Summary:
• We identify signs of visuo-spatial neglect through an automated analysis of saccadic eye trajectories using a series of machine learning classifiers.
• We provide a standardized pre-processing pipeline adaptable to other task-based eye-tracker measurements.
• Patient-wise, we benchmark the predictions form a 1D convolutional neural network with standardized paper-and-pencil test results.
• We evaluate white matter tracts by using Diffusion Tensor Imaging (DTI) and find a clear correlation with the microstructure of the third branch of the superior longitudinal fasciculus.
• Machine learning methods can efficiently and non-invasively characterize left spatial neglect.
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