Body posture alters brain imaging data
Bibliographic record
Abstract
In this thesis, I propose that body posture is an important, underappreciated, variable to consider in neuroimaging research. Thousands of brain imaging experiments are published each year, but few consider how the postures that participants assume may influence the data collected. Whereas participants in most behavioural, cognitive, and psychology experiments sit upright, one of the most prominent functional neuroimaging techniques, functional magnetic resonance imaging (fMRI), requires participants to lie supine. Many cognitive processes in our everyday life, moreover, are executed while neither sitting nor lying, but rather when standing, moving, or interacting with other people and the surrounding environment. A growing literature suggests that posture weighs heavily on both cognitive functions and physiological processes that are relevant to brain imaging. This thesis aims to elucidate how body posture shapes neuroimaging data.We directly investigated the effect of posture on spontaneous brain dynamics by recording electrical activity (EEG) in four orthostatic conditions (lying supine, inclined at 45°, sitting upright, and standing erect) and magnetic activity (MEG) in three postures (lying supine, sitting reclined, sitting upright). We found that posture altered electromagnetic brain imaging data. Upright postures (sitting and standing), compared to reclined and supine postures, were associated with widespread increases in high-frequency oscillatory activity regardless of whether participants were involved in a mental task or had their eyes open or closed. Using MEG recordings alongside associated structural MRI scans, we were able to more precisely localize posture-driven changes in brain activity. Sitting upright versus lying supine was associated with greater high-frequency (i.e., beta and gamma) activity in widespread parieto-occipital cortex. Moreover, upright and reclined postures correlated with dampened activity in prefrontal regions, especially across lower frequency bandwidths. Our findings highlight the importance of posture as a determinant in neuroimaging. Generalizing results—from supine neuroimaging measurements to erect positions typical of ecological human behavior—would call for considering the influence that posture wields on brain dynamics.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".