Cerebral Cortex

Salient sensory stimuli are known to evoke neural activations across distributed brain regions. However, the temporal dynamics of these responses over sub-second timescales remain poorly understood, in part due to limitations in the temporal resolution of non-invasive neuroimaging methods. We examined the spatiotemporal dynamics of neural activations evoked by salient sensory stimuli (rare sounds) using 1,194 widely distributed intracranial electrodes in 5 neurosurgical patients. Salient stimuli preferentially activated 263 of 1,194 electrodes (22%), with responses segregating into two largely distinct spatiotemporal patterns: (1) phasic activation in sensorimotor regions, and (2) sustained activation within the salience network. Cross-correlation analysis revealed that phasic sensorimotor activation preceded sustained salience network activation on a trial-by-trial basis. These findings support an updated view of salience processing in the human brain, revealing that salient stimuli evoke two sequential stages of neural activation--phasic sensorimotor responses followed by sustained salience network activity--rather than simultaneous widespread activation.

Pain captures attention and interferes with executive and motor processes but task performance may be preserved at the cost of more effort. In a preregistered fMRI study, 40 participants performed a visuomotor force-matching task at two force levels under individually calibrated painful or non-painful thermal stimulation, while reporting the intensity of perceived effort. Maintaining task performance under pain was associated with increased perceived effort and recruited brain regions involved in pain modulation and cognitive control. Region-of-interest analysis showed perceived effort was consistently linked to decreased anterior midcingulate cortex activity, whereas supplementary motor area contributions varied depending on its role in motor execution or pain processing. Across experimental condition, motor, pain-modulatory and cognitive-control regions were associated with effort perception. Independently of condition, effort perception was modulated by ventromedial prefrontal cortex and ventral striatum. These findings indicate that effort perception reflects brain activity within areas involved in motor, executive and valuation processes.

A central goal of neuroscience is to understand how neural circuit architecture supports learning. While recent work has clarified the computational role of depth in sensory cortical hierarchies, it remains unclear why predominantly feedforward, non-convolutional circuits such as the cerebellum and olfactory system also contain multiple processing layers. Theoretical work in deep learning has shown that two-hidden-layer networks can achieve classification capacity that scales quadratically with the number of intermediate neurons, but these results rely on nonlocal synaptic optimization and are therefore difficult to reconcile with biological learning rules. Here, we show analytically and numerically that a two-hidden-layer network with feedforward gating can achieve quadratic capacity using local three-factor Hebbian learning when intermediate activity is sparse. This architecture supports efficient one-shot learning and, in settings where backpropagation requires many repeated weight updates, offers an advantage in learning speed. Beyond random perceptron tasks, the model also performs well on structured cerebellum-related tasks, including reinforcement-learning-based motor control. Mapping the model onto cerebellar microcircuitry further suggests functional roles for dendritic compartmentalization, branch-specific inhibition, and disinhibitory interneuron pathways. Together, these results extend the Marr-Albus-Ito framework by showing how the presence of multiple intermediate layers in cerebellum-like circuits can support fast, local, and high-capacity learning.

Recent advances have shown that the complexity of neural signals tracks global states of consciousness, such as wakefulness versus sleep. However, it is still unclear to what extent neural complexity reflects fine-grained changes in conscious content within the same global state. Here, we investigate how the complexity of brain signals is affected by increased perceptual clarity of a stimulus. To this end, we estimated neural signal complexity using Complexity via State-space Entropy Rate (CSER) to EEG recordings from an auditory discrimination task. In this paradigm, auditory stimuli were presented at varying signal-to-noise ratios (SNRs), with higher SNRs corresponding to greater subjective audibility and perceptual clarity, enabling us to relate neural complexity to graded perceptual awareness within a constant global state of consciousness. Our results showed that, while broadband CSER remains constant across SNRs, its spectral decomposition displays frequency-specific effects, with higher SNRs associated with a decreased complexity in and {beta} bands, increased complexity in{delta} , and no significant changes in{gamma} . Additionally, a temporal investigation of CSER exhibited a significant increase in complexity with stimulus clarity, with deviations from baseline peaking approximately 30 ms before the ERP. Extending this analysis to pairs of brain regions, mutual information rate uncovered a sudden post-stimulus breakdown in long-range information transmission relative to baseline. Taken together, these results reveal that while aggregated complexity measures track global states of consciousness, time- and frequency-resolved information-theoretic measures can capture variations in perceptual awareness, demonstrating their sensitivity as estimators of the level of conscious experience.

