Neural Circuits Research Articles

A Complex Relationship Among the Circadian Rhythm, Reward Circuit and Substance Use Disorder (SUD).

The human brain not only controls the various physiological functions but is also the prime regulator of circadian rhythms, rewards, and behaviors. Environmental factors, professional stress, and social disintegration are regarded as the initial causative factors of addiction behavior. Shift work, artificial light exposure at night, and chronic and acute jet lag influence circadian rhythm dysfunction. The result is impaired neurotransmitter release, dysfunction of neural circuits, endocrine disturbance, and metabolic disorder, leading to advancement in substance use disorder. There is a bidirectional relationship between chronodisruption and addiction behavior. Circadian rhythm dysfunction, neuroadaptation in the reward circuits, and alteration in clock gene expression in the mesolimbic areas influence substance use disorder (SUD), and chronotherapy has potential benefits in the treatment strategies. This review explores the relationship among the circadian rhythm dysfunction, reward circuit, and SUD. The impact of chronotherapy on SUD has also been discussed.

Neural decoding and feature selection methods for closed-loop control of avoidance behavior

Objective.Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as the foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors.Approach.We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance.Main results.Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low training/inference time and memory usage, requiring<310 ms for training,<0.051 ms for inference, and 16.6 kB of memory, using a single core of AMD Ryzen Threadripper PRO 5995WX CPU.Significance.Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.

Sex-Dependent Synergism of an Edible THC: CBD Formulation in Reducing Anxiety and Depressive-like Symptoms Following Chronic Stress.

Cannabis has shown therapeutic potential in mood and anxiety-related pathologies. However, the two primary constituents of cannabis, cannabidiol (CBD) and Δ-9-tetrahydrocannabinol (THC) produce distinct effects on molecular pathways in neural circuits associated with affective disorders. Moreover, it has been proposed that the combination of THC: and CBD may have unique synergistic properties. In the present study, the effects of a 1:100 THC: CBD ratio edible formulation were tested in behavioural, neuronal and molecular assays for anxiety and depressive-like endophenotypes. Adult male and female Sprague-Dawley rats were stressed for 14 days. Then, for three weeks, open field, elevated plus maze, light/dark box, social interaction, sucrose preference, and the forced swim test were performed 90 minutes after acute consumption of CBD (30 mg/kg), THC (0.3 mg/kg), or 1:100 combination of THC:CBD. After behavioural tests, in vivo, neuronal electrophysiological analyses were performed in the ventral tegmental area and prefrontal cortex (PFC). Furthermore, western-blot experiments examined the expression of biomarkers associated with mood and anxiety disorders, including protein kinase B (Akt), glycogen synthase kinase-3 (GSK-3), BDNF, mTOR, D1, and D2 receptor in nucleus accumbens (NAc) and PFC.Edible THC:CBD produces significant anxiolytic and antidepressant effects only in stressed male rats. In most cases, the combination of THC and CBD had stronger effects than either phytochemical alone. These synergistic effects are associated with alterations in Akt/GSK3 and D2-R expression in NAc and BDNF expression in PFC. Furthermore, THC:CBD reverses chronic stress-induced alterations in PFC neuronal activity. These findings demonstrate a novel synergistic potential for THC:CBD edible formulations in stress-related pathologies.

Sleepless nights and social plights: medial septum GABAergic hyperactivity in a neuroligin 3-deficient autism model.

Social deficits represent a core symptom domain of autism spectrum disorder (ASD), which is often comorbid with sleep disturbances. In this issue of the JCI, Sun et al. explored a medial septum (MS) circuit linking these behaviors in a neuroligin 3 conditional knockout model of autism. They identified GABAergic neuron hyperactivity following neuroligin 3 deletion in the MS. This hyperactivity resulted in the inhibition of the downstream preoptic area (POA) and hippocampal CA2 region, resulting in sleep loss and social memory deficits, respectively. Inactivating the hyperactive MS GABA neurons or activating the POA or CA2 rescued the behavioral deficits. Together, these findings deepen our understanding of neural circuits underlying social and sleep deficits in ASD.

