A study of brain networks associated with Freezing of gait in Parkinson's disease using transfer entropy analysis

Emerging Trends and Hot Topics of Non-Invasive Electroencephalography Research in the Elderly: A Bibliometric Analysis from 2014 to 2023

Background

Electroencephalography (EEG) has been rapidly developed and is widely used in both clinical and scientific fields. Original studies on non-invasive EEG in the elderly have been of great importance owing to the global aging trend. The present study aimed to provide a bibliometric overview on current status and trends in this research field.

Methods

We searched the Web of Science Core Collection for articles published during 2014 and 2023. Synonyms for EEG and the elderly were combined as a retrieval strategy. Invasive EEG and secondary studies were excluded. Online filters and manual reviews were applied to select eligible articles. Basic bibliometric parameters were analyzed and visualized using VOSviewer and Excel software.

Results

A total of 1656 publications were filtered, and 655 of which were finally included. In general, publication counts have steadily increased over the last 10 years. A sharp rise in publications occurred in 2021, and then remained at a high level. Authors and institutions from high-income countries/regions such as the United States of America (USA), China, and Germany were more productive and made significant contributions. Journals specialized in neuroscience, such as Frontiers in Aging Neuroscience, Neurobiology of Aging, and Clinical Neurophysiology, were popular among authors. Articles on aging, Alzheimer's disease (AD), mild cognitive impairment (MCI), dementia, memory, event-related potentials, attention, and the brain were more likely to use EEG. The newer topics included anesthesia, postoperative delirium (POD), confusion assessment method, connectivity, validation, and power.

Conclusion

This bibliometric study provides fundamental knowledge on the current status and hot spots of the original studies on EEG in elderly, which is beneficial to researchers in paving future investigations of neuroscience and neural diseases.

Spectral Time-Varying Pattern Causality and Its Application

In this paper, a new method based on delayed pattern causality is proposed, called spectral time-varying pattern causality. Specifically, this method uses symbolic dynamics and phase space reconstruction to infer causality, systematically quantifies the causal relationship between different frequency components of the signal, and the generated spectrum provides a rich information representation of the time-varying potential causality. The causal intensity at different times is quantified by a sliding window, providing a dynamic perspective for the study of causality in complex systems. Through the simulation data, we verified the effectiveness of the method and its robustness to noise, and then applied it to physiological data to compare the differences in coupling between electrodes in different brain regions between normal and Parkinson's patients in the resting state. The study of causality in complex systems provides a new perspective to better capture the latent and elusive dynamic structures.

Hierarchical Bayesian Causality Network to Extract High-Level Semantic Information in Visual Cortex

Functional MRI (fMRI) is a brain signal with high spatial resolution, and visual cognitive processes and semantic information in the brain can be represented and obtained through fMRI. In this paper, we design single-graphic and matched/unmatched double-graphic visual stimulus experiments and collect 12 subjects' fMRI data to explore the brain's visual perception processes. In the double-graphic stimulus experiment, we focus on the high-level semantic information as "matching", and remove tail-to-tail conjunction by designing a model to screen the matching-related voxels. Then, we perform Bayesian causal learning between fMRI voxels based on the transfer entropy, establish a hierarchical Bayesian causal network (HBcausalNet) of the visual cortex, and use the model for visual stimulus image reconstruction. HBcausalNet achieves an average accuracy of 70.57% and 53.70% in single- and double-graphic stimulus image reconstruction tasks, respectively, higher than HcorrNet and HcasaulNet. The results show that the matching-related voxel screening and causality analysis method in this paper can extract the "matching" information in fMRI, obtain a direct causal relationship between matching information and fMRI, and explore the causal inference process in the brain. It suggests that our model can effectively extract high-level semantic information in brain signals and model effective connections and visual perception processes in the visual cortex of the brain.