eNeuro最新文献

Estrogens act through nuclear and membrane-initiated signaling. Estrogen receptor alpha (ERα) is critical for reproduction, but the relative contribution of its nuclear and membrane signaling to the central regulation of reproduction is unclear. To address this question, two complementary approaches were used: estetrol (E₄) a natural estrogen acting as an agonist of nuclear ERs but as an antagonist of their membrane fraction and the C451A-ERα mouse lacking mERα. E₄ dose-dependently blocks ovulation in female rats, but the central mechanism underlying this effect is unknown. To determine whether E₄ acts centrally to control ovulation, its effect was tested on the positive feedback of estradiol (E₂) on neural circuits underlying LH secretion. In ovariectomized females chronically exposed to a low dose of E₂, estradiol benzoate (EB) alone or combined with progesterone (P) induced an increase in the number of kisspeptin (Kp) and gonadotropin-releasing hormone (GnRH) neurons co-expressing Fos, as a marker of neuronal activation. However, E₄ blocked these effects of EB when provided alone, but not when combined to P. These results indicate that E₄ blocked the central induction of the positive feedback in the absence of P, suggesting an antagonistic effect of E₄ on mERα in the brain as shown in peripheral tissues. In parallel, as opposed to wild-type females, C451A-ERα females did not show the activation of Kp and GnRH neurons in response to EB unless they are treated with P. Together these effects supports a role for membrane-initiated estrogen signaling in the activation of the circuit mediating the LH surge.Significance statement Estrogen receptor alpha (ERα) is critical for the activation of the neural circuits underlying ovulation. However, the relative contribution of its nuclear and membrane signaling to this neuroendocrine phenomenon is unclear. Using two complementary approaches to block membrane ERα signaling the present study reveals that membrane ERα signaling is required for the activation by estrogens of gonadotropin-releasing hormone (GnRH) and kisspeptin (Kp) neurons, two key neuronal populations underlying the surge of luteinizing hormone (LH) which triggers ovulation. Interestingly, the absence of activation of Kp and GnRH neurons is alleviated in both models by progesterone (P). Collectively the results of these two approaches converge to provide evidence that membrane estrogen signaling contributes to this key event for the central regulation of reproduction.

Here we test the Stochastic Dynamic Operator (SDO) as a new framework for describing physiological signal dynamics relative to spiking or stimulus events. The SDO is a natural extension of existing spike-triggered average (STA) or stimulus-triggered average techniques currently used in neural analysis. It extends the classic STA to cover state-dependent and probabilistic responses where STA may fail. In simulated data, SDO methods were more sensitive and specific than the STA for identifying state-dependent relationships. We have tested SDO analysis for interactions between electrophysiological recordings of spinal interneurons, single motor units, and aggregate muscle electromyograms (EMG) of major muscles in the spinal frog hindlimb. When predicting target signal behavior relative to spiking events, the SDO framework outperformed or matched classical spike-triggered averaging methods. SDO analysis permits more complicated spike-signal relationships to be captured, analyzed, and interpreted visually and intuitively. SDO methods can be applied at different scales of interest where spike-triggered averaging methods are currently employed, and beyond, from single neurons to gross motor behaviors. SDOs may be readily generated and analyzed using the provided SDO Analysis Toolkit We anticipate this method will be broadly useful for describing dynamical signal behavior and uncovering state-dependent relationships of stochastic signals relative to discrete event times.Significance Statement Here the authors introduce new tools and demonstrate data analysis using a new probabilistic and state-dependent technique, which is an expansion and extension of the classical spike-triggered average, the Stochastic Dynamic Operator. Stochastic Dynamic Operator methods extend application into domains where classical spike-triggered averages fail, capture more information on spike correlations, and match or outperform the spike-triggered average when generating predictions of signal amplitude based on spiking events. A data and code package toolkit for utilizing and interpreting Stochastic Dynamic Operator methods is provided together with example simulated and physiological data analyses. Both the method and the associated toolkit are expected to be broadly useful in research domains where the spike triggered average is currently used for analysis, and beyond.

This article allows readers to assess their ability to detect errors in thinking in seven case histories of psychologists' thoughts about cognitive science. It explains the nature of the errors and shows that some of them reflect faulty reasoning. It presents a "model method" to improve reasoning. It is based on the theory of mental models, which gives a general account of how individuals think, both deductively and indicatively, and which postulates that individuals construct mental models of possibilities in the world. The model method enhances both the accuracy and speed of reasoning. The article concludes with some general reflections on the role of knowledge of meanings, the world, and context in thinking.

