bioRxiv Subject Collection: Neuroscience's Journal

Live access to the emotional dynamics of REM sleep dreams in lucid dreamers with narcolepsy
Sleep helps regulate emotions, but it is still unclear whether -and how- the emotions we experience in dreams contribute to this regulation. To uncover the potential function of dream emotions, we must first understand what they are and how they unfold in dreams. The emotional content of dreams has mostly been studied using post-sleep dream reports, which provide a biased and static snapshot of a complex and dynamic experience. In this study, we took a more direct approach, accessing dream emotions in real-time. We asked twenty-four lucid dreamers with narcolepsy to report the emotional valence of their dreams, - positive, negative or neutral-, while still asleep, using predefined facial codes during daytime naps monitored with polysomnography. Of the 126 naps recorded, 62 contained at least one emotional code during REM sleep, yielding 191 codes in total. The ratios of positive and negative codes were evenly balanced per nap. The 33 naps with at least two codes allowed us to track the dream emotional dynamics. Over half of these naps showed opposite emotional valences (positive and negative). By measuring the time elapsed between codes, we estimated the average duration of a given dream's emotional valence in REM sleep to be about one minute. Positive emotions emerged on average earlier than negative ones during lucid REM sleep. These findings confirm the highly emotional nature of dreams and, more importantly, highlight that emotions in REM sleep dreams are fluid and fast-changing. Such emotional dynamics during REM sleep dreams may help us to better understand the mechanisms of the emotional regulatory function of dreams.

Repeated Viewing of a Narrative Movie Changes Event Timescales in The Brain
Many experiences occur repeatedly throughout our lives: we might watch the same movie more than once and listen to the same song on repeat. How does the brain modify its representations of events when experiences are repeated? We hypothesized that, with repeated viewing of a narrative movie, brain regions would adapt their event representations by becoming either finer (more detailed) or coarser (more generalized). To test this hypothesis, we analyzed data from 30 human participants who underwent functional magnetic resonance imaging (fMRI) while watching three 90-second clips from "The Grand Budapest Hotel" six times each. We used hidden Markov models and pattern similarity analysis applied to searchlights across the brain to quantify the strength of event structure at different timescales for each clip presentation. We then tested how event structure strength changed at both slow and fast timescales with repeated viewings. Most brain regions exhibited stability in the strength of event structure at both slow and fast timescales. Other regions, however, showed flexible event representations that became more or less granular across repeated clip presentations. Notably, several brain regions exhibited consistent changes in the strength of event structure at a slow timescale across different movie clips. Furthermore, in lateral occipital cortex and middle temporal gyrus, greater loss of event structure at a slow timescale predicted more detailed memory recall. These results highlight that event dynamics in the brain are not fixed, but can change flexibly with experience.

Evaluation of Data-based Motion Correction Techniques for High Temporal Resolution Functional PET
Functional Positron Emission Tomography (fPET) data offers novel insights into brain energy demands and molecular connectivity. Recent advances in improving temporal resolutions for this imaging technique have opened up new research possibilities. However, lower signal-to-noise ratios (SNR) inherent to short PET frames bring into question whether current realignment approaches still provide appropriate motion correction. Thus, we aimed to evaluate the effectiveness of standard motion correction methods and explore potential improvements for high temporal resolution fPET with 3s frames. We investigated two techniques aimed at improving the SNR to facilitate more accurate realignment of fPET images, in comparison to conventional motion correction: a deep-learning technique based on the application of a conditional generative adversarial network and an exponentially weighted sliding window average. Performance was evaluated by correlating rigid motion parameters between approaches and with simultaneously acquired fMRI data, and by assessing magnitudes of task-induced activation. Our results indicate that neither of the two methods substantially improve mitigation of motion artefacts. Given the increased computational effort of both techniques, we propose that the standard motion correction procedure is adequate for processing high temporal resolution fPET data. Nevertheless, future development of targeted strategies to enhance motion correction may further advance this imaging technique.