Mosaic evolution of a learning and memory circuit in Heliconiini butterflies.

How do neural circuits accommodate changes that produce cognitive variation? We explore this question by analyzing the evolutionary dynamics of an insect learning and memory circuit centered within the mushroom body. Mushroom bodies are composed of a conserved wiring logic, mainly consisting of Kenyon cells, dopaminergic neurons, and mushroom body output neurons. Despite this conserved makeup, there is huge diversity in mushroom body size and shape across insects. However, empirical data on how evolution modifies the function and architecture of this circuit are largely lacking. To address this, we leverage the recent radiation of a Neotropical tribe of butterflies, the Heliconiini (Nymphalidae), which show extensive variation in mushroom body size over comparatively short phylogenetic timescales, linked to specific changes in foraging ecology, life history, and cognition. To understand how such an extensive increase in size is accommodated through changes in lobe circuit architecture, we combined immunostainings of structural markers, neurotransmitters, and neural injections to generate new, quantitative anatomies of the Nymphalid mushroom body lobe. Our comparative analyses across Heliconiini demonstrate that some Kenyon cell sub-populations expanded at higher rates than others in Heliconius and identify an additional increase in GABA-ergic feedback neurons, which are essential for non-elemental learning and sparse coding. Taken together, our results demonstrate mosaic evolution of functionally related neural systems and cell types and identify that evolutionary malleability in an architecturally conserved parallel circuit guides adaptation in cognitive ability.

DIETARY REGULATION OF SILENT SYNAPSES IN THE DORSOLATERAL STRIATUM.

Obesity and drugs of abuse share overlapping neural circuits and behaviors. Silent synapses are transient synapses that are important for remodeling brain circuits. They are prevalent during early development but largely disappear by adulthood. Drugs of abuse increase silent synapses during adulthood and may facilitate reorganizing brain circuits around drug-related experience, facilitating addiction and contributing to relapse during treatment and abstinence. Whether obesity causes alterations in the expression of silent synapses in a manner similar to drugs of abuse has not been examined. Using a dietary-induced obesity paradigm, mice that chronically consumed high fat diet (HFD) exhibited increased silent synapses in both direct and indirect pathway medium spiny neurons in the dorsolateral striatum. Both the time of onset of increased silent synapses and their normalization upon discontinuation of HFD occurs on an extended time scale compared to drugs of abuse. These data demonstrate that chronic consumption of HFD, like drugs of abuse, can alter mechanisms of circuit plasticity likely facilitating neural reorganization analogous to drugs of abuse.

Environmental complexity modulates information processing and the balance between decision-making systems.

Behavior in naturalistic scenarios occurs in diverse environments. Adaptive strategies rely on multiple neural circuits and competing decision systems. However, past studies of rodent decision making have largely measured behavior in simple environments. To fill this gap, we recorded neural ensembles from hippocampus (HC), dorsolateral striatum (DLS), and dorsomedial prefrontal cortex (dmPFC) while rats foraged for food under changing rules in environments with varying topological complexity. Environmental complexity increased behavioral variability, lengthened HC nonlocal sequences, and modulated action caching. We found contrasting representations between DLS and HC, supporting a competition between decision systems. dmPFC activity was indicative of setting this balance, in particular predicting the extent of HC non-local coding. Inactivating mPFC impaired short-term behavioral adaptation and produced long-term deficits in balancing decision systems. Our findings reveal the dynamic nature of decision-making systems and how environmental complexity modulates their engagement with implications for behavior in naturalistic environments.

Potentiation of active locomotor state by spinal-projecting serotonergic neurons.

