Abstract
AbstractAlthough the pathophysiology of pain has been investigated tremendously, there are still many open questions, especially with regard to specific pain entities and their pain-related symptoms. To increase the translational impact of (preclinical) animal pain neuroimaging studies, the use of disease-specific pain models, as well as relevant stimulus modalities, are critical. Yet, the challenges of identifying neuroimaging signatures at a pain entity- and modality-specific level are manifold. Therefore, we developed a comprehensive framework for brain network analysis in disease-specific pain models combining functional MRI with graph-theory and data classification by linear discriminant analysis. This enabled us to expand our knowledge of stimulus (mechanical vs. electrical) modality processing under incisional (INC) and pathogen-induced inflammatory (CFA) pain entities compared to acute pain conditions. In short, graph-theoretical analyses revealed distinct Network Signatures of Pain Hypersensitivity (NSPH) for INC and CFA, resulting in impaired discrimination of stimulus modalities in both pain models compared to control conditions (CTR). Such specific neuroimaging signatures are an important step toward identifying novel pain biomarkers for certain diseases and relevant outcomes to evaluate target engagement of novel therapeutic options, which ultimately can be translated to the clinic.
Publisher
Cold Spring Harbor Laboratory