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The great behavioral heterogeneity observed between individuals with the samepsychiatric disorder and even within one individual over time complicates bothclinical practice and biomedical research. However, modern technologies are anexciting opportunity to improve behavioral characterization. Existingpsychiatry methods that are qualitative or unscalable, such as patient surveysor clinical interviews, can now be collected at a greater capacity and analyzedto produce new quantitative measures. Furthermore, recent capabilities forcontinuous collection of passive sensor streams, such as phone GPS orsmartwatch accelerometer, open avenues of novel questioning that werepreviously entirely unrealistic. Their temporally dense nature enables acohesive study of real-time neural and behavioral signals.  To develop comprehensive neurobiological models of psychiatric disease, itwill be critical to first develop strong methods for behavioral quantification.There is huge potential in what can theoretically be captured by currenttechnologies, but this in itself presents a large computational challenge --one that will necessitate new data processing tools, new machine learningtechniques, and ultimately a shift in how interdisciplinary work is conducted.In my thesis, I detail research projects that take different perspectives ondigital psychiatry, subsequently tying ideas together with a concludingdiscussion on the future of the field. I also provide software infrastructurewhere relevant, with extensive documentation.  Major contributions include scientific arguments and proof of concept resultsfor daily free-form audio journals as an underappreciated psychiatry researchdatatype, as well as novel stability theorems and pilot empirical success for aproposed multi-area recurrent neural network architecture.

Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology