bioRxiv Subject Collection: Neuroscience's Journal

Riemannian Diffusion Kernel-smoothed Continuous Structural Connectivity On Cortical Surface
Atlas-free continuous structural connectivity has garnered increasing attention due to the limitations of atlas-based approaches, including the arbitrary selection of brain atlases and potential information loss. Typically, continuous structural connectivity is represented by a probability density function, with kernel density estimation as a common estimation method. However, constructing an appropriate kernel function on the cortical surface poses significant challenges. Current methods often inflate the cortical surface into a sphere and apply the spherical heat kernel, introducing distortions to density estimation. In this study, we propose a novel approach using the Riemannian diffusion kernel derived from the Laplace-Beltrami operator on the cortical surface to smooth streamline endpoints into a continuous density. Our method inherently accounts for the complex geometry of the cortical surface and exhibits computational efficiency, even with dense tractography datasets. Additionally, we investigate the number of streamlines or fiber tracts required to achieve a reliable continuous representation of structural connectivity. Through simulations and analyses of data from the Adolescent Brain Cognitive Development (ABCD) Study, we demonstrate the potential of the Riemannian diffusion kernel in enhancing the estimation and analysis of continuous structural connectivity.

Optimizing Electrotactile Stimulation Parameters for Responses Under Cognitive Load
Background: Prosthetic users mainly rely on their vision for feedback during control of the device in absence of the tactile and proprioceptive modalities. Overreliance on vision increases cognitive load during prosthesis use, resulting in reduced control performance. Provision of additional feedback through other modalities could reduce this overreliance on vision and improve closed-loop prosthetic control. Fast and accurate recognition of feedback is particularly important when we consider that both delays and inaccuracies can destabilize closed-loop control environments. Transcutaneous electrotactile stimulation is a promising technology with advantages including non-invasiveness and simplicity. It is not yet clear, however, which parameters influence the speed and accuracy of response to electrotactile stimuli. In this study, we set out to investigate how we can influence response characteristics to an instantaneous change in stimulation intensity by manipulating both stimulus-related and environmental variables. Methods: 20 participants completed a randomized reaction time test to both visual and electrotactile stimulation. Participants were asked to respond to incoming stimuli with a button press on a controller as fast as possible, without discriminating between modality of stimulation. In a second experiment, 20 participants completed an intensity discrimination task for electrotactile stimulation. Participants had to prioritize either speed or accuracy during specific blocks, while cognitive load, magnitude of intensity shift and direction of shift were manipulated. Results: The results of the reaction time experiment confirmed lower average response times for electrotactile stimuli compared to visual stimuli by a median of ~50ms (p=0.008). In the intensity discrimination experiment, increased shift intensities led to increased response accuracies and decreased response times (p<0.001 in both cases). The presence of cognitive load increased average response times (p=0.005), but did not affect response accuracies (p=0.420). Conclusions: The results of the intensity discrimination experiment imply that the magnitude of intensity shift needs to be provided via steps multiple times larger than the just noticeable difference to ensure fast and accurate responses to electrotactile stimulation. Moreover, results from the reaction time experiment confirmed faster average response times for electrotactile stimuli over visual stimuli. We argue that providing electrotactile stimulation using bigger steps in perceived intensity could result in the supplementary feedback making closed-loop control of prosthetic devices more reliable.

Efficient Working Memory Maintenance via High-Dimensional Rotational Dynamics
Working memory (WM) is fundamental to higher-order cognition, yet the circuit mechanisms through which memoranda are maintained in neural activity after removal of sensory input remain subject to vigorous debate. Prominent theories propose that stimuli are encoded in either stable and persistent activity patterns configured through attractor mechanisms or dynamic and time-varying activity patterns brought about through functionally-feedforward network architectures. However, cortical circuits exhibit heterogeneous responses during WM tasks that are challenging to reconcile with either hypothesis. We hypothesised that these complex response dynamics could emerge from an optimally noise-robust and energetically efficient solution to WM tasks. We show that, in contrast to previous theories, networks optimised for efficient WM encoding exhibit high-dimensional rotational dynamics. We find direct evidence for these rotational dynamics in large-scale recordings from monkey prefrontal cortex. Our findings suggest that the complex and dynamic response properties of WM circuits emerge from efficient coding principles.

Transcranial Direct Current Stimulation Modulates Resting Brain Hemodynamics and Autonomic Function: A Multimodal fNIRS-HRV Study
Transcranial direct current stimulation (tDCS) is a non-invasive neuromodulation technique that can influence brain activity and physiological function. We conducted a sham-controlled multimodal study to examine the effects of low-intensity (0.375 mA) bifrontal tDCS at rest in healthy adult males. The tDCS, with the anode placed over the left dorsolateral prefrontal cortex (DLPFC) and the cathode over the right DLPFC, was applied for twelve minutes, and outcomes were measured with functional near-infrared spectroscopy (fNIRS) for cortical hemodynamics and connectivity, heart rate variability (HRV), photoplethysmography (PPG) for autonomic function, and subjective surveys for emotional stress. The results showed that, compared to sham, active tDCS produced a decrease in oxyhemoglobin (HbO) concentration in the left DLPFC during stimulation, indicating reduced cortical oxygenation in the stimulated region. Functional connectivity analysis of fNIRS signals further revealed altered network connectivity, including modulation of intra- and inter-frontal connections, in the tDCS condition relative to sham. Concurrently, tDCS induced an increase in HRV indices such as RMSSD, SDNN, pNN50, SD1 and SD2, reflecting enhanced parasympathetic activity and autonomic regulation. Participants in the active tDCS group also reported selective reductions in both state and trait anger after stimulation, whereas the sham group showed minimal changes in mood. These findings demonstrate that even at a subthreshold intensity, bifrontal tDCS at rest engaged neurovascular and neurovisceral mechanisms, coupling changes in prefrontal cortical activity with autonomic outflow and mood. This study provides new evidence of cortical-autonomic coupling during neuromodulation and suggests that low-intensity frontal tDCS may promote a calm physiological and emotional state. The results have implications for future research on brain stimulation in emotion regulation and for developing neuromodulation-based interventions to improve autonomic balance and mood in both healthy individuals and clinical populations.

