eNeuro最新文献

Vestibular dysfunction constitutes a major medical concern, and regeneration of hair cells (HCs) is a primary target of gene therapy aimed at restoring vestibular functions. Thus far, therapeutic trials in animal models targeting vestibular loss associated with genetic diseases have yielded variable and partial results, and the functional identity and quantity of HCs required to restore minimal or normal vestibular function remain undefined. Indeed, direct comparisons between structural pathology and quantitative assessments of vestibular dysfunctions are lacking in humans and are rather limited in animal models, representing a significant gap in current knowledge. Here, we present an innovative methodology to bridge the gap between HC integrity and functional vestibular loss in individual mice of either sex. Gradual vestibular deficits were induced through a dose-dependent ototoxic lesion, quantified with canal or utricular-specific vestibulo-ocular reflex tests, and were then correlated in all individuals with the loss of type I and type II HCs in different regions of ampulla and macula. Our findings reveal that the structure-function relationship is nonlinear, with lower bound of ∼50% of HCs necessary to retain minimal vestibular function, and threshold exceeding 80% to preserve normal function, thus shedding light on population coding mechanisms for vestibular response. Our data further support the decisive role of type I, rather than type II, HC in the tested VOR functions.

Measuring neurite length is crucial in neurobiology because it provides valuable insights into the growth, development, and function of neurons. In particular, neurite length is fundamental to study neuronal development and differentiation, neurons responses to drugs, neurodegenerative diseases and neuronal plasticity. Surprisingly, there is currently a lack of tools for high-throughput neurite analysis. In this article, we present CABaNe, as an open-source, high-throughput, rule-based ImageJ macro for cell analysis, including their neurite length. This macro possesses a graphical interface, metadata production, as well as verification means before and after analysis. Rule-based and machine learning-based programming have been tested for cell identification. Cell tested were N2A, a mouse neuroblastoma cell line. After testing, we had better precision and adaptability using rule-based cell identification. We challenged CABaNe with currently used techniques, which are manual or assisted. When tested on a small sample, CABaNe analyzed the dataset of interest much faster than manual measurements, while maintaining or increasing precision. When tested on a large dataset, comparing different conditions, we successfully highlighted differences between conditions, in a fully automated manner. Therefore, CABaNe is viable as a high-throughput option for cell analysis, for neurite length and other parameters. It is a base of code that can be used for other analysis or to train deep learning models. In the future, we expect this tool to be widely used in both basic and applied neurobiology research.

Dopaminergic inputs to various brain regions, such as the striatum, orbitofrontal cortex, and amygdala, play a critical role in processing reward acquisition information. While reward-related activity is also observed more broadly in motor, parietal, and hippocampal regions, the functional significance and potential hierarchy of reward-related representation across these latter areas remain unclear. We investigated this by quantifying neural predictive power using machine learning. Specifically, neural activity was examined in six brain areas-the primary and secondary motor cortices (M1 and M2), posterior parietal cortex (PPC), dorsal and ventral CA1 (dCA1 and vCA1), and lateral entorhinal cortex (LEC)-in male rats performing a self-initiated left-right choice task. Machine learning models classified rewarded versus nonrewarded trials based on neuronal firing properties significantly above chance for all regions. Crucially, classification revealed a clear performance gradient, forming a functional hierarchy: models using hippocampal data (dCA1 and vCA1) performed best, followed by LEC and PPC, with M1 and M2 performing lowest. Furthermore, SHapley Additive exPlanations (SHAP) analysis revealed a qualitative transformation in coding strategies along this hierarchy: while neocortical regions relied on subtle, distributed high-order statistics, the hippocampus utilized precise, categorical representations. At this apex, distinct strategies emerged: dCA1 primarily utilized temporally precise post-reward spike distributions with transient increase of response, while vCA1 integrated both spike timing and firing rate changes with suppressive response. These findings provide quantitative evidence for a functionally hierarchical and qualitative evolution of reward-related representation, highlighting distinct roles of dCA1 and vCA1 in encoding reward-related events to potentially guide future behavior.

