Surrogate data analyses of the energy landscape analysis of resting-state brain activity
The spatiotemporal dynamics of resting-state brain activity can be characterized by switching between multiple brain states, and numerous techniques have been developed to extract such dynamic features from resting-state functional magnetic resonance imaging (fMRI) data. However, many of these techn...
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Neural Circuits |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fncir.2025.1500227/full |
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| author | Yuki Hosaka Takemi Hieda Ruixiang Li Kenji Hayashi Koji Jimura Teppei Matsui |
| author_facet | Yuki Hosaka Takemi Hieda Ruixiang Li Kenji Hayashi Koji Jimura Teppei Matsui |
| author_sort | Yuki Hosaka |
| collection | DOAJ |
| description | The spatiotemporal dynamics of resting-state brain activity can be characterized by switching between multiple brain states, and numerous techniques have been developed to extract such dynamic features from resting-state functional magnetic resonance imaging (fMRI) data. However, many of these techniques are based on momentary temporal correlation and co-activation patterns and merely reflect linear features of the data, suggesting that the dynamic features, such as state-switching, extracted by these techniques may be misinterpreted. To examine whether such misinterpretations occur when using techniques that are not based on momentary temporal correlation or co-activation patterns, we addressed Energy Landscape Analysis (ELA) based on pairwise-maximum entropy model (PMEM), a statistical physics-inspired method that was designed to extract multiple brain states and dynamics of resting-state fMRI data. We found that the shape of the energy landscape and the first-order transition probability derived from ELA were similar between real data and surrogate data suggesting that these features were largely accounted for by stationary and linear properties of the real data without requiring state-switching among locally stable states. To confirm that surrogate data were distinct from the real data, we replicated a previous finding that some topological properties of resting-state fMRI data differed between the real and surrogate data. Overall, we found that linear models largely reproduced the first order ELA-derived features (i.e., energy landscape and transition probability) with some notable differences. |
| format | Article |
| id | doaj-art-c452f3f560534e7d8b6f63426e37eb3e |
| institution | DOAJ |
| issn | 1662-5110 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Neural Circuits |
| spelling | doaj-art-c452f3f560534e7d8b6f63426e37eb3e2025-08-20T02:52:28ZengFrontiers Media S.A.Frontiers in Neural Circuits1662-51102025-03-011910.3389/fncir.2025.15002271500227Surrogate data analyses of the energy landscape analysis of resting-state brain activityYuki Hosaka0Takemi Hieda1Ruixiang Li2Kenji Hayashi3Koji Jimura4Teppei Matsui5Graduate School of Brain Science, Doshisha University, Kyotanabe, JapanGraduate School of Brain Science, Doshisha University, Kyotanabe, JapanGraduate School of Brain Science, Doshisha University, Kyotanabe, JapanGraduate School of Brain Science, Doshisha University, Kyotanabe, JapanDepartment of Informatics, Gumma University, Maebashi, JapanGraduate School of Brain Science, Doshisha University, Kyotanabe, JapanThe spatiotemporal dynamics of resting-state brain activity can be characterized by switching between multiple brain states, and numerous techniques have been developed to extract such dynamic features from resting-state functional magnetic resonance imaging (fMRI) data. However, many of these techniques are based on momentary temporal correlation and co-activation patterns and merely reflect linear features of the data, suggesting that the dynamic features, such as state-switching, extracted by these techniques may be misinterpreted. To examine whether such misinterpretations occur when using techniques that are not based on momentary temporal correlation or co-activation patterns, we addressed Energy Landscape Analysis (ELA) based on pairwise-maximum entropy model (PMEM), a statistical physics-inspired method that was designed to extract multiple brain states and dynamics of resting-state fMRI data. We found that the shape of the energy landscape and the first-order transition probability derived from ELA were similar between real data and surrogate data suggesting that these features were largely accounted for by stationary and linear properties of the real data without requiring state-switching among locally stable states. To confirm that surrogate data were distinct from the real data, we replicated a previous finding that some topological properties of resting-state fMRI data differed between the real and surrogate data. Overall, we found that linear models largely reproduced the first order ELA-derived features (i.e., energy landscape and transition probability) with some notable differences.https://www.frontiersin.org/articles/10.3389/fncir.2025.1500227/fullresting-state fMRIdynamic functional connectivityenergy landscape analysisspontaneous activitymaximum entropy model |
| spellingShingle | Yuki Hosaka Takemi Hieda Ruixiang Li Kenji Hayashi Koji Jimura Teppei Matsui Surrogate data analyses of the energy landscape analysis of resting-state brain activity Frontiers in Neural Circuits resting-state fMRI dynamic functional connectivity energy landscape analysis spontaneous activity maximum entropy model |
| title | Surrogate data analyses of the energy landscape analysis of resting-state brain activity |
| title_full | Surrogate data analyses of the energy landscape analysis of resting-state brain activity |
| title_fullStr | Surrogate data analyses of the energy landscape analysis of resting-state brain activity |
| title_full_unstemmed | Surrogate data analyses of the energy landscape analysis of resting-state brain activity |
| title_short | Surrogate data analyses of the energy landscape analysis of resting-state brain activity |
| title_sort | surrogate data analyses of the energy landscape analysis of resting state brain activity |
| topic | resting-state fMRI dynamic functional connectivity energy landscape analysis spontaneous activity maximum entropy model |
| url | https://www.frontiersin.org/articles/10.3389/fncir.2025.1500227/full |
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