Daniel M. Blumberger

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.

The paper identifies sgACC functional connectivity abnormalities in depression using large-scale brain imaging data and develops an individualized TMS targeting algorithm to improve treatment outcomes.

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

BOONStim uses Bayesian optimization to improve TMS targeting by integrating fMRI and TMS simulation tools, showing faster convergence and accuracy compared to grid optimization.

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.

This paper shows that individual beliefs about receiving active or placebo treatment better explain outcome variability than the actual treatment in neurostimulation studies.