Group Surrogate Data Generating Models and similarity quantification of multivariate time-series: A resting-state fMRI study
Advancements in non-invasive brain analysis through novel approaches such as big data analytics and in silico simulation are essential for explaining brain function and associated pathologies. In this study, we extend the vector auto-regressive surrogate technique from a single multivariate time-ser...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811923004809 |
| _version_ | 1797727638361473024 |
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| author | Takuto Okuno Junichi Hata Yawara Haga Kanako Muta Hiromichi Tsukada Ken Nakae Hideyuki Okano Alexander Woodward |
| author_facet | Takuto Okuno Junichi Hata Yawara Haga Kanako Muta Hiromichi Tsukada Ken Nakae Hideyuki Okano Alexander Woodward |
| author_sort | Takuto Okuno |
| collection | DOAJ |
| description | Advancements in non-invasive brain analysis through novel approaches such as big data analytics and in silico simulation are essential for explaining brain function and associated pathologies. In this study, we extend the vector auto-regressive surrogate technique from a single multivariate time-series to group data using a novel Group Surrogate Data Generating Model (GSDGM). This methodology allowed us to generate biologically plausible human brain dynamics representative of a large human resting-state (rs-fMRI) dataset obtained from the Human Connectome Project. Simultaneously, we defined a novel similarity measure, termed the Multivariate Time-series Ensemble Similarity Score (MTESS). MTESS showed high accuracy and f-measure in subject identification, and it can directly compare the similarity between two multivariate time-series. We used MTESS to analyze both human and marmoset rs-fMRI data. Our results showed similarity differences between cortical and subcortical regions. We also conducted MTESS and state transition analysis between single and group surrogate techniques, and confirmed that a group surrogate approach can generate plausible group centroid multivariate time-series. Finally, we used GSDGM and MTESS for the fingerprint analysis of human rs-fMRI data, successfully distinguishing normal and outlier sessions. These new techniques will be useful for clinical applications and in silico simulation. |
| first_indexed | 2024-03-12T11:03:29Z |
| format | Article |
| id | doaj.art-6fdbd93d883f4b76b02ce3a8b287ff26 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-03-12T11:03:29Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-6fdbd93d883f4b76b02ce3a8b287ff262023-09-02T04:31:18ZengElsevierNeuroImage1095-95722023-10-01279120329Group Surrogate Data Generating Models and similarity quantification of multivariate time-series: A resting-state fMRI studyTakuto Okuno0Junichi Hata1Yawara Haga2Kanako Muta3Hiromichi Tsukada4Ken Nakae5Hideyuki Okano6Alexander Woodward7Connectome Analysis Unit, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan; Corresponding author.Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan; Graduate School of Human Health Sciences, Tokyo Metropolitan University, 7-2-10 Higashiogu, Arakawa-ku, Tokyo 116-8551, JapanLaboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, JapanGraduate School of Human Health Sciences, Tokyo Metropolitan University, 7-2-10 Higashiogu, Arakawa-ku, Tokyo 116-8551, JapanCenter for Mathematical Science and Artificial Intelligence, Chubu University, 1200 Matsumoto-cho, Kasugai, Aichi 487-8501, JapanExploratory Research Center on Life and Living Systems, National Institutes of Natural Sciences, Aichi, JapanLaboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, JapanConnectome Analysis Unit, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, JapanAdvancements in non-invasive brain analysis through novel approaches such as big data analytics and in silico simulation are essential for explaining brain function and associated pathologies. In this study, we extend the vector auto-regressive surrogate technique from a single multivariate time-series to group data using a novel Group Surrogate Data Generating Model (GSDGM). This methodology allowed us to generate biologically plausible human brain dynamics representative of a large human resting-state (rs-fMRI) dataset obtained from the Human Connectome Project. Simultaneously, we defined a novel similarity measure, termed the Multivariate Time-series Ensemble Similarity Score (MTESS). MTESS showed high accuracy and f-measure in subject identification, and it can directly compare the similarity between two multivariate time-series. We used MTESS to analyze both human and marmoset rs-fMRI data. Our results showed similarity differences between cortical and subcortical regions. We also conducted MTESS and state transition analysis between single and group surrogate techniques, and confirmed that a group surrogate approach can generate plausible group centroid multivariate time-series. Finally, we used GSDGM and MTESS for the fingerprint analysis of human rs-fMRI data, successfully distinguishing normal and outlier sessions. These new techniques will be useful for clinical applications and in silico simulation.http://www.sciencedirect.com/science/article/pii/S1053811923004809Resting-state fMRIGroup Surrogate Data Generating ModelMultivariate Time-series Ensemble Similarity ScoreVector Auto-Regressive Deep Neural NetworkMarmosetState transition analysis |
| spellingShingle | Takuto Okuno Junichi Hata Yawara Haga Kanako Muta Hiromichi Tsukada Ken Nakae Hideyuki Okano Alexander Woodward Group Surrogate Data Generating Models and similarity quantification of multivariate time-series: A resting-state fMRI study NeuroImage Resting-state fMRI Group Surrogate Data Generating Model Multivariate Time-series Ensemble Similarity Score Vector Auto-Regressive Deep Neural Network Marmoset State transition analysis |
| title | Group Surrogate Data Generating Models and similarity quantification of multivariate time-series: A resting-state fMRI study |
| title_full | Group Surrogate Data Generating Models and similarity quantification of multivariate time-series: A resting-state fMRI study |
| title_fullStr | Group Surrogate Data Generating Models and similarity quantification of multivariate time-series: A resting-state fMRI study |
| title_full_unstemmed | Group Surrogate Data Generating Models and similarity quantification of multivariate time-series: A resting-state fMRI study |
| title_short | Group Surrogate Data Generating Models and similarity quantification of multivariate time-series: A resting-state fMRI study |
| title_sort | group surrogate data generating models and similarity quantification of multivariate time series a resting state fmri study |
| topic | Resting-state fMRI Group Surrogate Data Generating Model Multivariate Time-series Ensemble Similarity Score Vector Auto-Regressive Deep Neural Network Marmoset State transition analysis |
| url | http://www.sciencedirect.com/science/article/pii/S1053811923004809 |
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