KazumichiOta/elderly-brain-template: Elderly Brain Template v1.1

[](https://doi.org/10.5281/zenodo.16827113) # 1. OverviewThis repository contains supplementary data related to the Elderly Brain Template developed and evaluated in our study, *Developing and Validating an Elderly Brain Template: A Comprehensive Comparison with MNI152 for Age-Specific Neuroimaging Analyses* Specifically, it includes:- Subcortical Dice coefficients for n=282 QC-passed IXI participants (after intracranial-volume QC; see Methods §2.2.3).- Whole-brain metrics (3D-SSIM, CC, MSE) for the same 282 QC-passed IXI participants.- Demographics (ID, sex, age) for n=90 QC-passed older adults (65–84 years) from the OASIS-1 dataset, matched to the 2020 Japanese census distribution.- Iterative convergence metrics (MSE, CC, 3D-SSIM) capturing how the Elderly template was refined over 20 iterative updates (phases).- Binary masks for seven subcortical ROIs and for GM / WM / CSF tissue classes. --- # 2. File Contents ## 2.1 IXI_region_dice.csv- **Description**: Subcortical Dice coefficients for each IXI subject, comparing registration to the Elderly template or MNI152.- **Columns**: - **ID**: Subject ID from IXI - **Age**: Actual age in years - **Age Group**: Categorical label (A=20–29, B=30–39, C=40–49, D=50–59, E=60–69, F=≥70) for visualization only - **template**: “Elderly” or “MNI152” - **Region**: e.g., amygdala, brainstem, hippocampus, pallidum, etc. - **Dice**: Dice coefficient measuring subcortical overlap between the registered subject image and the chosen template ## 2.2 IXI_metrics(SSIM_CC_MSE).csv- **Description**: Whole-brain metrics (3D-SSIM, CC, MSE) for each IXI subject under Elderly vs. MNI152 registration.- **Columns**: - **ID**, **Age**, **Age Group**, **template** - **Metric**: One of “3D-SSIM”, “CC”, or “MSE” - **Value**: Numerical value of that metric ## 2.3 Oasis_Japan_demographics.csv- **Description**: Demographic information (ID, sex, age) for 90 QC-passed older adults (65–84 years) extracted from OASIS-1, matching the age and sex distributions reported in the 2020 Japanese census.- **Columns**: - **ID**: OASIS-1 subject ID - **Sex**: “F” or “M” - **Age (years)**: Actual age ## 2.4 ElderlyTemplate_construction(SSIM_CC_MSE).csv- **Description**: This file contains the iterative metrics used to evaluate the convergence of our Elderly template construction. We performed a total of 20 updates (phases), and after every two steps, we checked 3D-SSIM (three-dimensional structural similarity index), CC (cross-correlation), and MSE (mean squared error) to determine whether the template had sufficiently converged.- **Columns**: 1. **Phase–Iteration**: Labels like phase1-1, phase1-2, phase2-1, etc., indicating the phase and step number. 2. **FileName**: The warped subject image filename at that iteration (e.g., phase1_input0002-modality0-WarpedToTemplate.nii.gz). 3. **CC (Cross-Correlation)**: A voxelwise measure of intensity correlation. Higher CC implies better alignment. 4. **MSE (Mean Squared Error)**: The mean of squared intensity differences. Lower MSE indicates fewer discrepancies. 5. **SSIM (3D-SSIM)**: Structural similarity index focusing on spatial patterns. Higher SSIM signals better structural match. These iterative metrics reveal how each subject’s registration to the evolving Elderly template improved (or stabilized) at each step. In the main paper, Figure 2 provides a box-plot visualization of these changes in CC, MSE, and SSIM across the 20 updates. ## 2.5 Elderly_brain_T1_template_1mm.nii.gz- **Description**: The final high-resolution Elderly Brain template generated by our multi-subject SyN-based construction strategy (using [ANTs][ants-ref]). - **Dimensions**: 176 (x) × 208 (y) × 176 (z) voxels - **Voxel size**: 1 mm × 1 mm × 1 mm (isotropic) - **Orientation**: L–R, P–A, I–S (qform/sform set to Scanner Anat) - Incorporates age-specific morphological features (e.g., atrophy, ventricular enlargement) commonly seen in older adults (65+). ## 2.6 Mask files All masks are provided under `masks/`.