OBJECTIVES: To investigate the neural mechanisms of rhythmic activity in the hippocampal CA1 region and orbitofrontal cortex (OFC) during a spatial goal-directed task. METHODS: Four long-Evans rats were trained to perform a spatial goal-directed task in a land-based water maze (Cheese-board maze). The task was divided into 5 periods: Pre-test, Pre-sleep, Learning, Post-sleep, and Post-test. During the Learning phase, the task was split into two goal navigation and two reward acquisition processes with a total of 8 learning stages. Local field potentials (LFP) from the CA1 and the OFC were recorded, and power spectral density analysis was performed on Theta (6-12 Hz), Beta (15-30 Hz), Low gamma (30-60 Hz), and High gamma (60-90 Hz) bands. Coherence, phase-locking value (PLV), and phase-amplitude cross coupling (PAC) were used to assess the interactions between the CA1 and the OFC during learning and memory. RESULTS: During the task training, the rats showed consistent rhythms of OFC neural activity across the task states (P>0.05) while exhibiting significant changes in Beta and High gamma rhythms in the CA1 region (P<0.05). Coherence and PLV between the CA1 and the OFC were higher during goal navigation, especially in the stable learning phase (Stage 8 vs Stage 1, P<0.01). The rats showed stronger cross-frequency coupling between CA1-Theta and OFC-Low gamma in the Post-test phase than in the Pre-test phase (P<0.05). CONCLUSIONS Learning and memory consolidation in goal-directed tasks involve synchronized activity between the CA1 region and the OFC, and cross-frequency coupling plays a key role in maintaining short-term memory of reward locations in rats.
Animals ; Rats ; Rats, Long-Evans ; CA1 Region, Hippocampal/physiology* ; Memory Consolidation/physiology* ; Prefrontal Cortex/physiology* ; Maze Learning/physiology* ; Goals ; Male ; Memory/physiology* ; Learning/physiology*

General anesthesia, pivotal for surgical procedures, requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments. Traditional assessment methods, relying on physiological indicators or behavioral responses, fall short of accurately capturing the nuanced states of unconsciousness. This study introduces a machine learning-based approach to decode anesthesia depth, leveraging EEG data across different anesthesia states induced by propofol and esketamine in rats. Our findings demonstrate the model's robust predictive accuracy, underscored by a novel intra-subject dataset partitioning and a 5-fold cross-validation method. The research diverges from conventional monitoring by utilizing anesthetic infusion rates as objective indicators of anesthesia states, highlighting distinct EEG patterns and enhancing prediction accuracy. Moreover, the model's ability to generalize across individuals suggests its potential for broad clinical application, distinguishing between anesthetic agents and their depths. Despite relying on rat EEG data, which poses questions about real-world applicability, our approach marks a significant advance in anesthesia monitoring.
Animals ; Machine Learning ; Electroencephalography/methods* ; Ketamine/administration & dosage* ; Rats ; Male ; Propofol/administration & dosage* ; Rats, Sprague-Dawley ; Anesthesia, General/methods* ; Brain/physiology* ; Intraoperative Neurophysiological Monitoring/methods*

Learning-associated functional plasticity at hippocampal synapses remains largely unexplored. Here, in a single session of reward-based trace conditioning, we examine learning-induced synaptic plasticity in the dorsal CA1 hippocampus (dCA1). Local field-potential recording combined with selective optogenetic inhibition first revealed an increase of dCA1 synaptic responses to the conditioned stimulus (CS) induced during conditioning at both Schaffer collaterals to the stratum radiatum (Rad) and temporoammonic input to the lacunosum moleculare (LMol). At these dCA1 inputs, synaptic potentiation of CS-responding excitatory synapses was further demonstrated by locally blocking NMDA receptors during conditioning and whole-cell recording sensory-evoked synaptic responses in dCA1 neurons from naive animals. An overall similar time course of the induction of synaptic potentiation was found in the Rad and LMol by multiple-site recording; this emerged later and saturated earlier than conditioned behavioral responses. Our experiments demonstrate a cued memory-associated dCA1 synaptic plasticity induced at both Schaffer collaterals and temporoammonic pathways.
Animals ; CA1 Region, Hippocampal/physiology* ; Male ; Association Learning/physiology* ; Neuronal Plasticity/physiology* ; Cues ; Memory/physiology* ; Synapses/physiology* ; Conditioning, Classical/physiology* ; Excitatory Postsynaptic Potentials/physiology* ; Receptors, N-Methyl-D-Aspartate/antagonists & inhibitors* ; Rats ; Optogenetics

