Yuelu Liu1, Roee Admon2, Monika S Mellem1, Emily L Belleau3, Roselinde H Kaiser4, Rachel Clegg5, Miranda Beltzer5, Franziska Goer5, Gordana Vitaliano3, Parvez Ahammad1, Diego A Pizzagalli6. 1. BlackThorn Therapeutics, San Francisco, California. 2. Department of Psychology, University of Haifa, Haifa, Israel. 3. McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts. 4. Department of Psychology and Neuroscience, University of Colorado, Boulder, Colorado. 5. McLean Hospital, Belmont, Massachusetts. 6. McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts. Electronic address: dap@mclean.harvard.edu.
Abstract
BACKGROUND: Theoretical models have emphasized systems-level abnormalities in major depressive disorder (MDD). For unbiased yet rigorous evaluations of pathophysiological mechanisms underlying MDD, it is critically important to develop data-driven approaches that harness whole-brain data to classify MDD and evaluate possible normalizing effects of targeted interventions. Here, using an experimental therapeutics approach coupled with machine learning, we investigated the effect of a pharmacological challenge aiming to enhance dopaminergic signaling on whole-brain response to reward-related stimuli in MDD. METHODS: Using a double-blind, placebo-controlled design, we analyzed functional magnetic resonance imaging data from 31 unmedicated MDD participants receiving a single dose of 50 mgamisulpride (MDDAmisulpride), 26 MDD participants receivingplacebo (MDDPlacebo), and 28 healthy control subjects receivingplacebo (HCPlacebo) recruited through two independent studies. An importance-guided machine learning technique for model selection was used on whole-brain functional magnetic resonance imaging data probing reward anticipation and consumption to identify features linked to MDD (MDDPlacebo vs. HCPlacebo) and dopaminergic enhancement (MDDAmisulpride vs. MDDPlacebo). RESULTS: Highly predictive classification models emerged that distinguished MDDPlacebo from HCPlacebo (area under the curve = 0.87) and MDDPlacebo from MDDAmisulpride (area under the curve = 0.89). Although reward-related striatal activation and connectivity were among the most predictive features, the best truncated models based on whole-brain features were significantly better relative to models trained using striatal features only. CONCLUSIONS: Results indicate that in MDD, enhanced dopaminergic signaling restores abnormal activation and connectivity in a widespread network of regions. These findings provide new insights into the pathophysiology of MDD and pharmacological mechanism of antidepressants at the system level in addressing reward processing deficits among depressed individuals.
RCT Entities:
BACKGROUND: Theoretical models have emphasized systems-level abnormalities in major depressive disorder (MDD). For unbiased yet rigorous evaluations of pathophysiological mechanisms underlying MDD, it is critically important to develop data-driven approaches that harness whole-brain data to classify MDD and evaluate possible normalizing effects of targeted interventions. Here, using an experimental therapeutics approach coupled with machine learning, we investigated the effect of a pharmacological challenge aiming to enhance dopaminergic signaling on whole-brain response to reward-related stimuli in MDD. METHODS: Using a double-blind, placebo-controlled design, we analyzed functional magnetic resonance imaging data from 31 unmedicated MDDparticipants receiving a single dose of 50 mg amisulpride (MDDAmisulpride), 26 MDDparticipants receiving placebo (MDDPlacebo), and 28 healthy control subjects receiving placebo (HCPlacebo) recruited through two independent studies. An importance-guided machine learning technique for model selection was used on whole-brain functional magnetic resonance imaging data probing reward anticipation and consumption to identify features linked to MDD (MDDPlacebo vs. HCPlacebo) and dopaminergic enhancement (MDDAmisulpride vs. MDDPlacebo). RESULTS: Highly predictive classification models emerged that distinguished MDDPlacebo from HCPlacebo (area under the curve = 0.87) and MDDPlacebo from MDDAmisulpride (area under the curve = 0.89). Although reward-related striatal activation and connectivity were among the most predictive features, the best truncated models based on whole-brain features were significantly better relative to models trained using striatal features only. CONCLUSIONS: Results indicate that in MDD, enhanced dopaminergic signaling restores abnormal activation and connectivity in a widespread network of regions. These findings provide new insights into the pathophysiology of MDD and pharmacological mechanism of antidepressants at the system level in addressing reward processing deficits among depressed individuals.
Authors: Roee Admon; Roselinde H Kaiser; Daniel G Dillon; Miranda Beltzer; Franziska Goer; David P Olson; Gordana Vitaliano; Diego A Pizzagalli Journal: Am J Psychiatry Date: 2016-10-24 Impact factor: 18.112
Authors: Roee Admon; Laura M Holsen; Harlyn Aizley; Anne Remington; Susan Whitfield-Gabrieli; Jill M Goldstein; Diego A Pizzagalli Journal: Biol Psychiatry Date: 2014-10-02 Impact factor: 13.382
Authors: R Admon; L D Nickerson; D G Dillon; A J Holmes; R Bogdan; P Kumar; D D Dougherty; D V Iosifescu; D Mischoulon; M Fava; D A Pizzagalli Journal: Psychol Med Date: 2014-05-15 Impact factor: 7.723
Authors: Warren D Taylor; David H Zald; Jennifer C Felger; Seth Christman; Daniel O Claassen; Guillermo Horga; Jeffrey M Miller; Katherine Gifford; Baxter Rogers; Sarah M Szymkowicz; Bret R Rutherford Journal: Mol Psychiatry Date: 2021-08-17 Impact factor: 15.992