```textmasks/├─ subcortical/ # 7 binary ROI masks│ ├─ brainstem_mask.nii.gz│ ├─ amygdala_mask.nii.gz│ ├─ caudate_mask.nii.gz│ ├─ hippocampus_mask.nii.gz│ ├─ pallidum_mask.nii.gz│ ├─ putamen_mask.nii.gz│ └─ thalamus_mask.nii.gz└─ tissue/ # tissue-class masks (SynthSeg) ├─ gm_mask.nii.gz ├─ wm_mask.nii.gz └─ csf_mask.nii.gz``` # 3. Usage Guidelines ## 3.1 Analysis PipelinePlease refer to our Supplementary Materials or the main paper for details on the software (e.g., [ANTs][ants-ref], [FreeSurfer][freesurfer-ref]) used to compute these metrics, including exact versions and parameters. ## 3.2 Age Group NoteThe **Age Group** column (A, B, C, D, E, F) is intended purely for visualization (e.g., color-coding in figures). In the actual statistical analyses, age was treated as a continuous variable. ## 3.3 ReanalysisBy loading these CSV files into your preferred environment (Python, R, etc.), you can replicate or expand our findings on template performance across different ages—both in terms of subcortical overlap (Dice) and whole-brain metrics (3D-SSIM, CC, MSE). For the Elderly template construction process specifically, **ElderlyTemplate_construction(SSIM_CC_MSE).csv** documents each iteration’s alignment quality. ## 3.4 Reproduce numeric results (Tables 2–S6) To recompute all numeric tables (no plotting), run: `Rscript analysis/reproduce_metrics.R` ## 3.5 Potential application: descriptive deformation profiling The Elderly Brain Template (EBT) can also be used as an age-appropriate reference anatomy for descriptive deformation profiling in older adults. By nonlinearly registering an individual T1-weighted image to the EBT space, users can compute Jacobian determinant or log-Jacobian determinant maps from the resulting deformation fields. These maps can be used to visualize and summarize local expansion or contraction relative to the average anatomy of older adults represented by the EBT. This descriptive use does not require a separate control cohort, because the deformation pattern is interpreted relative to the EBT itself rather than as a statistical abnormality map. The interpretation of Jacobian-based measures depends on the direction of the transformation and should be explicitly specified by users. Appropriate registration quality control is essential. For group-level analyses, users should apply appropriate statistical modeling and correction for multiple comparisons. This repository does not include normative voxel-wise log-Jacobian z-score maps. Therefore, Jacobian or log-Jacobian maps generated using the EBT should be interpreted as deformation-derived morphometric features, not as diagnostic abnormality maps. # 4. License ## 4.1 Repository license (original materials)Unless otherwise noted below, files created by the authors in this repository are released under**Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).** ## 4.2 Exceptions / Third-party terms (apply in addition to the above)Some files are subject to third-party terms and are **not** relicensed by the authors. - **OASIS-1 (DUA applies)** `Oasis_Japan_demographics.csv` contains OASIS-1 subject IDs and demographics and is provided for reproducibility. **Use of this file must comply with the OASIS Data Use Agreement (DUA).** No OASIS subject-level images are redistributed here. - **IXI (CC BY-SA 3.0 applies)** We do not redistribute IXI subject-level images. Users who obtain IXI must comply with the IXI license. # 5. Citation If you use this template or any associated data, please cite **both** the article and the dataset: Ota K (Corresponding author), Nakazato Y., Oyama G.Developing and Validating an Elderly Brain Template:A Comprehensive Comparison with MNI152 for Age-Specific Neuroimaging Analyses.NeuroImage. 2025; 320:121473. https://doi.org/10.1016/j.neuroimage.2025.121473 Dataset (concept DOI; latest): https://doi.org/10.5281/zenodo.16249928 Dataset DOI: https://doi.org/10.5281/zenodo.16827113 (Elderly Brain Template v1.1) # 6. Contact- **Corresponding Author**: Kazumichi Ota* - **Email**: [e-mail] If you have any questions, feedback, or encounter issues with these files, please feel free to contact us. # 7. Acknowledgments- We thank the IXI and OASIS projects for providing open-access MRI data. - No specific funding was received for this study. --- [ants-ref]: https://github.com/ANTsX/ANTs [freesurfer-ref]: https://surfer.nmr.mgh.harvard.edu/ Metadata note: The record description was updated to clarify potential descriptive deformation profiling and log-Jacobian applications of the Elderly Brain Template. No archived files, template images, masks, voxel values, affine matrices, spatial metadata, or version DOI were modified.