Puneet Dheer

About Puneet

Mr Dheer received his Bachelor of Technology and Masters of Technology in Computer Science and Engineering from SRM University, Tamil Nadu, India.

Throughout his research career Puneet was a research intern at the Centre for Artificial Intelligence and Robotics, DRDO, Bangalore, India from 2015 to 2016. He has also worked as a Senior Research Fellow (DBT-Delhi) and Project Linked Researcher (ISI-Kolkata) in epilepsy studies at the Indian Statistical Institute, Bangalore, India (2016-2019) with UAB Epilepsy Center, The University of Alabama at Birmingham, USA; and, was simultaneously selected for Summer School CAMP at the National Centre for Biological Sciences, Bangalore, India.

Puneet’s current research interests include epilepsy, network neuroscience, neurostimulation, computational neural modelling, neural signal processing, functional magnetic resonance imaging (fMRI), and statistical data analysis and applied machine learning.

Project details

Information flow in the normal and epileptic brain

Abnormal function within brain networks is a key determinants of seizure generation, spread and termination. Brain networks are also likely to play an important part in determining therapeutic response and in cognitive impairment associated with epilepsy. The combination of high spatial resolution of functional magnetic resonance imaging (fMRI) and high temporal resolution of electroencephalogram (EEG) holds the potential to detect the abnormality in brain network. However, due to the complexity in modelling both kinds of data, limited understanding is available on the altered information flow in the brain of epileptic patient. The Garg lab at IITD have developed a method utilizing sparse (i.e. constrained) regression to model the fMRI signal as a multivariate auto-regressive process at the voxel level allowing the underlying brain dynamics to be modelled accurately and parsimoniously. The method provides insight into the information flow in the fMRI data, which is postulated to reflect the flow of information in the underlying neural networks. Using simultaneous EEG-fMRI data in control subjects and patients with focal epilepsy acquired at UQ, this project will further develop the autoregressive modelling to identify patterns of abnormal connectivity in focal epilepsy, to determine if abnormalities in connectivity allow the identification of the epileptogenic focus and to understand the role of interictal epileptic transients in detected network abnormalities.

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UQ Supervisor

Professor David Reutens

Centre for Advanced Imaging
IITD Supervisor

Professor Rahul Garg

Department of Computer Science and Engineering