Automated Measurements of Brain Morphometry Derived from T1-weighted Magnetic Resonance Imaging Fluctuate from Morning to Afternoon
Aaron Trefler1, Neda Sadeghi2, Adam Thomas1, Carlo Pierpaoli2, Chris Baker1, and Cibu Thomas3

1National Institute of Mental Health, Bethesda, MD, United States, 2National Institute of Child Health and Human Development, Bethesda, MD, United States, 3Center for Neuroscience and Regenerative Medicine, Bethesda, MD, United States

Synopsis

Automated measures of brain morphometry derived from T1-weighted (T1W) images are typically used as proxy measures to investigate the relation between brain structure and behavior. However, the computation of T1W morphometric measures can be influenced by subject-related factors such as head motion1 and level of hydration2. Here, we provide a comprehensive assessment of the impact of time-of-day (TOD) on widely used measures of brain morphometry in healthy young adults. Our results show that the apparent volume of all major tissue compartments as well as measures of brain morphometry such as cortical thickness and gray matter density are significantly influenced by TOD.

Purpose

To assess the effect of time-of-day (TOD) on automated measures of brain morphometry derived from T1-weighted (T1W) images.

Introduction

A recent study reported subtle, yet significant changes in total brain volume (TBV) from morning to evening in an exceptionally large group of patient populations as well as healthy elderly individuals [3]. However, it is unknown whether: (a) TOD can influence measures of brain morphometry derived from T1W images in studies involving relatively smaller groups of young healthy individuals; (b) TOD may differentially affect the apparent volume of the three main tissue compartments (gray matter (GM); white matter (WM), and cerebrospinal fluid (CSF) (c) specific brain structures and cortical regions are disproportionately impacted; (d) the apparent change in TBV impacts surface-based T1W morphometry measures (e.g. cortical thickness) as well as Voxel-Based-Morphometry (VBM) measures.

Methods

A group of 19 healthy young adults (mean age: 25.8 years, range: 20-38, 10 female) were scanned during two visits (each visit scheduled two weeks apart) on a 3 Tesla GE MR750 scanner using a GE 32-channel head coil. During both visits, participants were scanned from 10AM to 12PM and from 2PM to 4PM, with a two-hour rest period in between. Two T1W MPRAGE volumes were acquired during each session (scan parameters: accelerated sagittal 3D MPRAGE, 25 cm FOV, 256x256 matrix slice, 1 mm slice thickness, TR: 7 ms, TE: 3.42 ms, flip angle: 7 deg. For the volumetric and surface-based analysis, we used the automated longitudinal pipeline implemented in FreeSurfer (version 5.3) to build subject-specific templates, and each participant’s images were re-processed using information from their subject-specific template4 to derive various volumetric and surface-based metrics. All volumetric measurements were expressed as a percentage of intra cranial volume (ICV), which was automatically estimated by FreeSurfer. Surface-based measures, such as cortical thickness and surface area as well as indices of brain curvature were automatically extracted using FreeSurfer’s automatic reconstruction procedure and the spatial topography of the TOD effect on different cortical regions was investigated using an automatic parcellation procedure [5]. For the VBM analysis6 (FSL-VBM version 1.0), all T1W images were processed through a modified VBM-FSL pipeline designed for longitudinal analysis7. To test the relationship between each brain morphometry measure and TOD, we applied the linear mixed effect (LME) model using the lme4 package in R8. For each measure of brain morphometry, the model used consisted of fixed effects (i.e. TOD) for both intercept and slope and a random effect (i.e., the subject-specific morphometric measure) to account for individual variations and estimate a prediction for each value of the T1W structural measure being modeled. The residual error between the prediction and actual measurement was calculated and used for significance testing.