In vivo inhibition of stearoyl-CoA desaturase modulates the hippocampal fatty acid profile and restores density of dendritic spines in the aggressive 5xFAD model of Alzheimer s disease
While alterations in brain lipids are a central feature of Alzheimer s disease (AD), therapeutic strategies targeting brain lipid metabolism are still lacking. Prior preclinical work has shown that pharmacological inhibition of the fatty acid desaturase, stearoyl-CoA desaturase (SCD), leads to recovery of hippocampal synapses with associated improvements in learning and memory in the slow-progressing 3xTg AD mouse model. Here, we used the rapidly progressing, highly amyloidogenic 5xFAD AD model to further delve into the effect of the SCD inhibitor (SCDi) on AD-associated fatty acid alterations and synapse loss. Hippocampus, cortex and plasma samples were collected from male and female 5xFAD and non-carrier control mice for fatty acid profiling and assessment of disease hallmarks. Plaque pathology, gliosis, and fatty acid alterations that included an increase in the C16:1/C16:0 desaturation index, a measure of SCD enzymatic activity, were apparent in the female hippocampus at 5 months of age, with similar fatty acid changes appearing in males by 8 months. Intracerebroventricular infusion of SCDi via osmotic pump for 28 days in 5 months old female 5xFAD and NC mice modulated the SCD-related fatty acid disturbances as well as PUFA concentrations. Quantification of Golgi staining revealed an SCDi-induced recovery of dendritic spine density. The beneficial effects of SCDi treatment on fatty acid balance and hippocampal dendritic spines in this more aggressive amyloidogenic 5xFAD model further support SCD inhibition as a promising therapeutic avenue for AD.

Evidence of spinal cord comparator modules for rapid corrections of movements
Successful movement requires continuous adjustments in response to changes in internal and external environments. To do so, neural circuits continuously compare efference copies of motor commands with sensory input to respond to sensory prediction errors. Some responses need to be very fast and, for limbs, likely occur in as yet undefined spinal cord circuits. Here, we describe spinal circuits involving dI3 neurons, showing that they receive multimodal sensory inputs and direct efferent copies from both Renshaw cells and motor neurons. We further show that they form connections to motor pools, including diverging connections to antagonist motor nuclei. Reducing dI3 neuronal activity diminished stumbling responses, as did disrupting Renshaw cell circuits, providing evidence for a comparator role of dI3 neurons for online corrections. Together, our findings reveal a pivotal role for dI3 neurons functioning as comparators of internal predictions and external sensory feedback to mediate rapid corrections of ongoing movements.

Neurophysiological Markers for the Acute Pain Model in Rats Under Urethane-induced Anesthesia
Certain neurophysiological mechanisms of pain can be investigated in anesthetized animals, and such a model is potentially useful for the development of pain treatment. Yet, the interference of pain-related neural patterns and anesthesia-related patterns should be understood. Here we studied he interplay between acute pain-induced and urethane-induced EEG activity. We analyzed the activity of the rats' somatosensory cortex under urethane-induced anesthesia both before and after introducing a painful stimulus (formalin injection into the left hind limb). Our analysis of the EEG parameters, such as EEG amplitude, amplitude differences between the left and right hemispheres, and spectral entropy showed significant pain-related effects. The responses to painful stimuli depended on the anesthesia phase.

Species-specific Rates of Fatty Acid MetabolismSet the Scale of Temporal Patterning of Corticogenesisthrough Protein Acetylation Dynamics
Developmental processes display temporal differences across species, leading to divergence in organ size and composition. In the cerebral cortex, neurons of diverse identities are generated sequentially through a temporal patterning mechanism conserved throughout mammals. This corticogenesis process is considerably prolonged in the human species, leading to increased brain size and complexity, but the underlying molecular mechanisms remain largely unknown. Here we found that human cortical progenitors displayed lower levels of fatty acid oxidation than their mouse counterparts, in line with their protracted pattern. Treatments that enhance mitochondrial fatty acid oxidation (FAO) accelerated the development of human cortical organoids, including faster progression of neural progenitor cell fate and precocious generation of late-born neurons and glia. FAO accelerated temporal patterning through increased Acetyl-CoA-dependent protein acetylation, including on specific histone transcriptional marks. Thus, species-specific metabolic rates regulate the turnover of post-translation modifications to set the scale of temporal gene regulatory networks of corticogenesis.

Multi-Scale Anti-Correlated Neural States Dominate Naturalistic Whole-Brain Activity
The human brain's response to naturalistic stimuli is characterized by complex spatiotemporal dynamics. Within these dynamics there is a transitioning structure between sets of anti-correlated neural states that is frequently observed but has not been systematically investigated across scales. In this paper, we use three different naturalistic fMRI datasets to quantify anti-correlation in global and local neural states during naturalistic viewing or listening and investigate their interdependence and their relationship to changes in the stimuli. We demonstrate that continuous naturalistic brain activity shows an anti-correlational structure that spans both global and local spatial scales, with regions in the dorsal attention network showing strong alignment between local and global state transitions. On the global scale, ongoing dynamics are dominated by two antagonistic states that correspond to Default Mode Network and Task Positive Network configurations, with a third transitional state mediating between them. On the local scale, we observe anti-correlated neural states that are associated with periods of relatively high and low brain activity. Across the brain, these are driven by subsets of voxels that are systematically anti-correlated with their area's dominant activity pattern. This antagonism is related to stimulus changes, which tend to trigger a switch to the TPN state globally and to high activity states locally. On the local scale we also see a modality-specific pattern, with visual changes mostly driving transitions in visual cortical regions and auditory changes predominantly affecting auditory and language-related areas. The consistency of these findings across datasets with different stimulus types (audiovisual and purely auditory) indicates that anti-correlated neural states represent a domain-general organizational principle of brain function. We propose that anti-correlated dynamics functionally represent a convergent solution to the fundamental challenge of maintaining coherent internal representations while remaining responsive to meaningful changes in the environment.