Ischemic stroke (IS) poses a serious threat to patient survival. The inhibition of ferroptosis can effectively alleviate ischemia-reperfusion (I/R) injury, suggesting potential targets in the ferroptosis pathway for the treatment of IS. In this study, MCAO/R mice and OGD/R-induced HT22 cell were constructed. It was found that baicalein decreased ROS, MDA, and Fe²⁺ levels, upregulated GSH levels, and enhanced the expression of ferroptosis-related proteins (GPX4 and SLC7A11), downregulated the expression of proapoptotic proteins (Bax, cytochrome c, and cleaved caspase-3), and upregulated the expression of an antiapoptotic protein (Bcl-2), ameliorating cerebral I/R injury. In animal and cell models, Sirtuin6 (SIRT6) is downregulated, and Forkhead boxA2 (FOXA2) expression and acetylation levels are abnormally upregulated. SIRT6 inhibited FOXA2 expression and acetylation. Baicalein promoted FOXA2 deacetylation by upregulating SIRT6 expression. FOXA2 transcriptionally inhibits SLC7A11 expression. In conclusion, baicalein inhibited apoptosis and partially suppressed the role of ferroptosis to alleviate cerebral I/R injury via SIRT6-mediated FOXA2 deacetylation to promote SLC7A11 expression.

Despite decades of preclinical investigation, there remains limited understanding of the etiology and biological underpinnings of anxiety disorders. Sensitivity to potential threat is characteristic of anxiety-like behavior in humans and rodents, but traditional rodent behavioral tasks aimed to assess threat responsiveness lack translational value, especially with regard to emotionally valenced stimuli. Therefore, development of novel preclinical approaches to serve as analogues to patient assessments is needed. In humans, the fearful face task is widely used to test responsiveness to socially communicated threat signals. In rats, ultrasonic vocalizations (USVs) are analogous social cues associated with positive or negative affective states that can elicit behavioral changes in the receiver. It is therefore likely that when rats hear aversive alarm call USVs (22 kHz), they evoke translatable changes in brain activity comparable with the fearful face task. We used functional magnetic resonance imaging in male and female rats to assess changes in BOLD activity induced by exposure to aversive 22 kHz alarm calls emitted in response to threatening stimuli, prosocial (55 kHz) USVs emitted in response to appetitive stimuli, or a computer-generated 22 kHz tone. Results show patterns of regional activation that are specific to each USV stimulus. Notably, limbic regions clinically relevant to psychiatric disorders (e.g., amygdala, bed nucleus of the stria terminalis) are preferentially activated by either aversive 22 kHz or appetitive 55 kHz USVs. These results support the use of USV playback as a promising translational tool to investigate affective processing under conditions of distal threat in preclinical rat models.

Human and mouse dorsal root ganglia (hDRG and mDRG) neurons are important tools in understanding the molecular and electrophysiological mechanisms that underlie nociception and drive pain behaviors. One of the simplest differences in firing phenotypes is that neurons are single-firing (exhibit only one action potential) or multi-firing (exhibit 2 or more action potentials). To determine if single- and multi-firing hDRG neurons exhibit differences in intrinsic properties, firing phenotypes, and AP waveform properties, and if these properties could be used to predict multi-firing, we measured 22 electrophysiological properties by whole-cell patch-clamp electrophysiology of 94 hDRG neurons from six male and four female donors. We then analyzed the data using several machine learning models to determine if these properties could be used to predict multi-firing. We used 1,000 iterations of Monte Carlo cross-validation to split the data into different train and test sets and tested the logistic regression, k-nearest neighbors, random forest, support vector classifier, and XGBoost machine learning models. All models tested had a >80% accuracy on average, with support vector classifier, and XGBoost performing the best. We found that several properties correlated with multi-firing hDRG neurons and together could be used to predict multi-firing neurons in hDRG including a long decay time, a low rheobase, and long first spike latency. We also found that the hDRG models were able to predict multi-firing with 90% accuracy in mDRG neurons. Understanding these properties could be beneficial in the elucidation of targets on peripheral sensory neurons related to pain.

This study aims to explore the correlation of serum thyroid hormone levels to cognitive impairments in Parkinson's disease (PD) patients. In this retrospective study, 106 Chinese patients without cognitive impairments and 94 patients with cognitive impairments, including 55 with mild cognitive impairment (PD-MCI) and 39 with PD dementia (PDD), were analyzed. Clinical data regarding the PD assessments, including disease duration, Unified Parkinson's Disease Rating Scale (UPDRS) Part 3 scores, and Hoehn and Yahr (H-Y) staging, were analyzed. Cognitive functions were evaluated using the Montreal Cognitive Assessment score. Serum levels of thyroid-stimulating hormone (TSH), free thyroxine (FT4), and free triiodothyronine (FT3), were measured using ELISA. Significantly altered H-Y staging, disease duration, and UPDRS Part 3 scores were observed in PD patients with cognitive impairment compared with those without. Serum levels of FT3 were significantly decreased, while FT4 and TSH levels were significantly elevated in PD patients with cognitive impairment compared with those without. Combined detection of TSH, FT3, and FT4 showed value in distinguishing PD patients with and without cognitive impairment. Furthermore, a comparison of serum levels between PD-MCI and PDD patients revealed significant association between thyroid hormone levels and the degree of cognitive impairment in PD patients. Our findings suggest a relationship between changes in serum thyroid hormone levels and cognitive impairments in PD patients. Thyroid hormone levels, particularly FT3, may serve as potential markers for cognitive dysfunction in PD.

