ArticleFrontiers in neuroscience2019
Using Deep Learning and Resting-State fMRI to Classify Chronic Pain Conditions.
Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 52 citations in OpenAlex.
- Mathematical and Computational Models for Pain: A Systematic Review.Pain medicine (Malden, Mass.) · 2021Pooled it
- Brain dysconnectivity patterns associated with chronic back pain development.Brain informatics · 2026Article
- A predictive corticospinal model for pain perception.Cell reports. Medicine · 2026Article
- Tongue Characteristics and Musculoskeletal Pain in Patients with Type 2 Diabetes Mellitus: A Principal Component and Latent Profile Analysis.Journal of pain research · 2026Article
- Precision connectivity in osteoarthritis pain with permutation and network analysis: a key step toward clinical application.BMC medical imaging · 2025Article
- Precision Connectivity in Arthritis Pain with Permutation and Network Analysis: A Key Step Toward Clinical Application.Research square · 2025Article
- Multi-network dynamical structure of the human brain in the setting of chronic pain: a coordinate-based meta-analysis.Brain communications · 2025Article
- Extracting interpretable signatures of whole-brain dynamics through systematic comparison.PLoS computational biology · 2024Article
- Minimum and Maximum Pattern-Based Self-Organized Feature Engineering: Fibromyalgia Detection Using Electrocardiogram Signals.Diagnostics (Basel, Switzerland) · 2024Article
- Bibliometric analysis of functional magnetic resonance imaging studies on chronic pain over the past 20 years.Acta neurochirurgica · 2024Review
- Extracting interpretable signatures of whole-brain dynamics through systematic comparison.bioRxiv : the preprint server for biology · 2024Article
- Diagnosis of Chronic Musculoskeletal Pain by Using Functional Near-Infrared Spectroscopy and Machine Learning.Bioengineering (Basel, Switzerland) · 2023Article
- The hemodynamic response function as a type 2 diabetes biomarker: a data-driven approach.Frontiers in neuroinformatics · 2023Article
- Deep Learning Classification of Treatment Response in Diabetic Painful Neuropathy: A Combined Machine Learning and Magnetic Resonance Neuroimaging Methodological Study.Neuroinformatics · 2023Article
- Imaging the neural substrate of trigeminal neuralgia pain using deep learning.Frontiers in human neuroscience · 2023Article
- Detection of brain regions responsible for chronic pain in osteoarthritis: an fMRI-based neuroimaging study using deep learning.Frontiers in neurology · 2023Article
- Identification of Resting-State Network Functional Connectivity and Brain Structural Signatures in Fibromyalgia Using a Machine Learning Approach.Biomedicines · 2022Article
- Multi-Source Domain Adaptation Techniques for Mitigating Batch Effects: A Comparative Study.Frontiers in neuroinformatics · 2022Article
- Predicting escitalopram treatment response from pre-treatment and early response resting state fMRI in a multi-site sample: A CAN-BIND-1 report.NeuroImage. Clinical · 2022Article
- Chronic Pain Treatment and Digital Health Era-An Opinion.Frontiers in public health · 2021Article
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chronic pain is known as a complex disease due to its comorbidities with other symptoms and the lack of effective treatments. As a consequence, chronic pain seems to be under-diagnosed in more than 75% of patients. At the same time, the advance in brain imaging, the popularization of machine learning techniques and the development of new diagnostic tools based on these technologies have shown that these tools could be an option in supporting decision-making of healthcare professionals. In this study, we computed functional brain connectivity using resting-state fMRI data from one hundred and fifty participants to assess the performance of different machine learning models, including deep learning (DL) neural networks in classifying chronic pain patients and pain-free controls. The best result was obtained by training a convolutional neural network fed with data preprocessed using the MSDL probabilistic atlas and using the dynamic time warping (DTW) as connectivity measure. DL models had a better performance compared to other less costly models such as support vector machine (SVM) and RFC, with balanced accuracy ranged from 69 to 86%, while the area under the curve (ROC) ranged from 0.84 to 0.93. Also, DTW overperformed correlation as connectivity measure. These findings support the notion that resting-state fMRI data could be used as a potential biomarker of chronic pain conditions.
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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.