Dynamic Brain Functional Lateralization Varies Across High and Low Vigilance States
Poster No:
2025
Submission Type:
Abstract Submission
Authors:
Yuwei Su1, Suyu Zhong1
Institutions:
1Beijing University of Posts and Telecommunications, Beijing, China
First Author:
Co-Author:
Introduction:
Changes in states of consciousness are often accompanied by complex reorganization of brain functional activity (Li, 2023). However, several studies have shown that these changes are not entirely symmetrical between the left and right hemispheres (Tamaki, 2016; Liu, 2013). This observation suggests that brain functional changes across different states of consciousness may be closely related to functional lateralization. Moreover, adopting a dynamic perspective on functional lateralization has emerged as a novel trend (Ocklenburg, 2024). Integrating these perspectives, we aim to investigate the differences in dynamic functional lateralization between high and low vigilance states.
Methods:
We analyzed 605 right-handed subjects from the HCP dataset. The initial and terminal 400 fMRI frames were used as coarse proxies for high and low vigilance states. The dynamic laterality index (DLI) was calculated using a sliding window approach, based on Fisher z-transformed Pearson correlations between regional BOLD time series and global signals from the left and right hemispheres (Wu, 2022).
3 DLI-based dynamic laterality measures were derived: MLI (the time-averaged DLI), LF (the standard deviation of DLI), and LR (the number of laterality sign switches). Paired t-tests were used to compare these measures between high and low states. SVM was applied to classify the 2 states.
To investigate the spatial organization of dynamic lateralization, the GenLouvain community detection algorithm was applied to DLI-based correlation matrix, with its elements representing the Pearson correlations of DLI between brain region pairs. Finally, the spatial clustering results from healthy participants were utilized to explore potential atypical patterns of dynamic lateralization in neurodegenerative disorders, such as Alzheimer' s disease, using the MCAD dataset (Sun, 2024).
3 DLI-based dynamic laterality measures were derived: MLI (the time-averaged DLI), LF (the standard deviation of DLI), and LR (the number of laterality sign switches). Paired t-tests were used to compare these measures between high and low states. SVM was applied to classify the 2 states.
To investigate the spatial organization of dynamic lateralization, the GenLouvain community detection algorithm was applied to DLI-based correlation matrix, with its elements representing the Pearson correlations of DLI between brain region pairs. Finally, the spatial clustering results from healthy participants were utilized to explore potential atypical patterns of dynamic lateralization in neurodegenerative disorders, such as Alzheimer' s disease, using the MCAD dataset (Sun, 2024).
Results:
There were significant differences in dynamic laterality measures between high and low vigilance states. For MLI, both positive and negative t-values were observed, with positive values indicating greater leftward lateralization in high state (Fig. 1A). All regions demonstrated significantly higher LF values compared to low state (Fig. 1B). High state exhibited significantly reduced change frequency in the right medial prefrontal cortex and significantly increased change frequency in the left cuneus, superior frontal gyrus, and right orbitofrontal gyrus (Fig. 1C). In terms of classification, MLI achieved an AUC of 0.87, with LF and LR demonstrating moderate performance (Fig. 1D).
Spatial clustering revealed distinct patterns of brain organization across vigilance states. In high state, 2 clusters were identified. Cluster 1 was dominated by DMN and subcortical networks, while cluster 2 was dominated by visual and somatomotor networks (Fig. 1E). Low state revealed 3 clusters: cluster 1 was dominated by limbic system, frontoparietal network, and DMN; cluster 2 and 3 were dominated by primary network and subcortical network, respectively (Fig. 1F).
Regarding neurodegenerative disorders, distinct patterns of group differences emerged. Significant group differences were observed in both clusters in high state. MCI demonstrated significantly higher DLI values in cluster 1 and significantly lower DLI values in cluster 2 (Fig. 2A and B). In low state, no significant differences were observed in cluster 1 (Fig. 2C). However, in clusters 2 and 3, HC and AD exhibited significantly lower DLI values than other groups, respectively (Fig. 2D and E).
Spatial clustering revealed distinct patterns of brain organization across vigilance states. In high state, 2 clusters were identified. Cluster 1 was dominated by DMN and subcortical networks, while cluster 2 was dominated by visual and somatomotor networks (Fig. 1E). Low state revealed 3 clusters: cluster 1 was dominated by limbic system, frontoparietal network, and DMN; cluster 2 and 3 were dominated by primary network and subcortical network, respectively (Fig. 1F).
Regarding neurodegenerative disorders, distinct patterns of group differences emerged. Significant group differences were observed in both clusters in high state. MCI demonstrated significantly higher DLI values in cluster 1 and significantly lower DLI values in cluster 2 (Fig. 2A and B). In low state, no significant differences were observed in cluster 1 (Fig. 2C). However, in clusters 2 and 3, HC and AD exhibited significantly lower DLI values than other groups, respectively (Fig. 2D and E).
Conclusions:
This study revealed significant differences in dynamic functional lateralization between high and low vigilance states. Spatial clustering identified distinct brain organization patterns: 2 clusters in high state and 3 clusters in low state. Group differences were observed in specific clusters for disorders. These findings highlight the dynamic nature of brain lateralization across vigilance states and its potential as a marker for detecting neurodegenerative changes.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s)
Modeling and Analysis Methods:
Classification and Predictive Modeling
fMRI Connectivity and Network Modeling 2
Perception, Attention and Motor Behavior:
Consciousness and Awareness 1
Keywords:
Consciousness
FUNCTIONAL MRI
Other - Functional Lateralization
1|2Indicates the priority used for review
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Provide references using APA citation style.
Tamaki, M. (2016). Night Watch in One Brain Hemisphere during Sleep Associated with the First-Night Effect in Humans. Current Biology, 26(9), 1190–1194.
Liu, X. (2013). Differential Effects of Deep Sedation with Propofol on the Specific and Nonspecific Thalamocortical Systems. Anesthesiology, 118(1), 59–69.
Ocklenburg, S. (2024). Cross-hemispheric communication: Insights on lateralized brain functions. Neuron, 112(8), 1222–1234.
Wu, X. (2022). Dynamic changes in brain lateralization correlate with human cognitive performance. PLOS Biology, 20(3), e3001560.
Sun, Y. (2024). Structure–function coupling reveals the brain hierarchical structure dysfunction in Alzheimer’s disease: A multicenter study. Alzheimer’s & Dementia, 20(9), 6305–6315.