eNeuro最新文献

Blocking is a key statistical method introduced almost a century ago by Ronald Fisher. Blocking controls the effect of "nuisance" variables that are not of direct interest but introduce unwanted variation into the experimental response. Block factors, such as cage, litter, or time, are used to group experimental units into homogeneous subsets. There are two types of block designs: complete and incomplete. In complete block designs every treatment appears in every block. Examples include the Randomized Complete Block Design (RCBD) with a single block factor, and variants such as Latin square and Graeco-Latin square designs with multiple block factors. RCBDs are simple, flexible, and the most widely used. Replicated and nested Latin square designs allow more rigorous control of complex nuisance structures with minimal sample size. Incomplete block designs are extremely useful when practical constraints (e.g., caging density or varying litter sizes) restrict complete treatment replication across all blocks. Because not all treatments appear in every block, these designs require computer-generated allocation plans to obtain optimum balance and efficiency. Each of the seven blocking designs described in this paper includes a practical example from the research literature, the corresponding skeleton analysis of variance and R code for random allocation plans. By increasing precision and power to detect treatment effects, blocking promotes ethical research by maximizing the amount of information for a minimum number of animals, supporting the 3Rs principle of Reduction.

Rigorous, statistically grounded experimental design is central to ethical and effective animal research. Foundational principles for statistically based Design of Experiments (DOE) were established over a century ago by Sir Ronald Fisher. They have since been augmented by modern computational tools that now enable researchers to implement designs that maximize scientific information and benefit while minimizing harms. However, many preclinical investigators are unfamiliar with formal DOE methods. Poorly designed experiments followed by inappropriate statistical analyses contribute to poor reproducibility, translational failure, and unnecessary animal use. This first paper in a three-part series introduces neuroscience researchers to the fundamentals of statistically based experimental design as a substitute for traditional two-group comparisons. Key components of a designed experiment are defined, along with the importance of correctly identifying experimental units to avoid pseudo-replication. Fisher's three essential design principles-randomization, replication, and blocking-are presented as nonoptional practices for controlling bias, managing variation, and ensuring valid statistical inferences. Particular emphasis is placed on probability-based random allocation, the use of validated computer-generated randomization plans, and the role of blocking in reducing nuisance variation. By embedding robust design principles early in study planning, researchers can produce reliable, reproducible, and ethically justifiable data. Subsequent papers in the series will expand on methods for controlling unwanted variation through blocking (Part 2) and outline flexible multivariable design strategies (Part 3). Worked examples and R code are included.

Galanin-expressing neurons in the ventrolateral preoptic area (VLPO^galanin) are active during sleep and play an important role in regulating non-rapid eye movement (NREM) sleep. It is generally believed that VLPO^galanin neurons promote sleep via inhibitory actions in arousal-promoting regions of the brain. Histaminergic neurons are a population of wake-active neurons that receive strong projections from the sleep-active VLPO^galanin neurons. However, the ability of galanin to influence the activity of histaminergic neurons has received limited attention. Here, using whole-cell patch-clamp electrophysiological recordings from genetically identified histaminergic neurons in male mice, we explore the mechanisms by which galanin influences histaminergic neuron electrical excitability. Our results reveal that galanin is a powerful inhibitor of histaminergic neuron activity and demonstrate that the inhibitory effects of galanin are mediated by galanin receptor 1 (GALR1) and the subsequent opening of G-protein-coupled inwardly rectifying (GIRK) and large conductance calcium-activated potassium (BK) channels. Furthermore, we identify that histaminergic neurons highly express Galr1 mRNA and show that the GALR1-mediated hyperpolarization of histaminergic neurons is largely independent of action potential-dependent synaptic transmission or fast excitatory or inhibitory neurotransmitters. Together, these results suggest that direct postsynaptic activation of GALR1 expressed on histaminergic neurons mediates the inhibitory effects of galanin on these neurons. This data also supports the notion that the sleep-promoting effects of VLPO^galanin neuron activation may occur via the ability of galanin to inhibit the arousal-promoting histaminergic neurons.

Microglia, resident immune sentinels in the brain, are crucial in responding to tissue damage, infection, damage signals like purines (ATP/ADP), and clearing cellular debris. It is currently unknown how microglial reactivity progresses and contributes to seizure development following Theiler's murine encephalomyelitis virus (TMEV) infection. Previously, it has been demonstrated that purinergic signaling in microglia is disrupted in the hippocampus of TMEV-infected mice. However, whether reactive cortical microglia also exhibit changes in purinergic signaling, cytokine levels, and purinergic receptors is unknown. Thus, we seek to evaluate region-based differences in microglial reactivity in the TMEV model. We employed a custom triple transgenic mouse line expressing tdTomato and GCaMP6f under a CX3CR1 Cre promoter and exogenously applied ATP/ADP to acute brain slice preparations from TMEV-infected mice and controls of either sex. Interestingly and in contrast to what is observed in the hippocampus, we found that despite microglial reactivity in the cortex, microglia can respond to purinergic damage signals and engage calcium signaling pathways, comparable to PBS controls. Using a cytokine panel, we also found that proinflammatory cytokine levels (TNF-α, IL-1α, and IFN-γ) are brain region dependent in mice infected with TMEV. Using RNAscope FISH, we observed increases in expression of purinergic receptors responsible for microglial motility (P2Y₁₂R) and inflammation (P2X₇R) in the cortex. Collectively our results suggest that following TMEV infection, microglial response to novel damage signals, as well as the production of proinflammatory cytokines, varies as a function of the brain region.

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.