bioRxiv Subject Collection: Neuroscience's Journal

Predicting task-related brain activity from resting-state brain dynamics with fMRI Transformer
Accurate prediction of the brain's task reactivity from resting-state functional magnetic resonance imaging (fMRI) data remains a significant challenge in neuroscience. Traditional statistical approaches often fail to capture the complex, nonlinear spatiotemporal patterns of brain function. This study introduces SwiFUN (Swin fMRI UNet Transformer), a novel deep-learning framework designed to predict 3D task activation maps directly from resting-state fMRI scans. SwiFUN leverages advanced techniques such as shifted window-based self-attention, which helps to understand complex patterns by focusing on varying parts of the data sequentially, and a contrastive learning strategy to better capture individual differences among subjects. When applied to predicting emotion-related task activation in adults (UK Biobank, n=7,038) and children (ABCD, n=4,944), SwiFUN consistently achieved higher overall prediction accuracy than existing methods across all contrasts; it demonstrated an improvement of up to 27% for the FACES-PLACES contrast in ABCD data. The resulting task activation maps revealed individual differences across cortical and subcortical regions associated with sex, age, and depressive symptoms. This scalable, transformer-based approach potentially reduces the need for task-based fMRI in clinical settings, marking a promising direction for future neuroscience and clinical research that enhances our ability to understand and predict brain function.

Unveiling Stimulus Transduction Artifacts in Auditory Steady-State Response Experiments: Characterization, Risks, and Mitigation Strategies
This scientific paper addresses the potential risk of spurious responses in neuroscientific auditory steady-state response (ASSR) experiments attributed to transduction artifacts. The focus is particularly on click train stimuli, given their spectral content in the frequency range of interest (e.g., 40 Hz). Building upon a pilot experiment demonstrating the existence of the artifact in a phantom head, this study focuses on the characterization of stimulus artifacts in extended measurements with phantoms and the evaluation of associated risks in experiments involving human subjects. The investigation is divided into two parts: the first part scrutinizes stimulus artifact properties crucial for mitigation, while the second part assesses risks in ASSR experiments with human subjects based on the characterized artifact. The discussion covers stimulus characterization, experimental setups with phantoms, and experiments with human subjects, exploring potential sources of the artifact, its spatial properties, and the influence of re-referencing. The results reveal the role of headphone cables as a source of stimulus artifacts, along with the surprising impact of headphone transducers. The study emphasizes the need for careful experimental design and data analysis to avoid misinterpretations of stimulus artifacts as genuine brain responses in ASSR experiments.

Normative evidence weighting and accumulation in correlated environments
The brain forms certain deliberative decisions following normative principles related to how sensory observations are weighed and accumulated over time. Previously we showed that these principles can account for how people adapt their decisions to the temporal dynamics of the observations (Glaze et al., 2015). Here we show that this adaptability extends to accounting for correlations in the observations, which can have a dramatic impact on the weight of evidence provided by those observations. We tested online human participants on a novel visual-discrimination task with pairwise-correlated observations. With minimal training, the participants adapted to uncued, trial-by-trial changes in the correlations and produced decisions based on an approximately normative weighting and accumulation of evidence. The results highlight the robustness of our brain's ability to process sensory observations with respect to not just their physical features but also the weight of evidence they provide for a given decision.

Balanced bidirectional optogenetics reveals the causal impact of cortical temporal dynamics in sensory perception
Whether the fast temporal dynamics of neural activity in brain circuits causally drive perception and cognition remains one of most longstanding unresolved questions in neuroscience1-6. While some theories posit a "timing code" in which dynamics on the millisecond timescale is central to brain function, others instead argue that mean firing rates over more extended periods (a "rate code") carry most of the relevant information. Existing tools, such as optogenetics, can be used to alter temporal structure of neural dynamics7, but they invariably change mean firing rates, leaving the interpretation of such experiments ambiguous. Here we developed and validated a new approach based on balanced, bidirectional optogenetics that can alter temporal structure of neural dynamics while mitigating effects on mean activity. Using this new approach, we found that selectively altering cortical temporal dynamics substantially reduced performance in a sensory perceptual task. These results demonstrate that endogenous temporal dynamics in the cortex are causally required for perception and behavior. More generally, this new bidirectional optogenetic approach should be broadly useful for disentangling the causal impact of different timescales of neural dynamics on behavior.

Individual differences in the expression and control of anger are encoded in the same fronto-temporal GM-WM network
Anger can be deconstructed into distinct components: a temporary emotional state (state anger), a stable personality trait (trait anger), a tendency to outwardly express it (anger-out), or to internally suppress it (anger-in), and the capability to manage it (anger control). These aspects exhibit individual differences that vary across a continuum. Notably, the capacity to express and control anger are of great importance to modulate our reactions in interpersonal situations. The aim of this study was to test the hypothesis that anger expression and control are negatively correlated and that both can be decoded by the same patterns of grey and white matter features of a fronto-temporal brain network. To this aim, a data fusion unsupervised machine learning technique, known as transposed Independent Vector Analysis (tIVA), was used to decompose the brain into covarying GM-WM networks and then backward regression was used to predict both anger expression and control from a sample of 212 healthy subjects. Confirming our hypothesis, results showed that anger control and anger expression are negatively correlated, the more individuals control anger, the less they externalize it. At the neural level, individual differences in anger expression and control can be predicted by the same GM-WM network. As expected, this network included fronto-temporal regions as well as the cingulate, the insula and the precuneus. The higher the concentration of GM-WM in this brain network, the higher level of externalization of anger, and the lower the anger control. These results expand previous findings regarding the neural bases of anger by showing that individual differences in anger control and expression can be predicted by morphometric features.

Loss of the MeCP2 gene in parvalbumin interneurons leads to an inhibitory deficit in the amygdala and affects its functional connectivity.
Background: MECP2 gene is located in the X-chromosome and encodes a methyl-CpG-binding protein involved in transcription regulation. The loss-of-function mutation and the duplication of the MECP2 gene, causing the gain-of-function, lead to severe neurodevelopmental syndromes: Rett syndrome and MECP2 gene duplication syndrome. Clinical picture of both syndromes includes, among other symptoms, social deficits and heightened anxiety. The amygdala is involved in the regulation of social behavior as well as fear and anxiety. Here, we investigated the effect of the MeCP2 gene ablation in the parvalbumin interneurons on the microcircuit and functional connectivity of the amygdala in a genetic mouse model of Rett syndrome. Methods: Males with conditional knock-out of the MeCP2 gene in the parvalbumin interneurons were used as a genetic mouse model of Rett syndrome. Littermates with the intact gene were used as controls. Ex-vivo brain slice electrophysiology, combined with pharmacology and optogenetics, was used to characterize microcircuits within the lateral amygdala of a genetic mouse model. Synaptic currents and excitability of parvalbumin interneurons and principal neurons were analyzed by a whole-cell patch clamp. In-vivo functional ultrasound was used to visualize the connectivity within the amygdala-ventral hippocampus-prefrontal cortex triad. Results: Loss of MeCP2 in parvalbumin interneurons caused a significant reduction of the inhibitory drive to the principal neurons within the lateral amygdala microcircuitry. Decreased inhibitory synaptic connectivity was accompanied by an increase in the excitability of principal neurons in the lateral amygdala, which was associated with the deficit in the GABA drive and reduced potassium currents. The in vivo functional connectivity of the amygdala-ventral hippocampus and amygdala-prefrontal cortex was significantly reduced in conditional knock-outs compared to their littermates with the intact gene in the X chromosome. Conclusions: Our study characterized the consequences of the MeCP2 gene loss in the parvalbumin interneurons on the amygdala connectivity and microcircuit and provided evidence supporting the previous findings on the role of interneurons in the functional deficit observed in animal models with MeCP2 loss.

Disrupted Hippocampal Theta-Gamma Coupling and Spike-Field Coherence Following Experimental Traumatic Brain Injury
Traumatic brain injury (TBI) often results in persistent learning and memory deficits, likely due to disrupted hippocampal circuitry underlying these processes. Precise temporal control of hippocampal neuronal activity is important for memory encoding and retrieval and is supported by oscillations that dynamically organize single unit firing. Using high-density laminar electrophysiology, we discovered a loss of oscillatory power across CA1 lamina, with a profound, layer-specific reduction in theta-gamma phase amplitude coupling in injured rats. Interneurons from injured animals were less strongly entrained to theta and gamma oscillations, suggesting a mechanism for the loss of coupling, while pyramidal cells were entrained to a later phase of theta. During quiet immobility, we report decreased ripple amplitudes from injured animals during sharp-wave ripple events. These results reveal deficits in information encoding and retrieval schemes essential to cognition that likely underlie TBI-associated learning and memory impairments, and elucidate potential targets for future neuromodulation therapies.

Comparing Methods for Deriving the Auditory Brainstem Response to Continuous Speech in Human Listeners
Several tools have recently been developed to derive the Auditory Brainstem Response (ABR) from continuous natural speech, facilitating investigation into subcortical encoding of speech. These tools rely on deconvolution, which models the subcortical auditory pathway as a linear system, where a nonlinearly processed stimulus is taken as the input (i.e., regressor), the electroencephalogram (EEG) data as the output, and the ABR as the impulse response deconvolved from the recorded EEG and the regressor. In this study, we analyzed EEG recordings from subjects listening to both unaltered natural speech and synthesized "peaky speech." We compared the derived ABRs using three regressors: the half-wave rectified stimulus (HWR) from Maddox and Lee (2018), the glottal pulse train (GP) from Polonenko and Maddox (2021), and the auditory nerve modeled response (ANM) from Shan et al. (2024). Our evaluation focused on the fidelity, efficiency, and practicality of each method in different scenarios. The results indicate that the ANM regressor for both peaky and unaltered speech and the GP regressor for peaky speech provided the best performance, whereas the HWR regressor demonstrated relatively poorer performance. The findings in this study will guide future research in selecting the most appropriate paradigm for ABR derivation from continuous, naturalistic speech.