Animals produce diverse motor actions that enable expression of context-appropriate behaviors. Neuromodulators facilitate behavioral flexibility by altering the temporal dynamics and output of neural circuits. Discrete populations of serotonergic (5-HT) neurons target circuits in the brainstem and spinal cord, but their role in the control of motor behavior is unclear. Here we define the pre- and post-synaptic organization of the spinal-projecting serotonergic system and define a role in locomotor control. We show that while forebrain-targeting 5-HT neurons decrease their activity during locomotion, subpopulations of spinal projecting neurons increase their activity in a context-dependent manner. Optogenetic activation of ventrally projecting 5-HT neurons does not trigger initiation of movement, but rather enhances the speed and duration of ongoing locomotion over extended time scales. These findings indicate that the descending serotonergic system potentiates locomotor output and demonstrate a role for serotonergic neurons in modulating the temporal dynamics of motor circuits.

The Zebrafish Cerebellar Neural Circuits Are Involved in Orienting Behavior.

Deficits in social behavior are found in neurodevelopmental disorders, including autism spectrum disorders (ASDs). Since abnormalities in cerebellar morphology and function are observed in ASD patients, the cerebellum is thought to play a role in social behavior. However, it remains unknown whether the cerebellum is involved in social behavior in other animals and how cerebellar circuits control social behavior. To address this issue, we employed zebrafish stereotyped orienting behavior as a model of social behaviors, in which a pair of adult zebrafish in two separate tanks approach each other, with one swimming at synchronized angles (orienting angles) with the other. We harnessed transgenic zebrafish that express botulinum toxin, which inhibits the release of neurotransmitters, in either granule cells or Purkinje cells (PCs), and zebrafish mutants of reelin, which is involved in the positioning of cerebellar neurons, including PCs. These zebrafish, deficient in the function or formation of cerebellar neural circuits, showed a significantly shorter period of orienting behavior compared with their control siblings. We found an increase in c-fos and egr1 expression in the cerebellum after the orienting behavior. These results suggest that zebrafish cerebellar circuits play an important role in social orienting behavior.

Prediction of Rheological Properties of Flour From Physicochemical Properties Using Multiple Regression Techniques and Artificial Neuronal Networks

ABSTRACTThis study has two main objectives: (i) to determine the physicochemical and rheological properties of different flours and (ii) to estimate the alveograph parameters obtained as a result of experimental studies. In this context, physicochemical (protein, ash, falling number, wet gluten, gluten index, Zeleny, and delayed sedimentation) and alveograph parameters (P, L, G, W, P/L, and IE) of 150 different bread and pastry flours were determined. Multiple regression analysis (MRA) and artificial neural network (ANN) methods were then used to predict alveograph results from this experimentally obtained data set. Root mean square error (RMSE), mean absolute error (MAE), Nash‐Sutcliffe (NSEC), and relative error (RE) performance statistics were used to evaluate the CS prediction capabilities of the methods. It was found that the flours were in the range of 11.01%–13.82% protein, 325–403 s falling number, and 30–61 mL Zeleny and delayed sedimentation values. The ANN method showed better predictive performance than the regression‐based method. W was the best estimated parameter in the ANN model. This was followed by G, L, Ie, P/L, and P values. Considering the RMSE value of the W parameter, it was observed that the ANN method provided an improvement of 5.16, 1.76, and 2.15 times compared to the regression method for the training, validation, and test sets, respectively.

In silico modeling of reservoir-based predictive coding in biological neuronal networks on microelectrode arrays

Reservoir computing and predictive coding together yield a computational model for exploring how neuronal dynamics in the mammalian cortex underpin temporal signal processing. Here, we construct an in-silico model of biological neuronal networks grown on microelectrode arrays and explore their computing capabilities through a sine wave prediction task in a reservoir-based predictive coding framework. Our results show that the time interval between stimulation pulses is a critical determinant of task performance. Additionally, under a fixed feedback latency, pulse amplitude modulation is a favorable encoding scheme for input signals. These findings provide practical guidelines for future implementation of the model in biological experiments.

Oscillatory and nonoscillatory sleep electroencephalographic biomarkers of the epileptic network.

