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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.

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Journal Article

Direct Parametric Reconstruction for Improved Characterization of Neurotransmitter Release using Dynamic PET

TL;DR: In this paper, the authors developed a method for estimation of LSRRM parametric maps with significantly higher signal-to-noise ratio (SNR) by direct parameter estimation from raw dynamic PET projection data.
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Understanding Personal Control and the Brain Reward System for Psychopathology Is Challenging but Important.

TL;DR: A functional magnetic resonance imaging experiment explores the psychological and neural mechanisms related to the perception of control and asks the question: how might subclinical depression and motivational orientation impact neural responses to the opportunity to choose and to receiving rewards stemming from personal choice.
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Perseverative Cognition in the Positive Valence Systems: An Experimental and Ecological Investigation.

TL;DR: In this article, the effects of persistent cognition (PC) on reward sensitivity and learning rate were investigated in healthy individuals who performed the Probabilistic Reward Task (PRT) before and after the induction of PC or a waiting period.
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A multi-pronged investigation of option generation using depression, PET and modafinil.

TL;DR: In this article , the authors investigated option generation in Major Depressive Disorder (MDD) and how dopamine might modulate this process, as well as the effects of modafinil (a putative cognitive enhancer) on option generation.
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Default Mode and Frontoparietal Network Dynamics: Associations with Familial Risk for Depression and Stress Sensitivity

TL;DR: In this paper , a coactivation pattern (CAP) analysis was conducted to examine functional network dynamic properties, including time spent in each CAP (total number of volumes), CAP persistence (number of consecutive volumes in eachCAP), and number of transitions between posterior DMN-FPN and canonical DMN CAPs.