Research Areas

• The human brain is a complex system capable of producing nonstationary 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.

  Recently selected papers:

  Resting State Dynamics Meets Anatomical Structure: Temporal Multiple Kernel Learning (tMKL) Model Govinda Surampudi, Joyneel Mishra, Bapi Raju Surampudi, Gustavo Deco, Avinash Sharma, and Dipanjan Roy Neuroimage Volume 184, 1 January 2019, Pages 609-620 https://doi.org/10.1016/j.neuroimage.2018.09.054. 

  Multiscale Diffusion Kernels for Learning the Structural and functional connectivity Sriniwas Govinda Surampudi, Shruti Naik, Avinash Sharma, Raju S.Bapi, Dipanjan Roy Oct.2,2016; doi:http://dx.doi.org/10.1101/078766.Neural Information Processing Systems (NIPS 2016), Barcelona

  Promises and pitfalls of relating alteration of white matter pathways causing improvement in Cognitive performance. Cognitive Neuroscience Dipanjan Roy & V. S. Chandrasekhar Pammi (DOI: 10.1080/17588928.2016.1205577) July 2016 Cognitive Neuroscience 

  Role of learning, attention and Brain oscillations during unisensory and multisensory perception using EEG and MEG

  
  

  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 characterized 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 behavior with the goal of identifying underlying neurophysiological processes of the disease and informing clinical interventions using brain network recovery studies.

  Recently selected papers:

• Distinct Neurobehavioral Mechanisms for Expectancy Violation and Value Updating Moumita Das and Dipanjan Ray Journal of Neuroscience 3 January 2018, 38 (1) 26-28; DOI: https://doi.org/10.1523/JNEUROSCI.2708-17.2017

• Segregation and Integration of  cortical information processing underlying cross-modal perception Multisensory Research 2017, Vinodh G.Kumar, Neeraj Kumar,  Dipanjan Roy, Arpan Banerjee (accepted in press)

• 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
• Neurophysiological Investigation of Context Modulation based on Musical Stimulus Siddharth Mehrotra · Anuj Shukla · Dipanjan Roy July 2016 conference proceedings International conference in Music perception and Cognition ICMPC14, July 5–9, 2016, San Francisco, USA.
• Rodrigo Sigala, Sebastian Haufe, Dipanjan Roy, Hubert R. Dinse, Petra Ritter. The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models.  Front. Comput. Neurosci., 04 April 2014 | doi:10.3389/fncom.2014.0003 

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  Characterization of the role of noise, delay, criticality in large-scale brain models (Theory of Brain dynamics) using empirical  EEG dynamics

  

  Critical behavior 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 behavioral 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

  Recently selected papers:

     Does the regulation of local excitation-inhibition balance aid in the recovery of functional connectivity? A computational account  Anirudh Vattikonda, Bapi Raju, Arpan Banerjee, Gustavo Deco, Dipanjan Roy Neuroimage. 2016 Aug 1;136:57-67.doi:10.1016/j.neuroimage.2016.05.002. Epub 2016 May 10

Dipanjan Roy,  VK Jirsa. Inferring network properties of cortical neurons with synaptic coupling and parameter dispersion Roy D, Jirsa VK Front. Comput. Neurosci., 26 March 2013 | doi: 10.3389/fncom.2013.00020 Front. Comput. Neurosci., 26 March 2013 | doi: 10.3389/fncom.2013.00020. 

Dipanjan Roy, Anandamohan Ghosh, and Viktor K Jirsa. Phase description of Neural oscillators with global electric and synaptic coupling. Phys. Rev. E 83, 051909 (2011).

Anandamohan Ghosh, Dipanjan Roy, and Viktor K. Jirsa. A simple model for bursting dynamics. Phys. Rev. E 80, 041930 (2009).

Role of Brain stimulations and modulation of neurophysiological responses (adaptation, plasticity) in reorganization of sensory-motor functions

• 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. 

       Recently selected papers:

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

Konstantin Mergenthaler*, Dipanjan Roy*, Jeremy Petravicz, Mriganka Sur and Klaus Obermayer. Changes in V1 orientation tuning when blocking astrocytic glutamate transporters: models for extra and Intra synaptic mechanisms. BMC Neuroscience 2013, 14(Suppl 1):P298     doi:10.1186/1471-2202-14-S1- P298.

Mohit H. Adhikari, Dipanjan Roy, Pascale P. Quilchini, 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.

Dipanjan Roy, Yenni Tjandra, Konstantin Mergenthaler, Jeremy Petravicz, Caroline A. Runyan, Nathan R. Wilson, Mriganka Sur, Klaus Obermayer. Afferent specificity, feature specific connectivity influence orientation selectivity: A computational study in mouse primary visual cortex. The Journal of Neuroscience http://adsabs.harvard.edu/cgi-bin/bib_query?arXiv:1301.0996 2013 Feb ArXiv preprint