Global Reference Bias in Neuroimaging: A Female-Specific Template to Reduce Diagnostic Bias

Background. Standard neuroimaging templates derived predominantly from Western, male-weighted cohorts may not adequately represent ethnic- and sex-specific anatomical variation. Application of such templates to ethnically divergent populations propagates registration errors into regional volume estimates, segmentation labels, and downstream clinical decisions. Using them for clinical decision making can introduce systematic bias into artificial intelligence, machine learning based neuroimaging diagnostic tools. Central Asian populations, especially Kazakhs, are particularly absent from existing reference frameworks. Methods. . We constructed the first female-specific T1-weighted MRI brain template for the Kazakh population. Structural MRI scans from 58 cognitively healthy female participants (aged 25–75 years, MoCA ≥ 26) were acquired on a 1.5 T Siemens Magnetom Avanto scanner and quality controlled. Scans were acquired at an anisotropic voxel size of 0.89 × 0.89 × 1.5 mm³, with the through-plane resolution 40% coarser than the in-plane resolution. After shape harmonization, 58 subjects entered iterative unbiased template construction using Advanced Normalization Tools (ANTs) with Symmetric Normalization (SyN). Before cross-template comparison, all candidate templates were resampled to a common 1 mm isotropic resolution of 1 mm using sinc interpolation. Registration fidelity was quantified using the Dice Similarity Coefficient (DSC) and the Normalized Cross-Correlation (NCC). The resulting template was compared against the Montreal Neurological Institute’s 152 brain template (MNI152) (2009c) standard, the Chinese CN200 template, and three reference parcellations (Harvard-Oxford Cortical, Harvard-Oxford Subcortical, and Schaefer 2018) using Pearson correlation, Adjusted Rand Index (ARI), and Normalized Mutual Information (NMI). Results. The final Kazakh female template achieved high intracohort registration fidelity (DSC = 0.987 ± 0.004; NCC = 0.874 ± 0.071). Global alignment with MNI152 was strong (r = 0.718, MAE = 0.207) across 3,180,888 overlapping voxels; however, the coefficient of determination ( = 0.515) indicated that 48.5% of voxel-wise intensity variance remains unexplained by the MNI152 reference, demonstrating significant morphological divergence. Topological concordance with Western-derived parcellations was moderate to low (ARI: 0.3191–0.3598; NMI: 0.4352–0.7709), with the weakest agreement observed in the cortical mantle. Cross-template registration revealed that the Kazakh template required the lowest mean deformation magnitude (2.63 ± 0.35 mm) and near-zero volumetric bias (log J = -0.032). The MNI152 template required 135.4% greater deformation (Cohen's d = 4.49, p < 0.001), while the Chinese CN200 template required 23.4% greater deformation than the Kazakh template (Cohen's d = 1.46, p < 0.001). Conclusions. Global reference templates fail to adequately capture Kazakh female neuroanatomy, as demonstrated by the 48.5% unexplained voxel-wise variance against MNI152, the altered topological boundaries of Western-derived atlases, and the superior anatomical fit of the native population template. This population-specific reference framework provides a standardized baseline intended to improve voxel-based morphometry accuracy and minimize false positive atrophy detection during early Alzheimer’s disease screening in Kazakhstan.