Abstract
Analyses of intrinsic network activity have been instrumental in revealing cortical processes that are altered in chronic pain patients. In a novel approach, we aimed to elucidate how intrinsic functional networks evolve in regard to the fluctuating intensity of the experience of chronic pain. In a longitudinal study with 156 fMRI sessions, 20 chronic back pain patients and 20 chronic migraine patients were asked to continuously rate the intensity of their endogenous pain. We investigated the relationship between the fluctuation of intrinsic network activity with the time course of subjective pain ratings. For chronic back pain, we found increased cortical network activity for the salience network and a local pontine network, as well as decreased network activity in the anterior and posterior default mode network for higher pain intensities. Higher pain intensities in chronic migraine were accompanied with lower activity in a prefrontal cortical network. By taking the perspective of the individual, we focused on the variability of the subjective perception of pain, which include phases of relatively low pain and phases of relatively high pain. The present design of the assessment of ongoing endogenous pain can be a powerful and promising tool to assess the signature of a patient’s endogenous pain encoding.
Highlights
In a step, we ran a group ICA - separately for CBP and CM with temporally concatenated data of all recordings using MELODIC
The present investigation has targeted how the ongoing perception of chronic pain is subserved in the human brain
By taking into account the perspective of the individual, we focussed on the processes that matter for each patient, which are phases of relatively low pain and more straining phases of relatively high pain
Summary
We ran a group ICA - separately for CBP and CM with temporally concatenated data of all recordings using MELODIC. The rationale to run separate ICAs was to find components that are tuned as much as possible to the characteristics of the patient groups. The number of components was restricted to 100. Dual regression was used to derive the corresponding network time series and maps for all components and for each of the sessions.
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