exposure has previously been found to affect brain white matter (WM) tract integrity and language development in infants and children, the impacts of HIV and antiretroviral therapy (ART) exposure on central auditory tracts remain unclear. Moreover, no research to date has investigated the relationship between auditory WM tract development and language outcomes in infants exposed to HIV but uninfected (iHEU). Brain images were acquired at the age of 0-5 weeks for 31 infants whose mothers began ART pre-conception (iHEU-pre), 29 infants whose mothers began ART post-conception (iHEU-post) and 25 infants who were HIV-unexposed (iHU). Full-probabilistic diffusion tensor imaging (DTI) tractography was used to assess WM integrity in tracts connected to central auditory structures. Language assessments were carried out at 9-14 months using the Griffiths Mental Development Scales (GMDS). Linear regression analysis was used to compare DTI tractography results between iHEU and iHU and to assess the relationship between DTI measures and language. Finally, the impacts of HIV and ART exposure on associations between language and DTI measures were visualised using groupwise language-DTI correlation plots. There were no results after multiple comparison correction. Unadjusted results show recurring patterns of reduced fractional anisotropy (FA), driven by iHEU-post, in auditory tracts of iHEU compared to iHU. Both iHEU-pre and iHEU-post contributed to the patterns of uncorrected elevations in mean diffusivity (MD) observed in the entire iHEU group, with the left medial geniculate nucleus being the auditory structure most frequently observed within the affected tracts. Effect sizes of uncorrected differences, which were small-to-moderate in size, were similar to other infant DTI tractography studies. Groupwise assessment of the data revealed moderately strong correlations between GMDS language scores and DTI measures in some affected tracts, only for iHU. Our findings indicate that HIV/ART exposure may have subtle effects on auditory WM tract development in infants. Delays in auditory tract maturation appear to occur irrespective of ART exposure duration and may be HIV exposure-specific effects. Tracts connected to the left auditory thalamus have notably been implicated in our unadjusted results. HIV and ART exposure may interfere with the way in which auditory WM tracts mature, potentially impacting the role of a small number of these tracts in language processing.

PurposePrevious research has demonstrated effects of head impact exposure on cortical neurophysiology, which may help with understanding variability in clinical sequelae. In separate lines of research, neurochemical and gene transcription markers of vulnerability to traumatic brain injury (TBI) have been established. The purpose of this study was to examine whether these cortical neurochemical and gene transcription gradients are spatially aligned with neurophysiological effects. Methods and MaterialsMagnetoencephalography (MEG) data was collected at a total of 278 pre- and post-season timepoints from 91 high school football players across up to four seasons of play. Of the 91 football players, 10 experienced a concussion, and of the remaining 81 non-concussed players, 71 met the criteria for complete imaging and kinematic data, with post-season evaluations less than six weeks after the end of the season. Head impacts were tracked over the course of the season with helmet-mounted sensors. MEG data underwent source-imaging, frequency-transformation, spectral parameterization, and linear modeling to examine the effects of concussive and non-concussive head impact exposure on pre-to-post-season changes in rhythmic and arrhythmic neurophysiological activity. To determine clinical effects, parent reported Post-Concussive Symptom Inventory scores related to cognitive symptoms were correlated with cortical neurophysiological changes. Multi-atlas data of neurochemical system densities from neuromaps and gene expression from the Allen Human Brain Atlas were examined for alignment with head impact-related alterations in neurophysiology via nonparametric spin-tests with autocorrelation-preserving null models (5,000 Hungarian spins; pFDR <.05). ResultsConcussion-related reductions in cortical excitability (i.e., aperiodic exponent slowing) were aligned with atlas-based norepinephrine transporter (NET) and alpha-4 beta-2 nicotinic receptor (4{beta}2) densities, and with apolipoprotein E (APOE) and brain-derived neurotrophic factor (BDNF) expression levels. More severe cognitive symptoms associated with concussion-related slowing of aperiodic neurophysiology were also aligned with atlas-based NET and 4{beta}2 receptor densities. Similar changes in cortical excitability related to non-concussive head impact exposure were colocalized with serotonin receptor (5-HT1A) density maps and APOE and BDNF expression. Rhythmic alpha activity was reduced by concussion and colocalized with histamine (H3) and mu-opioid (MOR) receptors, among others, as well as with gene transcription atlases of APOE and C-C chemokine receptor 5 (CCR5). ConclusionsThese findings extend our previous work to show that the effects of head impact exposure on neurophysiology are strongest in cortical areas with specific neurochemical and genetic profiles that are known to signal vulnerability to traumatic brain injury, and that these spatial alignments are also associated with self-reported symptom severity. Clinical Relevance / ApplicationChange in cortical excitability, as measured here by MEG, has potential value as a clinical tool for concussion diagnosis and prognosis. We provide genetic and neurochemical contextualization for these changes that may extend their clinical applications, for example to concussion risk assessment and pharmacotherapies.