In addition to the oscillatory brain activity, the nonoscillatory (scale-free) components of the background electroencephalogram (EEG) may provide further information about the complexity of the underlying neuronal network. As epilepsy is considered a network disease, such scale-free metrics might help to delineate the epileptic network. Here, we performed an analysis of the sleep oscillatory (spindle, slow wave, and rhythmic spectral power) and nonoscillatory (H exponent) intracranial EEG using multiple interictal features to estimate whether and how they deviate from normalcy in 38 adults with drug-resistant epilepsy. To quantify intracranial EEG abnormalities within and outside the seizure onset areas, patients' values were adjusted based on normative maps derived from the open-access Montreal Neurological Institute open iEEG Atlas. In a subset of 29 patients who underwent resective surgery, we estimated the predictive value of these features to identify the epileptogenic zone in those with a good postsurgical outcome. We found that distinct sleep oscillatory and nonoscillatory metrics behave differently across the epileptic network, with the strongest differences observed for (1) a reduction in spindle activity (spindle rates and rhythmic sigma power in the 10-16 Hz band), (2) a higher rhythmic gamma power (30-80 Hz), and (3) a higher H exponent (steeper 1/f slope). As expected, epileptic spikes were also highest in the seizure onset areas. Furthermore, in surgical patients, the H exponent achieved the highest performance (balanced accuracy of .76) for classifying resected versus nonresected channels in good outcome patients. This work suggests that nonoscillatory components of the intracranial EEG signal could serve as promising interictal sleep candidates of epileptogenicity in patients with drug-resistant epilepsy. Our findings further advance the understanding of epilepsy as a disease, whereby absence or loss of sleep physiology may provide information complementary to pathological epileptic processes.

Cholangioscopy-based convoluted neuronal network vs. confocal laser endomicroscopy in identification of neoplastic biliary strictures.

Background and study aims Artificial intelligence (AI) models have demonstrated high diagnostic performance identifying neoplasia during digital single-operator cholangioscopy (DSOC). To date, there are no studies directly comparing AI vs. DSOC-guided probe-base confocal laser endomicroscopy (DSOC-pCLE). Thus, we aimed to compare the diagnostic accuracy of a DSOC-based AI model with DSOC-pCLE for identifying neoplasia in patients with indeterminate biliary strictures. Patients and methods This retrospective cohort-based diagnostic accuracy study included patients ≥ 18 years old who underwent DSOC and DSOC-pCLE (June 2014 to May 2022). Four methods were used to diagnose each patient's biliary structure, including DSOC direct visualization, DSOC-pCLE, an offline DSOC-based AI model analysis performed in DSOC recordings, and DSOC/pCLE-guided biopsies. The reference standard for neoplasia was a diagnosis based on further clinical evolution, imaging, or surgical specimen findings during a 12-month follow-up period. Results A total of 90 patients were included in the study. Eighty-six of 90 (95.5%) had neoplastic lesions including cholangiocarcinoma (98.8%) and tubulopapillary adenoma (1.2%). Four cases were inflammatory including two cases with chronic inflammation and two cases of primary sclerosing cholangitis. Compared with DSOC-AI, which obtained an area under the receiver operator curve (AUC) of 0.79, DSOC direct visualization had an AUC of 0.74 ( P = 0.763), DSOC-pCLE had an AUC of 0.72 ( P = 0.634), and DSOC- and pCLE-guided biopsy had an AUC of 0.83 ( P = 0.809). Conclusions The DSOC-AI model demonstrated an offline diagnostic performance similar to that of DSOC-pCLE, DSOC alone, and DSOC/pCLE-guided biopsies. Larger multicenter, prospective, head-to-head trials with a proportional sample among neoplastic and nonneoplastic cases are advisable to confirm the obtained results.

