Cohort Builder: A Software Pipeline for Generating Patient Cohorts with Predetermined Baseline Characteristics from Medical Records and Raw Ophthalmic Imaging Data.

In clinical research, the analysis of patient cohorts is a widely employed method for investigating relevant healthcare questions. The ability to automatically extract large-scale patient cohorts from hospital systems is vital in order to unlock the potential of real-world clinical data, and answer pivotal medical questions through retrospective research studies. However, existing medical data is often dispersed across various systems and databases, preventing a systematic approach to access and interoperability. Even when the data are readily accessible, clinical researchers need to sift through Electronic Medical Records, confirm ethical approval, verify status of patient consent, check the availability of imaging data, and filter the data based on disease-specific image biomarkers. We present Cohort Builder, a software pipeline designed to facilitate the creation of patient cohorts with predefined baseline characteristics from real-world ophthalmic imaging data and electronic medical records. The applicability of our approach extends beyond ophthalmology to other medical domains with similar requirements such as neurology, cardiology and orthopedics.

Homeostatic Reinforcement Theory Accounts for Sodium Appetitive State- and Taste-Dependent Dopamine Responding.

Seeking and consuming nutrients is essential to survival and the maintenance of life. Dynamic and volatile environments require that animals learn complex behavioral strategies to obtain the necessary nutritive substances. While this has been classically viewed in terms of homeostatic regulation, recent theoretical work proposed that such strategies result from reinforcement learning processes. This theory proposed that phasic dopamine (DA) signals play a key role in signaling potentially need-fulfilling outcomes. To examine links between homeostatic and reinforcement learning processes, we focus on sodium appetite as sodium depletion triggers state- and taste-dependent changes in behavior and DA signaling evoked by sodium-related stimuli. We find that both the behavior and the dynamics of DA signaling underlying sodium appetite can be accounted for by a homeostatically regulated reinforcement learning framework (HRRL). We first optimized HRRL-based agents to sodium-seeking behavior measured in rodents. Agents successfully reproduced the state and the taste dependence of behavioral responding for sodium as well as for lithium and potassium salts. We then showed that these same agents account for the regulation of DA signals evoked by sodium tastants in a taste- and state-dependent manner. Our models quantitatively describe how DA signals evoked by sodium decrease with satiety and increase with deprivation. Lastly, our HRRL agents assigned equal preference for sodium versus the lithium containing salts, accounting for similar behavioral and neurophysiological observations in rodents. We propose that animals use orosensory signals as predictors of the internal impact of the consumed good and our results pose clear targets for future experiments. In sum, this work suggests that appetite-driven behavior may be driven by reinforcement learning mechanisms that are dynamically tuned by homeostatic need.