Do Symptoms Matter? Investigating Symptom-Based Lesion Network Mapping.
Lesion network mapping (LNM) is an approach to map focal brain lesions to a common brain network through the use of a reference connectome dataset. Van den Heuvel and colleagues recently showed that results produced by LNM lack disease specificity. Here, we expand on symptom-based LNM (sLNM), a variant designed to focus on symptom-specificity, statistical rigor, and clinical utility. We show that sLNM maps from unrelated disorders nonetheless converge toward a common output, confirming a lack of disease specificity similar to LNM. Given this lack of disease specificity, it is puzzling why studies have shown clinical efficacy of sLNM-guided treatment. Our findings suggest that sLNM results converge to the first principal gradient, which describes the brain's sensorimotor-association organizational axis that has been linked to development and pathology. Therefore, sLNM maps may be clinically useful because they reflect this fundamental brain organizational axis rather than disease-specific networks. Taken together with the results from van den Heuvel et al, these insights open an important opportunity for integrating findings from sLNM with findings on the sensorimotor-association brain axis.

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A PRISMA-guided systematic review of musculoskeletal modelling approaches in lower-limb cycling biomechanics
Cycling is commonly employed in sports performance, rehabilitation, and clinical contexts, while musculoskeletal (MSK) simulations enable the investigation of internal biomechanics that cannot be measured experimentally. Despite growing use, the application, validation, and standardisation of MSK simulations in cycling remain unclear. This review aimed to systematically characterise the application, validation strategies, modelling assumptions, and reporting practices of musculoskeletal simulations in lower-limb cycling biomechanics. Searches were performed in Scopus, PubMed, IEEE Xplore, and Web of Science on 1 August 2024, covering studies from January 2010 to July 2024. Peer-reviewed English-language journal articles applying MSK simulations to lower-limb cycling were included; inverse kinematics-only was excluded. No protocol was registered, and no formal risk-of-bias assessment was conducted, as there were no intervention effects and no quantitative synthesis. Twenty-eight studies met the inclusion criteria. Most of them investigated bicycle-rider configuration, neuromuscular coordination, or electrical stimulation control, with participant cohorts overwhelmingly composed of young men and minimal female representation~(272 total). Model reporting was often incomplete, with wide variation in anatomical scope, inconsistent descriptions of degrees of freedom, and limited sharing of models or code. Use of experimental data was uneven across studies: while all incorporated kinematic measurements, only two-thirds included kinetic data, and only one study reported physiological measures. Model validation was generally based on literature values. Seventy-eight per cent of studies used optimisation, mainly with effort-based cost functions, and parameter variations were exploratory rather than systematic. The evidence base is limited by small, predominantly male cohorts, inconsistent reporting standards, and limited physiological validation. These results consolidate current practices and highlight the need for more transparent and open reporting, sex-balanced and clinically diverse participant representation, stronger validation, and more rigorous sensitivity analysis to enhance reproducibility and practical relevance. This review was funded by AGAUR (Spain), CAPES (Brazil) and FAP-DF (Brazil).

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From Head to Toe: Efficient Somatosensory Mapping with Fast Stimulation and Multivariate Pattern Analysis
Background: Somatosensory evoked potentials (SEPs) measured with electroencephalography (EEG) are widely used to study cortical responses to touch but most research has limited the focus on few body parts, typically a finger, and applied time-consuming testing protocols. Multivariate pattern analysis (MVPA) provides a complementary approach that may increase sensitivity and allow faster stimulation, yet its relationship to classical SEP analysis in somatosensory research remains largely unexplored. Methods: Fifteen participants received vibrotactile stimulation on the finger, hand, cheek, and foot while EEG was recorded. We compared a traditional 'slow' stimulation protocol (800-1200 ms inter-stimulus intervals) with a 'fast' protocol (300-500 ms). We compared temporal and topographical aspects between SEP and MVPA. Results: Both stimulation protocols produced highly similar SEP components (P100, N140, P200), topographies, and classification results, while the fast protocol reduced testing time by about 60%. SEPs revealed systematic body-part differences, with earlier components for cheek stimulation and delayed responses for the foot. Multivariate classification distinguished body parts with accuracies up to ~50-55% (chance: 25%), peaking around 100 ms after stimulus onset. Classifier weight maps closely matched SEP topographies over centroparietal electrodes, indicating that classification relied on physiologically meaningful somatosensory signals. Classification accuracy peaked around 100 ms after stimulus onset, coinciding with the SEP P100 component, but declined gradually thereafter, suggesting that early somatosensory responses contain particularly informative multivariate patterns that generalize over time. Conclusions: Faster stimulation protocols substantially increase efficiency without compromising interpretability. Combining classical SEP analysis with multivariate classification provides complementary insights and offers a powerful framework for mapping somatosensory representations across the body.

