  Brain imaging of mental health, neurodevelopmental and learning disorders has
coupled with machine learning to identify patients based only on their brain
activation, and ultimately identify features that generalize from smaller
samples of data to larger ones. However, the success of machine learning
classification algorithms on neurofunctional data has been limited to more
homogeneous data sets of dozens of participants. More recently, larger brain
imaging data sets have allowed for the application of deep learning techniques
to classify brain states and clinical groups solely from neurofunctional
features. Deep learning techniques provide helpful tools for classification in
healthcare applications, including classification of structural 3D brain
images. Recent approaches improved classification performance of larger
functional brain imaging data sets, but they fail to provide diagnostic
insights about the underlying conditions or provide an explanation from the
neural features that informed the classification. We address this challenge by
leveraging a number of network visualization techniques to show that, using
such techniques in convolutional neural network layers responsible for learning
high-level features, we are able to provide meaningful images for expert-backed
insights into the condition being classified. Our results show not only
accurate classification of developmental dyslexia from the brain imaging alone,
but also provide automatic visualizations of the features involved that match
contemporary neuroscientific knowledge, indicating that the visual explanations
do help in unveiling the neurological bases of the disorder being classified.