The role of miR-9a in modulating sensory neuron morphology and mating behavior in Drosophila melanogaster.
Following mating, female Drosophila melanogaster display profound behavioral changes, including sensory sensitivity and rejection of courting males. The molecular mechanisms governing this plasticity remain incompletely understood. Here, we identify the conserved microRNA, miR-9a, as a critical regulator of this process. We show that miR-9a mutant females exhibit a premature rejection phenotype, mimicking mated-female behavior, which is correlated with an aberrant overgrowth of adult body wall sensory neurons. We demonstrate that this neuronal phenotype is governed by a dual regulatory system. First, in a non-cell autonomous mechanism, miR-9a expression in the epidermis is required to constrain sensory neuron dendrite growth, indicating that an epithelial-derived signal patterns the underlying neuron. Second, within the neuron itself, miR-9a interacts genetically with the transcription factor senseless (sens) and the novel RNA-binding protein bruno2 (bru2). Reducing the dosage of either sens or bru2 rescues both the neuronal and behavioral defects of miR-9a mutants. Our findings reveal an integrated, inter-tissue signaling axis where epithelial miR-9a orchestrates a non-cell autonomous cue that modulates a cell-intrinsic network to ensure the precise development of sensory neurons, thereby calibrating behavioral responses critical for reproductive success.

Neutral lipid processing in glia is sexually dimorphic and promotes sleep through diacylglycerol catabolism
Sleep is thought to have a protective role in clearing toxic waste from the brain, which may include processing of damaged lipids. We recently showed that blocking endocytosis in glia increases sleep and report here that this block is associated with an increase in peroxidized lipids and glial lipid droplet accumulation, raising the possibility that accumulation of these lipids increases the need to sleep. Sleep gain induced by blocking glial transport is exaggerated by knockout of the lipid droplet coat protein, Lipid Storage Droplet 2 (Lsd2), suggesting that sleep-promoting lipids are not contained in lipid droplets. To identify lipids regulated by sleep state, we performed global, targeted lipidomics analysis on Drosophila neurons and glia, screening nearly 3,000 lipids across 11 major classes. This revealed that sex influences lipid composition in both cell types and lipid homeostasis following extended wakefulness. Female neurons and glia are enriched in ultra-long chain fatty acids, triacylglycerols, and diacylglycerols, with glial diacylglycerol enrichment correlating with elevated sleep need. Based on manipulations of neutral lipid metabolic pathways, we propose that monoacylglycerols, products of glial diacylglycerol catabolism, promote sleep.

Single-cell long-read sequencing of the experience-induced transcriptome
Neural activity drives transcriptional events that are critical for learning. Activity-induced transcript isoform expression and alternative splicing are cell-type specific events typically obscured by sequencing approaches that restrict read length. We combined single-cell transcriptomics with Nanopore long-read sequencing to resolve these phenomena, generating the first dataset profiling learning-induced gene and isoform expression in individual mouse hippocampal cells. Due to their length, the majority of reads we generated could be uniquely mapped to their respective gene and isoform features in the mouse reference. ~20k cells from 16 mouse samples were sequenced and clustered on the basis of gene expression, yielding 21 hippocampal cell types. Differential expression analysis revealed 1,266 significantly experience-variable isoforms, revealing novel splicing behavior in many synaptic genes. This work is the first to comprehensively profile activity-induced isoform expression, demonstrating that single cell long-read sequencing can reveal new layers of transcriptional complexity.

Pathogenic tau inhibits synaptic plasticity by blocking eIF4B-mediated local protein synthesis
Activity-dependent modulation of synaptic strength is critical for encoding memories and it is inhibited in tauopathies including Alzheimer's disease (AD) and Frontotemporal lobar degeneration with tau inclusions (FTLD-tau). Pathogenic tau accumulates in neurons where it obstructs synaptic plasticity. How tau blocks synaptic plasticity leading to memory loss is unclear. Here, we show that FTLD-tau inhibits plasticity by blocking activity-dependent protein synthesis in dendrites. In the plasticity-associated translatome, we identified a subset of downregulated translated mRNAs in FTLD-tau neurons that encode postsynaptic plasticity regulators. Protein synthesis was blocked by FTLD-tau binding to eIF4B which caused eIF4B dissociation from the translation initiation complex and reduced dendritic eIF4B levels. Inhibiting the tau-eIF4B interaction or enhancing eIF4B levels in FTLD-tau neurons restored local protein synthesis and synaptic plasticity. Together, this suggests that pathogenic tau binding to eIF4B disables the local synthesis of plasticity-related proteins that drive synapse strengthening and memory formation.

