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Neuroimage. 2015 Sep;118:126-32. doi: 10.1016/j.neuroimage.2015.05.077. Epub 2015 Jun 3.

Diurnal fluctuations in brain volume: Statistical analyses of MRI from large populations.

Author information

1
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Street, Montreal, Quebec H3A 2B4, Canada; Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue, ND20, Cleveland, OH 44195, USA. Electronic address: knakamura@mrs.mni.mcgill.ca.
2
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Street, Montreal, Quebec H3A 2B4, Canada.
3
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Street, Montreal, Quebec H3A 2B4, Canada; NeuroRx Research, 3575 Park Avenue, Suite #5322, Montreal, Quebec H2X 4B3, Canada.

Abstract

We investigated fluctuations in brain volume throughout the day using statistical modeling of magnetic resonance imaging (MRI) from large populations. We applied fully automated image analysis software to measure the brain parenchymal fraction (BPF), defined as the ratio of the brain parenchymal volume and intracranial volume, thus accounting for variations in head size. The MRI data came from serial scans of multiple sclerosis (MS) patients in clinical trials (n=755, 3269 scans) and from subjects participating in the Alzheimer's Disease Neuroimaging Initiative (ADNI, n=834, 6114 scans). The percent change in BPF was modeled with a linear mixed effect (LME) model, and the model was applied separately to the MS and ADNI datasets. The LME model for the MS datasets included random subject effects (intercept and slope over time) and fixed effects for the time-of-day, time from the baseline scan, and trial, which accounted for trial-related effects (for example, different inclusion criteria and imaging protocol). The model for ADNI additionally included the demographics (baseline age, sex, subject type [normal, mild cognitive impairment, or Alzheimer's disease], and interaction between subject type and time from baseline). There was a statistically significant effect of time-of-day on the BPF change in MS clinical trial datasets (-0.180 per day, that is, 0.180% of intracranial volume, p=0.019) as well as the ADNI dataset (-0.438 per day, that is, 0.438% of intracranial volume, p<0.0001), showing that the brain volume is greater in the morning. Linearly correcting the BPF values with the time-of-day reduced the required sample size to detect a 25% treatment effect (80% power and 0.05 significance level) on change in brain volume from 2 time-points over a period of 1year by 2.6%. Our results have significant implications for future brain volumetric studies, suggesting that there is a potential acquisition time bias that should be randomized or statistically controlled to account for the day-to-day brain volume fluctuations.

TRIAL REGISTRATION:

ClinicalTrials.gov NCT00420212 NCT01071083.

KEYWORDS:

Brain atrophy; Brain parenchymal fraction; Diurnal change; MRI

[Indexed for MEDLINE]

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