bioRxiv Subject Collection: Neuroscience's Journal

The preoptic Kisspeptin/nNOS/GnRH (KiNG) neuronal network regulates rhythmic LH release through a dual activation-inhibition mechanism
Gonadotropin-releasing hormone (GnRH) neurons are the final common target of a complex network of cells cooperating for the central control of reproduction. The balance between excitatory and inhibitory transsynaptic and non-synaptic inputs is crucial for the maintenance of the GnRH rhythms: the pulse and the surge. The precise mechanisms behind this remain under debate. In this work, we challenge the hypothesis that excitatory and inhibitory inputs from kisspeptin and neuronal nitric oxide (NO) synthase (nNOS)-expressing neurons orchestrates GnRH release, in a microcircuit that we call the Kisspeptin/nNOS/GnRH (KiNG) neuronal network. Our work specifically focuses on the role of nNOS neurons located in the organum vasculosum laminae terminalis (OV) and the median preoptic nucleus (MePO). nNOS and kisspeptin neurons interact anatomically and functionally, with the kisspeptin receptor (Kiss1r) being differentially regulated in nNOS-expressing neurons across the female estrous cycle. Using a novel viral tool allowing for the measurement of NO/cGMP levels with exquisite sensitivity, we demonstrate that kisspeptin is able to induce NO-dependent cGMP production in the OV/MePO, including in GnRH neurons in vivo. Using electrophysiological, genetic, chemogenetic and pharmacologic approaches, we reveal that NO production from nNOS neurons in the OV/MePO is needed to fine-tune the GnRH/LH response to kisspeptin, and specifically to turn off GnRH release, thus generating pulses. Our findings provide valuable insights into the tripartite KiNG neuronal network governing the regulation of the GnRH/LH pulse and surge.

Few-shot pattern detection by transient boosting of somato-dendritic coupling
Neurons are thought to detect salient patterns amidst noise in continuous information streams, but their rapidity tends to be overlooked. Consequently, theoretical neuron models lack key mechanistic features that are suggested to underlie biological neuron rapid learning of input patterns. To unravel these features, we propose a class of models endowed with biologically-plausible predictive learning rules. In these models, an error signal propagates somatic spiking activity to dendrites, facilitating unsupervised learning of repeatedly co-activated presynaptic-neuron communities. Spike-triggered transient boosting of dendritic coupling bestows plausibility and improves the signal-to-noise ratio of learning dramatically. We demonstrate that our plasticity rule enables neurons to swiftly establish a behavioral timescale reward-place association in spatial navigation tasks and showcase how cell assemblies pre-configured in recurrent networks learn multiple patterns within a few repetitions robustly. Our results shed light on the self-supervising function of backpropagating action potentials for pattern learning and its acceleration by pre-existing cell assemblies.

Neural Dynamics and Seizure Correlations: Insights from Neural Mass Models in a Tetanus Toxin Rat Model of Epilepsy
This study focuses on the use of a neural mass model to investigate potential relationships between functional connectivity and seizure frequency in epilepsy. We fitted a three- layer neural mass model of a cortical column to intracranial EEG (iEEG) data from a Tetanus Toxin rat model of epilepsy, which also included responses to periodic electrical stimulation. Our results show that some of the connectivity weights between different neural populations correlate significantly with the number of seizures each day, offering valuable insights into the dynamics of neural circuits during epileptogenesis. We also simulated single-pulse electrical stimulation of the neuronal populations to observe their responses after the connectivity weights were optimized to fit background (non-seizure) EEG data. The recovery time, defined as the time from stimulation until the membrane potential returns to baseline, was measured as a representation of the critical slowing down phenomenon observed in nonlinear systems operating near a bifurcation boundary. The results revealed that recovery times in the responses of the computational model fitted to the EEG data were longer during 5 min periods preceding seizures compared to 1 hr before seizures in four out of six rats. Analysis of the iEEG recorded in response to electrical stimulation revealed results similar to the computational model in four out of six rats. This study supports the potential use of this computational model as a model-based biomarker for seizure prediction when direct electrical stimulation to the brain is not feasible.

Cognitive abilities are associated with rapid dynamics of electrophysiological connectome states
Time-varying changes in whole-brain connectivity patters, or connectome state dynamics, hold significant implications for cognition. However, connectome dynamics at fast (> 1Hz) timescales highly relevant to cognition are poorly understood due to the dominance of inherently slow fMRI in connectome studies. Here, we investigated the behavioral significance of rapid electrophysiological connectome dynamics using source-localized EEG connectomes during resting-state (N=926 including twins, 473 females). We focused on dynamic connectome features pertinent to individual differences, specifically those with established heritability: Fractional Occupancy (i.e., the overall duration spent in each recurrent connectome state) in beta and gamma bands, and Transition Probability (i.e., the frequency of state switches) in theta, alpha, beta, and gamma bands. Canonical correlation analysis found a significant relationship between the heritable phenotypes of sub-second connectome dynamics and cognition. Specifically, principal components of Transition Probabilities in alpha (followed by theta and gamma bands) and a cognitive factor representing visuospatial processing (followed by verbal and auditory working memory) most notably contributed to the relationship. We conclude that the specific order in which rapid connectome states are sequenced shapes individuals' cognitive abilities and traits. Such sub-second connectome dynamics may inform about behavioral function and dysfunction and serve as endophenotypes for cognitive abilities.

Leveraging Multi-echo EPI to Enhance BOLD Sensitivity in Task-based Olfactory fMRI
Functional magnetic resonance imaging (fMRI) using blood-oxygenation-level-dependent (BOLD) contrast relies on gradient echo echo-planar imaging (GE-EPI) to quantify dynamic susceptibility changes associated with the hemodynamic response to neural activity. However, acquiring BOLD fMRI in human olfactory regions is particularly challenging due to their proximity to the sinuses where large susceptibility gradients induce magnetic field distortions. BOLD fMRI of the human olfactory system is further complicated by respiratory artifacts that are highly correlated with event onsets in olfactory tasks. Multi-Echo EPI (ME-EPI) acquires gradient echo data at multiple echo times (TEs) during a single acquisition and can leverage signal evolution over the multiple echo times to enhance BOLD sensitivity and reduce artifactual signal contributions. In the current study, we developed a ME-EPI acquisition protocol for olfactory task-based fMRI and demonstrated significant improvement in BOLD signal sensitivity over conventional single-echo EPI (1E-EPI). The observed improvement arose from both an increase in BOLD signal changes through a T2*-weighted echo combination and a reduction in non BOLD artifacts through the application of the Multi-Echo Independent Components Analysis (ME-ICA) denoising method. This study represents one of the first direct comparisons between 1E-EPI and ME-EPI in high-susceptibility regions and provides compelling evidence in favor of using ME-EPI for future task-based fMRI studies.

