Structured sparse multiset canonical correlation analysis of simultaneous fNIRS and EEG provides new insights into the human action-observation network
Abstract The action observation network (AON) is a network of brain regions involved in the execution and observation of a given action. The AON has been investigated in humans using mostly electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), but shared neural correlates of a...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-04-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-10942-1 |
| _version_ | 1818012356600397824 |
|---|---|
| author | Hadis Dashtestani Helga O. Miguel Emma E. Condy Selin Zeytinoglu John B. Millerhagen Ranjan Debnath Elizabeth Smith Tulay Adali Nathan A. Fox Amir H. Gandjbakhche |
| author_facet | Hadis Dashtestani Helga O. Miguel Emma E. Condy Selin Zeytinoglu John B. Millerhagen Ranjan Debnath Elizabeth Smith Tulay Adali Nathan A. Fox Amir H. Gandjbakhche |
| author_sort | Hadis Dashtestani |
| collection | DOAJ |
| description | Abstract The action observation network (AON) is a network of brain regions involved in the execution and observation of a given action. The AON has been investigated in humans using mostly electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), but shared neural correlates of action observation and action execution are still unclear due to lack of ecologically valid neuroimaging measures. In this study, we used concurrent EEG and functional Near Infrared Spectroscopy (fNIRS) to examine the AON during a live-action observation and execution paradigm. We developed structured sparse multiset canonical correlation analysis (ssmCCA) to perform EEG-fNIRS data fusion. MCCA is a generalization of CCA to more than two sets of variables and is commonly used in medical multimodal data fusion. However, mCCA suffers from multi-collinearity, high dimensionality, unimodal feature selection, and loss of spatial information in interpreting the results. A limited number of participants (small sample size) is another problem in mCCA, which leads to overfitted models. Here, we adopted graph-guided (structured) fused least absolute shrinkage and selection operator (LASSO) penalty to mCCA to conduct feature selection, incorporating structural information amongst the variables (i.e., brain regions). Benefitting from concurrent recordings of brain hemodynamic and electrophysiological responses, the proposed ssmCCA finds linear transforms of each modality such that the correlation between their projections is maximized. Our analysis of 21 right-handed participants indicated that the left inferior parietal region was active during both action execution and action observation. Our findings provide new insights into the neural correlates of AON which are more fine-tuned than the results from each individual EEG or fNIRS analysis and validate the use of ssmCCA to fuse EEG and fNIRS datasets. |
| first_indexed | 2024-04-14T06:19:04Z |
| format | Article |
| id | doaj.art-cb86cd8a111a492baf4a9c4a9be099d6 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-14T06:19:04Z |
| publishDate | 2022-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-cb86cd8a111a492baf4a9c4a9be099d62022-12-22T02:08:06ZengNature PortfolioScientific Reports2045-23222022-04-0112111310.1038/s41598-022-10942-1Structured sparse multiset canonical correlation analysis of simultaneous fNIRS and EEG provides new insights into the human action-observation networkHadis Dashtestani0Helga O. Miguel1Emma E. Condy2Selin Zeytinoglu3John B. Millerhagen4Ranjan Debnath5Elizabeth Smith6Tulay Adali7Nathan A. Fox8Amir H. Gandjbakhche9Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of HealthEunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of HealthEunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of HealthDepartment of Human Development and Quantitative Methodology, University of MarylandEunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of HealthLeibniz Institute for NeurobiologyBehavioral Medicine and Clinical Psychology Department, Cincinnati Children’s Hospital Medical CenterDepartment of Computer Science and Electrical Engineering, University of Maryland Baltimore CountyDepartment of Human Development and Quantitative Methodology, University of MarylandEunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of HealthAbstract The action observation network (AON) is a network of brain regions involved in the execution and observation of a given action. The AON has been investigated in humans using mostly electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), but shared