Brain connectivity and cognition in healthy and pathological aging Shruti is a MS by Research thesis student at IIIT Hyderabad since Dec, 2015. She is interested in Computational Neuroscience, Artificial Neural Networks, Machine Learning, Cognitive Neuroscience. Currently, she is working on brain connectivity, Cognitive Neuroscience and neurodynamic models to differentiate between healthy and pathological aging.
Nayanica Srivastava (JRF project)
Causal interaction among the neurocognitive networks
Nayanica Srivastava is currently working on the project Default-mode brain network dynamics in Cognition at CBCS, University of Allahabad. devices. She is a PostGraduate (M.Tech) in Digital Communication from DITU, Dehradun. Her area of interest covers Digital Signal Processing, Digital Communication. Currently, she is investigating fMRI connectivity analysis to understand dynamical interactions between prominent brain networks and their implication in Cognition.
Vatika Harlalka (MS by research)
ASD functional connectivity at rest using fMRI Simulating large-scale brain network dynamics using mean field models and constrained structural connectivity between brain areas. Simulated BOLD activity is constrained by neuroimaging resting state functional data acquired using fMRI. Designing benchmark simulations to understand the relationship between neuronal activity and metabolic hemodynamic response as measured by fMRI brain signals in Autism Spectrum Disorders (ASD).
Snigdha Dagar (MS by research)
How endogenous brain states interacts with therapeutic mechanisms where antagonising these subject-specific maladaptive alterations by regulation of cortical excitability/activity and induction of beneficial plasticity are crucial for re-installing efficient information transfer in the brain during neurorehabilitation. In this perspective article, we propose that innovative technologies of portable electroencephalography (EEG) and functional-near-infrared spectroscopy (fNIRS) neuroimaging will be able to objectively quantify the individual brain state with computational neural mass models in order to understand the impact of Brain State Dependent Electrotherapy (BSDE) in post-stroke rehabilitation.
Govinda Surampudi (Jointly with Avinash Sharma) (MS by research)
Multiscale diffusion kernels and machine learning techniques to learn functional connectivity features in the Human cortex
Govinda is a Research Scholar at IIIT-since July 2015. He graduated from Nirma University, Ahmedabad in computer science in 2013. He has worked at Samsung Research Institute, Noida for 2 years on Android OS and applications. He is interested in Brain-Computer Interface, Computer Vision and Machine Learning. Currently, he is presently working with CVIT and Cognitive Science Lab at IIIT-Hyderabad on Multiscale diffusion kernel to learn functional connectivity features in the Human Cortex.
Akanksha Gupta (Research Assistant project)
Akanksha Gupta is a graduate(B.Tech.) in Electronics & Communications Engineering from Dr.A.P.J. Abdul Kalam Technical University, Lucknow. Her area of interest covers Signal processing techniques and Communication systems. She is interested in understanding large-scale synchronisation/coherence and criticality in the brain dynamics using EEG auditory signals.
Saurabh graduated in physics from BIT Mesra and pursuing masters in cognitive science at CBCS, Allahabad. He is currently investigating effective connectivity in neurodynamic models. He wants to understand how Theory of mind (TOM) can be conceptualised from spatiotemporal dynamical changes in the large-scale neurocognitive networks.