Institute of Neurosciences and Medicine (INM)

Brain and Behaviour (INM-7)

Cognitive and clinical Neurosciences

About

Our lab investigates interindividual variability in brain–behavior phenotypes using machine learning approaches. A central focus of our work is to understand the sources of this variability across the lifespan. We integrate brain measures with the exposome—the cumulative environmental, lifestyle, and biological factors shaping human development—to build a more holistic account of interindividual variability in brain phenotype. We are particularly interested in heterogeneity in disease, asking why individuals with similar diagnoses show markedly different brain and behavioral profiles. By combining computational modeling, neuroimaging, and population-scale datasets, our goal is to move toward precision neuroscience: understanding the brain–behavior phenotype in context.

Research Topics

Brain organization, atlases, and normative modelling

We develop brain atlases and normative models that characterize typical patterns of brain structure and function across the lifespan. These resources provide reference frameworks for detecting individual deviations and support both basic neuroscience and clinical translation.

Multivariate exposomic-brain-behavior mapping and Brain-based prediction of behavior

Using advanced multivariate brain-behavior mapping and predictive modeling approaches, we identify distributed brain signatures that explain and predict cognitive, affective, and behavioral phenotypes. Our work emphasizes generalizability, reproducibility, and interpretability to establish robust brain–behavior mappings. By modeling continuous variation in brain–behavior phenotypes, we aim to uncover biologically meaningful axes of heterogeneity and their links to exposomic profiles.

Exposome-informed predictive modelling

We integrate environmental, lifestyle, and biological exposure variables (the exposome) into predictive frameworks. This allows us to quantify how cumulative life-course exposures shape brain organization and behavioral outcomes in both healthy and clinical populations.

Disease subtyping and stratification

We develop data-driven approaches to identify mechanistically informed disease subtypes based on brain imaging, behavioral, and exposome features. Our goal is to advance precision neuroscience by improving stratification, prognosis, and treatment strategies.