bioRxiv Subject Collection: Neuroscience's Journal

Parkinson's disease-associated shifts between DNA methylation and DNA hydroxymethylation in human brain
Background: The majority of PD cases are due to a complex interaction between aging, genetics, and environmental factors; epigenetic mechanisms are thought to act as important mediators of these risk factors. While multiple studies to date have explored the role of DNA modifications in PD, few focus on 5-hydroxymethylcytosine (5hmC), which is thought to be particularly important in the brain and the response to environmental exposures. Objectives: The goal of this study was to identify paired changes in 5hmC and 5-methylcytosine (5mC) in PD in enriched neuronal nuclie isolated from PD post-mortem parietal cortex and age- and sex-matched controls. Methods: We performed oxidative bisulfite (BS) conversion and paired it with our previously published BS-based EWAS to identify cytosines with significant shifts between these two related epigenetic marks. Interaction differentially modified cytosines (iDMCs) were identified using our recently published mixed effect model for co-analyzing {beta}mC and {beta}hmC data. Results: We identified 1,030 iDMCs with paired changes in 5mC and 5hmC (FDR < 0.05) that map to 695 genes, including the PD risk gene, DNAJC6 (PARK19). Conclusions: These data potentially links epigenetic regulation of the PARK19 locus in the pathogenesis of idiopathic PD. In addition, iDMC-containing genes have known functions in synaptic formation and function, cell cycle and senescence, neuroinflammation, and epigenetic regulation. These data suggest that there are significant shifts between 5mC and 5hmC associated with PD in genes relevant to PD pathogenesis that are not captured by analyzing BS-based data alone or by analyzing each mark as a distinct dataset.

Tau pathology leads to lonely non-traveling slow waves that mediate human memory impairment
Memory markedly declines with age, exaggerated in those with Alzheimer's disease, yet the mechanisms are still not resolved. Here, we show that frontal lobe tau pathology in humans leads to impaired en masse unity and cortical traveling propagation of NREM slow waves, consequentially impairing memory retention. We elucidate these findings using PET tau brain imaging, and then replicate and extend them using AD pathology markers derived from lumbar puncture CSF in an independent clinical cohort. Thus, tau-associated memory deficits are not wholly direct, but indirectly mediated through consequential 'lonely', non-traveling slow-wave events.

Robustly encoding certainty in a metastable neural circuit model
Localized persistent neural activity has been shown to serve delayed estimation of continuous variables. Common experiments require that subjects store and report the feature value (e.g., orientation) of a particular cue (e.g., oriented bar on a screen) after a delay. Visualizing recorded activity of neurons according to their feature tuning reveals activity bumps whose centers wander stochastically, degrading the estimate over time. Bump position therefore represents the remembered estimate. Recent work suggests that bump amplitude may represent estimate certainty reflecting a probabilistic population code for a Bayesian posterior. Idealized models of this type are fragile due to the fine tuning common to constructed continuum attractors in dynamical systems. Here we propose an alternative metastable model for robustly supporting multiple bump amplitudes by extending neural circuit models to include quantized nonlinearities. Asymptotic projections of circuit activity produce low-dimensional evolution equations for the amplitude and position of bump solutions in response to external stimuli and noise perturbations. Analysis of reduced equations accurately characterizes phase variance and the dynamics of amplitude transitions between stable discrete values. More salient cues generate bumps of higher amplitude which wander less, consistent with the experimental finding that greater certainty correlates with more accurate memories.

Astrocytic hemoglobin is an H2O2-decomposing peroxidase and therapeutic target for Alzheimer's disease
Hemoglobin (Hb) is well-known for transporting oxygen in red blood cells within blood vessels. However, its role in the brain has been largely unknown. Here, we report that Hb, found in hippocampal astrocytes of both animal models and Alzheimer's disease (AD) patients, displays significant antioxidant effects through its H2O2-decomposing peroxidase activity. To counteract the harmful effects of aberrant H2O2-production in AD, we developed KDS12025, a BBB-permeable small molecule that effectively enhances the H2O2-decomposing activity of Hb by 100-fold, even at very low level of Hb. KDS12025 reduces in astrocytes and reverses memory impairment in AD models. Gene-silencing of Hbbeta abrogates the enhancing effect of KDS12025 in both culture and animal models of AD. We propose boosting Hb's peroxidase activity as a new therapeutic approach for AD treatment.

