Skip to content

Ziad Nahas

Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.

2 papers in the library · 16 citations · publishing 2024-2026

Papers

Clinical characteristics and treatment exposure of patients with marked treatment-resistant unipolar major depressive disorder: A RECOVER trial report.

Brain stimulation January 1, 2024 Charles R Conway, Scott T Aaronson, Harold A Sackeim et al. 16 citations

Patients with treatment-resistant unipolar major depressive disorder who qualified for the RECOVER trial—the largest randomized sham-controlled study of vagus nerve stimulation for a psychiatric condition—had severe disability, a median of 11.0 prior failed antidepressant treatments, and high rates of suicidality (77% with suicidal ideation, 40% with previous suicide attempts). Seventy-one percent had received at least one prior interventional psychiatric treatment (electroconvulsive therapy, transcranial magnetic stimulation, or esketamine). Compared to those without such history, recipients of interventional treatments were younger, more severely depressed, had greater suicidal ideation, earlier onset of depression, and more failed medication trials.

Metastability of resting-state bold fMRI as a reliable biomarker of individual brain dynamics: An interrogation of within-subject variability as a function of total acquisition time.

Network neuroscience (Cambridge, Mass.) January 1, 2026 Hiba Sheheitli, Robert Hermosillo, Gracie Grimsrud et al.

Metastability of BOLD fMRI signals, a proxy for brain dynamics, shows within-subject reliability comparable to static functional connectivity when enough data are used, but the amount needed varies across brain networks. Combining network-specific metastability metrics into a single feature vector improves reliability by an order of magnitude. This finding was reproduced in the Midnight Scan Club dataset (10 subjects over 10 days). The measure also proved sensitive to change in brain dynamics under psilocybin. The authors conclude the combined feature vector is a promising candidate for individual-specific biomarkers and precision neuromodulation targets.