Confirmation of resting‐state BOLD fluctuations in the human brainstem and spinal cord after identification and removal of physiological noise
Bibliographic record
Abstract
Purpose Resting‐state functional MRI (rs‐fMRI) has been used to investigate networks within the cortex, but its use in the brainstem (BS) and spinal cord (SC) has been limited. This region presents challenges for fMRI, partly because of sources of physiological noise. This study aims to quantify noise contributions to rs‐fMRI, and to obtain evidence of resting‐state blood oxygenation level–dependent (BOLD) fluctuations. Methods Resting‐state‐fMRI data were obtained from the BS/SC in 16 participants, at 3 Tesla, with T2‐weighted single‐shot fast spin‐echo imaging. The peripheral pulse, respiration, and expired CO2 were recorded continuously. Physiological noise was modeled from these recordings, movement parameters, and white matter regions. Model fits were then subtracted from the data. BOLD contributions were then investigated through connectivity. Results Bulk motion was the largest contributor to the signal variance (19% of the total), followed by cardiac‐related motion (14%), nonspecific signal variations detected in white matter (10%), respiratory‐related motion (2.6%), and end‐tidal CO2 variations (0.7%). After noise was removed, significant left‐right connectivity was detected in the SC dorsal horns and ventral horns. Conclusions Resting‐state BOLD fluctuations are demonstrated in the SC, as are the dominant noise contributions. These findings are an essential step toward establishing rs‐fMRI in the BS/SC. Magn Reson Med 78:2149–2156, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".