Introduction: Folkloric claims have surrounded essential oils, including their enhancement of learning and memory through inhalational exposure. Few studies in humans have shown a benefit in cognition, albeit incremental. However, this benefit may not be entirely attributable to the essential oil aroma but may be confounded by psychological associations. We investigated rosemary, peppermint, lemon, and coffee aromas in a learning and memory model of Drosophila melanogaster to eliminate this confounder. Methods: We screened for concentrations of the four treatments that are non-stimulatory for altered locomotory behavior in the flies. At these concentrations, we determined if they were chemoneutral (i.e., neither chemoattractant nor chemorepellent) to the flies. Learning and memory of the flies exposed to these aromas were determined using an Aversive Phototaxis Suppression (APS) assay. Results: The aromas of rosemary, peppermint, and lemon that did not elicit altered mobility in the flies were from dilute essential oil solutions that ranged from 0.2 to 0.5% v/v; whereas for the aroma in coffee, it was at a higher concentration of 7.5% m/v. At these concentrations, the aromas used were found to be chemoneutral towards the flies. We observed no improvement in both learning and memory in the four aromas tested. While a significant reduction (p < 0.05) in learning was observed when flies were treated with the aromas of rosemary, peppermint, and coffee, a significant reduction (p < 0.05) in memory was only observed in the peppermint aroma treatment. Conclusion This study demonstrated that in the absence of psychological association, the four aromas do not enhance learning and memory
Drosophila melanogaster ; Learning ; Memory ; Rosmarinus ; Mentha piperita ; Citrus ; Coffea

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

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

Mice ; Animals ; Memory Disorders ; Cerebrospinal Fluid

For decades, memory research has centered on the role of neurons, which do not function in isolation. However, astrocytes play important roles in regulating neuronal recruitment and function at the local and network levels, forming the basis for information processing as well as memory formation and storage. In this review, we discuss the role of astrocytes in memory functions and their cellular underpinnings at multiple time points. We summarize important breakthroughs and controversies in the field as well as potential avenues to further illuminate the role of astrocytes in memory processes.
Astrocytes ; Neuronal Plasticity/physiology* ; Memory/physiology* ; Neurons/physiology* ; Cognition/physiology*

Macaque is a common animal model in drug safety assessment. Its behavior reflects its health condition before and after drug administration, which can effectively reveal the side effects of drugs. At present, researchers usually rely on artificial methods to observe the behavior of macaque, which cannot achieve uninterrupted 24-hour monitoring. Therefore, it is urgent to develop a system to realize 24-hour observation and recognition of macaque behavior. In order to solve this problem, this paper constructs a video dataset containing nine kinds of macaque behaviors (MBVD-9), and proposes a network called Transformer-augmented SlowFast for macaque behavior recognition (TAS-MBR) based on this dataset. Specifically, the TAS-MBR network converts the red, green and blue (RGB) color mode frame input by its fast branches into residual frames on the basis of SlowFast network and introduces the Transformer module after the convolution operation to obtain sports information more effectively. The results show that the average classification accuracy of TAS-MBR network for macaque behavior is 94.53%, which is significantly improved compared with the original SlowFast network, proving the effectiveness and superiority of the proposed method in macaque behavior recognition. This work provides a new idea for the continuous observation and recognition of the behavior of macaque, and lays the technical foundation for the calculation of monkey behaviors before and after medication in drug safety evaluation.
Animals ; Electric Power Supplies ; Macaca ; Recognition, Psychology

The causes of mental disorders are complex, and early recognition and early intervention are recognized as effective way to avoid irreversible brain damage over time. The existing computer-aided recognition methods mostly focus on multimodal data fusion, ignoring the asynchronous acquisition problem of multimodal data. For this reason, this paper proposes a framework of mental disorder recognition based on visibility graph (VG) to solve the problem of asynchronous data acquisition. First, time series electroencephalograms (EEG) data are mapped to spatial visibility graph. Then, an improved auto regressive model is used to accurately calculate the temporal EEG data features, and reasonably select the spatial metric features by analyzing the spatiotemporal mapping relationship. Finally, on the basis of spatiotemporal information complementarity, different contribution coefficients are assigned to each spatiotemporal feature and to explore the maximum potential of feature so as to make decisions. The results of controlled experiments show that the method in this paper can effectively improve the recognition accuracy of mental disorders. Taking Alzheimer's disease and depression as examples, the highest recognition rates are 93.73% and 90.35%, respectively. In summary, the results of this paper provide an effective computer-aided tool for rapid clinical diagnosis of mental disorders.
Humans ; Mental Disorders/diagnosis* ; Alzheimer Disease/diagnosis* ; Brain Injuries ; Electroencephalography ; Recognition, Psychology

