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Age-related vascular changes accompany or precede the development of Alzheimer's disease (AD) pathology. The comorbidity of AD and arterial stiffening suggests that vascular changes have a pathogenic role. Carotid artery mechanics and hemodynamics have been associated with age-related cognitive decline. However, the impact of hemodynamics and vascular mechanics on regional vulnerability within the brain has not been thoroughly explored. Compared with the arterial system, brain venous circulation in cognitive impairment is less understood despite the venous system's role in transport. To study vasculature impact on biochemistry in AD models, we must first establish the differences in vasculature mechanics and hemodynamics in a common AD model compared with healthy controls. With this baseline data, future studies on manipulating vasculature integrity in mice become feasible. Young and aged female 3xTg mice and age-matched controls were imaged using a combination of ultrasound and mass spectrometry. Wall shear stress varied across age and AD models. Mean velocity and pulsatility index varied across age and AD. Liquid chromatography-mass spectrometry of brain tissue revealed several lipids that were statistically different between age and AD, and matrix-assisted laser desorption/ionization MS imaging revealed region-specific differences between groups. Combining both ultrasound and mass spectrometry, we were able to detect significant changes in the vascular biomechanics of neck vasculature prior to observing significant changes in the brain biochemistry. Our work revealed significant vascular differences in the 3xTg compared with controls and, to our knowledge, is the first to study vascular biomechanics via ultrasound in the 3xTg AD mouse model.

The cerebellum is well established in subsecond motor timing, but its role in suprasecond interval timing remains unclear. Here, we investigated how cerebellar output influences time estimation over longer timescales. Male rats performed a nose-poke interval timing task in which reward availability could be predicted either from a fixed 2.5 s auditory cue (cued trials) or had to be estimated internally during uncued 3.5 s trials that demanded self-timing. Chemogenetic inhibition of the lateral cerebellar nucleus (LCN) produced bidirectional effects: delayed action initiation in predictable trials and premature (∼100-160 ms) responses when self-timing was required. Despite a slowing of movement, overall task success rates remained unchanged. Because motor slowing is likely to lead to later, not earlier, action initiation, these results implicate the LCN in computing internal time estimates. These findings demonstrate that the cerebellum integrates motor and cognitive processes for suprasecond timing, with differential effects on externally guided and self-generated timing.

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.Significance Statement Understanding how the brain responds to sound offers new ways to identify individuals and assess their cognitive state. This study introduces a deep learning model that can simultaneously recognize a person, determine whether the sound they heard was in their native language, and identify how the sound was delivered. By combining all three tasks, the system learns richer patterns from brain signals, making it more accurate and reliable. Our results show that this approach can improve the performance of brain-based identification systems while also tracking how people process sounds. This work opens new possibilities for secure, brain-driven authentication and real-time cognitive monitoring.

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 knockout 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 wildtype 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.Significance Statement Amyloid precursor protein (APP) plays a central role in Alzheimer's disease, yet its normal functions are not fully understood. In this study, we uncover a previously unrecognized role of the GABA B Receptor in mediating the inhibitory effects of sAPPα on neurite outgrowth. These findings provide mechanistic insight into how disruptions in APP signaling could influence both normal brain development and pathological processes in neurodevelopmental disorders and Alzheimer's disease.

Spastin is a conserved microtubule-severing enzyme mutated in hereditary spastic paraplegia. The role that spastin plays in the cell biology of axon regeneration and degeneration has recently been investigated in Drosophila We show that the C. elegans spastin ortholog, spas-1, is expressed in GABA motor neurons, in addition to the known expression in touch receptor neurons (TRNs) and that it is required for axon regeneration in the GABA motor neurons after in vivo laser axotomy. We identified no neuronal developmental defects in the GABA motor neurons and only minor branching variations in the TRNs. However, we show that spas-1 is required for the long-term maintenance of TRN axons in C. elegans, as older spas-1 null C. elegans show a significant increase in specific axonal morphological defects compared with the wild type as identified by confocal microscopy in aged animals. Together, our results suggest that spastin is required for regrowth and maintenance of axons in C. elegans, consistent with previous reports in Drosophila.

There are a wide variety of commercially available antibodies for labeling microglial cells based on different protein targets, as well as antibodies for the same protein target made in different species. While this array of targets and hosts allows for flexibility in immunohistochemical experiments, it is important to validate that different antibodies provide comparable and accurate immunodetection prior to experimental data collection. We found that a commercially available anti-Iba1 antibody, made in goat, produces irregular staining patterns in specific regions of the mouse brain in both sexes, prompting a further investigation into the phenomenon. This Iba1-goat antibody displayed increased numbers of labeled cells when compared with expression of a CX3CR1-GFP reporter and IHC detection of P2RY12, two common microglial markers. Furthermore, immunodetection by other common anti-Iba1 antibodies made in rabbit and chicken did not display the excessive cell labeling when compared with the CX3CR1-GFP reporter. Upon further investigation, this Iba1-goat antibody was observed to highly colocalize with vasopressin neurons in the paraventricular nucleus of the hypothalamus (PVN) and the supraoptic nucleus of the hypothalamus (SON), the two main sites of vasopressin production in the brain. Other anti-Iba1 antibodies made in other species did not show this same colocalization with vasopressin. Finally, this effect was species-specific, as Wistar rats did not display erroneous cell labeling by the Iba1-goat antibody. In sum, the present study employs both qualitative and quantitative data to highlight the importance of validating antibody efficacy and specificity in a region- and species-specific manner.