The rat dorsomedial (DMS) and dorsolateral striatum (DLS), equivalent to caudate nucleus and putamen in primates, are required for goal-directed and habit behaviour, respectively. However, it is still unclear whether and how this functional dichotomy emerges in the course of learning. In this study we investigated this issue by recording DMS and DLS single neuron activity in rats performing a continuous spatial alternation task, from the acquisition to optimized performance. We first applied a classical analytical approach to identify task-related activity based on the modifications of single neuron firing rate in relation to specific task events or maze trajectories. We then used an innovative approach based on Hawkes process to reconstruct a directed connectivity graph of simultaneously recorded neurons, that was used to decode animal behavior. This approach enabled us to better unravel the role of DMS and DLS neural networks across learning stages. We showed that DMS and DLS display different task-related activity throughout learning stages, and the proportion of coding neurons over time decreases in the DMS and increases in the DLS. Despite theses major differences, the decoding power of both networks increases during learning. These results suggest that DMS and DLS neural networks gradually reorganize in different ways in order to progressively increase their control over the behavioral performance.Significance statement Our study helps understanding the role of the dorsomedial (DMS) and dorsolateral striatum (DLS) during the acquisition and optimization of a behavioral strategy. It is generally believed that the DMS mediates action-outcome associations, whereas the DLS supports habit behavior, but it is still unclear how these processes emerges during learning. To analyze the dynamic changes of DMS and DLS network activity across learning stages, we used a mathematical analysis combining single neuron firing rate and connectivity between neurons to decode rat behavior in a goal-directed spatial task. We demonstrated that both DMS and DLS activity supports behavioral performance throughout all learning stages, thus challenging the hypothesis of a gradual shift from DMS to DLS activity.

The formation and precise positioning of axons and dendrites are crucial for the development of neural circuits. Although juxtracrine signaling via cell-cell contact is known to influence these processes, the specific structures and mechanisms regulating neuronal process positioning within the central nervous system (CNS) remain to be fully identified. Our study investigates motoneuron 24 (MN24) in the Drosophila embryonic CNS, which is characterized by a complex yet stereotyped axon projection pattern, known as 'axonal routing.' In this motoneuron, the primary dendritic branches project laterally toward the midline, specifically emerging at the sites where axons turn. We observed that Scp2-positive neurons contribute to the lateral fascicle structure in the ventral nerve cord (VNC) near MN24 dendrites. Notably, the knockout of the Down syndrome cell adhesion molecule (Dscam1) results in the loss of dendrites and disruption of proper axonal routing in MN24, while not affecting the formation of the fascicle structure. Through cell-type specific knockdown and rescue experiments of Dscam1, we have determined that the interaction between MN24 and Scp2-positive fascicle, mediated by Dscam1, promotes the development of both dendrites and axonal routing. Our findings demonstrate that the holistic configuration of neuronal structures, such as axons and dendrites, within single motoneurons can be governed by local contact with the adjacent neuron fascicle, a novel reference structure for neural circuitry wiring.Significance Summary We uncover a key neuronal structure serving as a guiding reference for neural circuitry within the Drosophila embryonic CNS, highlighting the essential role of an adjacent axonal fascicle in precisely coordinating axon and dendrite positioning in motoneuron 24 (MN24). Our investigation of cell-cell interactions between motoneurons and adjacent axonal fascicles-crucial for initiating dendrite formation, soma mislocation, and axonal pathfinding in MN24-emphasizes the neuronal fascicle's significance in neural circuit formation through Dscam1-mediated inter-neuronal communication. This enhances our understanding of the molecular underpinnings of motoneuron morphogenesis in Drosophila Given the occurrence of analogous axon fascicle formations within the vertebrate spinal cord, such structures may play a conserved role in the morphogenesis of motoneurons via Dscam1 across phyla.

A new era of disease-modifying therapy for Alzheimer's disease (AD) arrived in 2021 following the Food and Drug Administration's (FDA) decision to grant accelerated approval for aducanumab, an anti-β-amyloid (Aβ) monoclonal antibody designed to target Aβ aggregates, a biological component of AD. More recently, trial outcomes for lecanemab and donanemab, two additional antibodies of this drug class, have shown favorable and significant slowing of metrics for cognitive and functional decline. Lecanemab and donanemab have since received similar FDA approval to aducanumab in January 2023 and July 2024, respectively. Given that these therapies are a clearly emerging tool in the repertoire of clinicians treating AD and related dementias, a critical dialogue has been ongoing regarding the potential impacts and place for these therapies. Here, we seek to contextualize this debate by first considering factors involved in theoretically extrapolating current randomized control trial outcomes to estimate meaningful clinical impacts. In the process of this exercise, we outline a generally useful concept termed Summative Treatment-Associated Benefit measuring Long-term Efficacy/Effectiveness Area as a metric of summative benefits of treatment over the life course of an individual. Second, we consider current real-world factors, such as conditions of FDA approval and other points involved in clinical decision-making that will influence and/or temper the actual impacts of this drug class.