The prefrontal cortex and hippocampus may support sequential working memory beyond episodic memory and spatial navigation. This stereoelectroencephalography (SEEG) study investigated how the dorsolateral prefrontal cortex (DLPFC) interacts with the hippocampus in the online processing of sequential information. Twenty patients with epilepsy (eight women, age 27.6 ± 8.2 years) completed a line ordering task with SEEG recordings over the DLPFC and the hippocampus. Participants showed longer thinking times and more recall errors when asked to arrange random lines clockwise (random trials) than to maintain ordered lines (ordered trials) before recalling the orientation of a particular line. First, the ordering-related increase in thinking time and recall error was associated with a transient theta power increase in the hippocampus and a sustained theta power increase in the DLPFC (3-10 Hz). In particular, the hippocampal theta power increase correlated with the memory precision of line orientation. Second, theta phase coherences between the DLPFC and hippocampus were enhanced for ordering, especially for more precisely memorized lines. Third, the theta band DLPFC → hippocampus influence was selectively enhanced for ordering, especially for more precisely memorized lines. This study suggests that theta oscillations may support DLPFC-hippocampal interactions in the online processing of sequential information.
Adult ; Female ; Humans ; Young Adult ; Epilepsy ; Hippocampus ; Memory, Short-Term ; Mental Recall ; Prefrontal Cortex ; Theta Rhythm ; Male

With the widespread use of electrical equipment, cognitive functions such as working memory (WM) could be severely affected when people are exposed to 50 Hz electromagnetic fields (EMF) for long term. However, the effects of EMF exposure on WM and its neural mechanism remain unclear. In the present paper, 15 rats were randomly assigned to three groups, and exposed to an EMF environment at 50 Hz and 2 mT for a different duration: 0 days (control group), 24 days (experimental group I), and 48 days (experimental group II). Then, their WM function was assessed by the T-maze task. Besides, their local field potential (LFP) in the media prefrontal cortex (mPFC) was recorded by the in vivo multichannel electrophysiological recording system to study the power spectral density (PSD) of θ and γ oscillations and the phase-amplitude coupling (PAC) intensity of θ-γ oscillations during the T-maze task. The results showed that the PSD of θ and γ oscillations decreased in experimental groups I and II, and the PAC intensity between θ and high-frequency γ (hγ) decreased significantly compared to the control group. The number of days needed to meet the task criterion was more in experimental groups I and II than that of control group. The results indicate that long-term exposure to EMF could impair WM function. The possible reason may be the impaired communication between different rhythmic oscillations caused by a decrease in θ-hγ PAC intensity. This paper demonstrates the negative effects of EMF on WM and reveals the potential neural mechanisms from the changes of PAC intensity, which provides important support for further investigation of the biological effects of EMF and its mechanisms.
Humans ; Rats ; Animals ; Memory, Short-Term/physiology* ; Electromagnetic Fields/adverse effects* ; Prefrontal Cortex ; Cognition

Alzheimer's disease (AD) is a neurodegenerative disease characterized by cognitive impairment, with the predominant clinical diagnosis of spatial working memory (SWM) deficiency, which seriously affects the physical and mental health of patients. However, the current pharmacological therapies have unsatisfactory cure rates and other problems, so non-pharmacological physical therapies have gradually received widespread attention. Recently, a novel treatment using 40 Hz light flicker stimulation (40 Hz-LFS) to rescue the cognitive function of model animals with AD has made initial progress, but the neurophysiological mechanism remains unclear. Therefore, this paper will explore the potential neural mechanisms underlying the modulation of SWM by 40 Hz-LFS based on cross-frequency coupling (CFC). Ten adult Wistar rats were first subjected to acute LFS at frequencies of 20, 40, and 60 Hz. The entrainment effect of LFS with different frequency on neural oscillations in the hippocampus (HPC) and medial prefrontal cortex (mPFC) was analyzed. The results showed that acute 40 Hz-LFS was able to develop strong entrainment and significantly modulate the oscillation power of the low-frequency gamma (lγ) rhythms. The rats were then randomly divided into experimental and control groups of 5 rats each for a long-term 40 Hz-LFS (7 d). Their SWM function was assessed by a T-maze task, and the CFC changes in the HPC-mPFC circuit were analyzed by phase-amplitude coupling (PAC). The results showed that the behavioral performance of the experimental group was improved and the PAC of θ-lγ rhythm was enhanced, and the difference was statistically significant. The results of this paper suggested that the long-term 40 Hz-LFS effectively improved SWM function in rats, which may be attributed to its enhanced communication of different rhythmic oscillations in the relevant neural circuits. It is expected that the study in this paper will build a foundation for further research on the mechanism of 40 Hz-LFS to improve cognitive function and promote its clinical application in the future.
Humans ; Adult ; Rats ; Animals ; Memory, Short-Term/physiology* ; Rats, Wistar ; Neurodegenerative Diseases ; Hippocampus ; Prefrontal Cortex

