Evidence map›Paper›PMID 38168378›Full record

ArticlemedRxiv : the preprint server for health sciences2023

A common symptom geometry of mood improvement under sertraline and placebo associated with distinct neural patterns.

Lucie Berkovitch, Kangjoo Lee, Jie Lisa Ji, Markus Helmer, Masih Rahmati, Jure Demšar, Aleksij Kraljič, Andraž Matkovič, Zailyn Tamayo, John D Murray and 5 more

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors at 6 institutions in 5 countries.

Lucie BerkovitchDepartment of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA.
Kangjoo LeeDepartment of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA.
Jie Lisa JiManifest Technologies, Inc. New Haven, CT, USA.
Markus HelmerManifest Technologies, Inc. New Haven, CT, USA.
Masih RahmatiManifest Technologies, Inc. New Haven, CT, USA.
Jure DemšarDepartment of Psychology, University of Ljubljana, Ljubljana, Slovenia.
Aleksij KraljičDepartment of Psychology, University of Ljubljana, Ljubljana, Slovenia.
Andraž MatkovičDepartment of Psychology, University of Ljubljana, Ljubljana, Slovenia.
Zailyn TamayoDepartment of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA.
John D MurrayDepartment of Psychological and Brain Science, Dartmouth College, Hanover, NH, USA.
Grega RepovšDepartment of Psychology, University of Ljubljana, Ljubljana, Slovenia.
John H KrystalDepartment of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA.
William J MartinJohnson & Johnson Innovative Medicine, San Diego, CA, USA.
Clara FonteneauDepartment of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA.
Alan AnticevicDepartment of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA.
Yale University · USUniversity of Ljubljana · SIArtificial Cell Technologies (United States) · USDartmouth College · USJohnson & Johnson (United States) · USUniversité Paris Cité · FR

Funding

ProNET: Psychosis-Risk Outcomes NetworkU01MH124639 · NIMH · YALE UNIVERSITY · PI CARRIE E BEARDEN, JOHN M KANE · 2020 to 2026
$81.2M
NIMH NIH HHS U01 MH124639
6 · The paper itself

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.

Identifiers

PMID38168378
PMCPMC10760263
OpenAlexW4389871188

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.