Mapping Personality Traits in the Ecological Brain
Poster No:
762
Submission Type:
Abstract Submission
Authors:
Azman Akhter1, Arpan Banerjee1, Dipanjan Roy2
Institutions:
1National Brain Research Centre, Gurugram, Haryana, 2Indian Institute of Technology (IIT), Jodhpur, Rajasthan
First Author:
Co-Author(s):
Introduction:
Individually specific responses to environmental demands are central to understanding personality variation. Specific (personality) traits manifest particular behaviors in situations with particular features (Jackson, 2010; Hardikar, 2024). The static analyses have been at a failure to establish the personality influence on the naturalistic experience (Finn 2020). The association between brain response and traits must be inherently context-dependent and thus highly dynamic and transient during real-world experiences. To examine this phenomenon, we introduce a framework to identify moments where brain responses align with personality (unique moments: because during such moments the experiences would be idiosyncratic) and those where such associations are absent (universal moments).
Methods:
We used HCP 7T MOVIE dataset Session 1 (MOVIE1_7T_AP) ( n=90, unrelated individuals to avoid sibling/twin similarity to confound shared metric). We first calculated Intersubject Interregion Phase Synchrony (ISIRPS) based on Glerean 2012, with some modifications, for 380 BOLD parcels, including 300 Schaeffer cortical parcels and 80 AAL3 subcortical parcels. We used N-1 intersubject Synchrony and taken the Cosine distance between the phases to drive functional connectivity. Using Intersubject Representational Similarity Analysis (ISRSA), we link ISIRPS and personality scores at each timepoint. Representational Dissimilarity Matrices (RDMs) were generated pairwise between subject Euclidean distances for whole brain ISIRPS and personality scores ( Whole(itemwise)-whole questionnaire responses, and summary trait score (the big-five factors). We employed GLM to quantify associations at each time point following the equations given in Fig.1. Moments with significant associations (p-adjusted < 0.001) are labeled unique moments, while non-significant ones are universal moments.
Results:
The results presented in the figure illustrate the time series of whole-brain associations with the whole personality model (fig1 A), alongside the time series for each individual personality trait (fig1 B). These time series highlight the dynamic and transient nature of unique moments, showcasing significant variability in both the appearance of the traits and the strength of their associations across time. the distribution of these associations, providing a clear depiction of how the strength of unique moments varies. This supports our hypothesis that personality-driven brain responses are highly context-sensitive and dynamic.
Additionally, the pairwise correlations between the time series for individual traits reveal some level of interdependence among traits. However, the most observed correlations are not very high, suggesting that each trait contributes uniquely to the dynamic brain responses during naturalistic experiences.
To further explore these dynamics, we compared individual edges (connectivity metrics) during unique moments versus universal moments using the Wilcoxon test. By applying a stringent threshold of p < 0.0001, we identified 22,065 out of 72,010 edges that show significant differences in connectivity between the two-moment categories, making up about 30%, that expand across the whole brain. This highlights the widespread and distinct connectivity patterns that characterize unique moments compared to universal moments.
Additionally, the pairwise correlations between the time series for individual traits reveal some level of interdependence among traits. However, the most observed correlations are not very high, suggesting that each trait contributes uniquely to the dynamic brain responses during naturalistic experiences.
To further explore these dynamics, we compared individual edges (connectivity metrics) during unique moments versus universal moments using the Wilcoxon test. By applying a stringent threshold of p < 0.0001, we identified 22,065 out of 72,010 edges that show significant differences in connectivity between the two-moment categories, making up about 30%, that expand across the whole brain. This highlights the widespread and distinct connectivity patterns that characterize unique moments compared to universal moments.
Conclusions:
Current analysis reveals a dynamic, context-sensitive network of interregional synchrony transiently associated with personality traits during naturalistic experiences. By identifying a brain-wide network significantly differing between unique and universal moments, we highlight the limitations of static analyses in capturing such dynamics. This transient network reflects personality influences driven by contextual demands. Low-to-moderate correlations between trait-specific time series suggest individualized trait contributions. These results emphasize the need for dynamic approaches to studying personality-related brain function.
Emotion, Motivation and Social Neuroscience:
Social Neuroscience Other
Higher Cognitive Functions:
Higher Cognitive Functions Other 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 2
Multivariate Approaches
Keywords:
Cognition
Computational Neuroscience
Data analysis
Design and Analysis
Machine Learning
Modeling
1|2Indicates the priority used for review
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Provide references using APA citation style.
Finn, E. S., Glerean, E., Khojandi, A. Y., Nielson, D., Molfese, P. J., Handwerker, D. A., & Bandettini, P. A. (2020). Idiosynchrony: From shared responses to individual differences during naturalistic neuroimaging. NeuroImage, 215, 116828. https://doi.org/10.1016/j.neuroimage.2020.116828
Glerean, E., Salmi, J., Lahnakoski, J. M., Jääskeläinen, I. P., & Sams, M. (2012). Functional Magnetic Resonance Imaging Phase Synchronization as a Measure of Dynamic Functional Connectivity. Brain Connectivity, 2(2), 91–101. https://doi.org/10.1089/brain.2011.0068
Hardikar, S., McKeown, B., Turnbull, A., Xu, T., Valk, S. L., Bernhardt, B. C., Margulies, D. S., Milham, M. P., Jefferies, E., Leech, R., Villringer, A., & Smallwood, J. (2024). Personality traits vary in their association with brain activity across situations. Communications Biology, 7(1), 1–8. https://doi.org/10.1038/s42003-024-07061-0
Jackson, J. J., Wood, D., Bogg, T., Walton, K. E., Harms, P. D., & Roberts, B. W. (2010). What do conscientious people do? Development and validation of the Behavioral Indicators of Conscientiousness (BIC). Journal of Research in Personality, 44(4), 501–511. https://doi.org/10.1016/j.jrp.2010.06.005