bioRxiv Subject Collection: Neuroscience's Journal

Automated image segmentation uncovers the role of CD74high human microglia in cognitive decline
The role of activated microglia in Alzheimer's disease (AD) is well established; the proportion of stage III activated microglia has been associated with AD and cognitive decline, but this morphologically defined subtype is relatively uncommon (1-2% of microglia) and its cellular function is unknown. Single-cell RNA-sequencing revealed CD74 as a marker gene that is enriched in immunologically active microglial subtypes associated with AD. Here, we evaluated the relationship between CD74 expression, AD-related traits, and microglial morphology using dorsolateral prefrontal cortex samples from two brain collections (ROSMAP: n=63, NYBB: n=91). An image segmentation pipeline using CellProfiler was developed to extract features from entire tissue sections. The pipeline automatically delineated gray and white matter regions and segmented 1,120,780 gray matter microglia. In a meta-analysis of the two datasets, we find an increase in frequency of microglia with high CD74 expression (CD74high) in relation to AD dementia (p = 0.038), particularly in the phase of terminal, accelerated cognitive decline before death. These microglia have a more rounded, amoeboid shape (ROSMAP: p = 1.4x10-6; NYBB: p = 2x10-13) which is a characteristic morphology of activated stage III microglia. Results were consistent across both datasets, highlighting the robustness of our cellular segmentation approach. This study identifies a potential role for CD74high microglia and the CD74 ligand MIF in cognitive decline, and it provides evidence for a partially overlapping but distinct role for CD74high microglia and morphologically defined stage III microglia, whose functional properties have remained poorly understood. These CD74high microglia appear to be enriched for genes involved in cytokine response for class I and II antigen presentation, as well as regulation of T cell proliferation. These findings begin to link microglial subtypes defined by single-cell transcriptomic data with those characterized by classical morphological criteria to resolve the roles of different microglial functions to distinct stages in the trajectory to AD.

Embryonic Spinocerebellar Ataxia Type 37 AUUUC Repeat RNA Causes Neurodevelopmental Defects in Zebrafish
Onset of many neurodegenerative and neuromuscular diseases usually starts in adulthood; however, recent advances point toward neurodevelopmental changes as drivers of late neurodegeneration. How early neuropathological features occur in these conditions remains unclear, which is critical for timely therapeutic intervention. Here, we provide evidence that neurodevelopmental axonal defects initiate a motor phenotype in a zebrafish model of spinocerebellar ataxia type 37 (SCA37), a degenerative hereditary condition caused by an ATTTC repeat in the DAB1 gene. We investigated neuronal defects triggered by the embryonic AUUUC repeat RNA and their effects later in life by transiently expressing this RNA in embryos and analyzing innervation and motor function. We found abnormalities in motor neuron axonal outgrowth and muscle innervation. We also discovered disrupted embryonic motor activity and reduced locomotor distance and velocity in late adult zebrafish, demonstrating motor impairment. Moreover, we showed that NOVA2 expression rescues axonal defects, indicating dysfunction of NOVA2-regulated neurodevelopmental processes. Overall, our results establish embryonic expression of the AUUUC repeat RNA as a driver of axonal and synaptic abnormalities, interfering with neuronal circuits and culminating in adult motor dysfunction.

GephyrinΔ199-233 - an epileptogenic microdeletion
Gephyrin, as the main organizer of inhibitory synapses, is crucial for inhibitory signal transmission, and implicated in various neurological disorders. Various studies have identified gephyrin microdeletions in conditions of autism, schizophrenia, and epilepsy. Those deletions affected the N-terminal G-domain and/or the central C-domain of gephyrin while the receptor binding C-terminal E-domain was not affected. Here, we investigated the importance of a specific microdeletion ({triangleup}199-233) within the C-domain using a full-body knock-in mouse model. Homozygous mice displayed a severe phenotype characterized by reduced fertility, increased mortality, and neurological deficits at early developmental stages. Analyses in dissociated hippocampal neurons demonstrated disrupted synaptic targeting of gephyrin {triangleup}199-233 that harbors the functionally important S-palmitoylation site at Cys212. Simultaneously, we found adaptations at the excitatory synapse, with smaller, but more numerous clusters of the excitatory scaffolding protein PSD95. Although, gephyrin {triangleup}199-233 showed unexpectedly a facilitated receptor interaction, inhibitory signal transmission was reduced. We hypothesize, that the gephyrin {triangleup}199-233-mediated reduction of inhibition triggers compensatory excitation, which possibly fails and/or disrupts the excitation/inhibition ratio in our mouse model. These findings highlight the critical role of the gephyrin C-domain and its post-translational modifications in synaptic function and neuronal health, offering a novel mouse model for the development of potential therapeutic targets addressing gephyrin-associated neurological disorders.