Neuron-level prediction and noise can implement flexible reward-seeking behavior
We show that neural networks can implement reward-seeking behavior using only local predictive updates and internal noise. These networks are capable of autonomous interaction with an environment and can switch between explore and exploit behavior, which we show is governed by attractor dynamics. Networks can adapt to changes in their architectures, environments, or motor interfaces without any external control signals. When networks have a choice between different tasks, they can form preferences that depend on patterns of noise and initialization, and we show that these preferences can be biased by network architectures or by changing learning rates. Our algorithm presents a flexible, biologically plausible way of interacting with environments without requiring an explicit environmental reward function, allowing for behavior that is both highly adaptable and autonomous. Code is available at https://github.com/ccli3896/PaN.

Sex differences in olfactory behavior and neurophysiology in Long Evans Rats
In many species, olfactory abilities in females are deemed more sensitive than those in males. Studies in humans show that women have lower olfactory thresholds and are better able to discriminate and identify odors than men. In mice, odorants elicit faster activation from a higher number of olfactory sensory neurons in females than males. Our study explores sex differences in olfaction in Long Evans rats from a behavioral and electrophysiological perspective. Local field potentials (LFPs) in the olfactory bulb (OB) represent the coordinated activity of bulbar neurons. Olfactory gamma (65-120 Hz) and beta (15-30 Hz) oscillations have been functionally and anatomically linked to odor perception. Spontaneous and odor-evoked OB LFPs were recorded from awake rats at the same time for 12 days. Odors used included urine of both sexes and monomolecular odorants characterized previously for correlation of volatility with behavior and OB oscillations. Sampling duration, baseline gamma and beta power, and odor-elicited beta and gamma power were analyzed. We find that females sample odorants for a shorter duration than males. While baseline gamma and beta power do not show significant differences between the two sexes, odor-elicited gamma and beta power in females is lower than in males. Neither sampling duration nor beta and gamma power in females varied systematically with day of estrus. We further verify that variance of these behavioral and physiological measures is not different across sexes, adding to growing evidence that researchers need not be concerned about often-claimed additional variance in female subjects.

Consistency of resting-state correlations between fMRI networks and EEG band-power
Several simultaneous EEG-fMRI studies have aimed to identify the relationship between EEG band power and fMRI resting state networks (RSNs) to elucidate their neurobiological significance. Although common patterns have emerged, inconsistent results have also been reported. This study examines the consistency of these correlations across subjects and to understand how factors such as the hemodynamic response delay and the use of different EEG data spaces (source/scalp) influence them. Using three distinct EEG-fMRI datasets, acquired independently on 1.5T, 3T and 7T MRI scanners (comprising 42 subjects in total), we evaluate the generalizability of our findings across different acquisition conditions. We found consistent correlations between fMRI RSN and EEG band-power time-series across subjects in the three datasets studied, with systematic variations with RSN, EEG frequency-band, and HRF delay, but not with EEG space. Qualitatively, the majority of these correlations were similar across the three datasets, despite important differences in field strength, number of subjects and resting-state conditions. Our findings support consistent correlations across specific fMRI RSNs and EEG bands and highlight the importance of methodological considerations in interpreting them that may explain conflicting reports in existing literature.

Intrinsic dynamics and neural implementation of a hypothalamic line attractor encoding an internal behavioral state
Line attractors are emergent population dynamics hypothesized to encode continuous variables such as head direction and internal states. In mammals, direct evidence of neural implementation of a line attractor has been hindered by the challenge of targeting perturbations to specific neurons within contributing ensembles. Estrogen receptor type 1 (Esr1)-expressing neurons in the ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) show line attractor dynamics in male mice during fighting. We hypothesized that these dynamics may encode continuous variation in the intensity of an internal aggressive state. Here, we report that these neurons also show line attractor dynamics in head-fixed mice observing aggression. We exploit this finding to identify and perturb line attractor-contributing neurons using 2-photon calcium imaging and holographic optogenetic perturbations. On-manifold perturbations demonstrate that integration and persistent activity are intrinsic properties of these neurons which drive the system along the line attractor, while transient off-manifold perturbations reveal rapid relaxation back into the attractor. Furthermore, stimulation and imaging reveal selective functional connectivity among attractor-contributing neurons. Intriguingly, individual differences among mice in line attractor stability were correlated with the degree of functional connectivity among contributing neurons. Mechanistic modelling indicates that dense subnetwork connectivity and slow neurotransmission are required to explain our empirical findings. Our work bridges circuit and manifold paradigms, shedding light on the intrinsic and operational dynamics of a behaviorally relevant mammalian line attractor.

