bioRxiv Subject Collection: Neuroscience's Journal

Inflammatory reprogramming of human brain endothelial cells compromises blood-brain barrier integrity in Alzheimer's disease
Blood-brain barrier (BBB) dysfunction is an early feature of Alzheimer's disease (AD), yet the endothelial gene-regulatory programs involved remain incompletely understood. We integrate postmortem human single-nucleus transcriptomics with iPSC-based BBB models to define a conserved, inflammation-driven pathway that compromises barrier integrity. We identify an NF-KB-associated endothelial gene module endoM2 that is elevated in AD, inversely correlated with cognition, and enriched for inflammation and endothelial-to-mesenchymal transition signatures. Cytokine stimulation of iPSC-derived brain endothelial cells induces morphological remodeling, lipid accumulation, junctional disruption, and transcriptomic shifts that mirror endoM2. A targeted drug screen identifies the NF-KB inhibitor BAY11-7082 as protective against cytokine-induced changes. In our perfusable iPSC-derived BBB-Chip that recapitulates human BBB signatures, single-cell profiling reveals inflammatory endothelial state-specific programs reflecting those in AD brains and demonstrates that BAY11-7082 suppresses cytokine-triggered dysfunction and reverses inflammation-associated gene activation. Together, these findings position cerebrovascular inflammation as a therapeutic target to preserve BBB integrity in AD.

A hypothalamic circuit for anticipating future changes in energy balance.
AgRP neurons cause hunger, the drive to seek and consume food. Their activation by fasting is key for survival and is thought to be triggered by feedback when energy stores are low. However, we know that environmental cues can also regulate AgRP neurons, since cues that predict future food intake rapidly inhibit AgRP neurons. But is the converse true: can the prediction of future fasting rapidly activate AgRP neurons? Here we show that such rapid fasting activation of AgRP neurons does occur. This fasting response is driven by excitatory input from paraventricular hypothalamic neurons expressing Sim2, which are bidirectionally sensitive to predictions of future energy state. In this way, cognitively-processed contextual information conveyed by PVH-Sim2 neurons strongly activates AgRP neurons. Lastly, chronic silencing of PVH-Sim2 neurons causes persistent hypophagia. This PVH-Sim2 to AgRP neuron circuit, by anticipating and preventing negative energy balance, provides an important new dimension of hunger regulation.

Alpha-Band Phase Modulates Perceptual Sensitivity by Changing Internal Noise and Sensory Tuning
Alpha-band neural oscillations (8-13 Hz) are theorized to phasically inhibit visual processing based, in part, on results showing that pre-stimulus alpha phase predicts detection (i.e., hit rates). However, recent failures to replicate and a lack of a mechanistic understanding regarding how alpha impacts detection have called this theory into question. We recorded EEG while six observers (6,020 trials each) detected near-threshold Gabor targets embedded in noise. Using signal detection theory (SDT) and reverse correlation, we observed an effect of occipital and frontal pre-stimulus alpha phase on sensitivity (d'), not criterion. Hit and false alarm rates were counterphased, consistent with a reduction in internal noise during optimal alpha phases. Perceptual reports were also more consistent when two identical stimuli were presented during the optimal phase, suggesting a decrease in internal noise rather than signal amplification. Classification images revealed sharper spatial frequency and orientation tuning during the optimal alpha phase, implying that alpha phase shapes sensitivity by modulating sensory tuning towards relevant stimulus features.

Beta bursts mediate amygdala gating of hippocampal emotional encoding
The amygdala and hippocampus are central to emotional processing, yet the transient neural dynamics coordinating these regions remain unclear. We simultaneously recorded single-neuron activity and local field potentials from both regions in epilepsy patients during an emotional image-rating task. Neurons in both regions responded to images with firing rate changes that predicted subjective ratings of extreme pleasantness or unpleasantness. To examine the underlying oscillatory mechanisms, we analyzed beta bursts (13-30 Hz)-transient, high-power events-since conventional spectral analyses revealed no valence-specific patterns. Beta bursts were associated with increased gamma amplitude and enhanced phase coherence in both structures, with beta-gamma phase-amplitude coupling capturing emotion-related dynamics. Critically, amygdala beta bursts strongly suppressed hippocampal firing through interneuron activation during negative valence processing, whereas hippocampal bursts showed no reciprocal influence. These findings suggest that beta bursts provide a temporal code for emotion and represent a candidate mechanism for targeted neuromodulation in mood disorders.

Local thalamic interneurons drive spindle termination and enable sleep-dependent learning
The thalamus is central to fundamental brain functions including sensation, attention, and sleep through the precise generation and regulation of neuronal ensemble oscillatory activity. Sensory thalamic circuits are considered feedforward structures, lacking lateral connectivity, while recurrence in the network is mediated by interactions with inhibitory neurons of the thalamic reticular nucleus. Here, we define previously uncharacterized functional roles of local thalamic interneurons, a component of the sensory thalamus whose function has remained unexplored. We demonstrate that local interneuron activation induces rebound oscillations in thalamocortical relay neurons ex vivo and neocortical spindles in vivo, while their inhibition increases spindle occurrence, overall spindle duration and impairs sensory learning. Our findings reveal that local thalamic interneurons have shared and complementary functions to those of thalamic reticular neurons and are required for proper spindle formation and sleep-dependent learning. Together, this work establishes an important neural substrate of thalamocortical circuit function.

Dscam1 Controls Presynaptic Terminal Size to Regulate Synaptic Structure and Function in a Central Motor Circuit
The Drosophila giant fiber system is a well-characterized escape circuit that enables precise investigation of synaptic development and function. Here, we examined the role of the cell adhesion molecule Dscam1 in shaping presynaptic architecture and supporting circuit performance. Using RNAi-mediated knockdown of Dscam1 in giant fiber interneurons, we observed reduced presynaptic terminal volume, smaller synapse interfaces, and reduced levels of electrical (Shak-B) and chemical (Bruchpilot) synaptic proteins. Despite this reduction in overall abundance, protein localization and density remained unchanged. Functionally, Dscam1 knockdown impaired circuit performance, both increasing latency and reducing success at high frequency demand. Correlation and principal component analysis of our data set show that anatomical variables, particularly presynaptic terminal size, best predicted circuit output. These findings identify Dscam1 as a key developmental regulator of presynaptic terminal size, linking early axon terminal elaboration to adult synaptic function in a central motor circuit. More broadly, our results exemplify a volumetric scaling model where available presynaptic area constrains the distribution of synaptic proteins, both electrical and chemical.

Propagation Mapping: A Precision Framework for Reconstructing the Neural Circuitry of Brain Maps
Human brain mapping has traditionally relied on univariate approaches to characterize regional activity, an assumption that is increasingly being challenged. While functional connectivity offers a promising alternative, it fails to capture signal amplitude, which is essential for a comprehensive understanding of the brain biological organization. The current study introduced and validated propagation mapping, a novel framework designed to reconstruct the neural circuitry underlying human brain maps. By modeling the interdependence of regional signal amplitude and connectivity, the spatial organization of brain maps could be predicted with near-perfect accuracy. This precision remained stable across task contrasts, parcellation atlases, and both short- and long-range connections. Critically, propagation mapping preserved each individual functional fingerprint, enabling reliable study of inter-individual differences in brain behavior relationships. As a biologically informed and accessible representation of brain organization, propagation mapping provides a powerful alternative to traditional regional analyses and opens new avenues for discovery in neurological and psychiatric neuroimaging research.

