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Brain Cogn. 2019 Apr;131:4-9. doi: 10.1016/j.bandc.2018.06.005. Epub 2018 Jun 30.

Review on biomarkers in the resting-state networks of chronic pain patients.

Author information

1
Functional Imaging Unit, Center for Diagnostic Radiology, University of Greifswald, Greifswald, Germany; Athinoula A. Martinos Center for Biomedical Imaging, Department for Radiology, Massachusetts General Hospital, Charlestown, USA. Electronic address: jpfannmoeller@mgh.harvard.edu.
2
Functional Imaging Unit, Center for Diagnostic Radiology, University of Greifswald, Greifswald, Germany.

Abstract

Biomarkers indicating characteristic alterations in the brains of pain patients would in comparison to behavioral examinations allow for earlier diagnoses of pain disease development, a more immediate monitoring of pain disease progression, and for the development of interventions to reverse or compensate for the alterations. To reveal causal relations between an observed alteration and the pain disease longitudinal examinations are essential. Resting-state fMRI examinations can readily be included in large longitudinal cohorts allowing to achieve sufficiently large patient samples even for rare diseases. Our literature review on longitudinal resting-state fMRI examinations of pain patients indicates that pain chronicity is predicted by alterations to the brain's reward system and default mode network. A brain wide reorganization of the resting-state networks is associated with the emergence of the chronic pain state. The functional connectivity of the left frontoparietal network predicts the evolution of pain intensity in the chronic state. Further investigations are necessary concerning the generalization of the biomarkers across the phases in pain development especially for the healthy state, across different pain etiologies, and their specificity to chronic pain. The currently acquired representative longitudinal cohorts will allow for clarification of those issues within the next decades.

KEYWORDS:

Chronicity risk prediction; Large cohorts; Longitudinal examinations; Pain disease evolution; Pain management; Progression monitoring and prediction

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