Cortical determinants of loudness perception and auditory hypersensitivity
Parvalbumin-expressing inhibitory neurons (PVNs) stabilize cortical network activity, generate gamma rhythms, and regulate experience-dependent plasticity. Here, we observed that activation or inactivation of PVNs functioned like a volume knob in the mouse auditory cortex (ACtx), turning neural and behavioral classification of sound level up or down over a 20dB range. PVN loudness adjustments were ''sticky'', such that a single bout of 40Hz PVN stimulation sustainably suppressed ACtx sound responsiveness, potentiated feedforward inhibition, and behaviorally desensitized mice to loudness. Sensory sensitivity is a cardinal feature of autism, aging, and peripheral neuropathy, prompting us to ask whether PVN stimulation can persistently desensitize mice with ACtx hyperactivity, PVN hypofunction, and loudness hypersensitivity triggered by cochlear sensorineural damage. We found that a single 16-minute bout of 40Hz PVN stimulation session restored normal loudness perception for one week, showing that perceptual deficits triggered by irreversible peripheral injuries can be reversed through targeted cortical circuit interventions.

Distinct ventral tegmental area neuronal ensembles are indispensable for reward-driven approach and stress-driven avoidance behaviors
Assigning valence to stimuli for adaptive behavior is an essential function, involving the ventral tegmental area (VTA). VTA cell types are often defined through neurotransmitters (NT). However, valence function in VTA does not parse along NT-boundaries as, within each NT-class, certain neurons are excited by reward and others by stressors. Here we identify the co-activated mouse VTA neuronal ensembles for reward and stress, and determine their role in adaptive behaviors. We show that stimuli of opposite valence (opioid vs acute social stress) recruit two segregated intermingled small VTA neuronal ensembles. These two ensembles continue to be preferentially engaged by congruent valence stimuli. Stimulation of VTA stress- or reward ensembles is aversive/reinforcing, respectively. Strikingly, external valence stimuli fully require activity of these small discrete VTA ensembles for conferring approach/avoidance outcomes. Overall, our study identifies distinct small VTA ensembles for positive and negative valence coding and shows their indispensability for adaptive behavior.

Diverse perceptual biases emerge from Hebbian plasticity in a recurrent neural network model
Perceptual biases offer a glimpse into how the brain processes sensory stimuli. While psychophysics has uncovered systematic biases such as contraction and repulsion, a unifying neural network model for how such seemingly distinct biases emerge from learning is lacking. Here, we show that both contractive and repulsive biases emerge from continuous Hebbian plasticity in a single recurrent neural network. We test our model in three experimental paradigms: a working memory task, a reference memory task, and a novel "one-back task" that we design to test the robustness of the model. We find excellent agreement between model predictions and experimental data without fine-tuning the model to any particular paradigm. These results show that apparently contradictory perceptual biases can in fact emerge from a simple local learning rule in a single recurrent region of the brain.

Dopamine mediated plasticity preserves excitatory connections to direct pathway striatal projection neurons and motor function in a mouse model of Parkinson's disease
The cardinal symptoms of Parkinson's disease (PD) such as bradykinesia and akinesia are debilitating, and treatment options remain inadequate. The loss of nigrostriatal dopamine neurons in PD produces motor symptoms by shifting the balance of striatal output from the direct (go) to indirect (no-go) pathway in large part through changes in the excitatory connections and intrinsic excitabilities of the striatal projection neurons (SPNs). Here, we report using two different experimental models that a transient increase in striatal dopamine and enhanced D1 receptor activation, during 6-OHDA dopamine depletion, prevent the loss of mature spines and dendritic arbors on direct pathway projection neurons (dSPNs) and normal motor behavior for up to 5 months. The primary motor cortex and midline thalamic nuclei provide the major excitatory connections to SPNs. Using ChR2-assisted circuit mapping to measure inputs from motor cortex M1 to dorsolateral dSPNs, we observed a dramatic reduction in both experimental model mice and controls following dopamine depletion. Changes in the intrinsic excitabilities of SPNs were also similar to controls following dopamine depletion. Future work will examine thalamic connections to dSPNs. The findings reported here reveal previously unappreciated plasticity mechanisms within the basal ganglia that can be leveraged to treat the motor symptoms of PD

Amygdala engages non-emotional multi-item working memory maintenance through amygdala-hippocampus interaction
The process of holding information in working memory (WM) is an active one that requires neural activity within and between regions. The human amygdala (AMY) and hippocampus (HPC) are known to play crucial roles in emotional WM processing. Although human electrophysiological studies have made remarkable progress in revealing that HPC supports multi-item maintenance in a load-dependent manner, the characteristics of AMY and the circuit-level mechanisms underpinning AMY-HPC interactions remain largely unexplored. To address this gap in our knowledge, this study employed intracranial EEG recordings from AMY and HPC in nine epileptic patients to evaluate intra-regional neural representations and inter-regional communications during maintenance under different non-emotional WM loads. The results showed that high load enhances low-frequency power and intra-regional theta-gamma phase-amplitude coupling (PAC) in AMY and HPC. On the network level, high load elicits an increase in the strength of the modulation of HPC theta phase entraining AMY gamma amplitude. Furthermore, high load elevates AMY-aHPC theta phase synchrony and directional connectivity strength, with the direction being from anterior HPC (aHPC) to AMY. Conversely, posterior HPC (pHPC)-AMY synchrony is not affected by load variations. Overall, these findings highlight the importance of AMY in non-emotional WM tasks and shed new light on the neurophysiological basis of AMY-HPC interactions during WM maintenance.

Dysfunctional S1P/S1PR1 signaling in the dentate gyrus drives vulnerability of chronic pain-related memory impairment
Memory impairment in chronic pain patients is substantial and common, and few therapeutic strategies are available. Chronic pain-related memory impairment has susceptible and unsusceptible features. Therefore, exploring the underlying mechanisms of its vulnerability is essential for developing effective treatments. Here, combining two spatial memory tests (Y-maze test and Morris water maze), we segregated chronic pain mice into memory impairment-susceptible and -unsusceptible subpopulations in a chronic neuropathic pain model induced by chronic constrictive injury of the sciatic nerve. RNA-seq analysis and gain/loss-of-function study revealed that S1P/S1PR1 signaling is a determinant for vulnerability to chronic pain-related memory impairment. Knockdown of the S1PR1 in the DG promoted a susceptible phenotype and led to structural plasticity changes of reduced excitatory synapse formation and abnormal spine morphology as observed in susceptible mice, while overexpression of the S1PR1 and pharmacological administration of S1PR1 agonist in the DG promoted an unsusceptible phenotype and prevented the occurrence of memory impairment, and rescued the morphological abnormality. Finally, GO enrichment analysis and biochemical evidence indicated that down-regulation of S1PR1 in susceptible mice may impair DG structural plasticity via interaction with actin cytoskeleton rearrangement-related signaling pathways including Itga2 and its downstream Rac1/Cdc42 signaling and Arp2/3 cascade. These results reveal a novel mechanism and provide a promising preventive and therapeutic molecular target for vulnerability to chronic pain-related memory impairment.

A thalamic perspective of (un)consciousness in pharmacological and pathological states in humans
Currently, there is substantial ongoing discussion around the functional role of the thalamus in consciousness. What is missing in the literature, however, is a systematic investigation of the relevance of specific thalamic nuclei in pharmacologically and pathologically altered states of consciousness in humans. Using functional neuroimaging in both healthy anaesthetised volunteers and patients with disorders of consciousness (DOC), we sought to identify which specific thalamic subregions in both cohorts may be differentially significant for loss of consciousness. Our findings revealed that the pulvinar (Pu) and ventral-latero-ventral (VLV) nuclei, in anaesthesia, and the VLV, in DOC, had distinct functional connectivity patterns related to the default mode and somatomotor networks. Remarkably, among all nuclei, the Pu was found to have the strongest functional connectivity change with anaesthetic-induced loss of consciousness, while in DOC patients, we found the VLV revealed the strongest connectivity change in comparison with healthy controls. Furthermore, we provide evidence that this neural connectivity biomarker in patients also mirrors the changes observed at the behavioural level, which could have clinical implications for targeted deep brain stimulation in therapy for DOC.