p75NTR upregulation following perinatal hypoxia leads to deficits in parvalbumin-expressing GABAergic cell maturation, cortical activity and cognitive abilities in adult mice
Children who experienced moderate perinatal hypoxia are at risk of developing long lasting subtle cognitive and behavioral deficits, including learning disabilities and emotional problems. Understanding the underlying mechanisms is an essential step for designing targeted therapy. Fast-spiking, parvalbumin-positive (PV) GABAergic interneurons modulate the generation of gamma oscillations, which in turn regulate many cognitive functions including goal-directed attentional processing and cognitive flexibility. Due to their fast firing rate, PV cell function requires high levels of energy, which may render them highly vulnerable to conditions of metabolic and oxidative stress caused by perinatal hypoxia. Here, we show that adult mice that experienced moderate perinatal hypoxia (MPH) have decreased cortical PV expression levels in addition to specific impairments in the social, recognition memory and cognitive flexibility domain. We further found that the expression level of the neurotrophin receptor p75NTR, which limits PV cell maturation during the first postnatal weeks, is increased in MPH mice. Genetic deletion of p75NTR in GABAergic neurons expressing the transcription factor Nkx2.1, which include PV cells, protects mice from PV expression loss and the long-term cognitive effects of MPH. Finally, one week treatment with a p75NTR inhibitor starting after MPH completely rescues the cognitive and cortical activity deficits in adult mice. All together this data reveals a potential molecular target for the treatment of the cognitive alterations caused by MPH.

Alcohol Sipping Patterns, Personality, and Psychopathology in Children: Moderating Effects of Dorsal Anterior Cingulate Cortex (dACC)
Alcohol, the most consumed drug in the United States, is associated with various psychological disorders and abnormal personality traits. Despite extensive research on adolescent alcohol consumption, the impact of early alcohol sipping patterns on changes in personality and mental health over time remains unclear. There is also limited information on the latent trajectory of early alcohol sipping, beginning as young as 9-10 years old. The dorsal anterior cingulate cortex (dACC) is crucial for cognitive control and response inhibition. However, the role of dACC remains unclear in the relationship between early alcohol sipping and mental health outcomes and personality traits over time. Utilizing the large data from the Adolescent Brain and Cognitive Development study, we aim to comprehensively examine the longitudinal impact of early alcohol sipping patterns on psychopathological measures and personality traits in adolescents, filling crucial gaps in the literature. We identified three latent alcohol-sipping groups, which demonstrate distinct personality traits and depression score trajectories. Bilateral dACC activation during the stop-signal task moderated the effect of early alcohol sipping on personality and depression over time. Additionally, bidirectional effects were observed between alcohol sipping and personality traits. This study provides insights into the impact of early alcohol consumption on adolescent development.

Astrocytic CREB in nucleus accumbens promotes susceptibility to chronic stress
Background: Increasing evidence implicates astrocytes in stress and depression in both rodent models and human Major Depressive Disorder (MDD). Despite this, little is known about the transcriptional responses to stress of astrocytes within the nucleus accumbens (NAc), a key brain reward region, and their influence on behavioral outcomes. Methods: We used whole cell sorting, RNA-sequencing, and bioinformatic analyses to investigate the NAc astrocyte transcriptome in male mice in response to chronic social defeat stress (CSDS). Immunohistochemistry was used to determine stress-induced changes in astrocytic CREB within the NAc. Finally, astrocytic regulation of depression-like behavior was investigated using viral-mediated manipulation of CREB in combination with CSDS. Results: We found a robust transcriptional response in NAc astrocytes to CSDS in stressed mice, with changes seen in both stress-susceptible and stress-resilient animals. Bioinformatic analysis revealed CREB, a transcription factor widely studied in neurons, as one of the top-predicted upstream regulators of the NAc astrocyte transcriptome, with opposite activation states seen in resilient versus susceptible mice. This bioinformatic result was confirmed at the protein level with immunohistochemistry. Viral overexpression of CREB selectively in NAc astrocytes promoted susceptibility to chronic stress. Conclusions: Together, our data demonstrate that the astrocyte transcriptome responds robustly to CSDS and, for the first time, that transcriptional regulation in astrocytes contributes to depressive-like behaviors. A better understanding of transcriptional regulation in astrocytes may reveal unknown molecular mechanisms underlying neuropsychiatric disorders.

Automating the Human Connectome Project's Temporal ICA Pipeline
Functional magnetic resonance imaging (fMRI) data are dominated by noise and artifacts, with only a small fraction of the variance relating to neural activity. Temporal independent component analysis (tICA) is a recently developed method that enables selective denoising of fMRI artifacts related to physiology such as respiration. However, an automated and easy to use pipeline for tICA has not previously been available; instead, two manual steps have been necessary: 1) setting the group spatial ICA dimensionality after MELODIC's Incremental Group-PCA (MIGP) and 2) labeling tICA components as artifacts versus signals. Moreover, guidance has been lacking as to how many subjects and timepoints are needed to adequately re-estimate the temporal ICA decomposition and what alternatives are available for smaller groups or even individual subjects. Here, we introduce a nine-step fully automated tICA pipeline which removes global artifacts from fMRI dense timeseries after sICA+FIX cleaning and MSMAll alignment driven by functionally relevant areal features. Additionally, we have developed an automated "reclean" Pipeline for improved spatial ICA (sICA) artifact removal. Two major automated components of the pipeline are 1) an automatic group spatial ICA (sICA) dimensionality selection for MIGP data enabled by fitting multiple Wishart distributions; 2) a hierarchical classifier to distinguish group tICA signal components from artifactual components, equipped with a combination of handcrafted features from domain expert knowledge and latent features obtained via self-supervised learning on spatial maps. We demonstrate that the dimensionality estimated for the MIGP data from HCP Young Adult 3T and 7T datasets is comparable to previous manual tICA estimates, and that the group sICA decomposition is highly reproducible. We also show that the tICA classifier achieved over 0.98 Precision-Recall Area Under Curve (PR-AUC) and that the correctly classified components account for over 95% of the tICA-represented variance on multiple held-out evaluation datasets including the HCP-Young Adult, HCP-Aging and HCP-Development datasets under various settings. Our automated tICA pipeline is now available as part of the HCP pipelines, providing a powerful and user-friendly tool for the neuroimaging community.

