Emotion processing in joint hypermobility: A potential link to the neural bases of anxiety and related somatic symptoms in collagen anomalies.

BACKGROUND: Joint hypermobility syndrome (JHS) has repeatedly been associated with anxiety and anxiety disorders, fibromyalgia, irritable bowel syndrome and temporomandibular joint disorder. However, the neural underpinnings of these associations still remain unclear. This study explored brain responses to facial visual stimuli with emotional cues using fMRI techniques in general population with different ranges of hypermobility. METHODS: Fifty-one non-clinical volunteers (33 women) completed state and trait anxiety questionnaire measures, were assessed with a clinical examination for hypermobility (Beighton system) and performed an emotional face processing paradigm during functional neuroimaging. RESULTS: Trait anxiety scores did significantly correlate with both state anxiety and hypermobility scores. BOLD signals of the hippocampus did positively correlate with hypermobility scores for the crying faces versus neutral faces contrast in ROI analyses. No results were found for any of the other studied ROIs. Additionally, hypermobility scores were also associated with other key affective processing areas (i.e. the middle and anterior cingulate gyrus, fusiform gyrus, parahippocampal region, orbitofrontal cortex and cerebellum) in the whole brain analysis. CONCLUSIONS: Hypermobility scores are associated with trait anxiety and higher brain responses to emotional faces in emotion processing brain areas (including hippocampus) described to be linked to anxiety and somatic symptoms. These findings increase our understanding of emotion processing in people bearing this heritable variant of collagen and the mechanisms through which vulnerability to anxiety and somatic symptoms arises in this population.

Quantification of rat brain SPECT with (123)I-ioflupane: evaluation of different reconstruction methods and image degradation compensations using Monte Carlo simulation.

SPECT studies with (123)I-ioflupane facilitate the diagnosis of Parkinson's disease (PD). The effect on quantification of image degradations has been extensively evaluated in human studies but their impact on studies of experimental PD models is still unclear. The aim of this work was to assess the effect of compensating for the degrading phenomena on the quantification of small animal SPECT studies using (123)I-ioflupane. This assessment enabled us to evaluate the feasibility of quantitatively detecting small pathological changes using different reconstruction methods and levels of compensation for the image degrading phenomena. Monte Carlo simulated studies of a rat phantom were reconstructed and quantified. Compensations for point spread function (PSF), scattering, attenuation and partial volume effect were progressively included in the quantification protocol. A linear relationship was found between calculated and simulated specific uptake ratio (SUR) in all cases. In order to significantly distinguish disease stages, noise-reduction during the reconstruction process was the most relevant factor, followed by PSF compensation. The smallest detectable SUR interval was determined by biological variability rather than by image degradations or coregistration errors. The quantification methods that gave the best results allowed us to distinguish PD stages with SUR values that are as close as 0.5 using groups of six rats to represent each stage.