Heterogeneous brain region-specific responses to astrocytic mitochondrial DNA damage in mice
Astrocytes use Ca2+ signals to regulate multiple aspects of normal and pathological brain function. Astrocytes display context-specific diversity in their functions, and in their response to noxious stimuli between brain regions. Indeed, astrocytic mitochondria have emerged as key players in governing astrocytic functional heterogeneity, given their ability to dynamically adapt their morphology to regional demands on their ATP generation and Ca2+ buffering functions. Although there is reciprocal regulation between mitochondrial dynamics and mitochondrial Ca2+ signaling in astrocytes, the extent of this regulation into the rich diversity of astrocytes in different brain regions remains largely unexplored. Brain-wide, experimentally induced mitochondrial DNA (mtDNA) loss in astrocytes showed that mtDNA integrity is critical for proper astrocyte function, however, few insights into possible diverse responses to this noxious stimulus from astrocytes in different brain areas were reported in these experiments. To selectively damage mtDNA in astrocytes in a brain-region-specific manner, we developed a novel adeno-associated virus (AAV)-based tool, Mito-PstI, which expresses the restriction enzyme PstI, specifically in astrocytic mitochondria. Here, we applied Mito-PstI to two distinct brain regions, the dorsolateral striatum, and the hippocampal dentate gyrus, and we show that Mito-PstI can induce astrocytic mtDNA loss in vivo, but with remarkable brain-region-dependent differences on mitochondrial dynamics, spontaneous Ca2+ fluxes and astrocytic as well as microglial reactivity. Thus, AAV-Mito-PstI is a novel tool to explore the relationship between astrocytic mitochondrial network dynamics and astrocytic mitochondrial Ca2+ signaling in a brain-region-selective manner.

AxoDen: An Algorithm for the Automated Quantification of Axonal Density in defined Brain Regions
The rodent brain contains 70,000,000+ neurons interconnected via complex axonal circuits with varying architectures. Neural pathologies are often associated with anatomical changes in these axonal projections and synaptic connections. Notably, axonal density variations of local and long-range projections increase or decrease as a function of the strengthening or weakening, respectively, of the information flow between brain regions. Traditionally, histological quantification of axonal inputs relied on assessing the mean fluorescence intensity within a rectangle placed in the brain region-of-interest. Despite yielding valuable insights, this conventional method is notably susceptible to background fluorescence, post-acquisition adjustments, and inter-researcher variability. Additionally, it fails to account for the non-uniform innervation across brain regions, thus overlooking critical data such as innervation percentages and axonal distribution patterns. In response to these chal-lenges, we introduce AxoDen, an open-source semi-automated platform designed to increase the speed and rigor of axon quantifications for basic neuroscience discovery. AxoDen processes user-defined brain regions-of-interests incorporating dynamic thresholding of grayscales-transformed images to facilitate binarized pixel measurements. Thereby AxoDen segregates the image content into signal and non-signal categories, effectively eliminating background interference and enabling the exclusive measurement of fluorescence from axonal projections. AxoDen provides detailed and accurate representations of axonal density and spatial distribution. AxoDen's advanced yet user-friendly platform enhances the reliability and efficiency of axonal density analysis and facilitates access to unbiased high-quality data analysis with no technical background or coding experience required. AxoDen is freely available to everyone as a valuable neuroscience tool for dissecting axonal innervation patterns in precisely defined brain regions.

Rapid changes in plasma corticosterone and medial amygdala transcriptome profiles during social status change reveal molecular pathways associated with a major life history transition in mouse dominance hierarchies
Social hierarchies are a common form of social organization across species. Although hierarchies are largely stable across time, animals may socially ascend or descend within hierarchies depending on environmental and social challenges. Here, we develop a novel paradigm to study social ascent and descent within male CD-1 mouse social hierarchies. We show that mice of all social ranks rapidly establish new stable social hierarchies when placed in novel social groups with animals of equivalent social status. Seventy minutes following social hierarchy formation, previously socially dominant animals exhibit higher increases in plasma corticosterone and vastly greater transcriptional changes in the medial amygdala (MeA), which is central to the regulation of social behavior, compared to previously subordinate animals. Specifically, social descent is associated with reductions in MeA expression of myelination and oligodendrocyte differentiation genes. Maintaining high social status is associated with high expression of genes related to cholinergic signaling in the MeA. Conversely, social ascent is related to relatively few unique rapid changes in the MeA. We also identify novel genes associated with social transition that show common changes in expression when animals undergo either social descent or social ascent compared to maintaining their status. Two genes, Myosin binding protein C1 (Mybpc1) and -Crystallin (Crym), associated with vasoactive intestinal polypeptide (VIP) and thyroid hormone pathways respectively, are highly upregulated in socially transitioning individuals. Further, increases in genes associated with synaptic plasticity, excitatory glutamatergic signaling and learning and memory pathways were observed in transitioning animals suggesting that adaptations in these processes may support rapid social status changes.

Self-Supervised Grid Cells Without Path Integration
Grid cells, found in the medial Entorhinal Cortex, are known for their regular spatial firing patterns. These cells have been proposed as the neural solution to a range of computational tasks, from performing path integration, to serving as a metric for space. Their exact function, however, remains fiercely debated. In this work, we explore the consequences of demanding distance preservation over small spatial scales in networks subject to a capacity constraint. We consider two distinct self-supervised models, a feedforward network that learns to solve a purely spatial encoding task, and a recurrent network that solves the same problem during path integration. Surprisingly, we find that this task leads to the emergence of highly grid cell-like representations in both networks. However, the recurrent network also features units with band-like representations. We subsequently prune velocity inputs to subsets of recurrent units, and find that their grid score is negatively correlated with path integration contribution. Thus, grid cells emerge without path integration in the feedforward network, and they appear substantially less important than band cells for path integration in the recurrent network. Our work provides a minimal model for learning grid-like spatial representations, and questions the role of grid cells as neural path integrators. Instead, it seems that distance preservation and high population capacity is a more likely candidate task for learning grid cells in artificial neural networks.

Dynamics of EEG Microstates Change Across the Spectrum of Disorders of Consciousness
As a response to the environment and internal signals, brain networks reorganize on a sub-second scale. To capture this reorganization in patients with disorders of consciousness and understand their residual brain activity, we investigated the dynamics of electroencephalography (EEG) microstates. We analyze EEG microstate markers to quantify the periods of semi-stable topographies and the large-scale cortical networks they may reflect. To achieve this, EEG samples are clustered into four groups and then fit back into each time sample. We then obtain a time series of maps with different frequencies of occurrence and duration. One such occurrence of a map with a given duration is called a microstate. The goal of this work is to study the dynamics of these topographical patterns across patients with disorders of consciousness. Using the microstate time series, we calculate static and dynamic markers. In contrast to the static, the dynamic metrics depend on the specific temporal sequences of the maps. The static measure Ratio of Total Time covered (RTT) shows differences between healthy controls and patients, however, no differences were observed between the groups of patients. In contrast, some dynamic markers capture inter-patient group differences. The dynamic markers we investigated are Mean Microstate Durations (MMD), Microstate Duration Variances (MDV), Microstate Transition Matrices (MTM), and Entropy Production (EP). The MMD and MDV decrease with the state of consciousness, whereas the MTM non-diagonal transitions and EP increase. In other words, DoC patients have slower and closer to equilibrium (time-reversible) brain dynamics. In conclusion, static and dynamic EEG microstate metrics differ across consciousness levels, with the latter capturing the subtitler differences between groups of patients with disorders of consciousness.

Unveiling the hidden electroencephalographical rhythms during development: aperiodic and periodic activity in healthy subjects
Objective: The study analyzes power spectral density (PSD) components, aperiodic (AP) and periodic (P) activity, in resting-state EEG of 240 healthy subjects from 6 to 29 years old, divided into 4 groups. Methods: We calculate AP and P components using the (Fitting Oscillations and One-Over-f (FOOOF)) plugging in EEGLAB. All PSD components were calculated from 1-45Hz. Topography analysis, Spearman correlations, and regression analysis with age were computed for all the components. Results: AP and P activity show different topography across frequencies and age groups. Age-related decreases in AP exponent and offset parameters lead to reduced power, while P power decreases (1-6Hz) and increases (10-15Hz) with age. Conclusions: We support the distinction between the AP and P components of the PSD and its possible functional changes with age. AP power is dominant in the configuration of the canonical EEG rhythms topography, although P contribution to topography is embedded in the canonical EEG topography. Some EEG canonical characteristics are similar to those of P component, as topographies of EEG rhythms (embedded) and increases in oscillatory frequency with age. Significance: We support that spectral power parameterization improves the interpretation and neurophysiological and functional accuracy of brain processes.

Energy Deficit is a Key Driver of Sleep Homeostasis
Sleep and feeding are vital homeostatic behaviors, and disruptions in either can result in substantial metabolic consequences. The precise interplay between these fundamental behaviors remains incompletely understood. Here, we investigate concomitant changes in feeding and sleep behaviors that accompany various types of sleep loss. To understand the circuitry involved in sleep-feeding interactions, we measured changes in sleep and food intake in individual animals, as well as respiratory metabolic expenditure, that accompany behavioral and genetic manipulations that induce sleep loss in Drosophila melanogaster. We found that sleep disruptions resulting in energy deficit through increased metabolic expenditure and manifested as increased food intake were consistently followed by rebound sleep. In contrast, "soft" sleep loss, which does not induce rebound sleep, was not accompanied by increased metabolism and food intake. Our results support the notion that sleep functions to conserve energy and that sleep debt is linked to energy debt. These findings highlight the potential for novel therapeutic approaches to treating sleep or metabolic disorders through the manipulation of the other.

Visual detection of seizures in mice using supervised machine learning
Seizures are caused by abnormally synchronous brain activity that can result in changes in muscle tone, such as twitching, stiffness, limpness, or rhythmic jerking. These behavioral manifestations are clear on visual inspection and the most widely used seizure scoring systems in preclinical models, such as the Racine scale in rodents, use these behavioral patterns in semiquantitative seizure intensity scores. However, visual inspection is time-consuming, low-throughput, and partially subjective, and there is a need for rigorously quantitative approaches that are scalable. In this study, we used supervised machine learning approaches to develop automated classifiers to predict seizure severity directly from noninvasive video data. Using the PTZ-induced seizure model in mice, we trained video-only classifiers to predict ictal events, combined these events to predict an univariate seizure intensity for a recording session, as well as time-varying seizure intensity scores. Our results show, for the first time, that seizure events and overall intensity can be rigorously quantified directly from overhead video of mice in a standard open field using supervised approaches. These results enable high-throughput, noninvasive, and standardized seizure scoring for downstream applications such as neurogenetics and therapeutic discovery.

Spatial selective auditory attention is preserved in older age but is degraded by peripheral hearing loss
Interest in how ageing affects attention is long-standing, although interactions between sensory and attentional processing in older age have not been systematically studied. Here, we examined interactions between peripheral hearing and selective attention in a spatialised cocktail party listening paradigm, in which three talkers spoke different sentences simultaneously and participants were asked to report the sentence spoken by a talker at a particular location. By comparing a sample of older (N = 61; age = 55-80 years) and younger (N = 58; age = 18-35 years) adults, we show that, as a group, older adults benefit as much as younger adults from preparatory spatial attention. Although, for older adults, this benefit significantly reduces with greater age-related hearing loss. These results demonstrate that older adults without hearing loss retain the ability to direct spatial selective attention, but this ability deteriorates with age-related hearing loss. Thus, reductions in spatial selective attention likely contribute to difficulties communicating in social settings for older adults with age-related hearing loss. Overall, these findings demonstrate a relationship between mild perceptual decline and attention in older age.

Molecular and circuit determinants in the globus pallidus mediating control of cocaine-induced behavioral plasticity
The globus pallidus externus (GPe) is a central component of the basal ganglia circuit, receiving strong input from the indirect pathway and regulating a variety of functions, including locomotor output and habit formation. We recently showed that it also acts as a gatekeeper of cocaine-induced behavioral plasticity, as inhibition of parvalbumin-positive cells in the GPe (GPePV) prevents the development of cocaine-induced reward and sensitization. However, the molecular and circuit mechanisms underlying this function are unknown. Here we show that GPePV cells control cocaine reward and sensitization by inhibiting GABAergic neurons in the substantia nigra pars reticulata (SNrGABA), and ultimately, selectively modulating the activity of ventral tegmental area dopamine (VTADA) cells projecting to the lateral shell of the nucleus accumbens (NAcLat). A major input to GPePV cells is the indirect pathway of the dorsomedial striatum (DMSD2), which receives DAergic innervation from collaterals of VTADA-NAcLat cells, making this a closed-loop circuit. Cocaine likely facilitates reward and sensitization not directly through actions in the GPe, but rather in the upstream DMS, where the cocaine-induced elevation of DA triggers a depression in DMSD2 cell activity. This cocaine-induced elevation in DA levels can be blocked by inhibition of GPePV cells, closing the loop. Interestingly, the level of GPePV cell activity prior to cocaine administration is correlated with the extent of reward and sensitization that animals experience in response to future administration of cocaine, indicating that GPePV cell activity is a key predictor of future behavioral responses to cocaine. Single nucleus RNA-sequencing of GPe cells indicated that genes encoding voltage-gated potassium channels KCNQ3 and KCNQ5 that control intrinsic cellular excitability are downregulated in GPePV cells following a single cocaine exposure, contributing to the elevation in GPePV cell excitability. Acutely activating channels containing KCNQ3 and/or KCNQ5 using the small molecule carnosic acid, a key psychoactive component of Salvia rosmarinus (rosemary) extract, reduced GPePV cell excitability and also impaired cocaine reward, sensitization, and volitional cocaine intake, indicating its potential as a therapeutic to counteract psychostimulant use disorder. Our findings illuminate the molecular and circuit mechanisms by which the GPe orchestrates brain-wide changes in response to cocaine that are required for reward, sensitization, and self-administration behaviors.

Cerebellar tonic inhibition orchestrates the maturation of information processing and motor coordination
Tonic inhibition in cerebellar granule cells (GC) is crucial in information coding fidelity for motor coordination. It emerges in activity-dependent and -independent manners, and their interplay evolves with age. However, specific molecular and cellular mechanisms and how their change affects network-level computation and motor behavior remain unclear. Here we show that, while net tonic inhibitory current remains unchanged, the main source of tonic GABA switches from synaptic spillover (neuronal activity-dependent) to astrocytic Best1 (activity-independent) throughout adolescence (4-8 weeks) in mice. Computational modeling based on experimental data demonstrated that this switch down-regulates the internally generated network activity mediating mutual inhibition between GC clusters receiving different inputs, thereby enhancing their independence. Consistent with simulations, 3D-posture analysis revealed an age-dependent increase in independent limb movements during spontaneous motion, which was impaired in Best1 knockout mice. Our findings highlight the late-stage development of complex motor coordination driven by the emergence of astrocyte-mediated tonic inhibition.

CCR2 silencing in sensory neurons blocks bone cancer progression
The peripheral nervous system has been shown to contribute to cancer growth by expanding the immunological niche. How the nervous system affects bone cancer progression and how neuroimmune pathways can be targeted for cancer treatment are not yet clear. Here, we demonstrate a profound influence of the peripheral nervous system on tumor progression, which can be targeted by silencing neuronal chemokine receptor signaling. We show that axotomy in animals with bone cancer inhibits tumor progression. Conversely, intrathecal injection of a known tumor-associated proinflammatory chemokine, CCL2, promotes tumor growth and allodynia. Silencing CCR2 in DRG neurons through a newly developed gene therapy successfully impedes tumor progression and bone remodeling and relieves bone cancer-associated pain. We demonstrate that the mechanism underlying CCR2-mediated tumor progression involves decreased neuropeptide secretion by peripheral nerves that promote expansion of the tumor-associated macrophage population. Silencing the CCR2 receptor in DRG neurons successfully normalizes the neuropeptide milieu and ameliorates altered bone remodeling. Thus, we have developed a novel therapeutic pathway for targeting a neuroimmune axis that contributes to cancer progression.

The subcortical basis of subjective sleep quality
Study objectives: To assess the association between self-reported sleep quality and cortical and subcortical local morphometry. Methods: Sleep and neuroanatomical data from the full release of the young adult Human Connectome Project dataset were analyzed. Sleep quality was operationalized with the Pittsburgh Sleep Quality Index (PSQI). Local cortical and subcortical morphometry was measured with subject-specific segmentations resulting in voxelwise thickness measurements for cortex and relative (i.e., cross-sectional) local atrophy measurements for subcortical regions. Results: Relative atrophy across several subcortical regions, including bilateral pallidum, striatum, and thalamus, was negatively associated with both global PSQI score and sub-components of the index related to sleep duration, efficiency, and quality. Conversely, we found no association between cortical morphometric measurements and self-reported sleep quality. Conclusions: This work shows that subcortical regions such as the bilateral pallidum, thalamus, and striatum, might be interventional targets to ameliorate self-reported sleep quality.

Ex Vivo Cortical Circuits Learn to Predict and Spontaneously Replay Temporal Patterns
It has been proposed that prediction and timing are computational primitives of neocortical microcircuits, specifically, that neural mechanisms are in place to allow neocortical circuits to autonomously learn the temporal structure of external stimuli and generate internal predictions. To test this hypothesis, we trained cortical organotypic slices on two specific temporal patterns using dual-optical stimulation. After 24-hours of training, whole-cell recordings revealed network dynamics consistent with training-specific timed prediction. Unexpectedly, there was replay of the learned temporal structure during spontaneous activity. Furthermore, some neurons exhibited timed prediction errors. Mechanistically our results indicate that learning relied in part on asymmetric connectivity between distinct neuronal ensembles with temporally-ordered activation. These findings further suggest that local cortical microcircuits are intrinsically capable of learning temporal information and generating predictions, and that the learning rules underlying temporal learning and spontaneous replay can be intrinsic to local cortical microcircuits and not necessarily dependent on top-down interactions.

Reduced temporal and spatial stability of neural activity patterns predict cognitive control deficits in children with ADHD
This study explores the neural underpinnings of cognitive control deficits in ADHD, focusing on overlooked aspects of trial-level variability of neural coding. We employed a novel computational approach to neural decoding on a single-trial basis alongside a cued stop-signal task which allowed us to distinctly probe both proactive and reactive cognitive control. Typically developing (TD) children exhibited stable neural response patterns for efficient proactive and reactive dual control mechanisms. However, neural coding was compromised in children with ADHD. Children with ADHD showed increased temporal variability and diminished spatial stability in neural responses in salience and frontal-parietal network regions, indicating disrupted neural coding during both proactive and reactive control. Moreover, this variability correlated with fluctuating task performance and with more severe symptoms of ADHD. These findings underscore the significance of modeling single-trial variability and representational similarity in understanding distinct components of cognitive control in ADHD, highlighting new perspectives on neurocognitive dysfunction in psychiatric disorders.

