Narcissus reflected: Grey and white matter features joint contribution to the default mode network in predicting narcissistic personality traits

Khanitin Jornkokgoud,Teresa Baggio, Richard Bakiaj,Peera Wongupparaj,Remo Job,Alessandro Grecucci

EUROPEAN JOURNAL OF NEUROSCIENCE(2024)

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摘要
Despite the clinical significance of narcissistic personality, its neural bases have not been clarified yet, primarily because of methodological limitations of the previous studies, such as the low sample size, the use of univariate techniques and the focus on only one brain modality. In this study, we employed for the first time a combination of unsupervised and supervised machine learning methods, to identify the joint contributions of grey matter (GM) and white matter (WM) to narcissistic personality traits (NPT). After preprocessing, the brain scans of 135 participants were decomposed into eight independent networks of covarying GM and WM via parallel ICA. Subsequently, stepwise regression and Random Forest were used to predict NPT. We hypothesized that a fronto-temporo parietal network, mainly related to the default mode network, may be involved in NPT and associated WM regions. Results demonstrated a distributed network that included GM alterations in fronto-temporal regions, the insula and the cingulate cortex, along with WM alterations in cerebellar and thalamic regions. To assess the specificity of our findings, we also examined whether the brain network predicting narcissism could also predict other personality traits (i.e., histrionic, paranoid and avoidant personalities). Notably, this network did not predict such personality traits. Additionally, a supervised machine learning model (Random Forest) was used to extract a predictive model for generalization to new cases. Results confirmed that the same network could predict new cases. These findings hold promise for advancing our understanding of personality traits and potentially uncovering brain biomarkers associated with narcissism. The neural basis of narcissistic personality traits is explored using a novel approach that blends unsupervised and supervised machine learning techniques. Through the analysis of brain scans from 135 participants, a network showing grey and white matter changes in fronto-temporal, insular and cingulate areas is identified, and it is linked to the default mode network. This network accurately predicts narcissism but not other traits, demonstrating predictive capability for new cases. Findings provide valuable insights into the neural correlates of narcissism. image
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关键词
machine learning,narcissism,narcissistic traits,parallel ICA,Random Forest
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