It is well established that epilepsy is characterized by the destruction of the information capacity of brain network and the interference with information processing in regions outside the epileptogenic focus. However, the potential mechanism remains poorly understood. In the current study, we applied a recently proposed approach on the basis of resting-state fMRI data to measure altered local neural dynamics in mesial temporal lobe epilepsy (mTLE), which represents how long neural information is stored in a local brain area and reflect an ability of information integration. Using resting-state-fMRI data recorded from 36 subjects with mTLE and 36 healthy controls, we calculated the intrinsic neural timescales (INT) of neural signals by summing the positive magnitude of the autocorrelation of the resting-state brain activity. Compared to healthy controls, the INT values were significantly lower in patients in the right orbitofrontal cortices, right insula, and right posterior lobe of cerebellum. Whereas, we observed no statistically significant changes between patients with long- and short-term epilepsy duration or between left-mTLE and right-mTLE. Our study provides distinct insight into the brain abnormalities of mTLE from the perspective of the dynamics of the brain activity, highlighting the significant role of intrinsic timescale in understanding neurophysiological mechanisms. And we postulate that altered intrinsic timescales of neural signals in specific cortical brain areas may be the neurodynamic basis of cognitive impairment and emotional comorbidities in mTLE patients.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8693765 | PMC |
http://dx.doi.org/10.3389/fnhum.2021.772365 | DOI Listing |
Anal Methods
January 2025
School of Future Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Near-infrared (NIR) spectroscopy, with its advantages of non-destructive analysis, simple operation, and fast detection speed, has been widely applied in various fields. However, the effectiveness of current spectral analysis techniques still relies on complex preprocessing and feature selection of spectral data. While data-driven deep learning can automatically extract features from raw spectral data, it typically requires large amounts of labeled data for training, limiting its application in spectral analysis.
View Article and Find Full Text PDFPartial migration is a phenomenon where migratory and resident individuals of the same species co-exist within a population, and has been linked to both intrinsic (e.g., genetic) as well as environmental factors.
View Article and Find Full Text PDFCancer Discov
January 2025
Neurology Clinic and National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany.
Deepening our understanding of neuro-cancer interactions can innovate brain tumor treatment. This mini review unfolds the most relevant and recent insights into the neural mechanisms contributing to brain tumor initiation, progression, and resistance, including synaptic connections between neurons and cancer cells, paracrine neuro-cancer signaling, and cancer cells' intrinsic neural properties. We explain the basic and clinical-translational relevance of these findings, identify unresolved questions and particularly interesting future research avenues, such as central nervous system neuro-immunooncology, and discuss the potential transferability to extracranial cancers.
View Article and Find Full Text PDFJ Affect Disord
January 2025
Key Laboratory of Adolescent Cyberpsychology and Behaviour (Ministry of Education), Key Laboratory of Human Development and Mental Health of Hubei Province, National Intelligent Society Governance Experiment Base (Education), School of Psychology, Central China Normal University, Wuhan, China. Electronic address:
Background: The role of cortical networks in health anxiety remain poorly understood. This study aimed to develop a predictive model for health anxiety, using a machine-learning approach based on resting-state functional connectivity (rsFC) with functional near-infrared spectroscopy (fNIRS).
Method: One hundred and four university students experiencing school disclosure due to the Corona Virus Disease 2019 pandemic participated in the study, and the final sample consisted of 90 participants.
Sci Rep
January 2025
School of Electronic Information and Physics, Central South University of Forestry Science and Technology, Changsha, 410004, China.
Graph neural networks have excellent performance and powerful representation capabilities, and have been widely used to handle Few-shot image classification problems. The feature extraction module of graph neural networks has always been designed as a fixed convolutional neural network (CNN), but due to the intrinsic properties of convolution operations, its receiving domain is limited. This method has limitations in capturing global feature information and easily ignores key feature information of the image.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!