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Brain fingerprints along the language hierarchy
Juan Zhang1, Liping Zhuang2, Jiahao Jiang3
1State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
Brain scans reveal that complex language tasks enhance individual uniqueness, making brain connectivity a reliable identifier. Higher-order brain networks are more personalized than lower-order ones.
Area of Science:
- Neuroscience
- Cognitive Science
- Brain Imaging
Background:
- The brain's functional connectome acts as a unique identifier for individuals.
- The specific neural information contributing to this uniqueness is not well understood.
Purpose of the Study:
- To investigate how individual identifiability changes across different levels of the language processing hierarchy.
- To explore the relationship between task complexity, brain functional connectivity, and individual uniqueness.
Main Methods:
- Functional magnetic resonance imaging (fMRI) scans were acquired from subjects during rest and while listening to stories presented in various formats (backward, scrambled, forward).
- Individual identifiability was assessed by comparing functional connectivity profiles across different scan sessions and tasks.
- Analysis was conducted at the whole-brain and functional network levels, comparing auditory and frontoparietal networks.
Main Results:
- Individual identifiability increased with the complexity of the language task, indicating greater distinguishability in more complex conditions.
- High-order networks, such as the frontoparietal network, showed higher individualization compared to low-order networks like the auditory network.
- Increased individual identifiability correlated with increased inter-subject variability in functional connectivities.
Conclusions:
- Task complexity and hierarchical processing in the brain contribute to individual uniqueness.
- High-order cognitive functions engage more individualized neural patterns.
- These findings enhance understanding of brain individualization and may inform the development of connectivity-based biomarkers.
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