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

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Effects of gestational age at birth on perinatal structural brain development in healthy term-born babies

Multiple studies have demonstrated less favourable childhood outcomes in infants born in early term (37-38 weeks gestation) compared to …

Emerging functional connectivity differences in newborn infants vulnerable to autism spectrum disorders

Studies in animal models of autism spectrum disorders (ASD) suggest atypical early neural activity is a core vulnerability mechanism …

Investigating altered brain development in infants with congenital heart disease using tensor-based morphometry

Magnetic resonance (MR) imaging studies have demonstrated reduced global and regional brain volumes in infants with congenital heart …

Development of Microstructural and Morphological Cortical Profiles in the Neonatal Brain

Interruptions to neurodevelopment during the perinatal period may have long-lasting consequences. However, to be able to investigate …


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