Predicting Sensation-Seeking from Resting-State fMRI: The Need for Age-Specific Models
Keywords: rs-fMRI, Function Connectivity, Sensation-Seeking, Age-specific Modeling
TL;DR: This study uses resting-state fMRI data to predict sensation-seeking. Our proposed age-specific predictive model significantly outperformed the baseline that fitted a single model for the entire dataset.
Abstract: Sensation-seeking, as a sub-dimension of impulsivity, reflects an individual's tendency for novel and stimulating experiences. High sensation-seeking often involves novelty-seeking and risk-taking, which may lead to risky behaviors such as reckless driving, addiction, and substance use, significantly impacting individuals' social and personal functioning. Recent studies have utilized functional magnetic resonance imaging (fMRI) to study the neural mechanisms of sensation-seeking. However, the influence of demographic factors like age on the neural patterns associated with sensation-seeking remains unexplored. In this paper, we predicted sensation-seeking scores from resting-state fMRI data in a large-scale study involving $131$ male participants aged $20$ to $79$. By developing separate predictive models for different age groups (age-specific model), we achieved an $R^2$ of $0.38$ between the actual and predicted sensation-seeking scores. The proposed age-specific model significantly outperformed the baseline that fitted a single model for the entire dataset, indicating the importance of demographic factors in understanding the neural correlates of sensation-seeking. We identified key brain regions associated with sensation-seeking, including the prefrontal areas, cerebellum, subcortical regions, parietal lobe, and cerebral cortex areas. Notably, the brain connectivity patterns linked to sensation-seeking varied across age groups, further demonstrating the age-related variation in neural correlates of sensation-seeking. Our proposed age-specific modeling of sensation-seeking acknowledges the diversity in neural patterns across different aging stages and potentially offers more accurate insights into the neural correlates of sensation-seeking.
Track: 11. General Track
Registration Id: YCN4FBHZXRS
Submission Number: 397
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