1
|
Berkovitch L, Lee K, Ji JL, Helmer M, Rahmati M, Demšar J, Kraljič A, Matkovič A, Tamayo Z, Murray JD, Repovš G, Krystal JH, Martin WJ, Fonteneau C, Anticevic A. A common symptom geometry of mood improvement under sertraline and placebo associated with distinct neural patterns. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.15.23300019. [PMID: 38168378 PMCID: PMC10760263 DOI: 10.1101/2023.12.15.23300019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Importance Understanding the mechanisms of major depressive disorder (MDD) improvement is a key challenge to determine effective personalized treatments. Objective To perform a secondary analysis quantifying neural-to-symptom relationships in MDD as a function of antidepressant treatment. Design Double blind randomized controlled trial. Setting Multicenter. Participants Patients with early onset recurrent depression from the public Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study. Interventions Either sertraline or placebo during 8 weeks (stage 1), and according to response a second line of treatment for 8 additional weeks (stage 2). Main Outcomes and Measures To identify a data-driven pattern of symptom variations during these two stages, we performed a Principal Component Analysis (PCA) on the variations of individual items of four clinical scales measuring depression, anxiety, suicidal ideas and manic-like symptoms, resulting in a univariate measure of clinical improvement. We then investigated how initial clinical and neural factors predicted this measure during stage 1. To do so, we extracted resting-state global brain connectivity (GBC) at baseline at the individual level using a whole-brain functional network parcellation. In turn, we computed a linear model for each brain parcel with individual data-driven clinical improvement scores during stage 1 for each group. Results 192 patients (127 women), age 37.7 years old (standard deviation: 13.5), were included. The first PC (PC1) capturing 20% of clinical variation was similar across treatment groups at stage 1 and stage 2, suggesting a reproducible pattern of symptom improvement. PC1 patients' scores significantly differed according to treatment during stage 1, whereas no difference of response was evidenced between groups with the Clinical Global Impressions (CGI). Baseline GBC correlated to stage 1 PC1 scores in the sertraline, but not in the placebo group. Conclusions and Relevance Using data-driven reduction of symptoms scales, we identified a common profile of symptom improvement across placebo and sertraline. However, the neural patterns of baseline that mapped onto symptom improvement distinguished between treatment and placebo. Our results underscore that mapping from data-driven symptom improvement onto neural circuits is vital to detect treatment-responsive neural profiles that may aid in optimal patient selection for future trials.
Collapse
Affiliation(s)
- Lucie Berkovitch
- Department of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA
- Division of Neurocognition, Neurocomputation, Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, USA
- Université Paris Cité, Paris, France
- Department of Psychiatry, GHU Paris Psychiatrie et Neurosciences, Service Hospitalo-Universitaire, Paris, France
- Unicog, Saclay CEA Centre, Neurospin, Gif-Sur-Yvette Cedex, France
| | - Kangjoo Lee
- Department of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA
- Division of Neurocognition, Neurocomputation, Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, USA
| | - Jie Lisa Ji
- Manifest Technologies, Inc. New Haven, CT, USA
| | | | | | - Jure Demšar
- Department of Psychology, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Aleksij Kraljič
- Department of Psychology, University of Ljubljana, Ljubljana, Slovenia
| | - Andraž Matkovič
- Department of Psychology, University of Ljubljana, Ljubljana, Slovenia
| | - Zailyn Tamayo
- Department of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA
- Division of Neurocognition, Neurocomputation, Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, USA
| | - John D Murray
- Department of Psychological and Brain Science, Dartmouth College, Hanover, NH, USA
| | - Grega Repovš
- Department of Psychology, University of Ljubljana, Ljubljana, Slovenia
| | - John H Krystal
- Department of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA
- Division of Neurocognition, Neurocomputation, Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, USA
| | | | - Clara Fonteneau
- Department of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA
- Division of Neurocognition, Neurocomputation, Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, USA
| | - Alan Anticevic
- Department of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA
- Division of Neurocognition, Neurocomputation, Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, USA
- Department of Psychology, Yale University School of Medicine, New Haven, CT, USA
| |
Collapse
|