PurposeNavigational ability develops throughout childhood alongside the maturation of brain regions supporting egocentric and allocentric processing. In Autism Spectrum Disorder (ASD), atypical hippocampal development may impact flexible spatial memory; however, findings on navigational ability in autistic children remain inconsistent. This study aimed to compare both objective and perceived navigation ability in children with ASD and typically developing (TD) peers. MethodTwenty-six children with high-functioning ASD and twenty-five age- and gender-matched TD children (M_age = 12.04 years, SD = 1.64) completed a battery of navigational tasks from the Spatial Performance Assessment for Cognitive Evaluation (SPACE), including Path Integration, Egocentric Pointing, Mapping, Associative Memory, and Perspective Taking. Perceived navigation ability was assessed using the Santa Barbara Sense of Direction (SBSOD) scale. ResultsNo significant group differences were observed across any objective navigation tasks. However, children with ASD reported significantly lower perceived navigation ability compared to TD peers. ConclusionThese findings suggest a dissociation between perceived and actual navigational ability in ASD. By early adolescence, objective navigation performance appears intact, potentially reflecting sufficient maturation of underlying neural systems or the presence of compensatory mechanisms. The results underscore the importance of incorporating objective, task-based measures when assessing cognitive abilities in autistic populations.

BackgroundHippocampal place cells (PCs) undergo representational drift, i.e., a gradual change in their place fields despite unaltered behavior. While Ca2+ imaging enables long-term tracking of PC populations, distinct PC detection methods have been shown to yield different subpopulations of PCs, with only a few systematic comparisons between methods, especially in open arenas. New MethodWe provide an analysis protocol for one-photon PC data obtained during free foraging in two-dimensional arenas that allows us to compare two widely used PC detection methods, significance of spatial information (SI), and split-half correlation (SHC), and their effect on representational drift. The analysis is demonstrated on previously published Ca2+ data from dorsal CA1 of freely foraging mice, with cells tracked for 10 consecutive days. ResultsBoth criteria, SI and SHC, yielded proportions of approx. 17% PCs with only 40% overlap. SI-identified PCs demonstrated higher stability, higher rate map correlations, and a slower rate of representational drift than SHC-PCs. Comparison with existing methodsPrevious studies comparing SI and SHC PC detection methods in Ca2+ data did not focus on either open field behavior or representational drift. ConclusionOur results indicate that the choice of PC detection method significantly affects the estimate of representational drift in Ca2+ imaging studies.

