Respiratory motion introduces positional uncertainties in radiotherapy for thoracic and abdominal tumors, and system latencies between respiratory signal acquisition and
 beam adjustment further increase targeting errors. This study aims to develop a precise real-time prediction method for discontinuous respiratory motion under prolonged latency
 conditions.

 Approach. We propose DiscoTSNet, a phase-guided temporal-spatial network for discontinuous respiratory motion prediction. The model combines a Phase-Guided Respiratory Decomposition
 module for separating respiratory signals into aperiodic, inhalation, and exhalation components; a Disco2D mechanism for organizing historical sequences according to known system
 latency; and a Spatial Feature Extraction module for modeling relationships among the coordinate channels of the surface markers.

 Main results. DiscoTSNet was evaluated on a publicly available CyberKnife respiratory motion dataset and achieved the lowest mean absolute error (MAE) and mean squared error (MSE)
 and the highest coefficient of determination (R^2) and correlation coefficient (CORR) among the compared models across the evaluated latency conditions from 200 ms to 1000 ms. At
 1000 ms latency, its MAE, MSE, R^2, and CORR were 0.186, 0.107, 0.688, and 0.831, respectively.

 Significance. The proposed latency-guided discontinuous respiratory motion prediction method is tailored to radiotherapy motion compensation and has potential to improve real-time
 tumor tracking and enhance radiotherapy precision.

Some of the most demanding perceptual tasks require sustained vigilance to detect and classify infrequent, unpredictable targets within continuous sensory streams. Here, we designed a sustained listening task to ask whether cardinal classes of perceptual errors (misclassifications, miss, and false alarms) were associated with variability in event-related processing, ongoing cortical state, and autonomic arousal. We recorded 64-channel scalp EEG and pupil diameter in 39 adults while they monitored streams of random tone pips for occasional target sequences of repeating tones. A target-evoked negativity varied systematically with listening outcome: it was largest for correct reports, reduced for misclassifications, and nearly absent for misses. Notably, these differences emerged within the first 500 ms of the target sequence, before the information needed to classify its length was available. Related negativities also accompanied false alarms despite the absence of a target. By contrast, neural synchronization to acoustic cues in individual tone pips did not differ across perceptual outcomes. We then looked further back in time, to the 1 s period preceding target onset, and identified additional signatures of forthcoming misclassifications and false alarms in pupil diameter and EEG aperiodic slopes. Models evaluated with leave-one-subject-out cross-validation showed that baseline state measures and event-related negativity amplitude provided complementary information about the probability and type of forthcoming errors. These findings link perceptual errors during sustained listening to higher-order cortical and arousal-state dynamics rather than fluctuant encoding of bottom-up acoustic cues.

Non-oscillatory, aperiodic activity, is an emerging signal of interest whose physiological and functional origins are just beginning to be probed for their potential link to systems-level physiology and cognition. But there are several pernicious, practical, and statistical hazards to be aware of when deploying spectral parameterization tools for disentangling oscillations and aperiodic activity over short, noisy time windows. In this primer, we provide a set of recommendations and guidelines for time-resolved and event-related spectral parameterization to help researchers meet these challenges. Specifically, we provide recommendations on how to optimize and assess model fits, how to report analysis decisions to maximize reproducibility, and what to do when spectral parameterization fails to identify the presence of oscillations. These recommendations will help guide the study of the distinct dynamics of periodic and aperiodic neural activity in cognition, behavior, and disease at fine timescales.

