Detection of Resting-State Functional Connectivity from High-Density Electroencephalography Data: Impact of Head Modeling Strategies
Recent technological advances have been permitted to use high-density electroencephalography (hdEEG) for the estimation of functional connectivity and the mapping of resting-state networks (RSNs). The reliable estimate of activity and connectivity from hdEEG data relies on the creation of an accurat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/11/6/741 |
| _version_ | 1797531585160937472 |
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| author | Gaia Amaranta Taberna Jessica Samogin Marco Marino Dante Mantini |
| author_facet | Gaia Amaranta Taberna Jessica Samogin Marco Marino Dante Mantini |
| author_sort | Gaia Amaranta Taberna |
| collection | DOAJ |
| description | Recent technological advances have been permitted to use high-density electroencephalography (hdEEG) for the estimation of functional connectivity and the mapping of resting-state networks (RSNs). The reliable estimate of activity and connectivity from hdEEG data relies on the creation of an accurate head model, defining how neural currents propagate from the cortex to the sensors placed over the scalp. To the best of our knowledge, no study has been conducted yet to systematically test to what extent head modeling accuracy impacts on EEG-RSN reconstruction. To address this question, we used 256-channel hdEEG data collected in a group of young healthy participants at rest. We first estimated functional connectivity in EEG-RSNs by means of band-limited power envelope correlations, using neural activity estimated with an optimized analysis workflow. Then, we defined a series of head models with different levels of complexity, specifically testing the effect of different electrode positioning techniques and head tissue segmentation methods. We observed that robust EEG-RSNs can be obtained using a realistic head model, and that inaccuracies due to head tissue segmentation impact on RSN reconstruction more than those due to electrode positioning. Additionally, we found that EEG-RSN robustness to head model variations had space and frequency specificity. Overall, our results may contribute to defining a benchmark for assessing the reliability of hdEEG functional connectivity measures. |
| first_indexed | 2024-03-10T10:45:53Z |
| format | Article |
| id | doaj.art-6f6df0e51ff54cf2b8771c9e762acab0 |
| institution | Directory Open Access Journal |
| issn | 2076-3425 |
| language | English |
| last_indexed | 2024-03-10T10:45:53Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj.art-6f6df0e51ff54cf2b8771c9e762acab02023-11-21T22:36:58ZengMDPI AGBrain Sciences2076-34252021-06-0111674110.3390/brainsci11060741Detection of Resting-State Functional Connectivity from High-Density Electroencephalography Data: Impact of Head Modeling StrategiesGaia Amaranta Taberna0Jessica Samogin1Marco Marino2Dante Mantini3Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, BelgiumResearch Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, BelgiumResearch Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, BelgiumResearch Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, BelgiumRecent technological advances have been permitted to use high-density electroencephalography (hdEEG) for the estimation of functional connectivity and the mapping of resting-state networks (RSNs). The reliable estimate of activity and connectivity from hdEEG data relies on the creation of an accurate head model, defining how neural currents propagate from the cortex to the sensors placed over the scalp. To the best of our knowledge, no study has been conducted yet to systematically test to what extent head modeling accuracy impacts on EEG-RSN reconstruction. To address this question, we used 256-channel hdEEG data collected in a group of young healthy participants at rest. We first estimated functional connectivity in EEG-RSNs by means of band-limited power envelope correlations, using neural activity estimated with an optimized analysis workflow. Then, we defined a series of head models with different levels of complexity, specifically testing the effect of different electrode positioning techniques and head tissue segmentation methods. We observed that robust EEG-RSNs can be obtained using a realistic head model, and that inaccuracies due to head tissue segmentation impact on RSN reconstruction more than those due to electrode positioning. Additionally, we found that EEG-RSN robustness to head model variations had space and frequency specificity. Overall, our results may contribute to defining a benchmark for assessing the reliability of hdEEG functional connectivity measures.https://www.mdpi.com/2076-3425/11/6/741electroencephalographyfunctional connectivityresting-state networkshead modellingelectrode localizationhead tissue segmentation |
| spellingShingle | Gaia Amaranta Taberna Jessica Samogin Marco Marino Dante Mantini Detection of Resting-State Functional Connectivity from High-Density Electroencephalography Data: Impact of Head Modeling Strategies Brain Sciences electroencephalography functional connectivity resting-state networks head modelling electrode localization head tissue segmentation |
| title | Detection of Resting-State Functional Connectivity from High-Density Electroencephalography Data: Impact of Head Modeling Strategies |
| title_full | Detection of Resting-State Functional Connectivity from High-Density Electroencephalography Data: Impact of Head Modeling Strategies |
| title_fullStr | Detection of Resting-State Functional Connectivity from High-Density Electroencephalography Data: Impact of Head Modeling Strategies |
| title_full_unstemmed | Detection of Resting-State Functional Connectivity from High-Density Electroencephalography Data: Impact of Head Modeling Strategies |
| title_short | Detection of Resting-State Functional Connectivity from High-Density Electroencephalography Data: Impact of Head Modeling Strategies |
| title_sort | detection of resting state functional connectivity from high density electroencephalography data impact of head modeling strategies |
| topic | electroencephalography functional connectivity resting-state networks head modelling electrode localization head tissue segmentation |
| url | https://www.mdpi.com/2076-3425/11/6/741 |
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