Neural Correlates of Pain and its Associated Clinical and Psychological Correlates in the Dynamic Pain Connectome in Patients with Ankylosing Spondylitis
Bibliographic record
Abstract
Chronic pain is a major public health issue. Despite its prevalence, the brain and behavioural mechanisms underlying chronic pain are not well understood, hampering effective treatment. Both individual factors (such as how we adapt in response to stressors), and brain function contribute to variability in pain perception. Abnormalities in brain and behaviour may indicate (mal)adaptive response or functioning in chronic pain patients. The overall goal of this thesis is to elucidate brain and behavioral mechanisms involved in pain. I first explore brain communication within and between networks of the dynamic pain connectome (default mode network; DMN, salience network; SN, sensorimotor network; SMN) that reflect chronic pain symptoms. Second, I examine how resilience – an individual’s ability to adapt well in response to stress – contributes to individual differences in pain perception. Finally, I investigate the intersection of brain communication and resilience and how this relationship is altered in chronic pain. My participants included healthy controls and patients with ankylosing spondylitis (AS) experiencing chronic pain. Participants underwent a resting state fMRI scan and psychophysical testing, and completed questionnaires probing resilience and other factors. I found that 1) AS patients exhibit abnormal, heightened DMN-SN cross-network functional connectivity, 2) cross-network connectivity between the DMN, SN, SMN, and descending pain system tracks AS pain-related outcomes, 3) resilience, anxiety, and their interaction predict pain-related affect in response to experimental pain stimuli, 4) AS patients reporting high pain intensity and disease activity are less resilient, 5) Chronic pain intensity is related to (heightened) DMN – SMN cross network connectivity, and 6) within-DMN connectivity tracks resilience in healthy individuals and patients reporting minimal pain. This thesis demonstrates that AS patients have chronic pain-related abnormalities between brain networks of the dynamic pain connectome, and that resilience plays a role in the pain experience and its representation in the brain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".