Baggio, T. | bioRxiv Science

bioRxiv2025

Resting State connectivity patterns associated with trait anxiety in adolescence

Anxiety symptoms can vary across different life stages, with a higher frequency during adolescence and early adulthood, increasing the risk of developing future anxiety disorders. To date, neuroscientific research on anxiety has primarily focused on adulthood, thus limiting our understanding of how anxiety may characterize earlier stages of life, and employing mostly univariate approaches, thus discounting large-scale alterations of the brain. One intriguing hypothesis is that adolescents with trait anxiety may display similar abnormalities shown by adults in brain regions ascribed to the Default Mode Network (DMN) associated with self-referential thinking, awareness, and rumination-related processes. The present study aims to expand our previous knowledge on this topic using a large sample of young individuals to uncover the resting-state connectivity patterns associated with trait anxiety in a network approach. To test our hypotheses, we analyzed the rs-fMRI images of 1263 adolescents (mean age 20.55 years) as well as their scores on anxiety trait. A significant association between trait anxiety and resting-state functional connectivity in two networks was found, with some regions overlapping with the Default Mode Network. The first network included regions such as the cingulate gyrus and the middle temporal gyri known to be involved in self-referential processing and emotional perception and control, both altered in anxiety disorders. The second network included the precuneus, possibly related to rumination that characterizes anxiety. Of note, the higher the trait anxiety, the lower the connectivity within both networks, suggesting abnormal self-referential processing, awareness, and emotion regulation abilities in adolescents with high anxiety trait. These findings provided a better understanding of the association between trait anxiety and brain rs-functional connectivity, and may pave the way for the development of potential biomarkers in adolescents with anxiety.

neuroscience↗

bioRxiv2024

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

Despite the clinical significance of narcissistic personality, its neural bases have not been clear yet, primarily due to 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 gray (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 hypothesize that a fronto-temporo parietal network mainly related to the Default Mode Network, may be involved in NPT and white matter regions related to these 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 predict other personality traits (i.e., Histrionic, Paranoid, and Avoidant personalities). Notably, this network did not predict these personality traits. Additionally, a supervised machine learning model (Random Forest) was used to extract a predictive model to generalize 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.

neuroscience↗