Nonrapid eye movement (NREM) sleep is characterized by the interaction of multiple oscillations essential for memory consolidation, alongside a dynamic arrhythmic 1/f scale-free background that may also contribute to its functions. Recent spectral parametrization methods, such as fitting oscillation and one-and-over-F and irregular resampling auto-spectral analysis, enable the dissociation of rhythmic and arrhythmic components in the spectral domain; however, they do not resolve these processes in the time domain. Instantaneous measures of frequency, amplitude, and phase-amplitude coupling (PAC) are thus still confounded by fluctuations in arrhythmic activity. This limitation represents a pitfall for studies of NREM sleep relying on instantaneous estimates to investigate oscillatory coupling. To address this limitation, we introduce "Rhythms and Background" (RnB), a novel wavelet-based methodology designed to dynamically denoise time series data of arrhythmic interference. This enables the extraction of purely rhythmic time series, suitable for enhanced time-domain analyses of sleep rhythms. We first validate RnB through simulations, demonstrating it accurately estimates the spectral profiles of individual and multiple oscillations across a range of arrhythmic conditions. We then apply RnB to publicly available intracranial electroencephalogram sleep recordings, showing that it provides an improved spectral and time-domain representation of hallmark NREM rhythms. Finally, we demonstrate that RnB significantly enhances the assessment of PAC between cardinal NREM oscillations, outperforming traditional methods that conflate rhythmic and arrhythmic components. This methodological advance offers a substantial improvement in the analysis of sleep oscillations, providing greater precision in the study of rhythmic activity critical to NREM sleep functions.

There is a critical need for robust and reliable preclinical models for posttraumatic stress disorder (PTSD) to better understand pathophysiological mechanisms and support the development of novel treatments. The single prolonged stress (SPS) model has been previously utilized to investigate various acute behavioral effects and stress hormone changes in rodents. This study paired anxiety-like and social behavioral evaluations with corticosterone assessment as a complementary physiological biomarker to determine the presence of robust and intervenable phenotypes following SPS. Sprague Dawley rats (N = 36, 30 male and 6 female) received SPS model induction (e.g., restraint with odorant, forced-swim, diethyl ether exposure, and isolation) or control handling. Serum corticosterone and behavioral assessments, including the open field test (OFT) and a social motivation test (SMT), were investigated at 1 and 2 weeks following SPS induction. This SPS model did not induce anxiety-like or locomotive differences assessed in the OFT (p's > 0.05). Similarly, SPS did not appear to alter social preference or avoidance in the SMT (p's > 0.05), as groups had similar novel social and novel object interaction levels. SPS-paired cue re-exposure did not unmask group differences in these behaviors. Corticosterone levels were also unaltered between groups in the weeks following SPS (p = 0.178). In the absence of other stressors or modifications, the null behavioral and corticosterone findings in the weeks following SPS suggest that this SPS protocol may not reliably produce adequately robust or intervenable phenotypes.

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 Image J 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 data set, 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.Significance statement When studying neuronal cell differentiation, an important morphological parameter is neurite length. This parameter requires measuring the protrusions length of analysed cells. However, this analysis done manually can be long, as each individual cell must be measured independently. Currently, efficient single cell tools exist to assist the measurement, such as NeuronJ. However, there is currently no available automated tool for this analysis, and manual techniques suffer operator bias. In this paper, we present a macro to fully automatize neurite length and other parameters measurement, for each cell, in each image, in each condition.

Alcohol use disorder (AUD) is one of the top behavioral causes of global disease burden in the United States. Repeated cycles of alcohol intoxication and abstinence induce neuroplastic alterations which induce excessive drinking and cognitive impairments. A system deeply dysregulated by chronic drinking is norepinephrine (NE). At moderate levels, NE has beneficial effects on cognition and behavior, mediated by the α2 adrenergic receptor (AR) subtype. Whether α2 AR activation blunts alcohol consumption in models of heavy drinking has not been determined, and whether α2 AR activation improves cognitive performance following chronic alcohol consumption is unknown. Here, we show that the α2 AR agonist clonidine worsens ethanol-induced hypothermia and sedation in male mice, while the more selective α2 AR agonist guanfacine is devoid of these effects. We also observed that, in male and female mice, while both clonidine and guanfacine reduce heavy alcohol drinking, guanfacine does so with higher potency. Furthermore, guanfacine improved cognitive performance in a temporal order test and, partially, in a novel object recognition test but had no effect in a novel spatial location test, in male and female ethanol-experienced mice. Finally, we found that chronic intermittent ethanol drinking increases the number of persistently activated NE neurons in both the locus ceruleus and the nucleus of the tractus solitarius, in both male and female mice. Our results highlight a central role for the α2 AR system in heavy alcohol drinking and associated cognitive deficits, suggesting that α2 AR stimulation may represent a viable pharmacological strategy to treat AUD.