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Neural sensing of surface traction modulates proprioceptive activity and locomotion in Caenorhabditis elegans
Locomotion - whether walking, running, or crawling - depends on the precise coordination of forces between the body and its surroundings. Two critical factors in this process are the force that resists the relative motion between two bodies, and mechanosensation, the body's ability to sense and respond to mechanical forces. Together, they allow organisms to move efficiently, adapt to varying environments, and maintain balance. Here we show that the `gentle touch' receptor neurons (TRNs) in the Caenorhabditis elegans body wall are sensitive to dynamic surface traction. Using a combination of calcium recordings and traction force microscopy in freely moving animals, microfluidics, and whole connectome computer simulations, we show that MEC-4 DEG/ENaC ion channel activity depends on the crawling velocity and friction force. Mutations disrupting MEC-4 activity and body wall mechanoreceptor function produce lethargic worms with impaired proprioceptive regulation, suggesting functional coupling between surface mechanoreceptors and proprioceptors. Our data reveal a new role for classical touch receptors in locomotion and critically define the mechanical modality sensed by skin mechanosensors.

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Tracing the invisible: Quantifying mirroring and embodied attunement in dyadic and triadic Dance Movement Therapy
Background: Mirroring is a foundational method used in Dance Movement Therapy (DMT), assumed to foster empathy and therapeutic attunement, yet its embodied dynamics remain insufficiently studied. In this paper, we provide the first quantitative exploration of client/therapist mirroring across dyadic and triadic formats, examining how synchrony unfolded during a structured mirroring exercise in which participants alternated between leading and following roles. Methodology: Using optical motion capture and time series modelling, we quantified movement coordination in dyadic (female client/therapist; male client/therapist) and triadic (therapist with both clients) interactions. Results: In dyadic tasks, the female client/therapist interaction was marked by tight temporal alignment, significant synchrony, robust predictive accuracy, and clear client to therapist influence, consistent with kinaesthetic empathy and affect-sensitive entrainment. By contrast, the male client/therapist dyad exhibited weaker and more delayed temporal coupling, alongside reduced phase synchronisation and fewer directional dependencies, despite comparable levels of interpersonal proximity. In the triadic task, temporal entrainment attenuated: therapist movement had few matching qualities to clients movement, yet recurrent synchrony with both clients persisted, suggesting a strategic shift from fine-grained entrainment to stable postural scaffolding under divided attention. Discussion: These findings demonstrated that mirroring is not a uniform technique, but a family of embodied coordination modes flexibly recruited according to relational context and client expressivity. They align with theories of embodied simulation and affect attunement, implicating rapid motor resonance in dyadic entrainment and interoceptive/affective scaffolding in triadic stability. Clinically, the results underscore the need for training in flexible embodied strategies, split attention, and equitable allocation of attunement in group work. More broadly, they open a translational agenda linking kinematic synchrony to neural, interoceptive, and autonomic mechanisms, positioning mirroring as both an experiential hallmark and a measurable mechanism of change in embodied psychotherapy.

