https://doi.org/10.1140/epjs/s11734-026-02251-9
Regular Article
Hidden nodes of personality: functional brain networks and their trait correlates
1
Research Institute at Medical University of Plovdiv, Medical University of Plovdiv, 15A Vasil Aprilov Blvd., 4002, Plovdiv, Bulgaria
2
Department of Psychiatry and Medical Psychology, Medical University of Plovdiv, 15A Vasil Aprilov Blvd., 4002, Plovdiv, Bulgaria
3
Neuroscience Research Institute, FSBEI HE SamSMU MOH Russia, 89, Chapaevskaya Street, 443099, Samara, Russia
4
Research Institute of Applied Artificial Intelligence and Digital Solutions, Plekhanov Russian University of Economics, 36, Stremyanny Per., 115054, Moscow, Russia
a
This email address is being protected from spambots. You need JavaScript enabled to view it.
Received:
4
February
2026
Accepted:
4
March
2026
Published online:
11
March
2026
Abstract
This study investigates the associations between personality traits and the topology of resting-state functional brain networks, aiming to identify trait-specific global, nodal, and predictive network markers. Graph-theoretical metrics of functional connectivity were related to psychometric measures of personality traits using correlational analysis, unsupervised clustering, principal component analysis (PCA), and predictive modeling. Nonlinear relevance vector machine (RVM) and linear regression models were applied to assess the predictability of traits from network measures. Psychopathic traits were moderately and negatively associated with global integration measures, including mean node strength (r = − 0.264) and mean clustering coefficient (r = − 0.247), indicating reduced local segregation and integration. Masochistic traits showed a negative correlation with mean betweenness centrality (r = − 0.230), suggesting a more distributed network architecture. At the nodal level, eigenvector centrality was positively associated with masochistic traits in frontal mid regions (r = 0.28–0.32), left inferior frontal gyrus (r = 0.27–0.31), left postcentral gyrus (r = 0.26), and supramarginal gyrus (r = 0.27–0.31). Psychopathic traits were positively related to eigenvector centrality of Heschl’s gyrus (r = 0.25–0.26). A replicable association was observed between oral traits, node strength, and the left nucleus accumbens (r = 0.26), indicating increased centrality of reward-related circuits. PCA revealed three latent components explaining 75% of total variance, separating psychopathic–masochistic, oral–rigid, and masochistic trait dimensions. In predictive analyses, RVM models achieved the lowest errors (mean RMSE < 0.4), with the highest accuracy for psychopathic traits using functional connectivity (RMSE = 0.40) and for masochistic traits using node strength (RMSE = 0.346). Linear regression yielded exceptionally low error for psychopathic traits predicted by betweenness centrality (RMSE = 0.122). Our work offers a modest contribution to the development of a nomological network of traits within the bioenergetic analytical paradigm in the domain of personality neuroscience. Personality traits are associated with distinct and partially nonlinear patterns of functional brain network organization. Both global topology and trait-specific nodal centrality contribute to the neural expression and prediction of personality dimensions, supporting a multilevel network-based model of personality.
© The Author(s) 2026
modified publication 2026
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
