Comprehensive Summary
This study, presented by Meyer-Arndt et al., examines the extent to which neurobehavioral mechanisms of fear generalization contribute to anxiety in patients with Multiple Sclerosis (MS). MS and anxiety are often comorbid, with about 36% of the MS population experiencing anxiety. This is higher than the prevalence of anxiety in people without MS, which is about 30%. For this study, 77 participants were recruited: 18 with MS and anxiety, 36 with MS but without anxiety, and 23 with neither MS nor anxiety. The researchers used a functional MRI (fMRI) and a diffusion-weighted MRI. The fMRI assessed fear processing, while the diffusion-weighted MRI was used for graph-based structural connectome analyses. The data were analyzed with a machine learning model that was trained to associate fMRI fear-response patterns with fear ratings in healthy participants (HPs). The results showed that people with MS and anxiety displayed fear overgeneralization, meaning they perceived non-threatening stimuli as threatening. These findings suggest that general fear-processing mechanisms contribute to anxiety in MS.
Outcomes and Implications
Anxiety is prevalent in patients with MS; however, the neurobehavioral mechanisms are poorly understood. Conducting more research on how anxiety is expressed in patients with MS is important so that proper treatment can be provided. This research showed that the overgeneralization of fear in MS patients with anxiety is a generic mechanism that does not involve MS-specific factors. More machine learning models could be used in future studies to compare healthy individuals with people who have certain conditions. By utilizing machine learning models, we could expand our knowledge of neurobehavioral mechanisms across a wide range of psychiatric conditions.