A predictive model for the estimation of industrial [formula omitted] emissions for IoT-based devices

The paper is devoted to solving the problem, associated with increasing the forecasting accuracy of pollutant concentration level based on PM2.5 dust. The LANN model (LANN – Lagrange powered Artificial Neuron Network), proposed in the paper, allows you to take into account the mutual influence of pollution sources on the concentration level at monitoring points, which, on the one hand, allows to manage the volume of emissions from industrial enterprises in such a way as to avoid penalties and, on the other hand, allows the regulatory agencies to determine points for environmental control, at which permissible standards will be most likely exceeded. The suggested solution makes it possible to use low-cost IoT-based air quality monitoring devices, which were primarily considered not applicable due to their low accuracy and exposure to the influence of short-term stochastic factors; besides, no models were available, which would be capable to provide the necessary accuracy and forecast horizon on the base of IoT data. At the same time, the obtained model has low requirements for computing resources compared to dispersion models and is dependent on the accuracy of weather information; besides, the model allows you to take into account trends and obtain better forecast values in terms of accuracy than the models, that use regression methods, neural network models and ML models, stochastic models and their ensembles. The application of the model improves well-known pollutant dispersion estimation calculation techniques through the use of measurement data flow and dynamic adaptation to changing conditions The model was trained on the dataset over the period 2022-2023 collected from 5 monitoring devices, which were installed in the catchment area of the group, incl. 13 industrial points of PM2.5 pollutant sources.

Stress resilience is an active and multifactorial process manifested by structural, functional, and molecular changes in synapses

Stress resilience is the ability of neuronal networks to maintain their function despite the stress exposure. Using a mouse model we here investigate stress resilience phenomenon. To assess the resilient and anhedonic behavioral phenotypes developed after the induction of chronic unpredictable stress, we quantitatively characterized the structural and functional plasticity of excitatory synapses in the hippocampus using a combination of proteomic, electrophysiological, and imaging methods. Our results indicate that stress resilience is an active and multifactorial process manifested by structural, functional, and molecular changes in synapses. We reveal that chronic stress influences palmitoylation of synaptic proteins, whose profiles differ between resilient and anhedonic animals. The changes in palmitoylation are predominantly related with the glutamate receptor signaling thus affects synaptic transmission and associated structures of dendritic spines. We show that stress resilience is associated with structural compensatory plasticity of the postsynaptic parts of synapses in CA1 subfield of the hippocampus.

Astrocytes induce desynchronization and reduce predictability in neuron-astrocyte networks cultured on microelectrode arrays.

In this article, we aim to study how astrocytes control electrophysiological activity during neuronal network formation. We used a combination of spike/burst analysis, classification of spike waveforms based on various signal properties and tools from information theory to demonstrate how astrocytes modulate the electrical activity of neurons using microelectrode array (MEA) signals. We cultured rat primary cortical neurons and astrocytes on 60-electrode MEAs with different neuron/astrocyte ratios ranging from 'pure' neuronal cultures to co-cultures containing 50% neurons and 50% astrocytes. Our results show that astrocytes desynchronize the network and reduce predictability in the signals and affect the repolarization phase of the action potentials. Our work highlights that it is crucial to go beyond standard MEA analysis to assess how astrocytes control neuronal cultures and investigate any dysfunction that could potentially result in neuronal hyperactivity.

Adjacent Neuronal Fascicle Guides Motoneuron 24 Dendritic Branching and Axonal Routing Decisions through Dscam1 Signaling.

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.

Machine Learning as a Tool for Auxiliary Diagnosis of Osteoporosis

By making predictions from the model created with machine learning, specifically supervised learning, this essay focuses on the auxiliary diagnosis of osteoporosis. The chosen dataset includes highly relevant indexes that determine the diagnosis of osteoporosis. After doing the feature selection and one-hot encoding to preprocess the dataset, we then split the dataset into validation and training sets to avoid overfitting. Then, to train the model for prediction, we used the neuron network, chose ReLU and Sigmoid functions as activation functions, and changed the number of neurons and epochs to find the best-fit model. Through further measurement, evaluation, and application, including plotting the binary cross entropy error diagram and analyzing the classification report, the model is examined as highly accurate and desirable. With an appreciable degree of accuracy, this model for auxiliary diagnosis is able to prevent disease development or deterioration by detecting early symptoms and vulnerable populations, reduce the probability for doctors to misdiagnose, and improve the efficiency and quality of the osteoporosis diagnosis, therefore enhancing patient experiences.

