A Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal Neuroimaging Data.

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Abstract

Understanding the structural and functional mechanisms of the brain is challenging for mood and mental disorders. Many neuroimaging techniques are widely used to reveal hidden patterns from different brain imaging modalities. However, these findings are bounded by the limitation of each modality. In addition, the lack of validity of current psychosis nosology created more complications in understanding biomarkers. In this study, we introduced a deep convolutional framework to classify and identify label noises using structural and functional magnetic resonance imaging data. We applied our method to functional and structural MRI data from a schizophrenia dataset and evaluated the model’s performance in a cross-validated form. In addition, we introduced a noise criterion to distinguish a potentially noisy subject for each modality. Our results show the learned model using resting-state functional MRI data is more informative and has higher performance in comparison with structural MRI data. Lastly, based on the noise level, we investigated potential borderline subjects as possible subtypes and made a statistical analysis to distinguish differences between resting-state static functional connectivity features.Clinical Relevance- Results show schizophrenia patients are separable from the healthy control group based on their neuroimaging data and resting-state functional MRI data is more informative than structural MRI data and hence contains less label noise.