Our questions and how and why we try to answer them

Using advanced brain imaging techniques and computational models, we try to understand how the brain balances structural and functional stability and plasticity across the lifespan. At the core of our work is a focus on how the physical architecture of the brain shapes its many functions—and how these functions feed back to reshape that architecture over time. Given the extended period of human neurodevelopment and our reliance on social relationships for learning and growth, we pay special attention to the role social factors play in shaping brain structure and cognitive function.

To tackle these questions, we draw on diverse data sources, including MRI-based neuroanatomy, histology, genomics, geocoded information, task-based assessments, and self-reports. This broad approach helps us unravel the complex interplay between brain structure and function, genes and (social) environment, over time.

We believe in open and transparent science, sharing all our code on GitHub and publishing our findings with open access. Our lab culture values inquiry, collaboration, and mutual respect, where we learn and grow together. Below, we outline our key research areas and goals:

Principles of brain organisation

To understand how brain structure both constrains and enables function, we investigate its neurobiological, genetic, and evolutionary basis. Our previous research showed that brain structure is genetically organized along large-scale axes (Valk et al., 2020) and clarified the layer-specific organization of the human brain (Saberi et al., 2023). We also identified heritable intrinsic microstructural and functional asymmetries in regions involved in language and attention (Wan et al., 2022, eLife; Wan et al., 2024).

Biological factors

Our second research focus is how biological factors, such as genetics, hormones, and immune responses, influence brain structure and function throughout life. For example, stress hormones like cortisol affect brain function, while genetic predispositions interact with the social environment to shape neural development. Our recent studies revealed sex differences in brain structure and function, emphasizing the importance of considering sex-specific factors in brain research (Küchenhoff et al., 2024; Serio et al., 2024). Also, we have investigated the relationship between brain organization and metabolic markers (Wan, bioRxiv) and genetic variation (Wan, bioRxiv).

The living brain

While genetics shape brain structure, environmental factors play a crucial role in brain function across the lifespan. Because of the extended maturation of the cerebral cortex and the effects of sociocultural learning, we focus especially on the social environment. Previous work demonstrated that changes in social demands, such as through mental training, can alter cortical structure and function, improving social cognitive abilities (Valk, 2017; Valk, 2023). More recently, we showed that microstructural brain changes support resilient adaptation during adolescence, reflecting a complex interplay between neurobiology, internal models, and responses to adversity (Hettwer et al., 2024). Importantly, the brain is not a passive recipient of social input—it actively produces social cognition to navigate interactions. Recent findings identified the cerebellar crus I/II as central to developing social cognitive functions in early childhood (Manoli et al., 2025).

Translation and global neuroscience

We integrate our models of brain organization with maps of disorder impact to better understand how brain structure and function form biological axes in which mental disorders are embedded. These insights help use to understand alterations in brain structure and function in brain disorders and conditions, predict how they may progress, but also investigate what kind of interventions may help.

Tools and community

Led by Amin, we developed CUBNM, a toolbox that uses GPUs to efficiently simulate brain network models based on neural mass models connected through a connectome, fitting them to empirical neuroimaging data via integrated optimization algorithms. In collaboration with the mica-lab, we co-developed brainspace, a statistical and decoding toolbox in MATLAB and Python. By building and sharing these tools, we aim to contribute to robust and integrative neuroscience.

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