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Data for comparison of automated and manual co-registration for magnetoencephalography

dataset
posted on 30.04.2020 by Jon Houck, Eric Claus
De-identified, de-faced structural MRI files for 90 participants, as well as co-registration error data used in the computation of intra-class correlations.

Dataset supporting publication: Houck JM, Claus ED (2020) A comparison of automated and manual co-registration for magnetoencephalography. PLoS ONE 15(4): e0232100. https://doi.org/10.1371/journal.pone.0232100

Abstract: Magnetoencephalography (MEG) is a neuroimaging technique that accurately captures the rapid (sub-millisecond) activity of neuronal populations. Interpretation of functional data from MEG relies upon registration to the participant’s anatomical MRI. The key remaining step is to transform the participant’s MRI into the MEG head coordinate space. Although both automated and manual approaches to co-registration are available, the relative accuracy of two approaches has not been systematically evaluated. The goal of the present study was to compare the accuracy of manual and automated co-registration. Resting MEG and T1-weighted MRI data were collected from 90 participants. Automated and manual co-registration were performed on the same subjects, and the inter-method reliability of the two methods assessed using the intra-class correlation. Median co-registration error for both methods was within acceptable limits. Inter-method reliability was in the “good” range for co-registration error, and the “good” to “excellent” range for translation and rotation. These results suggest that the output of the automated co-registration procedure is comparable to that achieved using manual co-registration.

Funding

Neural Mechanisms of Behavior Change in a Community Sample of Drinkers

National Institute On Alcohol Abuse and Alcoholism

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Imaging Brain Activity in Substance Use Treatment

National Institute On Alcohol Abuse and Alcoholism

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  • AA - National Institute on Alcohol Abuse and Alcoholism (NIAAA)

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