https://doi.org/10.1140/epjs/s11734-024-01411-z
Regular Article
Identifying neural network structures explained by personality traits: combining unsupervised and supervised machine learning techniques in translational validity assessment
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
Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 14 Alexander Nevsky Street, 236016, Kaliningrad, Russia
4
Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Lytkino, 777, 141534, Solnechnogorsk, Russia
a
kristina.stoyanova@mu-plovdiv.bg
Received:
17
October
2024
Accepted:
14
November
2024
Published online:
9
December
2024
There have been studies previously the neurobiological underpinnings of personality traits in various paradigms such as psychobiological theory and Eysenck’s model as well as five-factor model. However, there are limited results in terms of co-clustering of the functional connectivity as measured by functional MRI, and personality profiles. In the present study, we have analyzed resting-state connectivity networks and character type with the Lowen bioenergetic test in 66 healthy subjects. There have been identified direct correspondences between network metrics such as eigenvector centrality (EC), clustering coefficient (CC), node strength (NS) and specific personality characteristics. Specifically, N Acc L and OFCmed were associated with oral and masochistic traits in terms of EC and CC, while Insula R is associated with oral traits in terms of NS and EC. It is noteworthy that we observed significant correlations between individual items and node measures in specific regions, suggesting a more targeted relationship. However, the more relevant finding is the correlation between metrics (NS, CC, and EC) and overall traits. A hierarchical clustering algorithm (agglomerative clustering, an unsupervised machine learning technique) and principal component analysis were applied, where we identified three prominent principal components that cumulatively explain 76% of the psychometric data. Furthermore, we managed to cluster the network metrics (by unsupervised clustering) to explore whether neural connectivity patterns could be grouped based on combined average network metrics and psychometric data (global and local efficiencies, node strength, eigenvector centrality, and node strength). We identified three principal components, where the cumulative amount of explained data reaches 99%. The correspondence between network measures (CC and NS) and predictors (responses to Lowen’s items) is 62% predicted with a precision of 90%.
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjs/s11734-024-01411-z.
© The Author(s) 2024
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/.
