CPMs demonstrate that neural networks associated with neuroticism differ across the adult lifespan
Poster No:
918
Submission Type:
Abstract Submission
Authors:
Adam Gorka1, James Teng1, Nathan McPherson1, Ruchika Prakash1
Institutions:
1The Ohio State University, Columbus, OH
First Author:
Co-Author(s):
Introduction:
Neuroticism is a risk factor for the development of adverse health outcomes as individuals age and accounts for approximately 10% of the conversion between mild cognitive impairment and Alzheimer's disease (Friedman, 2019; Terracciano et al., 2014). Previous studies utilizing connectome-based predictive models (CPM) to identify neural biomarkers of neuroticism have reported contradictory findings (Cai et al., 2020; Jiang et al., 2018). This has led researchers to suggest that differences in the age of study participants may explain inconsistencies across studies (Cai et al., 2020) and that neural models developed in samples of younger adults may not be capable of predicting neuroticism in older adults (Jiang et al., 2018). However, to date, no study had directly tested whether associations between functional connectivity and neuroticism identified using CPM are impacted by age. We set out to investigate the stability and plasticity of the brain-behavior relationship characterizing neuroticism across the adult lifespan.
Methods:
Data for this study comes from the HCP-Aging Lifespan 2.0 release (Harms et al., 2018). We restricted our sample (N=631, Men=43.4%, Age=60.19+/-15.84 SD) to participants who competed the NEO-FFI (Costa & McCrae, 1992) with at least one resting state scan with acceptable levels of head motion (average framewise displacement>0.5mm). Connectivity matrices were calculated by performing pairwise Pearson correlations on the mean BOLD time course from each node of the Shen atlas (Shen et al., 2013). The resulting 268x268 matrix was Fisher's z transformed for group-level analysis. A modified CPM approach was used to examine relationships between functional connectivity and neuroticism that were 1) age-invariant (i.e. significant across the lifespan while controlling for age) and 2) age-variant (i.e. significantly different as a function of age) using 10-fold cross validation (permuted p values based on the null distribution using 5000-iterations).
Results:
The age-invariant CPM model which tested for associations while controlling for age did not successfully predict within-sample neuroticism scores (all p perm>0.05). In contrast, the age-variant CPM model which tested for associations between functional connectivity and neuroticism that significantly differ as a function of age successfully predicted within-sample neuroticism scores (Combined model (r=0.104, p perm=0.023, Figure 1A); Positive interaction model (r=0.118, p perm=0.007, Figure 1B), Negative interaction model (r=0.097, p perm=0.006, Figure 1C)). The Positive interaction model identified edges that were overrepresented within multiple canonical networks (Figure 1D/E) including subcortical-visual association network connections. Identified edges between the subcortical and visual association network were negatively associated with neuroticism in younger adults but positively associated with neuroticism in older adults (Figure 2B). The Negative interaction model identified edges that were overrepresented within multiple canonical networks (Figure 1F/G) including within-network connections of the visual association network. Identified edges within the visual association network were positively associated with neuroticism in younger adults but negatively associated with neuroticism in older adults (Figure 2D).
·Figure 1
·Figure 2
Conclusions:
Our results demonstrate that the networks associated with neuroticism significantly differ across the adult lifespan. Researchers have suggested that accounting for neural degeneracy, which refers to the ability of the central nervous system to produce the same outcome using different pathways (Seghier & Price, 2018), can help us better identify brain-behavior relationships (Westlin et al., 2023). Our results suggests that that the directionality of the relationship between functional connectivity and neuroticism varies as a function of age which may shed light on neural biomarkers of adverse health outcomes in older adults.
Emotion, Motivation and Social Neuroscience:
Emotion and Motivation Other
Lifespan Development:
Aging 1
Modeling and Analysis Methods:
Classification and Predictive Modeling 2
Keywords:
Aging
Emotions
FUNCTIONAL MRI
Machine Learning
1|2Indicates the priority used for review
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3. Friedman, H. S. (2019). Neuroticism and health as individuals age. Personality Disorders: Theory, Research, and Treatment, 10(1), 25–32. https://doi.org/10.1037/per0000274
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