On the influence of the vascular architecture on Gradient Echo and Spin Echo BOLD fMRI signals across cortical depth: a simulation approach based on realistic 3D vascular networks
GE-BOLD contrast stands out as the predominant technique in functional MRI experiments for its high sensitivity and straightforward implementation. GE-BOLD exhibits rather similar sensitivity to vessels independent of their size at submillimeter resolution studies like those examining cortical columns and laminae. However, the presence of nonspecific macrovascular contributions poses a challenge to accurately isolate neuronal activity. SE-BOLD increases specificity towards small vessels, thereby enhancing its specificity to neuronal activity, due to the effective suppression of extravascular contributions caused by macrovessels with its refocusing pulse. However, even SE-BOLD measurements may not completely remove these macrovascular contributions. By simulating hemodynamic signals across cortical depth, we gain insights into vascular contributions to the laminar BOLD signal. In this study, we employed four realistic 3D vascular models to simulate oxygen saturation states in various vascular compartments, aiming to characterize both intravascular and extravascular contributions to GE and SE signals, and corresponding BOLD signal changes, across cortical depth at 7T. Simulations suggest that SE-BOLD cannot completely reduce the macrovascular contribution near the pial surface. Simulations also show that both the specificity and signal amplitude of BOLD signals at 7T depend on the spatial arrangement of large vessels throughout cortical depth and on the pial surface.

Not so griddy: Internal representations of RNNs path integrating more than one agent
Success in collaborative and competitive environments, where agents must work with or against each other, requires individuals to encode the position and trajectory of themselves and others. Decades of neurophysiological experiments have shed light on how brain regions [e.g., medial entorhinal cortex (MEC), hippocampus] encode the self's position and trajectory. However, it has only recently been discovered that MEC and hippocampus are modulated by the positions and trajectories of others. To understand how encoding spatial information of multiple agents shapes neural representations, we train a recurrent neural network (RNN) model that captures properties of MEC to path integrate trajectories of two agents simultaneously navigating the same environment. We find significant differences between these RNNs and those trained to path integrate only a single agent. At the individual unit level, RNNs trained to path integrate more than one agent develop weaker grid responses, stronger border responses, and tuning for the relative position of the two agents. At the population level, they develop more distributed and robust representations, with changes in network dynamics and manifold topology. Our results provide testable predictions and open new directions with which to study the neural computations supporting spatial navigation.

Multimodal Predictive Modeling: Scalable Imaging Informed Approaches to Predict Future Brain Health
Background Predicting future brain health is a complex endeavor that often requires integrating diverse data sources. The neural patterns and interactions identified through neuroimaging serve as the fundamental basis and early indicators that precede the manifestation of observable behaviors or psychological states. New Method In this work, we introduce a multimodal predictive modeling approach that leverages an imaging-informed methodology to gain insights into future behavioral outcomes. We employed three methodologies for evaluation: an assessment-only approach using support vector regression (SVR), a neuroimaging-only approach using random forest (RF), and an image-assisted method integrating the static functional network connectivity (sFNC) matrix from resting-state functional magnetic resonance imaging (rs-fMRI) alongside assessments. The image-assisted approach utilized a partially conditional variational autoencoder (PCVAE) to predict brain health constructs in future visits from the behavioral data alone. Results Our performance evaluation indicates that the image-assisted method excels in handling conditional information to predict brain health constructs in subsequent visits and their longitudinal changes. These results suggest that during the training stage, the PCVAE model effectively captures relevant information from neuroimaging data, thereby potentially improving accuracy in making future predictions using only assessment data. Comparison with Existing Methods The proposed image-assisted method outperforms traditional assessment-only and neuroimaging-only approaches by effectively integrating neuroimaging data with assessment factors. Conclusion This study underscores the potential of neuroimaging-informed predictive modeling to advance our comprehension of the complex relationships between cognitive performance and neural connectivity.

Multiple mechanisms of aminoglycoside ototoxicity are distinguished by subcellular localization of action
Mechanosensory hair cells of the inner ear are vulnerable to environmental insult, with damage resulting in hearing and balance disorders. Hair cells are sensitive to exposure to toxic agents including therapeutic drugs such as the aminoglycoside antibiotics neomycin and gentamicin. Here we describe distinct mechanisms for aminoglycoside-induced cell death in zebrafish lateral line hair cells. Neomycin exposure results in death from acute exposure with most cells dying within 1 hour of exposure. By contrast exposure to gentamicin results in delayed death, taking up to 24 hours for maximal effect. Washout experiments demonstrate that delayed death does not require continuous exposure, demonstrating two mechanisms where downstream responses differ in their timing. Acute damage is associated with mitochondrial calcium fluxes and can be alleviated by the mitochondrially-targeted antioxidant mitoTEMPO, while delayed death is independent of these factors. Conversely delayed death is associated with lysosomal accumulation and is reduced by altering endolysosomal function, while acute death is not sensitive to these manipulations. These experiments reveal the complexity of responses of hair cells to closely related compounds, suggesting that intervention focusing on early events rather than specific death pathways may be a successful therapeutic strategy.

Theoretical proposal for restoration of hand motor function utilizing plasticity of motor-cortical interhemispheric interaction
After stroke, the poorer recovery of motor function of upper extremities compared to other body parts is a longstanding problem. Based on our recent functional MRI evidence on healthy volunteers, this perspective paper proposes systematic hand motor rehabilitation utilizing the plasticity of interhemispheric interaction between motor cortices and following its developmental rule. We first discuss the effectiveness of proprioceptive intervention on the paralyzed (immobile) hand synchronized with voluntary movement of the intact hand to induce muscle activity in the paretic hand. In healthy participants, we show that this bilateral proprioceptive-motor coupling intervention activates the bilateral motor cortices (= bilaterally active mode), facilitates interhemispheric motor-cortical functional connectivity, and augments muscle activity of the passively-moved hand. Next, we propose training both hands to perform different movements, which would be effective for stroke patients who become able to manage to move the paretic hand. This bilaterally different movement training may guide the motor cortices into left-right independent mode to improve interhemispheric inhibition and hand dexterity, because we have shown in healthy older adults that this training reactivates motor-cortical interhemispheric inhibition (= left-right independent mode) declined with age, and can improve hand dexterity. Transition of both motor cortices from the bilaterally active mode to the left-right independent mode is a developmental rule of hand motor function and a common feature of motor function recovery after stroke. Hence, incorporating the brain's inherent capacity for spontaneous recovery and adhering to developmental principles may be crucial considerations in designing effective rehabilitation strategies.

Predicting Suicide Risk in Bipolar Disorder patients from Lymphoblastoid Cell Lines genetic signatures
This research investigates the genetic signatures associated with a high risk of suicide in Bipolar disorder (BD) patients through RNA sequencing analysis of lymphoblastoid cell lines (LCLs). By identifying differentially expressed genes (DEGs) and their enrichment in pathways and disease associations, we uncover insights into the molecular mechanisms underlying suicidal behavior. LCL gene expression analysis reveals significant enrichment in pathways related to primary immunodeficiency, ion channel, and cardiovascular defects. Notably, genes such as LCK, KCNN2, and GRIA1 emerged as pivotal in these pathways, suggesting their potential roles as biomarkers. Machine learning models trained on a subset of the patients and then tested on other patients demonstrate high accuracy in distinguishing low and high-risk of suicide in BD patients. Moreover, the study explores the genetic overlap between suicide-related genes and several psychiatric disorders. This comprehensive approach enhances our understanding of the complex interplay between genetics and suicidal behavior, laying the groundwork for future prevention strategies.

DENOISING: Dynamic Enhancement and Noise Overcoming in Multimodal Neural Observations via High-density CMOS-based Biosensors
Large-scale multimodal neural recordings on high-density biosensing microelectrode arrays (HD-MEAs) offer unprecedented insights into the dynamic interactions and connectivity across various brain networks. However, the fidelity of these recordings is frequently compromised by pervasive noise, which obscures meaningful neural information and complicates data analysis. To address this challenge, we introduce DENOISING, a versatile data-derived computational engine engineered to adjust thresholds adaptively based on large-scale extracellular signal characteristics and noise levels. This facilitates the separation of signal and noise components without reliance on specific data transformations. Uniquely capable of handling a diverse array of noise types (electrical, mechanical, and environmental) and multidimensional neural signals, including stationary and non-stationary oscillatory local field potential (LFP) and spiking activity, DENOISING presents an adaptable solution applicable across different recording modalities and brain networks. Applying DENOISING to large-scale neural recordings from mice hippocampal and olfactory bulb networks yielded enhanced signal-to-noise ratio (SNR) of LFP and spike firing patterns compared to those computed from raw data. Comparative analysis with existing state-of-the-art denoising methods, employing SNR and root mean square noise (RMS), underscores DENOISING's performance in improving data quality and reliability. Through experimental and computational approaches, we validate that DENOISING improves signal clarity and data interpretation by effectively mitigating independent noise in spatiotemporally structured multimodal datasets, thus unlocking new dimensions in understanding neural connectivity and functional dynamics.

Bifurcation enhances temporal information encoding in the olfactory periphery
Living systems continually respond to signals from the surrounding environment. Survival requires that their responses adapt quickly and robustly to the changes in the environment. One particularly challenging example is olfactory navigation in turbulent plumes, where animals experience highly intermittent odor signals while odor concentration varies over many length- and timescales. Here, we show theoretically that Drosophila olfactory receptor neurons (ORNs) can exploit proximity to a bifurcation point of their firing dynamics to reliably extract information about the timing and intensity of fluctuations in the odor signal, which have been shown to be critical for odor-guided navigation. Close to the bifurcation, the system is intrinsically invariant to signal variance, and information about the timing, duration, and intensity of odor fluctuations is transferred efficiently. Importantly, we find that proximity to the bifurcation is maintained by mean adaptation alone and therefore does not require any additional feedback mechanism or fine-tuning. Using a biophysical model with calcium-based feedback, we demonstrate that this mechanism can explain the measured adaptation characteristics of Drosophila ORNs.

