Frontiers in Neuroscience最新文献

Sleep in Drosophila melanogaster is regulated by a complex and distributed network of neural circuits that are influenced by factors such as internal state, circadian timing, and prior experiences. While no single "sleep center" has been identified, key brain regions-including the central complex, the mushroom bodies, and other associative structures-such as ventral nerve cord (VNC) contribute to the modulation of sleep and wakefulness. The roles of these regions appear to be dynamic, context-dependent, and often overlapping, reflecting the multifaceted nature of sleep regulation. At the circuit level, mechanisms such as changes in neuronal firing patterns, neurotransmitter systems (e.g., octopamine, dopamine, GABA), and experience-dependent synaptic plasticity have been shown to regulate sleep-wake cycles. On a molecular scale, a variety of genes-including shaker, fruitless, and GAT-influence sleep regulation through distinct pathways, with perturbations in these genes resulting in significant alterations in sleep duration, architecture, and homeostatic regulation. Recent studies, particularly those utilizing Drosophila sleep mutants, have provided valuable insights into the genetic and circuit-level interactions that govern sleep homeostasis and its coordination with the circadian system. These findings underscore sleep as an emergent property of interacting neural and genetic networks, providing a robust model for understanding the mechanisms of sleep in more complex organisms. This review synthesizes the latest advancements in Drosophila sleep research, with a focus on neural structures and the genetic basis of sleep regulation.

The sympathetic branch of the autonomic nervous system, known for its governance of the "fight or flight" response, has attracted newfound interest due to its role in maintaining bodily homeostasis in various tissue types. Sympathetic activity in the skin is often perturbed in neurological and neurodegenerative disorders. Notably, aberrant changes in the sympathetic skin response can be detected before clinical manifestations of diabetic neuropathy. Furthermore, sympathetic signaling at neuromuscular junctions in skeletal muscle has now been demonstrated to be critical for synapse integrity and proper functioning. Insufficient sympathetic signaling in skeletal muscle underlies the pathogenesis of muscle weakness in several disease states, such as myasthenia syndromes and sarcopenia. Additionally, surgical sympathectomies, a treatment method for conditions that involve heightened sympathetic activity, can give rise to other unwanted side effects, prompting the need for sympathetic trunk reconstruction. Therefore, the sympathetic nervous system, with renewed appreciation of its known functions and developing excitement for its recently discovered functions, remains a source for a wealth of potential discoveries that can further enable us to improve human health.

Auditory perception and localization are fundamental tasks for many species, allowing them to detect, identify, and spatially localize sound sources in their environment. While biological systems have evolved sophisticated neural mechanisms for auditory adaptation, artificial auditory systems still struggle to match their performance, particularly in dynamic and noisy environments. Our research focuses on whether sensor adaptation, driven by efferent feedback from the processing stage to the sensory stage, can improve localization performance. Inspired by human sound source localization based on interaural level differences (ILD) and efferent feedback, the proposed neuromorphic system architecture is composed of two bio-inspired acoustic sensors connected to a neural processing stage, represented by two neurons of the medial nucleus of the trapezoid body (MNTB) and two neurons of the lateral superior olive (LSO). The LSO neuron response was analyzed in the following ways: (i) using measured sensor responses at different ILD without efferent feedback and with a fixed local feedback for each sensor measurement; (ii) simulated with synthetically generated sounds with varying ILDs for four different feedback configurations from the LSO neuron to the acoustic sensors. Results from (i) showed how the feedback tuning can be used to overcome mismatches due to fabrication tolerances between different MEMS sensors, and (ii) showed the influence of different feedback configurations and simulation parameters on the LSO neuron response with respect to different ILDs.

Introduction: Disruptions in functional connectivity (FC) within the default mode network (DMN) are well established as a key neuropathology underlying cognitive impairment in type 2 diabetes mellitus (T2DM). Subcortical nuclei, including the basal forebrain (BF) and mediodorsal thalamus, play critical roles in regulating DMN-associated cognitive processes and are particularly vulnerable to hyperglycemia and brain insulin resistance. However, the specific FC patterns between these subcortical nuclei and DMN cortical regions in patients with T2DM, as well as their potential associations with cognitive impairment, remain incompletely elucidated.

Methods: Eighty-two patients with T2DM and 79 healthy controls (HCs) were enrolled in this study. Clinical data, neuropsychological assessments, and resting-state functional magnetic resonance imaging were collected from all participants. Resting-state (rs-FNC) and dynamic (dFNC) functional network connectivity analyses were performed to characterize connectivity between subcortical nuclei and DMN cortical regions. Correlation analyses explored associations between FNC metrics showing significant intergroup differences and participants' clinical and cognitive parameters.