Neural and behavioral correlates of evidence accumulation in human click-based echolocation
Echolocation enables blind individuals to perceive and navigate their environment by emitting clicks and interpreting their returning echoes. While expert blind echolocators demonstrate remarkable spatial accuracy, the behavioral and neural mechanisms supporting the temporal integration of spatial echoacoustic cues remain less explored. Here, we investigated the temporal dynamics of spatial information accumulation in human click-based echolocation using EEG. Blind expert echolocators and novice sighted participants localized virtual spatialized echoes derived from realistic synthesized mouth clicks, with trials presenting trains of 2-11 clicks. Behavioral results showed that blind expert echolocators significantly outperformed sighted controls in spatial localization. For these experts, localization thresholds decreased with more clicks, indicating cumulative integration of spatial cues across repeated samples. EEG decoding analyses revealed that neural representations significantly distinguished echo laterality and predicted overall spatial localization performance from the first click alone. Additionally, brain responses relative to the first click evolved systematically over successive clicks, paralleling psychophysical performance in blind echolocators and providing a possible index of perceptual information accumulation. These findings provide, to our knowledge, the first fine-grained account of temporal neural dynamics underlying click-based echolocation, directly linked to behavioral performance over multiple samples. They reveal how successive echoes are integrated over time into coherent spatial representations. Together, these results advance our understanding of the perceptual and neural mechanisms underlying echolocation and demonstrate adaptive sensory processing in the absence of vision.

Post-inhibitory rebound firing drives hypothalamic activation for male mating
Many behaviorally relevant limbic circuits are dominated by inhibitory connections, raising the question of how neuronal activation arises in such contexts. In male mating, both activation of mPOA and its inhibitory inputs are essential - a paradox previously ascribed to disinhibition. Here, we show that the mPOA largely lacks local inhibitory circuits, arguing against disinhibition. Instead, in vivo recordings reveal that mount-active mPOA neurons are suppressed prior to behavioral onset, and this inhibition negatively correlates with subsequent activation, consistent with post-inhibitory rebound firing (PIR). A biologically plausible conductance-based model demonstrates that synaptic inhibition interacts with T-type Ca 2+ currents - the ionic basis of PIR - to promote mPOA neuron firing. Experimentally, bidirectional manipulations of mPOA T-type Ca 2+ channels confirm this mechanism: knockdown reduces PIR and impairs male mating, while over-expression enhances mating. Together, these results identify PIR as a critical mechanism by which inhibition promotes activation in limbic circuits to drive male mating.

Asymmetric Social Representations in the Prefrontal Cortex for Cooperative Behavior
Cooperation is a hallmark of social species, enabling individuals to achieve goals that are unattainable alone. Across species, cooperative behaviors are often organized by distinct social roles such as leaders and followers, yet the neural mechanisms supporting such role-based coordination remain elusive. Here we introduce a new paradigm for studying cooperation in mice, where pairs of animals engage in a joint spatial foraging task that naturally gives rise to stable leader-follower roles predictive of learning speed. Disruption of medial prefrontal cortex (mPFC) activity, particularly in followers, impairs cooperation and induces reciprocal shifts in how animals weigh self- and partner-related cues for decision-making. Calcium imaging reveals that mPFC encodes both leadership dynamics and an egocentric social value map of the partner's position, each in an asymmetric, role-specific manner. Combining this behavior with a novel multi-agent inverse reinforcement learning framework, we identify latent value functions that guide cooperative decisions and are decodable from mPFC activity. These findings uncover fundamental neural computations that support cooperation, revealing how social roles shape decision-making in real time. Our work opens new avenues for investigating the cellular and circuit basis of social cognition and collective behavior.

Brain-wide organization of intrinsic timescales at single-neuron resolution
Variations in intrinsic neural timescales across the mammalian forebrain reflect the anatomical structure and functional specialization of brain areas and individual neurons. Yet, the organization of timescales beyond the forebrain remains unexplored. We analyzed intrinsic timescales of single neurons across the entire mouse brain. Median timescales were up to fivefold longer in the midbrain and hindbrain than in the forebrain. Spatial patterns of gene expression predicted timescale variation at a resolution finer than brain-area boundaries. Across neurons, the diversity of timescales revealed a multiscale architecture, in which fast timescales determined regional differences in medians, while slow timescales universally followed a power-law distribution with an exponent near 2, indicating a shared dynamical regime across the brain consistent with the edge of instability or chaos. These organizing principles for the dynamics of single neurons across the brain provide a foundation for linking cellular activity with regional specialization and brain-wide computation.