Auditory-evoked EEG signals contain rich temporal and cognitive features that reflect both the identity of individuals and their neural response to external stimuli. Traditional unimodal approaches often fail to fully leverage this multidimensional information fully, limiting their effectiveness in real-world biometric and neurocognitive applications. This study aims to develop a unified deep learning model capable of jointly performing biometric identification, auditory stimulus language classification, and device modality recognition, thereby exploiting both physiological and cognitive dimensions of auditory-evoked EEG. We introduce TriNet-MTL (Triple-Task Neural Transformer for Multitask Learning), a multi-branch deep learning framework composed of a shared temporal encoder and a transformer-based sequence modeling unit, trained and validated on auditory-evoked EEG data from 20 human participants (16 males and 4 females). The architecture is designed to simultaneously learn task-specific features via three dedicated output heads, each addressing one of the following: user identity (biometric), stimulus language (native vs non-native), and stimulus delivery mode (in-ear vs bone conduction). The model is trained using a sliding window approach and optimized through joint cross-entropy loss across tasks. TriNet-MTL demonstrates robust performance across all three classification tasks, achieving high accuracy in biometric identification (>93%) and strong generalization in cognitive state inference. Multi-task training further improves representation learning, reducing inter-task interference while enhancing task synergy. The proposed TriNet-MTL framework effectively captures both user-specific and cognitively informative patterns from auditory-evoked EEG, establishing a promising direction for integrated EEG-based biometric authentication and cognitive state monitoring in real-world systems.

Neurite outgrowth is essential for neural circuit formation and is tightly regulated by secreted factors and their receptors. The secreted extracellular domain of the amyloid precursor protein (sAPPα) has been shown to modulate neurite outgrowth. Recently, the gamma amino butyric acid receptor type-B subunit 1a (GABA_BR1a) was identified as an sAPPα binding partner that mediates its effects on synaptic transmission. Here, we investigated whether this interaction also regulates neurite outgrowth. In mouse primary hippocampal neurons of either sex, the GABA_BR agonist baclofen reduced axon length; whereas its antagonist CGP54626 increased axon length in primary hippocampal neurons. Moreover, GABA_BR1a knock-out increased axon length and abolished the effect of baclofen. Application of sAPPα reduced axon length, an effect that required the presence of both GABA_BR1a and the extension domain of sAPPα, which mediates its binding to GABA_BR1a. Similarly, the APP 17mer peptide, which is sufficient to bind GABA_BR1a and mimic the effects of sAPP on synaptic transmission, reduced axon outgrowth in wild-type but not in GABA_BR1a-deficient neurons. Together, these findings indicate that the 1a isoform contributes to GABA_BR-dependent suppression of neurite outgrowth and mediates the inhibitory effect of sAPPα on neurite outgrowth.

Social cognition, central to emotional and cognitive well-being, is particularly vulnerable to aging, where impairments can lead to isolation and functional decline. Despite compelling evidence that altered social behavior is associated with cognitive decline and dementia risk, experimental strategies for testing causative links remain scarce. To address this gap, we aimed to establish a rat model for research on social neurocognitive aging. We conducted a large-scale behavioral study in 169 male young (6 months) and aged (24-25 months) Long-Evans rats. In order to explore potential relationships among aging outcomes, we first documented individual differences in a widely validated water maze test of hippocampal learning and memory. Sociability and social novelty were then evaluated in the same subjects using the three-chamber social interaction test. Aging induced a selective shift in social novelty preference, marked by a striking familiarity bias in a substantial subpopulation of old rats, while sociability remained entirely normal. Changes in social novelty preference were completely independent of individual differences in spatial memory and unrelated to anxiety or sensorimotor function. Notably, neuromodulation via TMS enhanced social novelty preference selectively in aged rats that exhibited a social introversion phenotype before treatment, consistent with the possibility that this aging condition reflects a distinct and modifiable neural network state. Together, the results establish a valuable preclinical framework for developing a comprehensive neurobiology of social cognition in aging.