The effects of action-based predictions in early visual cortex
During voluntary movement, predictions about the sensory consequences of an action typically result in reduced sensory sensitivity. The forward model theory proposes that this reduced sensitivity is due to neural suppression or cancellation of sensory action outcomes. However, recently this theory has been challenged by three alternative theories: the pre-activation account, sharpening, and the opposing processes theory. In this fMRI study, we directly tested and compared these four theories using univariate and multivariate analyses both prior to and during stimulus presentation. Participants performed a visual orientation discrimination task on two sequentially presented gratings, which were either presented automatically (passive condition) or triggered by their own button press (active condition). Auditory cues at trial onset indicated the overall grating orientation, followed by a preparatory phase in which participants anticipated the upcoming stimuli. During this phase, the predicted stimulus orientation was decodable from early visual cortex activity in both active and passive conditions at levels significantly above chance, indicating pre-activation of the predicted orientation, but with no difference between active and passive conditions. BOLD responses did not emerge earlier in active conditions, arguing against the pre-activation theory. During stimulus presentation, actively generated stimuli elicited larger BOLD responses than passively presented ones, contradicting the forward model theory, which predicts overall response suppression. Decoding accuracy did not differ between conditions, inconsistent with the sharpening hypothesis, which predicts enhanced neural precision for actively generated stimuli. Instead, our findings align most closely with the opposing processes theory, which posits pre-activation in both conditions. However, the stronger BOLD response for actively generated stimuli is not predicted by any existing theory, suggesting that additional mechanisms - such as heightened attention or motor-related enhancement - may contribute to sensory processing during self-initiated actions.

Hierarchical Bayesian Modelling of Interoceptive Psychophysics
Interoception, the capacity to sense, perceive, and metacognitively appraise viscerosensory and homeostatic signals, is a growing focus in psychology and psychiatry. Adaptive psychophysical tasks now allow quantification of perceptual sensitivity, bias, and precision in cardiac and respiratory domains. However, accurately estimating these parameters often requires large numbers of trials or participants, posing practical challenges, especially in clinical research where participant availability and tolerance are limited. One approach to reduce participant burden while maintaining statistical rigour is to optimise data analysis. Here, we present hierarchical Bayesian models tailored for cardiac and respiratory interoceptive psychophysics that efficiently estimate sensitivity, bias, and precision at both individual and group levels. Using simulations and empirical data, we validate these models and demonstrate that they allow enhanced inference relative to conventional approaches. To support adoption, we provide openly-accessible resources, including a tutorial on how to implement and fit these models in R (written with researchers without modelling expertise in mind) and an app for sample-size justification. These tools facilitate robust, efficient, and generalisable modelling of interoceptive performance, enabling rigorous studies even with limited trials or participant availability.

Lifestyle and transcriptional signatures associated with ethnicity/race-related variations in the functional connectome
The functional connectome architecture of the human brain, which involves ethnicity/race characteristics, has significantly advanced our understanding of brain-behaviour relationships. However, the multifaceted underpinnings of ethnicity/race-related variations in the brain functional connectome remain largely unclear. In this study, we leverage precision individualized functional mapping to examine ethnicity/race-related differences in the brain's intrinsic functional organization, as well as their associations with lifestyle factors and transcriptional signatures. Our findings indicate that variations in network topography and functional connectivity across ethnic/racial groups follow a hierarchical pattern along the sensorimotor-association axis and are constrained by brain morphology. We identify lifestyle factors, primarily education and substance use, that mediate the associations between ethnicity/race and functional connectivity. Via the use of human brain gene expression data, we further demonstrate that cortical gene expression patterns are spatially associated with ethnicity/race-related variations in functional connectivity. Gene ontology analysis of ethnic/racial-associated genes reveals significant enrichment in biological processes, including synaptic signalling pathways and neuronal system development. Collectively, these results reveal the complex relationships between ethnicity/race-related differences in brain function and brain morphology, lifestyle factors, and transcriptomic profiles.

