Navigating the brain: How cerebral blood flow shifts with task complexity
Bibliographic record
Abstract
Monitoring middle cerebral artery blood velocity (MCAv) during maneuvers known to alter cerebral perfusion, such as supine-to-standing transitions or walking, may provide a more comprehensive assessment used to flag individuals susceptible to cerebral hypoperfusion in a way that cannot be achieved at rest. Furthermore, dual-tasks challenge the brain to match MCAv to meet increases in local demands of oxygen and energy in two different functional networks (motor and cognitive), potentially causing cerebral hypoperfusion when competing for shared and/or limited brain resources. We developed a dual-task paradigm comprising of five levels of task complexity, including single-tasks and dual-tasks. The main objective of the study was to evaluate changes in MCAv as task complexity increased, which was demonstrated through cognitive, motor, and combined cognitive-motor tasks in older adults with different cognitive function levels. A secondary objective was to assess the success rate (as a percentage) of obtaining MCAv signals during the dual-task protocol to determine the feasibility of measuring such metrics in older adults with varying levels of cognitive ability. Of the 88 participants (37 females, 75 ± 7 years, 27 ± 4 kg/m2), a MCAv signal was ascertained in 56 participants throughout both single-tasks and both dual-tasks. MCAv increased when transitioning from a simple single-task to a more complex dual-task, while also highlighting a decline in motor and cognitive performance. A full multi-modal signal acquisition (MCAv, blood pressure, and cerebral oxygenation) was acquired for 48 participants. Lower MCAv signal acquisition was observed in females and people with cognitive impairment. We have demonstrated how MCAv changes with increased task complexity, while also uncovering declines in gait and cognitive performance. By establishing the feasibility of obtaining MCAv signals during cognitive stress tests and dynamic movements in older adults with varying cognitive abilities, we can begin to assess cerebral hypoperfusion using a potentially more sensitive indicator linked to neural damage.
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 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 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".