A Normative Brain MRI Database of Neurotypical Participants from 5 to 90 Years of Age

Treit, Sarah; Rickard, Julia; Stolz, Emily; Solar, Kevin Grant; Seres, Peter; Emery, Derek; Beaulieu, Christian

doi:10.1017/cjn.2021.513

Cited by 2 publications

(6 citation statements)

References 7 publications

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“…The local healthy participant study was approved by the Human Research Ethics Board at the University of Alberta with written informed consent (and assent for children). First, test-retest diffusion data were acquired from 24 healthy participants (29 ± 8 (20 - 48) years; 13 females) on a 3T Siemens Prisma (64 channel head and neck coil) as part of the University of Alberta normative brain imaging database (Treit et al, 2023). Diffusion tensor imaging was acquired with a single-shot EPI spin-echo sequence: multi-band=2, GRAPPA R=2, 6/8 partial Fourier, 10 b0 s/mm 2 , 6 b500 s/mm 2 (not used here), 20 b1000 s/mm 2 , 64 b2500 s/mm 2 (not used here), TR=5160 ms, TE=67 ms, FOV=220 mm, 96 1.5 mm slices with no gap, 1.5×1.5 mm 2 , reconstructed over the 64 channels using sum of squares, with an acquisition time of 9:29 min (or 2:51 min for only the b0 and b1000 shells used for this study).…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…The full segmentation pipeline will be made available upon publication on the first author’s github page (https://github.com/grahamlittlephd/MicroBrain). All data from the University of Alberta normative sample (Treit et al, 2023) will be made publicly available following publication embargos. For download links and data access, please email the Principal Investigator, Christian Beaulieu at christian.…”

Section: Data Code and Availabilitymentioning

confidence: 99%

“…To assess age related DTI parameter trajectories in 6 deep GM structures, diffusion data were acquired from two separate lifespan cohorts. The first cohort (cohort #1), included 382 subjects recruited under the University of Alberta normative brain imaging database (Treit et al, 2023) and were scanned with the same 1.5 mm isotropic diffusion imaging and T1-weighted MPRAGE protocols as the test-retest cohort above. Amongst Prisma with a single-shot EPI spin-echo sequence: multi-band=2, GRAPPA R=2, 6/8 partial Fourier, 6 b0, 30 b1000 s/mm 2 , 30 b2000 s/mm 2 (not used here), TR = 4700 ms, TE = 64 ms, FOV = 220 mm, 90 1.5 mm slices with no gap, 1.5 × 1.5 mm 2 zero-filled to 0.75 × 0.75 mm 2 in-plane, reconstructed over the 64 channels using sum of squares, and a 6 min total acquisition time (although only 3.5 min for just b0 and b1000 used for the proposed analysis method).…”

Section: The Local Healthy Participant Study Was Approved By the Huma...mentioning

confidence: 99%

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Automated Surface-Based Segmentation of Deep Gray Matter Regions Based on Diffusion Tensor Images Reveals Unique Age Trajectories Over the Healthy Lifespan

Little,

Acosta-Franco,

Beaulieu

2023

Preprint

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Many studies have demonstrated unique trajectories of deep gray matter (GM) volumes over development and aging, suggesting but not measuring microstructural alterations over the lifespan. Only a few studies have measured diffusion tensor imaging (DTI) parameters in deep GM or reported these values across a wide age range in a large cohort. To enable efficient DTI studies of deep GM in large cohorts without the need of T1-weighted images, an automated segmentation technique is proposed here that works solely on parametric maps calculated from DTI. The algorithm segments the globus pallidus, striatum, thalamus, hippocampus and amygdala per hemisphere by deforming 3D models of these structures to their boundaries visible on the contrast provided by diffusion tensor maps and images alone. This new DTI-only method is compared against standard T1-weighted image segmentation for (i) 1.25 mm isotropic diffusion data from the Human Connectome Project (HCP) test-retest cohort (n=44) and (ii) 1.5 mm isotropic test-retest diffusion data from a local normative study (n=24). Dice coefficients of voxel overlap between methods in the HCP test-retest cohort were high (>0.7) for 7 of 10 structures, but were low for the left globus pallidus (0.54) and left/right amygdala (0.67, 0.69). The proposed DTI-only segmentation qualitatively appeared more accurate and yielded smaller volumes than T1w for 8/10 structures in both cohorts, with the exception of the globus pallidus which showed larger volumes in the HCP test-retest data but lower volumes in the local normative study data. The DTI-only segmentation method was then applied to two local single site development/aging ‘lifespan’ cohorts (cohort 1: n=365 5-90 years, cohort 2: n= 164 5-74 years) to assess age changes in volume, fractional anisotropy (FA) and mean diffusivity (MD). In both cohorts, MD trajectories were quadratic for all five structures, decreasing slightly and then increasing after ∼30-35 years. In cohort #1, FA trajectories remained flat from 5 to ∼25 years and then started to decrease for the globus pallidus and hippocampus and over 5 to 90 years, FA decreased linearly for amygdala, increased linearly for striatum, and remained constant for the thalamus. In the second cohort, using an alternate acquisition protocol, the FA trajectories of all 5 structures across all ages were similar, except for the globus pallidus and thalamus which both increased in value from 5 ∼ 20 years and likely reflect differences in acquisition details. Notably, the development and aging trajectories for DTI were distinct from those of the deep GM volumes. The proposed automated deep GM segmentation method on DTI-only will facilitate the analysis of deep GM DTI (currently ignored in nearly all studies despite the data there within the field-of-view) and will be advantageous particularly for studies that do not have a T1-weighted scan, as in many clinical populations.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Data Code and Availabilitymentioning