Connectome simulations identify a central pattern generator circuit for fly walking
Animal locomotion relies on rhythmic body movements driven by central pattern generators (CPGs): neural circuits that produce oscillating output without oscillating input. However, the circuit structure of a CPG for walking is not known in any animal. To identify the cells and synapses that underlie rhythmic leg movement in walking flies, we developed dynamic simulations of the Drosophila ventral nerve cord (VNC) connectomes. We used a computational activation screen to identify descending neurons from the brain that drive rhythmic activity in leg motor neurons, including a command neuron for walking (DNg100). By synthetic pruning of the VNC network, we isolated a minimal three-neuron rhythm-generating circuit consisting of one inhibitory and two excitatory interneurons. A model of this core CPG circuit is sufficient to generate motor rhythms, and the two excitatory neurons are necessary in the VNC network model. Connectome simulations also predicted that parallel descending neurons (DNb08) produce rhythmic leg movements, which we experimentally confirmed using optogenetics in behaving flies. Our results reveal the cellular identity and synaptic structure of a putative CPG circuit for fly walking.

The effect of hunger and state preferences on the neural processing of food images
Visual information plays a key role in guiding food-related decisions. While previous studies have shown that features such as calories and naturalness are encoded by the visual brain, it remains unclear how this encoding is shaped by the observer's current state. In this study, we explore the effect of 1) hunger state, 2) current individual preference, and 3) task relevance on the processing of visual food information. Participants underwent two EEG sessions: one after fasting overnight and the another after eating normally. We used multivariate decoding methods to assess the impact of hunger on the decoding of edibility and food identity, and Representational Similarity Analysis to determine the time-course of information related to food flavour, personal appeal, and arousal, both when participants attended to the stimuli and when their attention was distracted away. Results showed that the visual brain quickly encodes information about edibility and food identity, and this was not influenced by whether the participants were in a fasted or sated state. Information about the flavour profile was found to be represented regardless of attentive state. Personal appeal and arousal information emerged later and were only observed when the food was task relevant. Using RSA, we found that food appeal and arousal encoding was more closely aligned with behavioural ratings within rather than between sessions, suggesting the nature of the encoding was driven by current state. In conclusion, the study provides insights into how personal preferences and physiological states influence the representation of food information in the brain.

Pheromone circuits and transcriptional cascades modulating transcriptional and chromatin states in the Drosophila central brain with social experience
Social experience significantly influences the behavioral and physiological responses of animals, including humans. In many animals, social isolation increases aggression, courtship, locomotion, and feeding while disrupting sleep. This occurs when peripheral neurons detect social signals, such as pheromones, which activate decision-making circuits in the brain. However, the molecular and circuit mechanisms of how chronic social isolation or enrichment alter gene expression and affect neuronal function and behavior remain unclear. In this study, we examined how transcription patterns and chromatin marks in male Drosophila brains change in response to social experience, and the impact of pheromone circuits and transcription factors involved in social circuit function. We focused on pheromone receptors Or47b and Or67d, as well as transcription factors FruM and DsxM. Our findings suggest that social experience impacts multiple genes in the central brain. Disrupting Or47b, Or67d, FruM, and DsxM function normalized the transcriptional responses through antagonistic interactions. Specifically, Or47b circuits predominantly mediated transcriptional responses to social isolation through DsxM function, while Or67d and FruM regulated responses to group housing. Notably, mutants of fruM and dsxM exhibited more extensive transcriptional changes in the brain than Or mutants, especially for FruM/DsxM target genes. While social experience did not lead to detectable alterations in the overall chromatin profile in the whole brain, mutants of the four genes resulted in significant changes in the enrichment of H3K4me3 and RNA polymerase II (RNAPolII) compared to wild type. Furthermore, mutants in fruM and dsxM generally eliminated social experience-dependent changes in sleep and locomotion behaviors, whereas Or mutants exhibited more modest disruptions. Overall, our results uncover the pheromone circuits and transcriptional cascades in regulating molecular and behavioral responses to social experience.

Extreme Value Theory for Modeling Category Decision Boundaries in Visual Recognition
Several possibilities exist for modeling decision boundaries in category learning, with varying degrees of human fidelity. This paper finds evidence for preferentially focusing representational resources on the extremes of the distribution of visual inputs in a generative model as an alternative to the central tendency models that are commonly used for prototypes and exemplars. The notion of treating extrema near a decision boundary as features in visual recognition is not new, but a comprehensive statistical framework of recognition based on extrema has yet to emerge for category learning. Here we suggest that the statistical Extreme Value Theory provides such a framework. In Experiment 1, line segment stimuli that vary in a single dimension of length are used to assess how human subjects and statistical models assign category membership to a gap region between two categories shown as reference stimuli. A Weibull fit better predicts an observed human shift when moving from uniform to enriched or long tails as reference stimuli. In Experiment 2, more complex 2D rendered face sequences drawn from morphspaces are used as stimuli. Again, the Weibull fit better predicts an observed human shift when reference stimuli are sampled differently. An extrema-based model lends new insight into how discriminative information may be encoded in the brain with implications for the understanding of how decision making works in category learning.