Visual processing undergoes rapid refinement in the first year of life, supporting the emergence of higher-order cognitive, language, and motor functions. Visual evoked potentials (VEPs) provide a non-invasive measure of visual system maturation that may shed light on heterogeneous developmental trajectories among infants at high familial likelihood for autism. Infants with an older sibling with autism spectrum disorder (N = 177 at 6 months; N = 132 at 12 months) participated in the Infant Brain Imaging Study-Early Prediction (IBIS-EP) study. Pattern-reversal VEPs were recorded at 6 and 12 months, and developmental skills were assessed at 24 months using the Bayley Scales of Infant and Toddler Development (Bayley-III). VEP components were characterized by P1 amplitude, latency, and trial-to-trial variability in latency. Associations with 24-month cognitive, language, and motor scores were examined using general linear models controlling for age, site, sex, and trial count. Robust VEPs were observed at both timepoints, showing age-appropriate morphology and expected developmental changes, including decreases in P1 latency and amplitude from 6 to 12 months. Greater trial-to-trial variability in P1 latency at both timepoints was significantly associated with higher cognitive and language scores at 24 months. Variability in visual cortical response timing was the strongest neural correlate of developmental skills in infancy. These findings suggest that temporal variability in early neural responses may reflect adaptive sensory circuit flexibility rather than inefficiency, potentially facilitating experience-dependent tuning of visual pathways. VEPs offer a mechanistic window into how developing sensory systems scaffold individual differences in early developmental trajectories. Research HighlightsO_LITrial-to-trial variability in visual cortical response timing predicts cognitive and language outcomes at 24 months in infants at familial likelihood for autism. C_LIO_LIMean P1 latency did not predict outcomes, suggesting variability is a more sensitive early neural marker than average response timing. C_LIO_LIGreater neural response variability in infancy may reflect adaptive sensory circuit flexibility rather than noise or inefficient processing. C_LIO_LIVEP-based biomarkers provide a scalable mechanistic window into how early sensory processing scaffolds cognitive and language development. C_LI

Adeno-associated viral (AAV) vectors are foundational tools for dissecting brain structure-function relationships, but AAV serotype tropism varies across brain regions and species, requiring empirical validation to inform experimental design. This need is especially important in non-model organisms, where molecular neuroscience tools remain underdeveloped and access to research subjects is often limited. The Arctic ground squirrel (AGS, Urocitellus parryii) is a valuable model for studying extreme physiology, including metabolic suppression during hibernation and resistance to cerebral ischemia/reperfusion, yet no studies have evaluated AAV performance in the AGS brain. Here, we investigated the ability of AAV serotypes 1, 8, 9, and DJ to transduce the AGS hypothalamus using the human synapsin (hSyn) promoter and directly compared cellular transduction rates in a region implicated in thermoregulation and hibernation. To maximize data collection from a limited experimental population, we used a within-animal, contralateral stereotaxic injection design. Recombinant AAV vectors expressing enhanced green fluorescent protein or mCherry were delivered bilaterally, and reporter expression was analyzed four weeks later. All tested serotypes produced clear and reproducible reporter expression, establishing AAV as a viable molecular tool in the AGS hypothalamus. AAV1 produced significantly greater cellular transduction rates than AAV-DJ (17.2% {+/-} 3.5% vs 8.4% {+/-} 2.9%, paired t-test, p = 0.032). AAV8 and AAV9 showed transduction rates of 22.8% {+/-} 0.6% and 20.1% {+/-} 1.5%, respectively; however, with only two biological replicates per serotype, formal statistical comparison was not performed. These findings provide the first direct characterization of AAV-mediated gene delivery in the AGS brain and establish a foundation for future molecular interrogation of hypothalamic circuits in this extreme mammalian hibernator.