Short-interval intracortical inhibition (SICI) is the most widely used neurophysiological index of GABAergic inhibition in the human cortex. However, it is an indirect measure, inferring synaptic inhibition from suppression of peripherally recorded motor-evoked potentials (MEPs) elicited by transcranial magnetic stimulation (TMS). In the standard protocol, a subthreshold conditioning pulse suppresses the MEP evoked by a suprathreshold test pulse delivered 1-5 ms later. Interpretation is further complicated by temporal overlap with short-interval intracortical facilitation (SICF), reflecting excitatory interactions at interstimulus intervals of [~]1.5 and 2.7 ms. To overcome these limitations, we recorded immediate TMS-evoked EEG potentials (iTEPs; 1-10 ms post-stimulus) as a more direct measure of motor cortical activity in 16 healthy volunteers (20-35 years; 7 male). The conventional SICI protocol suppressed only later components of the iTEP, likely corresponding to late corticospinal volleys previously identified in epidural spinal recordings after suprathreshold TMS, while the earliest iTEP component was unaffected. Importantly, later iTEPs were suppressed to a similar extent whether conditioning-test intervals coincided with SICF peaks or troughs, and the magnitude of iTEP suppression correlated with concurrently recorded paired-pulse MEP suppression. SICI also reduced an early TEP component (N15; 10-20 ms), but paired-pulse N15 suppression showed a different dependence on stimulus intensity and did not correlate with MEP suppression. These findings demonstrate that SICI measured via MEPs does not reflect a global index of cortical GABAergic motor cortical inhibition but instead reflects inhibition within specific cortical circuits that can be investigated directly with iTEPs.

Early behavioral and temperamental differences are important indicators of later socioemotional development and psychopathology risk, yet their neural bases near birth remain incompletely understood. Using resting-state fMRI data from the Developing Human Connectome Project, we examined whether neonatal functional connectivity predicts 18-month behavioral and temperament outcomes in 397 infants (277 term-born, 120 preterm-born). Outcomes were assessed using the Child Behavior Checklist (CBCL) and the Early Childhood Behavior Questionnaire (ECBQ). We applied a stability-driven, ROI-constrained connectome-based predictive modeling framework to identify robust whole-brain connectivity features associated with later externalizing, internalizing, surgency, negative affect, and effortful control. Significant predictive models were observed for multiple outcomes across the whole cohort as well as within term-born and preterm-born groups, with clear differences in predictive architecture between cohorts. Across analyses, prefrontal and temporoparietal systems were repeatedly implicated, alongside medial temporal, fusiform, parahippocampal, and orbitofrontal-related regions. These findings indicate that large-scale neonatal functional organization is meaningfully related to later socioemotional and behavioral variation, and that preterm birth is associated with partly distinct predictive connectivity patterns.

Regular cannabis use has been associated with alterations in reward-related neural processes, yet findings remain inconsistent and the relationship between neural activity and behavioural performance is not fully understood. The present study aimed to characterise neural and behavioural correlates of reward processing in regular cannabis users (CU) compared with matched non-users (NU) using the Monetary Incentive Delay Task (MIDT). Firstly, we assessed behavioural performance through reaction times, accuracy and monetary earnings to determine whether potential neural alterations were reflected in task performance. Secondly, focusing on reward-related brain regions, we examined group differences in BOLD functional MRI activity during anticipation and outcome phases separately for monetary win and loss conditions. Finally, we explored the association between behavioural performance and neural activation. Our findings indicate that regular cannabis use is associated with altered engagement of key nodes within the mesocorticolimbic circuit during both anticipatory and outcome phases of reward processing, accompanied by impaired behavioural performance. Particularly, compared with NU, CU showed (I) lower striatal activity during anticipation of monetary win and higher ventral striatum and frontal pole activity during anticipation of monetary loss; (II) greater VTA activation during outcome of successful monetary win and loss avoidance and lower frontal pole activity during outcome of unsuccessful loss avoidance; (III) impaired behavioural performance, reflected in lower monetary rewards and a trend towards slower reaction times and reduced accuracy; (IV) disrupted brain-behaviour coupling. Results from this study may help inform future research on the neurobiological mechanisms underlying changes in reward function and the resultant behavioural consequences of cannabis use.