Inside Out: Functional Flexibility in Assigning the Body's inside-outside
Tactile surface perception is often assumed to reflect a fixed boundary between body and world, with the skin providing a stable oriented surface that separates inside from outside. We show that this assumption fails in functionally specific ways. Using moving tactile stimuli on the hand, participants perceived stimulus direction on the fingertips as reversed when hand posture changed - reporting motion as if it originated from the opposite side of the skin, while responses on the palm were less consistent and less sensitive to posture. Congenitally blind participants exhibited stable, individualized surface assignments on the fingertips, demonstrating that vision is not required to construct oriented tactile surfaces. However, visual experience modulated posture effects: sighted individuals showed posture-dependent skin-orientation assignments driven by motion direction, whereas congenitally blind individuals showed posture-invariant assignments determined by functional use. These findings indicate that tactile surface perception is not globally fixed but dynamically inferred according to functional demands; rather than enforcing a purely geometric body-world boundary, the brain flexibly assigns where the surface is and which side we occupy.

netneurotools: a trainee-oriented approach to network neuroscience
Brain imaging is an increasingly inter-disciplinary field, encompassing multiple data types and multiple analytic traditions. Projects typically involve many moving parts, such as building customized preprocessing pipelines, transforming between data formats, preparing datasets for analysis, and ultimately displaying results. The field is conventionally built on highly specialized software packages that solve these individual challenges well, but are not necessarily designed to be interoperable. Trainees new to the field are therefore often left to come up with isolated heuristics and workarounds to complete a project. Here we present a way to navigate the increasingly complex informatics ecosystem of brain imaging. netneurotools is our lab's internal Python toolkit that has been continuously developed and maintained by the lab's trainees. The philosophy of the toolkit is that it should be the Swiss army knife of the lab: functions and routines that we often use but that are not part of any established pipeline or package. Since its inception, the toolkit has been open and welcomes contribution from neuroscientists across the globe. netneurotools presents a necessary counterweight to out-of-the-box software packages and highlights the importance of smaller, ad hoc functions for implementing projects. By opening a window into the inner workings of a lab, netneurotools also presents an opportunity to begin a new type of discourse among groups and establish tangible links within the community.

Targeted degradation of pathologic tau aggregates via AUTOTAC ameliorates tauopathy
The pathogenesis of tauopathies including Alzheimer's disease (AD) and progressive supranuclear palsy (PSP) involves the misfolding and aggregation of tau. Here, we employed AUTOTAC to induce the lysosomal degradation of intraneuronal tau aggregates. ATB2005A is a 734-Da chimera that simultaneously binds {beta}-sheet-rich tau aggregates and the autophagic receptor p62/SQSTM1, leading to autophagosomal sequestration and lysosomal co-degradation. In mouse models of tauopathies, orally administered ATB2005A lowered intraneuronal tau aggregates and exerted the therapeutic efficacy in neuroinflammation as well as cognition, behavior, and muscle movements. A Phase 2 clinical trial (U34401-4/2023/14) with companion dogs carrying canine cognitive dysfunction (CCD) demonstrated the efficacy of ATB2005A, as a veterinary medicine, to reverse the disease progression. ATB2005A is under Phase 1 clinical trial with human participants in Korea (202300697). These results validate AUTOTAC as a versatile platform for developing therapeutics to eradicate toxic protein aggregates in a wide range of proteinopathies.

A comparison of movement-related neuronal activities in cerebellar- and basal ganglia-recipient regions of the macaque thalamus
The ventral lateral (VL) nucleus of the thalamus relays signals from the cerebellum (Cb) and basal ganglia (BG) to primary motor cortex (M1). In primates, glutamatergic Cb efferents from the deep cerebellar nuclei and GABAergic BG efferents from the internal segment of the globus pallidus (GPi) terminate in distinct subregions of VL: the posterior (VLp) and anterior (VLa) divisions, respectively. This anatomical segregation suggests that Cb- and BG-thalamocortical circuits may play distinct roles in motor control, which could be revealed by comparing movement-related activity in VLp and VLa. Here, we recorded single-unit activity from VLp and VLa, identified via electrical stimulation of superior cerebellar peduncle and GPi, during a choice reaction time reaching task. We also recorded from M1, which maintains bidirectional connections with both VLp and VLa. VLa neurons exhibited a significantly higher proportion of decrease-type responses compared with VLp and M1, consistent with inhibitory GPi input. Time-resolved general linear model analysis showed dynamic encoding of task parameters, particularly movement direction, in all three regions. Direction encoding was strongest in M1, moderate in VLp, and weakest in VLa. Direction encoding in VLa also lagged behind that in M1 and VLp. Clustering analysis of direction encoding strength and timing revealed a subpopulation of VLp neurons that encoded direction particularly strongly during the reaction time period. These results highlight a limitation of traditional assumptions that activity characteristics are distributed homogeneously across neural populations and point to a novel functional organization within VLp neurons.

Commonality and Variability in Functional Networks In Young Children Under 5 Years Old
Functional brain networks exhibit striking individual variability in adults, while less is known about the functional network variability in young children under 5 years. Conventional group-average approaches to obtain the group-level network may have obscured some fine developmental details. We combined data-driven template creation at a coarse parcel level and template-matching at a fine vertex level to map individualized functional networks from single fMRI scans in children aged 8-60 months in two datasets, revealing four key insights: (1) Individual-specific networks show moderate stability across longitudinal scans (Normalized Mutual Information ~ 0.52 with ~ 20 minutes of low-motion data) despite developmental changes; (2) Across the population, sensory cortices demonstrate higher consistency in network assignment than association cortices; (3) Network lateralization in the language network increases with age and correlates with age-normalized verbal developmental quotient (r ~ 0.17), suggesting a correspondence between the individual functional topography and the emergence of functional specialization; and (4) Our 23-network solution identifies finer subdivisions than previously reported using group-average approaches, with many networks resembling the subdivisions found in adult fMRI data. Our findings revealed neurodevelopmental variations in functional network topography that are reliable within individuals and behaviorally relevant, opening new avenues for investigating how person-specific network architecture supports emerging cognitive abilities.

