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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

  • Krishnan, ML, Wang, Z, Aljabar, P, Ball, G, Mirza, G, Saxena, A, Counsell, SJ, Hajnal, JV, Montana, G, Edwards, AD. Machine learning shows association between genetic variability in PPARG and cerebral connectivity in preterm infants. Proceedings of the National Academy of Sciences of the United States of America 114(52) : 13744 -13749 2017
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  • Ball, G, Counsell, SJ. Connectomics. 770 -774 2017
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  • Ball, G, Beare, R, Seal, ML. Network component analysis reveals developmental trajectories of structural connectivity and specific alterations in autism spectrum disorder. Human Brain Mapping 38(8) : 4169 -4184 2017
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  • Chung, AW, Schirmer, MD, Krishnan, ML, Ball, G, Aljabar, P, Edwards, AD, Montana, G. Characterising brain network topologies: A dynamic analysis approach using heat kernels. NeuroImage 141: 490 -501 2016
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  • Krishnan, ML, Wang, Z, Silver, M, Boardman, JP, Ball, G, Counsell, SJ, Walley, AJ, Montana, G, Edwards, AD. Possible relationship between common genetic variation and white matter development in a pilot study of preterm infants. Brain and Behavior 6(7) : e00434 2016
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