Correlation between brain volume change and T2 relaxation time induced by dehydration and rehydration: Implications for monitoring atrophy in clinical studies
Bibliographic record
Abstract
Brain volume change measured from magnetic resonance imaging (MRI) provides a widely used and useful in vivo measure of irreversible tissue loss. These measurements, however, can be influenced by reversible factors such as shifts in brain water content. Given the strong effect of water on T2 relaxation, we investigated whether an estimate of T2 relaxation time would correlate with brain volume changes induced by physiologically manipulating hydration status. We used a clinically feasible estimate of T2 ("pseudo-T2") computed from a dual turbo spin-echo MRI sequence and correlated pseudo-T2 changes to percent brain volume changes in 12 healthy subjects after dehydration overnight (16-hour thirsting) and rehydration (drinking 1.5 L of water). We found that the brain volume significantly increased between the dehydrated and rehydrated states (mean brain volume change = 0.36%, p = 0.0001) but did not change significantly during the dehydration interval (mean brain volume change = 0.04%, p = 0.57). The changes in brain volume and pseudo-T2 significantly correlated with each other, with marginal and conditional correlations (R (2)) of 0.44 and 0.65, respectively. Our results show that pseudo-T2 may be used in conjunction with the measures of brain volume to distinguish reversible water fluctuations and irreversible brain tissue loss (atrophy) and to investigate disease mechanisms related to neuro-inflammation, e.g., in multiple sclerosis, where edema-related water fluctuations may occur with disease activity and anti-inflammatory treatment.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".