This research demonstrates that the trans-aqueduct approach is a feasible, minimally invasive access pathway to the third ventricle, offering a potential route to the deep brain for therapeutic technologies. Further pre-clinical investigation is required to thoroughly evaluate physiological tolerance, trauma risk, and the long-term implications of intraventricular implantation. The third ventricle is a high-value site for neuromodulation due to its proximity to deep-brain targets, including the subthalamic nucleus (STN) and globus pallidus internus (GPi). This study defined the anatomical pathway; and evaluated the technical feasibility of retrograde access to the third ventricle via the cerebral aqueduct using minimally invasive interventional techniques. Evaluation was conducted in three phases using human MRI datasets (n=16; mean age 48.4 years) and cadaveric specimens (n=6; mean age 88.2 years). Phase 1 involved morphometric MRI analysis of the aqueduct and ventricles. Phase 2 tested trans-aqueduct access on cadaver specimens via fluoroscopically guided guidewires and catheters. Phase 3 utilized direct anatomical dissections on cadaver specimens (n=3) to morphometrically measure the third ventricular cavity and its relationship to deep-brain nuclei. Measurements across the sample groups showed a mean aqueduct diameter of 1.6 mm (SD=0.14). Third ventricle dimensions averaged 27.6 mm (ventral-dorsal), 19.9 mm (caudal-cranial), and 5.7 mm (lateral). Successful access to the third ventricle was achieved in 83% (5/6) of cadaveric specimens. The optimal technical configuration utilized a 0.018'' angled-tip guidewire and 5-6 Fr catheters; the aqueduct accommodated diameters up to 2.0 mm with minimal resistance. The STN and GPi were localized within 5-20 mm of the ventricular volumetric centroid. The trans-aqueduct approach is a technically feasible, minimally invasive pathway for accessing the third ventricle. This route offers a potential alternative for the delivery of therapeutic neurotechnologies. Further research is required to assess physiological tolerance, trauma risk, and the long-term safety of intraventricular implantation.

Neurodevelopmental disorders, including autism spectrum disorder (ASD), are influenced by both genetic abnormalities and environmental toxicants. Among environmental risk factors, endocrine-disrupting chemicals such as bisphenol A (BPA) and pharmaceutical drugs such as valproic acid (VPA) have been associated with an increased risk of autism. In this study, human induced pluripotent stem cell (iPSC)-derived forebrain organoids were used to model early neurodevelopmental disruptions induced by BPA and VPA exposure. On day 62 of differentiation, forebrain organoids were treated with physiologically relevant concentrations of BPA or VPA for 28 days. Following treatment, morphological, molecular, and electrophysiological changes were assessed across experimental conditions. Both compounds produced distinct alterations in organoid morphology, neurodevelopmental gene expression, and network electrical activity, with VPA inducing markedly stronger effects. Overall, these data suggest forebrain organoids as a robust, physiologically relevant in vitro model system for studying neurodevelopment. This platform enables systematic investigation of environmental and pharmacological risk factors implicated in the pathogenesis of neurodevelopmental disorders.

BackgroundPhelan McDermid syndrome (PMS), caused by SHANK3 haploinsufficiency, is a genetic form of autism spectrum disorder (ASD) that provides a genetically defined model for studying ASD-related circuit dysfunction. SHANK3 mutations disrupt synaptic organization and cortical synchrony, leading to attenuated gamma-band auditory steady-state responses (ASSRs). We investigated whether PMS-related electrophysiological signatures could be identified using machine learning and whether similar patterns are present in a subset of individuals with idiopathic ASD (iASD). MethodsEEG recorded during a 40-Hz ASSR paradigm was collected from 123 participants (42 TD aged 2-30, 56 iASD aged 3-31, 25 PMS aged 2-26). We extracted time-series, ERSP, FOOOF-derived spectral, and intertrial phase coherence (ITPC) features. XGBoost models with leave-one-out cross-validation classified PMS versus TD; the best age/sex-adjusted ITPC model was then applied to iASD participants to derive a Synchrony Atypicality Index (SAI). Unsupervised clustering of high-dimensional ITPC features was also performed. ResultsITPC-based models showed the strongest discrimination between TD and PMS participants (AUROC = 0.83). When applied to iASD participants, 35.7% exhibited elevated SAI, indicating a PMS-like gamma-band phase-locking profile. Classification of iASD versus PMS performed poorly in the full sample but improved markedly after excluding high-SAI iASD individuals, consistent with substantial heterogeneity within iASD. Unsupervised clustering of ITPC features identified PMS-enriched clusters that also captured high-SAI iASD participants. Results were consistent after controlling for age in sensitivity analyses. ConclusionsReduced 40-Hz ITPC is a mechanistically interpretable electrophysiological signature of PMS and identifies a biologically meaningful PMS-like subgroup within iASD, supporting biomarker-guided stratification.

