Research Areas

Lifespan associated alterations in large-scale brain network dynamics and cognitive functions 
Presentation2

We are 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 goal oriented task conditions. Our group combines three complementary approaches Neuroimaging, Computational methods including data driven analysis and machine learning, and behavioural experiments grounded on the neurocognitive and neuropsychological theories to understand how multisensory perception, attentional control together shapes up memory processing and how does cognitive Aging impacts perceptual integration, attentional variability, working memory and episodic memory processing. We also apply neurodynamical computational modelling framework to uncover specific influence of biophysical and physiological parameters such as noise, conduction delay, brain-states  on the large-scale brain network structural-functional connectivity relationship (modularity, small-world network topology, scale-free topology, centrality) and dynamics (synchrony, coherence, metastability, flexibility) of the Aging brain. Further, exploiting unisensory (Visual, Auditory, and Somatosensory) and multisensory (combining Visual-Auditory, Visual-tactile stimulus) fMRI and EEG paradigms we investigate brain networks, specific  interactions and fingerprinting multisensory brain connectivity in space and time.  For all understanding lifespan associated changes in brain functional connectivity and dynamics we use a combination of data analysis approach using signal processing theory, graph theory, and modern machine learning and deep learning approach, develop advance methods based on dynamical systems theory, mathematical modelling, and empirical studies to investigate the above research areas. 

selected papers:

Reconfiguration of directed functional connectivity among neurocognitive networks with ageing: Considering the role of thalamo-cortical interactions Moumita Das, Vanshika Singh, Lucina Q. Uddin, Arpan Banerjee, Dipanjan Roy Cerebral Cortex (2021) 31 (4), 1970-1986

MEG Oscillatory and Aperiodic neural dynamics contribute to different cognitive aspects of short-term memory decline through the lifespan. Thuwal, Kusum, Arpan Banerjee, and Dipanjan RoybioRxiv (2021).

Sahoo B, Pathak A, Deco G, Banerjee A, Roy D. Lifespan associated changes in global patterns of coherent communication Neuroimage 216, 116824(2020)

Integrative network analysis reveals the cell-type-specific changes in the hippocampus of young, aging and Alzheimer’s disease Vinay Lanke, S.T.R. Moolamalla, Dipanjan Roy and P.K.Vinod Frontiers in Aging Neuroscience  DOI: 10.3389/fnagi.2018.00153

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

Atypical brain network dynamics in neurodevelopmental disorderspicture-1-1

Neurophysiological processes and behavioral 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 behaviour, we apply these tools to understand perceptual learning, vision, and psychiatric disease.

selected papers:

Atypical core-periphery brain dynamics in autism: Implications for symptom severity Dipanjan Roy and Lucina Q. Uddin Network Neuroscience page 1-27 2021

Atypical flexibility in dynamic functional connectivity quantifies the severity in autism spectrum disorder” Harlalka, V., Bapi, R. S., Vinod, P. K., & Roy, D. DOI: 10.3389/fnhum.2019.00006 (2019)

Age, disease and their interaction effects on the intrinsic connectivity of children and adolescents in Autism Spectrum Disorder using functional connectomics Vatika Harlalka et al. Brain Connectivityhttp://doi.org/10.1089/brain.2018.0616 Published in Volume: 8 Issue 7: September 17, 2018, Online Ahead of Print: August 29, 2018 

The neural substrate of group mental health: Insights from a multi-brain reference frame in functional neuroimaging  Front. Psychol. 2017, Dipanjan Ray, Dipanjan RoyBrahmdeep Sindhu, Pratap Sharan and Arpan Banerjee

Multi-scale, Multimodal imaging and machine learning methods to characterise impact of brain lesions, axonal injury in structure-function-dynamics relationship in cognitive processing and functional recovery 

RP_Fig2 copy-page-001 (1)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.

selected papers:

Naskar, A., Vattikonda, A., Deco, G., Roy, D., & Banerjee, A. (2021) Multiscale dynamic mean field model (MDMF) to relate resting-state brain dynamics with local cortical excitatory-inhibitory neurotransmitter homeostasis. Network Neuroscience, 1-55 2021

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

Multiple Kernel Learning Model for Relating Structural and Functional Connectivity in the Brain Govinda Surampudi, Shruti Naik, Bapi Raju Surampudi, Viktor K.Jirsa, Avinash Sharma, and Dipanjan Roy Scientific Reports 8.1 (2018): 3265.

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

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

How sensory processing, perception, attention, predictive coding and learning give rise to memory and cognitive processing using behaviour, EEG, MEG and fMRI 

Figure11

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. KumarShrey DuttaSiddharth TalwarDipanjan RoyArpan 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 connectivity4(10), 791-811.

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

  • Figure_1Collective 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.

Funding 

Department of Biotechnology (DBT) Govt. of India 

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

NBRC core  

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