Individual differences drive social hierarchies in mouse societies
Social structures in naturalistic animal groups emerge from repeated interactions between individuals. Yet it remains unclear whether social position reflects a single behavioral axis or arises from multiple, dissociable processes, and to what extent social structures are shaped by stable traits of the individuals or by emergent group dynamics. Addressing these questions in mice requires continuous, long-term tracking of multiple behaviors of individuals in unperturbed groups of sufficient size. To this end, we developed the NoSeMaze, a high-throughput, non-invasive sensor-rich habitat enabling scalable study of naturalistic social interactions. Spontaneous dyadic tube competitions revealed stable, transitive social hierarchies, with individual ranks persisting even after group reshuffling. Similarly, proactive chasing showed strong, trait-like stability across different social contexts. However, social status was not monolithic: rank and chasing were only moderately aligned. Their relationship was stronger in groups with less clarified hierarchies, where mice relied more on aggressive signaling to assert their position. Additionally, chasing was disproportionately concentrated among top-ranking individuals, suggesting active status negotiation within the social elite. Despite its stability, chasing thus fulfilled distinct functional roles depending on social context. Our findings support a systems-level view in which social position results from the interplay between stable individual traits and group-level dynamics. The modular NoSeMaze platform thereby reveals the multidimensional nature of social rank organization in mice.

Precise temporal localisation of M/EEG effects with Bayesian generalised additive multilevel models
Time-resolved electrophysiological measurements such as those obtained through magneto- and electroencephalography (M/EEG) offer a unique window onto the neural activity underlying cognitive processes. Researchers are often interested in determining whether and when these signals differ across experimental conditions or participant groups. The conventional approach involves mass univariate statistical testing across time and space followed by corrections for multiple comparisons or some form of cluster-based inference. While effective for controlling error rates at the cluster-level, cluster-based inference comes with a significant limitation: by shifting the focus of inference from individual time points to clusters, it prevents drawing conclusions about the precise onset or offset of observed effects. Here, we present a *model-based* alternative for analysing M/EEG timeseries, such as event-related potentials or time-resolved decoding accuracy. Our approach leverages Bayesian generalised additive multilevel models, providing posterior odds that an effect exceeds zero (or chance) at each time point, while naturally accounting for temporal dependencies and between-subject variability. Using both simulated and empirical M/EEG datasets, we show that this approach substantially outperforms conventional methods in estimating the onset and offset of neural effects, yielding more precise and reliable estimates. We provide an open-source R package implementing the method and describe how it can be integrated into M/EEG analysis pipelines using MNE-Python.

Aberrant development of glutamatergic inputs to PV and SOM interneurons shape amygdala excitability and oscillatory dynamics after early life stress
Early-life stress (ELS) induces persistent amygdala dysfunction and affects amygdala-related emotional behaviors, yet the developmental and physiological mechanisms driving these effects remain poorly understood. Here, we provide a comprehensive electrophysiological characterization of the effects of ELS on parvalbumin and somatostatin interneurons (INs) as well as principal neurons (PNs) in the mouse lateral amygdala (LA) across development. Additionally, we correlate these findings to activity of the LA circuitry in vivo, using Neuropixels recordings in awake mice. In preweaning juveniles, the effects of ELS were remarkably similar in males and females, involving reduced IN excitability, elevated glutamatergic input to INs and shift in the PN E/I balance. While IN function was largely normalized in adult females, males developed a distinct pathological phenotype characterized by reduced glutamatergic input to INs, impaired recruitment of INs and hyperexcitability of PNs. This male-specific dysfunction correlated with aberrant LA oscillatory dynamics in awake mice and deficits in fear processing. Our data suggest that the impaired glutamatergic wiring of interneurons is a key mechanism underlying the aberrant circuit dynamics in the LA after ELS exposure and contribute to the ELS-induced defects in fear processing. These findings highlight sex-specific developmental trajectories of interneuron connectivity as a potential factor contributing to ELS-induced psychiatric vulnerability.

Separable neural population representations are constructed from mixed single neuron selectivity in the mouse early visual system
Both sensory and non-sensory brain regions receive mixed inputs from single neurons which require decomposition and integration before proceeding through a processing hierarchy. Whether mixed input signals are used in biological neural networks to derive pure single neuron representations, or distributed as new population representations from mixed single neurons, is not clear. In this study, we measured the distribution of single neuron hue and luminance tuning in the dorsolateral geniculate nucleus (dLGN) and primary visual cortex (V1) of mice, as well as the information about and structure of hue and luminance representations in populations of hundred of simultaneously sampled neurons. We compare single neuron and population encoding to null models expected for random integration and extraction of pure categorical single neuron representation. Using both univariate and multivariate regression techniques, we consistently noted that tuning for hue and luminance, rather than clustering into categorical response structures, formed uniform distributions. While the distribution of single neuron selectivity varied across the thalamocortical circuit, we found no evidence of categorical tuning organization emerging in the hierarchy. Nevertheless, populations contained complete information, in either high-dimensional linear representations or low-dimensional non-linear representations. In summary, we find that as early as primary sensory cortex and thalamus single neurons that have mixed selectivity for hue and luminance form a high dimensional representation of those variables, which can be non-linearly embedded in multiple separable representations.

