Physiological influences on neurovascular coupling: A systematic review of multimodal imaging approaches and recommendations for future study designs
Bibliographic record
Abstract
In this review, we have amalgamated the literature, taking a multimodal neuroimaging approach to quantify the relationship between neuronal firing and haemodynamics during a task paradigm (i.e., neurovascular coupling response), while considering confounding physiological influences. Original research articles that used concurrent neuronal and haemodynamic quantification in humans (n ≥ 10) during a task paradigm were included from PubMed, Scopus, Web of Science, EMBASE and PsychINFO. Articles published before 31 July 2023 were considered for eligibility. Rapid screening was completed by the first author. Two authors completed the title/abstract and full-text screening. Article quality was assessed using a modified version of the National Institutes of Health Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. A total of 364 articles were included following title/abstract and full-text screening. The most common combination was EEG/functional MRI (68.7%), with cognitive (48.1%) and visual (27.5%) tasks being the most common. The majority of studies displayed an absence/minimal control of blood pressure, arterial gas concentrations and/or heart rate (92.9%), and only 1.3% monitored these factors. A minority of studies restricted or collected data pertaining to caffeine (7.4%), exercise (0.8%), food (0.5%), nicotine (2.7%), the menstrual cycle (0.3%) or cardiorespiratory fitness levels (0.5%). The cerebrovasculature is sensitive to numerous factors; thus, to understand the neurovascular coupling response fully, better control for confounding physiological influences of blood pressure and respiratory metrics is imperative during study-design formulation. Moreover, further work should continue to examine sex-based differences, the influence of sex steroid hormone concentrations and cardiorespiratory fitness.
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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 | 0.103 | 0.235 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".