The long distance relationship of regional amyloid burden and tau pathology spread
Introduction: Consistent with the amyloid-cascade-hypothesis, we tested whether regional amyloid burden is associated with tau pathology increases in spatially independent brain regions and whether functional connectivity serves as a mediator bridging the observed spatial gap between these pathologies. Methods: Data of 98 amyloid-positive and 35 amyloid-negative subjects with baseline amyloid (18F-AV45) and longitudinal tau (18F-AV1451) PET were selected from ADNI. Annual tau change maps were computed. All images were z-transformed using the amyloid-negative subjects as reference. Z-maps of baseline amyloid and annual tau change were submitted to a parallel independent component analysis in GIFT, yielding six component pairs linking spatial patterns of baseline amyloid to longitudinal tau increase. Next, we used the region of maximum coefficient per component as seeds for functional connectivity analyses in a healthy control dataset. This resulted in six pairs of amyloid and tau seed-based networks (SBN). The spatial overlap between these SBNs and components (amyloid OR tau change) and the combined component pairs (amyloid AND tau change) were quantified. Results: Amyloid SBNs presented greater spatial overlap with their respective amyloid components (24%-54%) than tau SBNs with the respective tau change components (16%-40%). However, the spatial combination of amyloid and tau component pairs showed highest spatial overlap with the amyloid SBNs (up to 62% vs. 39% for the tau SBNs). Conclusion: Mechanistically, regional associations of amyloid and tau pathology may be driven by underlying large-scale functional networks. Functional connections may thereby transmit soluble amyloid to remote brain regions within the same network, likely triggering tau aggregation.

Opioid-driven disruption of the septal complex reveals a role for neurotensin- expressing neurons in withdrawal
Because opioid withdrawal is an intensely aversive experience, persons with opioid use disorder (OUD) often relapse to avoid it. The lateral septum (LS) is a forebrain structure that is important in aversion processing, and previous studies have linked the lateral septum (LS) to substance use disorders. It is unclear, however, which precise LS cell types might contribute to the maladaptive state of withdrawal. To address this, we used single-nucleus RNA-sequencing to interrogate cell type specific gene expression changes induced by chronic morphine and withdrawal. We discovered that morphine globally disrupted the transcriptional profile of LS cell types, but Neurotensin-expressing neurons (Nts; LS-Nts neurons) were selectively activated by naloxone. Using two-photon calcium imaging and ex vivo electrophysiology, we next demonstrate that LS-Nts neurons receive enhanced glutamatergic drive in morphine-dependent mice and remain hyperactivated during opioid withdrawal. Finally, we showed that activating and silencing LS-Nts neurons during opioid withdrawal regulates pain coping behaviors and sociability. Together, these results suggest that LS-Nts neurons are a key neural substrate involved in opioid withdrawal and establish the LS as a crucial regulator of adaptive behaviors, specifically pertaining to OUD.

Three-dimensional morphology of the vibrissal array of the harbor seal (Phoca vitulina)
Nearly all mammals have specialized facial hairs known as vibrissae (whiskers) that serve a variety of functions, including tactile exploration and flow sensing. The geometric arrangement of the whiskers on an animal's face as well as each whisker's individual geometry will play a critical role in determining the sensory information that the animal acquires. To date, however, the relationships between vibrissal morphology and functional use remain largely unexplored. In the present work, we quantify the three-dimensional morphology of the vibrissal array of the harbor seal (Phoca vitulina). Specifically, we develop relationships between a whisker's basepoint location and its arc length, its intrinsic curvature, and the angles at which it emerges from the mystacial pad. Results show that the arc length of seal whisker ranges between 20-120 mm, and the shape of a seal whisker is well-described by a cubic equation. The orientation at which each whisker emerges from the seal's face can be described by three angles. The angle of emergence in the horizontal plane depends on both the whisker's dorsal-ventral as well as its rostral-caudal basepoint location. In contrast, the angle of emergence in the elevation plane depends only on the whisker's dorsal-ventral basepoint location. Finally, the angle that the whisker "twists" about its own axis again depends on both dorsal-ventral and rostral-caudal basepoint location. We discuss how the morphology of the array could create a range of mechanical responses specialized for tactile or flow sensing.

Rapid dynamics of electrophysiological connectome states are heritable
Time-varying changes in whole-brain connectivity patterns, or connectome state dynamics, are a prominent feature of brain activity with broad functional implications. While infraslow (<0.1Hz) connectome dynamics have been extensively studied with fMRI, rapid dynamics highly relevant for cognition are poorly understood. Here, we asked whether rapid electrophysiological connectome dynamics constitute subject-specific brain traits and to what extent they are under genetic influence. Using source-localized EEG connectomes during resting-state (N=928 including twins, 473 females), we quantified heritability of multivariate (multi-state) features describing temporal or spatial characteristics of connectome dynamics. States switched rapidly every ~60-500ms. Temporal features were heritable, particularly, Fractional Occupancy (in theta, alpha, beta, and gamma bands) and Transition Probability (in theta, alpha, and gamma bands), representing the duration spent in each state and the frequency of state switches, respectively. Genetic effects explained a substantial proportion of phenotypic variance of these features: Fractional Occupancy in beta (44.3%) and gamma (39.8%) bands and Transition Probability in theta (38.4%), alpha (63.3%), beta (22.6%), and gamma (40%) bands. However, we found no evidence for heritability of spatial features, specifically states' Modularity and connectivity pattern. We conclude that genetic effects strongly shape individuals' connectome dynamics at rapid timescales, specifically states' overall occurrence and sequencing.

Dynamical models reveal anatomically reliable attractor landscapes embedded in resting state brain networks
Analyses of functional connectivity (FC) in resting-state brain networks (RSNs) have generated many insights into cognition. However, the mechanistic underpinnings of FC and RSNs are still not well-understood. It remains debated whether resting state activity is best characterized as noise-driven fluctuations around a single stable state, or instead, as a nonlinear dynamical system with nontrivial attractors embedded in the RSNs. Here, we provide evidence for the latter, by constructing whole-brain dynamical systems models from individual resting-state fMRI (rfMRI) recordings, using the Mesoscale Individualized NeuroDynamic (MINDy) platform. The MINDy models consist of hundreds of neural masses representing brain parcels, connected by fully trainable, individualized weights. We found that our models manifested a diverse taxonomy of nontrivial attractor landscapes including multiple equilibria and limit cycles. However, when projected into anatomical space, these attractors mapped onto a limited set of canonical RSNs, including the default mode network (DMN) and frontoparietal control network (FPN), which were reliable at the individual level. Further, by creating convex combinations of models, bifurcations were induced that recapitulated the full spectrum of dynamics found via fitting. These findings suggest that the resting brain traverses a diverse set of dynamics, which generates several distinct but anatomically overlapping attractor landscapes. Treating rfMRI as a unimodal stationary process (i.e., conventional FC) may miss critical attractor properties and structure within the resting brain. Instead, these may be better captured through neural dynamical modeling and analytic approaches. The results provide new insights into the generative mechanisms and intrinsic spatiotemporal organization of brain networks.