Cognitive reappraisal, the deliberate reinterpretation of emotional events, is widely considered an effective emotion regulation strategy, and modulation of the late positive potential (LPP) during negative affect reduction has become the primary electrophysiological evidence for volitional emotional control. Experimental instructions, however, impose dual-task demands that free viewing does not, confounding reappraisal with cognitive load. By including instructions to increase emotional responses to pictures ("enhance") as well as instructions to decrease ("suppress"), different predictions are generated. If the LPP reflects regulation, then, compared to free viewing, suppress instructions should decrease LPP amplitude, and enhance instructions should increase LPP amplitude. If modulation instead reflects cognitive load, both instructions should reduce the LPP, as both impose an additional cognitive task. In a sample of 107 participants, evaluative ratings confirmed that regulation instructions modulated reported emotional intensity in the expected directions (Enhance > View > Suppress), but that both enhance and suppress instructions reduced LPP amplitude compared to free viewing, with Bayesian model comparisons providing strong evidence against direction-specific regulation and in favor of cognitive load. Whole-scalp multivariate pattern analysis confirmed that no instruction-related neural signal exists at any scalp location or latency within the first second after stimulus onset. These data indicate that LPP modulation following both instruction types reflects dual-task cognitive load rather than volitional emotional control. Significance StatementCognitive reappraisal is considered the gold standard of emotion regulation, and reduced late positive potential (LPP) amplitude during negative emotion suppression is the primary neural evidence that humans can voluntarily control emotional responses. The current data are inconsistent with this regulatory account and instead support a cognitive load interpretation. Whether instructed to enhance or suppress emotional responses, LPP amplitude was reduced in both conditions relative to free viewing, consistent with attentional resource competition rather than directional regulatory control. The same participants reported successfully regulating emotional experience in opposite directions, producing a clear dissociation between neural and behavioral measures. These findings challenge a basic tenet of emotional regulation and raise questions concerning LPP modulation as a biomarker of regulatory capacity.

Substance use disorders run in families, yet the mechanisms underlying intergenerational transmission remain unclear. We investigated indirect genetic effects, pathways through which parental genotypes influence offspring phenotypes via the family environment, for alcohol use disorder (AUD), nicotine dependence (ND), and related quantitative outcomes, and aimed to identify family environmental factors through which such effects may operate. Using transmitted and non-transmitted polygenic scores (PGS) constructed for problematic alcohol use, tobacco use disorder, and general addiction liability, we analyzed 5972 European-ancestry adult offspring with at least one genotyped parent from the population-based Lifelines cohort (Netherlands). Offspring outcomes included lifetime DSM-5 AUD diagnosis, AUD symptom count, maximum drinks in 24 hours, Fagerstrom Test for Nicotine Dependence score, and cigarettes per day. AUD findings were meta-analyzed with data from the Brisbane Longitudinal Twin Study (N = 1368; Australia). We also examined parent-of-origin effects and mediation by parental substance use and socioeconomic status using structural equation modeling. Transmitted PGS robustly predicted all AUD and ND outcomes ({beta} = 0.07-0.16; OR = 1.20 for AUD diagnosis). Non-transmitted PGS, indexing indirect genetic effects, were negligible for all clinical syndrome outcomes. The only significant indirect genetic effect was on cigarettes per day ({beta} = 0.03, p = 0.01), mediated by parental smoking behavior but not socioeconomic status. These findings indicate that intergenerational transmission of risk for AUD and ND is driven primarily by direct genetic effects, with modest indirect genetic effects on smoking quantity. Larger samples and cross-trait analyses are needed to further elucidate these mechanisms.

