Neuroimaging-based biomarker discovery and validation.

Abstract

Though developing biological markers for chronic pain has been a major goal of the field for decades, such biomarkers have not yet made their way into clinical practice. However, given the potential uses of biomarkers in multiple aspects of prevention and treatment—such as pain and risk factor assessment, diagnosis, prognosis, treatment selection, drug discovery, and more—efforts to discover new pain biomarkers have been expanding [5; 6; 8; 30]. Recent advances in human neuroimaging, including functional and structural Magnetic Resonance Imaging (fMRI/sMRI) combined with machine learning techniques, are bringing us closer to the goal of developing objective, brain-based markers of the neural functions and neuropathology that underlie chronic pain [2; 7; 25; 33]. These brain measures are particularly promising as biomarkers for chronic pain. Though pain is reliably induced by peripheral nociceptive input, many forms of chronic pain may arise from neuropathology in the supra-spinal circuits that govern the construction of pain experience and long-term motivation [1; 14; 26; 32]. Particularly, structural neuroimaging measures could provide more stable markers of neuropathology of chronic pain, including stable features underlying pain risk and resilience [2; 3; 11; 19; 28; 29]. Gray-matter changes have also been associated with a number of conditions that are often co-morbid with chronic pain, including depression [4; 22; 24], stress [10; 12; 20], post-traumatic stress disorder [17; 21; 27], and early-life adversity [13; 18; 23; 31]. Therefore, structural measures may provide important clues about supra-spinal contributions to both pain and related risk factors (Fig. 1). Figure 1 Key common brain regions that show structural changes across different conditions related to chronic pain, including depression, stress, post-traumatic stress disorder (PTSD), and early-life adversity. In this issue, Labus et al. [16] developed a new neuroimaging biomarker for irritable bowel syndrome (IBS) using structural MRI data, based on a relatively large sample of 80 IBS patients and 80 healthy controls. They used sparse Partial Least Squares-Discriminant Analysis (sPLS-DA), a method that allowed them to both develop a classification model based on brain structure and identify the regions that make the most important contributions to the classification. They subsequently tested the predictive model on a “holdout” sample of 26 IBS patients and 26 healthy controls. The model discriminated patients from controls with 70% accuracy (compared to a chance accuracy of 50%), providing a moderate but reliable morphological brain signature for IBS. Rather than being the end of the story, this study serves as a starting point for biomarker discovery and validation. Like other brain ‘signatures’ [30], the signature they identified can become a ‘research product’ that can be tested on multiple samples from different laboratories, and validated or challenged in various ways. The more the marker for IBS status or IBS risk holds up to the scrutiny of being characterized across samples and populations, the more useful it will become. Importantly, there is a set of desirable characteristics that a useful neuroimaging biomarker should demonstrate throughout the biomarker development process. We briefly describe several such characteristics (summarized in Table 1), and then relate them to the findings of Labus et al. [16]. Table 1 Desirable characteristics of neuroimaging biomarkers