Invalidation of fMRI experiments secondary to neurovascular uncoupling in patients with cerebrovascular disease
Bibliographic record
Abstract
PURPOSE: Blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is a technique used to infer neuronal activity from the observed changes in blood flow. Cerebrovascular reactivity (CVR) is the ability of arterioles to increase blood flow in response to vasodilatory stimulus. We hypothesize that in areas of disease where there is exhausted vascular reserve and impaired CVR there will be diminished blood flow response following neuronal activation, and that these areas would appear as false-negative tests on BOLD fMRI. MATERIALS AND METHODS: Patients with steno-occlusive disease and unilateral hemodynamic impairment received a standardized hypercapnic stimuli while being imaged with BOLD fMRI to generate CVR maps. These were compared to traditional BOLD fMRI maps of neuronal activation in the motor cortex in response to a motor task. RESULTS: Neuronal activation from the motor task was found to be linearly correlated with CVR (n = 11 patients, R = 0.82). Regions with positive (normal) CVR showed positive activation on BOLD fMRI, while regions with negative CVR had attenuated neuronal activation on BOLD fMRI. CONCLUSION: In areas with cerebrovascular disease where CVR is impaired, there is uncoupling of neuronal activation and blood flow that confounds traditional BOLD fMRI. CVR mapping is a noninvasive MRI-based imaging technique that can provide information about the vascular reactivity of the brain that is important to consider when interpreting traditional BOLD fMRI studies. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1448-1455.
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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 | 0.020 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".