ObjectiveSocial difficulties are transdiagnostic in childhood, but their heterogeneity is poorly characterised and rarely treated as a primary neurodevelopmental phenotype. This matters because childhood and adolescence are sensitive periods for peer relationships and brain development. We used data-driven modelling and non-linear mapping to derive social profiles and test their clinical, cognitive, and neural correlates. MethodsParticipants were 992 children aged 5-18 years from CALM (Mage = 9.6). Social items from the SDQ, CCC-2, and Conners-3 were modelled using a regularised partial correlation network to derive core social dimensions. A self-organising map captured graded social profiles. Simulated archetypes, SVM-based island identification, and permutation testing defined profile regions and centroid-distance scores. Profiles were related to referral, diagnosis, cognition, BRIEF indices, and T1-derived MIND network structure in an MRI subsample (n = 431). ResultsWe identified four profiles: social engagement, friendship difficulties, social withdrawal, and peer victimisation. Profile expression tracked variation in referral and diagnostic pathways. Social withdrawal showed the clearest disadvantage across cognitive domains, whereas social engagement was associated with fewer executive function difficulties across BRIEF indices. MIND strength components covaried with profile expression (a significant PLS latent variable, p = 0.02), with covariance strongest for social withdrawal and peer victimisation. ConclusionsChildhood social functioning organises graded signatures that relate to clinically relevant pathways, cognitive and executive outcomes, and brain structure. Profiling social signatures provides a scalable framework for identifying social need beyond diagnostic categories, motivating studies to test directionality and improve developmental outcomes.

Background: Transcranial temporal interference stimulation (tTIS) is an emerging noninvasive neuromodulation approach that enables focal, frequency-specific modulation of deep brain regions, offering a novel method for investigating therapeutic mechanisms underlying brain and mental health disorders. Pain is a key target because it is a feature of multiple disorders and is increasingly understood to depend on brain circuits. Here, we tested the effects of tTIS on bilateral evoked pain, capitalizing on converging evidence from human and animal studies indicating that the primary motor cortex (M1) contains body-wide inter-effector regions and has descending projections to regions implicated in nociceptive, motivational, and autonomic processing, making it a key cortical target for pain modulation. Methods: We conducted a pre-registered, triple-blind, randomized crossover study (N = 32, 160 study sessions), investigating frequency-dependent effects of tTIS applied to the left M1 on experimentally evoked thermal pain in healthy adults. We tested four stimulation frequencies (10 Hz, 20 Hz, 70 Hz, and sham) on separate days (>10,000 pain trials total). Noxious heat was applied to both the right and left forearms using individually calibrated temperatures both pre- and post-stimulation. Results: Active tTIS produced significant analgesia at all stimulation frequencies (10 Hz, 20 Hz, and 70 Hz) relative to sham (Cohens d = 0.46-0.82, all p < 0.05). 10 Hz produced the greatest reduction (d = 0.82), and both 10 Hz and 20 Hz produced more analgesia than 70 Hz (d = 0.44 and 0.38, respectively; p < 0.05). Stimulation-related sensations were equivalent across frequencies, and participants were blind to condition. Pain reductions remained stable over a [~]40-min post-stimulation period and were bilateral, consistent with stimulation of body-wide inter-effector regions. Conclusions: These results provide the first evidence that tTIS can reliably reduce experimental pain perception in humans in a frequency-dependent manner, providing a foundation for noninvasive pain modulation with tTIS.

Parkinson's Disease (PD) is a complex neurodegenerative disorder that manifests through systemic, large-scale physiological reorganizations. While research often focuses on region-specific neural changes, there is a growing need for multidomain approaches to capture the complexity of the disease and its clinical heterogeneity. This study proposes an analytical pipeline to evaluate Brain-Heart Interplay (BHI) as a novel systemic biomarker for neurodegeneration and healthy ageing. In this study we assessed BHI across three open-source datasets (EEG and ECG signals). We compared Healthy Young, Healthy Elderly, and PD patients in resting state to investigate the effects of ageing and cognitive performance. Additionally, we studied BHI trends in PD patients in the moment of freezing of gait (FOG). Methodologically, brain network organization was quantified using coherence-based EEG connectivity and graph theory, while heart activity was analyzed through Poincare plot-derived measures of cardiac autonomic activity. The coupling between these two systems was measured using the Maximal Information Coefficient to capture linear and non-linear dependencies between global cortical organization and cardiac autonomic outflow. The results demonstrate that BHI is a sensitive biomarker for detecting early multisystem dysfunction in both neurodegeneration and ageing. Furthermore, the identification of specific BHI trends during FOG onset suggests new opportunities for understanding the physiological mechanisms driving motor complications in PD. Our proposed pipeline provides a guiding tool for large-scale physiological assessment in clinical research.

