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Categorizing Acquisition Intervals from Whole-Brain MEG Functional Connectivity
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DOI：10.1007/978-3-032-00656-1_30.png)
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Abstract
En 中文
The magnetoencephalography (MEG) functional connectome has been shown to hold rich information on cognitive states and the health status of the brain. In this study, we investigate whether multiple recordings of MEG can be classified as to whether they occurred less than a week apart or a week or more apart. We use the difference between session functional connectomes as the primary feature, followed by multi-agglomerative clustering and logistic regression with elastic net regularization. We run this with 5-split stratified folds, repeating the runs with replacement 20 times, totaling 100 folds. We achieve a mean AUROC score of 0.72.+/- 0.17SD in the theta band using phaselinearity-measurement (PLM) connectivity, indicating generalizable differentiable changes in the brain from both periods. In addition to longitudinal approaches to understanding MEG recordings, this finding could be applied to MEG fingerprinting work, where the re-identification of subjects largely depends on their longitudinal robustness.
Keywords:
Magnetoencephalography (MEG)
Test-retest reliability
Fingerprinting
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