OBJECTIVE: To propose a semi-supervised epileptic seizure prediction model (ST-WGAN-GP-Bi-LSTM) to enhance the prediction performance by improving time-frequency analysis of electroencephalogram (EEG) signals, enhancing the stability of the unsupervised feature learning model and improving the design of back-end classifier. METHODS: Stockwell transform (ST) of the epileptic EEG signals was performed to locate the time-frequency information by adaptive adjustment of the resolution and retaining the absolute phase to obtain the time-frequency inputs. When there was no overlap between the generated data distribution and the real EEG data distribution, to avoid failure of feature learning due to a constant JS divergence, Wasserstein GAN was used as a feature learning model, and the cost function based on EM distance and gradient penalty strategy was adopted to constrain the unsupervised training process to allow the generation of a high-order feature extractor. A temporal prediction model was finally constructed based on a bi-directional long short term memory network (Bi-LSTM), and the classification performance was improved by obtaining the temporal correlation between high-order time-frequency features. The CHB-MIT scalp EEG dataset was used to validate the proposed patient-specific seizure prediction method. RESULTS: The AUC, sensitivity, and specificity of the proposed method reached 90.40%, 83.62%, and 86.69%, respectively. Compared with the existing semi-supervised methods, the propose method improved the original performance by 17.77%, 15.41%, and 53.66%. The performance of this method was comparable to that of a supervised prediction model based on CNN. CONCLUSION The utilization of ST, WGAN-GP, and Bi-LSTM effectively improves the prediction performance of the semi-supervised deep learning model, which can be used for optimization of unsupervised feature extraction in epileptic seizure prediction.
Humans ; Memory, Short-Term ; Seizures/diagnosis* ; Electroencephalography

Working memory is an important foundation for advanced cognitive function. The paper combines the spatiotemporal advantages of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to explore the neurovascular coupling mechanism of working memory. In the data analysis, the convolution matrix of time series of different trials in EEG data and hemodynamic response function (HRF) and the blood oxygen change matrix of fNIRS are extracted as the coupling characteristics. Then, canonical correlation analysis (CCA) is used to calculate the cross correlation between the two modal features. The results show that CCA algorithm can extract the similar change trend of related components between trials, and fNIRS activation of frontal pole region and dorsolateral prefrontal lobe are correlated with the delta, theta, and alpha rhythms of EEG data. This study reveals the mechanism of neurovascular coupling of working memory, and provides a new method for fusion of EEG data and fNIRS data.
Electroencephalography/methods* ; Memory, Short-Term ; Neurovascular Coupling/physiology* ; Prefrontal Cortex ; Spectroscopy, Near-Infrared/methods*

Transcranial magneto-acoustic-electrical stimulation is a new non-invasive neuromodulation technology, in which the induced electric field generated by the coupling effect of ultrasound and static magnetic field are used to regulate the neural rhythm oscillation activity in the corresponding brain region. The purpose of this paper is to investigate the effects of transcranial magneto-acoustic-electrical stimulation on the information transfer and communication in neuronal clusters during memory. In the experiment, twenty healthy adult Wistar rats were randomly divided into a control group (five rats) and stimulation groups (fifteen rats). Transcranial magneto-acoustic-electrical stimulation of 0.05~0.15 T and 2.66~13.33 W/cm ² was applied to the rats in stimulation groups, and no stimulation was applied to the rats in the control group. The local field potentials signals in the prefrontal cortex of rats during the T-maze working memory tasks were acquired. Then the coupling differences between delta rhythm phase, theta rhythm phase and gamma rhythm amplitude of rats in different parameter stimulation groups and control group were compared. The experimental results showed that the coupling intensity of delta and gamma rhythm in stimulation groups was significantly lower than that in the control group ( P<0.05), while the coupling intensity of theta and gamma rhythm was significantly higher than that in the control group ( P<0.05). With the increase of stimulation parameters, the degree of coupling between delta and gamma rhythm showed a decreasing trend, while the degree of coupling between theta and gamma rhythm tended to increase. The preliminary results of this paper indicated that transcranial magneto-acoustic-electrical stimulation inhibited delta rhythmic neuronal activity and enhanced the oscillation of theta and gamma rhythm in the prefrontal cortex, thus promoted the exchange and transmission of information between neuronal clusters in different spatial scales. This lays the foundation for further exploring the mechanism of transcranial magneto-acoustic-electrical stimulation in regulating brain memory function.
Acoustics ; Animals ; Electric Stimulation ; Memory, Short-Term/physiology* ; Rats ; Rats, Wistar ; Theta Rhythm/physiology* ; Transcranial Direct Current Stimulation

