Trait anxiety relates to the steady propensity to experience and report negative emotions and thoughts such as fear and worries across different situations, along with a stable perception of the environment as characterized by threatening stimuli. Previous studies have tried to investigate neuroanatomical features related to anxiety mostly using univariate analyses and thus giving rise to contrasting results. The aim of this study is to build a predictive model of individual differences in trait anxiety from brain morphometric features, by taking advantage of a combined data fusion machine learning approach to allow generalization to new cases. Additionally, we aimed to perform a network analysis to test the hypothesis that anxiety-related networks have a central role in modulating other networks not strictly associated with anxiety. Finally, we wanted to test the hypothesis that trait anxiety was associated with specific cognitive emotion regulation strategies, and whether anxiety may decrease with ageing. Structural brain images of 158 participants were first decomposed into independent covarying gray and white matter networks with a data fusion unsupervised machine learning approach (Parallel ICA). Then, supervised machine learning (decision tree) and backward regression were used to extract and test the generalizability of a predictive model of trait anxiety. Two covarying gray and white matter independent networks successfully predicted trait anxiety. The first network included mainly parietal and temporal regions such as the postcentral gyrus, the precuneus, and the middle and superior temporal gyrus, while the second network included frontal and parietal regions such as the superior and middle temporal gyrus, the anterior cingulate, and the precuneus. We also found that trait anxiety was positively associated with catastrophizing, rumination, other- and self-blame, and negatively associated with positive refocusing and reappraisal. Moreover, trait anxiety was negatively associated with age. This paper provides new insights regarding the prediction of individual differences in trait anxiety from brain and psychological features and can pave the way for future diagnostic predictive models of anxiety.

Borderline personality disorder (BPD) is one of the most diagnosed disorders in clinical settings. Besides the fully diagnosed disorder, borderline personality traits (BPT) are quite common in the general population. Prior studies have investigated the neural correlates of BPD but not of BPT. This paper investigates the neural correlates of BPT in a subclinical population using a supervised machine learning method known as Kernel Ridge Regression (KRR) to build predictive models. Additionally, we want to determine whether the same brain areas involved in BPD are also involved in subclinical BPT. Recent attempts to characterize the specific role of resting state-derived macro networks in BPD have highlighted the role of the default mode network. However, it is not known if this extends to the subclinical population. Finally, we wanted to test the hypothesis that the same circuitry that predicts BPT can also predict histrionic personality traits. Histrionic personality is sometimes considered a milder form of BPD, and making a differential diagnosis between the two may be difficult. For the first time KRR was applied to structural images of 135 individuals to predict BPT, based on the whole brain, on a circuit previously found to correctly classify BPD, and on the five macro-networks. At a whole brain level, results show that frontal and parietal regions, as well as the Heschl's area, the thalamus, the cingulum, and the insula, are able to predict borderline traits. BPT predictions increase when considering only the regions limited to the brain circuit derived from a study on BPD, confirming a certain overlap in brain structure between subclinical and clinical samples. Of all the five macro networks, only the DMN successfully predicts BPD, confirming previous observations on its role in the BPD. Histrionic traits could not be predicted by the BPT circuit. The results have implications for the diagnosis of BPD and a dimensional model of personality.

Anxiety symptoms occur more frequently during adolescence and early adulthood, increasing the risk of future anxiety disorders. Neuroscientific research on anxiety has primarily focused on adulthood, employing mostly univariate approaches, discounting large-scale alterations of the brain. Indeed adolescents with trait anxiety may display similar abnormalities shown by adults in brain regions ascribed to the default mode network, associated with self-referential thinking and rumination-related processes. The present study aims to explore resting-state connectivity patterns associated with trait anxiety in a large sample of young individuals. We analyzed the rs-fMRI images of 1263 adolescents (mean age 20.55 years) and 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, such as the cingulate gyrus, the middle temporal gyri and the precuneus. 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.

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.