Habituation to pain: self-report, electroencephalography, and functional magnetic resonance imaging in healthy individuals. A scoping review and future recommendations.

ABSTRACT: Habituation to pain is a fundamental learning process and important adaption. Yet, a comprehensive review of the current state of the field is lacking. Through a systematic search, 63 studies were included. Results address habituation to pain in healthy individuals based on self-report, electroencephalography, or functional magnetic resonance imaging. Our findings indicate a large variety in methods, experimental settings, and contexts, making habituation a ubiquitous phenomenon. Habituation to pain based on self-report studies shows a large influence of expectations, as well as the presence of individual differences. Furthermore, widespread neural effects, with sometimes opposing effects in self-report measures, are noted. Electroencephalography studies showed habituation of the N2-P2 amplitude, whereas functional magnetic resonance imaging studies showed decreasing activity during painful repeated stimulation in several identified brain areas (cingulate cortex and somatosensory cortices). Important considerations for the use of terminology, methodology, statistics, and individual differences are discussed. This review will aid our understanding of habituation to pain in healthy individuals and may lead the way to improving methods and designs for personalized treatment approaches in chronic pain patients.

Habituation to Pain in Patients with Chronic Pain: Clinical Implications and Future Directions.

In this review, the latest insights into habituation to pain in chronic pain are summarized. Using a systematic search, results of studies on the evidence of habituation to (experimental) pain in migraine, chronic low back pain, fibromyalgia, and a variety of chronic pain indications are presented. In migraine, reduced habituation based on self-report and the EEG-based N1 and N2-P2 amplitude is reported, but the presence of contradictory results demands further replication in larger, well-designed studies. Habituation to pain in chronic low back pain seems not to differ from controls, with the exception of EEG measures. In fibromyalgia patients, there is some evidence for reduced habituation of the N2-P2 amplitude. Our analysis shows that the variability between outcomes of studies on habituation to pain is high. As the mechanisms underlying habituation to pain are still not fully understood and likely involve several pathways, it is now too early to conclude that habituation to pain is related to clinical outcomes and can be used as a diagnostic marker. The review ends with a discussion on future directions for research including the use of standard outcome measures to improve comparisons of habituation to pain in patients and controls, as well as a focus on individual differences.

The Amsterdam Open MRI Collection, a set of multimodal MRI datasets for individual difference analyses.

We present the Amsterdam Open MRI Collection (AOMIC): three datasets with multimodal (3 T) MRI data including structural (T1-weighted), diffusion-weighted, and (resting-state and task-based) functional BOLD MRI data, as well as detailed demographics and psychometric variables from a large set of healthy participants (N = 928, N = 226, and N = 216). Notably, task-based fMRI was collected during various robust paradigms (targeting naturalistic vision, emotion perception, working memory, face perception, cognitive conflict and control, and response inhibition) for which extensively annotated event-files are available. For each dataset and data modality, we provide the data in both raw and preprocessed form (both compliant with the Brain Imaging Data Structure), which were subjected to extensive (automated and manual) quality control. All data is publicly available from the OpenNeuro data sharing platform.

Neuroimaging-based biomarkers for pain: state of the field and current directions.

Chronic pain is an endemic problem involving both peripheral and brain pathophysiology. Although biomarkers have revolutionized many areas of medicine, biomarkers for pain have remained controversial and relatively underdeveloped. With the realization that biomarkers can reveal pain-causing mechanisms of disease in brain circuits and in the periphery, this situation is poised to change. In particular, brain pathophysiology may be diagnosable with human brain imaging, particularly when imaging is combined with machine learning techniques designed to identify predictive measures embedded in complex data sets. In this review, we explicate the need for brain-based biomarkers for pain, some of their potential uses, and some of the most popular machine learning approaches that have been brought to bear. Then, we evaluate the current state of pain biomarkers developed with several commonly used methods, including structural magnetic resonance imaging, functional magnetic resonance imaging and electroencephalography. The field is in the early stages of biomarker development, but these complementary methodologies have already produced some encouraging predictive models that must be tested more extensively across laboratories and clinical populations.