Rogier Kievit (PI)

Rogier Kievit is on the MRC Programme Leader Track at the Cognition and Brain Sciences Unit, a Sir Henry Wellcome fellow and a Fellow of Fitzwilliam College, University of Cambridge. He studies the rise and fall of cognitive functions across the lifespan by relating brain function, brain structure and cognition in developing populations using multivariate modeling techniques. Specifically, I use a variety of structural equation models to capture the changing relationship between mind and brain across lifetime.


Delia Fuhrmann (postdoctoral scientist)

I am interested in lifespan development and plasticity. My current research focus is executive functions, such as reasoning and working memory, and their neural substrates. I am investigating this topic using mostly large, publicly available data-sets and multivariate statistical tools like Structural Equation Modelling.

Sophia Borgeest (PhD student)

I am interested in healthy aging, and especially in why some people seem to age much better (or more successfully) than others. In my first project, I used psychometric techniques in a large-sample, multimodal dataset to further understand the associations between leading an active lifestyle and cognitive health. Currently, I am exploring how we can better measure brain structure throughout the lifespan. Here, I am looking at detailed structural MR imaging metrics that might allow for a biologically more accurate picture of the human brain, hopefully allowing us to better describe and quantify healthy neural aging. I like engaging the public and writing for popular audiences. I wish there were more women in leadership and that Cambridge were closer to the mountains.

Ivan Simpson-Kent (PhD student)

I want to understand how brain and behaviour interact with each other during development to produce intelligence, particularly in childhood and adolescence. My first project used psychometric modelling (e.g. structural equation modelling) to examine the associations between white matter connectivity and cognitive behaviour in both typically and atypically developing learners. Currently, I’m using insights from network science to hopefully tease apart the ‘causal’ influences between grey and white matter brain regions and cognitive performance. In my spare time, I co-host a podcast called Clever Ramblings (available on YouTube, shameless plug), watch anime, and daydream about my hometown of West Philadelphia.


Erik-Jan van Kesteren (visiting student)

Erik-Jan visited the lab in 2019 to improve exploratory factor analysis for structural brain imaging data. Specifically, he developed and tested methods for taking into account brain symmetry when extracting factors, as asymmetry in the brain may be of interest in development. He is currently working on his PhD in statistics at Utrecht University. You can read more about him here.

Theresa Fox (visiting student)

Theresa Fox worked in our group in 2019 to examine morphometric measures (surface-based measures) and their potential to reflect age-related cognitive and structural brain differences. We hereby examine the utility and reliability of a new pipeline called mindboggle that was used in the large samples for the CamCan and CALM project.

Marie Deserno

Marie Deserno visited the lab in 2018 to work on longitudinal developmental dynamics in children at risk for autism. She used latent growth curve models to study the interplay between motor and language development in early childhood. She is currently finishing her PhD at the University of Amsterdam on network approaches to autism. You can read about her current work here

Susanne de Mooij

Susanne de Mooij visited the lab in 2016 to work in psychometric approaches to aging. She implemented CFA SEM trees to study neural and cognitive age differentiation in the Cam-CAN sample. Her paper on this work can be found here. She is currently working on her PhD in a shared project between Birkbeck and the University of Amsterdam, working on educational games using psychometric techniques, eye tracking and adaptive learning.