Physical activity and the mediating effect of fear, depression, anxiety, and catastrophizing on pain related disability in people with chronic low back pain

The ability to infer emotional states through self-reports is often limited. Their measurement becomes even more challenging when considering emotional phenomena such as pain-related fear where different associated fear constructs have been proposed. Demonstrating significant predictive value regarding disability in patients with persistent musculoskeletal pain, pain-related fear is often assessed by questionnaires focusing on either fear of movement/(re)injury/kinesiophobia, fear avoidance beliefs or pain anxiety. Furthermore, the relationship of general anxiety measures such as trait anxiety to pain-related fear remains ambiguous. Advances in neuroimaging might help to support potential commonalities or differences across psychological constructs using appropriate machine learning techniques with the ability to reveal predictive relationships between neural information and questionnaire scores.Here, we applied a pattern regression approach using functional magnetic resonance imaging data of 20 non-specific chronic low back pain (LBP) patients. More specifically, we applied a novel approach using Multiple Kernel Learning that allows investigating the contribution of experimental conditions and regional neural information to a prediction model. We hypothesized to find evidence for or against a common fear construct by computing and comparing the prediction model of each questionnaire according to the contribution of fear-related neural information and conditions. The current results underpin the diversity of fear constructs among self-report measures of pain-related fear by demonstrating evidence of non-overlapping and differentially contributing neural sources within fear processing regions. Thus, the current approach might ultimately help to further understand and dissect the fear constructs captured by the various pain-related fear questionnaires. Significance. CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/251751 doi: bioRxiv preprint first posted online Jan. 25, 2018; Pain-related fear, often assessed through self-reports such as questionnaires, has shown prognostic value and clinical utility for a variety of musculoskeletal pain disorders. However, it remains difficult to determine a common underlying fear construct of pain-related fear due to several proposed constructs among questionnaires. The current study describes a novel neuroscientific approach using machine learning of neural patterns within the fear network of chronic LBP patients that might have the potential to identify neural commonalities or differences among the various fear constructs. Ultimately, this approach might afford a deeper understanding of the suggested fear constructs of pain-related fear and might be also applied to other domains where ambiguity exists between emotional phenomena and underlying psychological constructs.

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