Dysregulation of synaptic transcripts underlies network abnormalities in ALS patient-derived motor neurons
Amyotrophic lateral sclerosis (ALS) is characterized by dysfunction and loss of upper and lower motor neurons. Several studies have identified structural and functional alterations in the motor neurons before the manifestation of symptoms, yet the underlying cause of such alterations and how they contribute to the progressive degeneration of affected motor neuron networks remain unclear. Importantly, the short and long-term spatiotemporal dynamics of neuronal network activity make it challenging to discern how ALS-related network reconfigurations emerge and evolve. To address this, we systematically monitored the structural and functional dynamics of motor neuron networks with a confirmed endogenous C9orf72 mutation. We show that ALS patient-derived motor neurons display time-dependent neural network dysfunction, specifically reduced firing rate and spike amplitude, impaired bursting, but higher overall synchrony in network activity. These changes coincided with altered neurite outgrowth and branching within the networks. Moreover, transcriptional analyses revealed dysregulation of molecular pathways involved in synaptic development and maintenance, neurite outgrowth and cell adhesion, suggesting impaired synaptic stabilization. This study identifies early synaptic dysfunction as a contributing mechanism resulting in network-wide structural and functional compensation, which may over time render the networks vulnerable to neurodegeneration.

Excitation/Inhibition imbalance impairs multisensory causal inference by increasing the proneness to experience the sound-induced flash illusion in the schizophrenia spectrum
The spectrum of schizophrenia is characterised by an altered sense of self with known impairments in tactile sensitivity, proprioception, body-self boundaries, and self-recognition. These are thought to be produced by failures in multisensory integration mechanisms, commonly observed as enlarged temporal binding windows during audiovisual illusion tasks. To our knowledge, there is an absence of computational explanations for multisensory integration deficits in patients with schizophrenia and individuals with high schizotypy, particularly at the neurobiological level. We implemented a multisensory causal inference network to reproduce the responses of individuals who scored low in schizotypy in a simulated double flash illusion task. Next, we explored the effects of Excitation/Inhibition imbalance, feedback weights, and synaptic density on the visual illusory responses of the network. Using quantitative fitting to empirical data, we found that an increase in recurrent excitation or cross-modal connectivity in the network enlarges the temporal binding window and increases the overall proneness to experience the illusion, matching the responses of individuals scoring high in schizotypy. Moreover, we found that an increase in the E/I balance by either neural mechanism increases the probability of inferring a common cause from the stimuli. We propose an E/I imbalance account of reduced temporal discrimination in the SCZ spectrum and discuss possible links with Bayesian theories of schizophrenia. We highlight the importance of adopting a multisensory causal inference perspective to address body-related symptomatology of schizophrenia.

Genetic ablation of GABAB receptors from oligodendrocyte precursor cells protects against demyelination in the mouse spinal cord
GABAergic signaling and GABAB receptors play crucial roles in regulating the physiology of oligodendrocyte-lineage cells, including their proliferation, differentiation, and myelination. Therefore, they are promising targets for studying how spinal oligodendrocyte precursor cells (OPCs) respond to injuries and neurodegenerative diseases like multiple sclerosis. Taking advantage of the temporally controlled and cell-specific genetic removal of GABAB receptors from OPCs, our investigation addresses their specific influence on OPC behavior in the gray and white matter of the mouse spinal cord. Our results show that while GABAB receptors do not significantly alter OPC cell proliferation and differentiation under physiological conditions, they distinctly regulate the Ca2+ signaling of OPCs. In addition, we investigate the impact of OPC-GABAB receptors in two models of toxic demyelination, namely the cuprizone and the lysolecithin models. The genetic removal of OPC-GABAB receptors protects against demyelination and oligodendrocyte loss. Additionally, we observe enhanced resilience to cuprizone-induced pathological alterations in OPC Ca2+ signaling. Our results provide valuable insights into the potential therapeutic implications of manipulating GABAB receptors in spinal cord OPCs and deepen our understanding of the interplay between GABAergic signaling and spinal cord OPCs, providing a basis for future research.

Overlapping role of synaptophysin and synaptogyrin family proteins in determining the small size of synaptic vesicles
Members of the synaptophysin and synaptogyrin family are vesicle proteins with four transmembrane domains. In spite of their abundance in synaptic vesicle (SV) membranes, their role remains elusive and only mild defects at the cellular and organismal level are observed in mice lacking one or more family members. Here, we show that co-expression with synapsin of each of the four brain-enriched members of this family - synaptophysin, synaptoporin, synaptogyrin1 and synaptogyrin3 - in fibroblasts is sufficient to generate clusters of small vesicles in the same size range of SVs. Moreover, mice lacking all these four proteins have larger SVs. We conclude that synaptophysin and synaptogyrin family proteins play an overlapping function in the biogenesis of SVs and in determining their small size.

Gastrin Releasing Peptide Signaling in the Nucleus Accumbens Medial Shell Regulates Neuronal Excitability and Motivation
Neuropeptides are the largest class of neuromodulators, which can be co-released together with classical neurotransmitters. It has been shown that subpopulations of dopamine neurons express mRNA for the neuropeptide Gastrin-releasing peptide (GRP); however, its functional relevance in dopaminergic circuits is unknown. Here, we find that the GRP receptor (GRPR) is present in the nucleus accumbens medial shell (NAc MSh), which is targeted by GRP-expressing midbrain dopamine neurons as well as glutamatergic inputs from the hippocampus and amygdala. We show that the NAc MSh GRPR-positive cells are a subpopulation of D2 receptor-expressing neurons, which have high intrinsic excitability and can be activated by GRP in vivo. Selective deletion of Grpr from the NAc MSh increases motivation in a progressive ratio test, indicating a role for GRPR in motivated behaviors. These experiments establish GRP/GRPR signaling as a novel regulator of mesolimbic circuits and advance our understanding of neuropeptides in the striatum.

The diversity of SNCA transcripts in neurons, and its impact on antisense oligonucleotide therapeutics
The role of the SNCA gene locus in driving Parkinsons disease (PD) through rare and common genetic variation is well-recognized, but the transcriptional diversity of SNCA in vulnerable cell types remains unclear. We performed SNCA long-read RNA sequencing in human dopaminergic neurons and show that annotated SNCA transcripts account for only 5% of expression. Rather, the majority of expression (75%) at the SNCA locus originates from transcripts with alternative 5 and 3 untranslated regions. Importantly, 10% originates from transcripts encoding open reading frames not previously annotated, which are translated and detectable in human postmortem brain. Defining the 3 untranslated regions enabled the rational design of antisense oligonucleotides targeting the majority of SNCA transcripts, leading to the effective reversal of PD pathology, including protein aggregation, mitochondrial dysfunction, and toxicity. Resolving the complexity of the SNCA transcriptional landscape impacts RNA therapies and highlights differences in protein isoforms and their contribution to disease.

Periodic ER-plasma membrane junctions support long-range Ca2+ signal integration in dendrites
Neuronal dendrites must relay synaptic inputs over long distances, but the mechanisms by which activity-evoked intracellular signals propagate over macroscopic distances remain unclear. Here, we discovered a system of periodically arranged endoplasmic reticulum-plasma membrane (ER-PM) junctions tiling the plasma membrane of dendrites at ~1 m intervals, interlinked by a meshwork of ER tubules patterned in a ladder-like array. Populated with Junctophilin-linked plasma membrane voltage-gated Ca2+ channels and ER Ca2+-release channels (ryanodine receptors), ER-PM junctions are hubs for ER-PM crosstalk, fine-tuning of Ca2+ homeostasis, and local activation of the Ca2+/calmodulin-dependent protein kinase II. Local spine stimulation activates the Ca2+ modulatory machinery facilitating voltage-independent signal transmission and ryanodine receptor-dependent Ca2+ release at ER-PM junctions over 20 m away. Thus, interconnected ER-PM junctions support signal propagation and Ca2+ release from the spine-adjacent ER. The capacity of this subcellular architecture to modify both local and distant membrane-proximal biochemistry potentially contributes to dendritic computations.

Location- and feature-based selection histories make independent, qualitatively distinct contributions to urgent visuomotor performance
Attention mechanisms that guide visuomotor behaviors are classified into three broad types according to their reliance on stimulus salience, current goals, and selection histories (i.e., recent experience with events of many sorts). These forms of attentional control are clearly distinct and multifaceted, but what is largely unresolved is how they interact dynamically to determine impending visuomotor choices. To investigate this, we trained two macaque monkeys to perform an urgent version of an oddball search task in which a red target appears among three green distracters, or vice versa. By imposing urgency, performance can be tracked continuously as it transitions from uninformed guesses to informed choices, and this, in turn, permits assessment of attentional control as a function of time. We found that the probability of making a correct choice was strongly modulated by the histories of preceding target colors and target locations. Crucially, although both effects were gated by success (or reward), the two variables played dynamically distinct roles: whereas location history promoted an early motor bias, color history modulated the later perceptual evaluation. Furthermore, target color and location influenced performance independently of each other. The results show that, when combined, selection histories can give rise to enormous swings in visuomotor performance even in simple tasks with highly discriminable stimuli.