Frequency-specific maturation of cortical organization and social-cognitive links from childhood to adolescence
The human brain unfolds its functional architecture over multiple timescales during childhood and adolescence, yet we know little about how these developmental trajectories vary between individuals. Slow brain signals at different frequencies are thought to support distinct functional processes, but their contribution to shaping large-scale cortical organization across development remains unclear. Here, we map the maturation of cortical organization by decomposing spontaneous slow brain activity into multiple frequency bands in participants aged 6-19 years. We identify a reproducible three-stage progression - child-like, transitional, and adult-like configurations - whose timing depends on frequency: faster dynamics mature earliest, intermediate frequencies develop gradually, and lower-frequency dynamics reorganize abruptly around puberty. Individual cognitive and social traits modulate these trajectories: children with higher social anxiety or higher IQ reach adult-like configurations earlier in specific frequency bands, whereas lower-IQ children show generalized delays. These results reveal that cortical organization matures along multiple, frequency-specific timescales and that intellectual and socio-emotional factors shape its pace. Our multi-frequency framework provides a new perspective on hierarchical brain development and may inform biomarkers for atypical neurodevelopment.

Absence of short-term axon initial segment plasticity in human, mouse, and rat cortical circuits
Maintaining neuronal inputs and outputs within the physiological range relies on the ability of neurons to update their responsiveness to inputs dependent on changing activity levels. Termed homeostatic plasticity, the mechanisms that neurons employ to control their responsiveness are varied, and proposed to include structural changes to a key neuronal structure, the axon initial segment (AIS). As the site of action potential initiation, the AIS has been postulated to rapidly change its length in response to increased or decreased cellular and circuit activity. To date, AIS structural plasticity has only been tested in tissue cultures and rodent models. In our current study, we assess the ability of neurons to alter their AIS length over a variety of timescales in ex vivo rodent and human brain slices, human neurons derived from induced pluripotent stem cells, and in mice dark-reared during early life; using a combination of electrophysiology and immunohistochemistry. We find no evidence for changes to AIS length following depolarisation for up to 3 hours, despite positive controls confirming modulated activity. However, we do find that neuronal physiological properties are altered by changes in activity, but these are largely independent of action potential initiation associated with the AIS. In summary, we find no evidence supporting a role for AIS structural plasticity in mouse, rat, or human cortical neurons.

Dorsomedial Prefrontal Cortex links abstract planning to motor execution
Mental planning is essential for producing action sequences. Despite the contribution of planning to many everyday activities, the neurocognitive processes that map a selected plan into a concrete course of action are unclear. We asked whether planning and execution are linked by abstract task relationships that are divorced from the specifics of action implementation. Human participants underwent functional MRI while performing a task that required encoding and maintaining abstract representations in order to plan and later apply those representations to execute action sequences. Our analyses revealed that dorsomedial prefrontal cortex encoded representations during the planning period that shifted progressively from information based on perceptual input to information associated with the abstract task context. These abstract representations were then used to translate between the action plan and its execution irrespective of the perceptual and motor details, indicating that DMPFC builds and sustains an abstract representational bridge that links planning to action.

Coping strategy dynamics and resilience profiles after early life stress revealed by behavioral sequencing
Animal models can provide valuable insights into the mechanisms underlying stress-related disorders. Yet, significant translational challenges persist, as laboratory behavioral assays are often reductionistic, with limited attention to ethologically relevant behavioral diversity. Recent advances in high-throughput pose-estimation tools and computational ethology methods are addressing this limitation by enhancing the resolution and validity of behavioral phenotyping. In this context, it is known that early life stress (ELS) reshapes how animals handle subsequent threats later in life, but the fine-scale dynamics and ethological details of this shift remain elusive. To overcome this, we combined a deep-learning pose-estimation pipeline (DeepLabCut) with a supervised freezing classifier (SimBA) and an unsupervised behavioral motifs identification platform (keypoint MoSeq) to study in detail the diversity and dynamics of behavior in an auditory fear-conditioning (FC) paradigm in two independent cohorts of adult male mice that were exposed to ELS through the limited bedding and nesting (LBN) paradigm. We first validated the blunted freezing response after ELS in a supervised manner using SimBA. Next, keypoint MoSeq segmented the same pose-estimation data into ethologically meaningful motifs over time. When compared to control animals, ELS offspring showed an altered FC response, reduced behavioral entropy and limited diversity in their behavioral repertoire. Such response was characterized by longer active-behavior bouts and more recurrent transitions between states, indicating a more stereotyped and predictable response. Multidimensional scaling of time-binned behavioral vectors and distance metrics identified a resilient subpopulation within the ELS group that displayed a control-like behavioral profile, characterized by a steeper increase in freezing behavior during the FC task and a more diverse behavioral repertoire with reduced recurrence of stereotyped actions, less frequent and shorter active bouts and prolonged passive responses. Overall, our findings suggest that ELS shifts the balance between passive and active coping strategies and that resilience is marked by a less stereotypical yet more diverse and flexible behavioral response to a subsequent stressful demand. Finally, we further validated the unsupervised behavioral motifs with a predictive model that identified distinctive kinematic features of these responses, which could be used to build new behavioral classifiers that can be applied in other behavioral paradigms. These results demonstrate the potential of computational ethology to dissect complex behavioral patterns and improve our understanding of individual stress responses. By combining supervised and unsupervised behavioral analysis tools, we can deepen our understanding of the latent structure of stress behavior and identify objective markers of vulnerability and resilience.

