Anirudh Kaushik supervised by Prof. Jayanthi Sivaswamy received his  Master of Science –  Dual Degree in Computer Science and Engineering  (CSD). Here’s a summary of his research work on  The Anatomy of Synthesis: Simulating Changes in the Human Brain over Time through Diffeomorphic Deformations:

The human brain undergoes continuous structural changes throughout the lifespan, driven by a complex interplay of aging processes, environmental influences, and disease-related mechanisms. Patterns of structural change, particularly atrophy associated with tissue loss and shrinkage, emerge gradually over me and are observable using medical imaging techniques. While these changes are shaped by common biological mechanisms, they are also highly individualized, influenced by factors such as lifestyle, and neurological conditions like Alzheimer’s Disease (AD), Parkinson’s disease, tumors, and stroke. Understanding the progression of these changes, both at the individual level and across populations, is critical for advancing our knowledge of healthy aging and the dynamics of neurodegenerative disease. 

To study how brain structure evolves over me, researchers rely on longitudinal neuroimaging: repeated imaging of the same individuals at multiple timepoints. Unlike cross-sectional imaging, which captures a single snapshot per subject, longitudinal scans provide a temporal sequence that enables direct observation of anatomical trajectories. These sequences allow for the measurement of rates of change, identification of early biomarkers, and modeling of disease progression in a subject-specific manner. 

However, acquiring complete longitudinal datasets in practice remains challenging. Subject dropout, missed clinical visits, and protocol variability often result in missing scans, interrupting the temporal continuity required for accurate modeling. These gaps limit the effectiveness of methods that rely on temporally complete inputs and can bias downstream analyses. Imputing the missing scan to complete the subject’s imaging meline is therefore a critical step toward enabling robust longitudinal modeling and improving our understanding of neurodegenerative processes. 

This thesis addresses the challenges of modeling and analyzing longitudinal brain changes by developing anatomically grounded methods for data imputation, latent space disentanglement, and downstream trajectory analysis. We first introduce SynBADD, a deformation-based framework that synthesizes missing brain scans by predicting physiologically plausible stationary velocity fields (SVFs), parametric fields that encode smooth, invertible deformations over space and me, rather than directly genera ng full image intensities. By operating in the deformation space, SynBADD preserves anatomical coherence and spa al fidelity while mi ga ng the artifacts typically associated with intensity-based synthesis approaches. 

Building on this foundation, we propose DIVA, a metadata-informed variational autoencoder designed to learn a temporally disentangled latent space for modeling brain morphological changes. DIVA is trained on synthetically augmented Stationary Velocity Fields (SVFs), enriching intra-subject variation and improving the model’s capacity to generalize across limited real-world samples. Age, disease label, and temporal information are explicitly disentangled through conditional bottleneck supervision, allowing the learned latent representations to reflect meaningful clinical and chronological factors. This disentanglement enhances temporal predictability, supports more accurate subject-specific trajectory modeling, and enables the use of powerful transformer-based architectures for latent space interpolation on and prediction on. 

We further extend our analysis in an “Analysis by Synthesis” framework, investigating how metadata-informed conditioning affects genera on quality and how different temporal reasoning strategies (past-only, future-only, and bidirectional) impact anatomical plausibility. We perform trajectory-based analyses of generated scans, evaluating how well imputed data aligns with true subject-specific anatomical trends across key regions of interest (ROIs) such as the hippocampus and para-hippocampus. Additionally, we assess subgroup differences by age, sex, and disease status, and evaluate downstream task performance with and without synthetic imputation. Through comprehensive evaluations on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) longitudinal dataset, our approaches achieve substantial improvements over state-of-the-art baselines in both image fidelity and clinically relevant anatomical accuracy. Beyond technical advancements, this work provides new insights into the modeling of individualized brain aging patterns and opens pathways for data augmentation in clinical studies where longitudinal completeness is often unattainable.

July 2025