Quantifying the performance of MEG source reconstruction using resting state data.

ABSTRACT: In magnetoencephalography (MEG) research there are a variety of inversion methods to transform sensor data into estimates of brain activity. Each new inversion scheme is generally justified against a specific simulated or task scenario. The choice of this scenario will however have a large impact on how well the scheme performs. We describe a method with minimal selection bias to quantify algorithm performance using human resting state data. These recordings provide a generic, heterogeneous, and plentiful functional substrate against which to test different MEG recording and reconstruction approaches. We used a Hidden Markov model to spatio-temporally partition data into self-similar dynamic states. To test the anatomical precision that could be achieved, we then inverted these data onto l

SUBMITTER: Little S 

PROVIDER: S-EPMC6150947 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature