Neuroimaging Assessment of Cerebrovascular Reactivity in Concussion: Current Concepts, Methodological Considerations, and Review of the Literature
Bibliographic record
Abstract
Concussion is a form of traumatic brain injury (TBI) that presents with a wide spectrum of subjective symptoms and few objective clinical findings. Emerging research suggests that one of the processes that may contribute to concussion pathophysiology is dysregulation of cerebral blood flow (CBF) leading to a mismatch between CBF delivery and the metabolic needs of the injured brain. Cerebrovascular reactivity (CVR) is defined as the change in CBF in response to a measured vasoactive stimulus. Several magnetic resonance imaging (MRI) techniques can be used as a surrogate measure of CBF in clinical and laboratory studies. In order to provide an accurate assessment of CVR, these sequences must be combined with a reliable, reproducible vasoactive stimulus that can manipulate CBF. Although CVR imaging currently plays a crucial role in the diagnosis and management of many cerebrovascular diseases, only recently have studies begun to apply this assessment tool in patients with concussion. In order to evaluate the quality, reliability, and relevance of CVR studies in concussion, it is important that clinicians and researchers have a strong foundational understanding of the role of CBF regulation in health, concussion, and more severe forms of TBI, and an awareness of the advantages and limitations of currently available CVR measurement techniques. Accordingly, in this review, we (1) discuss the role of CVR in TBI and concussion, (2) examine methodological considerations for MRI-based measurement of CVR, and (3) provide an overview of published CVR studies in concussion patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".