Recently, deep learning has achieved impressive results in medical image tasks. However, this method usually requires large-scale annotated data, and medical images are expensive to annotate, so it is a challenge to learn efficiently from the limited annotated data. Currently, the two commonly used methods are transfer learning and self-supervised learning. However, these two methods have been little studied in multimodal medical images, so this study proposes a contrastive learning method for multimodal medical images. The method takes images of different modalities of the same patient as positive samples, which effectively increases the number of positive samples in the training process and helps the model to fully learn the similarities and differences of lesions on images of different modalities, thus improving the model's understanding of medical images and diagnostic accuracy. The commonly used data augmentation methods are not suitable for multimodal images, so this paper proposes a domain adaptive denormalization method to transform the source domain images with the help of statistical information of the target domain. In this study, the method is validated with two different multimodal medical image classification tasks: in the microvascular infiltration recognition task, the method achieves an accuracy of (74.79 ± 0.74)% and an F1 score of (78.37 ± 1.94)%, which are improved as compared with other conventional learning methods; for the brain tumor pathology grading task, the method also achieves significant improvements. The results show that the method achieves good results on multimodal medical images and can provide a reference solution for pre-training multimodal medical images.
Humans ; Algorithms ; Brain/diagnostic imaging* ; Brain Neoplasms/diagnostic imaging* ; Recognition, Psychology

Objective We aimed to investigate whether antagonism of the cannabinoid CB₁ receptor (CB₁R) could affect novel object recognition (NOR) memory in chronically rapid eye movement sleep-deprived (RSD) rats.Methods The animals were examined for recognition memory following a 7-day chronic partial RSD paradigm using the multiple platform technique. The CB₁R antagonist rimonabant (1 or 3 mg/kg, i.p.) was administered either at one hour prior to the sample phase for acquisition, or immediately after the sample phase for consolidation, or at one hour before the test phase for retrieval of NOR memory. For the reconsolidation task, rimonabant was administered immediately after the second sample phase.Results The RSD episode impaired acquisition, consolidation, and retrieval, but it did not affect the reconsolidation of NOR memory. Rimonabant administration did not affect acquisition, consolidation, and reconsolidation; however, it attenuated impairment of the retrieval of NOR memory induced by chronic RSD.Conclusions These findings, along with our previous report, would seem to suggest that RSD may affect different phases of recognition memory based on its duration. Importantly, it seems that the CB₁R may, at least in part, be involved in the adverse effects of chronic RSD on the retrieval, but not in the acquisition, consolidation, and reconsolidation, of NOR memory.
Rats ; Animals ; Rimonabant/pharmacology* ; Memory ; Sleep, REM ; Receptors, Cannabinoid ; Cannabinoids/pharmacology*

Animal survival necessitates adaptive behaviors in volatile environmental contexts. Virtual reality (VR) technology is instrumental to study the neural mechanisms underlying behaviors modulated by environmental context by simulating the real world with maximized control of contextual elements. Yet current VR tools for rodents have limited flexibility and performance (e.g., frame rate) for context-dependent cognitive research. Here, we describe a high-performance VR platform with which to study contextual behaviors immersed in editable virtual contexts. This platform was assembled from modular hardware and custom-written software with flexibility and upgradability. Using this platform, we trained mice to perform context-dependent cognitive tasks with rules ranging from discrimination to delayed-sample-to-match while recording from thousands of hippocampal place cells. By precise manipulations of context elements, we found that the context recognition was intact with partial context elements, but impaired by exchanges of context elements. Collectively, our work establishes a configurable VR platform with which to investigate context-dependent cognition with large-scale neural recording.
Animals ; Mice ; Rodentia ; Virtual Reality ; Cognition ; Recognition, Psychology