Dataset Information

Neuroimaging Assessment of Pain.

ABSTRACT: Pain is an unpleasant sensory and emotional experience. Understanding the neural mechanisms of acute and chronic pain and the brain changes affecting pain factors is important for finding pain treatment methods. The emergence and progress of non-invasive neuroimaging technology can help us better understand pain at the neural level. Recent developments in identifying brain-based biomarkers of pain through advances in advanced imaging can provide some foundations for predicting and detecting pain. For example, a neurologic pain signature (involving brain regions that receive nociceptive afferents) and a stimulus intensity-independent pain signature (involving brain regions that do not show increased activity in proportion to noxious stimulus intensity) were developed based on multivariate modeling to identify processes related to the pain experience. However, an accurate and comprehensive review of common neuroimaging techniques for evaluating pain is lacking. This paper reviews the mechanism, clinical application, reliability, strengths, and limitations of common neuroimaging techniques for assessing pain to promote our further understanding of pain.

SUBMITTER: Luo J

PROVIDER: S-EPMC9606166 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Neuroimaging Assessment of Pain.

Luo Jing J Zhu Hui-Qi HQ Gou Bo B Wang Xue-Qiang XQ

Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics 20220728 5

Pain is an unpleasant sensory and emotional experience. Understanding the neural mechanisms of acute and chronic pain and the brain changes affecting pain factors is important for finding pain treatment methods. The emergence and progress of non-invasive neuroimaging technology can help us better understand pain at the neural level. Recent developments in identifying brain-based biomarkers of pain through advances in advanced imaging can provide some foundations for predicting and detecting pain ...[more]

PMID: 35902535

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Project description:BackgroundPrimary dysmenorrhea (PDM) is the most common problem in menstruating women. A number of functional magnetic resonance imaging (fMRI) study have revealed that the brain plays a crucial role in the pathophysiology of PDM. However, these results have been inconsistent, and there is a lack of a comprehensive fMRI study to clarify the onset and long-term effects of PDM. The aim of this study was thus to investigate the onset and long-term effects of PDM in a cohort of patients with PDM.MethodsThis study employed a cross-sectional design with prospective data collection, in which 25 patients with PDM and 20 healthy controls (HCs) were recruited. The patients with PDM underwent fMRI scans both during the PDM during the pain phase (PDM-P) and nonpain phase (PDM-NP). The long-term effects of PDM on the brain was assessed by comparing PDM-NP findings with those of HCs, and the central mechanism of PDM was assessed by comparing the PDM-P findings with those of PDM-NP. To identify changes in brain function, the amplitude of low-frequency fluctuations and the regional homogeneity (ReHo) were measured. To assess changes in brain structure, voxel-based morphometry (VBM) was applied. The periaqueductal gray (PAG) was set as a region of for conducting seed-based whole-brain functional connectivity (FC) analysis. Subsequently, Pearson correlation analyses were employed to evaluate the associations between the abnormal brain region and the clinical information of the patients.ResultsThere were neither functional nor structural differences between patients in the PDM-NP and HCs. Compared with those in PDM-NP, those in PDM-P showed increased ReHo in the left dorsolateral prefrontal cortex (DLPFC) but decreased FC between PAG and right superior parietal gyrus, bilateral inferior parietal gyrus, right calcarine gyrus, left superior occipital gyrus, left precentral gyrus, right DLPFC, and left crus I of the cerebellar hemisphere.ConclusionsThe results from this study suggest that the mechanism of central pain hypersensitivity of PDM may be related to the disorder of the FC between the PAG and descending pain modulation system, default mode network (DMN), and occipital lobe. These findings could help us better understand the pathophysiology of PDM from a neuroimaging perspective.

Project description:Although self-report pain ratings are the gold standard in clinical pain assessment, they are inherently subjective in nature and significantly influenced by multidimensional contextual variables. Although objective biomarkers for pain could substantially aid pain diagnosis and development of novel therapies, reliable markers for clinical pain have been elusive. In this study, individualized physical maneuvers were used to exacerbate clinical pain in patients with chronic low back pain (N = 53), thereby experimentally producing lower and higher pain states. Multivariate machine-learning models were then built from brain imaging (resting-state blood-oxygenation-level-dependent and arterial spin labeling functional imaging) and autonomic activity (heart rate variability) features to predict within-patient clinical pain intensity states (ie, lower vs higher pain) and were then applied to predict between-patient clinical pain ratings with independent training and testing data sets. Within-patient classification between lower and higher clinical pain intensity states showed best performance (accuracy = 92.45%, area under the curve = 0.97) when all 3 multimodal parameters were combined. Between-patient prediction of clinical pain intensity using independent training and testing data sets also demonstrated significant prediction across pain ratings using the combined model (Pearson's r = 0.63). Classification of increased pain was weighted by elevated cerebral blood flow in the thalamus, and prefrontal and posterior cingulate cortices, and increased primary somatosensory connectivity to frontoinsular cortex. Our machine-learning approach introduces a model with putative biomarkers for clinical pain and multiple clinical applications alongside self-report, from pain assessment in noncommunicative patients to identification of objective pain endophenotypes that can be used in future longitudinal research aimed at discovery of new approaches to combat chronic pain.

Dataset Information

Neuroimaging Assessment of Pain.

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Neuroimaging Assessment of Pain.

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