Electrical stimulation elicits space- and parameter-dependent spiking responses in human cortical organoids
Electrical stimulation (ES) is used to treat neuropsychiatric disorders and investigate brain dynamics, yet its effects on human cortical microcircuits remain poorly understood. Cortical organoids provide a unique platform to investigate these mechanisms in isolation from subcortical and long-range cortical inputs. Here we illustrate how cortical organoids respond to ES, identifying the response profiles of isolated cortical circuits while detailing a roadmap of how ES parameters affect the organoid spiking activity. We employed a high-density multielectrode array to record neuronal activity from cortical organoids (n=417 units in N=7 organoids) during ES, systematically varying stimulation frequency, intensity, pulse width, and charge density. By analyzing single unit spiking activity, we found that ES elicits excitatory, inhibitory, and mixed responses in 39%, 12%, and 17% of the units, respectively. On average, this response lasted 100 ms and became stable within 26 trials. The magnitude of both excitatory and inhibitory responses was maximal near the stimulation site and decayed with distance. The response magnitude was inversely correlated with pulse intensity and duration, but not with stimulation frequency and charge density. These findings demonstrate that local cortical circuits are sufficient to initiate the early excitatory phase of the canonical ES response, whose magnitude depends on ES parameters, and can sustain the excitatory phase for over 100 ms. The reduced late inhibitory phase, together with the absence of late excitatory components observed 200 ms after ES in intact adult brains in-vivo, suggests that these phases may depend on neuronal maturation or inter-area connections. Our work thus establishes cortical organoids as a framework for studying the local contributions to ES-induced activity in a developmental model of the human cortex.

Targeting Lysosomal pH Restores Mitochondrial Quality Control in GBA1-Mutant Parkinsons Disease
Heterozygous mutations in the Glucocerebrosidase gene (GBA1), encoding the lysosomal hydrolase Beta-glucocerebrosidase (GCase), are a genetic risk factor for Parkinsons disease (PD). To explore the pathophysiological consequences of these mutations, we have used fibroblasts and dopaminergic neurons generated from induced pluripotent stem cells (iPSCs) derived from patients with GBA1-related PD. GCase activity, lysosomal acidification, protease activity, mitophagy and mitochondrial bioenergetic function were all impaired. Mitochondria were fragmented, with reduced membrane potential and oxygen consumption. We propose that impaired bioenergetic function is a consequence of impaired lysosomal acidification and compromised mitophagy. The V-ATPase complex drives lysosomal acidification. Its assembly is regulated by MTORC1, which is constitutively phosphorylated in mutant cells. FLIM-FRET measurements confirmed impaired V-ATPase assembly which reversed following rapamycin treatment. Acidic nanoparticles, which accumulate in lysosomes, rescued lysosomal pH, and restored mitophagy and mitochondrial membrane potential in GBA1 mutant dopaminergic neurons. These data identify a core pathway as a potential therapeutic target for the treatment of GBA1-mediated PD.

Intrinsic Gestational Timing Governs Human Cerebellar Development After Preterm Birth
Intrinsic biological clocks govern human brain development, but whether these programs recover from early disruption, such as premature birth, remains unknown. The cerebellum, with peak maturation in the last trimester, provides a model to address this challenge. We analyzed an unprecedented combination of in-vivo and postmortem cohorts of human postnatal cerebella spanning 22-42 weeks gestation, integrating longitudinal neuroimaging, spatial transcriptomics, and machine-learning-based histology to capture developmental states inaccessible to experimental models. Gestational age imposed lasting differences in postnatal cerebellar growth, architecture, and molecular programs. Spatially resolved gene expression data revealed lineage-specific rules: granule cells followed an immutable developmental clock, whereas Purkinje cells failed to undergo the maturation-linked reduction in cell numbers, retaining their population but with reduced dendritic complexity, reflected by a thinner molecular layer after early extrauterine transition. These findings redefine prematurity as a state-dependent arrest of intrinsic brain programs and provide a foundation for regenerative and neuroprotective interventions.

