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

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.

Investigation of the neural basis of expectation-based analgesia in the human brainstem and spinal cord by means of functional magnetic resonance imaging

“…Spatial normalization was guided by a combined anatomical reference image (template) spanning across brain (MNI152 template) and SC regions (PAM50 template), as described by De Leener et al (2018 ). Corresponding anatomical region-of-interest maps have also been defined from multiple sources, as described previously ( Lang and Bartram, 1982 , Talairach and Tournoux, 1988 , Millan, 2002 , Keren et al, 2009 , Naidich et al, 2009 , Whitfield-Gabrieli and Nieto-Castanon, 2012 , Leijnse and D'Herde, 2016 , De Leener et al, 2017 , Pauli et al, 2018 , Chiang et al, 2019 , Liebe et al, 2020 , Stroman et al, 2020 ) ( https://identifiers.org/neurovault.collection:3145 , www.med.harvard.edu/AANLIB/ ).…”

An Investigation of Descending Pain Modulation in Women With Provoked Vestibulodynia: Alterations of Brain Connectivity

“…The SEM and ANOVA analyses reported in the present study examine brain connectivity using a data-driven approach to provide greater information to the BOLD responses observed in brain regions relevant to pain processing ( 31 , 32 ). For this reason, the study results are summarized by connectivity strengths between regions and not by relative BOLD responses within each region of interest.…”

Music to My Senses: Functional Magnetic Resonance Imaging Evidence of Music Analgesia Across Connectivity Networks Spanning the Brain and Brainstem

“…Structural equation modelling (SEM) is a data-driven family of statistical techniques which are used to identify patterns of correlation/covariance among a set of BOLD responses within and across regions of interest (ROIs) ( 58 , 67 , 68 ). Our SEM methods are focused on characterising temporal relationships by explaining as much variance as possible through use of a pre-defined anatomical model of connections across the brain and brainstem.…”