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Ultrastructural preservation of a whole large mammal brain with a protocol compatible with human physician-assisted death
Building a high-fidelity computational model of the whole human brain will require preservation of the ultrastructure at the level of the entire organ, post-mortem. For such a model to reflect as closely as possible the brain in the living state, artifacts that arise during both the agonal phase and the postmortem interval will need to be minimized. This is potentially feasible if a terminally-ill patient donates their brain for research following physician-assisted death. In this paper, we modify a protocol for aldehyde-stabilized cryopreservation to make it compatible with physician-assisted death. We use pigs as a model, which resemble humans in cardiovascular and brain anatomy. Aldehyde-stabilized cryopreservation was designed to provide superior structural preservation of brains of any size, across all anatomical scales, compatible with diverse analytical assays and long-term storage without ultrastructural degradation. We demonstrate, with light microscopy and volume electron microscopy, that our brain preservation protocol results in connectomically traceable whole brains and propose an economically feasible storage modality that is expected to maintain stability of ultrastructure and macromolecules in the brain even for thousands of years. Most importantly, we establish that 14 min is the approximate length of the perfusability window--the time after the cardiac arrest during which blood washout needs to be initiated so that the brain ultrastructure is preserved.

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Neurocognitive deficits in controlling aversive memory among insomnia disorders
Background. Insomnia disorder is a common sleep disturbance characterized by adverse daytime cognitive and emotional impairments, such as repetitive negative thinking and increased psychological distress. Memory control, a key self-regulatory ability to control or inhibit unwanted thoughts and memories, plays an essential role in supporting cognitive functions and emotional well-being. Here, we delineate the neurocognitive mechanisms underlying memory control among individuals with insomnia. Methods. 41 participants meeting DSM-5 criteria for insomnia disorder and 40 healthy sleepers completed an emotional Think/No-Think task, during which participants either retrieved (Think) or suppressed the retrieval (No-Think) of aversive memories in response to memory cues while electroencephalograms were recorded. Results. Linear mixed model analyses with age and depression scores as covariates showed that participants with insomnia exhibited impaired memory control abilities, as evidenced by reduced suppression-induced forgetting in memory recall when compared to healthy sleepers. Electrophysiologically, healthy sleepers showed enhanced right prefrontal theta power in retrieval suppression than in retrieval, indicating elevated needs of inhibitory control during memory control. In sharp contrast, this difference was absent among those with insomnia. Notably, the greater the severity of insomnia symptoms, the smaller the retrieval vs. retrieval suppression theta power differences across participants, linking inefficient top-down control of unwanted memories with low sleep qualities. Conclusion. Individuals with insomnia showed impaired memory control of aversive memories and aberrant electrophysiological activities during retrieval suppression. Future research shall investigate the causal relationship between memory control abilities and insomnia symptoms.

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Modeling Multi-Modal Brain Connectomes for Brain Disorder Diagnosis via Graph Diffusion Optimal Transport Network
Network analysis of human brain connectivity provides a fundamental framework for identifying the neurobiological mechanisms that cause cognitive variations and neurological disorders. However, existing diagnostic models often treat structural connectivity (SC) as a fixed or optimal topological scaffold for functional connectivity (FC). This consequently overlooks the higher-order dependencies between brain regions that are critical for characterizing pathological alterations. Moreover, the distinct spatial organizations of SC and FC complicate their direct integration, as naive alignment methods may distort the inherent nonlinear patterns of brain connectivity. To address these limitations, we propose the Graph Diffusion Optimal Transport Network (GDOT-Net), which models disease-related topological evolution and achieves precise alignment between SC and FC. Unlike existing diffusion studies, the proposed model introduces an evolvable brain connectome modeling approach to infer the complex topological structure of brain networks, unveiling higher-order connectivity patterns linked to specific neuropsychiatric disorders. Furthermore, GDOT-Net incorporates a Pattern-Specific Alignment mechanism, leveraging optimal transport to align structural and functional topological representations in a geometry-aware manner. To capture nonlinear topological relationships between brain regions, a Neural Graph Aggregator Module was developed, which adaptively learns complex node interaction patterns in brain networks. By leveraging this module, GDOT-Net generates highly discriminative representations that form a robust basis for the precision diagnosis of brain disorders. Experiments on REST-meta-MDD and ADNI demonstrate that GDOT-Net surpasses SOTA methods in uncovering structural-functional misalignments and disorder-specific subnetworks. The source code is publicly available at this Link.

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Positive Affect Modulates Early Valuation and Conflict Processing in Social Decision-Making
Social decision making relies on dynamic affect cognition interactions across distributed brain networks, yet how incidental positive affect modulates these mechanisms at a millisecond timescale remains unclear. This study investigated the impact of music-induced positive emotion on the neural dynamics of decision-making in the Ultimatum Game. Fifty six participants were assigned to either a happy music group or an active control (rain sound) group. Fifty six participants were assigned to either a happy music group or an active control (rain sound) group, while electroencephalography was recorded to capture rapid neural dynamics. Behaviorally, happy music accelerated reaction times (RTs) and decoupled the ERP RT correlations observed in the control condition. Neurally, positive affect amplified event-related potential amplitudes during early conflict detection (220 to 280 ms) and late valuation (520 to 560 ms) stages. Multivariate pattern analysis further revealed that happy music enhanced the neural separability and temporal stability of decision states (accept vs. reject). Moreover, using support vector regression based on functional network features, we found that decision acceptance rates were predicted with significantly higher accuracy in the happy music group (R = 0.60) compared to controls (R = 0.41). Crucially, feature weight analysis indicated a topological shift in decision strategy: while the control group relied on frontal central edges (implicating executive control), the happy music group was characterized by central temporal connections (suggesting integrative processing). Collectively, these findings provide novel evidence that incidental emotion intervenes at the millisecond timescale to bias social choices, offering a dynamic network based account of the affect cognition interaction.

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Early Binding of Anti-Amyloid Antibodies to CAA Drives Complement Activation, Inflammation and ARIA in Mice
Anti-amyloid antibody treatment for Alzheimers disease is linked to Amyloid-Related Imaging Abnormalities (ARIA), including vasogenic edema (ARIA-E) and microhemorrhages (ARIA-H), especially in ApoE {epsilon} 4/4 carriers. To investigate mechanisms underlying ARIA, we examined the binding and temporal vascular effects of immunization with 3D6, the precursor to the anti-amyloid antibody bapineuzumab, in two aged Alzheimers disease amyloid mouse models. Acutely, 3D6 bound to cerebral amyloid angiopathy (CAA), resulting in C1q binding and classical complement activation. Weekly short-term immunization over 7 weeks resulted in elevated CAA- and plaque-associated complement deposition, red blood cell extravasation and microhemorrhages, and was accompanied by significant transcriptomic changes in genes related to complement, inflammation, vascular dysfunction, and endothelial lipid responses. Longer-term dosing over 13-15 weeks further increased complement deposition and was associated with blood-brain barrier disruption, MMP-9 upregulation, and microhemorrhages, accompanied by reduced amyloid burden and modest CAA clearance. C3 levels correlated with microhemorrhage severity. Perivascular macrophages co-localized with complement-decorated CAA in 3D6-treated mice. These findings implicate complement activation as an early key driver of ARIA and suggest that therapeutic targeting of complement may reduce ARIA risk.

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Enteric sensory neurons for nutrient detection and gut motility
The enteric nervous system (ENS) orchestrates gastrointestinal reflexes and brain-gut communication via molecularly diverse neurons. Among these, intrinsic primary afferent neurons (IPANs) are essential for detecting luminal nutrients and irritants, yet their molecular identities, sensory properties, and functions remain poorly resolved. Here, we establish a segment-resolved single-cell atlas of the murine ENS, including a comprehensive characterization of the gastric ENS. This resource defines a refined taxonomy of enteric neurons and glia and enabled the development of a genetic toolkit for molecularly defined IPANs. Using chemogenetics and calcium imaging, we discovered that myenteric neurons detect a wide range of nutrients, irritants, and cytokines. Nutrient detection depends on a functional connection between chemosensory epithelial cells and enteric neurons mediated by 5-HT-HTR3 axis. Through optogenetic analysis, we demonstrated segment-specific regulation of gut motility by different IPANs. Our work establishes a genetic and physiological framework for enteric-specific sensory mechanisms.