Aperiodic EEG Activity Provides a Linear, Bidirectional, and Spatially Uniform Marker of Subjective and Objective Vigilance in Humans, Both Within and Across States
Vigilance is increasingly conceived as a continuum, ranging from full alertness to deep sleep. Despite its fundamental role in cognition, behaviour, and health, reliable physiological markers of vigilance remain limited, and clinical assessments often rely on subjective or time-consuming evaluations. Traditionally, vigilance has been estimated through visual inspection of the electroencephalogram (EEG), identifying recognizable oscillatory patterns like rapid, wakefulness-defining alpha waves (~10 Hz) and large slow waves (~1 Hz) which typify sleep. However, these oscillatory features often appear only intermittently and follow complex, non-linear trajectories across time, space, and frequency, limiting their utility for automated, continuous tracking of vigilance. Recent research has shifted attention to the non-oscillatory, or aperiodic, component of the EEG, which may follow simpler dynamics and offer a more robust index of brain state. Yet most studies often continue to use narrowly defined, discrete vigilance states and transitions in only one direction (e.g., from wakefulness to sleep), without jointly examining oscillatory and aperiodic activity. Here, we address these key gaps by evaluating the capacity of both oscillatory and aperiodic features of EEG power spectra, derived from high-density recordings, to predict vigilance as a continuous variable. Across three independent datasets, we consistently show that although oscillatory features reliably track changes in vigilance, they are unequivocally outperformed by aperiodic activity. Aperiodic features demonstrate a stronger, more linear, and spatially consistent relationship with both objective and subjective indices of vigilance, offering a more robust and scalable physiological marker of this fundamental feature of the brain.

Zebrafish sleep displays distinct sub-states
Sleep is an essential and evolutionarily-conserved behavior. While mammals and several other species have been shown to exhibit well-defined sleep sub-states, some of which have been ascribed specific functions, it remains unclear to what extent such differentiation exists across the animal kingdom. Here we show, using long-term behavioral data combined with Hidden Markov Modeling, that larval zebrafish display distinct deep and light sleep sub-states. Although both states occur primarily at night, fish respond differently to sleep deprivation and arousing stimuli depending on which sleep sub-state they are in. Moreover, the proportions of deep and light sleep are selectively altered by genetic and pharmacological manipulations of melatonin, serotonin, and norepinephrine signaling, offering new insights into how these neuromodulators shape sleep architecture. These results support zebrafish as a tractable model for dissecting the regulation and function of sleep sub-states. More broadly, they demonstrate that structured, multi-state sleep is a conserved feature of vertebrate behavior.

Physical exercise and brain network dynamics: reduction of frontoparietal-striatal connectivity following 1 hour of aerobic cycling
Physical exercise is beneficial for metabolic health and cognitive performance. In addition, exercise can be highly pleasurable and and serves as an effective stress reliever. Extant studies have highlighted frontal, parietal, and subcortical brain changes following acute bouts of exercise. However, slower, slightly delayed brain correlates after exercise at the network level have not been studied. Therefore, this study's objective was to investigate the changes in dynamic functional connectivity after 60 minutes of exercise in healthy males. Here we measured a 6-minute resting state fMRI in 24 young males at baseline and after a 60-minute cycling exercise challenge. Apart from routine preprocessing, the data were denoised with FSL-FIX and modeled with i) leading eigenvector dynamics analysis (LEiDA) to probe whole brain network dynamics and ii) with group independent component analysis (ICA) and dual regression to quantify static brain connectivity. The within subject statistical tests compared baseline to post-exercise conditions. We found that a striato-fronto-parietal network is destabilized after exercise, as indicated by a lower probability of occurrence in dynamic analysis through LEiDA. The brain areas in the network include the bilateral caudate, putamen and pallidum as well as middle orbitofrontal, frontal operculum, frontal trigonum and inferior parietal cortices. No differences between baseline and post exercise conditions were found in the dual regression of the group ICA components. We conclude that 60 minutes of cycling causes a prolonged effect in brain network dynamics, reducing synchronization between the striatum and frontoparietal networks with respect to baseline. This provides insights into the network-level neural correlates of aerobic exercise, which may be directly linked with the stress relieving effects of physical exercise.

