Research Interests
“In theory, there is no difference between theory and practice. But, in practice, there is.”
— Manfred Eigen
At the core of my research is a search for the generative principles that govern the brain’s architecture. I aim to formalize these principles into predictive, multiscale models that unify diverse biological data and theoretical insights. My long-term goal is to understand how cognition arises from structure, and why at times, it falters.
What draws me to the brain is not only its extraordinary complexity, but the hidden order beneath it. I investigate the hypothesis that the brain follows an intrinsic biological syntax—a set of probabilistic, rule-based relationships by which structural features (such as gene expression gradients, cortical geometry, and network topology) give rise to cognitive function. These relationships are not fixed: they are shaped and reshaped over time through experience-dependent plasticity, developmental programs, and microstructural remodeling (e.g., synaptic pruning, myelination). Thus, my work centers on the dynamic interplay between structure and function, modeled not just as a biological necessity but as a source of flexibility.
Yet this relationship is fundamentally bidirectional: structure shapes function, and function reshapes structure. Through experience-dependent plasticity, developmental dynamics, and microstructural remodeling, brain networks are continuously refined. By modeling this dynamic interplay, we can deepen our understanding of the brain’s capacity for learning, adaptation, and its vulnerabilities in neurodevelopmental disorders.
The Fractal Brain
The brain does not just exhibit complexity, it exhibits fractal complexity. Across scales, from the folding of the cortex to dendritic arborization, self-similarity emerges as a fundamental organizing principle. I use fractal analysis to quantify this geometry and link it to cognition and its molecular underpinnings.
Higher fractal dimensions often correlate with greater cognitive flexibility and efficiency; conversely, reductions in cortical complexity have been observed in neurodevelopmental conditions such as autism. In prior work, I identified atypical fractal profiles in autism, particularly within the temporoparietal network, alongside plausible genetic contributors. I hypothesize that fractal complexity reflects an evolutionary strategy—a way to optimize between wiring cost and computational power, balancing efficiency with robustness within spatial constraints.
Brain Networks
Building on these insights, I study how local complexity and functional similarity shape the brain’s large-scale network organization. For example, I explore the principle of homophily: the tendency of structurally or transcriptionally similar regions to preferentially connect, analogous to clustering in social networks. These patterns reflect deep developmental constraints and evolutionary pressures, helping to coordinate processing across distributed systems.
I construct brain networks using multivariate features derived from neuroimaging (e.g., MRI), analyze their topological structure using graph theory, and relate these features to gene expression, behavior, and cognitive phenotypes. My work also examines trade-offs in wiring efficiency, the emergence of mesoscale motifs, and the overarching connectome architecture that supports flexible cognition.
