Clinical neuroscience is field with all the difficulties that come from high dimensional data, and none of the advantages that fuel modern-day breakthroughs in computer vision, automated speech recognition, and health informatics. It is a field of unavoidably small datasets, massive patient variability, environmental confounds, and an arguable lack of ground truth information. It is also a field where classification accuracy plays second fiddle to interpretability, particularly for functional neuroimaging modalities, such as EEG and fMRI. As a result of these challenges, deep learning methods have gained little traction in understanding neuropsychiatric disorders.
My lab tackles the challenges of functional data analysis by blending the interpretability of generative models with the representational power of deep learning. This talk will highlight three ongoing projects that span a range of “old school” methodologies and clinical applications. First, I will discuss a joint optimization framework that combines non-negative matrix factorization with artificial neural networks to predict multidimensional clinical severity from resting-state fMRI. Second, I will describe a probabilistic graphical model for epileptic seizure detection using multichannel EEG. The latent variables in this model capture the spatio-temporal spread of a seizure; they are complemented by a nonparametric likelihood based on convolutional neural networks. Finally, I will touch on a very recent initiative to manipulate emotional cues in human speech, as a possible assistive technology for autism. Our approach combines traditional speech analysis, diffeomorphic registration, and highway neural networks.