BigData Analysis for Medical Imaging

My research leverages the emergence of large-scale neuroimaging datasets to address one of the central unresolved questions in neuroscience: the characterization of brain structure trajectories across the entire lifespan. Despite extensive efforts, there is still no consensus on brain maturation and aging patterns, largely due to limitations in previous studies, which were restricted to narrow age ranges, small sample sizes, and heterogeneous processing methodologies. These limitations have led to inconsistent findings and hindered reproducibility across studies.

To overcome these challenges, I exploit the recent paradigm shift toward Big Data sharing in neuroimaging, combined with advances in image processing that enable the unified analysis of data from neonates to the elderly. My work focuses on developing methodological frameworks that ensure scalability, robustness, and consistency across highly heterogeneous datasets.

My main contributions in this field are threefold:

• Scalable and robust processing of large-scale neuroimaging data: I develop automated pipelines capable of processing massive datasets with high robustness to variability in acquisition protocols, image quality, and subject populations. These tools are designed to ensure reproducibility and consistency of quantitative measurements across thousands of scans.

• Lifespan modeling of brain structure trajectories: I have contributed to the first comprehensive analyses of brain structural variations across the entire lifespan, from early development to advanced aging. By applying unified processing frameworks, my work provides consistent and reproducible estimates of volumetric changes, enabling the characterization of normative brain trajectories at an unprecedented scale.

• Translation to neurodegenerative disease modeling: I extend these lifespan models to the study of neurological disorders, particularly neurodegenerative diseases, by identifying deviations from normative trajectories. This enables the development of quantitative biomarkers for disease characterization and progression, bridging large-scale population modeling with clinical applications.

These contributions are integrated into scalable platforms such as volBrain, enabling the systematic analysis of large and diverse cohorts.

Overall, my work establishes a data-driven and unified framework for lifespan neuroimaging, providing robust references for brain development and aging, and opening new avenues for the quantitative study of neurological diseases.