Predicting Empathy From Resting State Brain Connectivity: A Multivariate Approach
Recent task fMRI studies suggest that individual differences in trait empathy and empathic concern are mediated by patterns of connectivity between self-other resonance and top-down control networks that are stable across task demands. An untested implication of this hypothesis is that these stable...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2020-02-01
|
| Series: | Frontiers in Integrative Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fnint.2020.00003/full |
| _version_ | 1819208380959424512 |
|---|---|
| author | Leonardo Christov-Moore Leonardo Christov-Moore Leonardo Christov-Moore Leonardo Christov-Moore Leonardo Christov-Moore Nicco Reggente Pamela K. Douglas Pamela K. Douglas Pamela K. Douglas Jamie D. Feusner Jamie D. Feusner Marco Iacoboni Marco Iacoboni Marco Iacoboni |
| author_facet | Leonardo Christov-Moore Leonardo Christov-Moore Leonardo Christov-Moore Leonardo Christov-Moore Leonardo Christov-Moore Nicco Reggente Pamela K. Douglas Pamela K. Douglas Pamela K. Douglas Jamie D. Feusner Jamie D. Feusner Marco Iacoboni Marco Iacoboni Marco Iacoboni |
| author_sort | Leonardo Christov-Moore |
| collection | DOAJ |
| description | Recent task fMRI studies suggest that individual differences in trait empathy and empathic concern are mediated by patterns of connectivity between self-other resonance and top-down control networks that are stable across task demands. An untested implication of this hypothesis is that these stable patterns of connectivity should be visible even in the absence of empathy tasks. Using machine learning, we demonstrate that patterns of resting state fMRI connectivity (i.e. the degree of synchronous BOLD activity across multiple cortical areas in the absence of explicit task demands) of resonance and control networks predict trait empathic concern (n = 58). Empathic concern was also predicted by connectivity patterns within the somatomotor network. These findings further support the role of resonance-control network interactions and of somatomotor function in our vicariously driven concern for others. Furthermore, a practical implication of these results is that it is possible to assess empathic predispositions in individuals without needing to perform conventional empathy assessments. |
| first_indexed | 2024-12-23T05:38:29Z |
| format | Article |
| id | doaj.art-70fc4a68cd79472589f811d91998a18e |
| institution | Directory Open Access Journal |
| issn | 1662-5145 |
| language | English |
| last_indexed | 2024-12-23T05:38:29Z |
| publishDate | 2020-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Integrative Neuroscience |
| spelling | doaj.art-70fc4a68cd79472589f811d91998a18e2022-12-21T17:58:15ZengFrontiers Media S.A.Frontiers in Integrative Neuroscience1662-51452020-02-011410.3389/fnint.2020.00003507552Predicting Empathy From Resting State Brain Connectivity: A Multivariate ApproachLeonardo Christov-Moore0Leonardo Christov-Moore1Leonardo Christov-Moore2Leonardo Christov-Moore3Leonardo Christov-Moore4Nicco Reggente5Pamela K. Douglas6Pamela K. Douglas7Pamela K. Douglas8Jamie D. Feusner9Jamie D. Feusner10Marco Iacoboni11Marco Iacoboni12Marco Iacoboni13Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, Los Angeles, CA, United StatesBrain Research Institute, University of California, Los Angeles, Los Angeles, CA, United StatesDepartment of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United StatesInstitute for Simulation and Training, University of Central Florida, Orlando, FL, United StatesBrain and Creativity Institute, School of International Relations, University of Southern California, Los Angeles, CA, United StatesThe Tiny Blue Dot Foundation, Santa Monica, CA, United StatesBrain Research Institute, University of California, Los Angeles, Los Angeles, CA, United StatesDepartment of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United StatesInstitute for Simulation and Training, University of Central Florida, Orlando, FL, United StatesBrain Research Institute, University of California, Los Angeles, Los Angeles, CA, United StatesDepartment of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United StatesAhmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, Los Angeles, CA, United StatesBrain Research Institute, University of California, Los Angeles, Los Angeles, CA, United StatesDepartment of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United StatesRecent task fMRI studies suggest that individual differences in trait empathy and empathic concern are mediated by patterns of connectivity between self-other resonance and top-down control networks that are stable across task demands. An untested implication of this hypothesis is that these stable patterns of connectivity should be visible even in the absence of empathy tasks. Using machine learning, we demonstrate that patterns of resting state fMRI connectivity (i.e. the degree of synchronous BOLD activity across multiple cortical areas in the absence of explicit task demands) of resonance and control networks predict trait empathic concern (n = 58). Empathic concern was also predicted by connectivity patterns within the somatomotor network. These findings further support the role of resonance-control network interactions and of somatomotor function in our vicariously driven concern for others. Furthermore, a practical implication of these results is that it is possible to assess empathic predispositions in individuals without needing to perform conventional empathy assessments.https://www.frontiersin.org/article/10.3389/fnint.2020.00003/fullempathyempathic concernfMRIresting stateconnectivitymachine learning |
| spellingShingle | Leonardo Christov-Moore Leonardo Christov-Moore Leonardo Christov-Moore Leonardo Christov-Moore Leonardo Christov-Moore Nicco Reggente Pamela K. Douglas Pamela K. Douglas Pamela K. Douglas Jamie D. Feusner Jamie D. Feusner Marco Iacoboni Marco Iacoboni Marco Iacoboni Predicting Empathy From Resting State Brain Connectivity: A Multivariate Approach Frontiers in Integrative Neuroscience empathy empathic concern fMRI resting state connectivity machine learning |
| title | Predicting Empathy From Resting State Brain Connectivity: A Multivariate Approach |
| title_full | Predicting Empathy From Resting State Brain Connectivity: A Multivariate Approach |
| title_fullStr | Predicting Empathy From Resting State Brain Connectivity: A Multivariate Approach |
| title_full_unstemmed | Predicting Empathy From Resting State Brain Connectivity: A Multivariate Approach |
| title_short | Predicting Empathy From Resting State Brain Connectivity: A Multivariate Approach |
| title_sort | predicting empathy from resting state brain connectivity a multivariate approach |
| topic | empathy empathic concern fMRI resting state connectivity machine learning |
| url | https://www.frontiersin.org/article/10.3389/fnint.2020.00003/full |
| work_keys_str_mv | AT leonardochristovmoore predictingempathyfromrestingstatebrainconnectivityamultivariateapproach AT leonardochristovmoore predictingempathyfromrestingstatebrainconnectivityamultivariateapproach AT leonardochristovmoore predictingempathyfromrestingstatebrainconnectivityamultivariateapproach AT leonardochristovmoore predictingempathyfromrestingstatebrainconnectivityamultivariateapproach AT leonardochristovmoore predictingempathyfromrestingstatebrainconnectivityamultivariateapproach AT niccoreggente predictingempathyfromrestingstatebrainconnectivityamultivariateapproach AT pamelakdouglas predictingempathyfromrestingstatebrainconnectivityamultivariateapproach AT pamelakdouglas predictingempathyfromrestingstatebrainconnectivityamultivariateapproach AT pamelakdouglas predictingempathyfromrestingstatebrainconnectivityamultivariateapproach AT jamiedfeusner predictingempathyfromrestingstatebrainconnectivityamultivariateapproach AT jamiedfeusner predictingempathyfromrestingstatebrainconnectivityamultivariateapproach AT marcoiacoboni predictingempathyfromrestingstatebrainconnectivityamultivariateapproach AT marcoiacoboni predictingempathyfromrestingstatebrainconnectivityamultivariateapproach AT marcoiacoboni predictingempathyfromrestingstatebrainconnectivityamultivariateapproach |