Neuroscience informatics最新文献

Background

Results

Conclusions

Gut microbiota play a crucial role in complex interactions of the gut brain axis between the gastrointestinal system and the central nervous system. The intricate network of bidirectional communication between the gut and brain, mediated through neural, hormonal, and immunological pathways, known as the gut-brain axis, has been implicated in the pathophysiology of several mental, neurological and behavioral disorders. Alterations in the gut microbiota composition, or dysbiosis, have been associated with disorders like Alzheimer's disease, Parkinson's disease, Multiple Sclerosis, Autism Spectrum Disorder, Ischemic Stroke, Eating Disorders, depression, anxiety, stress and addiction. In this study, a Python package BERTopic, based on Artificial Intelligence based Natural Language Processing using Transformer model BERT, specializing in topic modeling, was applied to abstracts of 3,482 PubMed articles published from year 2014 until May 2024, to explore the mental, neurological, and behavioral diseases influenced by the gut microbiota. There were some variations in individual runs of BERTopic due to stochastic nature of one of its components, but overall the discovered topics corresponded to major neuropsychiatric diseases. To understand the impact of the variability in outcomes ten repeated runs of BERTopic were performed with keeping identical parameters. The major topics that were found consistently in all the ten repeated runs of BERTopic were Depression, Alzheimer Disease, Autism Spectrum Disorder, Parkinson's Disease, Multiple Sclerosis, Ischemic Stroke, Anorexia Nervosa and Schizophrenia.

Development of Parkinson's disease causes functional impairment in the brain network of Parkinson's patients. The aim of this study is to analyze brain networks of people with Parkinson's disease based on higher resolution parcellations and newer graphical features. The topological features of brain networks were investigated in Parkinson's patients (19 individuals) compared to healthy individuals (17 individuals) using graph theory. In addition, four different methods were used in graph formation to detect linear and nonlinear relationships between functional magnetic resonance imaging (fMRI) signals. The functional connectivity between the left precuneus and the left amygdala, as well as between the vermis 1-2 and the left temporal lobe was evaluated for the healthy and the patient groups. The difference between the healthy and patient groups was evaluated by parametric t-test and nonparametric U-test. Based on the results, Parkinson's patients exhibited a noteworthy reduction in centrality criterion compared to healthy subjects. Moreover, alterations in the regional features of the brain network were evident. Applying centrality criteria and correlation coefficients revealed significant distinctions between healthy subjects and Parkinson's patients across various brain areas. The results obtained for topological features indicate changes in the functional brain network of Parkinson's patients. Finally, similar areas obtained by all three methods of graph formation in the evaluation of connectivity between paired regions in the brain network of Parkinson's patients increased the reliability of the results.

The seamless integration of visual and auditory information is a fundamental aspect of human cognition. Although age-related functional changes in Audio-Visual Integration (AVI) have been extensively explored in the past, thorough studies across various age groups remain insufficient. Previous studies have provided valuable insights into age-related AVI using EEG-based sensor data. However, these studies have been limited in their ability to capture spatial information related to brain source activation and their connectivity. To address these gaps, our study conducted a comprehensive audio-visual integration task with a specific focus on assessing the brain maturation effects in various age groups, particularly in early-mid adulthood. We presented visual, auditory, and audio-visual stimuli and recorded EEG data from Young (18–25 years), Transition (26–33 years), and Middle (34–50 years) age cohort healthy participants. We utilized source-based features for the classification of these age groups. We aimed to understand how aging affects brain activation and functional connectivity among hubs during audio-visual tasks. Our findings unveiled diminished levels of brain activation among middle-aged individuals, which escalate when exposed to AVI stimuli. Lower frequency bands showed substantial changes with increasing age during AVI. Our results demonstrated that implementing the k-means elbow method during the AVI task successfully categorized brain regions into five distinct brain networks. Additionally, we observed increased functional connectivity in middle age, particularly in the frontal, temporal, and occipital regions. These results highlight the compensatory neural mechanisms involved in aging during cognitive tasks.

Cervical cancer has recently emerged as the leading cause of premature death among women. Around 85% of cervical cancer cases occur in underdeveloped countries. There are several risk factors associated with cervical cancer. This study describes a novel predictive model that uses early screening and risk trends from individual health records to forecast cervical cancer patients' prognoses. This study uses machine learning classification techniques to investigate the risk factors for cervical cancer. Additionally, use the voting method to evaluate all models and select the most appropriate model. The dataset used in this study contains missing values and shows a significant imbalance. Thus, the Random Oversampling technique was used as a sampling method. We used Principal Component Analysis (PCA) and XGBoost feature selection techniques to determine the most important features. To predict the accuracy, we used several machine learning classifiers, including Support Vector Machines (SVM), Random Forest (RF), k-nearest Neighbors (KNN), Decision Trees (DT), Naive Bayes (NB), Logistic Regression (LR), AdaBoost (AdB), Gradient Boosting (GB), Multilayer Perceptron (MLP), and Nearest Centroid Classifier (NCC). To demonstrate the efficacy of the suggested model, a comparison of its accuracy, sensitivity, and specificity was performed. We used the Random Oversampling approach along with the Ensemble ML method, hard voting on RF and MLP, and achieved 99.19% accuracy. It is demonstrated that the ensemble ML classifier (hard voting) performs better at handling classification problems when features are decreased and the high-class imbalance problem is handled.

