The impact of improved MEG–MRI co-registration on MEG connectivity analysis

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The impact of improved MEG–MRI co-registration on MEG connectivity analysis. / Chella, Federico; Marzetti, Laura; Stenroos, Matti; Parkkonen, Lauri; Ilmoniemi, Risto J.; Romani, Gian Luca; Pizzella, Vittorio.

julkaisussa: NeuroImage, Vuosikerta 197, 05.2019, s. 354-367.

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

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Chella, Federico ; Marzetti, Laura ; Stenroos, Matti ; Parkkonen, Lauri ; Ilmoniemi, Risto J. ; Romani, Gian Luca ; Pizzella, Vittorio. / The impact of improved MEG–MRI co-registration on MEG connectivity analysis. Julkaisussa: NeuroImage. 2019 ; Vuosikerta 197. Sivut 354-367.

Bibtex - Lataa

@article{7a072b4fdf46458eafb8ece4bbe86d78,
title = "The impact of improved MEG–MRI co-registration on MEG connectivity analysis",
abstract = "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.",
keywords = "Beamforming, Brain connectivity, Co-registration, MEG, Minimum-norm estimate, Volume-conductor modeling",
author = "Federico Chella and Laura Marzetti and Matti Stenroos and Lauri Parkkonen and Ilmoniemi, {Risto J.} and Romani, {Gian Luca} and Vittorio Pizzella",
note = "| openaire: EC/H2020/686865/EU//BREAKBEN",
year = "2019",
month = "5",
doi = "10.1016/j.neuroimage.2019.04.061",
language = "English",
volume = "197",
pages = "354--367",
journal = "NeuroImage",
issn = "1053-8119",

}

RIS - Lataa

TY - JOUR

T1 - The impact of improved MEG–MRI co-registration on MEG connectivity analysis

AU - Chella, Federico

AU - Marzetti, Laura

AU - Stenroos, Matti

AU - Parkkonen, Lauri

AU - Ilmoniemi, Risto J.

AU - Romani, Gian Luca

AU - Pizzella, Vittorio

N1 - | openaire: EC/H2020/686865/EU//BREAKBEN

PY - 2019/5

Y1 - 2019/5

N2 - 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.

AB - 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.

KW - Beamforming

KW - Brain connectivity

KW - Co-registration

KW - MEG

KW - Minimum-norm estimate

KW - Volume-conductor modeling

UR - http://www.scopus.com/inward/record.url?scp=85065224789&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2019.04.061

DO - 10.1016/j.neuroimage.2019.04.061

M3 - Article

VL - 197

SP - 354

EP - 367

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

ER -

ID: 33936300