Human sentence comprehension proceeds word-by-word, with prior research proposing two central sources of cognitive demand during incremental processing: forward-looking disambiguation of the incoming information stream, and backward-looking retrieval of information associated with previous words from working memory. Recent work has shown that Transformer-based language models successfully generate predictions about sentence processing load in human psycho- and neurolinguistic data by operationalizing disambiguation cost as next-token surprisal, and memory retrieval cost as normalized attention entropy (NAE). Such models, however, remain difficult to interpret as it is not well understood what factors play causally into the decision to assign a cost value to a given word in such artificial neural networks. Here, we present interpretable and cognitively grounded models of disambiguation and memory retrieval and evaluate their neural alignment and spatio-temporal correlates using human magnetoencephalography responses to naturalistic narrative speech. Multivariate temporal response function modeling demonstrates firstly that these human-bias-informed models fare equally well in accounting for observed human language processing data as their Transformer counterparts. This same modeling framework then suggests that surprisal and NAE temporally dissociate in the cortical language network -- surprisal being predictive of bilateral superior temporal gyrus and supramarginal gyrus activation [~]300-500 ms, and NAE being predictive of activity in the same regions, but later [~]750-850 ms. By demonstrating that interpretable neurocomputational models can achieve meaningful brain alignment while maintaining explanatory transparency, this work offers a methodological blueprint for bridging the gap between algorithmic theory and neural implementation.

Post-stroke cognitive recovery is difficult to predict using focal lesion characteristics alone. The brain's capacity to maintain cognitive function depends also on structural integrity of the whole brain. One way to measure brain health is through the severity of cerebral small vessel disease (CSVD) markers, which reflect aging-related pathologies that erode structural integrity. Here, we propose a composite measure of CSVD (cCSVD) integrating three independently validated biomarkers automatically quantified using T1-weighted MRIs: white matter hyperintensity volume (WMH; representing vascular injury), perivascular space count (PVS; putative glymphatic clearance), and brain-predicted age difference (brain-PAD; structural atrophy). We hypothesize that cCSVD, which captures the shared variance across these CSVD biomarkers, will be a robust indicator of whole-brain structural integrity and predict cognitive changes 3 months after stroke. We analyzed 65 early subacute stroke survivors with assessments within 21 days (baseline) and at 90 days (follow-up) post-stroke. WMH volume, PVS count, and brain-PAD were quantified from baseline T1-weighted MRIs, and then residualized for age, sex, days since stroke, and intracranial volume. Principal component analysis (PCA) of the residualized biomarkers was used to derive cCSVD. Beta regression with stability selection using LASSO was used to model three outcomes: baseline Montreal Cognitive Assessment (MoCA) scores, follow-up MoCA scores, and longitudinal change (follow-up score adjusted for baseline score). Logistic regression was used to test if baseline cCSVD predicted improvement in those with baseline cognitive impairment (MoCA < 26). The PCA revealed that the first principal component (PC1) explained 43.1% of the total variance among WMH volume, PVS count, and brain-PAD. The three biomarkers contributed nearly equally to PC1, which was subsequently used as the baseline cCSVD score. Lower baseline cCSVD was significantly associated with better MoCA scores at follow-up ({beta} = -0.19, p = 0.009), even after adjusting for baseline MoCA ({beta} = -0.12, p = 0.042), and, importantly, outperformed all individual biomarkers. Furthermore, lower cCSVD at baseline significantly increased the likelihood of improving to cognitively unimpaired status at three months (OR = 0.34, p = 0.036), independent of age and education. The composite CSVD captures the additive impact of vascular injury, glymphatic dysfunction, and structural atrophy on recovery in a way that individual measures do not. cCSVD accounts for shared variance across these domains, reflecting a patient's latent capacity for cognitive recovery, where relative integrity in one CSVD domain may mitigate effects of another. This automated, T1-based framework offers a scalable tool for predicting post-stroke recovery.