Closed-loop neuromodulation, especially using the phase of the electroencephalography (EEG) rhythm to assess the real-time brain state and optimize the brain stimulation process, is becoming a hot research topic. Because the EEG signal is non-stationary, the commonly used EEG phase-based prediction methods have large variances, which may reduce the accuracy of the phase prediction. In this study, we proposed a machine learning-based EEG phase prediction network, which we call EEG phase prediction network (EPN), to capture the overall rhythm distribution pattern of subjects and map the instantaneous phase directly from the narrow-band EEG data. We verified the performance of EPN on pre-recorded data, simulated EEG data, and a real-time experiment. Compared with widely used state-of-the-art models (optimized multi-layer filter architecture, auto-regress, and educated temporal prediction), EPN achieved the lowest variance and the greatest accuracy. Thus, the EPN model will provide broader applications for EEG phase-based closed-loop neuromodulation.
Humans ; Electroencephalography/methods* ; Brain/physiology* ; Machine Learning ; Signal Processing, Computer-Assisted ; Male ; Adult ; Neural Networks, Computer ; Brain Waves/physiology*

Alzheimer's disease (AD), a neurodegenerative disorder with complex etiologies, manifests through a cascade of pathological changes before clinical symptoms become apparent. Among these early changes, alterations in the expression of non-coding RNAs (ncRNAs) have emerged as pivotal events. In this study, we focused on the aberrant expression of ncRNAs and revealed that Lamr1-ps1, a pseudogene of the laminin receptor, significantly exacerbates early spatial learning and memory deficits in APP/PS1 mice. Through a combination of bioinformatics prediction and experimental validation, we identified the miR-29c/Bace1 pathway as a potential regulatory mechanism by which Lamr1-ps1 influences AD pathology. Importantly, augmenting the miR-29c-3p levels in mice ameliorated memory deficits, underscoring the therapeutic potential of targeting miR-29c-3p in early AD intervention. This study not only provides new insights into the role of pseudogenes in AD but also consolidates a foundational basis for considering miR-29c as a viable therapeutic target, offering a novel avenue for AD research and treatment strategies.
Animals ; Alzheimer Disease/pathology* ; Pseudogenes/genetics* ; Mice ; Memory Disorders/metabolism* ; MicroRNAs/genetics* ; Disease Models, Animal ; Spatial Learning/physiology* ; Mice, Transgenic ; Presenilin-1/genetics* ; Male ; Amyloid Precursor Protein Secretases/metabolism* ; Mice, Inbred C57BL ; Aspartic Acid Endopeptidases/metabolism*

Attempts have been made to modulate motor sequence learning (MSL) through repetitive transcranial magnetic stimulation, targeting different sites within the sensorimotor network. However, the target with the optimum modulatory effect on neural plasticity associated with MSL remains unclarified. This study was therefore designed to compare the role of the left primary motor cortex and the left supplementary motor area proper (SMAp) in modulating MSL across different complexity levels and for both hands, as well as the associated neuroplasticity by applying intermittent theta burst stimulation together with the electroencephalogram and concurrent transcranial magnetic stimulation. Our data demonstrated the role of SMAp stimulation in modulating neural communication to support MSL, which is achieved by facilitating regional activation and orchestrating neural coupling across distributed brain regions, particularly in interhemispheric connections. These findings may have important clinical implications, particularly for motor rehabilitation in populations such as post-stroke patients.
Humans ; Transcranial Magnetic Stimulation ; Motor Cortex/physiology* ; Male ; Electroencephalography ; Neuronal Plasticity/physiology* ; Female ; Adult ; Evoked Potentials, Motor/physiology* ; Young Adult ; Learning/physiology*

In modern society, people are increasingly exposed to chronic stress, leading to various mental disorders. However, the activities of brain regions, especially neural firing patterns related to specific behaviors, remain unclear. In this study, we introduce a novel approach, NeuroSync, which integrates open-field behavioral testing with electrophysiological recordings from emotion-related brain regions, specifically the central amygdala and the paraventricular nucleus of the hypothalamus, to explore the mechanisms of negative emotions induced by chronic stress in mice. By applying machine vision techniques, we quantified behaviors in the open field, and signal processing algorithms elucidated the neural underpinnings of the observed behaviors. Synchronizing behavioral and electrophysiological data revealed significant correlations between neural firing patterns and stress-related behaviors, providing insights into real-time brain activity underlying behavioral responses. This research combines deep learning and machine learning to synchronize high-resolution video and electrophysiological data, offering new insights into neural-behavioral dynamics under chronic stress conditions.
Animals ; Mice ; Male ; Emotions/physiology* ; Stress, Psychological/physiopathology* ; Action Potentials/physiology* ; Mice, Inbred C57BL ; Behavior, Animal/physiology* ; Machine Learning ; Amygdala/physiopathology* ; Neurons/physiology* ; Paraventricular Hypothalamic Nucleus/physiopathology* ; Brain/physiology*

