Reconstructing lost BOLD signal in individual participants using deep machine learning.

ABSTRACT: Signal loss in blood oxygen level-dependent (BOLD) functional neuroimaging is common and can lead to misinterpretation of findings. Here, we reconstructed compromised fMRI signal using deep machine learning. We trained a model to learn principles governing BOLD activity in one dataset and reconstruct artificially compromised regions in an independent dataset, frame by frame. Intriguingly, BOLD time series extracted from reconstructed frames are correlated with the original time series, even though the frames do not independently carry any temporal information. Moreover, reconstructed functional connectivity maps exhibit good correspondence with the original connectivity maps, indicating that the model recovers functional relationships among brain regions. We replicated this result in two h

SUBMITTER: Yan Y 

PROVIDER: S-EPMC7542429 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature