Advances and challenges in neuroimaging-based pain biomarkers

Abstract

Identifying neural biomarkers of pain has long been a central theme in pain neuroscience. Here, we review the state-of-the-art candidates for neural biomarkers of acute and chronic pain. We classify these potential neural biomarkers into five categories based on the nature of their target variables, including neural biomarkers of (1) within-individual perception, (2) between-individual sensitivity, and (3) discriminability for acute pain, as well as (4) assessment and (5) prospective neural biomarkers for chronic pain. For each category, we provide a synthesized review of candidate biomarkers developed using neuroimaging techniques including functional magnetic resonance imaging (fMRI), structural magnetic resonance imaging (sMRI), and electroencephalography (EEG). We also discuss the conceptual and practical challenges in developing neural biomarkers of pain. Addressing these challenges, optimal biomarkers of pain can be developed to deepen our understanding of how the brain represents pain and ultimately help alleviate patients' suffering and improve their well-being.

Keywords: EEG; MRI; biomarker; electroencephalography; machine learning; magnetic resonance imaging; pain.

Graphical abstract

Figure 1

Typology of biomarkers of pain Different types of neuroimaging-based biomarkers can be discerned for acute pain and chronic pain. For acute pain induced by transient noxious stimuli (e.g., radiant thermal laser, contact heat, and electrical stimuli) and tonic noxious stimuli (e.g., cold pressor and capsaicin), biomarkers are developed to distinguish pain from non-pain states and track pain severity (i.e., within-individual pain perception), or predict individual differences in pain sensitivity and discriminability. For chronic pain like chronic back pain and migraine, assessment biomarkers are identified to distinguish patients with chronic pain from healthy individuals or to identify patients with different subtypes of chronic pain (diagnostic), assessing the status of chronic pain like the severity of spontaneous pain or pain attack/free periods (monitoring); prospective biomarkers are developed to predict pain chronification (susceptibility/risk), recovery (prognostic), and responses to treatments (predictive). Using noninvasive neuroimaging techniques like magnetic resonance imaging (MRI) and electroencephalography (EEG) to sample brain activities and analysis methods like machine learning algorithms to build models, numerous potential neural biomarkers of pain have been developed. Any pain neural biomarker can be assessed in terms of model sensitivity, model specificity, and model generalizability.

Figure 2

Neuroimaging features for biomarkers of pain Magnetic resonance imaging (MRI) and electroencephalography (EEG) are the primary techniques used to develop neuroimaging-based biomarkers of pain. Different functional MRI (fMRI), structural MRI (sMRI), and EEG features can be extracted to identify biomarkers of pain.

Figure 3

Challenges of and viable solutions to developing pain biomarkers Developing biomarkers of pain faces conceptual and practical challenges. Conceptual challenges include the multidimensional nature of pain, huge heterogeneity of chronic pain, and few causal biomarkers. To address these challenges, future studies can develop different biomarkers for different dimensions of pain and individual chronic pain condition and adopt cross-species approaches to test the causal relationship between potential biomarkers and pain. Practical challenges include that most biomarkers still have few real clinical applications and deficient performance for clinical use, and the other is their low economic cost-effectiveness, which poses constraints on their widespread implementation in clinical settings. Future studies can conduct more direct investigations on clinically useful biomarkers, develop composite biomarkers, and train more powerful machine learning models.