Domain-general cognitive control processes in bilingual switching: evidence from midfrontal theta oscillations
Language control in bilingual speakers is thought to be implicated in effectively switching between languages, inhibiting the non-intended language, and continuously monitoring what to say and what has been said. It has been a matter of controversy concerning whether language control operates in a comparable manner to cognitive control processes in non-linguistic domains (domain-general) or if it is exclusive to language processing (domain-specific). As midfrontal theta oscillations have been considered as an index of cognitive control, examining whether a midfrontal theta effect is evident in tasks requiring bilingual control could bring new insights to the ongoing debate. To this end, we reanalysed the EEG data from two previous bilingual production studies where Dutch-English bilinguals named pictures based on colour cues. Specifically, we focused on three fundamental control processes in bilingual production: switching between languages, inhibition of the nontarget language, and monitoring of speech errors. Theta power increase was observed in switch trials compared to repeat trials, with a midfrontal scalp distribution. However, this midfrontal theta effect was absent in switch trials following a short sequence of same-language trials compared to a long sequence, suggesting a missing modulation of inhibitory control. Similarly, increased midfrontal theta power was observed when participants failed to switch to the intended language compared to correct responses. Altogether, these findings tentatively support the involvement of domain-general cognitive control mechanisms in bilingual switching.

General mechanisms of task engagement in the primate frontal cortex
Staying engaged with a task is necessary to maintain goal-directed behaviors. Although engagement varies with the specific task at hand it also exhibits continuous, intrinsic fluctuations widely. This intrinsic component of engagement is difficult to isolate behaviorally or neurally in controlled experiments with humans. By contrast, animals spontaneously move between periods of complete task engagement and disengagement, even in experimental settings. We, therefore, looked at behavior in macaques in a series of four tasks while recording fMRI signals. We identified consistent autocorrelation in task disengagement. This made it possible to build models capturing task-independent engagement and to link it to neural activity. Across all tasks, we identified common patterns of neural activity linked to impending task disengagement in mid-cingulate gyrus. By contrast, activity centered in perigenual anterior cingulate cortex (pgACC) was associated with maintenance of task performance. Importantly, we were able to carefully control for task-specific factors such as the reward history, choice value, and other motivational effects, such as response vigor, as indexed by response time, when identifying neural activity associated with task engagement. Moreover, we showed pgACC activity had a causal link to task engagement; in one of our tasks, transcranial ultrasound stimulation of pgACC, but not of control regions, changed task engagement/disengagement patterns.

Sex-specific mechanisms underlie long-term potentiation at hippocampus-nucleus accumbens synapses
Sex differences have complicated our understanding of the neurobiological basis of many behaviors that are key for survival. As such, continued elucidation of the similarities and differences between sexes is necessary in order to gain insight into brain function and vulnerability. The connection between the hippocampus (Hipp) and nucleus accumbens (NAc) is a crucial site where modulation of neuronal activity mediates reward-related behavior. Our previous work demonstrated that long-term potentiation (LTP) of Hipp-NAc synapses is rewarding, and that mice can make learned associations between LTP of these synapses and the contextual environment in which LTP occurred. Here, we investigate sex differences in the mechanisms underlying Hipp-NAc LTP using whole-cell electrophysiology and pharmacology. We found that males and females display similar magnitudes of Hipp-NAc LTP which occurs postsynaptically. However, LTP in females requires L-type voltage-gated Ca2+ channels (VGCC) for postsynaptic Ca2+ influx, while males rely on NMDA receptors (NMDAR). Additionally, females require estrogen receptor (ER) activity for LTP while males do not. These differential mechanisms converge as LTP in both sexes depends on CAMKII activity and occurs independently of dopamine-1 receptor (D1R) activation. Our results have elucidated sex-specific molecular mechanisms for LTP in an integral excitatory pathway that mediates reward-related behaviors, emphasizing the importance of considering sex as a variable in mechanistic studies. Continued characterization of sex-specific mechanisms underlying plasticity will offer novel insight into the neurophysiological basis of behavior, with significant implications for understanding how diverse processes mediate behavior and contribute to vulnerability to developing psychiatric disorders.

SIGNIFICANCE STATEMENTStrengthening of Hipp-NAc synapses drives reward-related behaviors. Male and female mice have similar magnitudes of long-term potentiation (LTP) and both sexes have a predicted postsynaptic locus of plasticity. Despite these similarities, we illustrate here that sex-specific molecular mechanisms are used to elicit LTP. Given the bidirectional relationship between Hipp-NAc synaptic strength in mediating reward-related behaviors, the use of distinct molecular mechanisms may explain sex differences observed in stress susceptibility or response to rewarding stimuli. Discovery and characterization of convergent sex differences provides mechanistic insight into the sex-specific function of Hipp-NAc circuitry and has widespread implications for circuits mediating learning and reward-related behavior.

Rapid learning of temporal dependencies at multiple timescales
Our environment contains temporal information unfolding simultaneously at multiple timescales. How do we learn and represent these dynamic and overlapping information streams? We investigated these processes in a statistical learning paradigm with simultaneous short and long timescale contingencies. Human participants (N=96) played a game where they learned to quickly click on a target image when it appeared in one of 9 locations, in 8 different contexts. Across contexts, we manipulated the order of target locations: at a short timescale, the order of pairs of sequential locations in which the target appeared; at a longer timescale, the set of locations that appeared in the first vs. second half of the game. Participants periodically predicted the upcoming target location, and later performed similarity judgements comparing the games based on their order properties. Participants showed context dependent sensitivity to order information at both short and long timescales, with evidence of stronger learning for short timescales. We modeled the learning paradigm using a gated recurrent network trained to make immediate predictions, which demonstrated multilevel learning timecourses and patterns of sensitivity to the similarity structure of the games that mirrored human participants. The model grouped games with matching rule structure and dissociated games based on low-level order information more so than high-level order information. The work shows how humans and models can rapidly and concurrently acquire order information at different timescales.

Segmentation-free measurement of locomotor frequency in Caenorhabditis elegans using image invariants
An animals locomotor rate is an important indicator of its motility. In studies of the nematode C. elegans, assays of the frequency of body bending waves have often been used to discern the effects of mutations, drugs, or aging. Traditional manual methods for measuring locomotor frequency are low in throughput and subject to human error. Most current automated methods depend on image segmentation, which requires high image quality and is prone to errors. Here, we describe an algorithm for automated estimation of C. elegans locomotor frequency using image invariants, i.e., shape-based parameters that are independent of object translation, rotation, and scaling. For each video frame, the method calculates a combination of 8 Hus moment invariants and a set of Maximally Stable Extremal Regions (MSER) invariants. The algorithm then calculates the locomotor frequency by computing the autocorrelation of the time sequence of the invariant ensemble. Results of our method show excellent agreement with manual or segmentation-based results over a wide range of frequencies. We show that compared to the segmentation method that analyzes a worms shape, our method is more robust to low image quality. We demonstrate the systems capabilities by testing the effects of serotonin and serotonin pathway mutants on locomotor frequency.

An Incremental Large Language Model for long text processing in the Brain
Accumulated evidence suggests that Large Language Models (LLMs) are beneficial in predicting neural signals related to narrative processing. The way LLMs integrate context over large timescales, however, is fundamentally different from the way the brain does it. In this study, we show that unlike LLMs that apply parallel processing of large contextual windows, the incoming context to the brain is limited to short windows of a few tens of words. We hypothesize that whereas lower-level brain areas process short contextual windows, higher-order areas in the default-mode network (DMN) engage in an online incremental mechanism where the incoming short context is summarized and integrated with information accumulated across long timescales. Consequently, we introduce a novel LLM that instead of processing the entire context at once, it incrementally generates a concise summary of previous information. As predicted, we found that neural activities at the DMN were better predicted by the incremental model, and conversely, lower-level areas were better predicted with short-context-window LLM.