Visual surface perception is a fundamental aspect of vision, yet its neural implementation remains poorly understood. Troxlers perceptual filling-in paradigm provides a tractable illusion for studying surface perception, in which a peripheral figure becomes perceptually assimilated into the surrounding background after a period of sustained fixation. Although neural correlates of this phenomenon have been reported in early visual cortex, the underlying mechanisms, particularly the contribution of feedback signaling, remain unresolved. Here we use ultra-high-field (7T) layer-fMRI to investigate perceptual filling-in in the human visual cortex. While experimentally controlling perceptual filling-in, we measured GE-BOLD responses in ten participants. Analyses across cortical depth in the independently localized figure representation in primary visual cortex (V1) revealed neural correlates of filling-in in deep cortical layers, which are associated with feedback input. These findings provide evidence that perceptual filling-in and visual surface perception in general are supported by feedback signals to early visual cortex.

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.

Objectives: Non-suicidal self-injury (NSSI) and self-harming sexual behaviours share functional and behavioural overlaps. However, the relationship between NSSI and problematic sexual behaviour (PSB) remains underexplored. This study aimed to investigate the association between NSSI and PSB in two cohorts - a non-clinical university cohort and a clinical PSB patient cohort. Methods: Data were collected from 2,189 university participants and 477 clinical PSB patients. NSSI was assessed via self-report, and PSB was measured with the Sexual Addiction Screening Test-Revised (SAST-R) Core. The four core addictive dimensions of PSB: relationship disturbance, loss of control, preoccupation, and affect disturbance, were also evaluated. Logistic regression analyses were conducted to examine the association between PSB (presence/absence and severity) and NSSI, looking at effects of gender and contributions of addictive dimensions of PSB. Results: Rates of NSSI were similar in the university (7.1%) and patient (5.7%) cohorts; stratified by gender, a higher proportion of women PSB patients had NSSI compared to in the university cohort (29.3% vs 9.3%). In the university group, who had milder PSB than patients, PSB was associated with NSSI (OR=2.11, p<0.001); a significant gender by PSB interaction was found showing that women with PSB were over four times more likely to have NSSI than men without PSB (OR=4.44, p=0.037). In contrast, PSB severity was not associated with NSSI in PSB patients (OR=1.10, p=0.25). Associations of the addictive dimensions of PSB with NSSI were observed only in the subgroup of university women, in the 'preoccupation' dimension (p<0.001). Conclusions: Our findings highlight gender-specific patterns in the association between PSB and NSSI, suggesting the need for further research and possibly targeted prevention and intervention strategies in women.

Chronotype reflects individual circadian preference for timing of sleep, wakefulness, and peak performance and has been linked to variability in prefrontal cognitive function across the day. Whether chronotype independently relates to dual-task gait cost (DTC) and whether this relationship differs by cognitive task domain is unclear. Sixty-nine healthy young adults (37 female; mean age 21.3 years) completed the Morningness-Eveningness Questionnaire (MEQ). Spatiotemporal gait parameters were recorded with three-dimensional motion capture during single-task walking and three dual-task conditions: backward word spelling (5LWB; phonological), serial subtraction by seven (SS7; arithmetic), and reverse month recitation (RMR; sequential). DTC was calculated for eight gait parameters. Condition differences were assessed with nonparametric tests and post-hoc comparisons. Multiple linear regression, adjusting for age, sex, BMI, and baseline gait velocity, tested the independent association between MEQ score and mean velocity DTC; exploratory Spearman correlations examined other parameters. SS7 produced the largest mean velocity DTC (-12.76%), significantly greater than 5LWB (-7.95%; p = 0.002) and RMR (-9.57%; p = 0.021). MEQ score independently predicted mean velocity DTC in 5LWB ({beta} = -0.51, p < 0.001, R{superscript 2} = 0.269) and RMR ({beta} = -0.55, p = 0.004, R{superscript 2} = 0.222), indicating greater morningness associated with better gait-speed preservation under cognitive load; the SS7 association was not significant ({beta} = -0.33, p = 0.071). Exploratory correlations showed MEQ-DTC associations across 7/8 parameters in 5LWB, 4/8 in RMR, and 3/8 in SS7. Chronotype is independently associated with dual-task gait cost in a task-domain-specific manner, with stronger effects for phonological and sequential tasks than for arithmetic processing. The SS7 condition yielded the largest interference but weakest chronotype modulation, suggesting arithmetic dual-task disruption may be less sensitive to circadian arousal. Fixed testing time and cross-sectional design warrant within-subject, multi-timepoint studies to confirm chronotype effects separate from time-of-day confounds.