Activity of prefrontal cortex serotonin 2A receptor expressing neurons is necessary for the head-twitch response of mice to psychedelic drug DOI in a sex-dependent manner
Serotonin 2A receptors (5-HT2ARs) mediate the effects of psychedelic drugs. 5-HT2AR agonists, such as (-)-2,5-dimethoxy-4-iodoamphetamine hydrochloride (DOI), that produce a psychedelic experience in humans induce a head-twitch response (HTR) behavior in rodents. However, it is unknown whether the activity of 5-HT2AR expressing neurons is sufficient to produce the HTR in the absence of an agonist, or in which brain region 5-HT2ARs control the HTR. Here, we use an optogenetic approach to examine whether activation of 5-HT2AR expressing neurons in the mouse prefrontal cortex (PFC) is sufficient to induce HTRs alone, or may augment the HTR produced by DOI, and if inhibition of these neurons prevents DOI-induced HTRs in mice. We crossed Htr2a-Cre mice to Cre-dependent optogenetic lines Ai32 (channelrhodopsin) and Ai39 (halorhodopsin) to selectively activate and inhibit (respectively) 5-HT2AR-expressing neurons in the PFC of adult mice. We found that optogenetic stimulation of PFC 5-HT2AR expressing neurons in the absence of an agonist does not increase HTRs in mice. In both male and female Ai32 mice that received vehicle, there was no difference in HTRs in mice that expressed Htr2a-Cre compared with control mice, indicating that optogenetic activation of 5-HT2AR+ cells in the PFC was not sufficient to produce HTRs in the absence of an agonist. In female mice, activation of PFC 5-HT2AR expressing neurons augmented the HTR produced by DOI. However, this result was not seen in male mice. In contrast, inhibition of 5-HT2AR expressing neurons in the PFC prevented the increase in HTR produced by DOI in male, but not in female, mice. Together, these findings suggest that activation of 5-HT2ARs in the PFC is not sufficient to induce HTRs in the absence of a 5-HT2AR agonist but is necessary for induction of HTRs by a 5-HT2AR agonist in a sex-dependent manner.

VesselBoost: A Python Toolbox for Small Blood Vessel Segmentation in Human Magnetic Resonance Angiography Data
Magnetic resonance angiography (MRA) performed at ultra-high magnetic field provides a unique opportunity to study the arteries of the living human brain at the mesoscopic level. From this, we can gain new insights into the brain's blood supply and vascular disease affecting small vessels. However, for quantitative characterization and precise representation of human angioarchitecture to, for example, inform blood-flow simulations, detailed segmentations of the smallest vessels are required. Given the success of deep learning-based methods in many segmentation tasks, we here explore their application to high-resolution MRA data, and address the difficulty of obtaining large data sets of correctly and comprehensively labelled data. We introduce VesselBoost, a vessel segmentation package, which utilizes deep learning and imperfect training labels for accurate vasculature segmentation. Combined with an innovative data augmentation technique, which leverages the resemblance of vascular structures, VesselBoost enables detailed vascular segmentations.

Frequency-Dependent Inter-Brain Synchrony is Modulated by Social Interaction in Freely Moving Mice
Social interaction, a pivotal aspect of human and animal behavior, involves a dynamic exchange of information that shapes behavioral responses, emotional states, and cognitive processes. To gain insights into the neural mechanisms underlying these processes, it is necessary to simultaneously investigate the brain activity of socially interacting subjects. Commonly, the simultaneous study of behavior and brain activity during the execution of social tasks is conducted through Hyperscanning in humans which limits the availability of interventions. Here we describe a new experimental platform that combines the development of a new miniaturized optical system, the 'MiCe-Scope', to monitor neural activity across the entire cortical mantle with a behavioral paradigm to perform a Hyperscanning study in freely moving mice engaged in social interaction. Our results revealed inter-brain synchrony across different frequency bands widespread over the entire cortical mantle, modulated by social behavior. This finding suggests that synchronization reflects the mutual prediction performed by the entire cortex in mice of interacting dyads. The presence of different synchronization maps in these frequency bands suggests a multiscale nature of interaction, extending the predictive nature of interaction to cortical areas beyond the medial prefrontal cortex. Our work provides an experimental framework to conduct Hyperscanning studies in an animal model that mirrors findings from human studies.

Clearing truncated tau protein restores neuronal function and prevents microglia activation in tauopathy mice
Tau protein truncated at aspartate 421 (Asp421) is a characteristic feature of Alzheimer's disease (AD) and other tauopathies. It is likely to have a role in their pathogenesis by promoting tau aggregation. Here, using two tauopathy mouse models, we show that a monoclonal antibody against Asp421, 5G2, led to a) a 59-74% clearance of insoluble tau protein in the brains of JNPL3 tauopathy mice following a thirteen-week treatment period, b) a 46% decrease of tau levels in brain interstitial fluid immediately following a single dose of 5G2 as examined by brain microdialysis in awake JNPL3 mice, c) improved neuronal function and d) reduced microglial activation as determined by two-photon imaging in awake PS19 tauopathy mice, where we also found tau accumulation earlier than signs of microglial activation. For mechanistic insight using culture models, 5G2 prevented toxicity of AD brain derived pathological tau protein, cleared intracellular tau, and prevented microgliosis. We also knocked down the intracellular Fc receptor and ubiquitin E3 ligase, TRIM21, and found a reduction in cellular retention of tau antibodies, which appeared to reduce the acute efficacy (24 h) of tau antibodies but not their longer-term efficacy (5 days). Overall, these findings strongly support the feasibility of targeting Asp421 truncated tau protein to treat tauopathies, indicate that tau-associated abnormalities of neuronal activity precede microglial activation and that antibody-mediated tau clearance via the TRIM21 pathway is mostly transient.

Tango-seq: overlaying transcriptomics on connectomics to identify neurons downstream of Drosophila clock neurons
Knowing how neural circuits change with neuronal plasticity and differ between individuals is important to fully understand behavior. Connectomes are typically assembled using electron microscopy, but this is low throughput and impractical for analyzing plasticity or mutations. Here, we modified the trans-Tango genetic circuit-tracing technique to identify neurons synaptically downstream of Drosophila s-LNv clock neurons, which show 24hr plasticity rhythms. s-LNv target neurons were labeled specifically in adult flies using a nuclear reporter gene, which facilitated their purification and then single cell sequencing. We call this Tango-seq, and it allows transcriptomic data and thus cell identity to be overlayed on top of anatomical data. We found that s-LNvs preferentially make synaptic connections with a subset of the CNMa+ DN1p clock neurons, and that these are likely plastic connections. We also identified synaptic connections between s-LNvs and mushroom body Kenyon cells. Tango-seq should be a useful addition to the connectomics toolkit.

Neural tracking of the speech envelope predicts binaural unmasking
Binaural unmasking is the remarkable phenomenon that it is substantially easier to detect a signal in noise, when the interaural parameters of the signal are different from those of the noise - a mechanism that comes in handy in so-called cocktail party scenarios. In this study, we investigated the effect of binaural unmasking on neural tracking of the speech envelope. We measured EEG in 8 participants who listened to speech in noise at a fxed signal-to-noise ratio (-12 dB or -9 dB, depending on the speech material), in two conditions: one where speech and noise had the same interaural phase difference (both speech and noise having an opposite waveform across ears, S{pi}N{pi}, and one where the interaural phase difference of the speech was different from that of the noise (only the speech having an opposite waveform across ears, S{pi}N0). We measured a clear benefit of binaural unmasking in behavioral speech understanding scores, accompanied with increased neural tracking of the speech envelope. Moreover, analyzing the temporal response functions revealed that binaural unmasking also resulted in decreased peak latencies and increased peak amplitudes. Our results are consistent with previous research using auditory evoked potentials and steady-state responses to quantify binaural unmasking at cortical levels. Moreover, they confirm that neural tracking of speech is modulated by speech understanding, even if the acoustic signal-to-noise ratio is kept constant.

Disentangling acute motor deficits and adaptive responses evoked by the loss of cerebellar output
Cerebellar patients exhibit a broad range of impairments when performing voluntary movements. However, the sequence of events leading to these deficits and the distinction between primary and compensatory processes remain unclear. We addressed this question by reversibly blocking cerebellar outflow in monkeys performing a planar reaching task. We found that the reduced hand velocity observed under cerebellar block is driven by a combination of a general decrease in muscle torque and a spatially tuned reduction in velocity, particularly pronounced in movements involving inter-joint interactions. The time course of these two processes was examined using repeated movements to the same target under cerebellar block. We found that the reduced velocity was driven by an acute onset of weakness superimposed on a gradually emergent strategy aimed to minimize passive inter-joint interactions. Finally, although the reduced velocity affected movements to all targets, it could not explain the enhanced motor noise observed under cerebellar block, which manifested as decomposed and variable trajectories. Our results suggest that cerebellar deficits lead to motor impairments through a loss of muscle strength and altered motor control strategy to compensate for the impaired control of limb dynamics. However, the loss of feedforward control also leads to increased motor noise, which cannot be strategically eliminated.