Synaptic communication within the microcircuits of pyramidal neurons and basket cells in the mouse prefrontal cortex
Basket cells are inhibitory interneurons in cortical structures with the potential to efficiently control the activity of their postsynaptic partners. Although their contribution to higher order cognitive functions associated with the medial prefrontal cortex (mPFC) relies on the characteristics of their synaptic connections, the way they are embedded into local circuits is still not fully uncovered. Here, we determined the synaptic properties of excitatory and inhibitory connections between pyramidal neurons (PNs), cholecystokinin-containing basket cells (CCKBCs) and parvalbumin-containing basket cells (PVBCs) in the mouse mPFC. By performing paired recordings, we revealed that PVBCs receive larger unitary excitatory postsynaptic currents from PNs with shorter latency and faster kinetic properties compared to events evoked in CCKBCs. Also, unitary inhibitory postsynaptic currents in PNs were more reliably evoked by PVBCs than by CCKBCs yet the former connections showed profound short-term depression. Moreover, we demonstrated that CCKBCs and PVBCs in the mPFC are mutually interconnected with each other. As alterations in PVBC function have been linked to neurological and psychiatric conditions like Alzheimers disease and schizophrenia and CCKBC vulnerability might play a role in mood disorders, a deeper understanding of the general features of basket cell synapses could serve as a reference point for future investigations with therapeutic objectives.

Increased Cholesterol Synthesis Drives Neurotoxicity in Patient Stem Cell-Derived Model of Multiple Sclerosis
Senescent neural progenitor cells have been identified in brain lesions of people with progressive multiple sclerosis (PMS). However, their role in disease pathobiology and contribution to the lesion environment remains unclear.

By establishing directly induced neural stem/progenitor cell (iNSC) lines from PMS patient fibroblasts, we studied their senescent phenotype in vitro. Senescence was strongly associated with inflammatory signaling, hypermetabolism, and the senescence associated secretory phenotype (SASP). PMS-derived iNSCs displayed increased glucose-dependent fatty acid and cholesterol synthesis, which resulted in the accumulation of cholesterol ester enriched lipid droplets. An HMG-CoA reductase-mediated lipogenic state was found to induce secretion of the SASP in PMS iNSC conditioned media via transcriptional regulation by cholesterol-dependent transcription factors. SASP from PMS iNSCs induced neurotoxicity. Chemical targeting of HMG-CoA reductase using the cholesterol-lowering drug simvastatin (SV) prevented SASP release and resulting neurotoxicity.

Our findings suggest a disease-associated, cholesterol-related, hypermetabolic phenotype of PMS iNSCs that leads to neurotoxic signaling and is rescuable pharmacologically.

Host brain environmental influences on transplanted medial ganglionic eminence progenitors
Interneuron progenitor transplantation can ameliorate disease symptoms in a variety of neurological disorders. This strategy is based on transplantation of embryonic medial ganglionic eminence (MGE) progenitors. Elucidating host brain environment influences on interneuron progenitors as they integrate is critical to optimizing this strategy across different disease states. Here, we systematically evaluated age and brain region influences on survival, migration and differentiation of transplant-derived cells. We find that early postnatal MGE transplantation yields superior survival and more extensive migratory capabilities compared to juvenile or adult. MGE progenitors migrate more widely in cortex compared to hippocampus. Maturation to interneuron subtypes is regulated by age and brain region. MGE progenitors transplanted into dentate gyrus sub-region of early postnatal hippocampus can differentiate into astrocytes. Our results suggest that host brain environment critically regulates survival, spatial distribution and maturation of MGE-derived interneurons following transplantation. These findings inform and enable optimal conditions for interneuron transplant therapies.

An intracranial dissection of human escape circuits
Predators attack at different spatiotemporal scales, spurring prey to elicit escape responses that range from simple motor reactions and strategic planning that involve more complex cognitive processes. Recent work in humans suggests that escape relies on two distinct circuits: the reactive and cognitive fear circuits. However, the specific involvement of these two circuits in different stages of human escaping remains poorly characterized. In this study, we recorded intracranial electroencephalography (iEEG) from epilepsy patients while they performed a modified flight initiation distance (FID) task. We found brain regions in the cognitive fear circuit, including the ventromedial prefrontal cortex and hippocampus, encoded the threat level during the information processing stage. The actual escaping stage, especially under rapid attack, prominently activated areas within the reactive fear circuit, including the midcingulate cortex and amygdala. Furthermore, we observed a negative correlation between the high gamma activity (HGA) of the amygdala and the HGA of the vmPFC and HPC under rapid attacks. This indicates that the amygdala may suppress the activity of the cognitive fear circuit under rapid attacks, enabling the organism to react quickly to ensure survival under the imminent threat. These findings highlight the distinct roles of the reactive and cognitive fear circuits in human escaping and provide accounts for the importance of fear in human survival decisions.

Enhanced legumain activity links progranulin deficiency to TDP-43 pathology in frontotemporal lobar degeneration
Loss-of-function mutations in GRN are a major cause of frontotemporal lobar degeneration (FTLD) with TDP-43-positive inclusions. Progranulin (PGRN) loss leads to lysosomal dysfunction, microglial hyperactivation, and TDP-43 deposition, yet the underlying pathomechanism remains unknown. We demonstrate that PGRN slows the maturation and limits the proteolytic activity of the lysosomal protease legumain (LGMN). Accordingly, LGMN activity is strongly elevated in Grn knockout (ko) mice, in human induced pluripotent stem cell-derived GRN ko microglia, and in FTLD-GRN patients brain. Secreted microglial LGMN is internalized by neurons, where it mediates pathological processing of TDP-43, which is prevented by selective LGMN inhibition. In contrast, AAV-mediated overexpression of LGMN in mouse brains promotes TDP-43 processing, the aggregation of phosphorylated TDP-43 and increases plasma neurofilament light chain (NfL), a marker for neuronal loss. Our findings identify LGMN as a link between PGRN haploinsufficiency and TDP-43 pathology in FTLD-GRN and suggest LGMN as a therapeutic target.