Results

Our results reveal that between morning and afternoon: (a) A highly significant reduction in apparent brain volume (1.145% of ICV/12 h) can be detected even in a small group of adult healthy volunteers (b) The apparent volume of all the three major tissue compartments, GM, WM and CSF are influenced, and the magnitude of the TOD effect varies across the tissue compartments (Fig. 1); (c) The increase in CSF volume was associated with the decrease in GM and WM volumes; (d) Measures of apparent cortical thickness, surface area and GMD were all affected by TOD (Fig. 2), while measures such as, curvature indices and sulcal depth were not impacted; (e) The effect of TOD appears to have a greater impact on morphometric measures of the frontal and temporal lobe compared to other major lobes of the brain (Fig. 2).

Discussion

Our findings suggest that automatically derived measures of brain morphometry that are thought to reflect properties such as cortical thickness, surface area, GM volume and GMD, change significantly from morning to afternoon, even in the absence of any experimental manipulation. The magnitude of the TOD related changes in measures such as cortical thickness is greater than the apparent change attributed to interventions, such as memory training9. Moreover, the spatial topography of the TOD effect on apparent cortical thickness and GMD indicate that the frontal and temporal lobes are disproportionately impacted. This suggests that controlling for the effect of TOD is crucial for proper interpretation of apparent structural differences measured using T1W morphometry with longitudinal and cross-sectional experimental designs. The strong correlation between changes in CSF volume and changes in GM and WM volumes from morning to afternoon, as well as reports of similar changes in the literature10 suggests that the TOD effect is a physiological phenomenon that warrants further investigation.

Acknowledgements

This work was supported by Intramural Research Programs of NICHD and NIMH (Grant Number MH002909-07). Salary support for CT was provided by funding from the Department of Defense in the Center for Neuroscience and Regenerative Medicine.

References

1. Reuter, M., et al., Head motion during MRI acquisition reduces gray matter volume and thickness estimates. NeuroImage, 2015. 107: p. 107-115.

2. Kempton, M.J., et al., Effects of acute dehydration on brain morphology in healthy humans. Human brain mapping, 2009. 30(1): p. 291-298.

3. Nakamura, K., et al., Diurnal fluctuations in brain volume: Statistical analyses of MRI from large populations. NeuroImage, 2015. 118: p. 126-132.

4. Reuter, M., et al., Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage, 2012. 61(4): p. 1402-1418.

5. Fischl, B., et al., Automatically parcellating the human cerebral cortex. Cerebral cortex, 2004. 14(1): p. 11-22.

6. Ashburner, J. and K.J. Friston, Voxel-based morphometry - the methods. NeuroImage, 2000. 11(6): p. 805-821.

7. Thomas, A., et al., Functional but not structural changes associated with learning: An exploration of longitudinal Voxel-Based Morphometry (VBM). NeuroImage, 2009. 48(1): p. 117-125.

8. Bates, D., et al., lme4: Linear mixed-effects models using Eigen and S4. R package version 1.1-7.

9. Engvig, A., et al., Effects of memory training on cortical thickness in the elderly. Neuroimage, 2010. 52(4): p. 1667-1676.

10. Hodkinson, D.J., et al., Circadian and homeostatic modulation of functional connectivity and regional cerebral blood flow in humans under normal entrained conditions. J Cereb Blood Flow Metab, 2014. 34(9): p. 1493-9.

Figures

Fig1. Significant changes in total brain volume, gray matter volume, white matter volume, and CSF volume expressed as percentages of ICV and plotted against the time-of-day from the two visits. The green lines indicate the model estimate of the fixed effect of time-of-day and the gray region surrounding the green line shows the 95% confidence interval of the fitted model. The blue circles indicate individual measurements. A decrease in brain volume was observed in 14 out of the 19 subjects.

Fig 2. The spatial distribution of changes in apparent cortical thickness and gray matter density from morning to afternoon. (a) The Freesurfer analysis shows measurement of cortical thickness in dorsal regions along the frontal and temporal lobe are disproportionality impacted by TOD (b) VBM analysis shows the cortical and subcortical regions where a significant reduction in gray matter density was observed due to TOD.



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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