Sleep, anesthesia, and coma share a number of neural features but the recovery profiles are radically different. To understand the mechanisms of reversibility of unconsciousness at the network level, we studied the conditions for gradual and abrupt transitions in conscious and anesthetized states. We hypothesized that the conditions for explosive synchronization (ES) in human brain networks would be present in the anesthetized brain just over the threshold of unconsciousness. To test this hypothesis, functional brain networks were constructed from multi-channel electroencephalogram (EEG) recordings in seven healthy subjects across conscious, unconscious, and recovery states. We analyzed four variables that are involved in facilitating ES in generic, non-biological networks: (1) correlation between node degree and frequency, (2) disassortativity (i.e., the tendency of highly-connected nodes to link with less-connected nodes, or vice versa), (3) frequency difference of coupled nodes, and (4) an inequality relationship between local and global network properties, which is referred to as the suppressive rule. We observed that the four network conditions for ES were satisfied in the unconscious state. Conditions for ES in the human brain suggest a potential mechanism for rapid recovery from the lightly-anesthetized state. This study demonstrates for the first time that the network conditions for ES, formerly shown in generic networks only, are present in empirically-derived functional brain networks. Further investigations with deep anesthesia, sleep, and coma could provide insight into the underlying causes of variability in recovery profiles of these unconscious states.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4720783 | PMC |
http://dx.doi.org/10.3389/fncom.2016.00001 | DOI Listing |
Brain Cogn
January 2025
Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China. Electronic address:
Human experiences are inherently shaped by individual perspectives, leading to diverse interpretations of the same events. However, shared activities, such as communal film watching or sports viewing, underscore the dual nature of these experiences: collective joy arises through social interactions, while individual emotional responses are influenced by personal preferences. The neural mechanisms underlying this interplay between shared and idiosyncratic experiences, particularly in the context of reward processing, remain insufficiently explored.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
Shanghai Maritime University, Shanghai 201306, China. Electronic address:
Background And Objective: Inferring large-scale brain networks from functional magnetic resonance imaging (fMRI) provides more detailed and richer connectivity information, which is critical for gaining insight into brain structure and function and for predicting clinical phenotypes. However, as the number of network nodes increases, most existing methods suffer from the following limitations: (1) Traditional shallow models often struggle to estimate large-scale brain networks. (2) Existing deep graph structure learning models rely on downstream tasks and labels.
View Article and Find Full Text PDFNeuromolecular Med
January 2025
Department of Pathology and Laboratory Medicine, University of California, Irvine, Irvine, CA, USA.
Down syndrome (DS) or trisomy 21 (T21) is present in a significant number of children and adults around the world and is associated with cognitive and medical challenges. Through research, the T21 Research Society (T21RS), established in 2014, unites a worldwide community dedicated to understanding the impact of T21 on biological systems and improving the quality of life of people with DS across the lifespan. T21RS hosts an international conference every two years to support collaboration, dissemination, and information sharing for this goal.
View Article and Find Full Text PDFAdv Sci (Weinh)
January 2025
College of Physics Science & Technology, School of Life Sciences, Institute of Life Science and Green Development, Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Hebei University, Baoding, 071002, China.
Hardware system customized toward the demands of graph neural network learning would promote efficiency and strong temporal processing for graph-structured data. However, most amorphous/polycrystalline oxides-based memristors commonly have unstable conductance regulation due to random growth of conductive filaments. And graph neural networks based on robust and epitaxial film memristors can especially improve energy efficiency due to their high endurance and ultra-low power consumption.
View Article and Find Full Text PDFBrain Topogr
January 2025
Department of Neurology, Yale University School of Medicine, 333 Cedar Street, New Haven, CT, 06520, USA.
Aberrant large-scale resting-state functional connectivity (rsFC) has been frequently documented in ischemic stroke. However, it remains unclear about the altered patterns of within- and across-network connectivity. The purpose of this meta-analysis was to identify the altered rsFC in patients with ischemic stroke relative to healthy controls, as well as to reveal longitudinal changes of network dysfunctions across acute, subacute, and chronic phases.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!