Research Areas

The human brain is a complex system capable of producing non-stationary spatiotemporal signals. Mathematical descriptions based on neural mass models describing population firing rate and time-dependent analysis of regional time series is capable of making predictions about systems dynamics. This further establishes a direct bridge between biologically inspired theory, simulations, and experimental design. This informed prediction from theory based on biological constraints serves as an important tool for designing novel sensory stimuli to probe brain dynamics at multiple spatial and temporal scales of organization. In our lab, we examine structural and functional brain networks using data from non-invasive neuroimaging techniques  (fMRI, MEG, MRI, DTI, DSI). Our goal is to determine fundamental organizational principles of both underlying anatomy and specificity of functional dynamics. Our results collectively point to principles of the topology of networks that supports certain function of modules, spatial and temporal scaling of network organization, and network adaptability in response to increasing cognitive demands or in the context of learning. We are also interested in the recently emerging field of computational neuropsychiatry where complementary evidence accumulates from neuropsychiatric disease, specifically schizophrenia, the Parkinsonian disease that exhibits disruption of normal connectivity patterns, the prevalence of wiring inefficiency, disruption of neurochemical balance and as a consequence impact directly whole brain network dynamics.

We aim to investigate the role of local oscillations in a normal human brain and also a functional role of abnormal oscillations in the neuropsychiatric disorder characterised by alterations in a distributed activity across brain areas. We use network methods to uncover changes in large-scale brain circuitry that impact on cognitive function and behaviour with the goal of identifying underlying neurophysiological processes of the disease and informing clinical interventions using brain network recovery studies. Critical behaviour in rest-state dynamics: The precise neuronal mechanism generating close to critical dynamics in the brain is hitherto unresolved despite numerous recent investigations. What features of this critical state can be observed in the brain to conjecture that it is critical? To mathematically describe empirical observations we study cortical long-range correlations in space and time. For short-range correlations specifically look at features such as neuronal avalanches providing stability. At behavioural level dynamics is burst-like (e.g. synchronized gamma band (40-80Hz) burst evoked during stimulus detection or attention). We also develop drift-diffusion models based on statistical physics to study the mesoscopic dynamics of neural masses distributed in the various graph like entities such as voxels, nodes comprising several brain areas connected via realistic structural connectivity matrix.

Organization of directed functional connectivity among nodes of ventral attention network reveals the common network mechanisms underlying saliency processing across distinct spatial and spatio-temporal scales. Ghosh, P., Roy, D., & Banerjee, A. (2021) NeuroImage, 231, 117869. doi: https://doi.org/10.1101/2019.12.25.888446

Contextual Prediction Errors Reorganize Episodic Memories in Time. Fahd Yazin, Moumita Das, Arpan Banerjee, and Dipanjan Roy^† Scientific Reports 2021 (accepted)  

VinodhG. Kumar, Shrey Dutta, Siddharth Talwar, Dipanjan Roy^†, Arpan Banerjee ^† Biophysical mechanisms governing large-scale brain network dynamics underlying individual-specific variability of perception European Journal of Neuroscience 52 (7), 3746-3762(2020)

Large-scale functional integration, rather than functional dissociation along dorsal and ventral streams, underlies visual perception and action Dipanjan Ray, Neelambari Hazare, Dipanjan Roy, Arpan Banerjee Journal of Cognitive Neuroscience, 1-15 (2020) 

Large-scale functional brain networks underlying temporal integration of audio-visual speech perception: An EEG study G. Vinodh Kumar, Tamesh Halder, Amit K. Jaiswal, Abhishek Mukherjee, Dipanjan Roy, and Arpan Banerjee  Front. Psychol.|doi: 10.3389/fpsyg.2016.01558 (2016)

Roy, D., Sigala, R., Breakspear, M., McIntosh, A. R., Jirsa, V. K., Deco, G., & Ritter, P. (2014). Using the virtual brain to reveal the role of oscillations and plasticity in shaping brain’s dynamical landscape. Brain connectivity, 4(10), 791-811.

Brain state dependent stimulations, feedback and modulation of neurophysiological responses and oscillatory changes 

• Collective dynamics can create complex patterns of the population of neurons. Spiking dynamics occur on a fast time scale typically observed in-vivo in neuronal microcircuits and bursting dynamics occur on a much slower time scales. We study the behavior of large populations constrained by properties and the paucity of physical connections between the connected units and subtype of synapses. In this work, we build classes of models and apply machine learning principles such as gradient descent algorithm, multi-parametric search to infer the underlying model state space, biophysical mechanisms responsible for observed neuronal dynamics. 

selected papers:

Arup Kumar Pal, Dipanjan Roy, G. Vinodh Kumar, Bipra Chatterjee, L. N. Sharma, Arpan Banerjee, and C.N. Gupta “Empirical Mode Decomposition Algorithms for Classification of Single-Channel EEG Manifesting McGurk Effect.” International Conference Series on Intelligent Human-Computer Interaction 2020 Springer Nature Switzerland AG 2020 U. S. Tiwary and S. Chaudhury (Eds.): IHCI 2019, LNCS 11886, pp. 49–60, 2020 March. 

Near-infrared spectroscopy (NIRS) – electroencephalography (EEG) based brain-state dependent electrotherapy (BSDE) to facilitate post-stroke neurorehabilitation: inhibition–excitation balance hypothesis Snigdha Dagar, Bapi Raju, Subhajit Raychoudhury, Anirban Dutta, Dipanjan Roy Front. Neurol. 7:123. doi:10.3389/fneur.2016.00123

The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models (Rodrigo Sigala, Sebastian Haufe, Dipanjan Roy, Hubert R. Dinse, Petra Ritter) Frontiers in Computational Neuroscience 2014 March Front. Comput. Neurosci. | doi:10.3389/fncom.2014.00036

Mohit H. Adhikari, Pascale P. Quilchini, Dipanjan Roy, Viktor K. Jirsa, Christophe Bernard. Brain state dependent post-inhibitory rebound in entorhinal cortex interneurons.  Journal of Neuroscience 2012 May 9;32(19):6501-10.