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Predicting the degree of trait emotional empathy from cortical features using surface-based morphometry.

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Paper

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The authors' code

R · 45 lines · 2.2 KB · CC-BY-4.0

  1. # Clinically significant change
  2. #......................................................
  3. # Documentation
  4. #' @title Reliable Change Index (RCI)
  5. #' @description This function calculates Reliable Change Index (RCI) as modifed by Wiger and Solberg (2001, p.148).
  6. #'
  7. #' @param SD_0 standard deviation of the non-clinical population
  8. #' @param test.ret.rel test-retest reliability of the instrument
  9. #'
  10. #' @return numeric vector
  11. #'
  12. #' @docType data
  13. #'
  14. #' @format numeric vector of values
  15. #'
  16. #' @details This function computes value corresponding to "the minimum amount of change that could not be attributed to the error of measurement" (Biescad & Timulak, 2014, p. 150). If score change from before to post treatment
  17. #' is lower that value resulting from this function, than change in client score can not be attributed to the effectiveness of the therapy but rather other factors such as a measurement error (Biescad & Timulak, 2014). This function
  18. #' is a result of modification of the original Jacobson and Truax (1991) formula by Wiger and Solberg (2001, p.148).
  19. #'
  20. #' @references Jacobson, N. S., & Truax, P. (1991). Clinical significance: A statistical approach to defining meaningful change in psychotherapy research. Journal of Consulting and Clinical Psychology, 59(1), 12-19, DOI:
  21. #' [https://doi.org/10.1037/0022-006X.59.1.12]()
  22. #'
  23. #' Matus Biescad & Ladislav Timulak (2014). Measuring psychotherapy
  24. #'outcomes in routine practice: Examining Slovak versions of three commonly used outcome
  25. #'instruments, European Journal of Psychotherapy & Counselling, 16:2, 140-162, DOI:
  26. #' [https://doi.org/10.1080/13642537.2014.895772]()
  27. #'
  28. #'Wiger, D. E., & Solberg, K. B. (2001). Tracking Mental Health Outcomes:
  29. #'A Therapist’s Guide to Measuring Client Progress, Analyzing Data,
  30. #'and Improving Your Practice (1., Vol. 2001). Wiley.
  31. #'
  32. #' @seealso [psychtoolbox::clin_sig_chang()] function for calculation of the clinical cut-off scores
  33. #' @author Lukas Novak, \email{lukasjirinovak@@gmail.com}
  34. #'
  35. #' @examples
  36. #'
  37. #' re.ch.in = RCI(SD_0 = 4.87, test.ret.rel = 0.66)
  38. #' re.ch.in
  39. #'
  40. #' @export
  41. #......................................................
  43. RCI <- function(SD_0,test.ret.rel) {
  44. 1.96*(sqrt(2*SD_0^2*(1-test.ret.rel)))
  45. }

RCI.R at commit afa2a0d, under CC-BY-4.0 · at the source

Overview

Authors: Lukas Novak1,2, Klara Malinakova1, Jitse P van Dijk1,2,3, Peter Tavel1, William Penny4, Petr Hluštík5
ORCID iDs: Lukas Novak, Klara Malinakova, Jitse P van Dijk, Peter Tavel, William Penny, Petr Hluštík
  1. OUSHI - Olomouc University Social Health Institute, Palacký University Olomouc, Univerzitni 244/22, Olomouc, 771 11 Czech Republic
  2. Department of Community and Occupational Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
  3. Graduate School Kosice Institute for Society and Health, P.J. Safarik University in Kosice, Kosice, Slovak Republic
  4. School of Psychology, University of East Anglia, Norwich, UK
  5. Department of Neurology, Faculty of Medicine and Dentistry, Palacký University, Olomouc, Czech Republic
Institutions: University Medical Center Groningen (Netherlands); University of Groningen (Netherlands); Palacký University Olomouc (Czechia); University of Pavol Jozef Šafárik (Slovakia); University of East Anglia (United Kingdom)
Journal: Scientific reports, volume 16, issue 1, article 14893
Dates: received 9 October 2025; accepted 9 March 2026; published online 25 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41598-026-44137-9 · PMID 41876724 · PMCID PMC13168272 · OpenAlex W7140204266
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: structural MRI / diffusion (modality), human (organism), cognitive (subfield)
Methods: Connectivity, Statistics, Machine learning, fMRI & imaging, Smoothing, state filtering, decompositions, Preprocessing
Keywords: Empathy, MRI, Neural basis, Machine learning, Surface-based morphometry, Sulcus depth, Cortical thickness, Gyrification, SPLSR, Neuroscience, Psychology
MeSH: Cerebral Cortex*, Emotions*, Empathy*, Adult, Brain Mapping, Female, Humans, Machine Learning, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Topic: Empathy and Medical Education (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: Czech Ministry of Education, Youth and Sports (CZ.02.01.01/00/22_008/0004583)
Citations: not cited yet (Europe PMC); 54 references in the paper

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.

Supplementary Information: The online version contains supplementary material available at 10.1038/s41598-026-44137-9.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above.

OSF 5zc47

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 0 files, 0 scripts
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
At the source: doi.org/10.17605/OSF.IO/5ZC47](https:/doi.org/10.17605/OSF.IO/5ZC47)

lukas.novak/psychtoolbox

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: afa2a0db5ec1224abfd29262e02c12d44b99c31b, 24 August 2026
Languages: R (21), Shell (1)
Size: 54 files, 22 scripts
Software Heritage: not archived
Found in: the references
Holds: README, license file, environment (DESCRIPTION), tests, continuous integration, documentation, 1 notebook
Not found: CITATION.cff
Tools: tidyverse (9 files), easystats (4 files), car (2 files), reshape2 (2 files), rstatix (2 files), broom (1 file), lavaan (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
24 files
At the source: gitlab.com/lukas.novak/psychtoolbox

The paper's code and data availability statement is in the Data section.

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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).

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 11 keywords, 11 MeSH terms, 1 funder, 49 references.

Cite

This paper

Novak, L., Malinakova, K., van Dijk, J. P., Tavel, P., Penny, W., & Hluštík, P. (2026). Predicting the degree of trait emotional empathy from cortical features using surface-based morphometry. Scientific reports, 16(1), 14893. https://doi.org/10.1038/s41598-026-44137-9

BibTeX

@article{novak2026predicting,
author = {Novak, Lukas and Malinakova, Klara and van Dijk, Jitse P and Tavel, Peter and Penny, William and Hluštík, Petr},
title = {{Predicting the degree of trait emotional empathy from cortical features using surface-based morphometry}},
journal = {Scientific reports},
year = {2026},
month = mar,
volume = {16},
number = {1},
pages = {14893},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-44137-9},
url = {https://doi.org/10.1038/s41598-026-44137-9},
pmid = {41876724},
pmcid = {PMC13168272}
}

RIS

TY - JOUR
AU - Novak, Lukas
AU - Malinakova, Klara
AU - van Dijk, Jitse P
AU - Tavel, Peter
AU - Penny, William
AU - Hluštík, Petr
TI - Predicting the degree of trait emotional empathy from cortical features using surface-based morphometry
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/03/25
VL - 16
IS - 1
SP - 14893
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-44137-9
UR - https://doi.org/10.1038/s41598-026-44137-9
LA - en
ER -

CSL-JSON

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