Cognitive Computational Model Reveals Repetition Bias in a Sequential Decision-Making Task
Humans tend to repeat past actions due to rewarding outcomes. Recent computational models propose that the probability of selecting a specific action is also, in part, based on how often this action was selected before, independent of previous outcomes or reward. However, these new models so far lack empirical support. Here, we present evidence of a repetition bias using a novel sequential decision-making task and computational modeling to reveal the influence of choice frequency on human value-based choices. Specifically, we find that value-based decisions can be best explained by concurrent influence of both goal-directed reward seeking and a repetition bias. We also show that participants differ substantially in their repetition bias strength, and relate these measures to task performance. The new task enables a novel way to measure the influence of choice repetition on decision-making. These findings can serve as a basis for further experimental studies on the interplay between rewards and choice history in human decision-making.

Single neuron contributions to the auditory brainstem EEG
The auditory brainstem response (ABR) is an acoustically evoked EEG potential that is an important diagnostic tool for hearing loss, especially in newborns. The ABR originates from the response sequence of auditory brainstem nuclei, and a click-evoked ABR typically shows three positive peaks ('waves') within the first six milliseconds. However, an assignment of the waves of the ABR to specific sources is difficult, and a quantification of contributions to the ABR waves is not available. Here, we exploit the large size and physical separation of the barn owl first-order cochlear nucleus magnocellularis (NM) to estimate single-cell contributions to the ABR. We simultaneously recorded NM neurons' spikes and the EEG, and found that > 5,000 spontaneous single-cell spikes are necessary to isolate a significant spike-triggered average response at the EEG electrode. An average single-neuron contribution to the ABR was predicted by convolving the spike-triggered average with the cell's peri-stimulus time histogram. Amplitudes of predicted contributions of single NM cells typically reached 32.9 nV (mean, range: 2.5 - 162.7 nV), or 0.07% (median, range: 0.01 - 4.0%) of the ABR amplitude. The time of the predicted peak coincided best with the peak of the ABR wave II, and this coincidence was independent of the click sound level. Our results suggest that wave II of the ABR is shaped by a small fraction of NM units.

Ventricle stimulation as a potential gold-standard control stimulation site for transcranial focused ultrasound stimulation
This research investigates whether ventricular-focused ultrasound stimulation (ventricle-FUS) can serve as an effective control in studies using transcranial FUS, a non-invasive technology for brain modulation. FUS has notable potential for therapeutic applications but requires a robust control to accurately assess its effects. We evaluated the effectiveness of ventricle-FUS, as an active, non-cerebrum control for FUS research, comparing it to sham stimulation. We conducted a comprehensive assessment of ventricle-FUS, employing both questionnaires and multiple neuroimaging metrics such as grey matter and white matter volumes, and functional connectivity. Ventricle-FUS did not alter any of these metrics, thereby successfully retaining the auditory, somatosensory, and experiential elements of FUS without affecting brain structure or function. Importantly, participants were unable to distinguish whether they received ventricle-FUS or sham FUS. Our findings confirm that ventricle-FUS establishes it as a reliable control approach in FUS research, crucial for advancing its therapeutic applications

Learning Conjunctive Representations
Hippocampal place cells are known for their spatially selective firing patterns, which has led to the suggestion that they encode an animal's location. However, place cells also respond to contextual cues, such as smell. Furthermore, they have the ability to remap, wherein the firing fields and rates of cells change in response to environmental changes. How place cell responses emerge, and how these representations remap is not fully understood. In this work, we propose a similarity-based objective function that translates proximity in space, to proximity in representation. We show that a neural network trained to minimize the proposed objective learns place-like representations. We also show that the proposed objective is trivially extended to include other sources of information, such as context information, in the same way. When trained to encode multiple contexts, networks learn distinct representations, exhibiting remapping behaviors between contexts. The proposed objective is invariant to orthogonal transformations. Such transformations of the original trained representation (e.g. rotations), therefore yield new representations distinct from the original, without explicit relearning, akin to remapping. Our findings shed new light on the formation and encoding properties of place cells, and also demonstrate an interesting case of representational reuse.

Amplitude entropy captures chimera-like behavior in epileptic seizure dynamics
Epilepsy is a neurological disease characterized by epileptic seizures, which manifest with localized high-synchrony, high-amplitude activity that spreads from an onset zone to the rest of the epileptic network. Chimeras, defined as states of co-occurring synchrony and asynchrony in symmetrically coupled networks are increasingly invoked for characterization of seizures. In particular, chimera-like states have been observed during the transition from a normal (asynchronous) to a seizure (synchronous) network state. However, chimeras in epilepsy have only been investigated with respect to the varying phases of oscillators. We propose a novel method capturing the characteristic pronounced changes in the recorded EEG amplitude during seizures by estimating chimera-like states directly from the signals in a frequency- and time-resolved manner. We test the method on a publicly available intracranial EEG dataset of 16 patients with focal epilepsy. We show that the proposed measure, titled Amplitude Entropy, is sensitive to seizure onset dynamics, demonstrating its significant increases during seizure as compared to before and after seizure. This finding is robust across patients, their seizures, and different frequency bands. In the future, Amplitude Entropy could serve as a tool for seizure detection, but also help to characterize amplitude chimeras in other networked systems with characteristic amplitude dynamics.

Cued probabilistic expectations do not modulate grating-evoked event-related potentials in the visual system
We can rapidly learn recurring patterns that occur within our sensory environments. This knowledge allows us to form expectations about future sensory events. Several influential predictive coding models posit that, when a stimulus matches our expectations, the activity of feature-selective neurons in visual cortex will be suppressed relative to when that stimulus is unexpected. However, after accounting for known critical confounds, there is currently scant evidence for these hypothesised effects from studies recording electrophysiological neural activity. To provide a strong test for expectation effects on stimulus-evoked responses in visual cortex, we performed a probabilistic cueing experiment while recording electroencephalographic (EEG) data. Participants (n=48) learned associations between visual cues and subsequently presented gratings. A given cue predicted the appearance of a certain grating orientation with 10%, 25%, 50%, 75%, or 90% validity. We did not observe any stimulus expectancy effects on grating-evoked event-related potentials. Bayes factors generally favoured the null hypothesis throughout the time-courses of the grating-evoked responses. Multivariate classifiers trained to discriminate between grating orientations also did not substantively differ in their performance across stimulus expectancy conditions. Our null findings provide further evidence against modulations of prediction error signalling by probabilistic expectations as specified in contemporary predictive coding models.

BOLD Contrast Response Characteristics of Aberrant Voxels with Bilateral Visual Population Receptive Fields in Human Albinism
Albinism is an inherited disorder characterized by disrupted melanin production in the eye, and often in the skin and hair. This retinal hypopigmentation is accompanied by pathological decussation of many temporal retinal afferents at the optic chiasm during development, ultimately resulting in partially superimposed representations of opposite visual hemifields in each cortical hemisphere. Within these aberrant regions of hemifield overlap, individual voxels have been shown to have bilateral, dual population receptive fields (pRFs) responding to roughly mirror-image locations across the vertical meridian. Nonetheless, how these two conflicting inputs combine to determine a voxel's response to image contrast is still unknown. To address this, we stimulated the right and left hemifields with separately controlled sinusoidal gratings, each having a variety of contrasts (0, 8, 20, 45, 100%), and extracted voxel-wise BOLD response amplitudes to each contrast combination in visual areas V1-V3. We then compared voxels' responses to each hemifield stimulated individually with conditions when both hemifields were stimulated simultaneously. We hypothesized that simultaneous stimulation of the two pRF components will result in either a suppressive or facilitative interaction. However, we found that BOLD responses to simultaneous stimulation appeared to reflect simple summation of the neural activity from the individual hemifield conditions. This suggests that the superimposed opposite hemifield representations do not interact. Thus, dual pRFs in albinism likely reflect two co-localized, but functionally independent populations of neurons each of which respond to a single hemifield. This finding is commensurate with psychophysical studies which have shown no clear perceptual interaction between opposite visual hemifields in human albinism.

Multiparametric quantitative MRI uncovers putamen microstructural changes in Parkinson's Disease
Parkinson's disease (PD) is a progressive neurodegenerative disorder dominated by motor and non-motor dysfunction. Despite extensive research, the in vivo characterization of PD-related microstructural brain changes remains an ongoing challenge, limiting advancements in diagnostic and therapeutic strategies. The putamen, a critical structure within the basal ganglia, plays a key role in regulating movement and is profoundly affected in early PD-related neurodegeneration. In this study, we collected multiparametric quantitative MRI (qMRI) brain data of PD patients and healthy controls, to investigate microstructural alterations in the putamen in PD. We utilized a gradient analysis technique to analyze the spatial variations of various qMRI parameters, including relaxation rates (R1, R2, R2*), water fraction (WF), susceptibility, magnetization transfer saturation (MTsat), and diffusion metrics (MD, FA). Our findings reveal significant spatial gradients and interhemispheric asymmetries in these biophysical properties along the anterior-posterior axis of the putamen. Notably, PD patients exhibited increased water fraction and altered transverse relaxation rate R2*, particularly in the posterior putamen, correlated with motor symptom laterality. These microstructural changes suggest underlying tissue atrophy or neuroinflammatory processes associated with PD. The study underscores the importance of the posterior putamen as a focal point for PD pathology and highlights the potential of localized gradient analysis in detecting subtle yet clinically significant brain changes. The new qMRI dataset provides valuable insights into PD pathology, potentially aiding in the development of more precise diagnostic tools and targeted therapies.

