Frequency-Resolved Dynamic Functional Connectivity Reveals Scale-Stable Features of Connectivity-States
Investigating temporal variability of functional connectivity is an emerging field in connectomics. Entering dynamic functional connectivity by applying sliding window techniques on resting-state fMRI (rs-fMRI) time courses emerged from this topic. We introduce frequency-resolved dynamic functional...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-06-01
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| Series: | Frontiers in Human Neuroscience |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fnhum.2018.00253/full |
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| author | Markus Goldhacker Markus Goldhacker Ana M. Tomé Ana M. Tomé Mark W. Greenlee Elmar W. Lang Elmar W. Lang |
| author_facet | Markus Goldhacker Markus Goldhacker Ana M. Tomé Ana M. Tomé Mark W. Greenlee Elmar W. Lang Elmar W. Lang |
| author_sort | Markus Goldhacker |
| collection | DOAJ |
| description | Investigating temporal variability of functional connectivity is an emerging field in connectomics. Entering dynamic functional connectivity by applying sliding window techniques on resting-state fMRI (rs-fMRI) time courses emerged from this topic. We introduce frequency-resolved dynamic functional connectivity (frdFC) by means of multivariate empirical mode decomposition (MEMD) followed up by filter-bank investigations. In general, we find that MEMD is capable of generating time courses to perform frdFC and we discover that the structure of connectivity-states is robust over frequency scales and even becomes more evident with decreasing frequency. This scale-stability varies with the number of extracted clusters when applying k-means. We find a scale-stability drop-off from k = 4 to k = 5 extracted connectivity-states, which is corroborated by null-models, simulations, theoretical considerations, filter-banks, and scale-adjusted windows. Our filter-bank studies show that filter design is more delicate in the rs-fMRI than in the simulated case. Besides offering a baseline for further frdFC research, we suggest and demonstrate the use of scale-stability as a possible quality criterion for connectivity-state and model selection. We present first evidence showing that connectivity-states are both a multivariate, and a multiscale phenomenon. A data repository of our frequency-resolved time-series is provided. |
| first_indexed | 2024-12-21T15:59:59Z |
| format | Article |
| id | doaj.art-a80f3559827c4fdf9a7e9b96c4bb671d |
| institution | Directory Open Access Journal |
| issn | 1662-5161 |
| language | English |
| last_indexed | 2024-12-21T15:59:59Z |
| publishDate | 2018-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-a80f3559827c4fdf9a7e9b96c4bb671d2022-12-21T18:58:00ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612018-06-011210.3389/fnhum.2018.00253348842Frequency-Resolved Dynamic Functional Connectivity Reveals Scale-Stable Features of Connectivity-StatesMarkus Goldhacker0Markus Goldhacker1Ana M. Tomé2Ana M. Tomé3Mark W. Greenlee4Elmar W. Lang5Elmar W. Lang6CIML Lab, Department of Biophysics, University of Regensburg, Regensburg, GermanyDepartment of Experimental Psychology, University of Regensburg, Regensburg, GermanyCIML Lab, Department of Biophysics, University of Regensburg, Regensburg, GermanyDepartamento de Eletrónica, Telecomunicações e Informática (DETI), Instituto de Engenharia Electrónica e Telemática de Aveiro (IEETA), Universidade de Aveiro, Aveiro, PortugalDepartment of Experimental Psychology, University of Regensburg, Regensburg, GermanyCIML Lab, Department of Biophysics, University of Regensburg, Regensburg, GermanyDepartamento de Eletrónica, Telecomunicações e Informática (DETI), Instituto de Engenharia Electrónica e Telemática de Aveiro (IEETA), Universidade de Aveiro, Aveiro, PortugalInvestigating temporal variability of functional connectivity is an emerging field in connectomics. Entering dynamic functional connectivity by applying sliding window techniques on resting-state fMRI (rs-fMRI) time courses emerged from this topic. We introduce frequency-resolved dynamic functional connectivity (frdFC) by means of multivariate empirical mode decomposition (MEMD) followed up by filter-bank investigations. In general, we find that MEMD is capable of generating time courses to perform frdFC and we discover that the structure of connectivity-states is robust over frequency scales and even becomes more evident with decreasing frequency. This scale-stability varies with the number of extracted clusters when applying k-means. We find a scale-stability drop-off from k = 4 to k = 5 extracted connectivity-states, which is corroborated by null-models, simulations, theoretical considerations, filter-banks, and scale-adjusted windows. Our filter-bank studies show that filter design is more delicate in the rs-fMRI than in the simulated case. Besides offering a baseline for further frdFC research, we suggest and demonstrate the use of scale-stability as a possible quality criterion for connectivity-state and model selection. We present first evidence showing that connectivity-states are both a multivariate, and a multiscale phenomenon. A data repository of our frequency-resolved time-series is provided.https://www.frontiersin.org/article/10.3389/fnhum.2018.00253/fulldynamic functional connectivitymultivariateempirical mode decompositionfilter-bankmultiscalefMRI |
| spellingShingle | Markus Goldhacker Markus Goldhacker Ana M. Tomé Ana M. Tomé Mark W. Greenlee Elmar W. Lang Elmar W. Lang Frequency-Resolved Dynamic Functional Connectivity Reveals Scale-Stable Features of Connectivity-States Frontiers in Human Neuroscience dynamic functional connectivity multivariate empirical mode decomposition filter-bank multiscale fMRI |
| title | Frequency-Resolved Dynamic Functional Connectivity Reveals Scale-Stable Features of Connectivity-States |
| title_full | Frequency-Resolved Dynamic Functional Connectivity Reveals Scale-Stable Features of Connectivity-States |
| title_fullStr | Frequency-Resolved Dynamic Functional Connectivity Reveals Scale-Stable Features of Connectivity-States |
| title_full_unstemmed | Frequency-Resolved Dynamic Functional Connectivity Reveals Scale-Stable Features of Connectivity-States |
| title_short | Frequency-Resolved Dynamic Functional Connectivity Reveals Scale-Stable Features of Connectivity-States |
| title_sort | frequency resolved dynamic functional connectivity reveals scale stable features of connectivity states |
| topic | dynamic functional connectivity multivariate empirical mode decomposition filter-bank multiscale fMRI |
| url | https://www.frontiersin.org/article/10.3389/fnhum.2018.00253/full |
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