CCL5/CCR5 signaling modulates depression-relevant behavior, neuronal oscillations, and long-term depression of synaptic activity.
Major depressive disorder (MDD) is a debilitating disorder, often associated with perseverative thinking and anxiety. Localized reductions in pyramidal cell activity may contribute to associated symptoms, and effective antidepressant treatments typically enhance overall neuronal excitation. CCL5 is a chemokine that has been shown to reduce excitatory-neuronal activity, and is also increased with MDD and conditions that increase MDD risk. Here, we investigate the CCL5/CCR5 axis for its ability to modulate depression-relevant endpoints that are diminished in MDD, including neuronal oscillations, as well as biochemical and behavioral correlates of the disorder. In comparison to wildtype mice, CCR5 knockouts had increased gamma and theta power, and stronger theta/high-gamma phase amplitude coupling during dark-cycle EEG recordings. Compared to strain-matched wildtype mice, CCR5 knockouts also demonstrated reduced anxiety, increased sucrose preference, and improved associative memory. Proteomic analysis of the hippocampus showed that CCR5 knockouts had reduced levels of the GABA receptor alpha-4 subunit, which mediates tonic inhibition and restricts pyramidal cell plasticity. In complementary primary neuronal culture studies, CCL5 diminished GSK-beta activity and impaired NMDA-dependent long-term depression (LTD), a form of plasticity that promotes cognitive flexibility. In addition,CCL5 signaling reduced parvalbumin expression in GABAergic neurons through a CCR5-dependent manner. In combination with the ability of CCR5 to restrain gamma oscillation power and LTD, our data raise the possibility that CCL5/CCR5 signaling inhibits neuronal excitation through increased PV+ interneuron activity. Moreover, data are consistent with the possibility that CCR5 antagonists might share the ability of established antidepressants to both increase PC excitation and reduce PC inhibition.

High-density extracellular recordings from the interior of intact brain organoids enable automated high-throughput functional assay
Bridging the gap between preclinical screening and clinical outcomes remains a major challenge in drug development for neurological disorders. Brain organoids, derived from human induced pluripotent stem cells, offer a scalable and physiologically relevant platform to model human neural circuits. We develop a fully automated system to record neural activity from the interior of intact human cortical organoids using a high-density microfabricated probe. The robotic system completes insertion within minutes while preserving organoid integrity and enables immediate recording of spontaneous spikes. We extract physiologically grounded and deterministic spike features, and train a long short-term memory classifier to distinguish between organoids derived from healthy individuals and those harboring familial Alzheimer's disease (AD) mutations in the amyloid precursor protein. Despite intra-class variability, the classifier differentiates between organoid classes with 100% accuracy. The model classifies AD organoids treated with a drug candidate that reduces amyloid-{beta} levels as retaining an AD-like electrophysiological phenotype, demonstrating that functional readout can contradict molecular markers. The findings establish a high-throughput functional framework that may complement and extend existing drug screening assays.

Distinct Implicit Contributions to Action Selection and Action Execution in Sensorimotor Adaptation
The sensorimotor system is continuously adjusted to minimize error. Current theories assume that this adaptation process entails the operation of multiple learning systems, with a key division between implicit and explicit components. Recent studies have revealed several inconsistencies regarding the characteristics and constraints of the implicit system, suggesting that the current framework is incomplete. Here, we propose that these conflicting findings can be understood by recognizing that there are multiple implicit subcomponents, distinguished by their distinct computational goals. One well-studied component is implicit recalibration, a process critical for action execution which uses sensory-prediction errors to automatically refine the sensorimotor map. Here we describe a second, novel component, implicit aiming, a process which contributes to action selection to achieve the specific goals. Through a series of studies, we find compelling evidence that those two implicit processes show a clear separation in their temporal stabilities and contextual modulations. These distinct properties correspond to different computational frameworks attributing learning dynamics to either contextual inference or cancellation of competing neural populations, respectively. Together, these findings suggest an alternative framework for sensorimotor adaptation based on the computational goals of the system rather than phenomenology.

A phenotypic brain organoid atlas for neurodevelopmental disorders
Thousands of genes are associated with neurodevelopmental disorders (NDDs), yet mechanisms and targeted treatments remain elusive. To fill these gaps, we present a CIRM-initiated NDD biobank of 352 publicly-available genetically-diverse patient-derived iPSCs, along with clinical details, brain imaging and genomic data, representing four major categories of disease: microcephaly (MIC), polymicrogyria (PMG), epilepsy (EPI), and intellectual disability (ID). From 35 representative patients, we studied over 6000 brain organoids for histology and single cell transcriptomics. Compared with an organoid library from ten neurotypicals, patients showed distinct cellular defects linked to underlying clinical disease categories. MIC showed defects in cell survival and excessive TTR+ cells, PMG showed intermediate progenitor cell junction defects, EPI showed excessive astrogliosis, and ID showed excessive generation of TTR+ cells. Our organoid atlas demonstrates both conserved and divergent NDD category-specific phenotypes, bridging genotype and phenotype. This NDD iPSC biobank can support future disease modeling and therapeutic approaches.

