Neural processes linking joint hypermobility and anxiety: Key roles for the amygdala and insular cortex
Bibliographic record
Abstract
Abstract Background and Aims Anxiety symptoms are elevated among people with joint hypermobility. The underlying neural mechanisms are attributed theoretically to effects of variant connective tissue on the precision of interoceptive representations contributing to emotions. Methods We used functional magnetic resonance neuroimaging (fMRI) to quantify regional brain responses to emotional stimuli (facial expressions) in patients with generalised anxiety disorder (N=30) and a non-anxious comparison group (N=33). All participants were assessed for joint laxity and were classified (using Brighton Criteria) for the presence and absence of Hypermobility Syndrome (HMS: now considered Hypermobility Spectrum Disorder). Results HMS participants showed attenuated neural reactivity to emotional faces in specific frontal (inferior frontal gyrus, pre-supplementary motor area), midline (anterior mid and posterior cingulate cortices), and parietal (precuneus and supramarginal gyrus) regions. Notably, interaction between HMS and anxiety was expressed in reactivity of left amygdala (a region implicated in threat processing) and mid insula (primary interoceptive cortex) where activity was amplified in HMS patients with generalised anxiety disorder. Severity of hypermobility in anxious, compared to non-anxious, individuals correlated with activity within anterior insula (implicated as the neural substrate linking anxious feelings to physiological state). Amygdala-precuneus functional connectivity was stronger in HMS, compared to non-HMS, participants. Conclusions The predisposition to anxiety in people with variant connective tissue reflects dynamic interactions between neural centres processing threat (amygdala) and representing bodily state (insular and parietal cortices). Correspondingly, interventions to regulate of amygdala reactivity while enhancing interoceptive precision may have therapeutic benefit for symptomatic hypermobile individuals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".