Dissecting push/pull interactions in the rat subcortical auditory pathway
The role of subcortical structures in binaural integration is of great interest for auditory processing. The inferior colliculus (IC) is a main auditory midbrain center where ascending and descending auditory projections converge, which was suggested to encode auditory information via a push-pull mechanism between the two ICs. However, the origin of this push-pull mechanism in the brain and how it interacts with other upstream/downstream subcortical areas remain to be elucidated. Here, we harness functional MRI (fMRI) in combination with IC lesions in the rat to dissect the push-pull interaction from a brain-wide perspective. We find evidence for the push-pull mechanism in IC through negative/positive fMRI signals in the ipsilateral/contralateral ICs upon monaural stimulation. By unilaterally lesioning the corresponding contralateral IC, we demonstrate the necessity of intercollicular interactions for the push-pull interaction. Using binaural stimulation and IC lesions, we show that the push-pull interaction is exerted also in binaural processing. Finally, we demonstrate that, at least at the population level revealed by fMRI, the main push-pull interactions occur first at the IC level, and not earlier, and that the outcome of the push-pull "calculation" is relayed downstream to MGB. This dissection of the push-pull interaction sheds light into subcortical auditory function.

Fast and robust objective EEG audiometry
The current gold standard of audiometry relies on subjective behavioral responses, which is impractical and unreliable for certain groups such as children, individuals with severe disabilities, or the disabled elderly. This study presents a novel electroencephalography (EEG) system that is easy to setup and estimates audiometric thresholds quickly. Air-conduction audiometric thresholds at 250, 500, 1000, 2000, 4000, and 8000 Hz and 5 dB resolution were estimated from ten elderly patients with asymmetric sensorineural hearing loss and five normal hearing young adults using three different systems: the novel EEG system, conventional pure-tone audiometry (PTA), and an automated behavioral test with the same stimulus properties as in the EEG test. EEG data was collected for 15 minutes from 32 semi-dry EEG electrodes. Later, the EEG system was trimmed to 8 electrodes and 7.5 minutes of data with satisfactory results. Correlation and regression analysis validated the hearing thresholds derived from both EEG configurations relative to the behavioral hearing thresholds: Pearson correlation of 0.82 between PTA and 8-electrode 7.5-minute EEG data. The results of this study open the door to fast and objective hearing threshold estimation with EEG.

Associating EEG Functional Networks and the Effect of Sleep Deprivation as Measured Using Psychomotor Vigilance Tests
People are routinely forced to undertake cognitive challenges under the effect of sleep deprivation, due to professional and social obligations forcing them to ignore their circadian clock. However, low intra-individual and high inter-individual differences in behavioural outcomes are known to occur when people are sleep deprived, leading to the conclusion that trait-like differences to sleep deprivation could explain the differing levels of resilience. Within this study we consider if trait-like resilience to sleep deprivation, measured using psychomotor vigilance tests over a 40h constant routine protocol, could be associated with graph metrics (mean node strength, clustering coefficient, characteristic path length and stability) calculated from EEG functional networks acquired when participants are well rested (baseline). Furthermore, we investigated how stability (the consistency of a participant's functional network over time measured using 2-D correlation) changed over the constant routine. We showed evidence of strong significant correlations between high mean node strength, low characteristic path length and high stability at baseline with a general resilience to extended sleep deprivation, although the same features lead to vulnerability during the period of natural sleep onset, highlighting non-uniform correlations over time. We also show significant differences in the levels of stability between resilient and vulnerable groups.