Transcriptional dynamics of the oligodendrocyte lineageand its regulation by the brain erythropoietin system
Oligodendrocytes differentiate from oligodendrocyte progenitor cells (OPC) in early postnatal development, but some oligodendrogenesis is maintained throughout adulthood, where oligodendrocyte lineage dynamics may contribute to neuroplasticity, adaptive myelination, and myelin repair. Here, we studied the effect of erythropoietin (EPO) and its receptor (EPOR) on oligodendrocyte lineage dynamics employing murine hippocampus and its myelinated fibers as model region. Using multiple stage-specific markers and single-nuclei-RNA-seq data, we find that EPO stimulates all oligodendroglial lineage cells directly, driving differentiation/maturation. Differential gene expression analysis reveals multiple EPO-regulated mRNAs, including downregulated transcripts for GABA-A receptors, fitting the known inhibition of oligodendrocyte maturation by GABA. Importantly, analogous oligodendrocyte responses are seen when endogenous EPO expression in brain is stimulated by hypoxia. Mice lacking EPOR from mature oligodendrocytes show subtle deficiencies of adult myelination in hippocampal fimbria and mild working memory deficits. These gain- and loss-of-function experiments may further suggest EPO as clinically safe treatment for remyelination therapies.

Frequency-dependent coupling in responses to oscillatory inputs in networks of electrically coupled nodes: Gap junction networks and spatially extended neurons
Neuronal filters describe the neuronal information processing building blocks where specific frequency components of the output are enhanced over others. The result of the interaction of neuronal filters across nodes in a network is captured by the frequency-dependent amplitude and phase-difference coupling coefficients (CCs), respectively, which extend the notion of the steady-state CC to include frequency-dependent processes. These metrics are shaped by the interplay of the participating ionic and synaptic currents and the external inputs. The underlying biophysical and dynamic mechanisms are largely unknown beyond electrically coupled passive cells. In this paper, we investigate the mechanism of interaction of cellular neuronal filters in electrically coupled networks. We use biophysically plausible (conductance-based) mathematical modeling, numerical simulations, analytical calculations and dynamical systems tools. We focus on a family of models that include gap junction connected cells and compartmental models of spatially extended neurons. We consider a network of two electrically connected nodes receiving oscillatory inputs. This is the minimal network architecture that allows for a systematic study of the biophysical and dynamic mechanisms that shape the CC profiles. The participating neurons are either passive cells (low-pass filters) or resonators (band-pass filter) and exhibit lagging or mixed leading-lagging phase-shift responses as the input frequency increases. First, we use linear models to understand how the participating building blocks (e.g., conductances, negative and positive feedback effects) operating at the single node level give rise to the amplitude and phase CC profiles with band-pass filtering properties. Then, we extend our investigation and use linearized models of nonlinear of neurons with biophysically realistic ionic currents to investigate (to the linear level of approximation) how the presence of cellular ionic currents and cellular resonance/phasonance contribute to shaping the corresponding (amplitude and phase) CC profiles and determine their preferred coupling frequencies. Motivated by experimental results on subthreshold resonance in hippocampal CA1 pyramidal cells, we use three ionic currents: persistent sodium (amplifying), mixed sodium-potassium h- (resonant and hyperpolarization-activated) and M-type slow potassium (resonant and depolarization-activated). Finally, we extend our investigation to include the model nonlinearities to understand how the CC profiles are shaped by the interaction between the pre-J and post-J cells, and not just the post-J cells as it is the case for linear models. For gap junction connected networks, the connectivity is typically symmetric, while for compartmental models is asymmetric as the result of the different geometries of the participating compartments. We investigate the effects of asymmetric connectivity to understand how the compartmental geometry interacts with the neuronal biophysical properties to shape the CC profiles and determine the preferred coupling frequencies. The formalism and tools we develop and use in this paper can be extended to larger networks with an arbitrary number of nodes, to spatially extended multicompartment neuronal models, and to neurons having other types of ionic currents such as the T-type calcium current. The principles that emerge from our study are directly applicable to these scenarios. Our results make experimentally testable predictions and have implications for the understanding of spike transmission, synchronic firing and coincidence detection in electrically coupled networks in the presence of oscillatory inputs.

Sequential sampling from memory underlies perceptual decisions unyoked from actions
Perceptual decision-making refers to the class of decisions in which sensory evidence is used to categorize percepts and guide actions. Conventionally, categorical decisions are thought to precede motor actions. However, recent studies in nonhuman primates challenge this assumption -- when perceptual decisions were uncoupled from the actions they bear upon, animals postponed the decisions until relevant response options were revealed. To determine whether this postponement stems from cognitive limitations unique to nonhuman primates, we conducted a similar experiment with human subjects. Naive subjects viewed a random-dot motion (RDM) stimulus that was difficult to categorize. After a delay period following the RDM, two choice targets were presented and subjects decided which target lay closer to the perceived motion direction. Decision accuracy varied across subjects, reflecting individual differences in ability to integrate motion evidence. Notably, subjects with higher decision accuracy showed prolonged deliberation after choice-target presentation. Furthermore, the time they took to report their decisions depended on the strength of the motion evidence. This pattern of accuracy and decision reporting time could be accounted for by a bounded diffusion model in which subjects sequentially sample stored sensory information from memory during the target selection phase. When the RDM was challenging to categorize, the subsequent appearance of the targets provided a framework to interrogate stored evidence and render a decision. Our results reveal a strategic feature of working memory of retaining information based on its future utility. This observation opens new avenues for investigating how memory and decision-making interact.