Comparative Computational Modeling of Approach-Avoidance Biases in Suicidal Populations via Hierarchical Bayesian Inference
Pavlovian "approach or avoid" impulses are critical behavioral biases that, in excess, are linked to multiple psychiatric conditions. To investigate how such biases contribute to suicidal thoughts and behaviors, we analyzed data from two clinical populations completing an aversive Go/NoGo task. This task disentangles motor action (Go or NoGo) from outcome valence (escape from, or avoidance of, an aversive stimulus), enabling the isolation of Pavlovian biases from instrumental learning processes. We compared multiple computational models that had previously been proposed to explain Pavlovian tendencies, including reinforcement learning, active inference, and drift diffusion-based approaches. We employed a hierarchical Bayesian inference procedure that treats model identity as a random factor at the individual level, allowing an unbiased determination of which mechanisms most accurately captured participants behavior. Across both datasets, models featuring Pavlovian context biases plus a value-decay mechanism best accounted for performance. By contrast, policy-based Pavlovian models and more complex approaches, such as those integrating working memory or active inference, were supported by fewer study participants. These findings suggest that reflexive biases exert a persistent influence on decision-making, and that value decay plays a critical role in shaping behavior over time. Our results demonstrate the importance of systematically comparing and accounting for relevant cognitive processes to explain observed task behaviors. Understanding the factors contributing to task performance may help clarify how Pavlovian tendencies relate to psychopathology, including, in our case, elevated suicide risk. Finally, we illustrate how a complete hierarchical model selection framework can be applied to identify the most plausible mechanisms underlying Pavlovian biases, offering a robust approach for advancing our understanding of task behaviors and establishing clinical utility in future studies.

Male-specific analgesic effects of minocycline in sickle cell disease are mediated by microglia and the microbiome
Over 50% of individuals with sickle cell disease (SCD) experience chronic pain that is phenotypically distinct from their acute, vaso-occlusive crisis pain. Chronic SCD pain is commonly managed with opioid-based drugs that are associated with unwanted side effects, incomplete pain relief, and, in this population, accessibility issues. Thus, new treatments for chronic SCD pain are desperately needed. Here, we examined the analgesic efficacy of acute minocycline treatment in transgenic SCD mice. SCD mice exhibit gut dysbiosis and chronic inflammation. Therefore, we hypothesized that minocycline would provide robust analgesia in this model given the antibiotic and anti-inflammatory properties of the drug. Six days of minocycline treatment reversed chronic mechanical hypersensitivity only in male SCD mice. We identified two potential mechanisms underlying these sex-specific effects. First, we observed increased microgliosis only in the dorsal horn of male SCD mice. Minocycline treatment had opposite effects on microglial number in male and female SCD spinal cords. Second, minocycline treatment altered the gut microbiota in a sex-specific fashion; fecal microbiota transplant (FMT) from minocycline-treated female SCD mice induced widespread pain in recipients whereas FMT from minocycline-treated male SCD mice did not. In summary, these experiments highlight novel sex-specific mechanisms of minocycline analgesia and support future exploration of minocycline use for SCD pain management, but only in male patients.

A theory for self-sustained balanced states in absence of strong external currents
Recurrent neural networks with balanced excitation and inhibition exhibit irregular asynchronous dynamics, which is fundamental for cortical computations. Classical balance mechanisms require strong external inputs to sustain finite firing rates, raising concerns about their biological plausibility. Here, we investigate an alternative mechanism based on short-term synaptic depression (STD) acting on excitatory-excitatory synapses, which dynamically balances the network activity without the need of external inputs. By employing accurate numerical simulations and theoretical investigations we characterize the dynamics of a massively coupled network made up of N rate-neuron models. Depending on the synaptic strength J0, the network exhibits two distinct regimes: at sufficiently small J0, it converges to a homogeneous fixed point, while for sufficiently large J0, it exhibits Rate Chaos. For finite networks, we observe several different routes to chaos depending on the network realization. The width of the transition region separating the homogeneous stable solution from Rate Chaos appears to shrink for increasing N and eventually to vanish in the thermodynamic limit (N [->] {infty}). The characterization of the Rate Chaos regime performed by employing Dynamical Mean Field (DMF) approaches allow us on one side to confirm that this novel balancing mechanism is able to sustain finite irregular activity even in the thermodynamic limit, and on the other side to reveal that the balancing occurs via dynamic cancellation of the input correlations generated by the massive coupling. Our findings show that STD provides an intrinsic self-regulating mechanism for balanced networks, sustaining irregular yet stable activity without the need of biologically unrealistic inputs. This work extends the balanced network paradigm, offering insights into how cortical circuits could maintain robust dynamics via synaptic adaptation.

