Decoding the Spatiotemporal Dynamics of Neural Response Similarity in Auditory Processing: A Multivariate Analysis Based on OPM‐MEG

Human Brain Mapping, Journal Year: 2025, Volume and Issue: 46(4)

Published: Feb. 27, 2025

ABSTRACT The brain represents information through the encoding of neural populations, where activity patterns these groups constitute content this information. Understanding and their dynamic changes is significant importance to cognitive neuroscience related research areas. Current studies focus more on regions that show differential responses stimuli, but they lack ability capture about representational or process‐level dynamics within regions. In study, we recorded data from 10 healthy participants during auditory experiments using optically pumped magnetometer magnetoencephalography (OPM‐MEG) electroencephalography (EEG). We constructed similarity matrices (RSMs) investigate response decoding. results indicate RSA can reveal in pattern different stages processing reflected by OPM‐MEG. Comparisons with EEG showed both techniques captured same processes early However, differences sensitivity at later highlighted common distinct aspects representation between two modalities. Further analysis indicated process involved widespread network activation, including Heschl's gyrus, superior temporal middle inferior parahippocampal orbitofrontal gyrus. This study demonstrates combination OPM‐MEG sufficiently sensitive detect identify anatomical origins, offering new insights references for future application other multivariate methods MEG field.

Language: Английский

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Exploring brain dysfunction in IBD: A study of EEG-fMRI source imaging based on empirical mode diagram decomposition

Mathematical Biosciences & Engineering, Journal Year: 2025, Volume and Issue: 22(4), P. 962 - 987

Published: Jan. 1, 2025

Patients with inflammatory bowel disease (IBD) often suffer from mood disorders and cognitive decline, which has prompted research into abnormalities in emotional brain regions their functional analysis. However, most IBD studies only focus on single-modality neuroimaging technologies. Due to a limited spatiotemporal resolution, it is unfeasible fully explore deep source activities accurately evaluate the connectivity. Therefore, we propose an electroencephalography (EEG)-functional magnetic resonance imaging (fMRI)source method based empirical mode diagram decomposition (EMDD) performed synchronous EEG-fMRI analysis 21 patients 11 healthy subjects. The high-frequency spatial components of fMRI were extracted through EMDD as prior constraints compared EEG entire prior. Then, cortical time series reconstructed according Desikan-Killiany atlas for effective connectivity results showed that had better performance, average log model evidence increased by 29.60% explained variance 19.12%. There significant differences activation intensity abnormal between controls, some newly discovered: uncus, claustrum, lentiform nucleus, lingual gyrus. Moreover, findings signals revealed information flow loss frontal lobes, central areas, left parietal lobe, right temporal gyrus was enhanced.

Language: Английский

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Recognition of brain activities via graph-based long short-term memory-convolutional neural network

Frontiers in Neuroscience, Journal Year: 2025, Volume and Issue: 19

Published: March 24, 2025

Introduction Human brain activities are always difficult to recognize due its diversity and susceptibility disturbance. With unique capability of measuring activities, magnetoencephalography (MEG), as a high temporal spatial resolution neuroimaging technique, has been used identify multi-task activities. Accurately robustly classifying motor imagery (MI) cognitive (CI) from MEG signals is significant challenge in the field brain-computer interface (BCI). Methods In this study, graph-based long short-term memory-convolutional neural network (GLCNet) proposed classify MI CI tasks. It was characterized by implementing three modules graph convolutional (GCN), convolution memory (LSTM) effectively extract time-frequency-spatial features simultaneously. For performance evaluation, our method compared with six benchmark algorithms FBCSP, FBCNet, EEGNet, DeepConvNets, Shallow ConvNet MEGNet on two public datasets MEG-BCI BCI competition IV dataset 3. Results The results demonstrated that GLCNet outperformed other models average accuracies 78.65% 65.8% for classification four dataset, respectively. Discussion concluded enhanced model’s adaptability handling individual variability robust performance. This would contribute exploration activates neuroscience.

Language: Английский

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Dynamic Neural Network States During Social and Non-Social Cueing in Virtual Reality Working Memory Tasks: A Leading Eigenvector Dynamics Analysis Approach

Brain Sciences, Journal Year: 2024, Volume and Issue: 15(1), P. 4 - 4

Published: Dec. 24, 2024

This research investigates brain connectivity patterns in reaction to social and non-social stimuli within a virtual reality environment, emphasizing their impact on cognitive functions, specifically working memory. Employing the LEiDA framework with EEG data from 47 participants, I examined dynamic network states elicited by avatars compared stick cues during VR memory task. Through integration of deep learning graph theory analyses, unique associated cue type were discerned, underscoring substantial influence processes. LEiDA, conventionally utilized fMRI, was creatively employed detect swift alterations states, offering insights into processing dynamics. The findings indicate distinct neural for cues; notably, correlated state characterized increased self-referential memory-processing networks, implying greater engagement. Moreover, attained approximately 99% accuracy differentiating contexts, highlighting efficacy prominent eigenvectors analysis. Analysis also uncovered structural disparities, signifying enhanced contexts involving cues. multi-method approach elucidates cognition, establishing basis VR-based rehabilitation immersive learning, wherein signals may significantly enhance function.

Language: Английский

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