ArticlePloS one2020
A comparison of the effectiveness of functional MRI analysis methods for pain research: The new normal.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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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.
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Who cites it
13 citing papers in PubMed.
- Exploring Neural Signaling Patterns and Their Physiological Origins in Fibromyalgia by Means of Functional MRI Guided by a Review of the Literature.Brain sciences · 2025Article
- Investigating Descending Pain Regulation in Fibromyalgia and the Link to Altered Autonomic Regulation by Means of Functional MRI Data.Brain sciences · 2024Article
- Pain is what you think: functional magnetic resonance imaging evidence toward a cognitive and affective approach for pain research.Frontiers in pain research (Lausanne, Switzerland) · 2024Article
- Structural and Physiological Modeling (SAPM) for the Analysis of Functional MRI Data Applied to a Study of Human Nociceptive Processing.Brain sciences · 2023Article
- A Comparison of Functional Connectivity in the Human Brainstem and Spinal Cord Associated with Noxious and Innocuous Thermal Stimulation Identified by Means of Functional MRI.Brain sciences · 2023Article
- Proof-of-concept of a novel structural equation modelling approach for the analysis of functional magnetic resonance imaging data applied to investigate individual differences in human pain responses.Human brain mapping · 2023Article
- Evaluating metallic artefact of biodegradable magnesium-based implants in magnetic resonance imaging.Bioactive materials · 2022Article
- Evidence for Integration of Cognitive, Affective, and Autonomic Influences During the Experience of Acute Pain in Healthy Human Volunteers.Frontiers in neuroscience · 2022Article
- Altered Pain in the Brainstem and Spinal Cord of Fibromyalgia Patients During the Anticipation and Experience of Experimental Pain.Frontiers in neurology · 2022Article
- Music to My Senses: Functional Magnetic Resonance Imaging Evidence of Music Analgesia Across Connectivity Networks Spanning the Brain and Brainstem.Frontiers in pain research (Lausanne, Switzerland) · 2022Article
- An Investigation of Descending Pain Modulation in Women With Provoked Vestibulodynia (PVD): Alterations of Spinal Cord and Brainstem Connectivity.Frontiers in pain research (Lausanne, Switzerland) · 2021Article
- An Investigation of Descending Pain Modulation in Women With Provoked Vestibulodynia: Alterations of Brain Connectivity.Frontiers in pain research (Lausanne, Switzerland) · 2021Article
- Investigation of the neural basis of expectation-based analgesia in the human brainstem and spinal cord by means of functional magnetic resonance imaging.Neurobiology of pain (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Studies of the neural basis of human pain processing present many challenges because of the subjective and variable nature of pain, and the inaccessibility of the central nervous system. Neuroimaging methods, such as functional magnetic resonance imaging (fMRI), have provided the ability to investigate these neural processes, and yet commonly used analysis methods may not be optimally adapted for studies of pain. Here we present a comparison of model-driven and data-driven analysis methods, specifically for the study of human pain processing. Methods are tested using data from healthy control participants in two previous studies, with separate data sets spanning the brain, and the brainstem and spinal cord. Data are analyzed by fitting time-series responses to predicted BOLD responses in order to identify significantly responding regions (model-driven), as well as with connectivity analyses (data-driven) based on temporal correlations between responses in spatially separated regions, and with connectivity analyses based on structural equation modeling, allowing for multiple source regions to explain the signal variations in each target region. The results are assessed in terms of the amount of signal variance that can be explained in each region, and in terms of the regions and connections that are identified as having BOLD responses of interest. The characteristics of BOLD responses in identified regions are also investigated. The results demonstrate that data-driven approaches are more effective than model-driven approaches for fMRI studies of pain.
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