A spatially subsampled 1D representation of rsfMRI time courses fed to a simple 1D CNN matches state-of-the-art accuracy for autism spectrum disorder classification with minimal preprocessing.
ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data
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Simple 1-D Convolutional Networks for Resting-State fMRI Based Classification in Autism
A spatially subsampled 1D representation of rsfMRI time courses fed to a simple 1D CNN matches state-of-the-art accuracy for autism spectrum disorder classification with minimal preprocessing.