Transcranial direct current stimulation (tDCS) is a promising intervention for chronic disorders of consciousness (DoC), yet therapeutic responsiveness varies substantially across patients and does not conform to diagnostic boundaries. Contemporary theories of consciousness implicate the posterior parietal cortex as a critical node for conscious processing, but the electrophysiological mechanisms engaged by posterior parietal stimulation remain poorly characterized. High-definition tDCS (HD-tDCS) was applied over Pz in 42 patients with chronic DoC, who were stratified as responders (R+, n = 32) or non-responders (R-, n = 10) according to the emergence of new behavioral signs on the Coma Recovery Scale-Revised. Resting-state electroencephalography (EEG) was analyzed using spectral parameterization (FOOOF), band-limited estimates within two frequency windows informed by the ABCD framework (5-9 Hz and 20-35 Hz), recurrence quantification analysis (RQA), Lempel-Ziv complexity (LZc), and normalized spatial complexity (NSC). In R+ only, the aperiodic exponent decreased at four centro-parietal electrodes, power in the 20-35 Hz window increased at nine electrodes, RQA metrics decreased across 13-18 electrodes, LZc increased at 11 electrodes, and NSC in the 20-35 Hz window increased significantly (p = 0.0222). No corresponding changes were detected in R-. Relief-based feature selection identified the aperiodic exponent and the 20-35 Hz NSC feature from eight candidate biomarkers for training a Bayesian-optimized support vector machine. SHapley Additive exPlanations indicated comparable contributions from both features. These findings identify responder-specific changes in periodic and aperiodic spectral parameters and in nonlinear EEG descriptors following Pz-targeted HD-tDCS. They are consistent with modulation of cortical state and large-scale dynamics, but do not by themselves demonstrate excitation-inhibition rebalancing or thalamocortical loop reinforcement, and the single-arm design without sham stimulation precludes attribution of the observed changes specifically to HD-tDCS.

Background/Objectives: Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are frequently co-occurring neurodevelopmental conditions with partially overlapping neurophysiological profiles. Electroencephalography (EEG) provides non-invasive access to candidate biomarkers, yet the literature remains largely organized around single-diagnosis frameworks, limiting comparison across conditions and constraining translation into intervention selection. This review compares EEG signatures across ASD and ADHD from a transdiagnostic perspective and examines how such signatures might inform the selection of non-pharmacological interventions. Methods: A structured search of PubMed, Scopus, IEEE Xplore and Web of Science identified peer-reviewed studies published between 2010 and 2026 reporting EEG findings in ASD and/or ADHD, spanning resting-state, task-based, connectivity, event-related potential, machine learning and intervention studies. Sixty-eight sources were synthesized thematically. Given substantial heterogeneity in acquisition parameters and analytic pipelines, evidence was integrated interpretively rather than pooled quantitatively, and no formal risk-of-bias assessment was undertaken. Results: Shared features across both conditions frequently included low-frequency theta excess, reduced alpha modulation under cognitive load, and flattened aperiodic (1/f) slopes-a pattern compatible with, though not a direct measurement of, altered excitation/inhibition balance. While substantial heterogeneity exists, disorder-specific signatures often comprised the ASD "U-shaped" spectral profile alongside elevated epileptiform activity, and frontally pronounced theta/beta ratio elevation in subsets of individuals with ADHD. Machine-learning studies increasingly emphasize interpretable, multidomain feature sets over binary classification. Mindfulness-based and neurofeedback interventions converge on theta reduction and alpha enhancement, although reported effects are frequently conditional on responder status, task context, or outcome-rater blinding. Conclusions: Convergent EEG features support a transdiagnostic account of neurodevelopmental dysregulation. A biomarker-informed framework for intervention selection is proposed, which requires prospective validation before clinical application.

Parkinson's disease (PD) diagnosis relies on subjective clinical examination of motor signs that can be mild, intermittent, or absent early in the disease course, motivating objective, low-cost, non-invasive markers for earlier, more consistent detection. Voice recordings, acquirable with nothing more than a microphone, are a strong candidate, and this study develops a machine learning pipeline for voice-based PD screening built on the Competitive Swarm Optimizer (CSO), which jointly searches the acoustic feature subset, classifier configuration, and binarization transfer function, instead of optimizing the feature subset alone as most prior pipelines do. Evaluated on two public, subject-grouped voice datasets, Oxford and Naranjo, against five baselines under an identical protocol across 20 runs per method, our proposed pipeline attains the highest mean balanced accuracy on Naranjo with 0.847 and the second-highest on Oxford with 0.810, accuracies consistent with the wider voice-based PD screening literature; because this evidence comes from two small, single-recording-protocol, retrospective public datasets of 31 and 80 subjects each, we present it as an initial, encouraging step toward a first-pass triage or between-visit monitoring tool, pending external validation on a prospectively collected, multi-site cohort, not as a standalone diagnostic instrument. As an initial step toward clinical interpretability, we check which acoustic features are selected most consistently across 20 repeated runs of the proposed pipeline against established physiological correlates of Parkinsonian dysphonia; agreement between any two runs' complete feature subsets is weak, but pitch period entropy, a nonlinear-dynamical measure of aperiodic pitch period variability, is selected far more often than chance on both datasets, consistent with the underlying pathophysiology and not merely predictive. These results support voice-based, metaheuristic-optimized screening as a plausible, interpretable, low-burden tool for telemedicine and home monitoring.

Bidirectional communication between the heart and the brain shapes emotion, self-regulation, and adaptive behaviour, yet how the brain's intrinsic organisation supports cardiac interoception remains unclear. Two assays dominate the literature - heartbeat counting (HBC) and heart-rate discrimination (HRD) - but whether they engage separable resting-brain networks at high spatial density is untested. We analysed 256-channel resting EEG from 47 healthy adults (5 min eyes-open, 5 min eyes-closed) with the Schandry HBC task (Schandry, 1981; Garfinkel et al., 2015) and the psychophysical HRD task (Legrand et al., 2022), which includes a tone-versus-tone exteroceptive control block. Source-space connectivity was evaluated with the network-based statistic (NBS), complemented by modified multi-scale sample entropy. HBC accuracy was unrelated to HRD sensitivity in either modality (d', slope), though it covaried with the HRD interoceptive threshold, which indexes bias rather than precision (ρ= +0.41, p = .004), a parameter that indexes bias rather than precision. Entropy yielded the most robust association: higher eyes-closed source-space sample entropy co-occurred with better HBC accuracy across a broad multi-network cortical mantle (50 of 100 parcels at q<0.05); no other behaviour produced an FDR-significant parcel. This entropy-accuracy association survived adjustment for alpha power and the aperiodic (1/f) exponent, and all headline associations were unchanged after controlling pulse-derived heart rate and heart-rate variability and after excluding reversed responders identified by influence diagnostics. NBS returned four family-wise-significant subnetworks. Three were anchored on default-mode parcels but tracked d' in the exteroceptive rather than the interoceptive block; the fourth, a disjoint theta-band dorsal-attention-somatosensory cluster, tracked HBC awareness. Because 128 NBS families were screened and the reported clusters re-test a pre-selected subset of them, the yield of significant clusters does not clearly exceed chance expectation; the connectivity results are therefore reported as hypothesis-generating. The findings support a behavioural and neural dissociation between HBC and HRD and caution against pooling them as interchangeable indices of interoceptive accuracy.

We introduce a physics-based parametric generative model for a class of wave-like, organic aesthetic patterns that streamlines the conventional two-stage design workflow. Unlike conventional approaches that rely on separate texture design and manual invariant mappings for seamless tiling, our method simultaneously derives texture and layout from the explicit solution of the thin-plate vibration equation. By encoding symmetry and tiling constraints directly as boundary conditions, the model inherently guarantees seamless, symmetric patterns without ad-hoc mapping functions. Within this framework, artists can intuitively control style and structure through a compact set of visually interpretable parameters, enabling efficient generation of diverse periodic and aperiodic tilings. Extensive experiments validate the method's efficiency, structural coherence, and stylistic richness, demonstrating clear advantages over conventional approaches in both visual diversity and structural integrity. This work offers a novel, interpretable, and efficient approach to controllable digital art creation.

Aperiodic neural activity - historically dismissed as noise - is independently modulated from neural oscillations during visual working memory maintenance. In the context of aging, "flatter" trait aperiodic activity is associated with poorer visual working memory performance. Here, we causally test the role of aperiodic neural activity in visual working memory in younger adults. We introduce a novel noninvasive neurostimulation method - transcranial random aperiodic stimulation (tRAS) - to causally manipulate aperiodic activity to be either steeper or flatter. In a randomized, double-blind, placebo-controlled, crossover neurostimulation design (n = 30), we show that online noninvasive tRAS improves visual working memory precision when aperiodic activity is causally steepened. Our novel stimulation method paves the way for causal studies of non-oscillatory, aperiodic activity in human cognition, aging, and disease.

