About this Research Topic
In the past few decades, neuroimaging has provided remarkable insights into the central contributions to pain mechanisms, both in healthy and patient groups. Historically, pain neuroimaging studies have been unimodal i.e. using one modality (fMRI, PET, DTI, etc.) to explain the neural underpinnings of pain, which, therefore, provides limited understanding of pain. However, pain is a complex phenomenon with multiple dimensions, and any single modality is unlikely to capture comprehensive information about the multitude of events, circuits and networks involved in pain and its processing. Hence, synergies across multiple modalities are needed i.e. combining data across different modalities of neuroimaging modalities that will allow creation of biomarkers with higher explanatory and predictive power. Eventually these combinatorial biomarkers can be used for multiple avenues of work, from analgesic development to discovery of patient endophenotypes and biomarkers.
The goal of this Research Topic is to bring together a collection of papers that use multimodal neuroimaging modalities (i.e. neural data from more than one modality) in order to explain unique and differential aspects of central nervous system contribution to pain. In so doing, these insights will identify regions, circuits, and networks that provide greater explanatory and predictive power across modalities for a certain aspect of pain, explain differential dimensions of pain, or can be used to subgroup/subtype various forms of pain characteristics (or patient phenotyping for clinically-oriented questions).
Across both pain-free controls or/and patient populations (chronic, subacute, etc.), we welcome the submission of manuscripts that combine multiple data modalities (at least 2), including but not limited to:
• Neuroimaging only (e.g., fMRI, ASL, anatomical, DTI, MRS, PET, MEG, EEG, fNIRS, etc.)
• Neuroimaging assessing multiple tasks or states (e.g., resting state fMRI combined with task fMRI)
• Neuroimaging along with other biologically relevant markers (e.g., electrocardiogram, electromyogram, autonomic activities, imaging of visceral organs such as the gut or bladder, blood inflammatory markers, facial expressions, etc.)
• Neuroimaging along with patient-reported outcomes or questionnaire data
We encourage papers that employ both univariate (e.g., GLM) and multivariate (e.g., machine learning) analyses. For multivariate analyses, we welcome supervised (e.g., prediction, classification), unsupervised (e.g., clustering), or/and any novel data-fusion techniques. We additionally welcome any randomized or/and longitudinal therapeutic/clinical trials that have a neuroimaging component.
Keywords: multimodal, neuroimaging, task, resting state, biological measure, self-report, neuroinflammation, machine learning, clinical trial
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.