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Diego A. Pizzagalli
Researcher at Harvard University
Publications - 393
Citations - 27176
Diego A. Pizzagalli is an academic researcher from Harvard University. The author has contributed to research in topics: Anhedonia & Medicine. The author has an hindex of 74, co-authored 327 publications receiving 21846 citations. Previous affiliations of Diego A. Pizzagalli include Stanford University & McLean Hospital.
Papers
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Journal ArticleDOI
P01-299 Reward sensitivity and response to treatment in major depression
TL;DR: The hypothesis that impairment of reward responsiveness might influence response to treatment in patients with major depressive disorder is supported.
Journal ArticleDOI
Rostral anterior cingulate cortex morphology predicts treatment response to internet-based CBT for depression
Christian A. Webb,Elizabeth A. Olson,William D.S. Killgore,Diego A. Pizzagalli,Scott L. Rauch,Isabelle M. Rosso +5 more
TL;DR: In this article, the authors examined whether rACC and/or sgACC morphology predicts treatment response to internet-based cognitive behavioral therapy (iCBT) for major depressive disorder (MDD).
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A double dissociation of regular and irregular English past-tense production revealed by Low-Resolution Electromagnetic Tomography (LORETA)
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253. Utilizing a Behavioral Assay of Reward Learning to Predict Clinical Response to a Dopamine Agonist in Individuals With Depression
Alexis E. Whitton,Jenna Reinen,Mark Slifstein,Patrick J. McGrath,Dan V. Iosifescu,Anissa Abi-Dargham,Diego A. Pizzagalli,Franklin R. Schneier +7 more
Journal ArticleDOI
Trait- and state-like co-activation pattern dynamics in current and remitted major depressive disorder.
Emily L. Belleau,Daifeng Dong,Xiaoqiang Sun,Ge Xiong,Diego A. Pizzagalli,Randy P. Auerbach,Xiang Wang,Shuqiao Yao +7 more
TL;DR: In this article , the authors investigated dynamic functional connectivity alternations in unmedicated individuals with current or past major depressive disorder (MDD) using co-activation pattern analyses and identified trait-like brain network dynamics that might increase vulnerability to future MDD.