Cortical myelination networks reflect neuronal gene expression and track adolescent age in marmosets
Structural similarity provides a powerful framework for measuring coordinated macro- and microstructural variation across the cortex of a single brain. Similarity networks derived from myelin-sensitive MRI sequences undergo marked reorganisation during adolescence, linked to psychosocial outcomes in humans and rodents. However, the cellular mechanisms of myelination similarity and its development in non-human primate cortex remain unexplored. Here, we used myelin-sensitive T1w/T2w ratio images from a cross-sectional sample of 446 common marmosets (aged 0.62 to 12.75 years) to estimate cortical similarity networks in individual animals. Cortical areas with similar myeloarchitecture showed highly similar patterns of gene co-expression in glutamatergic neurons and PV+ and VIP+ interneurons, reflecting the activity dependence of myelination. A reliable age-based signal exists within network features, with coordinated developmental trajectories observed across the cortical hierarchy from primary to transmodal association cortices - a pattern that mirrors findings in human cortex. Taken together, marmosets demonstrate phylogenetically conserved patterns of myelination network development, potentially underpinned by key neuronal cell types that shape the functional specialisation of cortical areas.

Short Oxygen Pulses Enhance Creative Problem-Solving
Creativity is central to human innovation, yet it often fluctuates from moment to moment. Identifying simple interventions to reliably boost creativity has broad scientific and societal value. Here, we tested whether short-pulse oxygen inhalation enhances creative problem-solving. Sixty participants performed two established tasks: the Alternative Uses Test (AUT), capturing divergent idea generation, and the Fusion Innovation Test (FIT), assessing both divergent and convergent thinking. Oxygen (~40% FiO2) was delivered in 1-minute pulses at 3-4-minute intervals, designed to align with intrinsic brain flexibility rhythms. Responses were scored for novelty, feasibility, and goal attainment using a validated GPT-based method. Linear mixed-effects regression revealed that oxygen significantly enhances both the quality and quantity of creative ideas across tasks. These findings demonstrate that a safe, low-cost physiological intervention can augment creative performance, providing a new link between oxygen metabolism, neural flexibility, and problem-solving.

Depression reduces structurally informed network flexibility in premanifest Huntington's disease
Objective: The extent to which structural connectivity constrains effective connectivity in both depression and neurodegenerative contexts remains poorly understood. In particular, the relationship between structural connectivity aberrations and effective dysconnectivity associated with depression in Huntingtons disease remains uncharacterized. Here, we applied a novel procedure that implements structural connectivity-informed spectral dynamic causal modelling to examine how structural connectivity shapes directed inter-regional influences in premanifest Huntingtons disease gene expansion carriers (HDGECs) with and without depression history. Method: Using spectral dynamic causal modeling embedded in a hierarchical empirical Bayes framework, we analyzed fMRI data from 98 premanifest HDGECs across default mode network and striatum (caudate and putamen). HDGECs were split into two groups based on either having a history of depression or not, and depression severity on both the Beck Depression Inventory, 2nd Edition (BDI-II) and Hospital Anxiety and Depression Scale, Depression Subscale (HADS-D) was used to measure clinically elevated depression symptoms. Leave-one-out cross-validation was implemented to test predictive validity. Results: Model evidence substantially favored structurally informed over uninformed approaches across all participants. For HDGECs, having a history of depression was associated with reduced baseline variability in effective connectivity (decreased parameter), with particularly tight regularization of near-zero-valued structural connections toward zero effective connectivity values while leaving strongly connected pathways relatively unaffected. Effects converged on striatal self-connectivity and hippocampal-striatal pathways, with distinct patterns emerging between depression history groups. Notably, clinically elevated depression revealed differential connectivity signatures, with right caudate self-connectivity showing positive correlations with clinical cut-offs for HDGECs with and without depression history. In leave-one-out cross-validation, specific connections including DMN-to-striatum (BDI: r = -0.31, p = .002; HADS-D: r = -0.33, p = .001), right hippocampus-to-left caudate (BDI: r = -0.46, p < .001; HADS-D: r = -0.30, p = .002), and left caudate-to-left putamen (BDI: r = -0.48, p < .001; HADS-D: r = -0.30, p = .003) significantly predicted individual differences in depression severity scores. Conclusion : Together, these findings link reduced network flexibility to depression vulnerability in premanifest neurodegeneration, providing a mechanistic bridge between anatomical constraints, effective connectivity alterations, and clinical depression phenotypes.

A neural geometry for forelimb proprioception in the cervical spinal cord
Precise, real-time somatosensory feedback is essential for coordinated movement. While the anatomy and physiology of these sensory pathways is well described, their neural code and its construction remains unclear. Here we show that neurons in the cervical spinal cord generate a precise neural representation of the forelimb's kinematic state using muscle and tendon sensory afferent inputs. We identify two classes of movement responsive neurons - the first encodes speed, position and direction of the limb, while the second exhibits precise firing at specific limb positions or grid-like firing patterns that tile space. Their composite population activity is constrained to a low dimensional manifold that is an ordered representation of the position and velocity of the limb. Ablating muscle and tendon sensory afferents, but not cutaneous sensory afferents, disrupts this neural manifold. Moreover, transient perturbations of muscle and tendon afferents in freely moving mice reaching to spatial targets cause end-point errors as predicted by the deficits in the neural code. Our findings demonstrate that spinal networks, one synapse from the periphery, perform the complex computations necessary to represent forelimb movement.

Presence Hallucination Induction through Robotically Mediated Somatomotor Conflicts: a pooled analysis of 26 experiments
Hallucinations are significant symptoms in psychiatric and neurodegenerative diseases, that may indicate advanced disease progression or worse disease forms. They are also frequent in healthy individuals, especially elderly or bereaved. Despite their relevance, studying hallucinations in controlled laboratory conditions remains challenging given the limited procedures inducing clinically relevant hallucinations. We have previously developed a robotics-based protocol capable of inducing a specific clinically relevant hallucination, presence hallucination (PH), in both healthy individuals and patients. Using this approach, we have systematically investigated the sensitivity and intensity of PH-induction, as well as its effects on various sensory, behavioral and cognitive aspects. In the present study, we pooled individual-participant data from 26 in-house experiments (totaling 580 individuals) and conducted a Bayesian analysis to estimate effects and moderators of PH-induction. PH-induction was reliably induced with a medium effect-size, and individuals with schizotypal traits were more sensitive. Furthermore, we identified that PH-induction may not strongly depend on altered agency, but found a synergistic relationship between passivity and induced PH. Collectively these results elucidate the role of somatomotor processes in aberrant own body perceptions, advance understanding of psychosis, and provide powerful statistical priors for future studies.

Resveratrol enhances associative learning and memory function in C. elegans Models of Alzheimer's Disease: A chemotaxis-based behavioral analysis
Alzheimer's disease affects over 50 million individuals worldwide and is characterized by progressive cognitive decline through amyloid-{beta} plaque accumulation and neurofibrillary tangles. Current treatments provide limited therapeutic benefit, creating urgent need for neuroprotective compounds. This study investigated resveratrol's effects on associative learning and memory function in Caenorhabditis elegans models of Alzheimer's disease using chemotaxis-based behavioral assays. Wild-type C. elegans and GMC101 transgenic worms expressing human A{beta} peptides were exposed to six experimental conditions combining E. coli OP50, sodium chloride, and resveratrol treatments. Chemotaxis index values were analyzed using two-way ANOVA with Tukey's post hoc testing across five independent replicates. Results showed resveratrol significantly enhanced chemotaxis performance in GMC101 worms, particularly under combined E. coli, NaCl, and resveratrol conditions (p < 0.0001). Two-way ANOVA revealed significant main effects for genotype (p = 0.002258) and treatment condition (p < 0.0001). GMC101 worms demonstrated 214% improvement in behavioral responses under optimal treatment conditions, suggesting partial restoration of learning-associated cognitive functions. These findings support resveratrol's therapeutic potential for neurodegenerative diseases, though efficacy appears context-dependent and may require synergistic environmental stimuli for optimal effect.