Electrocardiogram (ECG) can visually reflect the physiological electrical activity of human heart, which is important in the field of arrhythmia detection and classification. To address the negative effect of label imbalance in ECG data on arrhythmia classification, this paper proposes a nested long short-term memory network (NLSTM) model for unbalanced ECG signal classification. The NLSTM is built to learn and memorize the temporal characteristics in complex signals, and the focal loss function is used to reduce the weights of easily identifiable samples. Then the residual attention mechanism is used to modify the assigned weights according to the importance of sample characteristic to solve the sample imbalance problem. Then the synthetic minority over-sampling technique is used to perform a simple manual oversampling process on the Massachusetts institute of technology and Beth Israel hospital arrhythmia (MIT-BIH-AR) database to further increase the classification accuracy of the model. Finally, the MIT-BIH arrhythmia database is applied to experimentally verify the above algorithms. The experimental results show that the proposed method can effectively solve the issues of imbalanced samples and unremarkable features in ECG signals, and the overall accuracy of the model reaches 98.34%. It also significantly improves the recognition and classification of minority samples and has provided a new feasible method for ECG-assisted diagnosis, which has practical application significance.
Algorithms ; Arrhythmias, Cardiac/diagnosis* ; Electrocardiography ; Humans ; Memory, Short-Term ; Neural Networks, Computer ; Signal Processing, Computer-Assisted

To compare the performance of generalized additive model (GAM) and long short-term memory recurrent neural network (LSTM-RNN) on the prediction of daily admissions of respiratory diseases with comorbid diabetes. Daily data on air pollutants, meteorological factors and hospital admissions for respiratory diseases from Jan 1st, 2014 to Dec 31st, 2019 in Beijing were collected. LSTM-RNN was used to predict the daily admissions of respiratory diseases with comorbid diabetes, and the results were compared with those of GAM. The evaluation indexes were calculated by five-fold cross validation. Compared with the GAM, the prediction errors of LSTM-RNN were significantly lower [root mean squared error (RMSE): 21.21±3.30 vs. 46.13±7.60, <0.01; mean absolute error (MAE): 14.64±1.99 vs. 36.08±6.20, <0.01], and the value was significantly higher (0.79±0.06 vs. 0.57±0.12, <0.01). In gender stratification, RMSE, MAE and values of LSTM-RNN were better than those of GAM in predicting female admission (all <0.05), but there were no significant difference in predicting male admission between two models (all >0.05). In seasonal stratification, RMSE and MAE of LSTM-RNN were lower than those of GAM in predicting warm season admission (all <0.05), but there was no significant difference in value (>0.05). There were no significant difference in RMSE, MAE and between the two models in predicting cold season admission (all >0.05). In the stratification of functional areas, the RMSE, MAE and values of LSTM-RNN were better than those of GAM in predicting core area admission (all <0.05). has lower prediction errors and better fitting than the GAM, which can provide scientific basis for precise allocation of medical resources in polluted weather in advance.
Beijing/epidemiology* ; Diabetes Mellitus/epidemiology* ; Female ; Hospitalization ; Humans ; Male ; Memory, Short-Term ; Neural Networks, Computer

OBJECTIVE: Exploring the effectiveness of using EEG linear and nonlinear features for accessing mental workload in different tasks. METHODS: Working memory tasks with different information types and various mental loads were designed based on N-Back paradigm. EEG signals from 18 normal adults were acquired when tasks were being performed. Linear and nonlinear features of EEGs were then extracted. Indices that can effectively reflect mental workload levels were selected by using multivariate analysis of variance statistical approach. RESULTS: With the increment of task load, power of frontal Theta, Theta/Alpha ratio, and sample entropies (scales>10) in parietal regions increased significantly first and decreased slightly then, while the power of central-parietal Alpha decreased significantly first and increased slightly then. No difference in power of frontal Theta, central-parietal Alpha, and sample entropies (scales>10) of parietal regions were found between verbal and object tasks, as well as between two spatial tasks. No difference of frontal Theta/Alpha ratio was found in all the four tasks. CONCLUSIONS The results can provide evidence for the mental workload evaluation in tasks with different information types.
Electroencephalography ; Memory, Short-Term ; Workload

Astrocytes are increasingly recognized to play an active role in learning and memory, but whether neural inputs can trigger event-specific astrocytic Ca²⁺ dynamics in real time to participate in working memory remains unclear due to the difficulties in directly monitoring astrocytic Ca²⁺ dynamics in animals performing tasks. Here, using fiber photometry, we showed that population astrocytic Ca²⁺ dynamics in the hippocampus were gated by sensory inputs (centered at the turning point of the T-maze) and modified by the reward delivery during the encoding and retrieval phases. Notably, there was a strong inter-locked and antagonistic relationship between the astrocytic and neuronal Ca²⁺ dynamics with a 3-s phase difference. Furthermore, there was a robust synchronization of astrocytic Ca²⁺ at the population level among the hippocampus, medial prefrontal cortex, and striatum. The inter-locked, bidirectional communication between astrocytes and neurons at the population level may contribute to the modulation of information processing in working memory.
Animals ; Astrocytes ; Hippocampus/physiology* ; Humans ; Memory, Short-Term/physiology* ; Mice ; Neurons/physiology* ; Population Dynamics