Glaucoma is a chronic optic neuropathy characterized by progressive retinal ganglion cell loss. To better visualize glaucomatous spatial patterns of nerve loss, we propose the attribute-based booster variational autoencoder (abVAE), which enables controllable latent representations without compromising reconstruction performance. Building upon the booster VAE (bVAE) framework and inspired by the attribute alignment loss introduced in Attri-VAE [1], the abVAE preserves reconstruction fidelity while enabling attribute-specific controllability over the inferior temporal (IT) and superior temporal (ST) sectors within the elliptical annulus of the retinal ganglion cell plus inner plexiform layer (GCIPL) thickness map from optical coherence tomography scans. By design, the latent space montage maps reveal that thicker regions are concentrated in the upper right corner, while thinner regions appear in the lower left. Quantitatively, the linear relationships between the latent variables and anatomical attributes ( \(d_1\) – \(T^*_{IT}\) and \(d_2\) – \(T^*_{ST}\) ) are reflected in the mean values of \(R^2\) : \(0.95\pm 0.01\) and \(0.86\pm 0.04\) for the abVAE model, \(0.76\pm 0.06\) and \(0.45\pm 0.22\) for the bVAE model, and \(0.64\pm 0.21\) and \(0.23\pm 0.32\) for the \(\beta \) -VAE model. The model also achieves high reconstruction quality, with a Dice score of 0.99 and a structural similarity index (SSIM) of 0.73. These results demonstrate that abVAE effectively balances anatomical interpretability and reconstruction accuracy, making it suitable for modeling spatial patterns of retinal thinning in glaucoma.