Smoothness Harmonic: A Graph-Based Approach to Reveal Spatiotemporal Patterns of Cortical Dynamics in fMRI Data
Despite fMRI data being interpreted as time-varying graphs in graph analysis, there has been more emphasis on learning sophisticated node embeddings and complex graph structures rather than providing a macroscopic description of cortical dynamics. In this paper, I introduce the notion of smoothness...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/12/7130 |
| _version_ | 1797596263954251776 |
|---|---|
| author | Wenjun Bai |
| author_facet | Wenjun Bai |
| author_sort | Wenjun Bai |
| collection | DOAJ |
| description | Despite fMRI data being interpreted as time-varying graphs in graph analysis, there has been more emphasis on learning sophisticated node embeddings and complex graph structures rather than providing a macroscopic description of cortical dynamics. In this paper, I introduce the notion of smoothness harmonics to capture the slowly varying cortical dynamics in graph-based fMRI data in the form of spatiotemporal smoothness patterns. These smoothness harmonics are rooted in the eigendecomposition of graph Laplacians, which reveal how low-frequency-dominated fMRI signals propagate across the cortex and through time. We showcase their usage in a real fMRI dataset to differentiate the cortical dynamics of children and adults while also demonstrating their empirical merit over the static functional connectomes in inter-subject and between-group classification analyses. |
| first_indexed | 2024-03-11T02:49:05Z |
| format | Article |
| id | doaj.art-c49e773319d34a7e8a6bf906dbe9b930 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T02:49:05Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c49e773319d34a7e8a6bf906dbe9b9302023-11-18T09:09:27ZengMDPI AGApplied Sciences2076-34172023-06-011312713010.3390/app13127130Smoothness Harmonic: A Graph-Based Approach to Reveal Spatiotemporal Patterns of Cortical Dynamics in fMRI DataWenjun Bai0Department of Computational Brain Imaging, Neural Information Analysis Laboratories, Advanced Telecommunication Research Institute International (ATR), Kyoto 619-0288, JapanDespite fMRI data being interpreted as time-varying graphs in graph analysis, there has been more emphasis on learning sophisticated node embeddings and complex graph structures rather than providing a macroscopic description of cortical dynamics. In this paper, I introduce the notion of smoothness harmonics to capture the slowly varying cortical dynamics in graph-based fMRI data in the form of spatiotemporal smoothness patterns. These smoothness harmonics are rooted in the eigendecomposition of graph Laplacians, which reveal how low-frequency-dominated fMRI signals propagate across the cortex and through time. We showcase their usage in a real fMRI dataset to differentiate the cortical dynamics of children and adults while also demonstrating their empirical merit over the static functional connectomes in inter-subject and between-group classification analyses.https://www.mdpi.com/2076-3417/13/12/7130computational neurosciencefMRI datagraph analysisconnectome Laplacian analysiscortical dynamics |
| spellingShingle | Wenjun Bai Smoothness Harmonic: A Graph-Based Approach to Reveal Spatiotemporal Patterns of Cortical Dynamics in fMRI Data Applied Sciences computational neuroscience fMRI data graph analysis connectome Laplacian analysis cortical dynamics |
| title | Smoothness Harmonic: A Graph-Based Approach to Reveal Spatiotemporal Patterns of Cortical Dynamics in fMRI Data |
| title_full | Smoothness Harmonic: A Graph-Based Approach to Reveal Spatiotemporal Patterns of Cortical Dynamics in fMRI Data |
| title_fullStr | Smoothness Harmonic: A Graph-Based Approach to Reveal Spatiotemporal Patterns of Cortical Dynamics in fMRI Data |
| title_full_unstemmed | Smoothness Harmonic: A Graph-Based Approach to Reveal Spatiotemporal Patterns of Cortical Dynamics in fMRI Data |
| title_short | Smoothness Harmonic: A Graph-Based Approach to Reveal Spatiotemporal Patterns of Cortical Dynamics in fMRI Data |
| title_sort | smoothness harmonic a graph based approach to reveal spatiotemporal patterns of cortical dynamics in fmri data |
| topic | computational neuroscience fMRI data graph analysis connectome Laplacian analysis cortical dynamics |
| url | https://www.mdpi.com/2076-3417/13/12/7130 |
| work_keys_str_mv | AT wenjunbai smoothnessharmonicagraphbasedapproachtorevealspatiotemporalpatternsofcorticaldynamicsinfmridata |