Trial reportArthritis & rheumatology (Hoboken, N.J.)2021
Prediction of Differential Pharmacologic Response in Chronic Pain Using Functional Neuroimaging Biomarkers and a Support Vector Machine Algorithm: An Exploratory Study.
Trial report in Arthritis & rheumatology (Hoboken, N.J.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled 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.
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
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- Short-term efficacy and safety of sublingual cyclobenzaprine for fibromyalgia: A systematic review and meta-analysis.Clinical rheumatology · 2026Pooled it
- Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025Pooled it
- Utilizing Artificial Intelligence for the Diagnosis, Assessment, and Management of Chronic Pain.Journal of biomedical physics & engineering · 2025Review
- Innovations in acute and chronic pain biomarkers: enhancing diagnosis and personalized therapy.Regional anesthesia and pain medicine · 2025Review
- Applications of Artificial Intelligence in Pain Medicine.Current pain and headache reports · 2024Review
- Cross-sectional Study on Medical Attitude Towards Artificial Intelligence Use in Fibromyalgia: Insights From the Annual Thinking Lab on Fibromyalgia Syndrome (ATLAS 2024).Translational medicine @ UniSa · 2024Article
- Does practice make perfect? Functional connectivity of the salience network and somatosensory network predicts response to mind-body treatments for fibromyalgia.Frontiers in pain research (Lausanne, Switzerland) · 2024Article
- Decreased Functional Connectivity of the Core Pain Matrix in Herpes Zoster and Postherpetic Neuralgia Patients.Brain sciences · 2023Article
- Altered habenular connectivity in chronic low back pain: An fMRI and machine learning study.Human brain mapping · 2023Article
- Effects of Alterations in Resting-State Neural Networks on the Severity of Neuropathic Pain after Spinal Cord Injury.Bioengineering (Basel, Switzerland) · 2023Article
- Resting state functional connectivity in SLE patients and association with cognitive impairment and blood-brain barrier permeability.Rheumatology (Oxford, England) · 2023Article
- Efficacy prediction of acupuncture treatment for migraine without aura based on multimodal MRI: A study protocol.Frontiers in neurology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveThere is increasing demand for prediction of chronic pain treatment outcomes using machine-learning models, in order to improve suboptimal pain management. In this exploratory study, we used baseline brain functional connectivity patterns from chronic pain patients with fibromyalgia (FM) to predict whether a patient would respond differentially to either milnacipran or pregabalin, 2 drugs approved by the US Food and Drug Administration for the treatment of FM.
methodsFM patients participated in 2 separate double-blind, placebo-controlled crossover studies, one evaluating milnacipran (n = 15) and one evaluating pregabalin (n = 13). Functional magnetic resonance imaging during rest was performed before treatment to measure intrinsic functional brain connectivity in several brain regions involved in pain processing. A support vector machine algorithm was used to classify FM patients as responders, defined as those with a ≥20% improvement in clinical pain, to either milnacipran or pregabalin.
resultsConnectivity patterns involving the posterior cingulate cortex (PCC) and dorsolateral prefrontal cortex (DLPFC) individually classified pregabalin responders versus milnacipran responders with 77% accuracy. Performance of this classification improved when both PCC and DLPFC connectivity patterns were combined, resulting in a 92% classification accuracy. These results were not related to confounding factors, including head motion, scanner sequence, or hardware status. Connectivity patterns failed to differentiate drug nonresponders across the 2 studies.
conclusionOur findings indicate that brain functional connectivity patterns used in a machine-learning framework differentially predict clinical response to pregabalin and milnacipran in patients with chronic pain. These findings highlight the promise of machine learning in pain prognosis and treatment prediction.
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.