Epilepsy is a severe and common neurological disease that causes sudden and irregular seizures, necessitating patient-specific detection models for effective management. The proposed methodology, Epilepsy Tracking META-Set Analysis, establishes portability rules that identify similar patients, enabling the transfer of these detection models from one patient to another. Main issue is to identify clusters of patients analyzing a set of meta-features of each patient in terms of clinical descriptors, performance metrics of a machine learning model for seizure detection, and data complexity measures. The investigation of complexity measures represents a novelty in such a medical field, allowing to compare patients and to support automated seizure detection methods. The proposed methodology is validated using the well-known Epileptic Seizure EEG Database from the Epilepsy Center of the University Hospital of Freiburg and demonstrates promising results in transferring detection models to new cases.

The human visual system can effortlessly group small components into entities to form an object, but the role of the hemispheres in this processing is still unknown. Understanding the hemispherical processing of perceptual grouping is crucial for unraveling the complexities of visual perception. We have attempted to examine the processing of perceptual grouping in both hemispheres of the human brain. The neural data was collected for 15 healthy subjects while they viewed displays featuring either ‘structure’ (line segments composed of dots) or ‘non-structure’ (random dots). ERPs were recorded and assessed in both frontal and occipital regions of the left and right hemispheres for structure and non-structure stimuli. Our results revealed higher activation for structure compared to non-structure in both brain hemispheres, with notably amplified activity observed in the right hemisphere. Moreover, a decrease in task-related alpha power and an increase in PLI functional connectivity were observed during the perceptual grouping of structures. A novel finding that the Granger causality exhibits a higher value for perceptual grouping when information flows from the right to the left hemisphere, in contrast to communication from left to right, is obtained. Thus, the right hemisphere demonstrated distinct dominance in activation amplitude, task-related alpha power, functional connectivity, and directional functional connectivity related to perceptual grouping. Furthermore, our findings suggest that perceptual grouping involves communication between the frontal and occipital brain regions. By elucidating the hemispherical mechanisms underlying perceptual grouping, this research not only advances our understanding of basic cognitive processes but also offers practical implications for fields such as neurorehabilitation and artificial intelligence.

Objective

Endovascular embolization has an important role in the management of brain arteriovenous malformations (AVMs). A Tsinghua AVM grading system has been proposed for patient selection and complete obliteration. The authors sought to validate this system in an independent patient cohort and compare it to the Buffalo grading system.

Methods

Consecutive 52 patients underwent endovascular AVM embolization between January 2019 and December 2021 according to Tsinghua AVM grading system. Each AVM was also graded using Buffalo grading system. Baseline clinical characteristics, complications, and AVM obliteration were compared between Tsinghua and Buffalo scales.

Results

Complete obliteration of AVM was obtained in 29 patients (55.8%). Three complications were encountered, one bleeding (1.9%) and 2 ischemic (3.8%), in 3(5.7%) patients who recovered completely at follow-up. The Tsinghua scale (p=0.017) was predictor of complete obliteration as well as Buffalo scale (p=0.002) on ROC curve analysis and their AUCs were not significantly different (p=0.672). The Tsinghua scale was also associated with the initial patient status (p=0.003) and injected Onyx volume (p=0.003) on linear regression test. Because of the low complication rate, neither the Tsinghua scale nor the Buffalo scale predicted complication risk related to AVM embolization.

Conclusions

The bleeding complication rate of 1.9% is within the range of rupture risk reported in the natural history of AVMs. In addition to predicting complete AVM obliteration as well as Buffalo scale, the Tsinghua scale can also predict the patients' status and the volume of Onyx avoid over injection.

Key messages

The Tsinghua grading system for endovascular AVM embolization will guide patient selection of AVM embolization.

Cognitive neuroscience investigates the intricate connections between brain function and mental processing to understand the cognitive architecture. Exploring the human brain, the epicenter of cognitive activity, offers valuable insights into underlying cognitive processes. To monitor brain states corresponding to various mental activities, appropriate measurement tools are essential. Electroencephalogram (EEG) signals serve as a valuable tool for recording patterns and changes in electrical brain activities. Leveraging non-linear signal processing techniques holds promise for advancing our understanding of brain activities during cognitive tasks. In this study, we analyze the electrical activity of the brain using EEG data collected from subjects engaged in a cognitive workload task. Employing wavelet-based analysis, we capture changes in the structure of EEG signals before and during a mental arithmetic task. Additionally, spectral analysis is conducted to discern alterations in the distribution of spectral contents of EEG signals. Our findings underscore the efficacy of wavelet-based analysis and spectral entropy in quantifying the time-varying and non-stationary nature of EEG recordings, offering effective frameworks for distinguishing between different cognitive activities. Consequently, these methods afford deeper insights into the cognitive architecture by tracking changes in the distribution of the time-varying spectrum.