A multi-frequency whole-brain neural mass model with homeostatic feedback inhibition
Whole-brain models are valuable tools for understanding brain dynamics in health and disease by enabling the testing of causal mechanisms and identification of therapeutic targets through dynamic simulations. Among these models, biophysically inspired neural mass models have been widely used to simulate electrophysiological recordings, such as MEG and EEG. However, traditional models face limitations, including susceptibility to hyperexcitation, which constrains their ability to capture the full richness of neural dynamics. Here, we developed and characterized a new version of the Jansen-Rit neural mass model aimed at overcoming these limitations. Our model incorporates inhibitory synaptic plasticity (ISP), which adjusts inhibitory feedback onto pyramidal neurons to clamp their firing rates around a target value. Further, the model combined two subpopulations of neural cortical columns oscillating in and {gamma}, respectively, to generate a richer EEG power spectrum. We analyzed how different model parameters modulate oscillatory frequency and connectivity. We considered a models showcase, simultaneously fitting EEG and fMRI recordings during NREM sleep. Bifurcation analysis showed that ISP increases the parameters range in which the model exhibited sustained oscillations; the target firing rate acts as a bifurcation parameter, moving the system across the bifurcation point, producing different oscillatory regimes, from slower to faster. High frequency activity emerged from low global coupling, high firing rates, and a high proportion of {gamma} versus subpopulations. Importantly, ISP was necessary in the multi-frequency model to successfully fit EEG functional connectivity across frequency bands. Finally, ISP-controlled reductions in excitability reproduced both the slow-wave activity and the reduced connectivity in NREM sleep. Altogether, our model is compatible with biological evidence of the effects of E/I balance on modulating brain rhythms and connectivity, as observed in sleep, neurodegeneration, and chemical neuromodulation. This biophysical model with ISP provides a springboard for realistic brain simulations in health and disease.

Connectome-based predictive modelling predicts frailty levels in older adults
Frailty is characterized by a persistent and progressive decline in physiological reserves, leading to increased vulnerability to stressors and a heightened risk of adverse health outcomes, both physically and mentally. Despite the prevalence of frailty in older adults, there is limited research on its neural substrates, especially using task-based brain functional connectivity. In this study, we used connectome-based predictive modelling (CPM) to find a linear relationship between task-based connectomes, taken from tasks that involved similar handgrip manipulations, and a separate measure of frailty: the maximum grip strength in older adults. We observed that the task-based connectomes were able to explain individual differences in grip strength, with the Subcortical and Cerebellum network, particularly the caudate nucleus, functional connectivity being the strongest predictor. These findings demonstrate that task-based functional connectomes can serve as personalized markers that can predict individual behavioral measures, including handgrip strength, and point to involvement of the caudate nucleus in frailty.

Response-driven Serial Dependence: When Response Mode Consistency Matters More than Shared Memory Encoding
Serial dependence - the bias from recent experience on present response - is often linked to shared memory representations. Yet, it remains unclear whether this bias across tasks when stimulus features are shared but response modes differ. To test this, we interleaved temporal reproduction and bisection tasks using a post-cue design that held duration encoding constant while varying motor output across trials. Using structural equation modeling (SEM), we dissociated perceptual (stimulus-driven) and decisional (response-driven) components of serial dependence. With consecutive same tasks, we replicated repulsive perceptual serial dependence and attractive decisional carryover. Critically, these effects vanished across tasks, despite identical stimulus processing, suggesting that consistent motor responses, not shared memory alone, drive sequential biases. Moreover, SEM uncovered repulsive perceptual influences that standard regression missed, highlighting its power to isolate overlapping effects. Together, these findings reveal that response-specific reactivation underpins serial dependence, pointing to motor-context binding as a key factor in temporal decision-making.

Toward Robust Neuroanatomical Normative Models: Influence of Sample Size and Covariates Distributions
Normative models are increasingly used to characterize individual-level brain deviations in neuroimaging studies, but their performance depends heavily on the reference sample used for training or adaptation. In this study, we systematically investigated how sample size and covariate composition of the reference cohort influence model fit, deviation estimates, and clinical readouts in Alzheimers disease (AD). Using a discovery dataset (OASIS-3, n = 1032), we trained models on healthy control (HC), subsamples ranging from 5 to 600 individuals, while varying age and sex distributions to simulate biases in reference populations. We further assessed the use of adaptive transfer learning by pre-training models on the UK Biobank (n = 42,747) and adapting them to the clinical dataset applying the same sub-sampling strategies. We evaluated model performance on a fixed HC test set and quantified deviation score errors, outlier detection, and classification accuracy in both the HC test set and the AD cohort. The findings were replicated in an external validation sample (AIBL, n = 463). Across all settings, model performance improved with increasing sample size, but demographic alignment of the covariates, particularly in age, was essential for reliable deviation estimates. Models trained directly within the dataset achieved stable fit with approximately 200 HCs, while adapted models reached comparable performance with as few as 50 individuals when pre-trained on large-scale data. These results show that robust individual-level modeling can be achieved using moderately sized but demographically matched cohorts, supporting broader application of normative modeling in ageing and neurodegeneration research.