Erythropoietin alleviates intellectual disability and autism-like behavior of mice caused by Zbtb20 haploinsufficiency, a construct-valid model of Primrose syndrome
Among the known genetic causes of syndromic autism spectrum disorders (ASD) are transcription factor deficiencies. In this regard, haploinsufficiency of the Zinc finger and broad complex, tramtrack, bric and brac domain-containing protein 20 (ZBTB20) leads to a prototypical clinical picture, referred to as Primrose syndrome, comprising severe ASD symptoms together with intellectual disability. Here, we present a comprehensive behavioral and phenotypical characterization of Zbtb20+/-mice, a construct valid model of this thus far untreatable human condition. Zbtb20+/- mice exhibit diminished sociability, reduced vocalization, distinct repetitive behaviors, impaired cognitive flexibility, hyperactivity and hypoalgesia. Magnetic resonance imaging reveals increased volumes of hippocampus, cerebellum, brain matter, and whole brain, confirmed by postmortem brain weight measurements. Due to our previous observation of enhanced ZBTB20 expression in CA1 pyramidal neurons upon recombinant human erythropoietin (rhEPO) injections, we anticipated a mitigating effect of rhEPO treatment for Primrose syndrome. Indeed, after just three weeks of alternate-day rhEPO injections, a remarkable improvement in the behavioral phenotype was observed. Our results higlight rhEPO as a first promising treatment for Primrose syndrome.

Consistency in phonetic categorization predicts successful speech-in-noise perception
Listeners bin continuous changes in the speech signal into phonetic categories. But they vary in how consistently/discretely they assign speech sounds to categories, which may relate to speech-in-noise (SIN) perception. Yet, it is unclear if and how perceptual gradience, consistency, and other cognitive factors (e.g., working memory) collectively predict SIN performance. Here, we estimated perceptual gradiency and response consistency during vowel labeling and assessed working memory and SIN performance. We found perceptual consistency and working memory were the best predictors of listeners' composite SIN scores. Our findings emphasize the importance of perceptual consistency over categoricity for noise-degraded speech perception.

Neural Encoding of Immediate and Instrumental Value During Planning
Planning is a key executive function enabling humans to anticipate future outcomes by mentally simulating action sequences, balancing immediate gains against long-term goals. However, prior research on planning often conflated it with either spatial navigation or reward-based learning, and little is known about the relative encoding of the immediate and instrumental value of the same action. We used a novel fMRI task in which participants repeatedly chose between two options, each with an immediate monetary value and a known future-oriented instrumental value. Our results show that striatal activity is positively correlated with instrumental value, whereas activity in the dorsomedial prefrontal cortex (dmPFC) and bilateral insula is negatively correlated with the instantaneous point value. These dissociable patterns support specialization in valuation neural circuits during planning.

Redefining Parkinson's Disease by Dysregulated Genetic Networks in Distinct Cell Types
Parkinson's disease (PD) is classically linked to dopaminergic neuron loss, but emerging evidence suggests broader cellular involvement. Here we show that PD risk variants converge on distinct molecular networks across specific brain cell types, enabling stratification of patients into six subgroups: dopaminergic, oligodendrocyte progenitor cells (O), excitatory (E), dopaminergic/excitatory, dopaminergic/oligodendrocyte and other. While all subgroups exhibit motor symptoms, the E-group individuals also display more severe non-motor symptoms, including dementia, hyposmia, and REM sleep behavior disorder. The O-group individuals exhibit reduced myelin integrity, as demonstrated by diffusion tensor imaging, implicating NRG6 (formerly C1orf56), a previously uncharacterized high-risk PD gene. We show that NRG6 encodes a conserved epidermal growth factor-like domain structurally and functionally analogous to neuregulin-1, which is critical for oligodendrocyte development and myelination. These findings redefine the cellular architecture of PD vulnerability and identify neuregulin-like signaling in oligodendrocytes as a potential contributor to non-motor symptoms.

Subacute effects of ketamine on neural correlates of reward processing
Objectives: Ketamine's prohedonic properties have been linked to enhanced reward-related brain activation during the early post-infusion phase. Its effects during the subacute period (~2-24 h post-infusion), when psychotomimetic symptoms fade and neuroplastic adaptations emerge, are less well characterised. This study assessed ketamine's subacute effects on reward processing using the Monetary Incentive Delay (MID) task. Methods: In a randomised, placebo-controlled, crossover study, 28 healthy participants received 0.5 mg/kg racemic ketamine or placebo via 40-minute intravenous infusion. Functional magnetic resonance imaging (fMRI) was acquired ~5 h post-infusion. Plasma concentrations of ketamine and norketamine were obtained for individual area under the curve (AUC) estimation. Analyses focused on the contrast between expected and actual trial outcomes. Results: At five hours post-infusion, ketamine did not significantly modulate MID task-related brain activation, despite pronounced subjective drug effects. Pharmacokinetic modelling confirmed expected ketamine and norketamine profiles, but neither drug exposure (AUC) nor subjective measures correlated with neural activation. Conclusions: Prohedonic effects of ketamine may not sufficiently manifest in MID task-related activation in healthy individuals ~5 hours after infusion. The lack of significant effects provides valuable extension of the existing literature, as ketamine's effects might be confined to a more acute time window or differ in clinical populations.