Multimodal Imaging and Logistic Weighted Cognitive Scores for Classification of MCI, AD, and FTD Subtypes
Background: Differentiating between mild cognitive impairment (MCI), Alzheimers disease (AD), and frontotemporal dementia (FTD) subtypes remains a clinical challenge due to overlapping cognitive symptoms, structural atrophy, and metabolic patterns, especially in the early stages. Multimodal classification approaches integrating neuroimaging and cognitive scores may offer early and accurate characterization and subsequently improved diagnostic precision. Methods: In this study, we included 100 participants (50 AD, 30 FTD, including 14 bvFTD and 16 PPA, and 20 MCI) who underwent simultaneous structural MRI and FDG-PET imaging. Cortical thickness (CTH) from anatomical MRI and standardized uptake values from FDG-PET were extracted using FreeSurfer and PETSurfer pipelines, respectively. CTH and FDG-PET features were combined into a single vector through a logistic weighting function derived from ACE-III scores, capturing the progressive nature of cognitive decline in dementia. A Naive Bayes classifier was then trained to differentiate diagnostic groups based on the merged features. Results: The model achieved classification accuracies of 83% for MCI vs. dementia (AD + FTD), 85% for MCI vs. FTD, 87% for MCI vs. PPA, 71% for MCI vs. bvFTD, 64% for MCI vs. AD, and 69% for AD vs. FTD. The overall classification accuracy was 68%, with the highest discriminative performance observed in separating MCI from FTD subtypes. Conclusions: This study presents a novel, cognition-weighted multimodal approach combining structural and metabolic imaging to enhance the classification of neurodegenerative syndromes. Findings from this study, underscore the potential of integrating ACE-III scores with neuroimaging biomarkers for accurate characterization and, early-stage differentiation of MCI, AD, and FTD variants. Keywords: Multimodal Neuroimaging, Cortical Thickness, FDG-PET, Logistic Weighting, Dementia Classification, Naive Bayes Classifier.

Reactivation-coupled brain stimulation enables complete learning generalization
Generalization of learned knowledge to new contexts is essential for adaptive behavior. Despite extensive research on the brain plasticity mechanisms underlying learning specificity, the mechanisms that facilitate generalization remain poorly understood. Here, we investigate whether using brain stimulation to disrupt offline consolidation in visual cortex promotes learning generalization. Separate groups of participants (N = 144) were trained on visual detection tasks using either a reactivation-based protocol or conventional full-practice, combined with anodal or sham transcranial direct current stimulation (tDCS) over the visual cortex. Strikingly, only combination of reactivation-based learning with anodal tDCS produced complete generalization from trained to untrained stimuli, an effect consistently replicated across features (orientation, motion direction). In contrast, reactivation-based learning alone and conventional full-practice, whether with or without brain stimulation, yielded stimulus-specific learning. Importantly, reactivation-coupled brain stimulation achieved generalization with an 80% reduction in training trials while maintaining learning gains comparable to full-practice. These findings demonstrate that reactivation and neuromodulation interact to unlock learning generalization, revealing a key brain plasticity mechanism and offering a rapid, translatable strategy for sensory rehabilitation.

Brain Network Differences in Second Language Learning Depend on Individual Competencies
Integrating new words into an existing semantic network is a core challenge of second language (L2) acquisition. We investigated how evidence-based learning strategies and individual performance shape the neurocognitive dynamics of vocabulary learning. Eighty-three adults with German or French as their native language (L1) learned 48 Finnish (L2) nouns over 14 days using a mobile app that systematically varied retrieval practice, corrective feedback, multisensory learning, and distributed learning. Before and after training, EEG was recorded during a translation recognition task designed to elicit the N400, an index of semantic integration. Vocabulary accuracy increased from 0.41% pre-learning to 75.5% post-learning (dz = 3.96), and the N400 incongruity effect increased significantly, F(1, 75) = 99.52, p < .001, 2g = .32, reflecting successful integration of new L2 words into the mental lexicon. High performers showed larger N400 responses and distinct ERP template-map preponderance (i.e., the proportion of epoch time points assigned to a given template map) indicating more efficient and specialized neural processing. Despite systematic manipulation of learning strategies, no single approach yielded consistent behavioral or neural advantages, suggesting that overall exposure and cumulative practice - rather than any specific strategy - were the key drivers of robust learning. ERP template-map analyses further revealed that learning not only amplified neural responses but also shifted the preponderance of maps in the N400 window, signaling a qualitative reorganization of semantic processing. These findings bridge cognitive neuroscience and language education, suggesting that the depth and success of vocabulary learning may depend more on the degree of integration achieved than on the specific instructional strategy employed.

Multiscale predictive modeling robustly improves the accuracy of pseudo-prospective seizure forecasting in drug-resistant epilepsy
Extensive research over the past two decades has focused on identifying a preictal period in scalp as well as intracranial EEG (iEEG). This has led to a plethora of seizure prediction and forecasting algorithms which have reached only moderate success on curated and pre-segmented EEG datasets (accuracy/AUC [≥] 0.8). Furthermore, when tested on their ability to pseudo-prospectively predict seizures from continuous EEG recordings, all existing algorithms suffer from low sensitivity (large false negatives), high time in warning (large false positives), or both. In this study, we provide pilot evidence that predictive modeling of the dynamics of iEEG features (biomarkers), seizure risk, or both at the scale of tens of minutes can significantly improve the pseudo-prospective accuracy of almost any state-of-the-art seizure forecasting model. In contrast to the bulk of prior research that has focused on designing better features and classifiers, we start from off-the-shelf features and classifiers and shift the focus to learning how iEEG features (classifier input) and seizure risk (classifier output) evolve over time. Using iEEG from n = 5 patients undergoing presurgical evaluation at the Hospital of the University of Pennsylvania and six state-of-the-art baseline models, we first demonstrate that a wide array of iEEG features are highly predictable over time, with over 99% and 35% of studied features, respectively, having R2 > 0 for 10-second- and 10-minute-ahead prediction (mean R2 of 0.85 and 0.2). Furthermore, in almost all patients and baseline models, we observe a strong correlation between feature predictability (with some features remaining predictable up to 30 minutes) and classification-based feature importance. As a result, we subsequently demonstrate that adding an autoregressive model that predicts iEEG features on 12{+/-}4 minutes into the future is almost universally beneficial, with a mean improvement of 28% in terms of area under pseudo-prospective sensitivity-time in warning curve (PP-AUC). The addition of the second autoregressive predictive model at the level of seizure risk further improved accuracy, with a total mean improvement of 51% in PP-AUC. Our results provide pioneering evidence for the long-term predictability of seizure-relevant iEEG features and the vast utility of time series predictive modeling for improving seizure forecasting using continuous intracranial EEG.

Noninvasive profiling of input-output excitability curves in human prefrontal cortex
Background: The human prefrontal cortex plays a critical role in cognitive control and behavior, and its dysfunction has been linked to numerous psychiatric and neurological disorders. However, noninvasive measurement of prefrontal activity remains challenging, limiting our understanding of how to optimize prefrontal treatments. Input-output relationships reveal how neural circuits respond to different inputs and are essential for determining optimal treatment parameters and understanding individual variability in treatment response, yet systematic investigation of prefrontal input-output relationships has been lacking. Objective: To characterize human prefrontal excitability with input-output (I/O) curves. Methods: We employed transcranial magnetic stimulation (TMS) with electroencephalography in a randomized mixed-block design with 28 healthy participants receiving single-pulse TMS to left dorsolateral prefrontal cortex (dlPFC) across 12 stimulation intensities (60-140% of resting motor threshold). We quantified prefrontal excitability using early local TMS-evoked potentials (EL-TEPs), local cortical responses measured locally 20-60 ms post-stimulus. Results: We observed a strong effect of TMS intensity on prefrontal EL-TEPs. Sigmoidal EL-TEP I/O curves were observed in 57% of participants, with the sigmoidality partially explained by the signal quality of the EL-TEP. Correlations were observed between EL-TEP and motor-evoked potential curve parameters, but intensity parameterization approaches did not significantly differ in explaining inter-individual EL-TEP response variability. Reliable EL-TEPs could be obtained using fewer TMS pulses at higher intensities, and test-retest assessments revealed robust I/O curve profiles. Conclusions: These findings provide a systematic noninvasive characterization of prefrontal input-output physiology in humans, establishing a validated framework for estimating prefrontal excitability. The comparison of various intensity parameterizations motivates the need for enhanced models and individualized measurement of stimulation responses.

