Multi-session statistics on beamformed MEG data
Beamforming has been widely adopted as a source reconstruction technique in the analysis of magnetoencephalography data. Most beamforming implementations incorporate a spatially-varying rescaling (which we term weights normalisation) to correct for the inherent depth bias in raw beamformer estimates...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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Academic Press Inc.
2014
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| _version_ | 1826342827639963648 |
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| author | Luckhoo, H Brookes, M Woolrich, M |
| author_facet | Luckhoo, H Brookes, M Woolrich, M |
| author_sort | Luckhoo, H |
| collection | OXFORD |
| description | Beamforming has been widely adopted as a source reconstruction technique in the analysis of magnetoencephalography data. Most beamforming implementations incorporate a spatially-varying rescaling (which we term weights normalisation) to correct for the inherent depth bias in raw beamformer estimates. Here, we demonstrate that such rescaling can cause critical problems whenever analyses are performed over multiple sessions of separately beamformed data, for example when comparing effect sizes between different populations. Importantly, we show that the weights-normalised beamformer estimates of neural activity can even lead to a reversal in the inferred sign of the effect being measured. We instead recommend that no weights normalisation be carried out; any depth bias is instead accounted for in the calculation of multi-session (e.g. group) statistics. We demonstrate the severity of the weights normalisation confound with a 2-D simulation, and in real MEG data by performing a group statistical analysis to detect differences in alpha power in eyes-closed rest compared with continuous visual stimulation. © 2014 The Authors. |
| first_indexed | 2024-03-07T02:00:34Z |
| format | Journal article |
| id | oxford-uuid:9d3be0af-319f-44bf-98da-dc45f838b9d1 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T02:00:34Z |
| publishDate | 2014 |
| publisher | Academic Press Inc. |
| record_format | dspace |
| spelling | oxford-uuid:9d3be0af-319f-44bf-98da-dc45f838b9d12022-03-27T00:41:23ZMulti-session statistics on beamformed MEG dataJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:9d3be0af-319f-44bf-98da-dc45f838b9d1EnglishSymplectic Elements at OxfordAcademic Press Inc.2014Luckhoo, HBrookes, MWoolrich, MBeamforming has been widely adopted as a source reconstruction technique in the analysis of magnetoencephalography data. Most beamforming implementations incorporate a spatially-varying rescaling (which we term weights normalisation) to correct for the inherent depth bias in raw beamformer estimates. Here, we demonstrate that such rescaling can cause critical problems whenever analyses are performed over multiple sessions of separately beamformed data, for example when comparing effect sizes between different populations. Importantly, we show that the weights-normalised beamformer estimates of neural activity can even lead to a reversal in the inferred sign of the effect being measured. We instead recommend that no weights normalisation be carried out; any depth bias is instead accounted for in the calculation of multi-session (e.g. group) statistics. We demonstrate the severity of the weights normalisation confound with a 2-D simulation, and in real MEG data by performing a group statistical analysis to detect differences in alpha power in eyes-closed rest compared with continuous visual stimulation. © 2014 The Authors. |
| spellingShingle | Luckhoo, H Brookes, M Woolrich, M Multi-session statistics on beamformed MEG data |
| title | Multi-session statistics on beamformed MEG data |
| title_full | Multi-session statistics on beamformed MEG data |
| title_fullStr | Multi-session statistics on beamformed MEG data |
| title_full_unstemmed | Multi-session statistics on beamformed MEG data |
| title_short | Multi-session statistics on beamformed MEG data |
| title_sort | multi session statistics on beamformed meg data |
| work_keys_str_mv | AT luckhooh multisessionstatisticsonbeamformedmegdata AT brookesm multisessionstatisticsonbeamformedmegdata AT woolrichm multisessionstatisticsonbeamformedmegdata |