Computational and Systems Neuroscience
Creating new techniques to understand the brain
Led by Associate Professor Adeel Razi (pictured right), the interdisciplinary team uses the latest MBI imaging technologies for their investigations in collaboration with a large network of national and international partners.
They use advanced computational and theoretical tools borrowed from dynamical systems theory, signal processing, and machine learning to develop new techniques to understand how the brain is organised.
Research within this group is geared towards developing new (dynamic causal) models that can explain how the brain’s measured data is caused. These generative models are used to gain mechanistic insights about how the brain is functionally organised. In other words, these generative models are used to:
- Understand how different parts of the brain are connected with each other to process information in health.
- Characterise brain dysfunction in neurodegenerative and neurodevelopmental disorders.
New PhD students join the lab
Devon Stoliker is a PhD student from Canada and has an interest in using mind altering drugs to understand the neural basis of consciousness. During his PhD research in Associate Professor Razi's lab, he will use brain imaging and computational modelling to understand subjective effects of classic psychedelics such as ego dissolution and the unitive experience.
Paul Aswin is a new PhD student enrolled in the IITB-Monash Academy in India. His research interest is in obtaining a novel synthesis of the laws governing emergence of an arrow of sophistication in nature, and a better understanding of the resulting biological self-organisation.
Trippy tech explores brain power under LSD
To learn more about our recent work exploring how the brain processes information when under the influence of hallucinogens, read ‘Trippy tech explores brain power under LSD’.
K. J. Friston, J. Kahan, A. Razi, K. E. Stephan, O. Sporns (2014) “On nodes and modes in resting state fMRI”, NeuroImage, Volume 99, pp. 533-547. Link
A. Razi, and K. J. Friston (2016), “The connected brain: Causality, models and intrinsic dynamics”, IEEE Signal Processing Magazine. vol. 33, no. 3, pp. 14-35. doi: 10.1109/MSP.2015.2482121 Link
A. Razi, M. L. Seghier, Y. Zhou, P. McColgan, P. Zeidman , H. J. Park, O. Sporns, G. Rees, K. J. Friston, (2017) ``Large-scale DCMs for resting state fMRI", Network Neuroscience. vol. 1, no. 3, pp. 222-241. Link
H.J. Park, K. J. Friston, C. Pae, B. Park, A. Razi, (2018) ``Dynamic effective connectivity in resting state fMRI", NeuroImage. vol. 180 (part B), pp. 594-608, 2018. Link.
K. Preller*, A. Razi*, P. Zeidman, P. Stämpfli, K. J. Friston, F. X. Vollenweider, (2019) ``Effective connectivity changes in LSD-induced altered states of consciousness", Proceeding of National Academy of Sciences. vol. 116, no. 7, pp. 2743-2748. Link
Y. Zhou, K. J. Friston, P. Zeidman, J. Chen, S. Li, A. Razi, (2018) ``The hierarchical organisation of the default, dorsal attention and salience networks in adolescents and young adults", Cerebral Cortex. vol. 28, no. 2, pp. 726–737. Link
P. McColgan, A. Razi, S. Gregory, K. K. Seunarine, A. Durr, R. A.C. Roos, B. Leavitt, R. Scahill, C. A. Clark, D. Langbehn, G. Rees and S. J. Tabrizi, and the Track-HD Investigators, (2017) ``Structural and functional brain network correlates of depressive symptoms in premanifest Huntington’s disease", Human Brain Mapping. vol. 38, no. 6, pp. 2819–2829. Link
S. Gregory*, J. Long*, S. Kloeppel, A. Razi, Elisa Schiller, Lora Minkova, , E. Johnson, A. Durr, B. Leavitt, R. A.C. Roos, J. Mills, J. C. Stout, R. Scahill, S. J. Tabrizi, G. Rees and the Track-HD Investigators, (2018) `` Testing a longitudinal compensation model in premanifest Huntington’s disease ", Brain. vol. 141, no. 7, pp. 2156–2166. Link