Temporal complexity of fMRI is reproducible and correlates with higher order cognition
It has been hypothesized that resting state networks (RSNs), extracted from resting state functional magnetic resonance imaging (rsfMRI), likely display unique temporal complexity fingerprints, quantified by their multiscale entropy patterns (McDonough and Nashiro, 2014). This is a hypothesis with a...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-04-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811921000379 |
| _version_ | 1831636715157061632 |
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| author | Amir Omidvarnia Andrew Zalesky Sina Mansour L Dimitri Van De Ville Graeme D. Jackson Mangor Pedersen |
| author_facet | Amir Omidvarnia Andrew Zalesky Sina Mansour L Dimitri Van De Ville Graeme D. Jackson Mangor Pedersen |
| author_sort | Amir Omidvarnia |
| collection | DOAJ |
| description | It has been hypothesized that resting state networks (RSNs), extracted from resting state functional magnetic resonance imaging (rsfMRI), likely display unique temporal complexity fingerprints, quantified by their multiscale entropy patterns (McDonough and Nashiro, 2014). This is a hypothesis with a potential capacity for developing digital biomarkers of normal brain function, as well as pathological brain dysfunction. Nevertheless, a limitation of McDonough and Nashiro (2014) was that rsfMRI data from only 20 healthy individuals was used for the analysis. To validate this hypothesis in a larger cohort, we used rsfMRI datasets of 987 healthy young adults from the Human Connectome Project (HCP), aged 22-35, each with four 14.4-min rsfMRI recordings and parcellated into 379 brain regions. We quantified multiscale entropy of rsfMRI time series averaged at different cortical and sub-cortical regions. We performed effect-size analysis on the data in 8 RSNs. Given that the morphology of multiscale entropy is affected by the choice of its tolerance parameter (r) and embedding dimension (m), we repeated the analyses at multiple values of r and m including the values used in McDonough and Nashiro (2014). Our results reinforced high temporal complexity in the default mode and frontoparietal networks. Lowest temporal complexity was observed in the subcortical areas and limbic system. We investigated the effect of temporal resolution (determined by the repetition time TR) after downsampling of rsfMRI time series at two rates. At a low temporal resolution, we observed increased entropy and variance across datasets. Test-retest analysis showed that findings were likely reproducible across individuals over four rsfMRI runs, especially when the tolerance parameter r is equal to 0.5. The results confirmed that the relationship between functional brain connectivity strengths and rsfMRI temporal complexity changes over time scales. Finally, a non-random correlation was observed between temporal complexity of RSNs and fluid intelligence suggesting that complex dynamics of the human brain is an important attribute of high-level brain function. |
| first_indexed | 2024-12-19T06:38:14Z |
| format | Article |
| id | doaj.art-585f0bc4678a456e8197e29f33e6abfa |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-19T06:38:14Z |
| publishDate | 2021-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-585f0bc4678a456e8197e29f33e6abfa2022-12-21T20:32:11ZengElsevierNeuroImage1095-95722021-04-01230117760Temporal complexity of fMRI is reproducible and correlates with higher order cognitionAmir Omidvarnia0Andrew Zalesky1Sina Mansour L2Dimitri Van De Ville3Graeme D. Jackson4Mangor Pedersen5Institute of Bioengineering, Center for Neuroprosthetics, Center for Biomedical Imaging, EPFL, Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia; Corresponding author at: Institute of Bioengineering, Center for Neuroprosthetics, Center for Biomedical Imaging, EPFL, Lausanne, Switzerland.Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, Australia; Department of Biomedical Engineering, The University of Melbourne, Melbourne, AustraliaMelbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, Australia; Department of Biomedical Engineering, The University of Melbourne, Melbourne, AustraliaInstitute of Bioengineering, Center for Neuroprosthetics, Center for Biomedical Imaging, EPFL, Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, SwitzerlandThe Florey Institute of Neuroscience and Mental Health, Melbourne Brain Centre, Melbourne, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia; Department of Neurology, Austin Health, Melbourne, AustraliaFlorey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia; Department of Psychology and Neuroscience, Auckland University of Technology, Auckland, New ZealandIt has been hypothesized that resting state networks (RSNs), extracted from resting state functional magnetic resonance imaging (rsfMRI), likely display unique temporal complexity fingerprints, quantified by their multiscale entropy patterns (McDonough and Nashiro, 2014). This is a hypothesis with a potential capacity for developing digital biomarkers of normal brain function, as well as pathological brain dysfunction. Nevertheless, a limitation of McDonough and Nashiro (2014) was that rsfMRI data from only 20 healthy individuals was used for the analysis. To validate this hypothesis in a larger cohort, we used rsfMRI datasets of 987 healthy young adults from the Human Connectome Project (HCP), aged 22-35, each with four 14.4-min rsfMRI recordings and parcellated into 379 brain regions. We quantified multiscale entropy of rsfMRI time series averaged at different cortical and sub-cortical regions. We performed effect-size analysis on the data in 8 RSNs. Given that the morphology of multiscale entropy is affected by the choice of its tolerance parameter (r) and embedding dimension (m), we repeated the analyses at multiple values of r and m including the values used in McDonough and Nashiro (2014). Our results reinforced high temporal complexity in the default mode and frontoparietal networks. Lowest temporal complexity was observed in the subcortical areas and limbic system. We investigated the effect of temporal resolution (determined by the repetition time TR) after downsampling of rsfMRI time series at two rates. At a low temporal resolution, we observed increased entropy and variance across datasets. Test-retest analysis showed that findings were likely reproducible across individuals over four rsfMRI runs, especially when the tolerance parameter r is equal to 0.5. The results confirmed that the relationship between functional brain connectivity strengths and rsfMRI temporal complexity changes over time scales. Finally, a non-random correlation was observed between temporal complexity of RSNs and fluid intelligence suggesting that complex dynamics of the human brain is an important attribute of high-level brain function.http://www.sciencedirect.com/science/article/pii/S1053811921000379Temporal complexityMultiscale entropyResting state networkFunctional MRIHuman connectome projectFluid intelligence |
| spellingShingle | Amir Omidvarnia Andrew Zalesky Sina Mansour L Dimitri Van De Ville Graeme D. Jackson Mangor Pedersen Temporal complexity of fMRI is reproducible and correlates with higher order cognition NeuroImage Temporal complexity Multiscale entropy Resting state network Functional MRI Human connectome project Fluid intelligence |
| title | Temporal complexity of fMRI is reproducible and correlates with higher order cognition |
| title_full | Temporal complexity of fMRI is reproducible and correlates with higher order cognition |
| title_fullStr | Temporal complexity of fMRI is reproducible and correlates with higher order cognition |
| title_full_unstemmed | Temporal complexity of fMRI is reproducible and correlates with higher order cognition |
| title_short | Temporal complexity of fMRI is reproducible and correlates with higher order cognition |
| title_sort | temporal complexity of fmri is reproducible and correlates with higher order cognition |
| topic | Temporal complexity Multiscale entropy Resting state network Functional MRI Human connectome project Fluid intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S1053811921000379 |
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