The application of deep learning–based generative models has brought a paradigm shift to medical imaging, particularly in MRI-based brain studies involving modality translation and multimodal fusion. This work introduces GM-LDM, a novel latent diffusion framework designed to improve the efficiency and accuracy of MRI generation. To ensure statistical consistency, GM-LDM incorporates a KL-regularized 3D autoencoder pre-trained on large-scale datasets from the ABCD and UK Biobank studies. A Vision Transformer (ViT)-based encoder-decoder serves as the denoising network, enhancing the fidelity of generated images. Crucially, GM-LDM integrates conditional information—specifically, functional network connectivity (FNC)—to enable personalized brain image synthesis, functional-guided structural synthesis, and biomarker discovery in neurological disorders such as schizophrenia. Experimental results demonstrate that GM-LDM generates subject-specific 3D gray matter volumes with high accuracy, showing strong potential for clinical neuroscience applications, including precise disease diagnosis and group-level biomarker localization.