neural correlates of action observation and action execution are still unclear due to lack of ecologically valid neuroimaging measures. In this study, we used concurrent EEG and functional Near Infrared Spectroscopy (fNIRS) to examine the AON during a live-action observation and execution paradigm. We developed structured sparse multiset canonical correlation analysis (ssmCCA) to perform EEG-fNIRS data fusion. MCCA is a generalization of CCA to more than two sets of variables and is commonly used in medical multimodal data fusion. However, mCCA suffers from multi-collinearity, high dimensionality, unimodal feature selection, and loss of spatial information in interpreting the results. A limited number of participants (small sample size) is another problem in mCCA, which leads to overfitted models. Here, we adopted graph-guided (structured) fused least absolute shrinkage and selection operator (LASSO) penalty to mCCA to conduct feature selection, incorporating structural information amongst the variables (i.e., brain regions). Benefitting from concurrent recordings of brain hemodynamic and electrophysiological responses, the proposed ssmCCA finds linear transforms of each modality such that the correlation between their projections is maximized. Our analysis of 21 right-handed participants indicated that the left inferior parietal region was active during both action execution and action observation. Our findings provide new insights into the neural correlates of AON which are more fine-tuned than the results from each individual EEG or fNIRS analysis and validate the use of ssmCCA to fuse EEG and fNIRS datasets.https://doi.org/10.1038/s41598-022-10942-1 |
| spellingShingle | Hadis Dashtestani Helga O. Miguel Emma E. Condy Selin Zeytinoglu John B. Millerhagen Ranjan Debnath Elizabeth Smith Tulay Adali Nathan A. Fox Amir H. Gandjbakhche Structured sparse multiset canonical correlation analysis of simultaneous fNIRS and EEG provides new insights into the human action-observation network Scientific Reports |
| title | Structured sparse multiset canonical correlation analysis of simultaneous fNIRS and EEG provides new insights into the human action-observation network |
| title_full | Structured sparse multiset canonical correlation analysis of simultaneous fNIRS and EEG provides new insights into the human action-observation network |
| title_fullStr | Structured sparse multiset canonical correlation analysis of simultaneous fNIRS and EEG provides new insights into the human action-observation network |
| title_full_unstemmed | Structured sparse multiset canonical correlation analysis of simultaneous fNIRS and EEG provides new insights into the human action-observation network |
| title_short | Structured sparse multiset canonical correlation analysis of simultaneous fNIRS and EEG provides new insights into the human action-observation network |
| title_sort | structured sparse multiset canonical correlation analysis of simultaneous fnirs and eeg provides new insights into the human action observation network |
| url | https://doi.org/10.1038/s41598-022-10942-1 |
| work_keys_str_mv | AT hadisdashtestani structuredsparsemultisetcanonicalcorrelationanalysisofsimultaneousfnirsandeegprovidesnewinsightsintothehumanactionobservationnetwork AT helgaomiguel structuredsparsemultisetcanonicalcorrelationanalysisofsimultaneousfnirsandeegprovidesnewinsightsintothehumanactionobservationnetwork AT emmaecondy structuredsparsemultisetcanonicalcorrelationanalysisofsimultaneousfnirsandeegprovidesnewinsightsintothehumanactionobservationnetwork AT selinzeytinoglu structuredsparsemultisetcanonicalcorrelationanalysisofsimultaneousfnirsandeegprovidesnewinsightsintothehumanactionobservationnetwork AT johnbmillerhagen structuredsparsemultisetcanonicalcorrelationanalysisofsimultaneousfnirsandeegprovidesnewinsightsintothehumanactionobservationnetwork AT ranjandebnath structuredsparsemultisetcanonicalcorrelationanalysisofsimultaneousfnirsandeegprovidesnewinsightsintothehumanactionobservationnetwork AT elizabethsmith structuredsparsemultisetcanonicalcorrelationanalysisofsimultaneousfnirsandeegprovidesnewinsightsintothehumanactionobservationnetwork AT tulayadali structuredsparsemultisetcanonicalcorrelationanalysisofsimultaneousfnirsandeegprovidesnewinsightsintothehumanactionobservationnetwork AT nathanafox structuredsparsemultisetcanonicalcorrelationanalysisofsimultaneousfnirsandeegprovidesnewinsightsintothehumanactionobservationnetwork AT amirhgandjbakhche structuredsparsemultisetcanonicalcorrelationanalysisofsimultaneousfnirsandeegprovidesnewinsightsintothehumanactionobservationnetwork |