The nucleus accumbens to ventral pallidum pathway regulates social play behavior via sex-specific mechanisms in juvenile rats
Social play behavior is a rewarding behavior predominantly displayed by juveniles of various mammalian species, including humans and rats. Although the mesolimbic reward system is involved in the regulation of social play, how brain regions in this system interact to regulate social play behavior is unknown. Here, we determined the involvement of the ventral pallidum (VP) as well as inputs from the nucleus accumbens (NAc) to the VP in the regulation of social play in male and female juvenile rats. We show that acute pharmacological inactivation of the VP, via microinfusion of the GABA-A receptor agonist muscimol, decreased social play behaviors in both sexes. Next, using Gad1-iCre rats, we show that chemogenetic stimulation of NAcGABA terminals in the VP decreased VP neuronal activation and decreased social play behaviors in both sexes. These findings together indicate that reduced inhibitory NAc input to the VP permits activation of the VP which facilitates the expression of social play behaviors. Lastly, we show that the equal expression of social play behavior in males and females is associated with a female-specific increase in NAc shell activation and a male-specific decrease in activation of the NAc shell neurons projecting to the VP. These sex-specific changes in NAc activity following social play exposure eliminated baseline sex differences in NAc activity. In conclusion, these findings support a model in which the sex-specific modulation of NAc inhibitory input to the VP facilitates activation of the VP that is necessary for the typical and equal expression of social play behavior in male and female juvenile rats.

How the characteristics of a virtual environment affects the perception of travel distance through it
Although simulated self-motion through virtual environments has been widely used to investigate perceptual odometry, the characteristics of the virtual environments used, and the reported results have varied greatly. Here, we systematically vary the characteristics of the environment through which observers are moved in order to explore the effect of (1) the structure of an environment including the presence and texture of a ground surface, (2) the naturalism and scale of an environment, (3) colour, and (4) the density of a starfield and how it might affect perceived travel distance. In all four experiments, participants were visually moved forwards through a virtual environment and perceived travel distance was estimated by either (1) stopping at the location of a previously seen target (the Move-To-Target Task) or (2) adjusting the position of a target to indicate a previously travelled distance (the Adjust-Target Task). Data were analyzed in terms of gain (perceived travel distance/actual travel distance). Results show no significant differences that depended on the structure of an environment or on the presence or absence of a ground surface (Experiment 1), or on the naturalism of the environment (Experiment 2), or on whether the environment was in colour or in black and white (Experiment 3). However, there was a small effect of the texture of the ground surface and of the scale of the environment. In Experiment 4, we show that there may be a very low ceiling effect in the density of a starfield needed to accurately estimate travel distance. Together these experiments have implications for the design of real and virtual environments where perceived motion is important and will enable us to further predict our perception of moving through an environment.

RetINaBox: A hands-on learning tool for experimental neuroscience
An exciting aspect of neuroscience research is developing and testing hypotheses via experimentation. However, due to logistical and financial hurdles, this compelling part of neuroscience research is generally lacking in classroom education. To address this issue, here we introduce RetINaBox: a low-cost open-source electronic retina simulator that provides users with a hands-on system to discover how the visual system builds feature detectors. RetINaBox features an LED array for generating visual stimuli and a photodiode array that acts as a mosaic of model photoreceptors. Custom software on a Raspberry Pi computer reads out responses from model photoreceptors and allows users to control the polarity and delay of the signal transfer from model photoreceptors to model retinal ganglion cells. Interactive lesson plans are provided, guiding users to discover different types of visual feature detectors - including center-surround, orientation selective, and direction selective receptive fields - as well as their underlying circuit computations.

Altered Use of Prior Expectations and Modified Neural Dynamics in a Mouse Model of Autism
In dynamic environments, updating beliefs based on past experiences (priors) is essential for optimal decision-making. Prior utilization is often impaired in psychiatric disorders, affecting perception and behavior. We investigate how Neurexin1 (Nrxn1) loss-of-function disrupts this process, providing insight into circuit deficits underlying sensorimotor dysfunction. While the synaptic role of Nrxn1 role is well studied, its impact on network dynamics and decision-making behavior remain unclear. Using widefield calcium imaging, we assess cortex-wide activity in mice performing a two-choice task to probe how priors influence visually-guided decisions. This task requires the mouse to combine sensory evidence with the prior probability over the stimulus side. We find Nrxn1 KO mice underutilized priors and were slower to update choices based on feedback. During decision-making, cortex-wide cortical activity is both elevated and increasingly correlated in Nrxn1 KO mice, independent of task period. Moreover, a larger fraction of cortical variance was explained by movement variables, consistent with stronger coupling of cortical activity to motor signals and a bias toward movement-related dynamics. These findings suggest that core computations underlying decision-making, such as integrating past experience with current evidence, depend on intact synaptic mechanisms shaped by genes like Nrxn1.

Neural responses to binocular in-phase and anti-phase stimuli
Binocular vision fuses similar inputs from the two eyes into a single percept, whereas incompatible inputs can produce rivalry, lustre, or diplopia. We measured neural responses to binocular stimuli with different phase relationships to test predictions from contemporary binocular combination models. Steady-State Visually Evoked Potentials (SSVEPs) were recorded from 15 observers in response to monocular and binocular stimulation at 3 Hz, using either On/Off or counterphase flicker with varied spatial and temporal phase relationships. On/Off and counterphase flicker elicited responses at the expected fundamental frequency (3 Hz and 6 Hz, respectively) and their harmonics. Manipulating phase relationships modulated these response patterns, including a reduction in the fundamental amplitude for On/Off flicker. The data were modeled with a series of binocular combination algorithms, ranging in complexity from a simple linear sum to a two-stage binocular gain-control model with parallel monocular and binocular phase-selective channels. The model required parallel monocular channels to account for our data, whereas phase selectivity was not essential. Overall, the two-stage contrast gain-control model remains a powerful and flexible framework for describing binocular combinations across various experimental conditions and modalities.