Results: rs-FNC analysis revealed decreased FC between the BF and the dorsomedial prefrontal cortex (dMPFC), the BF and the temporal pole, and the dMPFC and the anteromedial prefrontal cortex in patients with T2DM (network-based statistic correction; edge p < 0.001, component p < 0.05). dFNC analyses indicated increased frequency and prolonged mean dwell time (MDT) of State 1 (high-frequency low-connectivity), as well as decreased frequency and shortened MDT of State 2 (high-frequency high-connectivity) compared with HCs (all p < 0.05). Reduced FC between the dMPFC and BF was positively correlated with Montreal Cognitive Assessment scores (r = 0.353, p = 0.001), whereas frequency (r = -0.434, p < 0.001) and MDT (r = -0.376, p = 0.001) of State 2 were negatively correlated with T2DM disease duration after Bonferroni correction.

Conclusion: These findings indicate that T2DM duration correlates with reduced highly efficient DMN connectivity, and that the BF may regulate cognitive function via the dMPFC subsystem. The results reveal temporal and functional specificity in abnormal DMN connectivity in patients with T2DM and enrich the neural atlas of DMN dysfunction in this population.

Gaucher disease (GD) is a lysosomal storage disorder caused by biallelic GBA1 variants. Epilepsy is uncommon in GD and rarely manifests as progressive myoclonus epilepsy (PME), making early recognition difficult. We describe a 20-year-old man with childhood-onset myoclonus that progressed to drug-resistant generalized seizures and cognitive decline. Video-electroencephalography (VEEG) showed generalized polyspike-wave discharges associated with myoclonic jerks, whereas brain magnetic resonance imaging was initially normal. Cerebrospinal fluid studies, metabolic screening, and autoimmune encephalitis antibody panels were unremarkable. Glucocerebrosidase activity was markedly reduced, and a targeted myoclonic-epilepsy gene panel identified two GBA1 variants: c.907C > A (p. Leu303Ile) and c.1505G > A (p. Arg502His), indicating a presumed compound-heterozygous state consistent with neuronopathic GD type 3. No hepatosplenomegaly or skeletal abnormalities were detected. Seizure control remained poor despite multiple antiseizure medications and vagus nerve stimulation (VNS). To contextualize this case, we systematically reviewed 22 publications encompassing 71 GD3-PME patients. Most cases presented in childhood, frequently showed typical electrophysiological patterns of generalized or multifocal polyspike-wave discharges, and had early normal MRI followed by later cerebellar or brainstem atrophy. Recurrent compound-heterozygous GBA1 variants, markedly reduced enzyme activity, and poor therapeutic response were common findings. The accompanying systematic review highlights the heterogeneity and therapeutic limitations of GD3-associated PME and underscores the importance of incorporating metabolic and genetic testing into the evaluation of unexplained PME for timely diagnosis and tailored management.

Introduction: Vagus nerve stimulation (VNS) represents an alternative treatment option in drug-resistant epilepsy. VNS patients can be categorized as responders (R, ≥50% seizure reduction) or non-responders (NR, < 50% seizure reduction). We demonstrate that VNS responders and VNS non-responders differ in their electrophysiological characteristics based on pre-implantation EEG analysis, specifically evaluated using relative mean power (RPW) and various information Entropy estimators computed in both he frequency and time domains. Based on the RPW and the Entropy estimators, we define and analyze the Unique Characteristics (UCs) of the individual (R and NR) groups of epileptic patients as well as Common Characteristics (CCs) that differentiate epileptic patients from healthy controls (HCs).

Methods: We investigated pre-implantation time series in 59 epileptic patients treated with VNS (24 VNS responders, 35 VNS non-responders). Subsequently, we acquired the EEG time series for 37 age- and gender-matched HCs. The EEG recordings of these three groups were filtered into standard frequency bands (theta, alpha, beta, and gamma) and segmented into eight consecutive time intervals, containing specific types of stimulation and resting states. For each of these segments, the RPW and seven Entropy estimators were calculated. We focused on the distribution of features differentiating between the epileptic patients (VNS responders or non-responders) and the HCs.

Results: We identified 41 UCs (7 in RPW, 34 in Entropy) of VNS responders, in contrast to 19 UCs (4 in RPW, 15 in Entropy) of VNS non-responders. The UCs of VNS responders exhibit a specific pattern, showing their binding in the frequency domain to the alpha band and temporal binding to the segments of hyperventilation stimulation. The UCs of VNS non-responders were also temporally linked to hyperventilation, but mainly in the theta and gamma frequency bands.

Conclusion: The VNS responders exhibit more differences when compared to HCs than VNS non-responders. These differences can be observed in RPW, but they become more pronounced when Entropy analysis is applied. It seems that the distinct response to hyperventilation is present in both VNS responders and non-responders, differentiating them from HCs. However, the binding of this response to frequency bands differs among VNS responders and non-responders. In particular, the reaction among the VNS responders is strongly associated with the alpha frequency band.

Background/objectives: Schizophrenia is a highly heritable psychiatric disorder that affects approximately 1% of the global population. Genome-wide association studies (GWAS) have mapped most schizophrenia risk variants to noncoding regions, highlighting the role of regulatory processes and noncoding RNAs in schizophrenia pathology. Despite this, and schizophrenia's association with 5-hydroxytryptamine (serotonin) system dysfunction, HTR5A-AS1, a long noncoding RNA (lncRNA) antisense to the serotonin receptor (HTR, 5-hydroxytryptamine receptor) gene HTR5A, remains virtually unstudied. This study provides the first systematic characterization of HTR5A-AS1, validating its transcript structure and investigating its genetic associations, expression dynamics, developmental regulation, and potential synaptic and GABAergic functions in schizophrenia.

