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Brain imaging of mental health, neurodevelopmental and learning disorders hascoupled with machine learning to identify patients based only on their brainactivation, and ultimately identify features that generalize from smallersamples of data to larger ones. However, the success of machine learningclassification algorithms on neurofunctional data has been limited to morehomogeneous data sets of dozens of participants. More recently, larger brainimaging data sets have allowed for the application of deep learning techniquesto classify brain states and clinical groups solely from neurofunctionalfeatures. Deep learning techniques provide helpful tools for classification inhealthcare applications, including classification of structural 3D brainimages. Recent approaches improved classification performance of largerfunctional brain imaging data sets, but they fail to provide diagnosticinsights about the underlying conditions or provide an explanation from theneural features that informed the classification. We address this challenge byleveraging a number of network visualization techniques to show that, usingsuch techniques in convolutional neural network layers responsible for learninghigh-level features, we are able to provide meaningful images for expert-backedinsights into the condition being classified. Our results show not onlyaccurate classification of developmental dyslexia from the brain imaging alone,but also provide automatic visualizations of the features involved that matchcontemporary neuroscientific knowledge, indicating that the visual explanationsdo help in unveiling the neurological bases of the disorder being classified.

Visual Explanation for Identification of the Brain Bases for Dyslexia on fMRI Data