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Can grid cells produce hexadirectional signals?
Hexadirectional analysis is widely used to infer population-level grid cell activity in humans, yet this signature has not been reproduced in rodent electrophysiology, where grid cells are best characterized. Moreover, it remains unclear how grid cell populations could generate such a signal. We address this issue theoretically and empirically by evaluating three prevailing hypotheses and the null model, while critically examining the analysis framework itself. We show that the standard approach is insensitive to grid firing per se. Instead, hexadirectional modulation emerges in firing variance, which we find in ratemaps of single-cell, MEC recordings of freely moving rats. Empirically, conjunctive grid-by-head-direction tuning does not produce hexadirectional signals, whereas specific nonlinear transformations can. We argue that false positive inferences can occur and suggest approaches for improved robustness and confidence. This work has critical implications for studies on hexadirectional signals and sheds light on the neural basis of fMRI.

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A Novel Rapidly Manufacturable Flexible Subdural Electrode Array for Intraoperative Mapping of Cortical Activity
Flexible and biocompatible neurointerfaces are crucial elements for intraoperative monitoring and chronic neural recordings. However, existing fabrication methods often involve complex cleanroom processes, limiting rapid prototyping and customization. In this study, we present a fast, low-cost method for manufacturing a flexible subdural electrode array based on polydimethylsiloxane (PDMS) and gold conductive layer. The fabrication process utilizes a laser cutter for both mask generation and direct patterning of metal traces on a PDMS substrate, achieving a resolution of up to 30 m. A detachable interface was developed for reliable connectivity during testing. The electrochemical and mechanical properties of the array were characterized, demonstrating Ohmic behavior and stable conductivity after 50 cycles of mechanical bending, with a degradation of less than 10%. Electrochemical impedance spectroscopy (EIS) confirmed the viability of the electrodes for recording physiological signals. The functionality of the array was validated in vivo by performing simultaneous recordings of local field potentials (LFPs) and electrocorticography (ECoG) in the rat somatosensory cortex. The signals from the flexible subdural array showed a statistically significant (p < 0.001 ) median cross-correlation of 0.35 with LFPs recorded at a depth of 600-800 m by industrial electrode. We demonstrate here a robust and accessible approach for producing functional neural interfaces, suitable for rapid iteration and customization in research and clinical applications.

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Xylazine's k-opioid agonist activity is not shared with other FDA-approved alpha2-adrenergic agonists
Xylazine is a a2-adrenergic agonist typically used in as a sedative and analgesic in veterinary medicine. For some years, xylazine has been reported as an additive to fentanyl on the illicit drug market and has been associated with severe side-effects including severe ulcerations and potential amputations at the sites of injection along with an increased risk of respiratory depression and death. We recently reported that xylazine has modest k-opioid agonist activity in vitro and in vivo and asked if other alpha2-adrenergic agonists had similar off-target activities. To test this hypothesis, we profiled US FDA-approved alpha2-adrenergic agonists at 320 G protein coupled receptors (GPCRs) to identify potentially deleterious and/or beneficial off-targets. Although all other tested alpha2-adrenergic agonists were devoid of k opioid agonist activity, each had a distinct pattern of activity at various GPCRs and differential patterns of signaling bias at alpha2-receptor subtypes. These findings suggest potential molecular targets for both side-effects and therapeutic activities among known alpha2-adrenergic agonists.

