Brain connectivity and Neurodynamics Lab

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Welcome to our Computational Cognitive Neuroscience lab at National Brain Research Centre (NBRC), Manesar, India.

What exactly are the research questions that we are after?

In general, we are interested in how do hierarchical brain areas communicate using underlying neuronal dynamics to shape up memory, attention, action selection and cognition? We do experiments and combine it with theory developing new methods, model and data analysis to answer these questions.

Researchers in our group is currently investigating how do different brain areas to communicate and allocate resources, routes information and optimize information processing during various task conditions (emotion, perception, multisensory integration).

What exactly do we mean by optimal cognitive processing? What is the time-scale of slow versus fast cognitive processing? Under what context slowness of processing is beneficial versus not beneficial at all? How do we systematically learn new cognitive processes across lifespans? What specific role can we ascribe for perturbation such as traumatic brain injury, lesion in disturbing the balance or optimal functioning of the Brain? How do we compensate or restore such functions?  What exactly is the nature of the causal relationship between functional brain areas and networks under the resting and neurocognitive task conditions? How do the functional responses (e.g. emotion processing, multisensory integration, language processing, attention) change in aging, disease states?

What are our methods in understanding cognitive dynamics?

Our group is also concerned with questions in the area of developing intelligent data analysis combining machine learning, nonlinear dynamical systems, network analysis to uncover interrelationships and dependencies among variables observed from state-of-the-art neuroimaging data recorded at high spatial and temporal precision from fMRI, EEG, MEG.

In the information age, intelligent data analysis goes beyond the pure collection and organization the of data. Hence, our group focuses on intelligent methods, algorithms to understand structural organization of the human brain and their functional impact. With multiple national and international collaboration with leading neuroimaging labs currently the group is engaged in research to the above important questions that arise frequently in cognitive neuroscience, especially to understand the role of learning, context-dependent modulation and noise in shaping up functional and perhaps behavioral responses. Learning is thought to change the connections between the neurons in the brain, a process called synaptic plasticity. Using mathematical modeling and computational tools, we model synaptic plasticity across different time scales (fast time scales milliseconds activity) to very slow synaptic activity (in the order of multiple of minutes, days) that reproduces experimental findings.

We develop computational methods, benchmark simulation platforms, connectivity tool boxes to understand concerted activity such as global oscillations involving multiple brain areas exhibited at different temporal scale (EEG, MEG neuroimaging techniques). At the same time, we also look at the change in the spatial functional connectivity across brain areas (due to aging, learning, and structural lesions) that can be empirically measured using high spatial resolution functional MRI and theoretically predictable, understandable using large-scale simulation and systems neuroscience theory. We are working in tight collaboration with several experimental (national and international) neuroimaging and behavioral laboratories, which performs structural, functional MRI and behavioral responses in human participants. We are also actively engaged in the design of novel stimuli (multi-sensory, unisensory perceptual stimuli) to elicit specific behavioral responses that we are interested in. This allows systematic measurement and neural correlate at the ensemble level responsible for specific behavioral response and manifestation. This would allow us to systematically unravel the timing and the dynamics of cognitive processes.