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Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)
Published on: November 27, 2019
Quantitative analysis of MEG using modified sLORETA for clinical application.
Y Terakawa1, N Tsuyuguchi1, H Tanaka2
1Department of Neurosurgery, Osaka City University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, 545-8585 Osaka, Japan.
Summary
Standardised low-resolution brain electromagnetic tomography modified for a quantifiable method (sLORETA-qm) reliably quantifies magnetoencephalography (MEG) data. This method shows promise for analyses where equivalent current dipole models are unsuitable.
Area of Science:
- Neuroscience
- Biophysics
- Medical Imaging
Background:
- Magnetoencephalography (MEG) is a non-invasive neuroimaging technique.
- Quantitative analysis of MEG data is crucial for understanding brain activity.
- Equivalent Current Dipole (ECD) models are commonly used but have limitations.
Purpose of the Study:
- To evaluate the utility of standardised low-resolution brain electromagnetic tomography modified for a quantifiable method (sLORETA-qm) for quantitative MEG analysis.
- To assess the reliability of sLORETA-qm in analyzing somatosensory evoked fields (SEFs).
Main Methods:
- Somatosensory evoked fields (SEFs) were recorded from healthy volunteers.
- Median nerve stimulation was applied at varying intensities.
- sLORETA-qm was used to quantify N20m intensity changes.
- Relationships between sLORETA-qm intensity and ECD moment were analyzed.
Main Results:
- sLORETA-qm intensity showed a linear increase with stimulus intensity up to 1.5x TMT.
- A plateau or decrease in intensity was observed at higher stimulus intensities.
- A strong correlation (r(s)=0.91, p<0.001) was found between ECD moment and sLORETA-qm intensity.
- The distribution of sLORETA-qm intensity was normal after logarithmic transformation.
Conclusions:
- sLORETA-qm is a reliable method for quantitative analysis of MEG data, particularly for N20m.
- This technique offers a viable alternative to ECD models, especially in cases where ECD models are inappropriate.
