There is an extensive literature in machine learning demonstrating extraordinary ability to predict labels based off an abundance of data, such as object and voice recognition. Multiple scientific domains are poised to go through a data revolution, in which the quantity and quality of data will increase dramatically over the next several years. One such area is neuroscience, where novel devices will collect data orders of magnitude larger than current measurement technologies. In addition to being a “big data” problem, this data is incredibly complex. Machine learning approaches can adapt to this complexity to give state-of-the-art predictions. However, in many neurological disorders we are most interested in methods that are not only good at prediction, but also interpretable such that they can be used to design causal experiments and interventions.
Towards this end, we are building machine learning tools to analyze local field potentials recorded from electrodes implanted at many sites of the brain concurrently. Our machine learning techniques learn predictive and interpretable features that can generate data-driven hypotheses. By associating behavior outcomes with the learned features or brain networks, we can then generate a data-driven hypothesis about how the networks should be modulated in a causal experiment. Collaborators have developed optogenetic techniques to test these theories in a mouse model of depression, validating the machine learning approach. We have also demonstrated their utility in modeling data collected from a clinical trial to explore a novel treatment in Autism Spectrum Disorder.