A call for a unified and multimodal definition of cellular identity in the enteric nervous system
The enteric nervous system (ENS) is a tantalizing frontier in neuroscience. With the recent emergence of single cell transcriptomic technologies, this rare and poorly understood tissue has begun to be better characterized in recent years. A precise functional mapping of enteric neuron diversity is critical for understanding ENS biology and enteric neuropathies. Nonetheless, this pursuit has faced considerable technical challenges. By leveraging different methods to compare available primary mouse and human ENS datasets, we underscore the urgent need for careful identity annotation, achieved through the harmonization and advancements of wet lab and computational techniques. We took different approaches including differential gene expression, module scoring, co-expression and correlation analysis, unbiased biological function hierarchical clustering, data integration and label transfer to compare and contrast functional annotations of several independently reported ENS datasets. These analyses highlight substantial discrepancies stemming from an overreliance on transcriptomics data without adequate validation in tissues. To achieve a comprehensive understanding of enteric neuron identity and their functional context, it is imperative to expand tissue sources and incorporate innovative technologies such as multiplexed imaging, electrophysiology, spatial transcriptomics, as well as comprehensive profiling of epigenome, proteome, and metabolome. Harnessing human pluripotent stem cell (hPSC) models provides unique opportunities for delineating lineage trees of the human ENS, and offers unparalleled advantages, including their scalability and compatibility with genetic manipulation and unbiased screens. We encourage a paradigm shift in our comprehension of cellular complexity and function in the ENS by calling for large-scale collaborative efforts and research investments.

Cerebral Blood Volume Modulates Glymphatic Influx Through Extra-ventricular Cerebrospinal Fluid Volume
The glymphatic system facilitates waste removal via cerebrospinal fluid (CSF) influx alongside perivascular spaces throughout the brain. Vasomotion, the slow motion of blood vessel (0.1-0.3 Hz), has been found to be one of the driving forces for perivascular clearance, but it is not clear whether more chronical change of vessel diameter, as reflected by macroscopic cerebral blood volume (CBV), has any impact on glymphatic function. Combining multimodal mouse MRI techniques, we investigated the relationship among glymphatic influx, CBV, CSF volume and EEG power under six different conditions (awake, dexmedetomidine, isoflurane, isoflurane/dexmedetomidine, ketamine/xylazine and awake with caffeine). We found dexmedetomidine and caffeine enhanced glymphatic influx, while isoflurane reduced it compared with awake condition. Quantitative CBV imaging revealed that glymphatic influx was negatively correlated to CBV across the above conditions. Furthermore, such negative correlation was found to be mediated in part by changes of extra-ventricular CSF volume, which was quantified using T1 MRI. Taken together, our results suggest that CBV is a consciousness independent modulator of glymphatic function and modulates glymphatic influx through extra-ventricular CSF volume. This new finding opens potential avenues to enhance brain waste clearance by regulating CBV, which could be beneficial for protein deposition related neurological diseases.

TeaserCBV is a consciousness independent modulator of glymphatic function and modulates glymphatic influx through extra-ventricular CSF volume.

Mouse Auditory Cortex Undergoes Asynchronous Maturation in the Right and Left Hemispheres
Despite the significance of lateralized auditory processing in human cognition, there are limited studies in animal models exploring the developmental mechanisms of this cortical specialization. Here, we find that cellular and network signs of maturity in the Auditory Cortex (ACx) appear earlier in the right hemisphere in male mice. We further demonstrate that persistent, experience dependent map reorganization is confined to the hemisphere that is actively maturing and can be differentially engaged by temporally limited manipulations of the sensory environment. Our data suggests that differential timing in hemisphere development could lead to lateralized auditory functioning.

Elucidation of neuronal activity in mouse models of TMJ injury by in vivo GCaMP Ca2+ imaging of intact trigeminal ganglion neurons
Patients with temporomandibular disorders (TMD) typically experience facial pain and discomfort or tenderness in the temporomandibular joint (TMJ), causing disability in daily life. Unfortunately, existing treatments for TMD are not always effective, creating a need for more advanced, mechanism-based therapies. In this study, we used in vivo GCaMP3 Ca2+ imaging of intact trigeminal ganglia (TG) to characterize functional activity of the TG neurons in vivo, specifically in TMJ animal models. This system allows us to observe neuronal activity in intact anatomical, physiological, and clinical conditions and to assess neuronal function and response to various stimuli. We observed a significant increase in spontaneously and transiently activated neurons responding to mechanical, thermal, and chemical stimuli in the TG of forced mouth open (FMO) mice. An inhibitor of the CGRP receptor significantly attenuated FMO-induced facial hypersensitivity. In addition, we confirmed the attenuating effect of CGRP antagonist on FMO-induced sensitization by in vivo GCaMP3 Ca2+ imaging of intact TG. Our results contribute to unraveling the role and activity of TG neurons in the TMJ pain animal models of TMD, bringing us closer understanding the pathophysiological processes underlying TMD. Our study also illustrates the utility of in vivo GCaMP3 Ca2+ imaging of intact TG for studies aimed at developing more targeted and effective treatments for TMD.

Assessing the relationship between neural entrainment and altered states of consciousness induced by electronic music
In electronic music events, the driving four-on-the-floor music appears pivotal for inducing altered states of consciousness (ASCs). While various physiological mechanisms link repetitive auditory stimuli to ASCs, entrainment--a brainwave synchronization through periodic external stimuli-- has garnered primary focus. However, there are no studies systematically exploring the relationship between entrainment and ASCs. In the present study, we depart from the finding that entrainment to auditory stimuli peaks for stimulation rates around 2 Hz compared to others. Twenty participants listened to six one-minute electronic music excerpts at different tempos (1.65 Hz, 2.25 Hz, and 2.85 Hz). For each excerpt, they performed cognitive tasks and reported phenomenological experiences related to ASCs through questionnaires. Brain activity was recorded with electroencephalography to assess whether a modulation in entrainment by the beat of electronic music affected objective and subjective proxies of ASCs. Our results revealed a tempo-driven entrainment modulation, peaking at 1.65 Hz. Similarly, participants experience of unity during listening to the music was higher for the excerpts at 1.65 Hz, yet no relationship with entrainment was found. Critically, a correlation was found between entrainment and participants reaction time. Further studies are granted to explore how individual traits, such as musical training, modulate the relationship.

Single dopaminergic neuron DAN-c1 in Drosophila larval brain mediates aversive olfactory learning through D2-like receptors
The dopaminergic system plays critical roles in Drosophila olfactory associative learning. In this study, we identified DAN-c1, a single dopaminergic neuron (DAN) in each brain hemisphere, that is both necessary and sufficient for Drosophila larval aversive associative learning. Compared to well-known roles of excitatory D1-like receptors in learning, the role of D2-like receptors (D2Rs) has not been fully investigated. We observed that D2Rs were expressed in DANs and the mushroom body (MB) in third instar larval brains. Knockdown of D2Rs in DAN-c1 by microRNA impaired aversive learning. Optogenetic activation of DAN-c1 during training led to an aversive learning deficit as well, indicating that D2R achieves its functions via autoreceptor inhibition. Interestingly, knockdown of D2R in MB impaired both appetitive and aversive learning. These results reveal that D2Rs in different brain structures play important but distinct roles in Drosophila larval olfactory learning, providing new insights into molecular mechanisms underlying associative learning.

