Does hydration status affect MRI measures of brain volume or water content?
Bibliographic record
Abstract
PURPOSE: To determine whether differences in hydration state, which could arise from routine clinical procedures such as overnight fasting, affect brain total water content (TWC) and brain volume measured with magnetic resonance imaging (MRI). MATERIALS AND METHODS: Twenty healthy volunteers were scanned with a 3T MR scanner four times: day 1, baseline scan; day 2, hydrated scan after consuming 3L of water over 12 hours; day 3, dehydrated scan after overnight fasting of 9 hours, followed by another scan 1 hour later for reproducibility. The following MRI data were collected: T2 relaxation (for TWC measurement), inversion recovery (for T1 measurement), and 3D T1 -weighted (for brain volumes). Body weight and urine specific gravity were also measured. TWC was calculated by fitting the T2 relaxation data with a nonnegative least-squares algorithm, with corrections for T1 relaxation and image signal inhomogeneity and normalization to ventricular cerebrospinal fluid. Brain volume changes were measured using SIENA. TWC means were calculated within 14 tissue regions. RESULTS: Despite indications of dehydration as demonstrated by increases in urine specific gravity (P = 0.03) and decreases in body weight (P = 0.001) between hydrated and dehydrated scans, there was no measurable change in TWC (within any brain region) or brain volume between hydration states. CONCLUSION: We demonstrate that within a range of physiologic conditions commonly encountered in routine clinical scans (no pretreatment with hydration, well hydrated before MRI, and overnight fasting), brain TWC and brain volumes are not substantially affected in a healthy control cohort. J. Magn. Reson. Imaging 2016;44:296-304.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".