Background and Purpose: Ambient air pollution is an established risk factor for incident stroke, but whether post-discharge pollutant exposure influences stroke recurrence remains unknown. We investigated the association between post-discharge exposure to six ambient air pollutants and stroke recurrence in patients with acute ischemic stroke. Methods: We analyzed data from 27,346 patients in the CRCS-K-NIH nationwide multicenter registry of acute ischemic stroke patients (2014-2021) with confirmed ischemic stroke, residential address data, and matched air quality records. The primary exposure was the 3-month post-discharge average concentration of PM10, PM2.5, NO2, SO2, CO, and O2, assessed at the district level using inverse-distance weighted interpolation. The primary outcome was stroke recurrence from 3 to 15 months post-discharge. Cause-specific Cox proportional hazards models accounting for the multilevel data structure were used, with all-cause mortality as a competing risk. Restricted cubic splines assessed nonlinear dose-response relationships. Results: During follow-up (median 364.8 days), 765 patients experienced stroke recurrence and 471 died. Among the six pollutants, only SO2 showed a statistically significant association with recurrence (P for overall association in the restricted cubic spline analysis = 0.024). A potential threshold was identified at approximately 8.2 ppb, above which recurrence risk increased progressively (P for non-linearity = 0.095). The association was numerically stronger among older adults ([&ge;]75 years; P for interaction = 0.051) and women (P for interaction = 0.062). The highest SO2 concentrations were observed in harbor cities (Incheon, Ulsan, Busan), consistent with maritime shipping emissions. No significant associations were observed for the other five pollutants. Conclusions: Elevated post-discharge SO? exposure is associated with increased stroke recurrence risk, particularly in harbor regions and among older adults and women. These findings support incorporating ambient air quality monitoring into secondary stroke prevention strategies.

ObjectiveSeizure dogs are service animals trained to respond supportively to seizures in people with epilepsy; some are also trained to detect seizure-specific scents, particularly ictal volatile organic compounds (VOCs). This survey study examines feasibility and safety of incorporating a seizure service dog (SSD) into an inpatient setting, as well as patient perceptions of having an SSD in the Epilepsy Monitoring Unit (EMU). MethodsOur SSD underwent specialized training for seizure response and seizure recognition based on seizure-specific VOCs, and accompanied his epileptologist owner in the EMU on rounds for over four years prior to the study. We administered surveys to patients hospitalized in the EMU before and after interactions with a trained seizure dog. The surveys assessed the patients comfort with the dog, perceived usefulness of service dogs, safety, and tolerability. Select case examples are also presented in which seizure dog spontaneously alerted prior to epileptic seizures; seizures later confirmed by independent EEG review. ResultsPatient responses underscored overall high enthusiasm for seizure dog therapy, with 93% of participants reporting feeling "very comfortable" or "extremely comfortable" with a seizure dog present. No adverse concerns or negative experiences were reported by participants. 91% reported personally experiencing benefits of working with the seizure dog, citing emotional and comfort benefits during their hospitalization. 94% of participants were comfortable with physical contact with the dog or had no proximity preference. ConclusionThese findings suggest that seizure service dogs can be safely integrated into the inpatient EMU setting and have potential to enhance patient care and emotional well-being during EMU monitoring. Summary PointsO_LITotal of 98 patients admitted to EMU were surveyed about opinions regarding seizure dogs and comfort with integration of seizure dog in EMU setting, with 35 patients completing post-test surveys after interacting with the seizure dog. C_LIO_LI93% of surveyed EMU patients completing post-test surveys felt very or extremely comfortable with the seizure dog; no negative experiences or safety concerns were reported. C_LIO_LI91% reported personally experiencing emotional benefits of working with the seizure dog. C_LIO_LISelect case examples demonstrate that the trained seizure dog in our study may be able to spontaneously identify epileptic seizures. C_LI

