A permutation testing framework to compare groups of brain networks
Brain network analyses have moved to the forefront of neuroimaging research over the last decade. However, methods for statistically comparing groups of networks have lagged behind. These comparisons have great appeal for researchers interested in gaining further insight into complex brain function...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2013-11-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00171/full |
| _version_ | 1819074382632321024 |
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| author | Sean L Simpson Robert G Lyday Satoru eHayasaka Anthony P Marsh Paul J Laurienti |
| author_facet | Sean L Simpson Robert G Lyday Satoru eHayasaka Anthony P Marsh Paul J Laurienti |
| author_sort | Sean L Simpson |
| collection | DOAJ |
| description | Brain network analyses have moved to the forefront of neuroimaging research over the last decade. However, methods for statistically comparing groups of networks have lagged behind. These comparisons have great appeal for researchers interested in gaining further insight into complex brain function and how it changes across different mental states and disease conditions. Current comparison approaches generally either rely on a summary metric or on mass-univariate nodal or edge-based comparisons that ignore the inherent topological properties of the network, yielding little power and failing to make network level comparisons. Gleaning deeper insights into normal and abnormal changes in complex brain function demands methods that take advantage of the wealth of data present in an entire brain network. Here we propose a permutation testing framework that allows comparing groups of networks while incorporating topological features inherent in each individual network. We validate our approach using simulated data with known group differences. We then apply the method to functional brain networks derived from fMRI data. |
| first_indexed | 2024-12-21T18:08:38Z |
| format | Article |
| id | doaj.art-66fee54286bd4761abd636ace0fc901e |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-21T18:08:38Z |
| publishDate | 2013-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-66fee54286bd4761abd636ace0fc901e2022-12-21T18:54:51ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882013-11-01710.3389/fncom.2013.0017166591A permutation testing framework to compare groups of brain networksSean L Simpson0Robert G Lyday1Satoru eHayasaka2Anthony P Marsh3Paul J Laurienti4Wake Forest School of MedicineWake Forest School of MedicineWake Forest School of MedicineWake Forest UniversityWake Forest School of MedicineBrain network analyses have moved to the forefront of neuroimaging research over the last decade. However, methods for statistically comparing groups of networks have lagged behind. These comparisons have great appeal for researchers interested in gaining further insight into complex brain function and how it changes across different mental states and disease conditions. Current comparison approaches generally either rely on a summary metric or on mass-univariate nodal or edge-based comparisons that ignore the inherent topological properties of the network, yielding little power and failing to make network level comparisons. Gleaning deeper insights into normal and abnormal changes in complex brain function demands methods that take advantage of the wealth of data present in an entire brain network. Here we propose a permutation testing framework that allows comparing groups of networks while incorporating topological features inherent in each individual network. We validate our approach using simulated data with known group differences. We then apply the method to functional brain networks derived from fMRI data.http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00171/fullNeuroimagingconnectivityfMRISmall-worldgraph theoryJaccard |
| spellingShingle | Sean L Simpson Robert G Lyday Satoru eHayasaka Anthony P Marsh Paul J Laurienti A permutation testing framework to compare groups of brain networks Frontiers in Computational Neuroscience Neuroimaging connectivity fMRI Small-world graph theory Jaccard |
| title | A permutation testing framework to compare groups of brain networks |
| title_full | A permutation testing framework to compare groups of brain networks |
| title_fullStr | A permutation testing framework to compare groups of brain networks |
| title_full_unstemmed | A permutation testing framework to compare groups of brain networks |
| title_short | A permutation testing framework to compare groups of brain networks |
| title_sort | permutation testing framework to compare groups of brain networks |
| topic | Neuroimaging connectivity fMRI Small-world graph theory Jaccard |
| url | http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00171/full |
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