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Distinct beta burst motifs exhibit opposing error relationships during motor adaptation
Beta-band activity (13-30 Hz) is a hallmark of human movement, yet a unifying account of its functional role remains unresolved. Although typically described as a sustained oscillation, beta activity is increasingly recognised to consist of transient bursts. More recently, beta bursts have been shown to exhibit heterogeneous waveforms. Here, we ask whether variability in burst shape corresponds to separable computational roles during motor adaptation. Using high-density MEG, we recorded neural activity while participants performed a visuomotor rotation task under either implicit (sensorimotor adaptation) or explicit (strategic re-aiming) learning conditions. Conventional metrics, beta power and burst rate, showed context-dependent modulation during preparation but provided limited insight into trial-by-trial behaviour. In contrast, sorting bursts according to their waveforms revealed a repertoire of burst types with dissociable temporal dynamics and context-dependent modulation. Crucially, during post-movement evaluation, distinct burst subtypes showed opposing and temporally specific relationships with behavioural error: one subtype decreased with increasing error, whereas others increased. Together, these findings indicate that beta activity comprises separable transient events with specific computational roles, and that accounting for waveform diversity is essential for understanding how cortical beta supports adaptive behaviour.

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Decoding Phonetic Features: Somatotopic and Sensorimotor Representations in Native and Non-native Consonant Perception
Speech perception relies on the integration of auditory and articulatory information, yet the precise role of motor regions remains debated. Cross-linguistic approaches and challenging listening situations can help fill this gap. We combined behavioral measures and fMRI with multivariate pattern analyses to investigate cortical representations of native French and non-native Mandarin consonant perception under clear and noisy conditions. Cross-modal classification analysis showed that articulatory features of degraded native labial and dental consonants are mapped somatotopically in right lip and tongue motor areas, regions also activated during consonant production. These representations may support phoneme categorization by compensating for degraded input. Representational similarity analysis further revealed that a network encompassing bilateral temporal and frontal motor regions encodes phonetic features of native and non-native consonants, including place and manner of articulation. Our findings highlight that speech perception relies on embodied sensorimotor representations, which contribute to decoding phonetic features both within and across languages.

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Linking cross-species trajectories of cerebrovascular remodeling in aging and Alzheimer's disease to brain vessel transcriptome
Cerebrovascular remodeling driven by subtle molecular changes starts early in the asymptomatic stage of Alzheimer's disease (AD). Despite progress in human vascular imaging and postmortem tissue analysis, there is limited data on the early features of small vessel reorganization, particularly in the context of cell-specific molecular drivers. This is largely because of the invasive nature of the tools for direct cellular observation and analysis. Since early detection is key, histopathology falls short with end-point data from people that died in late stages of the disease. This is a critical knowledge gap, because the early vascular processes are thought to be strongly correlated with health outcomes, tipping the scales from mild cognitive impairment to AD. To meet these translational challenges, we performed near life-span in vivo two-photon imaging and MRI of the cerebrovascular tree in a mouse model of amyloidosis. We identified precisely when subtle abnormalities in vessel tortuosity and red blood cell velocity first emerge in the context of differential amyloid accumulation in vessels walls and tissues. We then isolated the brain vessels for transcriptional analysis at this flagship timepoint and performed cross-species analysis linking changes in vascular cells to genes and pathways common to both mice and humans. Importantly, using 7T MRI of aging humans, we directly associated vascular remodeling trajectories of mice and humans and identified a remarkably analogous tortuosity course in the smallest brain vessels. Our integrated framework across scales and species advances neuroimaging biomarker understanding and uncovers early mechanistic routs of dysfunctional angiogenesis and actin-mediated contractility.

