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Details

Role Senior Research Fellow
Research area Clinical Sciences

Contact

Available for student supervision
Studying brain development from the time of birth, through childhood and into adolescence.

Dr Ball's research combines Magnetic Resonance Imaging, bioinformatics and state-of-the-art machine learning models to discover the patterns that underlie typical brain development and identify the mechanisms that can lead to common neurodevelopmental disorders such as autism or ADHD. He is particularly interested in the impact of preterm birth on early brain development, and the long-term effects of early life adversity on a child's cognitive and functional outcomes.

Other aspects of Dr Ball's research program focus on the application of machine learning to identify abnormal movement patterns in infants at risk of developing cerebral palsy, the characterisation of pathological tissue types in paediatric brain tumour and modelling of structural connectivity networks in the brain.

Available projects include:
- Mapping cortical networks in the developing brain
- Modelling infant movements using video capture
- Combining neuroimaging and transcriptomics to model cortical development
Studying brain development from the time of birth, through childhood and into adolescence.

Dr Ball's research combines Magnetic Resonance Imaging, bioinformatics and state-of-the-art machine learning models to discover the patterns that underlie...
Studying brain development from the time of birth, through childhood and into adolescence.

Dr Ball's research combines Magnetic Resonance Imaging, bioinformatics and state-of-the-art machine learning models to discover the patterns that underlie typical brain development and identify the mechanisms that can lead to common neurodevelopmental disorders such as autism or ADHD. He is particularly interested in the impact of preterm birth on early brain development, and the long-term effects of early life adversity on a child's cognitive and functional outcomes.

Other aspects of Dr Ball's research program focus on the application of machine learning to identify abnormal movement patterns in infants at risk of developing cerebral palsy, the characterisation of pathological tissue types in paediatric brain tumour and modelling of structural connectivity networks in the brain.

Available projects include:
- Mapping cortical networks in the developing brain
- Modelling infant movements using video capture
- Combining neuroimaging and transcriptomics to model cortical development

Top Publications

  • Pua, EPK, Ball, G, Adamson, C, Bowden, S, Seal, ML. Quantifying individual differences in brain morphometry underlying symptom severity in Autism Spectrum Disorders. Scientific Reports 9(1) : 9898 2024
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  • Cullen, H, Krishnan, ML, Selzam, S, Ball, G, Visconti, A, Saxena, A, Counsell, SJ, Hajnal, J, Breen, G, Plomin, R, et al. Polygenic risk for neuropsychiatric disease and vulnerability to abnormal deep grey matter development. Scientific Reports 9(1) : 1976 2024
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  • Ball, G, Adamson, C, Beare, R, Seal, ML. Modelling neuroanatomical variation during childhood and adolescence with neighbourhood-preserving embedding. Scientific Reports 7(1) : 17796 2024
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  • Stolp, HB, Ball, G, So, P-W, Tournier, J-D, Jones, M, Thornton, C, Edwards, AD. Voxel-wise comparisons of cellular microstructure and diffusion-MRI in mouse hippocampus using 3D Bridging of Optically-clear histology with Neuroimaging Data (3D-BOND). Scientific Reports 8(1) : 4011 2024
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  • Krishnan, ML, Van Steenwinckel, J, Schang, A-L, Yan, J, Arnadottir, J, Le Charpentier, T, Csaba, Z, Dournaud, P, Cipriani, S, Auvynet, C, et al. Integrative genomics of microglia implicates DLG4 (PSD95) in the white matter development of preterm infants. Nature Communications 8(1) : 428 2024
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