confidence: 99%

Section: The Local Healthy Participant Study Was Approved By the Huma...mentioning

confidence: 99%

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Automated Surface-Based Segmentation of Deep Gray Matter Regions Based on Diffusion Tensor Images Reveals Unique Age Trajectories Over the Healthy Lifespan

Little,

Acosta-Franco,

Beaulieu

2023

Preprint

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“…The healthy lifespan dataset includes 856 healthy participants (ages 5—91 years, 367 (43%) males) who were scanned once at one of these three sites (Site 1: UofA n = 534, Site 2: ACH n = 52, Site 3: FMC n = 270; Table 1 ). Healthy development participants were recruited separately at each site as part of independent studies of typical brain development and/or aging ( 13 , 14 ), through advertising and word of mouth, and were screened for psychiatric, neurological and developmental disorders as well as contraindications to MRI. Traveling phantom participants were recruited locally through word of mouth and were all familiar with undergoing MRI scans to ensure compliance (i.e., mostly graduate students from Edmonton and Calgary).…”

Section: Methodsmentioning

confidence: 99%

Lifespan Volume Trajectories From Non–harmonized T1–Weighted MRI Do Not Differ After Site Correction Based on Traveling Human Phantoms

et al. 2022

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Multi–site imaging consortiums strive to increase participant numbers by pooling data across sites, but scanner related differences can bias results. This study combines data from three research MRI centers, including three different scanner models from two vendors, to examine non–harmonized T1–weighted brain imaging protocols in two cohorts. First, 23 human traveling phantoms were scanned twice each at all three sites (six scans per person; 138 scans total) to quantify within–participant variability of brain volumes (total brain, white matter, gray matter, lateral ventricles, thalamus, caudate, putamen and globus pallidus), and to calculate site–specific correction factors for each structure. Sample size calculations were used to determine the number of traveling phantoms needed to achieve effect sizes for observed differences to help guide future studies. Next, cross–sectional lifespan volume trajectories were examined in 856 healthy participants (5—91 years of age) scanned at these sites. Cross–sectional trajectories of volume versus age for each structure were then compared before and after application of traveling phantom based site–specific correction factors, as well as correction using the open–source method ComBat. Although small systematic differences between sites were observed in the traveling phantom analysis, correction for site using either method had little impact on the lifespan trajectories. Only white matter had small but significant differences in the intercept parameter after ComBat correction (but not traveling phantom based correction), while no other fits differed. This suggests that age–related changes over the lifespan outweigh systematic differences between scanners for volumetric analysis. This work will help guide pooling of multisite datasets as well as meta–analyses of data from non–harmonized protocols.

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A Normative Brain MRI Database of Neurotypical Participants from 5 to 90 Years of Age

Cited by 2 publications

References 7 publications

Automated Surface-Based Segmentation of Deep Gray Matter Regions Based on Diffusion Tensor Images Reveals Unique Age Trajectories Over the Healthy Lifespan

Automated Surface-Based Segmentation of Deep Gray Matter Regions Based on Diffusion Tensor Images Reveals Unique Age Trajectories Over the Healthy Lifespan

Lifespan Volume Trajectories From Non–harmonized T1–Weighted MRI Do Not Differ After Site Correction Based on Traveling Human Phantoms

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