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A normative reference for large-scale human brain dynamics across the lifespan
Human brain function emerges from dynamic reconfigurations of large-scale neural networks. While population-level reference charts have transformed the study of static brain structure and connectivity, an equivalent normative framework for intrinsic brain dynamics has been lacking. This gap has limited our ability to characterize individual variability, development, ageing, and mental health conditions at scale. Here, we establish a population-level normative reference for large-scale human brain dynamics using resting-state fMRI data from more than 10,000 individuals spanning the lifespan and 91 scanning sites. We derive a compact set of recurring brain-state configurations that are reproducible across scanners and acquisition paradigms and that generalize to previously unseen cohorts. Anchoring these dynamic states to normative lifespan models enables the quantification of individual deviations relative to population reference distributions. We show that intrinsic brain dynamics undergo systematic reorganization across development and ageing, with pronounced changes before early adulthood and more gradual modulation thereafter. Applying this framework across multiple mental health conditions reveals disorder-specific and highly heterogeneous deviations in brain dynamics that are not captured by static neuroimaging measures. Robust transfer to independent cohorts and longitudinal analyses demonstrate that normative brain dynamics can be reliably assessed out of distribution. These results delineate a population-scale dynamic architecture of the human brain and extend normative brain mapping from static phenotypes to the temporal domain, providing a reference framework for studying brain function across the lifespan in health and disease.

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Rapid Orthographic and Delayed Phonological Processing: ERP and Oscillatory Evidence from Masked Priming in Korean
A central question in visual word recognition concerns whether orthographic and phonological codes are coordinated sequentially or in parallel during lexical access. Korean Hangul, an alpha-syllabic writing system with morphophonemic spelling principles, allows independent manipulation of orthographic and phonological syllable overlap within a single experimental design. In a masked priming lexical decision task with EEG, we contrasted orthographically identical primes, phonologically overlapping primes, and unrelated primes. Event-related potentials and time-frequency representations (theta: 4-8 Hz, lower beta: 13-20 Hz, upper beta: 20-30 Hz) were analyzed to capture both evoked and oscillatory neural dynamics. Orthographic priming produced a cascade of facilitative effects: early fronto-central P200 enhancement (150--250 ms) with upper beta synchronization (30-290 ms), followed by centro-parietal N400 reduction (350-550 ms) with frontal theta suppression (400-730 ms), and behavioral facilitation. Phonological priming, by contrast, elicited sustained lower beta activity over central regions (310-590 ms) but produced no early electrophysiological modulation and no behavioral facilitation. This spatiotemporal dissociation provides converging neural evidence that orthographic syllable processing emerges at pre-lexical stages and cascades into lexical-level processing, whereas phonological syllable effects are confined to later stages of lexical access. These findings provide support for a sequential or cascaded account of orthographic-phonological coordination, as predicted by dual-route models, while challenging strong forms of parallel activation, and suggest that the alpha-syllabic structure of Korean may enable a processing strategy in which orthographic parsing serves as an efficient entry route to the lexicon.

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GluN2D-containing NMDA receptors regulate dentate gyrus function by facilitating granule cell activity and mediating synaptic plasticity
N-Methyl-D-aspartate ionotropic glutamate receptors (NMDARs) are crucial for synaptic transmission, long-term plasticity, neuronal activity, and cognition. Consistent with these functions, NMDAR dysfunction is linked to several brain disorders, including Alzheimer's disease, autism, schizophrenia, and depression. NMDARs are tetrameric complexes composed of two essential GluN1 subunits and two distinct GluN2 subunits (GluN2A-D) that define their functional characteristics. Although the roles of GluN2A and GluN2B, which are highly expressed in the brain, have been extensively studied, much less is known about GluN2D in brain function. Using selective GluN2D antagonists in the mature rodent brain and a conditional GluN2D knockout model, we assessed the role of GluN2D-containing NMDARs in dentate granule cells. We found these receptors are tonically active, mainly extrasynaptic, and promote granule cell action-potential firing. Additionally, physiologically relevant presynaptic and postsynaptic activity patterns induced strong long-term potentiation of NMDAR-mediated transmission at medial perforant path synaptic inputs, and this plasticity was driven by GluN2D lateral diffusion and facilitated by non-canonical glutamate delta-1 (GluD1) receptors. Finally, removing GluN2D from granule cells impaired spatial memory. Overall, our findings demonstrate that GluN2D-containing NMDARs are vital for hippocampal function by modulating granule cell activity and supporting synaptic plasticity.

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