Abnormal Low-Frequency Oscillations Reflect Trait-Like Pain Ratings in Chronic Pain Patients Revealed through a Machine Learning Approach
Bibliographic record
Abstract
Measures of moment-to-moment fluctuations in brain activity of an individual at rest have been shown to be a sensitive and reliable metric for studying pathological brain mechanisms across various chronic pain patient populations. However, the relationship between pathological brain activity and clinical symptoms are not well defined. Therefore, we used regional BOLD signal variability/amplitude of low-frequency oscillations (LFOs) to identify functional brain abnormalities in the dynamic pain connectome in chronic pain patients that are related to chronic pain characteristics (i.e., pain intensity). Moreover, we examined whether there were sex-specific attributes of these functional brain abnormalities and whether functional brain abnormalities in patients is related to pain intensity characteristics on different time scales. We acquired resting-state functional MRI and quantified frequency-specific regional LFOs in chronic pain patients with ankylosing spondylitis. We found that patients exhibit frequency-specific aberrations in LFOs. Specifically, lower-frequency (slow-5) abnormalities were restricted to the ascending pain pathway (thalamus and S1), whereas higher-frequency abnormalities also included the default mode (i.e., posterior cingulate cortex; slow-3, slow-4) and salience (i.e., mid-cingulate cortex) networks (slow-4). Using a machine learning approach, we found that these abnormalities, in particular within higher frequencies (slow-3), can be used to make generalizable inferences about patients9 average pain ratings (trait-like pain) but not current (i.e., state-like) pain levels. Furthermore, we identified sex differences in LFOs in patients that were not present in healthy controls. These novel findings reveal mechanistic brain abnormalities underlying the longer-lasting symptoms (trait pain intensity) in chronic pain. SIGNIFICANCE STATEMENT Measures of moment-to-moment fluctuations in brain activity of an individual at rest have been shown to be a reliable metric for studying functional brain associated with chronic pain. The current results demonstrate that dysfunction in these intrinsic fluctuations/oscillations in the ascending pain pathway, default mode network, and salience network during resting state display sex differences and can be used to make inferences about trait-like pain intensity ratings in chronic pain patients. These results provide robust and generalizable implications for investigating brain mechanisms associated with longer-lasting/trait-like chronic pain symptoms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".