Upregulation of FasII underlies synergistic neuropathological and behavioral defects in a Drosophila model of myotonic dystrophy
Myotonic dystrophy type 1 (DM1) is a multisystemic disorder that has been extensively studied for decades, yet our understanding of its neuropathological aspect remains rudimentary. In this study, we characterized a novel model of DM1 neuropathology by expressing untranslated expanded CUG repeats at the Drosophila larval neuromuscular junction. In this model, both pre- and postsynaptic expression of CUG repeats participate to induce reduction of synaptic boutons, increase of arbor disassembly and impairment of larval locomotor activity. We found that the expression of CUG repeats caused an upregulation of the cell adhesion molecule, FasII (NCAM1 in mammals), in both the motor neurons and the body wall muscles. Knockdown of fasII was sufficient to rescue bouton numbers and locomotor impairment in this model. Further analyses identified the upregulation of the FasII-C isoform as a major contributor of these phenotypes. Remarkably, overexpressing the FasII-A-PEST+ isoform rescued the synaptic and behavioral defects, likely by outcompeting the upregulated FasII-C. Our study provided the foundation for a basic mechanism of synapse dysregulation in DM1.

Impaired Hippocampal Reactivation Preceding Robust Aβ Deposition in a Model of Alzheimer's Disease
Current therapeutic strategies for Alzheimer's disease (AD) target amyloid-beta (A{beta}) fibrils and high molecular weight protofibrils associated with plaques, but other bioactive species may directly contribute to neural systems failure in AD. Employing hippocampal electrophysiological recordings and dynamic calcium imaging across the sleep-wake cycle in young mice expressing human A{beta} and A{beta} oligomers, we reveal marked impairments of hippocampal function long before amyloid plaques predominate. In slow wave sleep (SWS), A{beta} increased the proportion of hypoactive cells and reduced place-cell reactivation. During awake behavior, A{beta} impaired theta-gamma phase-amplitude coupling (PAC) and drove excessive synchronization of place cell calcium fluctuations with hippocampal theta. Remarkably, the on-line impairment of hippocampal theta-gamma PAC correlated with the SWS impairment of place-cell reactivation. Together, these results identify toxic effects of A{beta} on memory encoding and consolidation processes before robust plaque deposition and support targeting soluble A{beta}-related species to treat and prevent AD.

Human sensorimotor cortex reactivates recent visuomotor experience during awake rest
Previous studies have suggested that awake rest after training is helpful in improving motor performance and memory consolidation in visuomotor learning. Re-emergence of task-related activation patterns during awake rest has been reported, which play a role in memory consolidation or perceptual learning. This study aimed to test whether such reactivation occurs after visuomotor learning in the primary sensorimotor cortex. During fMRI scanning, 42 normal participants learned visuomotor tracking, while a rotational perturbation was introduced between a cursor position and a joystick angle. This visuomotor learning block was interleaved with the control block, during which the participants passively viewed a replay of previously performed cursor movements of their own. Half of the participants used their right hand, and the other half used their left hand to control the joystick. The resting-state scans were measured before and after the visuomotor learning sessions. A multivariate pattern classifier was trained to classify task and control blocks and then tested with resting scans before and after learning. Results revealed a significant increase in the number of volumes classified as the task in the post-learning rest compared with the pre-learning, indicating a re-emergence of task-related activities. Representational similarity analysis also showed a more similar pattern of activity with the task during the post-learning rest period. Furthermore, this effect is specific to the primary sensorimotor cortex contralateral to the hand used and significantly correlated with motor improvement after rest. Our finding revealed the reactivation of task-related patterns in the primary sensorimotor cortex for offline visuomotor learning.

Working memory enhancement using real-time phase-tuned transcranial alternating current stimulation
Background: Prior work has shown that transcranial alternating current stimulation (tACS) of parietooccipital alpha oscillations (8 - 14 Hz) can modulate working memory (WM) performance as a function of the phase lag to endogenous oscillations. However, leveraging this effect using real-time phase-tuned tACS was not feasible so far due to stimulation artifacts. Objectives/Hypothesis: We aimed to develop a system that tracks and adapts the phase lag between tACS and ongoing parietooccipital alpha oscillations in real-time. We hypothesized that such real-time phase-tuned tACS enhances working memory performance, depending on the phase lag. Methods: We developed real-time phase-tuned closed-loop amplitude-modulated tACS (CLAM-tACS) targeting parietooccipital alpha oscillations. CLAM-tACS was applied at six different phase lags relative to ongoing alpha oscillations while participants (N = 21) performed a working memory task. To exclude that behavioral effects of CLAM-tACS were mediated by other factors such as sensory co-stimulation, a second group of participants (N = 25) received equivalent stimulation of the forehead. Results: WM accuracy improved in a phase lag dependent manner (p < 0.05) in the group receiving parietooccipital stimulation, with the strongest enhancement observed at 330 degrees phase lag between tACS and ongoing alpha oscillations (p < 0.01, d = 0.976). Moreover, across participants, modulation of frontoparietal alpha oscillations correlated both in amplitude (p < 0.05) and phase (p < 0.05) with the modulation of WM accuracy. No such effects were observed in the control group receiving frontal stimulation. Conclusions: Our results demonstrate the feasibility and efficacy of real-time phase-tuned CLAM-tACS in modulating both brain activity and behavior, thereby paving the way for further investigation into brain-behavior relationships and the exploration of innovative therapeutic applications.

Real-time optimization to enhance noninvasive cortical excitability assessment in the human dorsolateral prefrontal cortex
Objective: We currently lack a robust noninvasive method to measure prefrontal excitability in humans. Concurrent TMS and EEG in the prefrontal cortex is usually confounded by artifacts. Here we asked if real-time optimization could reduce artifacts and enhance a TMS-EEG measure of left prefrontal excitability. Methods: This closed-loop optimization procedure adjusts left dlPFC TMS coil location, angle, and intensity in real-time based on the EEG response to TMS. Our outcome measure was the left prefrontal early (20-60 ms) and local TMS-evoked potential (EL-TEP). Results: In 18 healthy participants, this optimization of coil angle and brain target significantly reduced artifacts by 63% and, when combined with an increase in intensity, increased EL-TEP magnitude by 75% compared to a non-optimized approach. Conclusions: Real-time optimization of TMS parameters during dlPFC stimulation can enhance the EL-TEP. Significance: Enhancing our ability to measure prefrontal excitability is important for monitoring pathological states and treatment response.

Strong Protection by Bazedoxifene Against Chemically-Induced Ferroptotic Neuronal Death In Vitro and In Vivo
Ferroptosis is a form of regulated cell death characterized by excessive iron-dependent lipid peroxidation. Ferroptosis can be induced in cultured cells by exposure to certain chemicals (e.g., erastin and RSL3). Recently it was shown that protein disulfide isomerase (PDI) is a mediator of chemically-induced ferroptosis and also a target for ferroptosis protection. In this study, we find that bazedoxifene (BAZ), a selective estrogen receptor modulator with reported neuroprotective actions in humans, can inhibit PDI function and also strongly protect against chemically-induced ferroptosis in cultured neuronal cells. We find that BAZ can directly bind to PDI in vitro and in intact neuronal cells, and also can inhibit PDIs catalytic activity. Computational modeling analysis reveals that BAZ forms a hydrogen bond with PDI-His256. Inhibition of PDI by BAZ markedly reduces nNOS and iNOS dimerization and NO accumulation, which have recently been shown to play a crucial role in mediating chemically-induced ferroptosis. In addition, the direct antioxidant activity of BAZ may also partially contribute to its protective effect against chemically-induced ferroptosis. Behavioral analysis shows that mice treated with BAZ are strongly protected against kainic acid-induced memory deficits and hippocampal neuronal damage in vivo. In conclusion, the results of this study demonstrate that BAZ is an inhibitor of PDI and can strongly prevent chemically-induced ferroptosis in hippocampal neurons both in vitro and in vivo. These observations offer a novel, estrogen receptor-independent mechanism for the recently-reported neuroprotective actions of BAZ in humans.

Selective Labeling Meets Semi-Supervised Neuron Segmentation
Semi-supervised learning holds promise for cost-effective neuron segmentation in Electron Microscopy (EM) volumes. This technique fully leverages extensive unlabeled data to regularize supervised training for robust predictions. However, diverse neuronal patterns and limited annotation budgets may lead to distribution mismatch between labeled and unlabeled data, hindering the generalization of semi-supervised models. To address this issue, we propose an improved pipeline for cost-effective neuron segmentation by integrating selective labeling and semi-supervised training. For selective labeling, we present an unsupervised heuristic tailored for labeled dataset selection in EM volumes. Guided by self-supervised learning on local patches, representative sub-volumes comprising spatially associated patches are greedily selected via a coverage-based criterion. Those sub-volumes can effectively reflect unlabeled data distribution within a limited budget. For semi-supervised training, we introduce spatial mixing into neuron segmentation and integrate it within a Siamese architecture. This enhancement allows us to reinforce cross-view consistency through intra- and inter-mixing of labeled and unlabeled datasets. The proposed strategies bridge the distribution gap and encourage the model to learn shared semantics across datasets, promoting more effective semi-supervised learning. Extensive experiments on public datasets consistently demonstrate the effectiveness of the proposed pipeline, providing a practical and efficient solution for large-scale neuron reconstruction. Codes and data will be available.