Brain Development Lab

Our group focuses on the study of large-scale organization of the brain in both typical and atypical states. The North Star of our research is that fMRI signals are far more than snapshots of brain activity - they are a systems-level observatory for how the brain functions, connects, and develops.

• The research interests of our group are diverse and all grounded in the curiosity of understanding how the brain operates as a system of functionally interconnected regions.

• Key goals of our research are to understand i) how the brain develops across the lifespan, ii) what ensembles of neural connections constitutes a unique “fingerprint” of our brains, iii) why functional interactions between brain areas are atypical in neurodevelopmental conditions such as autism, and iv) how brain regions dynamically respond to sensory input.

• To achieve this, we model the brain as a network by combining functional connectomics and personalized approaches. We often integrate the insights we gain from fMRI with those of other brain imaging techniques - such as structural connectivity, brain morphometry and electroencephalography - as well as with neuroscientific resources like transcriptional databases and cognitive atlases.

In the lab, we pursue distinct yet interconnected research directions:

• Beyond resting state: brain networks responses to sensory stimuli. We have extensively investigated resting state “functional connectivity” i.e. the intrinsic spatiotemporal patters of communication across brain regions. Building on this foundation, we now examine how the developing brain processes multisensory inputs such as audiovisual stimuli. By analyzing naturalistic fMRI signals, we seek to open a new window into how brain functions are (a)typically organized in children and adolescents with neurodevelopmental conditions.

• Unveiling idiosyncrasy in autism. Guided by the principle that no two individuals are alike, we aim to search for the neural basis that makes individuals with autism similar and those that make them different from one another. For example, by applying neurosubtyping methods to fMRI, we have identified two dominant autism groups characterized by distinct and reproducible atypical functional connectivity. By developing precision neurocomputational approaches, we aim to shed light on the origins of inter-individual neural variability in autism.

• Not only fMRI: Predictive Brain Morphometry. Clinical and cognitive outcomes in children with autism are highly variable and often difficult to predict. To improve prognosis, we examine a wide range of brain volumetric and cortical thickness measures in a large Italian sample of preschoolers with autism and integrate them with phenotypic observations. By developing MRI-based predictive models, we aim to improve the prediction of evolutive trajectories in autism, with particular attention to those that cannot be primarily explained through behavioral observation.