Tools
Tools
Novak, Lukas, Malinakova, Klara, van Dijk, Jitse P., Tavel, Peter, Penny, William 
ORCID: https://orcid.org/0000-0001-9064-1191 and Hluštík, Petr
(2026)
Predicting the degree of trait emotional empathy from cortical features using surface-based morphometry.
Scientific Reports, 16 (1).
ISSN 2045-2322
Preview |
PDF (s41598-026-44137-9)
- Published Version
Available under License Creative Commons Attribution. Download (2MB) | Preview |
Abstract
The link between neuroanatomy and empathy was a primary aim of many studies in the past. However, these studies are often limited by partial volume effects inherent to Voxel-Based Morphometry (VBM), which hinder the accurate assessment of cortical features. To overcome these limitations, we used Surface-Based Morphometry (SBM) to investigate the relationship between cortical features such as sulcal depth and trait emotional empathy for the first time. We also tested several hypotheses based on previous research. Our study sample consisted of 62 adults. Trait empathy was measured by the Toronto Empathy Questionnaire. We used methods from machine learning (e.g., Leave-one-out cross-validation – LOO-CV – in conjunction with Sparse Partially Least Squares regression - SPLSR) to evaluate whether cortical features (i.e., gyrification, sulcal depth, and cortical thickness) could accurately predict the degree of trait emotional empathy. We tested the hypotheses of the present study using Linear Mixed Effects models. The Regions of Interest (ROI) tested within these hypotheses included bilateral insula and dorsal Anterior Cingulate Cortex (dACC). A significant negative association was found between the cortical thickness of the left insula and trait emotional empathy score (B = -4.72; 95% CI [-8.52, -0.92]; p = 0.019). In addition, there was a negative association between cortical thickness in the left dACC and trait emotional empathy (B = -5.47; 95% CI [-9.48, -1.46]; p = 0.008). The SPLSR models showed moderate training fit (≈ 60% in gyrification) but failed to generalize to unseen data, with negative cross-validated values across all 3 cortical features. While ROI analyses revealed significant negative associations between cortical thickness and trait emotional empathy, machine learning models failed to achieve reliable out-of-sample prediction, likely due to the high-dimensional feature space relative to sample size. The predictive findings should therefore be considered exploratory. Future studies should use larger samples and explore how differences in white matter structure can explain individual differences in empathy.
| Item Type: | Article |
|---|---|
| Additional Information: | Data availability: Anonymised data, code, trained ML model, and other materials related to this study are available at the Open Science Framework (OSF) website under the following DOI: [https://doi.org/10.17605/OSF.IO/5ZC47](https:/doi.org/10.17605/OSF.IO/5ZC47). |
| Uncontrolled Keywords: | cortical thickness,empathy,gyrification,mri,machine learning,neural basis,splsr,sulcus depth,surface-based morphometry,general ,/dk/atira/pure/subjectarea/asjc/1000 |
| Faculty \ School: | Faculty of Social Sciences > School of Psychology |
| UEA Research Groups: | Faculty of Social Sciences > Research Centres > Centre for Behavioural and Experimental Social Sciences |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 04 Aug 2026 13:39 |
| Last Modified: | 05 Aug 2026 13:48 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/103974 |
| DOI: | 10.1038/s41598-026-44137-9 |
Downloads
Downloads per month over past year
Actions (login required)
 |
View Item |