Common terns (Sterna hirundo) are regionally threatened migratory seabirds that form large breeding colonies during the North American summer months. They are highly vocal and serve as important bioindicators of aquatic ecosystems. Historically, acoustic studies on colonial seabirds have proven difficult due to the dense aggregations of individuals and high rate of call overlap. However, as passive acoustic monitoring (PAM) becomes increasingly common for studying seabird colonies, quantitative descriptions of species vocalizations are needed to accurately interpret behavioral information from colony soundscapes and support automated analysis of large acoustic datasets. This study aims to quantify the vocal repertoire of adult common terns. We deployed AudioMoths to collect acoustic data at a tern colony on Seavey Island, New Hampshire, USA from across the breeding season. Using RavenPro, unique call types were identified through visual and aural inspection of the acoustic data in the spectrogram. For each call, we then extracted measurements of peak frequency (Hz), bandwidth 90% (Hz), syllable duration 90% (s), and total bout duration (s) to quantify the characteristics of each call type. Statistical analyses for acoustic parameters by call type were performed using Kruskal-Wallis tests, followed by post-hoc Dunn tests. Our results demonstrate that each call type is significantly different from another by at least one parameter, with the exception of the kek and kip/tjuk calls. These findings present the first quantitative analysis of common tern vocalizations for North America. By defining temporal and spectral characteristics for multiple call types, this work helps translate colony soundscape into biologically meaningful information about tern behavior and colony dynamics. These descriptions also provide key parameters for developing automated tools to detect and classify vocalizations in dense, noisy colonies. Integrating quantified vocal characteristics with PAM offers a promising approach for monitoring colony activity and behavior while minimizing disturbance relative to traditional methods.

ContextGrid cells in the medial entorhinal cortex (MEC) of head-fixed mice exhibit ultraslow (<0.01 Hz) oscillations (USO) during walking in a 1D running wheel in darkness. It was proposed that these oscillations may have a connection with navigational behavior. ProblemThere is no clear link between the functional role of these oscillations and path integration, a fundamental navigation strategy used by animals to calculate their current position and orientation by continuously summing self-motion cues. HypothesisGiven the synaptic projections from MEC to the hippocampus, we hypothesized that ultraslow oscillations have a role in linking spatiotemporal memories acquired during navigation. MethodologyA realistic computational model of entorhinal-grid with ultraslow oscillations and hippocampal-place cells is proposed using synaptic plasticity between cell types, sustaining path integration of a rodent-like simulated animal. ResultsUltraslow oscillations induced persistent changes in the grid cell dynamics, represented as a positional drift of grid fields. Such drift resulted in position estimation errors but generated new grid-place cell associations when combined with synaptic plasticity. >DiscussionsUltraslow entorhinal oscillations were found to shape spatial memory through grid cell drifting, which could serve as a mechanism for flexibly accessing different spatial memories during navigation. HIGHLIGHTSO_LIPath integration dynamics hide ultraslow oscillations despite coexistence. C_LIO_LIUltraslow oscillations significantly degrade position estimation in path integration. C_LIO_LIGrid and place fields drift after the effect of ultraslow oscillations. C_LIO_LINew spatial memories were created as a result of the ultraslow oscillation drift. C_LIO_LIUltraslow oscillations enable dynamic access of different spatial memories C_LI

Neural information flow describes the movement of activity between neurons or brain areas. Advances in experimental methods have allowed production of large amounts of observational data related to neuronal activity from the single-neuron to population level. Most current methods for analysing these data are based on pairwise comparison of activity, and fall short of reliably extracting neural information flow network structure. Dynamic Bayesian networks may overcome some of these limitations. Here we evaluate the performance of a range of Bayesian network scoring metrics against the performance of multivariate Granger causality and LASSO regression for their ability to learn the connectivity underlying simulated single-neuron and neuronal population data. We find that discrete dynamic Bayesian networks are the best performing method for single-neuron data, and perform consistently for neural-population data. Continuous dynamic Bayesian networks have a tenancy to learn overly dense structures for both data types, but may have utility in scoping studies on single-neuron data. Multivariate Granger causality is the most robust method for learning structure of neural information flow between neural-populations, but performs poorly on single-neuron data. Significance testing within multivariate Granger causality produces variable results between data types. Overall, this work highlights how the analysis of neural information flow can vary depending on they type and structure of underlying data, and promotes discrete dynamic Bayesian networks as a useful and consistent tool for neural information flow analysis.