Sensory plasticity of dorsal horn silent neurons: a critical mechanism for neuropathic pain
The spinal cord dorsal horn (DH) integrates and modulates sensory processing but undergoes critical plasticity following nerve injury, leading to pain hypersensitivity. Mechanical allodynia, or touch-evoked pain, is a highly prevalent and debilitating symptom of neuropathic pain. It has been proposed that, after nerve injury, innocuous sensory neurons gain access to nociceptive-specific (NS) circuits in the DH due to altered spinal inhibitory controls, thereby converting touch into pain. It is however unclear how sensory processing is reorganized in these conditions across the different laminae of the DH to generate this symptom. In this study, we developed a novel ex vivo somatosensory preparation to selectively analyze excitatory neuronal activity across all DH laminae simultaneously, following physiological stimulations of the skin. Using two-photon calcium (Ca2+) imaging, we studied the DH activity under physiological conditions, after spinal disinhibition or nerve injury, and generated a computational model to reveal the sensory plasticity of individual DH neurons that leads to neuropathic pain. We demonstrate that spinal disinhibition, whether pharmacologically induced or resulting from nerve injury, converts most DH excitatory neurons into highly polymodal cells. We further show that such disinhibition unmasks an unprecedented number of previously silent neurons in both superficial and deep DH laminae, responding to a wide dynamic range (WDR) of sensory modalities. The computational model pinpoints that neuropathic pain does not result primarily from the transformation of excitatory NS neurons into WDR neurons, but rather from the activation of a previously dormant excitatory circuit. This newly active circuit spans both superficial and deep DH laminae and is predominantly composed of WDR excitatory neurons The identification of this extensive silent neuronal network provides critical insights into DH plasticity mechanisms underlying neuropathic pain, and should guide future therapeutic strategies.

Protein Biomarker in Focal Cortical Dysplasia: Molecular Clues to Pathogenesis
Focal Cortical Dysplasia (FCD) is a major cause of drug-resistant epilepsy (DRE), particularly in pediatric and young adult populations characterized by structural abnormalities in cortical development. This study investigated 60 patients with histologically confirmed FCD, combining clinical, histopathological, and molecular data to identify subtype-specific molecular signatures. we investigated a panel of candidate protein biomarkers (AKT, PTEN, mTOR, HTR6, RHEB, KCNT1, RALA, DEPDC5) across FCD subtypes using patient-derived tissue samples. Our results reveal subtype-specific alterations in biomarker expression, particularly within the mTOR signaling pathway, supporting its central role in cortical malformations. Key dysregulated genes AKT, mTOR, PTEN, RHEB, DEPDC5, KCNT1, RALA, and HTR6 involved in mTOR signaling, neuronal excitability, and cortical development. Western blotting and IHC revealed marked upregulation of AKT and mTOR in FCD III, consistent with mTOR pathway hyperactivation. In contrast, KCNT1, RALA, and DEPDC5 were significantly downregulated across all subtypes, suggesting disrupted inhibitory signaling and GATOR1 complex dysfunction. ELISA assays validated increased expression of AKT, mTOR, and HTR6, particularly in higher-grade lesions. This study bridges clinical, histopathological, and protein-level data, providing novel insight into the molecular basis of FCD and highlighting candidate biomarkers for future diagnostic and therapeutic applications. Keywords: FCD, drug-resistant epilepsy, mTOR, AKT, DEPDC5, KCNT1, precision medicine

Examination of Balancing in a Real-World Inverted Pendulum
Motor adaptation is typically studied using simplified virtual tasks. Here, we investigated how humans learn to stabilize a physically unstable, underactuated system, and how participants cope with changes in the system's dynamics. Twelve right-handed adults balanced a real inverted pendulum by moving a cart along a linear rail. Study 1 characterized the passive mechanical properties of three pendulums (short, medium, and long) using free-oscillatory decay and fall to absolute 30 degrees trials, after their release from the upright position. Longer rods exhibited slower decay and lower natural frequencies, as well as a longer duration before falling, indicating greater passive stability. Study 2 assessed human motor control of the pendulum to maintain balance. Human participants trained with the medium pendulum (30 trials) and were then tested with all three pendulums (20 trials each). During training, balance performance improved significantly, with time to failure increasing over trials. During testing, performance scaled with pendulum length, and longer rods were easier to balance. Similar peak cart velocities were observed across conditions, suggesting equivalent actuation effort. Additionally, as expected, pendulum angular velocities decreased with rod length, reflecting underlying inertial differences. Pendulum passive dynamics closely matched behavioral performance, supporting a strong link between intrinsic system properties and balancing outcomes. These findings show that motor learning in physically unstable environments is not only shaped by feedback and effort, but also by the alignment of human control strategies and abilities with the natural dynamics of the plant. We note that in this study, we used a modified pendulum rig previously employed to examine control engineering approaches to modelling balance, thereby generating a dataset that can later be used to compare human performance with real-time computer control implementations of the same tasks.

Precision Imaging for Intraindividual Investigation of the Reward Response
The reliance of fMRI research on between-person comparisons is limited by low test retest reliability and inability to explain within-person processes. Intraindividual studies are needed to understand how changes in brain functioning relate to changes in behavior. Here, we present open data and analysis of a novel intensively sampled fMRI study, the Night Owls Scan Club. This precision imaging dataset includes 44 sessions acquired across four participants at a roughly biweekly rate. In each session, participants completed multiple reward-related tasks and mood and alertness ratings, and mood induction behavioral manipulation. In this study, we examined how the reward response reflects between-person or within-person variance. Test-retest-reliability of the reward response was very low and not explained my measurement error, suggesting little utility for between-person comparisons. At an intraindividual level, the mood induction showed small increases in the reward anticipation response. Additionally, mood and alertness explained notable intraindividual variance of the reward response, including as much as 31% for one participant. Overall, results suggest that BOLD activation to reward tasks, and likely other fMRI tasks, is more appropriate for within-person study than between-person study, highlighting a need for intensive longitudinal neuroimaging designs.

Dynamical models reveal distance to criticality in ageing brain dynamics.
Understanding how the brain changes with age remains a central question in neuroscience. Here, we combine magnetoencephalography (MEG) recordings from young and older adults with a whole-brain dynamical model to explore how brain dynamics evolve across the lifespan. Using a network of coupled Stuart-Landau oscillators constrained by empirical structural connectivity, we systematically vary three model parameters to identify the settings that best reproduce alpha-band features observed in MEG data. Our findings reveal age-related shifts in these model parameters: older individuals exhibit stronger global coupling and more positive values of the bifurcation parameter, consistent with a transition to a supercritical regime. These results align with prior work suggesting altered excitation-inhibition balance in ageing and indicate a systematic reconfiguration of whole-brain dynamics. By situating empirical observations within a dynamical systems framework, this study provides a principled approach for quantifying the brains distance to criticality and lays the groundwork for future clinical applications.

Harnessing cGAS-STING signaling to counteract the genotoxic-immune nexus in tauopathy
Tauopathies are progressive neurodegenerative disorders characterized by aberrant tau aggregation, cognitive decline, and persistent neuroinflammation, yet the mechanisms driving neuroinflammation and disease progression remain incompletely understood. Here, utilizing human postmortem AD brains and a mouse model of tauopathy, we report that genotoxic stress-induced cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) immune pathway form a self-amplifying loop that fuels neuropathology and cognitive deficits. Targeted disruption of this cycle through either genetic deletion of cGAS or pharmacological inhibition of STING restores immune homeostasis and attenuates tau pathology and cognitive deficits. Our results showed a significant accumulation of DNA double-strand breaks (DDSBs) and impaired DNA repair function, alongside elevated cGAS-STING signaling and type I interferon (IFN-I) responses in human AD brains compared to non-AD. In the PS19 transgenic (PS19Tg) mouse model of tauopathy, we found significantly elevated levels of DDSBs and altered expression of DNA repair proteins during early stages of disease, which preceded the dysregulation of cGAS-STING signaling and emergence of significant neuropathology in the later stage. Interestingly, genetic deletion of cGAS shifted microglial polarization from a pro-inflammatory M1 phenotype toward an anti-inflammatory M2 state, accompanied by a reduction in IFN-I signaling and improved cognitive performance in PS19Tg mice. Pharmacological STING inhibition reshaped the transcriptomic landscape, revealing selective regulation of pathways governing synaptic plasticity, and immune responses. This transcriptional reprogramming was accompanied by suppression of inflammatory responses, reduction in synaptic pathology, and attenuation of tau pathology in PS19Tg mice, underscoring STING as a therapeutic target for tauopathy. In conclusion, our findings reveal that genotoxic-immune crosstalk drives neuroinflammation and tau pathology and identify a conserved, druggable cGAS-STING axis that can be targeted to impede or slow disease progression in tauopathies.

Using Propensity Score Matching to Control for MRI Scan Quality
Movement during MRI scanning complicates distinguishing between the different tissues in the brain (e.g., grey and white matter). Standard practice excludes scans based on researcher-determined visual quality thresholds. Unfortunately, children, elderly, and clinical populations are shown to move more, resulting in higher exclusion rates. This disproportionate exclusion creates systematic bias in the literature and makes research findings less generalizable. Furthermore, the artifacts caused by motion are demonstrated to continue to confound data, even after visual quality control has occurred. We aimed to minimize the confounding factor of systematic group differences in movement. To achieve this, we used a post-scanning statistical technique called propensity score matching (PSM) that matches control and patient populations on scan quality metrics, leading to more comparable groups, greater inclusion, and more generalizable results. We found that PSM can attenuate significant differences in scan quality between groups while allowing for greater sample diversity than standard exclusion protocols. Crucially, using PSM can also alter the results of neuroimaging-based analyses. Using three datasets (total n = 1536), we compared voxel based morphometry analyses based on different quality control protocols. In particular, we observed discrepant results between PSM and strict threshold exclusion, with PSM magnifying some regional group differences and diminishing others. Overall, PSM is a customizable way to mitigate the impact of confounds in neuroimaging research and a powerful method to help distinguish true effects from artifacts.

Dopamine receptor sensitivity and Pavlovian conditioned approach
Understanding the determinants of individual differences in cue-reactivity and drug sensitivity is critical to identifying neurobiological mechanisms underlying vulnerability to addiction. In this study, we examined the relationship between dopamine D1 and D2 receptor sensitivity and the attribution of incentive salience to reward cues and sensitivity to cocaine. Male Sprague Dawley rats were classified as having high or low sensitivity to the D2 receptor agonist quinpirole, and a subset was tested with the D1 receptor agonist SKF 82958. Cue-reactivity was assessed using a Pavlovian conditioned approach (PavCA) task, which distinguishes between sign-tracking (approach to a cue that predicts reward) and goal-tracking (approach to the site of reward delivery). Cocaine sensitivity was measured by locomotor activity and 50-kHz ultrasonic vocalizations (USVs), a putative measure of appetitive states. High D2 responders exhibited more sign-tracking and greater cocaine-induced USVs than low responders despite no difference in cocaine-induced locomotion. Sign-trackers also showed greater locomotor sensitivity to D1 receptor stimulation than goal-trackers and produced more cocaine-induced USVs. Rats with high sensitivity to both D1 and D2 receptor stimulation showed the strongest sign-tracking behavior and affective response to cocaine. These findings suggest that dopamine receptor sensitivity is associated with the propensity to attribute incentive salience to reward cues and potentially the appetitive effects of cocaine. This dopaminergic phenotype may reflect a mechanism contributing to both individual differences in cue-reactivity and drug responsiveness.

Robustness and fidelity of voltage imaging analysis pipelines
Tracking neuronal voltage changes using fluorescent voltage indicators is rapidly reshaping neuroscientific research. Voltage imaging enables direct visualization of electrical signals from subcellular compartments to large-scale networks, yet requires sophisticated image-analysis procedures. Here, we present a comprehensive study of current voltage imaging analysis pipelines and discuss the experimental conditions for which they are most suitable. We compare strengths and limitations of these pipelines in motion correction, denoising and segmentation routines, and discuss how different signal processing strategies can influence data integrity and interpretation. Our results show that most real-time analyses require GPU accelerated algorithms and that denoising prior to signal processing is needed for analysis of subcellular dynamics. We conclude that voltage imaging analyses needs to be tailored to different experimental settings: we propose a decision-tree to determine analysis strategies for diverse experimental conditions. Together, these insights pave the way for reproducible, high-fidelity voltage imaging studies.

Outdoor Air Pollution, Perivascular Space Morphology, and Cognition in Preadolescence
Background: Ambient air pollution exposure is associated with structural brain differences and poorer cognition in children; however, mechanisms of toxicity remain unclear. Perivascular spaces (PVS), key for brain waste clearance, may play a role in the neurotoxicity of air pollution. This study explored associations between air pollution exposure, PVS morphology, and cognition in preadolescents. Methods: We analyzed cross-sectional Adolescent Brain Cognitive DevelopmentSM (ABCD) Study data from 6,949 9-10-year-old participants. Annual average exposures to PM2.5, O3, NO2, and 15 PM2.5 components were estimated using spatiotemporal models mapped to residential addresses. PVS count and volume were derived from T1w and T2w MRI, and cognition was estimated using NIH Toolbox scores. Linear mixed-effects models examined independent associations between air pollution, PVS, and cognition; weighted quantile sum regression assessed co-exposure effects of PM2.5 mixtures. Findings: Linear models revealed that exposures to Zn, NH4+, and Br were positively associated with PVS count in several regions. Higher PVS count in five key regions was associated with poorer cognitive performance across several NIH Toolbox domains. Higher Ca, Zn, and NH4+ exposures were associated with poorer cognition (PFDR < 0.01). Higher frontal lobe PVS count mediated the association between Zn exposure and poorer total cognition (P < 0.01). Co-exposure models revealed that PM2.5 mixtures were associated with higher temporal and cingulate PVS count, and poorer working memory and crystallized intelligence (P < 0.01). Interpretation: Outdoor air pollution was associated with higher PVS count and reduced cognition, suggesting that brain clearance may be a novel mechanism linking pollution to neurodevelopmental harm in preadolescents.

