Research Areas

 

Figure1Discovering fundamental principle of normal cognitive functions and their alterations

Dipanjan Roy’s group is currently studying large-scale dynamics of brain networks under specific physical, anatomical constraints inferred from modern-day neuroimaging methods EEG, MEG, fMRI, DTI/DWI using resting and task conditions. Our group combines three complementary approaches Neuroimaging, Psychophysics, and Computational modeling to understand emotions, aging, perception, working memory, learning and neuroplasticity and individual brain states. More specifically, in our research we look at the specific influence physical parameters such as noise, conduction delay, brain-states has on the underlying network organization of the brain (structural modularity, small world network topology, scale-free topology etc.). Further, using Visual, Auditory, and Somatosensory stimulation paradigms we look at neural adaptation, learning on the efficiency of signal propagation and synchronization between interconnected brain areas. We use a combination of data analysis using signal processing theories, graph theory methods, and modern machine learning approach, developing sophisticated nonlinear methods based on dynamical systems theory, mathematical modeling, and empirical studies to investigate these phenomena.

Investigating atypical development of cognitive, affective and social brain connectivity in individuals with neurodevelopmental disorders

Neurophysiological processes and behavioural responses in human subjects are measurable using indirectly noninvasive fMRI, directly using surface recording using EEG, MEG–  Brain oscillations, normal responses change due to cortical lesion,  learning, memory consolidation — and interacts dynamically with the intrinsic spontaneous oscillations  that are inevitably present in the brain due to large scale anatomy and connectivity between the brain modules (inter and intra-hemispheric connectivity) in the absence of external stimuli. We are interested in the specific alteration in the cognitive response and try to understand mechanistically underlying neuronal changes from which they derive. To address the above research problem we systematically develop mathematical tools for understanding how brain networks reconfigure over multiple time scales. Along with our collaborators with their expertise in human behavior, we apply these tools to understand perceptual learning, vision, and psychiatric disease.

Recently selected papers:

Metastability in Senescence Trends in Cognitive Sciences 2017 Shruti Naik, Bapi S.Raju, Arpan Banerjee, Gustavo Deco, Dipanjan Roy Trends Cogn Sci. 2017 May 9. pii: S1364-6613(17)30079-7. doi: 10.1016/j.tics.2017.04.007

Metastability of Cortical BOLD Signals in Maturation and Senescence IEEE  Xplore conference proceedings IJCNN 2017 Shruti Naik, Subbareddy Oota, Arpan Banerjee, DipanjanRoy, Bapi S.Raju DOI: 10.1109/IJCNN.2017.7966435Publisher: IEEEElectronic ISSN: 2161-4407

Combining Multiscale Diffusion Kernels for Learning the Structural and Functional Brain Connectivity Sriniwas Govinda SurampudiShruti NaikAvinash ShramaRaju Surampudi BapiDipanjan Roy July 2017 Scientific Reports Nature Publishing (In revision)

Does the regulation of local excitation-inhibition balance aid in a 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, Rodrigo Sigala, Michael Breakspear, Anthony Randal McIntosh, Viktor K. Jirsa, Gustavo Deco, Petra Ritter. Revealing how local and global plasticity shapes the brain’s dynamical landscape. Brain Connectivity Journal 01 oct 2014 dx.doi.org/10.1089/brain.2014.0252.

Developing new computational methods for characterizing the structural and functional architecture of the human brain

  • RP_Fig2 copy-page-001 (1)The human brain is a complex system capable of producing nonstationary spatio temporal 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, 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:

    Multiscale Diffusion Kernels for Learning the Structural and functional connectivity Sriniwas Govinda Surampudi, Shruti Naik, Avinash Shrama, 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 

    Identifying large-scale neurocognitive networks to uncover the role of context, learning, attention for unisensory and multisensory perception using EEG 
    Figure11

    We aim to investigate the role of local oscillations under normal human brain and also functional role of abnormal oscillations in 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 behaviour with the goal of identifying underlying neurophysiological processes of the disease and informing clinical interventions using brain network recovery studies.

    Recently selected papers:

    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 
  • Fig2_082014_halpha_2_2 

     

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

    Figure8

    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:

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).

  • Constrained mean field activity and generators using EEG, tDCS to understand maladaptive neuroplasticity 

    Figure_1Collective dynamics can create complex patterns of the population of neurons. Spiking dynamics occurs on a fast time scale typically observed in-vivo in neuronal microcircuits and bursting dynamics occurs on a much slower time scales. We study behavior of large populations constrained by properties and 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

Funding 

[1] Department of Biotechnology (DBT) Ramalingaswami re-entry fellowship Govt. of India 

[2] Department of Science and Technology (DST) Cognitive Science Research Initiative Govt. of India 

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