This method would allow researchers to navigate the limitations of EMRs while taking full advantage of their scalability and inclusivity, ultimately producing more trustworthy research outcomes.

In this paper, I propose an “EMR phenotyping” framework that carefully combines patients’ conditions with rigorous validation steps.

Electronic medical records (EMRs) offer a scalable, inclusive, and cost-effective tool for research, but they were not built for this purpose. Diagnosis codes are often incomplete, outdated, or entered mainly for billing, which creates challenges for scientific use.

Kosha Ruparel, Bailey C. Spangler, Scott Troyan, Rusell T. Shinohara, @ted-satterthwaite.bsky.social, and Erica B. Baller.

Huge thanks to the wonderful team that helped this project come together!! @elenaccooper.bsky.social, Matthew K. Schindler, Amit Bar-Or, Rachel B. Brandstadter, Monica E. Calkins, Ruben C. Gur, Dina A. Jacobs, Clyde E. Markowitz, Tyler M. Moore, Laura R. Naydovich, Christopher M. Perrone...

This study will also explore the contribution of anxiety, fatigue, and physical functioning to depression in MS. By combining cognitive, mood, and neuroimaging data, the Baller Lab is working to uncover network-level mechanisms that drive depression, and other psychopathology, in MS.

We’re collecting clinical, cognitive, and neuroimaging data from 250 people with MS. Using 3T MRI, we plan to evaluate white matter disease burden and its relationship to depressive symptoms.

We’re using multiple sclerosis (MS) as a model to explore how disruption in brain networks may contribute to depression. Up to 50% of individuals with MS experience depression or depressive-like symptoms, yet the neurobiological underpinnings remain unclear.

You can read our full Comment at the link above or here for a pre-proof version: singlesp.github.io/papers/natmh...

s/o to the amazing sypres team: Brooke Sevchik, @vandyatvandy.bsky.social, Eric Strain, Sandeep Nayak, Bob Dworkin, @jcobbscott.bsky.social, & @ted-satterthwaite.bsky.social