Study of Sex Differences in the Whole Brain White Matter Using Diffusion MRI Tractography and Suprathreshold Fiber Cluster Statistics
Sex-specific characteristics demonstrate a substantial influence on the human brain white matter, suggesting distinct brain structural connectivity patterns between females and males. Diffusion MRI (dMRI) tractography is an important tool in assessing white matter connectivity and brain tissue microstructure across different populations. Whole brain white matter analysis using dMRI tractography for group statistical comparison is a challenging task due to the large number of white matter connections. In this work, we study whole-brain white matter connectivity differences between females and males using dMRI tractography. We study a large cohort of 707 healthy adult subjects from the Human Connectome Project Young Adult dataset. By applying a well-established fiber clustering pipeline and a suprathreshold fiber cluster statistical method, we evaluated tracts in the cerebral cortex, as well as those connecting to regions such as the cerebellum, which have been relatively less studied using dMRI tractography. We identified several tracts that differed significantly between females and males in terms of their fractional anisotropy and/or mean diffusivity. These included several deep white matter tracts (e.g., arcuate fasciculus, corticospinal tract, and corpus callosum) that have been previously shown to have sex differences, as well as superficial white matter tracts in the frontal lobe. However, there were relatively few cortical association tracts that exhibited significant sex differences. We also identified cerebellar tracts with sex differences. Finally, correlation analysis revealed that these white matter differences were linked to a range of neurobehavioral measures, with the strongest and most consistent associations observed for motor function. Overall, these findings provide characterizations of sex differences in the white matter and indicate that the circuits underlying motor function may be an important focus of future work on sex differences in the human brain.

Intrinsic Neural Oscillations Predict Verbal Learning Performance and Encoding Strategy Use
Individuals adopt different encoding strategies to facilitate learning. However, few studies have investigated the neurophysiological basis that support these different encoding strategies across individuals. The present work addresses this gap by extending our previous findings on the direct relationship between cortical spectral power, measured via resting-state magnetoencephalography, and performance on standard cognitive test results. Our results highlight the complex interactions between endogenous brain oscillations, learning and verbal encoding strategies assessed by the California Verbal Learning Test (CVLT-2). First, we found that resting-state theta oscillations were significantly associated with verbal learning and subjective clustering strategies. Second, we observed that semantic clustering is facilitated by oscillatory patterns in left sensory-motor brain regions. Finally, our analyses revealed that serial and semantic clustering strategies are related to opposite regression patterns, indicating a competitive interaction. Together, these findings provide novel insights into the neural oscillatory dynamics that support diverse encoding strategies in verbal learning.

Domain-Specific Functional Network Adaptations Supporting Dual-Task Performance in Older Adults
Aging is associated with declines in both motor and cognitive functions, which are well captured by dual task gait paradigms. However, the functional brain network mechanisms supporting motor and cognitive aspects of dual task performance in aging remain unclear. We examined 40 older adults (50 to 80 years) and 20 younger adults (20 to 40 years) who performed a motor single task (pedaling), a cognitive single-task (Go/NoGo), and a combined cognitive motor dual task during functional magnetic resonance Imaging (fMRI) using a custom-built MRI compatible pedaling device. Behaviorally, older adults showed significant dual task costs in motor performance, while cognitive performance was preserved. Neurally, older adults showed selective increases in connectivity within executive and motor-planning regions of cognitive networks, consistent with compensatory recruitment, whereas motor networks underwent broader reorganization, with strengthened frontoparietal control circuits but weakened cerebello-parietal and sensorimotor pathways. Multivariate analyses further revealed age related differences in latent connectivity behavior relationships: motor network patterns in older adults were more dispersed, reflecting heterogeneous reorganization, whereas cognitive-network patterns were more overlapping across groups, suggesting relative preservation. These findings suggest that aging involves a domain specific balance of resilience and vulnerability across brain networks and highlight motor-network adaption as a promising target for understanding why some older adults maintain function while others decline.

In-situ glial cell-surface proteomics identifies pro-longevity factors in Drosophila
Much focus has shifted towards understanding how glial dysfunction contributes to age-related neurodegeneration due to the critical roles glial cells play in maintaining healthy brain function. Cell-cell interactions, which are largely mediated by cell-surface proteins, control many critical aspects of development and physiology; as such, dysregulation of glial cell-surface proteins in particular is hypothesized to play an important role in age-related neurodegeneration. However, it remains technically difficult to profile glial cell-surface proteins in intact brains. Here, we applied a cell-surface proteomic profiling method to glial cells from intact brains in Drosophila, which enabled us to fully profile cell-surface proteomes in-situ, preserving native cell-cell interactions that would otherwise be omitted using traditional proteomics methods. Applying this platform to young and old flies, we investigated how glial cell-surface proteomes change during aging. We identified candidate genes predicted to be involved in brain aging, including several associated with neural development and synapse wiring molecules not previously thought to be particularly active in glia. Through a functional genetic screen, we identified one surface protein, DIP-{beta}, which is down-regulated in old flies and can increase fly lifespan when overexpressed in adult glial cells. We further performed whole-head single-nucleus RNA-seq, and revealed that DIP-{beta} overexpression mainly impacts glial and fat cells. We also found that glial DIP-{beta} overexpression was associated with improved cell-cell communication, which may contribute to the observed lifespan extension. Our study is the first to apply in-situ cell-surface proteomics to glial cells in Drosophila, and to identify DIP-{beta} as a potential glial regulator of brain aging.

Behavioral Decoding Reveals Cortical Endocannabinoid Potentiation during Δ9-THC Impairment
How {Delta}9-tetrahydrocannabinol (THC) impairs natural behaviors in mice remains unknown. We developed a video monitored behavioral platform with machine learning classifiers to unravel discrete changes in natural mouse behaviors. THC infusion into the medial prefrontal cortex (mPFC) disrupted walking kinematic features characteristic of impairment responses. THC predominantly increased mPFC GABAergic activity preceding walk initiation shifting the mPFC excitatory/inhibitory (E/I) balance. Pose-defined closed loop photo-stimulation of mPFC GABAergic neurons demonstrated that THC exacerbates selected parameters of motor impairment. Surprisingly, THC also induced a time locked, movement-induced, transient potentiation of mPFC endocannabinoid (eCB) release and ensuing CB1R-mediated synaptic inhibition. Here we establish that THC-modifies mPFC E/I balance to excitation via dynamic changes in eCB release which acts to induce behavioral impairment.

Combinatorial protein barcodes enable self-correcting neuron tracing with nanoscale molecular context
Mapping nanoscale neuronal morphology with molecular annotations is critical for understanding healthy and dysfunctional brain circuits. Current methods are constrained by image segmentation errors and by sample defects (e.g., signal gaps, section loss). Genetic strategies promise to overcome these challenges by using easily distinguishable cell identity labels. However, multicolor approaches are spectrally limited in diversity, whereas nucleic acid barcoding lacks a cell-filling morphology signal for segmentation. Here, we introduce PRISM (Protein-barcode Reconstruction via Iterative Staining with Molecular annotations), a platform that integrates combinatorial delivery of antigenically distinct, cell-filling proteins with tissue expansion, multi-cycle imaging, barcode-augmented reconstruction, and molecular annotation. Protein barcodes increase label diversity by >750-fold over multicolor labeling and enable morphology reconstruction with intrinsic error correction. We acquired a [~]10 million m3 volume of mouse hippocampal area CA2/3, multiplexed across 23 barcode antigen and synaptic marker channels. By combining barcodes with shape information we achieve an 8x increase in automatic tracing accuracy of genetically labelled neurons. We demonstrate PRISM supports automatic proofreading across micron-scale spatial gaps and reconnects neurites across discontinuities spanning hundreds of microns. Using PRISM's molecular annotation capability, we map the distribution of synapses onto traced neural morphology, characterizing challenging synaptic structures such as thorny excrescences (TEs), and discovering a size correlation among spatially proximal TEs on the same dendrite. PRISM thus supports self-correcting neuron reconstruction with molecular context.

