Analysis of the contribution of experimental bias, experimental noise, and inter-subject biological variability on the assessment of developmental trajectories in diffusion MRI studies of the brain

Abstract

Metrics derived from the diffusion tensor, such as fractional anisotropy (FA) and mean diffusivity (MD) have been used in many studies of postnatal brain development. A common finding of previous studies is that these tensor-derived measures vary widely even in healthy populations. This variability can be due to inherent inter-individual biological differences as well as experimental noise. Moreover, when comparing different studies, additional variability can be introduced by different acquisition protocols. In this study we examined scans of 61 individuals (aged 4-22 years) from the NIH MRI study of normal brain development. Two scans were collected with different protocols (low and high resolution). Our goal was to separate the contributions of biological variability and experimental noise to the overall measured variance, as well as to assess potential systematic effects related to the use of different protocols. We analyzed FA and MD in seventeen regions of interest. We found that biological variability for both FA and MD varies widely across brain regions; biological variability is highest for FA in the lateral part of the splenium and body of the corpus callosum along with the cingulum and the superior longitudinal fasciculus, and for MD in the optic radiations and the lateral part of the splenium. These regions with high inter-individual biological variability are the most likely candidates for assessing genetic and environmental effects in the developing brain. With respect to protocol-related effects, the lower resolution acquisition resulted in higher MD and lower FA values for the majority of regions compared with the higher resolution protocol. However, the majority of the regions did not show any age-protocol interaction, indicating similar trajectories were obtained irrespective of the protocol used.

Keywords: Brain development; DTI; Experimental variability; Mixed effects model.

Published by Elsevier Inc.

Figures

Fig. A.1

Plots of fractional anisotropy (FA) versus age for white matter and gray matter regions.

Fig. B.1

Plots of mean diffusivity (MD (μm2/s)) versus age for white matter and gray matter regions.

Fig. C.1

Sources of variability. Left: biological variability and experimental noise of axial diffusivity (AD). Right: biological variability and experimental noise of radial diffusivity (RD).

Fig. 1

Regions of interest are shown overlaid on the FA map computed from the template of 18–21 year-old subjects. Note that the ROIs are 3D structures; however, here axial slices are used as an example. The following ROIs were analyzed: 1 — superior longitudinal fasciculus (SLF), 2 — cingulum, 3 — body of corpus callosum (BCC), 4 — lateral part of splenium of corpus callosum (SCC lateral), 5 — middle part of splenium of corpus callosum (SCC medial), 6 — anterior limb of internal capsule (ALIC), 7 — genu of internal capsule (GIC), 8 — posterior limb of internal capsule (PLIC), 9 — optic radiations, 10 — lateral part of genu of corpus callosum (GCC lateral), 11 — middle part of genu of corpus callosum (GCC medial), 12 — caudate, 13 — putamen, 14 — thalamus, 15 — corticospinal tract (CST), 16 — middle cerebellar peduncle (MCP), and 17-medial lemniscus (ML).

Fig. 2

Plots of the estimated mean and standard error of the intercept term, parameter A in the model, for MD. The simulation results are computed for an assumed true value of A of 755 μm2/s.

Fig. 3

Plots of the estimated mean and standard error of the slope, parameter B in the model, for MD. The simulation results are computed for an assumed true value of B of −3.52 μm2/s per year.

Fig. 4

Plots of estimated σb and standard error of σb of MD.

Fig. 5

Plots of FA and MD (μm2/s) versus age for medial lemniscus (ML). Regions are overlaid on the FA and MD maps computed from the template of 18–21 year-old subjects. There is a protocol-dependent bias, and the effect of age is different depending on the protocol used.

Fig. 6

Plots of FA and MD (μm2/s) versus age for posterior limb of internal capsule (PLIC). Regions are overlaid on the FA and MD maps computed from the template of 18–21 year-old subjects. Similar trajectories are observed for both cDTI and eDTI, however, there is a protocol dependent bias.

Fig. 7

Sources of variability. Left: biological variability and experimental noise of FA. Right: biological variability and experimental noise of MD.