НЕЙРОМЕРЕЖЕВІ МАТЕМАТИЧНІ МОДЕЛІ ЛЮДИНОМАШИННОГО СПІЛКУВАННЯ ТА РОЗПІЗНАВАННЯ

The study is devoted to the neural network interpretation of the task of human-machine communication and recognition by multiple criteria, considered as a task of assignment. The main goal is to reduce this problem to a standard form, where the number of criteria groups is equal to the number of ranked documents. The study defined the architecture of a neural network and proposed the use of a network of binary neurons, which is a matrix of a certain dimension. The proposed ranking model is based on a neural network that contains arbitrary feedback. This allows the excitation to be transmitted back to the neuron, which contributes to the repeated performance of its function. However, in dynamic neural networks instability occurs, which is manifested in a random change in the states of neurons without reaching stationary states. The question of stability of the dynamics of such systems remains open. The considered discrete Hopfield neural network has the following characteristics: one layer of elements, each element is connected to all others, but not to itself; only one element is updated per stage; elements are updated in random order, but each is updated with the same frequency; the output function is binary (value "0" or "1"). A Hopfield neural network is recurrent: the output of the network is reused as input until a steady state is reached. After starting, the neural network changes its state, gradually moving to a stable mode, which allows identifying a plan for evaluating the process of human-machine communication according to a set of criteria. Random search procedures are used to refine the results. The proposed energy function is minimized to ensure that the constraints are met and the problem is solved. The constructed function reaches a minimum only in the states corresponding to the assignment plans. The definition of the network parameters is carried out by comparing the obtained functions with the energy function in general form. The practical implementation of the model demonstrated that Hopfield's neural network can be successfully applied to document ranking in human-machine communication and recognition systems, providing high accuracy and efficiency in solving ranking problems.

Reciprocal regulation of oxidative stress and mitochondrial fission augments parvalbumin downregulation through CDK5-DRP1- and GPx1-NF-κB signaling pathways

Loss of parvalbumin (PV) expressing neurons (PV neurons) is relevant to the underlying mechanisms of the pathogenesis of neurological and psychiatric diseases associated with the dysregulation of neuronal excitatory networks and brain metabolism. Although PV modulates mitochondrial morphology, volume and dynamics, it is largely unknown whether mitochondrial dynamics affect PV expression and what the molecular events are responsible for PV neuronal degeneration. In the present study, L-buthionine sulfoximine (BSO, an inhibitor of glutathione synthesis) did not degenerate PV neurons under physiological condition. However, BSO-induced oxidative stress decreased PV expression and facilitated cyclin-dependent kinase 5 (CDK5) tyrosine (Y) 15 phosphorylation, dynamin-related protein 1 (DRP1)-mediated mitochondrial fission and glutathione peroxidase-1 (GPx1) downregulation in PV neurons. Co-treatment of roscovitine (a CDK5 inhibitor) or mitochondrial division inhibitor-1 (Mdivi-1, an inhibitor of mitochondrial fission) attenuated BSO-induced PV downregulation. WY14643 (an inducer of mitochondrial fission) reduced PV expression without affecting CDK5 Y15 phosphorylation. Following status epilepticus (SE), CDK5 Y15 phosphorylation and mitochondrial fission were augmented in PV neurons. These were accompanied by reduced GPx1-mediated inhibition of NF-κB p65 serine (S) 536 phosphorylation. N-acetylcysteine (NAC), roscovitine and Mdivi-1 ameliorated SE-induced PV neuronal degeneration by mitigating CDK5 Y15 hyperphosphorylation, aberrant mitochondrial fragmentation and reduced GPx1-mediated NF-κB inhibition. Furthermore, SN50 (a NF-κB inhibitor) alleviated SE-induced PV neuronal degeneration, independent of dysregulation of mitochondrial fission, CDK5 hyperactivation and GPx1 downregulation. These findings provide an evidence that oxidative stress may activate CDK5-DRP1- and GPx1-NF-κB-mediated signaling pathways, which would be possible therapeutic targets for preservation of PV neurons in various diseases.