BackgroundAutistic individuals experience higher rates of sleep problems throughout their lives, and there is considerable heterogeneity in manifestations of these issues that remains unexplained. Here, we examine associations over time of heterogenous sleep trajectories with autism diagnosis, and behavioural and genetic factors related to autism. MethodWe used data from the Avon Longitudinal Study of Parents and Children (N=13,886, autistic n=150). The primary outcome was parent and self-reported night-time sleep duration, measured on 10 occasions (between 0.5y and 15.5y). The independent variables were autism diagnosis, autism polygenic score (PGS) and four parent-reported autistic traits: repetitive behaviour, social communication, speech coherence, and sociability. Latent class growth analysis was conducted to identify heterogenous classes of sleep trajectories, and these trajectory classes were regressed onto the independent variables. ResultsFour night-time sleep duration trajectory subclasses were identified; shorter (n=512, 4.1%), longer (n=1654, 13.1%), intermediate-shorter (n=3630, 28.8%), and intermediate-longer (used as the reference class; n=6825, 54.1%). An autism diagnosis was associated with a shorter or intermediate-shorter sleep duration trajectory, compared to the reference class. Similarly, higher scores in domains of repetitive behaviour, speech coherence and social communication were associated with shorter sleep duration trajectories. The autism PGS and sociability were not associated with any sleep trajectories compared to the intermediate-longer sleep trajectory (reference group). ConclusionAn autism diagnosis and specific autistic traits were associated with poorer long-term sleep outcomes across childhood and adolescence, highlighting the need for early, sustained sleep interventions, and the potential of trait-specific mechanisms for sleep problems. HighlightsO_LIFour distinct night-time sleep duration trajectories were identified across development C_LIO_LIAutism diagnosis predicted shorter and intermediate-shorter sleep trajectories C_LIO_LISpecific (but not all) autistic traits were linked to shorter sleep trajectories C_LIO_LIAutism PGS did not predict sleep duration trajectories C_LI

BackgroundIntegrating multimodal data into medical artificial intelligence (AI) tools and evaluating whether they outperform human experts remains a critical challenge. Epilepsy surgery offers a unique paradigm for this evaluation, as it provides an expert-independent measure (Engel score) of post-surgical outcome. Currently, evaluation for epilepsy surgery relies on the visual interpretation and human synthesis of multimodal data. While clinical evaluations are individualized and account for complex anatomical variability, integrating these diverse, high-dimensional modalities to generate a probability of surgical success remains challenging. Here, we leverage this objective outcome score to investigate the feasibility of a data-driven, phenotype-based model against the current clinical gold standard. MethodsThe evaluation was performed on an epilepsy-type controlled cohort of 57 patients from six tertiary epilepsy surgery centers who underwent resective/ablative surgery in the mesiotemporal lobe. Multimodal data, namely, patient demographics, semiology, invasive electrophysiology monitoring, and neuroimaging, were utilized. We first estimated how human experts perceive surgery success. Subsequently, we developed a data-driven model integrating these modalities to predict surgery outcomes. The model performance was compared to the current clinical gold standard (three independent human experts) and published outcome calculators. Finally, modality-level phenotypes were derived based on the models predictions. ResultsPredictions by human experts correlated poorly with post-surgical outcomes, and published outcome calculators did not perform better than the experts (DeLongs p = 0.367). Our model incorporating multimodal data achieved an area under the receiver operating characteristic curve (AUROC) of 0.801. It performed statistically better than the best human expert (DeLongs p = 0.043) and achieved a higher AUROC than the best published surgical outcome calculator (0.801 vs. 0.694). ConclusionsWe demonstrated the proof-of-concept that data-driven multimodal phenotypes can inform personalized surgery planning in epilepsy. Furthermore, we provide a framework for integrating multimodal data and benchmarking medical AI performance against human experts.