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KCL

Computational Modelling of the Neonatal Brain Connectivity

King’s College London Department of Forensic and Neurodevelopmental Sciences
✓ Fully Funded ⏰ Closing Soon 🎓 Bioengineering 🎓 Data Science 🎓 Neuroscience mathematical modelling computational modelling network theory mri neurodevelopmental disorders neonatal brain functional connectivity structural connectivity

Develop advanced computational models of neonatal brain connectivity using multimodal MRI data. Investigate neurodevelopmental conditions through integrated structural and functional brain networks, leveraging mathematical and biophysical methods in a novel EU-funded doctoral network.

AI-generated overview

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Why This Research Matters

This research could redefine understanding of early brain development by creating models that connect brain structure and function in neonates. It has significant implications for diagnosing and developing interventions for neurodevelopmental disorders like autism and ADHD, improving human health outcomes through better-targeted treatments.

Computational Neuroscience Brain Networks Graph Theory Neurodevelopment

Project Description

Project Overview

This PhD position focuses on developing computational models of the neonatal brain by combining mathematical and biophysical approaches with advanced diffusion and functional MRI data. The project aims to understand neuronal connectivity in both typically and atypically developing populations to yield new insights into neurodevelopmental disorders such as autism and attention deficit hyperactivity disorder (ADHD).

Using multimodal MRI data from a large cohort of infants including clinical subgroups, the research will create biologically plausible computational models incorporating network theory and neuronal dynamic characteristics. This integrated approach goes beyond conventional single-modality analyses, specifically tailored for studying the developing brain.

What You Will Do

You will work at the intersection of neuroscience and computational methods, using mathematical descriptions of neuronal spontaneous activity and network theory to model neonatal brain connectivity. The role involves collaboration with academic and industry partners across Europe as part of an EU-funded MSCA Doctoral Network, completing a PhD thesis, undertaking secondments with partner institutions, and participating in international training events.

Expected Outcomes

Development of next-generation in vitro and in silico neuroscience tools that accurately model structural and functional brain networks in neonates. The project is expected to improve understanding of neurodevelopmental conditions and advance computational neuroscience methods aimed at early brain development.

Why This Matters

Current understanding of brain connectivity is limited in early life stages. This research addresses this gap by adapting computational models for the neonatal brain, potentially transforming diagnosis and treatment of developmental brain disorders and providing a foundation for future neuroscience applications.

Entry Requirements

Applicants must not hold a PhD/doctoral degree and must comply with MSCA mobility rules (no residency or main activity in the UK for more than 12 months in the last 36 months). Background in computational neuroscience, bioengineering, biophysics, biotechnology, data science, or related fields is implied but not explicitly stated.

How to Apply

Apply online via the King's College London admissions portal by 2026-05-04 with a CV and cover letter (max 500 words). Use the Department of Forensic and Neurodevelopmental Sciences Research MPhil/PhD Full-time programme. Include reference DB-VISIONB-26 in the funding section. Contact Dr Dafnis Batalle at dafniss.batalle@kcl.ac.uk for project queries.

Eligibility

UK/Home
EU
International

Supervisor Profile

DD
Dr Dafnis Batalle
King’s College London, Department of Forensic and Neurodevelopmental Sciences
3702 Citations
35 h-index
Google Scholar

Dr Dafnis Batalle is a researcher specializing in computational neuroscience and neonatal brain imaging. He integrates advanced MRI techniques with mathematical and biophysical modelling to investigate brain development and neurodevelopmental disorders. His work lies at the intersection of applied neuroscience and computational methods, with a strong emphasis on early life brain connectivity.

Key Publications

2017 309 citations
Early development of structural networks and the impact of prematurity on brain connectivity
2022 226 citations
The developing human connectome project neonatal data release
2021 216 citations
The Developing Human Connectome Project: typical and disrupted perinatal functional connectivity
2012 211 citations
Altered small-world topology of structural brain networks in infants with intrauterine growth restriction and its association with later neurodevelopmental outcome
2019 152 citations
From pattern classification to stratification: towards conceptualizing the heterogeneity of Autism Spectrum Disorder

Research Contributions

Studied the early development of structural brain networks and the impact of prematurity on brain connectivity.
Improved understanding of how prematurity affects brain development, informing neonatal care and interventions.
Contributed to the Developing Human Connectome Project with neonatal data on brain connectivity.
Provided a foundational dataset to advance research on typical and disrupted perinatal brain functional connectivity.
Explored altered small-world topology in structural brain networks of infants with intrauterine growth restriction.
Linked brain network alterations to later neurodevelopmental outcomes, aiding early diagnosis and treatment planning.
Applied computational neuroscience and graph theory methods to understand brain networks and neurodevelopment.
Enhanced the modeling and interpretation of complex brain connectivity patterns relevant to neurodevelopmental disorders.

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