Brain Mecp2 Gene Dosage and Gene Therapy Shape Multi-Omic Signatures and Biomarkers in Rett Syndrome
Rett syndrome (RTT) is a neurodevelopmental disorder caused by MECP2 mutations. Like other genetic neurodevelopmental disorders, it lacks molecular biomarkers to evaluate disease and therapeutic outcomes. We present a strategy to define biomarkers of MeCP2 dysfunction in brain with potential to delineate mechanisms and monitor therapeutic interventions. This strategy relies on a library of proteins responsive to Mecp2 gene dosage and correlated with molecular and clinical outcomes after AAV9-mediated MECP2 gene therapy in Mecp2 KO mice. Gene rescue restored MeCP2 in brain, improved clinical phenotypes, and reverted transcriptome and proteome abnormalities. We identified 327 shared proteins among 1852 cortical and hippocampal proteins responsive to Mecp2/MECP2. Of these, 119 also displayed Mecp2/MECP2-dependent transcript changes. Both the Mecp2 responsive proteome and transcript/protein pairs were enriched in synaptic and metabolic pathways, including central carbon and NAD+ metabolism. We used this therapy-responsive protein library to guide selection of candidate cerebrospinal fluid (CSF) biomarkers in RTT. CSF composition from neurotypical and RTT groups was analyzed using ultrasensitive nucleic acid-based multiplexed ELISA. Twenty eight proteins were altered in RTT, nine overlapping with Mecp2 dosage and therapy sensitive proteins. Multivariate regression linked several candidates to Mecp2/MeCP2 abundance and phenotypic improvement in mice. This paradigm provides a rigorous molecular systems level framework integrating genetics, preclinical gene therapy, and clinical metrics to define robust cross species biomarkers and mechanisms in RTT, with potential applicability to other neurodevelopmental disorders.

Differential effects of sodium on agonist-induced conformational transitions and signaling at μ and κ opioid receptors
Sodium ions are classically conceptualized as negative allosteric modulators for G protein coupled receptors (GPCRs), although there have been reports of either positive allosteric modulation or no effect of sodium on GPCR function. Here we identified opposing actions of sodium on the mu and kappa-opioid receptors. We utilized a variety of methods including radioligand binding, real-time conformational monitoring of transitions using bioluminescence resonance energy transfer and signaling assays using the TRUPATH resource. At the mu receptors, sodium behaved as a negative allosteric modulator of binding, conformational transitions and signaling. Intriguingly, bitopic mu agonists were unaffected by sodium concentrations. By contrast, at the kappa opioid receptor sodium negatively modulated agonist binding and positively modulated conformational transitions and signaling. Taken together, these findings support the notion that the differential sensitivities to sodium concentrations will result in opposing effects on cell surface and intracellular signaling.

Functional comparison of control and 3' deletion human NRXN1 isoforms in C. elegans
Neuropsychiatric conditions including Schizophrenia are historically difficulty to study in vivo due to the extensive genetic heterogeneity present between patients. Recent exome sequencing of a Schizophrenia patient cohort identified a set of 3' deletions present within multiple highly expressed-isoforms of the synaptic adhesion molecule neurexin1 (NRXN1), a gene that is broadly implicated across many neurodevelopmental and neuropsychiatric disorders. To understand how isoform differences and mutations within isoforms impact neuronal function and behaviors, we expressed 8 NRXN1 isoform variants in C. elegans and tested their effects on a stereotyped food-deprivation response and social feeding behavior. Overall, expression of the NRXN1 isoforms followed a pattern similar to that of the endogenous ortholog NRX-1, with expression primarily localized to the nerve ring in the head of the animal. However, several isoform variants displayed distinct localization, with ectopic or abnormal expression in neuronal cell bodies. We observed in the food deprivation response behavior that the isoforms (both control and 3' deletion variants) fell into one of three phenotypic categories; unchanged, partial rescue, or gain of function. A similar trend was observed for social feeding behavior; most of the NRXN1 isoforms had no impact compared with npr-1;nrx-1 controls, but some had partial rescue or induced stronger phenotypes. In summary, NRXN1 isoforms are able to partially rescue behavioral defects caused by the loss of nrx-1, suggesting that they may functionally replace the endogenous protein. Further, we identified differential impacts between some control and 3' deletion isoform variants, confirming pathogenic impact of 3' deletion isoforms in behavior. Overall, C. elegans presents a genetically tractable model in which to study the impacts of protein coding deletions associated with human neuropsychiatric conditions that impact genes with many isoforms on robust and stereotyped behaviors in vivo.

Cryptic splicing in synaptic and membrane excitability genes links TDP-43 loss to neuronal dysfunction
TDP-43 pathology is a defining pathological hallmark of multiple neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). A major feature of TDP-43 pathology is its nuclear depletion, leading to the aberrant inclusion of cryptic exons during RNA splicing. STMN2 and UNC13A have emerged as prominent TDP-43 splicing targets, but the broader impact of TDP-43-dependent cryptic splicing on neuronal function remains unclear. Here, we report new TDP-43 splicing targets critical for membrane excitability and synaptic function, including KALRN, RAP1GAP, SYT7 and KCNQ2. Using human stem cell-derived neurons, we show that TDP-43 reduction induces cryptic splicing and downregulation of these genes, resulting in impaired excitability and synaptic transmission. In postmortem brains from patients with FTD, these cryptic splicing events occur selectively in neurons with TDP-43 pathology. Importantly, suppressing individual cryptic splicing events using antisense oligonucleotides partially restores neuronal function, and combined targeting almost fully rescues the synaptic deficit caused by TDP-43 loss. Together, our findings provide evidence that cryptic splicing in these synaptic and membrane excitability genes is not only a downstream marker but instead a direct driver of neuronal dysfunction, establishing a mechanistic link between TDP-43 pathology and neurodegeneration in ALS and FTD.

