ReviewF1000Research2017
Recent advances in predicting responses to antidepressant treatment.
Review in F1000Research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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.
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.
Who cites it
18 citing papers in PubMed.
- Cortical Connectivity Moderators of Antidepressant vs Placebo Treatment Response in Major Depressive Disorder: Secondary Analysis of a Randomized Clinical Trial.JAMA psychiatry · 2020Trial
- S-Adenosylmethionine (SAMe) monotherapy for depression: an 8-week double-blind, randomised, controlled trial.Psychopharmacology · 2020Trial
- Antidepressant and Related Neurobiological and Neurophysiological Effects of Add-On Transcranial Direct Current Stimulation in Major Depressive Disorder with Residual Symptoms: A Randomized, Double-Blind Clinical Trial Protocol.Methods and protocols · 2025Article
- Do cognitive and neurophysiological effects of acute memantine "challenge" predict its clinical benefits in Alzheimer's Disease?Psychiatry research · 2025Article
- Brain structural correlates of an impending initial major depressive episode.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2025Article
- An enhanced CNN-Bi-transformer based framework for detection of neurological illnesses through neurocardiac data fusion.Scientific reports · 2025Article
- Ginsenoside Rg1 exerts antidepressant effect by regulating hepatic kynurenine metabolism through promoting the interaction between HNF4α and PGC1α.Journal of ginseng research · 2025Article
- An exploration of the postural, location- and social contact- related sub-characteristics of inactive but awake behaviour as a depression-like indicator in mice.Applied animal behaviour science · 2024Article
- Real-world assessment of treatment inertia in the management of patients treated for major depressive disorder in the USA.Journal of comparative effectiveness research · 2024Observational
- Article
- Using Machine Learning to Predict Antidepressant Treatment Outcome From Electronic Health Records.Psychiatric research and clinical practice · 2023Article
- Waking inactivity as a welfare indicator in laboratory mice: investigating postures, facial expressions and depression-like states.Royal Society open science · 2022Article
- Antidepressant-like Effects of BDNF and NGF Individual Loop Dipeptide Mimetics Depend on the Signal Transmission Patterns Associated with Trk.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Interactions between maternal fluoxetine exposure, the maternal gut microbiome and fetal neurodevelopment in mice.Behavioural brain research · 2021Article
- Using Biomarkers to Predict Memantine Effects in Alzheimer's Disease: A Proposal and Proof-Of-Concept Demonstration.Journal of Alzheimer's disease : JAD · 2021Article
- Review
- Chronic Fluoxetine Treatment Induces Maturation-Compatible Changes in the Dendritic Arbor and in Synaptic Responses in the Auditory Cortex.Frontiers in pharmacology · 2019Article
- Understanding and Predicting Antidepressant Response: Using Animal Models to Move Toward Precision Psychiatry.Frontiers in psychiatry · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Major depressive disorder is one of the leading causes of disability in the world since depression is highly frequent and causes a strong burden. In order to reduce the duration of depressive episodes, clinicians would need to choose the most effective therapy for each individual right away. A prerequisite for this would be to have biomarkers at hand that would predict which individual would benefit from which kind of therapy (for example, pharmacotherapy or psychotherapy) or even from which kind of antidepressant class. In the past, neuroimaging, electroencephalogram, genetic, proteomic, and inflammation markers have been under investigation for their utility to predict targeted therapies. The present overview demonstrates recent advances in all of these different methodological areas and concludes that these approaches are promising but also that the aim to have such a marker available has not yet been reached. For example, the integration of markers from different systems needs to be achieved. With ongoing advances in the accuracy of sensing techniques and improvement of modelling approaches, this challenge might be achievable.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.