AimAutistic children have a high but varied prevalence of internalizing and externalizing problems. This study aimed to identify the subtypes of internalizing and externalizing problems among autistic preschool children in Japan, examine their temporal stability, and investigate differences in participation in daily life and family outcomes across these subtypes. MethodsA prospective cohort study was conducted with 275 caregivers of autistic children aged 51-75 months. Internalizing and externalizing problems were assessed using the Strengths and Difficulties Questionnaire. ResultsLatent transition analysis identified five subtypes: Low-symptom, High-emotional, Externalizing, Comorbid, and Peer-difficulty groups. Membership in the High-emotional and Externalizing groups was relatively stable over time, whereas the Peer-difficulty group showed frequent transitions to subtypes with higher levels of internalizing or externalizing problems. Significant differences in participation in daily life and family outcomes were observed across subtypes, but these patterns were inconsistent with a simple gradient of symptom levels. ConclusionsThe novel findings that the temporal stability of subtype membership varied and that differences in participation in daily life and family outcomes were observed across the subtypes suggest that the heterogeneity of internalizing and externalizing problems may be associated with variations in childrens participation in daily life and family outcomes over time. Plain Language SummaryAutistic preschool children often experience emotional and behavioral difficulties, but the way these difficulties manifest varies widely across individuals. This study aimed to identify the patterns of these difficulties, examine how they change over time, and investigate how participation in daily life and family outcomes differ across autistic preschool children. We conducted a study with 275 caregivers of autistic children aged 4-6 years in Japan. From caregiver reports of childrens emotional and behavioral difficulties, five distinct patterns were identified: a group with mainly emotional difficulties, a group with mainly behavioral difficulties, a group with both types of difficulties, a group with relatively low levels of difficulties, and a group characterized primarily by peer-related difficulties. Our findings suggest that different patterns of emotional and behavioral difficulties are associated with differences in childrens participation in daily life and family outcomes. These differences could not be explained simply by the overall severity of difficulties but rather reflect distinct patterns based on the type of difficulty. The results indicate that autistic children face diverse difficulties that change over time.

Hidden (semi-) Markov Models (HsMMs) are increasingly being used to segment neurophysiological signals into sequences of latent cognitive processes. The idea: different processes will leave distinct traces in trial-level recordings of (multivariate) neuro-physiological signals. Markov models, equipped with an emission model of these traces and a latent process model describing the progression through the different latent processes involved in a task, can then be used to infer the most likely process for any time-point and trial. However, the currently used HsMMs remain limited in two important ways. First, they cannot account for subject-level heterogeneity in the latent and emission process. Instead, a single group-level model is assumed to explain the entire data. Second, they cannot account for the potentially non-linear effects of experimental covariates on the latent and emission process. To address these problems, we present a modeling framework in which the HsMM parameters of the emission and latent process are replaced with mixed additive models, including smooth functions of experimental covariates and random effects. We derive all necessary quantities for empirical Bayes and fully Bayesian inference for all parameters and provide a Python implementation of all estimation algorithms. To demonstrate the advantages offered by this framework, we apply such a multi-level model to an existing lexical decision dataset. We show that, even in such a simple task, not all subjects rely on the same processes equally and that at least two semi-Markov states, previously believed to reflect distinct processes, might actually relate to the same cognitive process.