Atlas-based MRI investigations on older adults often utilize young adult standardized templates, such as those of the ICBM. Additionally, a thorough, quantitative assessment of how available standardized, study-specific and age-specific structural templates perform in studies on older adults has not yet been conducted. Here, a new standardized T1-weighted template was developed specifically for studies on older adults, and was compared to 25 other standardized, study-specific, and age-specific templates, in terms of image quality and inter-subject spatial normalization accuracy.
Development of the IIT-Aging Template:
T1-weighted brain MRI data from 222 non-demented older adults (65-95 age-range, male: female=1:1) participating in the Rush Memory and Aging Project1 (MAP) were collected on a 3T MRI scanner (1mm isotropic) and used in this work to construct a structural template of the older adult brain based on ANTs registration2,3,4 In the following, this template is referred to as the IIT-Aging template. Figure 1A shows a schematic representation of the template construction technique. To investigate the effect of the number of participants included in template construction on the performance of the template, additional templates were generated with different number of participants and the same distribution of age and sex.
Comparison to other Standardized and Study-Specific Templates:
IIT-Aging, along with 25 other standardized templates5-12,13 and one study specific template were compared in terms of the inter-subject spatial normalization accuracy achieved when used as references for normalization4 of T1-weighted data from 222 non-demented ADNI14 participants (65-95 age range, male: female=1:1). The data from these ADNI participants were used to construct the study specific template, denoted as ADNI_65-95, using the same template-building method as for IIT-Aging.
Comparison to Age-Specific Templates:
Five age-specific templates were constructed using data from 60 MAP participants each for the age-ranges: 65-75, 70-80, 75-85, 80-90, 80-95 years, while maintaining male:female=1:1. Each age-specific template was compared to IIT-Aging in terms of the inter-subject spatial normalization accuracy achieved when used as references for normalization of T1-weighted data from groups of 60 non-demented ADNI participants having the same age-range and sex-distribution as the age-specific template. All of the above templates were compared in terms of image sharpness by means of the normalized power-spectral density. In all comparisons, spatial normalization accuracy was assessed for each template by means of the average pairwise overlap of Freesurfer-generated15 regional gray matter labels (Generalized Tanimoto Coefficient) over all spatially normalized ADNI participants. The standard deviation and average of the absolute log-Jacobian determinant of deformations in the gray matter of ADNI participants were also computed.
1. A Bennett D, A Schneider J, S Buchman A, et al. Overview and findings from the rush Memory and Aging Project. Current Alzheimer Research. 2012;9(6):646-63.
2. Avants BB, Yushkevich P, Pluta J, et al. The optimal template effect in hippocampus studies of diseased populations. Neuroimage. 2010;49(3):2457-66.
3. Avants BB, Tustison NJ, Song G, et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage. 2011;54(3):2033-44.
4. Avants BB, Epstein CL, Grossman M, et al. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical image analysis. 2008;12(1):26-41.
5. UNC-Adult, UNC-Elderly (https://www.nitrc.org/projects/unc_brain_atlas)
6. Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage. 2007;38(1):95-113.
7. Rohlfing T, Zahr NM, Sullivan EV, et al. The SRI24 multichannel atlas of normal adult human brain structure. Human brain mapping. 2010;31(5):798-819.
8. Fonov V, Evans AC, Botteron K, et al. Brain Development Cooperative Group. Unbiased average age-appropriate atlases for pediatric studies. Neuroimage. 2011;54(1):313-27.
9. Fonov VS, Evans AC, McKinstry RC, et al. Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage. 2009(47):S102.
10. Holmes CJ, Hoge R, Collins L, et al. Enhancement of MR images using registration for signal averaging. Journal of computer assisted tomography. 1998;22(2):324-33.
11. LPBA40, ICBM452, ICBM305 (http://www.loni.usc.edu/resources/atlases)
12. Schwarz CG, Gunter JL, Ward CP, et al. THE MAYO CLINIC ADULT LIFE SPAN TEMPLATE: BETTER QUANTIFICATION ACROSS THE LIFE SPAN. Alzheimer's & Dementia: The Journal of the Alzheimer's Association. 2017;13(7):P93-4.
13. Tustison NJ, Cook PA, Klein A, et al. Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements. Neuroimage. 2014;99:166-79.
14. Weiner MW, Veitch DP, Aisen PS, et al. The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception. Alzheimers Dement. 2013;9(5):e111-94.
15. Fischl B. FreeSurfer. Neuroimage. 2012;62(2):774-81.