Cognitive flexibility, a mental process crucial for adaptive behavior, involves multiscale functioning across several neuronal organization levels. While its neural underpinnings have been studied for decades, limited knowledge exists about the structure and age-related differentiation of the white matter (WM) subserving brain regions implicated in cognitive flexibility. This study investigated the population-level relationship between cognitive flexibility and WM properties across two periods of human adulthood, aiming to discern how these associations vary over different life stages and brain tracts among men and women. We propose a novel framework to study age effects in brain structure-function associations. First, a meta-analysis was conducted to identify neural regions associated with cognitive flexibility. Next, projections of these neural regions were traced through the Human Connectome Project tractography template to identify the subserving WM associated with cognitive flexibility. Then, a cohort analysis was performed to characterize myelin-related macromolecular features using a subset of the UK Biobank magnetic resonance imaging (MRI) data, which has a companion functional/behavioral dataset. We found that (1) the wiring of cognitive flexibility is defined by a subset of brain tracts, which present undifferentiated features early in adulthood and significantly differentiated types in later life. (2) These MRI-derived properties are correlated with individual subprocesses of cognition closely related to cognitive flexibility. (3) In late life, homogeneity of specific WM tracts implicated in cognitive flexibility declines with age, a phenomenon not observed in early life. Our findings support the age-related differentiation of WM implicated in cognitive flexibility as a natural substrate of adaptive cognitive function.

Neurexins (Nrxns) are presynaptic cell adhesion molecules essential for synapse development and function. Of the many neurexin isoforms, only β-Nrxns contain the histidine-rich domain (HRD). While the HRD has been implicated in several pathological contexts, its normal physiological role remains unclear. To address this, we used a CRISPR-Cas9 method to generate a new mouse line expressing in-frame truncated Nrxn1β lacking the HRD. We found that HRD deletion did not affect mouse viability, gross brain development, or general behavior of either sex. However, loss of the HRD significantly altered neuroligin-1-dependent excitatory, but not inhibitory, presynaptic differentiation in primary cultured neurons. Moreover, this deletion affected presynaptic short-term plasticity, but not basal synaptic transmission, at hippocampal Schaffer collateral→CA1 synapses. These findings identify the Nrxn1β HRD as a potential contributor to excitatory presynaptic organization and function, providing new insight into the molecular diversity and specialization of Nrxns.

Natural environments contain behaviorally relevant information along many stimulus dimensions, each of which sensory systems must encode in order to guide behaviors. For example, the mammalian visual cortex encodes features of visual scenes such as spatial information related to object identity and temporal information about the motion of those objects in space. In order to reliably encode these behaviorally relevant visual features, neural representations should be robust to changes in environmental conditions. Further, information about changes in environmental conditions, such as the luminance changes that occur over the course of a day, is also important for guiding behaviors. In this study, we asked whether mouse primary visual cortex (V1) jointly represents the spatial properties of visual stimuli along with changes in the mean luminance of the visual scene. We find that while V1 neurons, in mice of either sex, encode spatial aspects of visual information in an invariant manner across luminance conditions, the V1 population response also contains a robust representation of luminance. Importantly, V1 populations encode changes in stimulus orientation and mean luminance along orthogonal axes in the neural response space, such that a change in one stimulus variable is encoded independently from the other.

Dementia-causing diseases, including Alzheimer's disease (AD), are one of the greatest health concerns facing the aging world population. A key feature of AD is excessive accumulation of amyloid-beta, leading to synapse and cell loss in brain structures, such as the hippocampus. This neurodegeneration is preceded by impaired neuron function, notably reduced synaptic inhibition. Metabotropic GABA_B receptors (GABA_BRs) may be modulated by amyloid precursor protein (APP) and are reported to be progressively lost from neuronal membranes of hippocampal pyramidal neurons. However, it remains unknown whether functional GABA_BR-mediated signaling changes over aging and whether or not pharmacological intervention can prevent receptor loss. In this study, we combine electrophysiological and biochemical analysis of hippocampal neurons in the Amyloid Precursor Protein/Presenilin-1 (APP/PS1) mouse model of AD from acute brain slices and organotypic slice cultures prepared from male and female mice to determine if functional GABA_BRs are lost and the effect of pharmacological modulation. Overall, we found that GABA_BR expression decreased with age, independent of genotype, with no evidence for postsynaptic GABA_BR loss in CA1 pyramidal cells at any age. We did observe a genotype-dependent reorganization of postsynaptic GABA_BR-mediated IPSCs, which was independent of age. Presynaptic GABA_BR-mediated inhibition was impaired in APP/PS1 mice, also independent of age. We observed that chronic GABA_BR modulation differentially regulated function but was independent of genotype. Overall, our data show that functional GABA_BR signaling is altered in APP/PS1 mice, independent of age, increasing our understanding of amyloidopathy-induced dysfunction.