Poster No:
1608
Submission Type:
Abstract Submission
Authors:
Bolong Wang1,2, Chenghui Zhang1,2, Xiangzhen Kong1,2
Institutions:
1Zhejiang University, Hangzhou, China, 2Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China
First Author:
Bolong Wang
Zhejiang University|Department of Psychology and Behavioral Sciences, Zhejiang University
Hangzhou, China|Hangzhou, China
Co-Author(s):
Chenghui Zhang
Zhejiang University|Department of Psychology and Behavioral Sciences, Zhejiang University
Hangzhou, China|Hangzhou, China
Xiangzhen Kong, PhD Supervisor
Zhejiang University|Department of Psychology and Behavioral Sciences, Zhejiang University
Hangzhou, China|Hangzhou, China
Introduction:
Regional neural dynamics facilitate hierarchical information processing in the brain and provide a framework for understanding the neural underpinnings of cognitive functions. However, the relationship between spontaneous neural dynamics and cognition remains incompletely characterized. To address this, we employed a comprehensive set of neurodynamic features alongside cognitive performance measures of a large-scale samples to elucidate the associations between spontaneous neural dynamics and cognitive functions.
Methods:
Human Connectome Projects dataset utilized in this study is a large-scale, pedigree-based resource that includes monozygotic and dizygotic twin pairs, as along with other siblings. We analyzed 24 neurodynamic features derived from functional magnetic resonance imaging (fMRI) time-series data and 29 behavioral measures to assess spontaneous neural dynamics and cognitive functions. We also quantified topographic typicality by measuring the spatial similarity between individual neurodynamic map and the group-averaged map.
At the whole-brain level, we employed a multivariate correlation method-partial least squares-correlation (PLSC)-to bridge the gap between neurodynamic features and behavioral measures. At the regional level, we performed region-specific correlation analyses between neurodynamic features and behavioral measures to examine regional specificity across the brain. Finally, we estimated the heritability of these neurodynamic features and behavioral measures and their genetic correlations using the pedigree structure of the dataset.
Results:
The first PLSC analysis revealed that neurodynamic features, particularly temporal autocorrelation and variability-related metrics, showed robust correlations with individual differences in behavioral measures (first component: r_Pearson=0.145,p=1.3×10^(-6)). Temporal autocorrelation of brain activity is considered to reflect the temporal integration window for information processing, whereas variability indicates the neural system's capacity to generate diverse responses to varying inputs.
In the second PLSC analysis, we observed that higher topographic typicality was consistently associated with better behavioral performance (first component: r_Pearson=0.203,p<1×10^(-5)). At the regional level, correlations between regional neurodynamic features and behavioral measures suggest a unimodal-to-transmodal gradient, reflecting the brain's functional hierarchy.
Importantly, both neurodynamic features and behavioral measures demonstrated significant heritability (for each measure: p<0.001). While neurodynamic features and their topographic typicality scores showed significant genetic correlations with cognitive functions (for each pair: p<0.001), neurodynamic features and their topographic typicality scores demonstrate independent genetic correlation(r_g=0.001,p=0.18). In addition, regional genetic analyses of neurodynamic features revealed similar hierarchical patterns across the brain, providing a biological foundation for the observed functional hierarchy.
Conclusions:
This study provides novel insights into the relationship between spontaneous neural dynamics and cognitive functions. The findings also reveal that both neural dynamics and cognitive functions are significantly heritable, with independent genetic correlations between neurodynamic features, their topographic patterns, and cognitive functions.
Genetics:
Genetics Other
Higher Cognitive Functions:
Higher Cognitive Functions Other 2
Modeling and Analysis Methods:
Multivariate Approaches 1
Keywords:
Cognition
Data analysis
MRI
Multivariate
NORMAL HUMAN
Other - genetic
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
Functional MRI
Behavior
Computational modeling
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
Other, Please list
-
HCP preprocessing pipeline
Provide references using APA citation style.
Glasser, M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80, 105–124 (2013).
No