Uncovering population contributions to the extracellular potential in the mouse visual system using Laminar Population Analysis
The local field potential (LFP), the low-frequency part of the extracellular potential, reflects transmembrane currents in the vicinity of the recording electrode. Thought mainly to stem from currents caused by synaptic input, it provides information about neural activity complementary to that of spikes, the output of neurons. However, the many neural sources contributing to the LFP, and likewise the derived current source density (CSD), can often make it challenging to interpret. Efforts to improve its interpretability have included the application of statistical decomposition tools like principal component analysis (PCA) and independent component analysis (ICA) to disentangle the contributions from different neural sources. However, their underlying assumptions of, respectively, orthogonality and statistical independence are not always valid for the various processes or pathways generating LFP. Here, we expand upon and validate a decomposition algorithm named Laminar Population Analysis (LPA), which is based on physiological rather than statistical assumptions. LPA utilizes the multiunit activity (MUA) and LFP jointly to uncover the contributions of different populations to the LFP. To perform the validation of LPA, we used data simulated with the large-scale, biophysically detailed model of mouse V1 developed by the Allen Institute. We find that LPA can identify laminar positions within V1 and the temporal profiles of laminar population firing rates from the MUA. We also find that LPA can estimate the salient current sinks and sources generated by feedforward input from the lateral geniculate nucleus (LGN), recurrent activity in V1, and feedback input from the lateromedial (LM) area of visual cortex. LPA identifies and distinguishes these contributions with a greater accuracy than the alternative statistical decomposition methods, PCA and ICA. Lastly, we also demonstrate the application of LPA on experimentally recorded MUA and LFP from 24 animals in the publicly available Visual Coding dataset. Our results suggest that LPA can be used both as a method to estimate positions of laminar populations and to uncover salient features in LFP/CSD contributions from different populations.

Author summaryTo make the best use of all the data collected in neuroscientific experiments, we need to develop appropriate analysis tools. In extracellular electrophysiological recordings, that is, measurements of electrical signals outside of cells produced by neural activity, the low-frequency part of the signal referred to as the local field potential (LFP) is often difficult to interpret due to the many neurons and biophysical processes contributing to this signal. Statistical tools have been used to decompose the recorded LFP with the aim of disentangling contributions from different neural populations and pathways. However, these methods are based on assumptions that can be invalid for LFP in the structure of interest. In this study, we extend and validate a method called laminar population analysis (LPA), which is based on physiological rather than statistical assumptions. We tested, developed, and validated LPA using simulated data from a large-scale, biophysically detailed model of mouse primary visual cortex. We found that LPA is able to tease apart several of the most salient contributions from different external inputs as well as the total contribution from recurrent activity within the primary visual cortex. We also demonstrate the application of LPA on experimentally recorded LFP.

Does early-stage Alzheimer's disease affect the dynamics of motor adaptation?
Alzheimers disease (AD) is characterized by an initial decline in declarative memory, while non-declarative memory processing remains relatively intact. Error-based motor adaptation is traditionally seen as a form of non-declarative memory, but recent findings suggest that it involves both fast, declarative and slow, non-declarative adaptive processes. If the declarative memory system shares resources with the fast process in motor adaptation, it can be hypothesized that the fast, but not the slow, process is disturbed in AD patients. To test this, we studied 20 early-stage AD patients and 21 age-matched controls of both sexes using a reach adaptation paradigm that relies on spontaneous recovery after sequential exposure to opposing force fields. Adaptation was measured using error clamps and expressed as an adaptation index (AI). Although patients with AD showed slightly lower adaptation to the force field than the controls, both groups demonstrated effects of spontaneous recovery. The time course of the AI was fitted by a hierarchical Bayesian two-state model in which each dynamic state is characterized by a retention and learning rate. Compared to controls, the retention rate of the fast process was the only parameter that was significantly different (lower) in the AD patients, confirming that the memory of the declarative, fast process is disturbed by AD. The slow adaptive process was virtually unaffected. Since the slow process learns only weakly from error, our results provide neurocomputational evidence for the clinical practice of errorless learning of everyday tasks in people with dementia.

Forecasting EEG time series with WaveNet
ObjectiveForecasting electroencephalography (EEG) signals, i.e., estimating future values of the time series based on the past ones, is essential in many real-time EEG-based applications, such as brain- computer interfaces and closed-loop brain stimulation. As these applications are becoming more and more common, the importance of a good prediction model has increased. Previously, mainly the autoregressive model (AR) has been employed for this task -- however, its prediction accuracy tends to fade quickly as multiple steps are predicted. We aim to improve on this by applying deep learning to make robust long-range forecasts.

MethodsWe applied the deep neural network model WaveNet to forecast resting-state EEG in theta- (4-7.5 Hz) and alpha-frequency (8-13 Hz) bands. We also compared WaveNet to the AR model, which has previously been widely used in real-time EEG applications.

ResultsWaveNet reliably forecasted EEG signals in both theta and alpha frequencies. It outperformed the AR model in estimating the signal amplitude and phase.

ConclusionWe demonstrate for the first time that deep learning can be utilised to forecast resting-state EEG time series over 100 ms ahead.

SignificanceIn the future, the developed model can enhance the real-time estimation of brain states in brain-computer interfaces and brain stimulation protocols. It may also be useful for answering neuroscientific questions and for diagnostic purposes.

Forced mouth opening induces post-traumatic hyperalgesia and associated peripheral sensitization after temporomandibular joints injury in mice
Temporomandibular disorder (TMD) is the most prevalent painful condition in the craniofacial area. The pathophysiology of TMD is not fully understood, and it is necessary to understand pathophysiology underlying painful TMD conditions to develop more effective treatment methods. Recent studies suggested that external or intrinsic trauma to TMJ is associated with chronic TMD in patients. Here, we investigated the effects of the TMJ trauma through forced-mouth opening (FMO) in mice to determine pain behaviors and peripheral sensitization of trigeminal nociceptors. FMO increased mechanical hyperalgesia assessed by von Frey test, spontaneous pain-like behaviors assessed by mouse grimace scale, and anxiety-like behaviors assessed by open-field test. In vivo GCaMP Ca2+ imaging of intact trigeminal ganglia (TG) showed increased spontaneous Ca2+ activity and mechanical hypersensitivity of TG neurons in the FMO compared to the sham group. Ca2+ responses evoked by cold, heat, and capsaicin stimuli were also increased. FMO-induced hyperalgesia and neuronal hyperactivities were not sex dependent. TG neurons sensitized following FMO were primarily small to medium-sized nociceptive afferents. Consistently, most TMJ afferents in the TG were small-sized peptidergic neurons expressing calcitonin gene-related peptides, whereas nonpeptidergic TMJ afferents were relatively low. FMO-induced intraneural inflammation in the surrounding tissues of the TMJ indicates potentially novel mechanisms of peripheral sensitization following TMJ injury. These results suggest that the TMJ injury leads to persistent post-traumatic hyperalgesia associated with peripheral sensitization of trigeminal nociceptors.