Replating induces mTOR-dependent rescue of protein synthesis in Charcot-Marie-Tooth diseased neurons
Charcot-Marie-Tooth disease (CMT) is an inherited peripheral neuropathy characterized by sensory dysfunction and muscle weakness, manifesting in the most distal limbs first and progressing more proximal. Over a hundred genes are currently linked to CMT with enrichment for activities in myelination, axon transport, and protein synthesis. Mutations in tRNA synthetases cause dominantly inherited forms of CMT and animal models with CMT-linked mutations in these enzymes display defects in neuronal protein synthesis. Rescuing protein synthesis in CMT mutant neurons could offer exciting therapeutic options beyond symptom management. To address this need, we expressed CMT-linked variants in tyrosyl tRNA synthetase (YARS-CMT) in primary sensory neurons and evaluated impacts on protein synthesis and cell viability. YARS-CMT expression reduced protein synthesis in these neurons prior to the onset of caspase-dependent axon degeneration and cell death. To determine how YARS-CMT expression affects axon outgrowth, we dissociated and replated these neurons to stimulate axon regeneration. To our surprise, axonal regrowth occurred normally in replated YARS-CMT neurons. Moreover, replating YARS-CMT neurons rescued protein synthesis. Inhibiting mTOR suppressed rescue of protein synthesis after replating, consistent with its significant role in protein synthesis during axon regeneration. These discoveries identify new avenues for augmenting protein synthesis in diseased neurons and restoring protein synthesis in CMT or other neurological disorders.

Functional rather than anatomic connectivity predicts seizure propagation in a multi-node model of focal neocortical epilepsy
Seizures propagate through the brain either locally or via widespread networks of anatomically and functionally connected nodes. These sites can be manipulated in the surgical treatment of human patients through ablation or stimulation. However, we still lack a full understanding of how seizures, ablation, and stimulation recruit or alter recruitment of these distant sites. Here, we apply widefield calcium imaging in a non-anesthetized rodent multi-nodal bilateral neocortical network model of focal epilepsy to examine excitatory and inhibitory cell recruitment. When we initiate seizures in somatosensory cortex (S1), they preferentially spread to an ipsilateral node in frontal cortex (M2) rather than across the corpus callosum to contralateral mirror somatosensory cortex. On the other hand, seizures rapidly spread across the corpus callosum in regions that connect M2 with its mirror M2 focus, indicating that this frontal region acts an amplifier for secondary generalization. Accordingly, ablation of M2 radically altered seizure propagation. Electrical stimulation of S1 revealed that S1 preferentially recruits excitatory cells in ipsilateral M2 but inhibitory cells in contralateral S1, which may explain the preferred propagation pathway. We also observed that the stimulation frequency can differentially determine the response of excitatory versus inhibitory neurons. Altogether, our findings suggest that seizures do not propagate homogeneously through anatomically connected nodes but are preferentially pulled to specific locations by excitatory/inhibitory balance. Thus, functional connectivity rather than anatomic connectivity will be more predictive of ictal spread, and more informative for ablative and stimulation-based therapeutics.

To jump or not to jump: Comparing effects of phenotypic plasticity on the visual responses and escape behavior of locusts and grasshoppers
Locusts exhibit remarkable phenotypic plasticity changing their appearance and behavior from solitary grasshoppers to gregarious locusts when population density increases. These changes include morphological differences in the size and shape of brain regions, but little is known about plasticity within individual neurons and alterations in behavior not directly related to aggregation or swarming. We examined looming escape behavior and the properties of a well-studied collision-detection neuron in gregarious and solitarious animals of three closely related species, the desert locust (Schistocerca gregaria), the Central American locust (S. piceifrons) and the American bird grasshopper (S. americana). For this neuron, the lobula giant movement detector (LGMD), we examined dendritic morphology, membrane properties, gene expression, and looming responses. Gregarious animals reliably jumped in response to looming stimuli, but surprisingly solitarious desert locusts did not produce escape jumps. These solitarious animals also had smaller LGMD dendrites. This is the first study done on three different species of grasshoppers to observe the effects of phenotypic plasticity on the jump escape behavior, physiology and transcriptomics of these animals. Surprisingly, there were little differences in these properties between the two phases except for behavior. For all the three species, gregarious animals jumped more than solitarious animals, but no significant differences were found between the two phases of animals in the electrophysiological and transcriptomics studies. Our results suggest that phase change impacts mainly the motor system and that the physiological properties of motor neurons need to be characterized to understand fully the variation in jump escape behavior across phases.

