A comparison of the effectiveness of functional MRI analysis methods for pain research: The new normal

Autor: Gabriela Ioachim, Patrick W. Stroman, Jocelyn M Powers, Kaitlin McNeil, Howard J M Warren
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Computer science
Brain mapping
Nervous System
Pain processing
030218 nuclear medicine & medical imaging
Diagnostic Radiology
0302 clinical medicine
Thalamus
Functional Magnetic Resonance Imaging
Medicine and Health Sciences
Image Processing
Computer-Assisted
Analysis method
Pain Measurement
Brain Mapping
Multidisciplinary
medicine.diagnostic_test
Radiology and Imaging
Brain
Middle Aged
Magnetic Resonance Imaging
medicine.anatomical_structure
Spinal Cord
Hypothalamus
Medicine
Female
Brainstem
Anatomy
Research Article
Adult
Imaging Techniques
Science
Central nervous system
Pain
Image processing
Neuroimaging
Research and Analysis Methods
Structural equation modeling
03 medical and health sciences
Young Adult
Signs and Symptoms
Diagnostic Medicine
medicine
Humans
Biology and Life Sciences
Magnetic resonance imaging
Spinal cord
Neuroanatomy
Clinical Medicine
Functional magnetic resonance imaging
Neuroscience
030217 neurology & neurosurgery
Brain Stem
Zdroj: PLoS ONE
PLoS ONE, Vol 15, Iss 12, p e0243723 (2020)
ISSN: 1932-6203
Popis: 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.
Databáze: OpenAIRE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje