fMRI and machine learning differentiate bipolar from unipolar depression

ML model using spontaneous brain activity from fMRI achieved AUC 0.894 for distinguishing bipolar vs. unipolar depression. Adding disease duration in a nomogram raised C-index to 0.926. Preliminary, single-center study requiring external validation.

• Diagnostic accuracy study in 158 patients (79 bipolar, 79 unipolar depression).
• Nomogram combining imaging signatures and disease duration had C-index 0.926, with good calibration and net benefit.
• Single-center, no external validation; small sample limits generalizability.

Automated summary

RadPigeon summaries are original and for information only. They are not clinical advice.