Consistency of local activation parameters at sensor- and source-level in neural signals
| Autor: | Víctor Rodríguez-González, Marcos Revilla-Vallejo, Hideyuki Hoshi, Roberto Hornero, Jesús Poza, Yoshihito Shigihara, Carlos Gómez |
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| Rok vydání: | 2020 |
| Předmět: |
0206 medical engineering
Biomedical Engineering 02 engineering and technology Electroencephalography Statistical parametric mapping Signal 03 medical and health sciences Cellular and Molecular Neuroscience 0302 clinical medicine Minimum-variance unbiased estimator Consistency (statistics) Distortion medicine Mathematics Brain Mapping medicine.diagnostic_test business.industry Brain Magnetoencephalography Pattern recognition 020601 biomedical engineering Sample entropy Artificial intelligence business 030217 neurology & neurosurgery Algorithms |
| Zdroj: | Journal of neural engineering. 17(5) |
| ISSN: | 1741-2552 |
| Popis: | Objective. Although magnetoencephalography and electroencephalography (M/EEG) signals at sensor level are robust and reliable, they suffer from different degrees of distortion due to changes in brain tissue conductivities, known as field spread and volume conduction effects. To estimate original neural generators from M/EEG activity acquired at sensor level, diverse source localisation algorithms have been proposed; however, they are not exempt from limitations and usually involve time-consuming procedures. Connectivity and network-based M/EEG analyses have been found to be affected by field spread and volume conduction effects; nevertheless, the influence of the aforementioned effects on widely used local activation parameters has not been assessed yet. The goal of this study is to evaluate the consistency of various local activation parameters when they are computed at sensor- and source-level. Approach. Six spectral (relative power, median frequency, and individual alpha frequency) and non-linear parameters (Lempel-Ziv complexity, sample entropy, and central tendency measure) are computed from M/EEG signals at sensor- and source-level using four source inversion methods: weighted minimum norm estimate (wMNE), standardised low-resolution brain electromagnetic tomography (sLORETA), linear constrained minimum variance (LCMV), and dynamical statistical parametric mapping (dSPM). Main results. Our results show that the spectral and non-linear parameters yield similar results at sensor- and source-level, showing high correlation values between them for all the source inversion methods evaluated and both modalities of signal, EEG and MEG. Furthermore, the correlation values remain high when performing coarse-grained spatial analyses. Significance. To the best of our knowledge, this is the first study analysing how field spread and volume conduction effects impact on local activation parameters computed from resting-state neural activity. Our findings evidence that local activation parameters are robust against field spread and volume conduction effects and provide equivalent information at sensor- and source-level even when performing regional analyses. |
| Databáze: | OpenAIRE |
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