Methods: Transcriptome-wide association study (TWAS) summary statistics were integrated with postmortem RNA sequencing (RNA-seq), BrainSpan developmental transcriptomes, UCSC Genome Browser annotations, and functional prediction tools. These complementary approaches enabled validation of the transcript's structure, quantification of regional and developmental expression, and assessment of potential molecular functions.

Results: HTR5A-AS1 showed significant TWAS associations with schizophrenia in the hippocampus and dorsolateral prefrontal cortex (dlPFC). In postmortem schizophrenia donor tissue, expression was significantly reduced in the hippocampus, with a non-significant but directionally similar decrease in the dlPFC; sex-stratified analyses revealed that hippocampal reductions were strongest in male donors. Parallel analyses showed modest hippocampal downregulation of the paired receptor gene HTR5A, again driven primarily by males. Developmental transcriptomes revealed region-specific developmental trajectories, with steep increases during adolescence, aligning with the age range of typical schizophrenia onset. HTR5A-AS1 was strongly co-expressed with HTR5A, and functional predictions implicated involvement in synaptic and GABAergic signaling, consistent with cortico-hippocampal circuit disruption in schizophrenia.

Conclusions: These findings provide the first evidence that HTR5A-AS1 is a bona fide antisense transcript with developmental and synaptic roles that may contribute to schizophrenia risk. Future single-cell and functional perturbation studies are needed to test causality and define mechanisms of regulation.

Introduction: Monogenic neurodevelopmental disorders (mNDDs) such as SNAREopathies exhibit complex electrophysiological features and diversity among clinical symptoms, complicating the mapping of electro-clinical relationships, essential for improving diagnosis and treatment monitoring. Establishing robust normative electrophysiological feature distributions from typically developing populations enables precise, individualized quantification of patient-specific abnormalities. Here, we introduce a multivariate framework to reveal patient-specific electrophysiological phenotypes and clinical severity dimensions of direct relevance for individual prognosis and therapeutic tracking.

Methods: We analyzed resting-state electroencephalography (EEG) data from15 SNAREopathy subjects (STXBP1 and SYT1) and 96 age-matched healthy controls. EEG biomarkers, including absolute power, relative power, and long-range temporal correlations (LRTC), were estimated across frequency bands and functional networks. Normative baselines of EEG features were established using principal component analysis (PCA) on controls. We computed patient deviations from normative distributions using Mahalanobis distances. We summarized clinical severity by applying PCA to assessments of motor, manual, communication, adaptive functioning, and severity ranking of qualitative EEG.

Results: The normative qEEG space identified diffuse, spectro-spatial patterns for absolute power, while relative power and LRTC displayed frequency-specific distributions. Clinical PCA identified a primary dimension of clinical impairment integrating deficits in mobility, hand function, communication, and adaptive behavior, whereas the secondary component captured the severity of qualitative EEG abnormalities. Patient deviations from normative absolute and relative power correlated with the primary, while LRTC deviations aligned with the secondary severity component.

Discussion: Normative qEEG deviance metrics correspond to distinct clinical severity dimensions in SNAREopathies, making them promising for tracking disorder progression and therapeutic response.

Spiking Neural Networks (SNNs) offer a paradigm of energy-efficient, event-driven computation that is well-suited for processing asynchronous sensory streams. However, training deep SNNs robustly in an online and continual manner remains a formidable challenge. Standard Backpropagation-through-Time (BPTT) suffers from a prohibitive memory bottleneck due to the storage of temporal histories, while local plasticity rules often fail to balance the trade-off between rapid acquisition of new information and the retention of old knowledge (the stability-plasticity dilemma). Motivated by the tripartite synapse in biological systems, where astrocytes regulate synaptic efficacy over slow timescales, we propose Astrocyte-Gated Multi-Timescale Plasticity (AGMP). AGMP is a scalable, online learning framework that augments eligibility traces with a broadcast teaching signal and a novel astrocyte-mediated gating mechanism. This slow astrocytic variable integrates neuronal activity to dynamically modulate plasticity, suppressing updates in stable regimes while enabling adaptation during distribution shifts. We evaluate AGMP on a comprehensive suite of neuromorphic benchmarks, including N-Caltech101, DVS128 Gesture, and Spiking Heidelberg Digits (SHD). Experimental results demonstrate that AGMP achieves accuracy competitive with offline BPTT while maintaining constant $O\left(1\right)$ temporal memory complexity. Furthermore, in rigorous Class-Incremental Continual Learning scenarios (e.g., Split CIFAR-100), AGMP significantly mitigates catastrophic forgetting without requiring replay buffers, outperforming state-of-the-art online learning rules. This work provides a biologically grounded, hardware-friendly path toward autonomous learning agents capable of lifelong adaptation.