Exercise-induced DNA damage response and memory formation in mice
This study reveals that acute aerobic exercise enhances memory formation through a controlled DNA damage mechanism, offering crucial insights into Alzheimer's disease (AD) prevention. This work challenges the traditional view that DNA damage is inherently harmful, demonstrating that minor, reversible DNA single-strand breaks (SSBs) induced by exercise serve as necessary primers for memory consolidation - a mechanism that may be impaired in AD pathogenesis. AD affects 1 in 9 adults over 65, with ~95% being late-onset cases where up to 40% of risk factors are modifiable through lifestyle interventions like exercise. While exercise demonstrably lowers AD risk, underlying mechanisms remain unclear. This study provides a missing mechanistic link by showing how exercise-induced DNA damage repair systems could counteract the DNA damage accumulation and repair dysregulation that are established hallmarks of brain aging and AD. In data presented herein, young mice showed significantly higher SSB rates in active genomic regions compared to aged mice, suggesting the decline of a protective mechanism (i.e., hormesis) with aging - potentially explaining increased AD susceptibility in older adults. The present study also suggests that exercise-induced SSBs are not random cellular damage but precisely targeted events that occur at genes essential for neuroplasticity, synaptic function, and memory formation. These breaks activate PARP1 (Poly ADP ribose polymerase 1), a crucial DNA damage sensor that simultaneously initiates repair processes while facilitating transcriptional programs necessary for memory consolidation. This mechanism may represent how exercise "primes" the brain against the pathological DNA damage accumulation seen in AD. In support of this, in behavioral experiments, a single exercise bout converted sub-threshold learning into robust long-term memory formation. This memory enhancement correlated with upregulation of both neurotrophic genes (BDNF, Fos) and DNA repair enzymes (PARP1, PARP2), demonstrating coordinated damage-repair processes that appear compromised in AD. We identify HPF1 as a critical cofactor enabling PARP1 to perform trans-ADP-ribosylation of histones, linking DNA damage sensing to epigenetic chromatin remodeling required for memory-related gene expression. This pathway represents a novel therapeutic target for AD, as restoring efficient DNA repair mechanisms might slow or prevent memory loss.

Sustained dynamics of saccadic inhibition and adaptive oculomotor responses during continuous exploration
In natural environments, stimuli often recur across time and space, requiring the visual system to remain sensitive to novelty while managing predictability. A central question in systems neuroscience is how motor systems adapt to repeated sensory events without compromising responsiveness. We investigated this adaptive capacity using saccadic inhibition (SI), a reflexive suppression of eye movements triggered by sudden visual transients, as a probe of oculomotor dynamics during naturalistic viewing. Human participants (N = 21) freely explored visual arrays while brief gaze-contingent flashes appeared five times at random intervals, either foveally or parafoveally. SI reliably occurred ~120 ms post-flash across repetitions and locations, indicating robust sensory-driven inhibition. However, the rebound phase, reflecting saccade reprogramming, showed a progressive decline. In a second experiment (N = 19), only the first or the fifth flash was visible on each trial. In this case, neither inhibition nor rebound was altered, suggesting that the rebound decline is driven by repeated sensory stimulation rather than exploration time. This dissociation reveals selective habituation of motor re-engagement mechanisms, while reflexive inhibitory gating remains stable. We propose that inhibition is mediated by circuitry that transiently suppresses saccade initiation and resists habituation. By contrast, the weakening rebound reflects a separate, habituation-prone route that reduces saccade generation to irrelevant events. Functionally, this imbalance implies a recalibration within the saccade generator, preserving inhibitory capacity while constraining motor output. Our findings uncover a distinct form of oculomotor habituation and demonstrate how SI reveals dynamic decoupling of sensory input and motor output under repeated stimulation.

Postpartum enhancement of spatial learning and cognitive flexibility: an IntelliCage study
The transition to motherhood has been shown to result in significant changes in the structure and function of the brain, with particular emphasis on the enhancement of cognitive abilities that are essential for survival. Specifically, in murine models, spatial learning and cognitive flexibility have been identified as critical components of mothering. These abilities facilitate efficient navigation of the environment, resource acquisition, and responsiveness to offspring's needs. While cognitive enhancements during the postpartum period have been observed in various experimental setups, research using long-term data collection with automated monitoring in home cage setup is completely lacking even though it provides a more reliable approach than other experimental procedures. This study aims to address this knowledge gap by systematically examining spatial learning and cognitive flexibility in female mice during the reproductive stages using IntelliCage, with the objective of offering a more comprehensive understanding of maternal cognitive adaptations. Utilising the IntelliCage system, we observed that female mice in the postpartum phase outperformed pregnant and nulliparous females in place learning, reversal learning, and fixed schedule drinking tasks, demonstrating faster adaptation and superior retention of information. These paradigms mirror real-world challenges faced by mothers, such as navigating resources and balancing caregiving with self-maintenance. The enhanced performance demonstrated by the mothers could be attributed to their heightened motivation and cognitive abilities, potentially influenced by substantial hormonal shifts, which have been known to modify neuroplasticity in critical brain regions. The identification of these improvements in maternal behaviour may offer novel insights into the impact of reproductive experiences on brain function, with implications for maternal health and broader cognitive research.

PRRT2 as an auxiliary regulator of Nav channel slow inactivation
During sustained activity, voltage-gated sodium (Nav) channels enter a slow inactivation state to limit cellular hyperexcitability. Disruption of this regulatory process has been implicated in skeletal, cardiac and neurological disorders. While the kinetics of this process are well characterized, its endogenous modulators remain unclear. Here, we identify Proline-Rich Transmembrane Protein 2 (PRRT2) as a native regulator of Nav channel slow inactivation. We show that PRRT2 facilitates the entry of Nav channels into slow-inactivated state and delays their recovery, a regulatory effect conserved from zebrafish to humans. PRRT2 forms molecular complexes with Nav channels both in vitro and in vivo. In the mouse cortex, PRRT2 deficiency impairs the slow inactivation of Nav channels in neuronal axons, leading to reduced cortical resilience in response to hyperexcitable challenges. Together, these findings establish PRRT2 as a physiological modulator of Nav channel slow inactivation and reveal a mechanism that supports cortical resilience to pathological perturbations.

Mapping the Structural Brain Network of Psychopathy: Convergent Evidence from Humans and Chimpanzees
Psychopathy, a condition characterized by profound emotional and interpersonal deficits, has long been hypothesized to stem, in part, from structural brain abnormalities. Yet, neuroimaging findings remain inconsistent. To address these discrepancies, we applied a novel structural network mapping approach combined with cross-species analyses. Traditional meta-analysis revealed weak spatial convergence across 20 samples. Nevertheless, we found that heterogeneous peak locations coalesced into a distributed set of regions encompassing the insular, and prefrontal cortices. This network overlapped strikingly (r = .93) with a lesion-derived network causally linked to antisocial behaviour, and variation within it predicted volumetric risk scores in both humans (n = 107, R2 = .16) and chimpanzees (n = 148, R2 = .21). These findings suggest that psychopathy reflects abnormalities across a collection of distributed regions rather than isolated areas, providing a unifying explanation for decades of inconsistent results and advancing our understanding of its clinical, biological, and evolutionary bases.

Dendritic Interaction of Timescales in Afterdepolarization Potentials and Nonmonotonic Spike--adding
Depolarizations that occur after action potentials, known as afterdepolarization potentials or ADPs, are important for neuronal excitability and stimulus evoked transient bursting. Slow inward and fast outward currents underlie the generation of such ADPs with modulation of ADP amplitudes occurring as a result of neuronal morphology. However, the relative contribution and role of these slow inward and fast outward currents in ADP generation is poorly understood in the context of somatic and dendritic localization as well as with varied dendritic properties. Using a two compartmental Hodgkin-Huxley type model, the role of somatic and dendritic compartmentalization of ADP associated currents is investigated, revealing that dendritic (rather than somatic) slow inward and fast outward currents are the main contributors to ADP and spike-adding during both brief step current and AMPA current input. Additionally, dendritic size and passive properties of the dendrites were found to be key modulators of ADP amplitude. However, increasing magnitudes of NMDA current conductance resulted in nonmonotonic spike-adding in a manner dependent on dendritic Ca2+ influx and Ca2+ activated K+ currents, which was found to be the result of tight regulation of stimulus evoked transient bursting through positive feedback on action potential generation by dendritic Ca2+ and subsequent negative feedback through Ca2+ activated K+ currents. This novel mechanism of ADP and spike-adding regulation highlights the role of currents with slow timescales in ADPs, stimulus evoked bursting and neuronal excitability with implications for Ca2+ dependent synaptic plasticity and neuromodulation.