Tirzepatide attenuates dopamine reward signaling and suppresses alcohol drinking and relapse-like behaviors in rodents
Alcohol use disorder (AUD) remains a major public health problem, with few effective medications currently available. However, peptides of the gut-brain axis appear to offer promising therapeutic targets for AUD as they influence the mesolimbic reward circuitry. Here, we examined the effects of tirzepatide, a long-acting dual glucagon-like peptide-1 receptor (GLP-1R) and glucose-dependent insulinotropic polypeptide receptor (GIPR) agonist approved for diabetes and obesity, using behavioral assays, alcohol intake paradigms, and molecular analyses in rodents. First, tirzepatide effectively attenuated the rewarding properties of alcohol, measured through locomotor stimulation, conditioned place preference, and accumbal dopamine release. Subsequently, this GLP-1R/GIPR agonist dose-dependently reduced voluntary alcohol consumption, prevented binge and relapse-like drinking, and maintained efficacy during repeated administration. Finally, tirzepatide induced sustained synaptic depression in the lateral septum and further altered histone regulatory proteins in this region, suggesting a potential neural substrate for its effects. Moreover, the GLP-1R/GIPR agonist affected metabolic parameters including body weight, adipose tissue mass, hepatic triglycerides and circulating pro-inflammatory cytokines. Together, our findings suggest tirzepatide modulates alcohol-related behaviors through reward-related mechanisms while also affecting physiological consequences associated with long-term alcohol use. Given tirzepatide's established clinical use and the consistency of effects observed here, these results support further investigation for treating AUD and associated complications.

Preliminary First-in-Human Pharmacokinetic Evaluation, Dosimetry and Safety of 7--Fluorotryptophan as PET Imaging Agent to Visualize Serotonin Synthesis in the Brain
Tryptophan (Trp) is the precursor for serotonin synthesis and other biologically relevant metabolites. We evaluated the novel radiotracer 7-[18F]Fluorotryptophan (7-[18F]FTrp) to assess its biodistribution, dosimetry, and potential for imaging brain Trp metabolism in humans. Six healthy volunteers underwent whole-body PET/CT imaging over 5.5 hours following intravenous injection of 7-[18F]FTrp. An additional four subjects underwent dynamic brain PET imaging for 2 hours. Time-activity curves (TACs) were extracted for source organs using VOIs defined on co-registered CT and PET images, and dosimetry was calculated using OLINDA software. The radiotracer showed rapid uptake and distribution, with highest activity observed in the liver, pancreas, salivary glands, in combination with urinary excretion. Brain pharmacokinetic analyses with image-derived input function (IDIF) determined that Patlak analyses were the best fit for brain image analyses. Brain uptake was modest, with highest region-specific accumulation in the pineal gland, which is a known site for serotonin synthesis. The estimated effective dose was within the expected range for 18F-labeled compounds (14.1 {+/-} 0.2 Sv/MBq). Our findings indicate that 7-[18F]FTrp is safe for human use, demonstrates favorable kinetics for studying both brain and peripheral Trp metabolism, and warrants further exploration in patients with serotonin metabolism disorders.

Dynamic neural states underpin bradykinesia severity in Parkinsons disease
Background: Bradykinesia in Parkinson's disease (PD) may arise due to transient, network-wide neural dynamics that extend beyond beta-band oscillatory activity within the motor cortical-subthalamic nucleus (STN) circuit. Methods: We address this question by using Hidden Markov Models (HMMs) to identify neural states from chronic motor cortical and STN recordings in five PD patients (1,046 hours from 10 hemispheres), with concurrent measurements of bradykinesia using wearable sensors. Findings: We identified four neural states with distinct spectral and temporal features. Two states exhibited spectral signatures--particularly STN low and high gamma, STN delta/alpha, cortical beta, and cortico-STN beta coherence--that predicted worsening bradykinesia. However, STN beta power alone was not consistently predictive, challenging traditional beta-centric views. These states also displayed compensatory features associated with bradykinesia amelioration, including cortical delta/alpha activity, cortical high gamma, and cortico-STN high gamma coherence. Two additional states affected bradykinesia through temporal rather than spectral properties. Prolonged lifetimes of one of these state worsened symptoms, whereas increased occurrences of another, marked by local beta without cortico-STN beta coherence, improved motor function. Interpretation: Our findings highlight the multidimensional nature of bradykinesia and suggest that state-aware, adaptive interventions targeting state features--rather than single frequency bands--may offer new opportunities for improved deep brain stimulation in PD. Keywords: Hidden Markov Model (HMM), Neural states, Bradykinesia, Parkinson's disease