BACKGROUND: Gluconeogenesis is a critical metabolic pathway for maintaining glucose homeostasis, and its dysregulation can lead to glycometabolic disorders. This study aimed to identify hub biomarkers of these disorders to provide a theoretical foundation for enhancing diagnosis and treatment. METHODS: Gene expression profiles from liver tissues of three well-characterized gluconeogenesis mouse models were analyzed to identify commonly differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA), machine learning techniques, and diagnostic tests on transcriptome data from publicly available datasets of type 2 diabetes mellitus (T2DM) patients were employed to assess the clinical relevance of these DEGs. Subsequently, we identified hub biomarkers associated with gluconeogenesis-related glycometabolic disorders, investigated potential correlations with immune cell types, and validated expression using quantitative polymerase chain reaction in the mouse models. RESULTS: Only a few common DEGs were observed in gluconeogenesis-related glycometabolic disorders across different contributing factors. However, these DEGs were consistently associated with cytokine regulation and oxidative stress (OS). Enrichment analysis highlighted significant alterations in terms related to cytokines and OS. Importantly, osteomodulin ( OMD ), apolipoprotein A4 ( APOA4 ), and insulin like growth factor binding protein 6 ( IGFBP6 ) were identified with potential clinical significance in T2DM patients. These genes demonstrated robust diagnostic performance in T2DM cohorts and were positively correlated with resting dendritic cells. CONCLUSIONS Gluconeogenesis-related glycometabolic disorders exhibit considerable heterogeneity, yet changes in cytokine regulation and OS are universally present. OMD , APOA4 , and IGFBP6  may serve as hub biomarkers for gluconeogenesis-related glycometabolic disorders.
Machine Learning ; Humans ; Computational Biology/methods* ; Biomarkers/metabolism* ; Diabetes Mellitus, Type 2/genetics* ; Animals ; Mice ; Gluconeogenesis/physiology* ; Gene Expression Profiling ; Transcriptome/genetics* ; Gene Regulatory Networks/genetics* ; Clinical Relevance

Emotion classification and recognition is a crucial area in emotional computing. Physiological signals, such as electroencephalogram (EEG), provide an accurate reflection of emotions and are difficult to disguise. However, emotion recognition still faces challenges in single-modal signal feature extraction and multi-modal signal integration. This study collected EEG, electromyogram (EMG), and electrodermal activity (EDA) signals from participants under three emotional states: happiness, sadness, and fear. A feature-weighted fusion method was applied for integrating the signals, and both support vector machine (SVM) and extreme learning machine (ELM) were used for classification. The results showed that the classification accuracy was highest when the fusion weights were set to EEG 0.7, EMG 0.15, and EDA 0.15, achieving accuracy rates of 80.19% and 82.48% for SVM and ELM, respectively. These rates represented an improvement of 5.81% and 2.95% compared to using EEG alone. This study offers methodological support for emotion classification and recognition using multi-modal physiological signals.
Humans ; Emotions/physiology* ; Electroencephalography ; Support Vector Machine ; Electromyography ; Signal Processing, Computer-Assisted ; Galvanic Skin Response/physiology* ; Machine Learning ; Male

Cardiotocography (CTG) is a non-invasive and important tool for diagnosing fetal distress during pregnancy. To meet the needs of intelligent fetal heart monitoring based on deep learning, this paper proposes a TWD-MOAL deep active learning algorithm based on the three-way decision (TWD) theory and multi-objective optimization Active Learning (MOAL). During the training process of a convolutional neural network (CNN) classification model, the algorithm incorporates the TWD theory to select high-confidence samples as pseudo-labeled samples in a fine-grained batch processing mode, meanwhile low-confidence samples annotated by obstetrics experts were also considered. The TWD-MOAL algorithm proposed in this paper was validated on a dataset of 16 355 prenatal CTG records collected by our group. Experimental results showed that the algorithm proposed in this paper achieved an accuracy of 80.63% using only 40% of the labeled samples, and in terms of various indicators, it performed better than the existing active learning algorithms under other frameworks. The study has shown that the intelligent fetal heart monitoring model based on TWD-MOAL proposed in this paper is reasonable and feasible. The algorithm significantly reduces the time and cost of labeling by obstetric experts and effectively solves the problem of data imbalance in CTG signal data in clinic, which is of great significance for assisting obstetrician in interpretations CTG signals and realizing intelligence fetal monitoring.
Humans ; Pregnancy ; Female ; Cardiotocography/methods* ; Deep Learning ; Neural Networks, Computer ; Algorithms ; Fetal Monitoring/methods* ; Heart Rate, Fetal ; Fetal Distress/diagnosis* ; Fetal Heart/physiology*