The impact of improved MEG–MRI co-registration on MEG connectivity analysis
| Autor: | Gian Luca Romani, Matti Stenroos, Lauri Parkkonen, Laura Marzetti, Vittorio Pizzella, Risto J. Ilmoniemi, Federico Chella |
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| Přispěvatelé: | Gabriele d'Annunzio University, Department of Neuroscience and Biomedical Engineering, Aalto-yliopisto, Aalto University |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Cognitive Neuroscience Models Neurological Co registration ta3112 050105 experimental psychology 03 medical and health sciences 0302 clinical medicine Beamforming Range (statistics) Image Processing Computer-Assisted Humans 0501 psychology and cognitive sciences Instrumentation (computer programming) Brain connectivity Volume-conductor modeling Brain Mapping Co-registration MEG business.industry 05 social sciences Brain Magnetoencephalography Reproducibility of Results Pattern recognition Magnetic Resonance Imaging Minimum-norm estimate Neurology Artificial intelligence business 030217 neurology & neurosurgery |
| Zdroj: | NeuroImage |
| Popis: | openaire: EC/H2020/686865/EU//BREAKBEN Co-registration between structural head images and functional MEG data is needed for anatomically-informed MEG data analysis. Despite the efforts to minimize the co-registration error, conventional landmark- and surface-based strategies for co-registering head and MEG device coordinates achieve an accuracy of typically 5–10 mm. Recent advances in instrumentation and technical solutions, such as the development of hybrid ultra-low-field (ULF)MRI–MEG devices or the use of 3D-printed individualized foam head-casts, promise unprecedented co-registration accuracy, i.e., 2 mm or better. In the present study, we assess through simulations the impact of such an improved co-registration on MEG connectivity analysis. We generated synthetic MEG recordings for pairs of connected cortical sources with variable locations. We then assessed the capability to reconstruct source-level connectivity from these recordings for 0–15-mm co-registration error, three levels of head modeling detail (one-, three- and four-compartment models), two source estimation techniques (linearly constrained minimum-variance beamforming and minimum-norm estimation MNE)and five separate connectivity metrics (imaginary coherency, phase-locking value, amplitude-envelope correlation, phase-slope index and frequency-domain Granger causality). We found that beamforming can better take advantage of an accurate co-registration than MNE. Specifically, when the co-registration error was smaller than 3 mm, the relative error in connectivity estimates was down to one-third of that observed with typical co-registration errors. MNE provided stable results for a wide range of co-registration errors, while the performance of beamforming rapidly degraded as the co-registration error increased. Furthermore, we found that even moderate co-registration errors (>6 mm, on average)essentially decrease the difference of four- and three- or one-compartment models. Hence, a precise co-registration is important if one wants to take full advantage of highly accurate head models for connectivity analysis. We conclude that an improved co-registration will be beneficial for reliable connectivity analysis and effective use of highly accurate head models in future MEG connectivity studies. |
| Databáze: | OpenAIRE |
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