Central Nervous System Changes in Asymptomatic Spinal Cord Compression and Surgical Outcome Prediction
Bibliographic record
Abstract
The goal of this thesis was to investigate the patterns of functional and structural connectivity in patients with asymptomatic cervical spinal cord compression (ASCC) compared to healthy controls (HCs) and use these patterns to predict surgical outcome in degenerative cervical myelopathy (DCM) patients.These studies consisted of 45 ASCC patients, 35 HCs, and 42 DCM patients with resting-state functional MRI (rs-fMRI) and diffusion tensor imaging (DTI) scans. The ASCC patients also had sagittal and axial T2-weighted cervical spinal MR imaging. Brain images were parcellated into regions-of-interest (ROIs) using the Montreal Neurological Institute (MNI) atlas and Multi-Domain Task Battery (MDTB) atlas. Correlation between rs-fMRI in ROIs was used to measure strength of functional connectivity, while streamline number from probabilistic tractography and fractional anisotropy (FA) were used to measure structural connectivity.ASCC patients had stronger functional and structural connectivity between visual and motor regions when compared to HCs, with the intracalcarine cortex (occipital cortex) being the largest hub of connection strength differences. Pre-surgical measures of functional and structural connectivity in the five connections with the greatest difference between ASCC patients and HCs were able to predict which DCM patients would experience the greatest benefit from spinal decompression surgery. This thesis indicates that functional and structural brain changes are already evident before neurological symptoms are seen. These alterations in connectivity patterns reflect a systematic reorganization of neural dynamics, suggesting the brain adaptively reconfigures its computational architecture to compensate for compromised signal transmission through the compressed spinal cord. ASCC patients appear to rely more on visual information to maintain normal sensorimotor function as proprioception information is likely compromised due to spinal compression. These early adaptations in brain computation may serve as crucial biomarkers for disease progression, potentially enabling more precise timing of clinical interventions in this challenging patient population. Finally, the finding that the same brain network changes in ASCC patients are also predictive of surgical outcome in DCM patients suggests there is a set of brain connections that define a core of adaptations the brain uses over multiple stages of this disease. The state of these connections could prove highly useful for clinicians in deciding when and if patients should be recommended for surgical intervention.
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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 | 0.000 |
| 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) | 0.001 | 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".