Dynamical Diversity in Conductance-Based Neuron Response to kilohertz Electrical Stimulation
Brain neuron networks are notably rich in structure and functioning, consequently rather diverse in their dynamical feedback to stimuli. Thus, accurately characterizing the neural response to external signals is a crucial first step in understanding these networks, which conceivably could enable neuroscience and medical applications. In particular, kilohertz (kHz) neuronal electrical stimulation is a technique capable of inducing behaviors and drives unseen in more conventional lower frequency ranges. This fact could be used in the development of neuromodulation therapies for neurological pathologies, e.g., Deep Brain Stimulation (DBS). Here, we investigate neuronal response and excitability of conductance-based models to kHz frequencies stimulation in a still unexplored parameter space region. We show that the dynamics exhibited by the paradigmatic Hodgkin-Huxley model under kilohertz stimulation is highly diverse, displaying from regular spiking to chaotic behavior, as well as regions of complete activity suppression. By extending the analyses to models of mammalian central nervous system regions we show that, despite presenting similar low-frequency dynamics, such neurons tend to respond rather differently under kilohertz. Further, based on dynamical markers, we propose a method for systematically mapping these behaviors on a stimulation parameter space. These results establish a quantitative framework for ultra-high-frequency neuromodulation protocols, paving the way for future computational neuroscience approaches to stimulation-based procedures.

Circadian control of dopaminergic signaling to the mushroom body regulates sleep through rhythmic Pka-C1 transcription in Drosophila
Despite the progress in understanding the circadian pacemaker, the specific mechanism by which it regulates sleep remains incompletely understood. We have previously demonstrated that a substantial number of genes are rhythmically expressed in the mushroom body (MB) Kenyon cells (KCs), including Pka-C1, which encodes the catalytic subunit of protein kinase A (PKA). PKA-C1 plays a crucial role in promoting daytime wakefulness; however, the underlying mechanism remains elusive. Here, we show that the {gamma}-lobe is the primary site of rhythmic Pka-C1 expression using a newly developed in vivo luciferase reporter. Through a combination of in silico analysis, CRISPR mutagenesis, and chromatin immunoprecipitation, we identify the transcription factor Onecut as a regulator of Pka-C1 transcriptional rhythms in {gamma}-KCs. Furthermore, genetic trans-synaptic connectivity mapping and neuronal activity imaging reveal that the dorsal Lateral clock Neurons (LNds) provide inhibitory input to a subset of dopaminergic (DA) neurons in the protocerebral anterior medial (PAM) cluster, PAM-{gamma}5, rhythmically modulating their activity. This, in turn, rhythmically activates MB {gamma}-KCs via excitatory Dop1R signaling. Resulting {gamma}-neuron activity rhythms drive Pka-C1 transcriptional rhythms through Onecut. Furthermore, these PKA-C1 rhythms reinforce neuronal activity rhythms, creating a feedback cycle between transcriptional and neural activity rhythms that promote daytime wakefulness. Our findings highlight the conserved role of DA in promoting wakefulness and offer mechanistic insights into its complex regulation. More generally, this work provides a mechanistic framework for how circadian rhythms are translated into neural activity to orchestrate complex behaviors like sleep.

Lewy pathology accumulates in swollen corticostriatal synapses in α-synucleinopathies
Lewy body diseases (LBD), including Parkinson's disease (PD), Parkinson's disease dementia (PDD), and dementia with Lewy bodies (DLB), are neurodegenerative disorders characterized by the accumulation of misfolded -synuclein in the form of Lewy pathology. A hallmark of these diseases is degeneration of the nigrostriatal pathway, resulting in loss of dopaminergic input to the striatum and consequent motor dysfunction. Lewy pathology is present in many regions outside the substantia nigra, and cortical Lewy pathology is the best correlate of cognitive decline in individuals that develop dementia. In addition, there is a high burden of neuritic Lewy pathology in the putamen, though the neuronal origin of this pathology is unclear. In the current study, we quantified the burden of Lewy pathology in the putamen across the spectrum of LBDs. Immunohistochemistry was used to quantify the Lewy burden in the putamen, cingulate and frontal cortices of 9 controls and 24 LBD cases. Even in PD cases without dementia, we observed a nearly complete striatal dopaminergic denervation among LBDs. Consistent with this denervation, most -synuclein pathology did not co-localize with dopaminergic terminals, but was instead enriched in excitatory, VGLUT1-positive terminals. This enrichment in glutamatergic terminals was associated with swollen axons, but not with overall loss of VGLUT1 terminals. These findings suggest that Lewy pathology accumulates at excitatory corticostriatal synapses prior to overt synaptic degeneration and could contribute to cognitive decline in LBDs.