Parkinson's disease (PD) is characterized by abnormal oscillatory activity and altered interregional coupling within the cortico-subthalamic (M1-STN) circuit. Transcranial magneto-acoustic stimulation (TMAS) combines focused ultrasound with a static magnetic field, but its modeled physical responses and circuit-level effects remain incompletely characterized. We integrated multiphysics simulations with behavioral testing, simultaneous M1-STN local field potential recordings, Golgi-Cox staining, and histological analyses in MPTP-induced parkinsonian mice. Simulations predicted an acoustic field concentrated in M1, magneto-acoustically induced motional source-current hotspots mainly in superficial cortex, and small passive membrane-potential polarization (approximately ± 0.03 mV) during one 400-ms active sonication period. The estimated mechanical index (∼0.71) and temperature rise (≤3 °C) indicated relatively low predicted mechanical and thermal risk; H&E examination showed no overt cortical histopathological abnormalities. In vivo, TMAS partially ameliorated selected behavioral deficits, attenuated abnormal aperiodic-adjusted beta activity in M1 and STN with region-dependent low-gamma effects, reduced beta-gamma phase-amplitude coupling, and reduced pathologically elevated M1-STN coherence after local bipolar re-referencing. TMAS also increased cortical dendritic spine density and partially attenuated MPTP-related reductions in SNc TH-positive neurons and striatal TH-positive fiber immunoreactivity. These results provide a cross-scale characterization of TMAS-associated physical, electrophysiological, behavioral, and structural changes. Because the acute simulations and chronic biological outcomes were not causally linked and component-specific controls were not included, the findings reflect effects associated with the overall TMAS paradigm rather than proof of a single physical mechanism.

In neurotypical adults, mind-wandering (MW) is associated with increases in posterior alpha power, reflecting inward-directed mentation, but tonic, cortical arousal remains largely unexplored in adults with attention-deficit/hyperactivity disorder (ADHD), particularly prone to MW. The main aim of this study was to characterize stimulus-independent oscillatory activity during MW and determine how global and spatially localized MW-related cortical arousal differs between ADHD patients and controls. In total, 28 medication-naïve ADHD patients and 28 controls completed a Go/No-Go task, which included thought-probes to detect MW episodes. The electroencephalography (EEG) analysis focused on cortical arousal measures indexed by pre-stimulus alpha power and its variability, using both global scalp-wise analyses and cluster-based permutation approaches. Deliberate and spontaneous MW were associated with distinct spatial patterns of alpha power synchronization, indicating that deliberate (unlike spontaneous) MW maintains relatively active attentional systems. Furthermore, permutation analyses revealed that controls exhibited significantly higher global alpha power without increased variability during MW, consistent with a stable internally oriented attentional state. Patients displayed no alpha power modulations between attentional states and higher alpha power variability during MW, reflecting unstable attentional engagement. These findings highlight dysregulated and variable alpha power dynamics as markers of unstable internal distraction in ADHD.

We investigate linear perturbations of an incompressible, weakly collisional, anisotropic plasma in the low frequency limit using an extended 16-moment fluid description that retains parallel and perpendicular heat fluxes. We identify a class of linear perturbations associated with a conserved thermomechanic invariant, a time independent, aperiodic structure involving coupled perturbations of heat fluxes, velocity, and magnetic field. In the standard CGL limit, where heat fluxes are neglected, no direct analog of this invariant exists. Retaining heat flux dynamics alters the linear structure of the system promoting third order velocity moments to autonomous variables and gives rise to a thermomechanic mode with coupled thermal, kinetic, and magnetic components. The associated perturbations are inherently localized, favoring compact, filamentary aperiodic structures. The thermomechanic invariant reveals a stationary sector of collisionless anisotropic plasma dynamics, characterized by a fixed algebraic polarization that enforces time independent relations among the relevant perturbation fields.

We present an explicit construction of the Freidlin-Wentzell quasipotential of a stochastic system with two degrees of freedom and nonreciprocal interactions. This model undergoes noise-induced transitions between four metastable attractors, forming recurrent but aperiodic "Escher cycles," similar to the cyclic nucleation dynamics observed in the nonreciprocal Ising model. We calculate the quasipotential analytically to first order in nonreciprocality. We characterize it along a one-dimensional reaction coordinate that connects the attractors, and we also obtain the full two-dimensional landscape, at leading order in perturbation theory. The resulting landscapes feature flat regions and extended plateaus, together with nondifferentiable switching lines. These singular structures arise from two geometric mechanisms: the handover of dominance between competing transition paths, and the competition between basins of attraction. The system provides a rare case where the geometry of nonequilibrium rare events can be fully resolved, and a simple analytically tractable example of a quasipotential in more than one coordinate that captures a rich set of nonequilibrium features.