Tmem117, an oligodendrocyte-enriched regulator of NCX activity, links myelin homeostasis to counterregulation and metabolic health.
The counterregulatory response (CRR) to hypoglycemia is a fundamental, evolutionarily conserved homeostatic mechanism orchestrated by the central nervous system (CNS) to ensure survival during glucose scarcity. In individuals with diabetes, this response is frequently impaired, contributing to life-threatening episodes of hypoglycemia. Tmem117 was previously identified in a genetic screen as a promising hypothalamic regulator of CRR. Our previous work highlighted its contribution to CRR through regulation of vasopressin secretion. Here, we reveal that Tmem117 is also enriched in cells of the oligodendrocytic lineage and we characterize the contribution of oligodendrocytic Tmem117 in CRR. We show that depletion of Tmem117 from either all oligodendrocyte lineage cells or only mature oligodendrocytes leads to myelin deficits and male-specific defects in CRR. Furthermore, we reveal that transient, adult-onset depletion of Tmem117 in mature oligodendrocytes is sufficient to induce long-lasting metabolic imbalances in male mice, suggesting that defects in oligodendrocytes and myelin can affect peripheral glucose homeostasis. Mechanistically, we provide for the first-time insights on the function of Tmem117 showing that it regulates intracellular calcium dynamics through its interaction with the sodium-calcium exchanger NCX1. Together, these results redefine our understanding of the cellular contributors to the CRR, highlight the importance of oligodendrocytes in systemic glucose regulation, and position Tmem117 as a promising molecular target for cell-specific manipulation of NCX activity.

Parkinson's disease LRRK2 mutations dysregulate iron homeostasis and promote oxidative stress and ferroptosis in human neurons and astrocytes
Background: Iron accumulation is a hallmark of sporadic and familial Parkinson's disease (PD) pathology and correlates with clinical motor symptom severity. The biochemical mechanisms driving iron dyshomeostasis in PD brain and whether these are early or late event in the neurodegenerative process remain unknown. Elevated nigral iron levels have been reported in LRRK2 mutation carriers, both in PD patients compared to idiopathic PD and in asymptomatic carriers relative to controls, suggesting that iron accumulation precedes clinical onset in LRRK2-associated PD. However, the precise consequence of pathogenic LRRK2 mutations on cellular iron handling within neurons and glial cells remains unclear. Methods: Here, we investigated different readouts of iron homeostasis in iPSCs and iPSC-derived neurons and astrocytes from PD patients harboring G2019S or R1441C/G LRRK2 mutations or healthy controls, as well as an isogenic iPSC panel with the same variants. By using high-content and super-resolution microscopy of iron-specific probes, we assayed iron content and distribution in cells and examined the downstream effects of iron dyshomeostasis on ferroptosis signaling. Results: We show that heterozygous LRRK2 mutations dysregulate cellular iron across iPSCs, neurons and astrocytes, in a kinase-dependent manner. Lysosomal ferrous iron storage was consistently elevated across iPSCs, iPSC-derived neurons and astrocytes carrying LRRK2 mutations. LRRK2 regulates Rab GTPase function through their direct phosphorylation, and our prior work revealed significant but divergent lysosomal phenotypes between Rab8a and Rab10 knockout models. Here, we report that Rab8a knockout recapitulates key aspects of LRRK2 mutation phenotypes on intracellular iron and ferritin levels, although with differences in magnitude and specificity. By contrast, Rab10 deficiency showed opposing effects, suggesting distinct roles for these well-established LRRK2 substrates in iron homeostasis. Finally, we show that basal lipid peroxidation and ROS levels are elevated in isogenic LRRK2 mutant neurons, while iron chelation was sufficient to reduce LRRK2-dependent ROS. Conclusions: Together, our findings demonstrate that LRRK2 mutations disrupt iron homeostasis across disease-relevant cell types and establish a discrete biochemical pathway linking LRRK2 signaling and vulnerability to ferroptosis. Ongoing work aims to further dissect the roles of Rab substrates in these pathways.

Human patient-specific FOXG1 syndrome mouse model revealed FOXG1-MYCN-mediated regulation of protein homeostasis in neurodevelopmental disorder
Neurodevelopmental disorders are characterized by disruptions in brain development, resulting in cognitive, behavioral, and neurological impairments. FOXG1 syndrome (FS), caused by heterozygous mutations in the FOXG1 gene, exemplifies a severe monogenic neurodevelopmental disorder. To investigate its pathogenesis, we generated a patient-specific W300X mouse model carrying a truncation variant of FOXG1. We found that the truncated FOXG1 protein in W300X-heterozygous (W300X-Het) mice is more abundant and more nuclear-localized than the full-length FOXG1 protein, implicating a pathogenic mechanism involving the truncated protein. Interestingly, W300X-Het mice exhibited profound abnormalities in the dentate gyrus, including disrupted neurogenesis, impaired granule cell migration, and altered dendritic morphology. Transcriptomic profiling identified broad dysregulation in protein homeostasis pathways, particularly ribosomal biogenesis, translation, and proteostasis. Disruption of the FOXG1-MYCN pathway, critical for robust protein synthesis during neural stem cell division, synaptogenesis, and synaptic plasticity, emerged as a key mechanism underlying these defects. In parallel, microglial activation and inflammation were markedly increased in the dentate gyrus, contributing to a pro-inflammatory environment that exacerbates neurogenic and structural deficits. Consistent with hippocampal dysfunction in FS patients, W300X-Het mice exhibited significant spatial learning and memory impairments. Together, our study highlights disrupted protein homeostasis and neuroinflammation as key drivers of FS pathogenesis, providing a framework for developing therapeutic strategies targeting these pathways.

Human dorsal root ganglia neuronal cell line to study nociceptive signaling: a new pipeline for pain therapy.
Nociceptive afferent neurons within the dorsal root ganglion (DRG) detect and relay painful stimuli from the periphery to the brain, and the malfunctioning of this process leads to sustained pain states. Animal model studies have been invaluable for demonstrating the importance of the DRG nociceptor in pain sensation and the development of related analgesic targets. However, there are functional biological differences between human and animal model nociceptors. Therefore, a complementary in vitro model of human nociception is critical to confirming the relevance of preclinical findings for therapeutic drug development. We characterized the nociceptive properties of differentiated cells from the human DRG-derived immortalized cell line HD10.6. Within differentiated HD10.6 cells, we documented the abundance and localization of nociceptive machinery central to regulating excitability and linked with pain sensation including ion channels TRPV1 and NaV1.7 and afferent peptides CGRP and Substance P. Using calcium influx imaging assays, we confirmed the electrical functionality of TRPV1 and NaV1.7 in HD10.6 cells, and through whole-cell patch clamp, we found similar baseline electrophysiological parameters of HD10.6 cells to those previously observed in human patient DRGs. Further, we found that differentiated HD10.6 cells express the mu opioid receptor 1 protein, and DAMGO, a mu agonist, blocks depolarization-evoked calcium influx in a naloxone-reversible fashion. Importantly, using an inflammatory cocktail, excitation and peripheral sensitization are induced within HD10.6 cells, mirroring nociceptors in a pain state during or after tissue damage or inflammation. Finally, HD10.6 cells were also cultured into dual-chambered microfluidic devices to mirror the biological anatomy of the nociceptor. Within this system, we demonstrated the uptake of adeno-associated-virus (AAV) by the peripheral terminals and AAV transport to the soma. Altogether, we have developed the use of HD10.6 cells to create a system of human nociceptive signaling on a chip to study human nociceptor physiology and intervention.