Nanoscopic tau aggregates in Parkinson's disease
Post-mortem tau pathology is frequently observed in Parkinson's disease (PD) using immunohistochemistry (IHC) to measure large inclusions, however, small protein aggregates that precede inclusions are considered a major driver of toxicity in neurodegenerative disease. We aimed to uncover the distribution of nanoscopic aggregates across six brain regions in post-mortem tissue from 14 PD and 15 controls using the single-molecule pull-down assay (SiMPull). In the hippocampus and amygdala, tau IHC and SiMPull were associated with advanced age in controls and dementia status in PD. Despite negligible tau IHC-labelled aggregates in the putamen, we identified a unique population of high-intensity nanoscopic tau aggregates for a subset of PD cases using SiMPull, ranging from 10-1,000 epitopes per aggregate and 30-1,000 nm in length. Previous evidence linking nigrostriatal tau pathology and motor deficits indicates that the nanoscopic tau aggregates identified in this study may contribute to striatal dysfunction in PD.

GSDME unlocks astrocyte-driven neurotoxicity in Alzheimer's Disease
Astrocytic calcium dysregulation and reactivity precede Abeta; deposition in amyloid-{beta} deposition in Alzheimer's disease (AD) but the neurotoxic mechanisms remain unclear. We show that GSDME acts as a switch, linking MAM-mediated calcium release to astrocyte-driven neurotoxicity. Specifically, Abeta-activated microglial signals activate astrocytic GSDME, releasing its N-terminal fragment, which targets MAMs and triggers ER calcium efflux. This induces biphasic CaMKIIalpha; phosphorylation, initially boosting NRF2 defenses, then activating NF-kappaB-driven inflammation, shifting astrocytes from protective to toxic states. GSDME activation also drives astrocyte-derived exosomes (ADEs) to carry neurotoxic tau, proinflammatory miRNAs, and toxic lipids, propagating toxicity. GSDME deletion in AD mice reduces Abeta; burden, restores NF-kappaB/NRF2 balance, reprograms astrocytes and ADEs to protective states, and rescues cognition. Multi-omics profiling of serum ADEs from AD patients reveals a disease-specific signature with central neurotoxicity and peripheral immune regulation. These findings position GSDME as a promising dual diagnostic and therapeutic target for early AD invention.

Voltage-dependent reversal potentials in spiking recurrent neural networks enhance energy efficiency and task performance
Spiking recurrent neural networks (SRNNs) rival gated RNNs on various tasks, yet they still lack several hallmarks of biological neural networks. We introduce a biologically grounded SRNN that implements Dale's law with voltage-dependent AMPA and GABA reversal potentials. These reversal potentials modulate synaptic gain as a function of the postsynaptic membrane potential, and we derive theoretically how they make each neuron's effective dynamics and subthreshold resonance input-dependent. We trained SRNNs on the Spiking Heidelberg Digits dataset, and show that SRNN with reversal potentials cuts spike energy by up to 4x, while increasing task accuracy. This leads to high-performing Dalean SRNNs, substantially improving on Dalean networks without reversal potentials. Thus, Dale's law with reversal potentials, a core feature of biological neural networks, can render SRNNs more accurate and energy-efficient.

Lepr Haploinsufficiency Accelerates Alzheimer-like Neurodegeneration via CDK5 Hyperactivation
Leptin signaling has neuroprotective effects and is increasingly linked to Alzheimer's disease (AD). Beyond metabolism, leptin modulates {beta}-amyloid metabolism, tau phosphorylation, and synaptic plasticity. While homozygous Lepr mutations are well studied, the impact of heterozygous mutations on neurodegeneration is unclear. To assess partial Lepr loss, one-year-old db/+ mice were evaluated for metabolic, behavioral, and neuropathological changes. Tests included glucose tolerance, memory assays, A{beta}42 and tau levels, CDK5 activity, and transcriptomics. Human LEPR variants were curated and classified using ACMG guidelines. Aged db/+ mice showed metabolic dysfunction, cognitive deficits, and AD-like pathology. Compared to controls, db/+ mice had increased body weight, insulin resistance, memory impairments, elevated A{beta}42, tau hyperphosphorylation, CDK5 hyperactivation, and astrocyte activation. Transcriptomics revealed altered synaptic and mitochondrial pathways. Thirty-three pathogenic or likely pathogenic human LEPR variants were identified. Lepr haploinsufficiency contributes to age-related cognitive decline and AD-like pathology, suggesting it as a genetic risk factor and therapeutic target.