Inferring task-related networks using independent component analysis in magnetoencephalography.
A novel framework for analysing task-positive data in magnetoencephalography (MEG) is presented that can identify task-related networks. Techniques that combine beamforming, the Hilbert transform and temporal independent component analysis (ICA) have recently been applied to resting-state MEG data a...
| Main Authors: | , , , , , , |
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| Format: | Journal article |
| Language: | English |
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2012
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| _version_ | 1797057091381231616 |
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| author | Luckhoo, H Hale, JR Stokes, M Nobre, A Morris, P Brookes, M Woolrich, M |
| author_facet | Luckhoo, H Hale, JR Stokes, M Nobre, A Morris, P Brookes, M Woolrich, M |
| author_sort | Luckhoo, H |
| collection | OXFORD |
| description | A novel framework for analysing task-positive data in magnetoencephalography (MEG) is presented that can identify task-related networks. Techniques that combine beamforming, the Hilbert transform and temporal independent component analysis (ICA) have recently been applied to resting-state MEG data and have been shown to extract resting-state networks similar to those found in fMRI. Here we extend this approach in two ways. First, we systematically investigate optimisation of time-frequency windows for connectivity measurement. This is achieved by estimating the distribution of functional connectivity scores between nodes of known resting-state networks and contrasting it with a distribution of artefactual scores that are entirely due to spatial leakage caused by the inverse problem. We find that functional connectivity, both in the resting-state and during a cognitive task, is best estimated via correlations in the oscillatory envelope in the 8-20 Hz frequency range, temporally down-sampled with windows of 1-4s. Second, we combine ICA with the general linear model (GLM) to incorporate knowledge of task structure into our connectivity analysis. The combination of ICA with the GLM helps overcome problems of these techniques when used independently: namely, the interpretation and separation of interesting independent components from those that represent noise in ICA and the correction for multiple comparisons when applying the GLM. We demonstrate the approach on a 2-back working memory task and show that this novel analysis framework is able to elucidate the functional networks involved in the task beyond that which is achieved using the GLM alone. We find evidence of localised task-related activity in the area of the hippocampus, which is difficult to detect reliably using standard methods. Task-positive ICA, coupled with the GLM, has the potential to be a powerful tool in the analysis of MEG data. |
| first_indexed | 2024-03-06T19:31:20Z |
| format | Journal article |
| id | oxford-uuid:1d8df227-fc7d-41a5-acfd-6d4a3b07799f |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T19:31:20Z |
| publishDate | 2012 |
| record_format | dspace |
| spelling | oxford-uuid:1d8df227-fc7d-41a5-acfd-6d4a3b07799f2022-03-26T11:11:37ZInferring task-related networks using independent component analysis in magnetoencephalography.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:1d8df227-fc7d-41a5-acfd-6d4a3b07799fEnglishSymplectic Elements at Oxford2012Luckhoo, HHale, JRStokes, MNobre, AMorris, PBrookes, MWoolrich, MA novel framework for analysing task-positive data in magnetoencephalography (MEG) is presented that can identify task-related networks. Techniques that combine beamforming, the Hilbert transform and temporal independent component analysis (ICA) have recently been applied to resting-state MEG data and have been shown to extract resting-state networks similar to those found in fMRI. Here we extend this approach in two ways. First, we systematically investigate optimisation of time-frequency windows for connectivity measurement. This is achieved by estimating the distribution of functional connectivity scores between nodes of known resting-state networks and contrasting it with a distribution of artefactual scores that are entirely due to spatial leakage caused by the inverse problem. We find that functional connectivity, both in the resting-state and during a cognitive task, is best estimated via correlations in the oscillatory envelope in the 8-20 Hz frequency range, temporally down-sampled with windows of 1-4s. Second, we combine ICA with the general linear model (GLM) to incorporate knowledge of task structure into our connectivity analysis. The combination of ICA with the GLM helps overcome problems of these techniques when used independently: namely, the interpretation and separation of interesting independent components from those that represent noise in ICA and the correction for multiple comparisons when applying the GLM. We demonstrate the approach on a 2-back working memory task and show that this novel analysis framework is able to elucidate the functional networks involved in the task beyond that which is achieved using the GLM alone. We find evidence of localised task-related activity in the area of the hippocampus, which is difficult to detect reliably using standard methods. Task-positive ICA, coupled with the GLM, has the potential to be a powerful tool in the analysis of MEG data. |
| spellingShingle | Luckhoo, H Hale, JR Stokes, M Nobre, A Morris, P Brookes, M Woolrich, M Inferring task-related networks using independent component analysis in magnetoencephalography. |
| title | Inferring task-related networks using independent component analysis in magnetoencephalography. |
| title_full | Inferring task-related networks using independent component analysis in magnetoencephalography. |
| title_fullStr | Inferring task-related networks using independent component analysis in magnetoencephalography. |
| title_full_unstemmed | Inferring task-related networks using independent component analysis in magnetoencephalography. |
| title_short | Inferring task-related networks using independent component analysis in magnetoencephalography. |
| title_sort | inferring task related networks using independent component analysis in magnetoencephalography |
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