CoDe Neuro Lab

CoDe Neuro Lab


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.



Dr. Dafnis Batallé

Lab Lead

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).


Dr. Lucas França

Postodoctoral Research Associate

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.


Dr. Oliver Gale-Grant

PhD Student

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.


Ioannis Valasakis

PhD Student

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!


Laila Hadaya

PhD Student

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.


Ryan Stanyard

PhD Student

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.


Sunniva Fenn-Moltu

PhD Student

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.


Yilan Dong

PhD Student

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.


Dynamic functional connectivity in neonates during active and quiet sleep

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).

This project is funded by a Wellcome Trust Seed Award in Science.

PhD projects available as part of the MRC DTP (Theme 2) and the EPSRC CDT

Recent Publications

Quickly discover relevant content by filtering publications.

Neonatal brain dynamic functional connectivity: impact of preterm birth and association with early childhood neurodevelopment

Abstract Brain functional dynamics have been linked to emotion and cognition in mature individuals, where alterations are associated …

Development of neonatal brain functional centrality and alterations associated with preterm birth

Abstract Formation of the functional connectome in early life underpins future learning and behavior. However, our understanding of how …

Montelukast reduces grey matter abnormalities and functional deficits in a mouse model of inflammation-induced encephalopathy of prematurity

Abstract Encephalopathy of prematurity (EoP) affects approximately 30% of infants born textless 32 weeks gestation and is highly …

Parsing brain-behavior heterogeneity in very preterm born children using integrated similarity networks

Abstract Very preterm birth (VPT; ≤ 32 weeks’ gestation) is associated with altered brain development and cognitive and behavioral …


Find us at the IoPPN:

  • Main IoPPN Building, 1st Floor FANS department, 16, De Crespigny Park, SE5 8AF, London, UK

And at Centre for the Developing Brain:

  • 1st Floor, South WIng, Perinatal Imaging Department, St Thomas’ Hospital, SE1 7EH, London, UK