Jonathan H. Downar

The paper develops a population model of motor evoked potentials from TMS to enable in-silico testing and tuning of closed-loop procedures without experimental data.

The paper introduces NSI, an objective measure to assess data quality for precision functional MRI, aligning with expert evaluations and enabling principled decisions on data sufficiency.

This study evaluates the relationship between proximity to individualized functional targets (iFTs) and clinical outcomes in rTMS trials for treatment-resistant depression, finding no association between proximity and improved outcomes.

This study identifies neurophysiological signatures predicting response to TMS in treatment-resistant depression using combined EEG and computational modeling.

The paper develops and evaluates dimensional and categorical approaches to identify depression subtypes using brain-behavior dimensions and clustering in a large single-site sample of MDD patients and controls.

The paper identifies a two-fold expansion of the frontostriatal salience network in individuals with depression using longitudinal neuroimaging data and shows its stability over time and predictive value for future symptoms.

The paper re-evaluates the stability of canonical correlations between functional connectivity and depressive symptoms using optimized statistical methods and demonstrates how optogenetic fMRI can test subtype-specific circuit mechanisms in depression.