Approximation of bone mineral density and subcutaneous adiposity using T1-weighted images of the human head
Bones and brain are intricately connected and scientific interest in their interaction is growing. This has become particularly evident in the framework of clinical applications for various medical conditions, such as obesity and osteoporosis. The adverse effects of obesity on brain health have long been recognised, but few brain imaging studies provide sophisticated body composition measures. Here we propose to extract the following bone- and adiposity-related measures from T1-weighted MR images of the head: an approximation of skull bone mineral density (BMD), skull bone thickness, and two approximations of subcutaneous fat (i.e., the intensity and thickness of soft non-brain head tissue). The reliability and validity of these four distinct measures were tested in two large-scale databases, the UK Biobank and OASIS-3. The measures pertaining to skull BMD, skull bone thickness, and intensity-based adiposity proxy proved to be reliable (r=.93/.83/.74, p<.001) and valid, with high correlations to DXA-derived head BMD values (rho=.70, p<.001) and MRI-derived abdominal subcutaneous adipose volume (rho=.62, p<.001). Thickness-based adiposity proxy had only a low retest reliability (r=.58, p<.001). The outcomes of this study constitute an important step towards extracting relevant non-brain features from available brain scans.

Encoding seizures with partial synchronization: A spiking neural network for biosignal monitoring on a mixed signal neuromorphic processor
Long-term monitoring of biomedical signals is crucial for modern clinical treatment of neurological disorders such as epilepsy. Encoding epileptic seizures with partial synchronization using a spiking neural network (SNN) offers a promising avenue as such networks can be implemented on ultra-low-power neuromorphic processors. Indeed, such bio-inspired neuromorphic systems containing mixed-signal asynchronous electronic circuits can perform always-on monitoring of biomedical signals for extended periods of time, without having to employ traditional clocked analog-to-digital conversion or cloud-based processing platforms. Here, we present a novel SNN architecture, co-designed and implemented with a mixed-signal neuromorphic chip, for monitoring epileptic seizures. Our hardware-aware SNN captures the phenomenon of partial synchronization within brain activity during seizures. We validate the network on a full-custom mixed-signal neuromorphic hardware using real-time analog signals converted from an Electroencephalographic (EEG) seizure data-set, and encoded as streams of events by an asynchronous delta modulation (ADM) circuit, directly integrated, together with its analog front-end (AFE) signal conditioning circuits, on the same die of the neuromorphic SNN chip. We demonstrate the ability of the hardware SNN to extract local synchronization patterns from the event streams and show that such patterns can facilitate seizure detection using a simple linear classifier. This research represents a significant advancement toward developing embedded intelligent ``wear and forget'' units for resource-constrained environments. These units could autonomously detect and log relevant EEG events of interest in out-of-hospital environments, offering new possibilities for patient care and management of neurological disorders.

Linear models replicate the energy landscape and dynamics of resting-state brain activity
The brain is constantly and spontaneously active even in the absence of sensory stimulation or motor tasks. The spatiotemporal dynamics of this resting-state brain activity are commonly assumed to be non-stationary and non-linear. It has been proposed that dynamic features (e.g., brain states) extracted from measured resting-state brain activity can better explain subject-specific phenotypes (e.g., fluid intelligence score) than static features. However, several recent studies reported that some analysis methods used to extract dynamic features from resting-brain activity as measured by functional magnetic resonance imaging (fMRI) do not truly reflect non-stationarity and/or non-linear statistical properties of the data. In this study, we examined energy landscape analysis (ELA) to assess whether dynamic features extracted by ELA accurately represent the non-stationary and/or non-linear aspects of resting-brain fMRI activity. To specify the statistical properties being extracted, we applied ELA to both real fMRI data and surrogate data generated by linear and stationary models. We found that ELA results obtained with both the real and simulated data were almost indistinguishable, suggesting that the features extracted by ELA can be explained by covariance and autocorrelation structures. These results corroborate a recent proposal that resting-state fMRI activity can be adequately described by linear models.

Effects of age on responses of principal cells of the mouse anteroventral cochlear nucleus in quiet and noise
Older listeners often report difficulties understanding speech in noisy environments. It is important to identify where in the auditory pathway hearing-in-noise deficits arise to develop appropriate therapies. We tested how encoding of sounds is affected by masking noise at early stages of the auditory pathway by recording responses of principal cells in the anteroventral cochlear nucleus (AVCN) of aging CBA/CaJ and C57BL/6J mice in vivo. Previous work indicated that masking noise shifts the dynamic range of single auditory nerve fibers (ANFs), leading to elevated tone thresholds. We hypothesized that such threshold shifts could contribute to increased hearing-in-noise deficits with age if susceptibility to masking increased in AVCN units. We tested this by recording the responses of AVCN principal neurons to tones in the presence and absence of masking noise. Surprisingly, we found that masker-induced threshold shifts decreased with age in primary-like units and did not change in choppers. In addition, spontaneous activity decreased in primary-like and chopper units of old mice, with no change in dynamic range or tuning precision. In C57 mice, which undergo early onset hearing loss, units showed similar changes in threshold and spontaneous rate at younger ages, suggesting they were related to hearing loss and not simply aging. These findings suggest that sound information carried by AVCN principal cells remains largely unchanged with age. Therefore, hearing-in-noise deficits may result from other changes during aging, such as distorted across-channel input from the cochlea and changes in sound coding at later stages of the auditory pathway.

