Welcome to the Computational Developmental Neuroscience Lab (CoDe-Neuro)
Our research focuses on studying the emergence of brain organization during early development and how subtle alterations in key developmental processes lead to neurodevelopmental disorders. To do so, we use multi-modal MRI, graph theory, whole-brain computational models, machine learning and other signal processing tools to characterise structural and functional connectivity. By these means, we aim to develop biomarkers of typical and atypical development allowing us to predict the heterogeneous outcome of children with a higher likelihood of showing neurodevelopmental conditions.
We work alongside the Forensic and Neurodevelopmental Science (FANS) department at the Institute of Psychiatry, Psychology & Neuroscience and the Centre for the Developing Brain (CDB) at the School of Biomedical Engineering & Imaging Sciences and collaborate with the developing Human Connectome and the AIMS-2-TRIALS projects:
We are grateful to the Wellcome Trust for funding a Seed Awards in Science project.
Both an engineer (MEng Telecommunications) and a neuroscientist (MSc, PhD), I use mathematical tools and computational models to study the emergence of brain organization during early development. Since 2018 I am a Lecturer in Neurodevelopmental Science at the IoPPN (KCL).
I have a BSc in physics, a MSc in Physics (Statistical Physics and Complex Systems), and a PhD in Neuroscience. My research interests include Complex Systems, Computational Neuroscience and Machine Learning.
My undergraduate degree was in medicine at Imperial College London. Following this I worked as a general doctor and psychiatrist. My PhD focuses on the relationship between socio-economic status, brain biology and early life outcomes.
As part of my PhD, I am exploring methods for predicting phenotypes for autism spectrum disorder (ASD) from neonatal brain connectivity, which is quite thrilling!
I have a Neuroscience (BSc) and Psychiatric Research (MSc) background. I am particularly interested in identifying predictive biomarkers of mental health outcomes and trajectories using neuroimaging and machine learning approaches.
Originally trained in Neuroscience and Psychology (BSc, Keele), I then specialised in Neuroimaging (MSc, MRes, King’s College London). My PhD utilises graph theory, neuroimaging methods and whole-brain computational modelling to examine how neurodevelopmental disease modulates the coupling of brain structure and function across the lifespan.
I completed my undergraduate degree in Neuroscience at the University of Glasgow, before joining the MRC Doctoral Training Partnership in Biomedical Sciences at King’s College London. My PhD focuses on functional brain network topology and dynamics in typical and atypical development.
BSc in Biomedical Engineering, Northeastern University China and MSc in Healthcare Technologies King’s College London. Current project focuses is on using machine learning to uncover the early origins of neurodevelopmental disorders.
Functional MRI (fMRI) tells us how different areas of the brain function together. The brain moves through different states, and this dynamic functional connectivity (FC) is key to understanding how the brain works in health, and in neurodevelopmental conditions like autism. However, dynamic FC has only ever been examined in adults. Because how the brain matures in early childhood impacts upon later development; and because neurodevelopmental conditions start early in life; we must look at infants. We aim to identify active and quiet sleep states in neonates using breathing patterns and map dynamic FC during sleep states in more than 600 fMRI datasets already acquired from sleeping newborns. We can then compare dynamic FC from a test sample of babies who are at a higher likelihood of developing conditions, like autism, against this reference. If we are successful with this pilot, future studies will examine i) what alters the maturation of dFC (informing prevention); and ii) whether newborn dynamic FC predicts childhood outcomes (informing intervention).