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Claire Punturieri

Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, Bethesda, MD, United States.

2 papers in the library · 43 citations · publishing 2023

Papers

Review: The use of functional magnetic resonance imaging (fMRI) in clinical trials and experimental research studies for depression

Frontiers in Neuroimaging June 27, 2023 Vasileia Kotoula, Jennifer W. Evans, Claire Punturieri et al. 26 citations

Functional magnetic resonance imaging (fMRI) can identify brain areas linked to depression symptoms and potential treatment targets. A review of fMRI studies on selective serotonin reuptake inhibitors (SSRIs) and ketamine found that both conventional and fast-acting antidepressants normalize amygdala hyperactivity in response to negative emotional stimuli, suggesting a common pathway for antidepressant action. Ketamine's rapid effects on brain activity and connectivity also trend toward normalizing depression-related abnormalities. While fMRI shows promise for identifying treatment targets, improved methodology and study design are needed before its findings can be used as primary clinical trial outcomes.

Functional MRI markers for treatment-resistant depression: Insights and challenges.

Progress in brain research January 1, 2023 Vasileia Kotoula, Jennifer W Evans, Claire Punturieri et al. 17 citations

Imaging studies of treatment-resistant depression (TRD) have examined brain activity, structure, and metabolite concentrations to identify critical areas of investigation and potential treatment targets. This chapter reviews findings from structural MRI, functional MRI, and magnetic resonance spectroscopy. Decreased connectivity and metabolite concentrations in frontal brain areas appear to characterize TRD, though results are not consistent across studies. Treatments including rapid-acting antidepressants and transcranial magnetic stimulation have shown some efficacy in reversing these changes while alleviating depressive symptoms. However, few TRD imaging studies exist, often with small sample sizes or varied methods, making firm conclusions difficult. Larger studies with unified hypotheses and data sharing could improve characterization of the illness and identify new treatment targets.