Flexible behavioral adjustment to frustrative nonreward in anticipatory behavior, but not in consummatory behavior, requires the dorsal hippocampus
The hippocampus (HC) is recognized for its pivotal role in memory-related plasticity and facilitating adaptive behavioral responses to reward shifts. However, the nature of its involvement in the response to reward downshifts remains to be determined. To bridge this knowledge gap, we explored the HC's function through a series of experiments in various tasks involving reward downshifts and using several neural manipulations in rats. In Experiment 1, complete excitotoxic lesions of the HC impaired choice performance in an 8-maze task after reducing the quantity of sugar pellet rewards. In Experiment 2, whereas chemogenetic inhibition of the dorsal HC left consummatory responses unaffected after a sucrose downshift, it significantly disrupted anticipatory behavior following a food-pellet reward reduction. Experiments 3-5 used peripheral lipopolysaccharide (LPS) treatment and found an increase in cytokine levels in the dorsal HC (dHC, Experiment 3), impaired anticipatory choice (Experiment 4), but no effect on consummatory behavior in two reward-downshift tasks. In Experiment 6, after a sucrose downshift, we found no evidence of increased activation in either the dorsal or ventral HC, as measured by c-Fos expression. These findings highlight the HC's pivotal role in adaptively modulating anticipatory behavior in response to frustrative nonreward, while having no effect on adjustments of consummatory behavior. Spatial orientation, memory update, choice of reward signals of different value, and anticipatory vs. consummatory adjustments to reward downshift are discussed as potential mechanisms that could elucidate the specific effects observed from HC manipulations.

Modeling the connectome of joint attention in infancy predicts Theory of Mind in preschool-age
A pivotal developmental milestone is reached around 9 months when infants begin to coordinate their attention with others. Joint attention acts as a catalyst for infants' learning and is proposed to predict later social cognitive development, including understanding others' minds (Theory of Mind, ToM). However, neural markers predicting joint attention development and their predictive value for later social cognitive abilities remain unknown. Here, we trained a model to identify whole-brain connectivity patterns predictive of joint attention from resting-state fMRI data of 8-15-month-old infants. The model significantly predicted joint attention scores in an independent infant sample, beyond general development. Dominant connections lay within the default network and its interaction with the ventral attention network. Crucially, this connectome also predicted later ToM in children aged 2-5 years. Beyond providing an early marker for individual differences in social cognitive development, these findings have high potential for the early diagnosis of social cognitive disorders.

Extracellular vesicles from mucopolysaccharidosis type III microglia impair neurite growth
In the neurodegenerative mucopolysaccharidosis type III (MPSIII), accumulation of abnormal glycosaminoglycans (GAGs) induces severe neuroinflammation by triggering the microglial pro-inflammatory cytokines production via a TLR4-dependent pathway. But the extent of the microglia contribution to the MPSIII neuropathology remains unclear. Extracellular vesicles (EVs) mediate intercellular communication and are known to participate in the pathogenesis of adult neurodegenerative diseases. However, characterization of the molecular profiles of EVs released by MPSIII microglia and their effects on neuronal functions have not been described. Here, we isolated EVs secreted by the BV-2 microglial cells after treatment with GAGs purified from urines of MPSIII patients. Proteomics and RNA sequencing analysis of MPSIII-EVs revealed a specific content involved in neuroinflammation and neurodevelopment pathways. Treatment of cortical neurons with MPSIII-EVs induced a disease-associated signature with somato-dendritic compartment alterations. This study is the first to show that MPSIII-EVs can propagate neuroinflammation, deprive neurons of neurodevelopmental factors and deliver a specific molecular message to surrounding naive cells. This work provides a framework for further studies of biomarkers to evaluate efficiency of emerging therapies.