Coordinated social interactions are supported by integrated neural representations
Joint actions are defined as coordinated interactions of two or more agents towards a shared goal, often requiring different and complementary individual contributions. However, how humans can successfully act together without the interfering effects of observing incongruent movements is still largely unknown. It has been proposed that interpersonal predictive processes are at play to allow the formation of a Dyadic Motor Plan, encompassing both agents shares. Yet, direct empirical support for such an integrated motor plan is still limited. In this study, we aimed at testing the properties of these anticipated representations. We collected EEG data while human participants (N = 36; 27 females) drew shapes simultaneously to a virtual partner, in two social contexts: either they had to synchronize and act jointly, or they performed the movements alongside, but independently. We adopted a multivariate approach to show that the social context influenced how the upcoming action of the partner is anticipated during the interval preceding the movement. We found evidence that acting jointly induces an encoding of the partners action that is strongly intertwined with the participants action, supporting the hypothesis of an integrative motor plan in joint but not in parallel actions.

Amygdalo-nigral inputs target dopaminergic and GABAergic neurons in the primate: a view from dendrites and soma
The central nucleus (CeN) of the amygdala is an important afferent to the DA system that mediates motivated learning. We previously found that CeN terminals in nonhuman primates primarily overlap the elongated lateral VTA (parabrachial pigmented nucleus, PBP, A10), and retrorubral field(A8) subregion. Here, we examined CeN afferent contacts on cell somata and proximal dendrites of DA and GABA neurons, and distal dendrites of each, using confocal and electron microscopy (EM) methods, respectively. At the soma/proximal dendrites, the proportion of TH+ and GAD1+ cells receiving at least one CeN afferent contact was surprisingly similar (TH = 0.55: GAD1=0.55 in PBP; TH = 0.56; GAD1 =0.51 in A8), with the vast majority of contacted TH+ and GAD1+ soma/proximal dendrites received 1-2 contacts. Similar numbers of tracer-labeled terminals also contacted TH-positive and GAD1-positive small dendrites and/or spines (39% of all contacted dendrites were either TH- or GAD1-labeled). Overall, axon terminals had more symmetric (putative inhibitory) axonal contacts with no difference in the relative distribution in the PBP versus A8, or onto TH+ versus GAD1+ dendrites/spines in either region. The striking uniformity in the amygdalonigral projection across the PBP-A8 terminal field suggests that neither neurotransmitter phenotype nor midbrain location dictates likelihood of a terminal contact. We discuss how this afferent uniformity can play out in recently discovered differences in DA:GABA cell densities between the PBP and A8, and affect specific outputs.

Significance statementThe amygdalas central nucleus (CeN) channels salient cues to influence both appetitive and aversive responses via DA outputs. In higher species, the broad CeN terminal field overlaps the parabrachial pigmented nucleus ( lateral A10) and the retrorubral field (A8). We quantified terminal contacts in each region on DA and GABAergic soma/proximal dendrites and small distal dendrites. There was striking uniformity in contacts on DA and GABAergic cells, regardless of soma and dendritic compartment, in both regions. Most contacts were symmetric (putative inhibitory) with little change in the ratio of inhibitory to excitatory contacts by region.

We conclude that post-synaptic shifts in DA-GABA ratios are key to understanding how these relatively uniform inputs can produce diverse effects on outputs.

Development and characterization of phospho-ubiquitin antibodies to monitor PINK1-PRKN signaling in cells and tissue
The selective removal of dysfunctional mitochondria, a process termed mitophagy, is critical for cellular health and impairments have been linked to aging, Parkinson disease, and other neurodegenerative conditions. A central mitophagy pathway is orchestrated by the ubiquitin (Ub) kinase PINK1 together with the E3 Ub ligase PRKN/Parkin. The decoration of damaged mitochondrial domains with phosphorylated Ub (p-S65-Ub) mediates their elimination though the autophagy system. As such p-S65-Ub has emerged as a highly specific and quantitative marker of mitochondrial damage with significant disease relevance. Existing p-S65-Ub antibodies have been successfully employed as research tools in a range of applications including western blot, immunocytochemistry, immunohistochemistry, and ELISA. However, physiological levels of p-S65-Ub in the absence of exogenous stress are very low, therefore difficult to detect and require reliable and ultrasensitive methods. Here we generated and characterized a collection of novel recombinant, rabbit monoclonal p-S65-Ub antibodies with high specificity and affinity in certain applications that allow the field to better understand the molecular mechanisms and disease relevance of PINK1-PRKN signaling. These antibodies may also serve as novel diagnostic or prognostic tools to monitor mitochondrial damage in various clinical and pathological specimens.

Molecular and cellular dynamics of the developing human neocortex at single-cell resolution
The development of the human neocortex is a highly dynamic process and involves complex cellular trajectories controlled by cell-type-specific gene regulation. Here, we collected paired single-nucleus chromatin accessibility and transcriptome data from 38 human neocortical samples encompassing both the prefrontal cortex and primary visual cortex. These samples span five main developmental stages, ranging from the first trimester to adolescence. In parallel, we performed spatial transcriptomic analysis on a subset of the samples to illustrate spatial organization and intercellular communication. This atlas enables us to catalog cell type-, age-, and area-specific gene regulatory networks underlying neural differentiation. Moreover, combining single-cell profiling, progenitor purification, and lineage-tracing experiments, we have untangled the complex lineage relationships among progenitor subtypes during the transition from neurogenesis to gliogenesis in the human neocortex. Specifically, we find a tripotential intermediate progenitor subtype termed Tri-IPC responsible for the local production of GABAergic neurons. Furthermore, by integrating our atlas data with large-scale GWAS data, we created a disease-risk map highlighting enriched ASD risk in second-trimester intratelencephalic projection neurons. Our study sheds light on the gene regulatory landscape and cellular dynamics of the developing human neocortex.

Arl2 Associates with Cdk5rap2 to Regulate Cortical Development via Microtubule Organization
ADP ribosylation factor-like GTPase 2 (Arl2) is crucial for controlling mitochondrial fusion and microtubule assembly in various organisms. Arl2 regulates the asymmetric division of neural stem cells in Drosophila via microtubule growth. However, the function of mammalian Arl2 during cortical development was unknown. Here, we demonstrate that mouse Arl2 plays a new role in corticogenesis via regulating microtubule growth, but not mitochondria functions. Arl2 knockdown leads to impaired proliferation of neural progenitor cells (NPCs) and neuronal migration. Arl2 knockdown in mouse NPCs significantly diminishes centrosomal microtubule growth and delocalization of centrosomal proteins Cdk5rap2 and {gamma}-tubulin. Moreover, Arl2 physically associates with Cdk5rap2 by in silico prediction using AlphaFold Multimer and in vitro binding assays. Remarkably, Cdk5rap2 overexpression significantly rescues the neurogenesis defects caused by Arl2 knockdown. Therefore, Arl2 plays an important role in mouse cortical development through microtubule growth via the centrosomal protein Cdk5rap2.