Research Areas
**Brain Aging, Brain health, and neurocompensatory mechanisms in large-scale brain network dynamics and cognitive functions **(attention, perception, memory, and emotion)
We are currently studying large-scale brain network dynamics 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:
Local homeostasis preserves global neural dynamics compensating for structural loss during human lifespan aging. Saha, S., Chakraborty, P., Naskar, A., Roy, D. , & Banerjee, A. (2025). Communications Biology , 8(1), 1251.
Contributions of short-and long-range white matter tracts in dynamic compensation with aging. Chakraborty, P., Saha, S., Deco, G., Banerjee, A., & Roy, D. (2025). Cerebral Cortex , 35(2).
Stability of the sensorimotor network sculpts the dynamic repertoire of the resting state over the lifespan. Sastry, N. C., Roy, D., & Banerjee, A. (2023). Cerebral Cortex , 33(4), 1246-1262.
Biophysical mechanism underlying compensatory preservation of neural synchrony over the adult lifespan.Pathak, A., Sharma, V., Roy, D., & Banerjee, A. (2022). Communications Biology , 5(1), 567.
Characterizing the Dynamic Reorganization in Healthy Aging and Classification of Brain Age. Dash, A., Bapi, R. S., Roy, D., & Vinod, P. K. (2022, July). In 2022International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). IEEE.
**Reconfiguration of directed functional connectivity among neurocognitive networks with aging: 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
Aperiodic and periodic components of ongoing oscillatory brain dynamics link distinct functional aspects of cognition across the adult lifespan. Thuwal, Kusum, Arpan Banerjee, and Dipanjan Roy. ** Eneuro **8, no. 5 (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 o _f 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
Mapping mental states using machine learning models to predict social cognition and brain connectivity associated with atypical neurodevelopment in childrenusing** fMRI, EEG, and naturalistic task **
selected papers:
- Explainable deep-learning framework: decoding brain states and prediction of individual performance in false-belief task at early childhood stage. Bhavna, K., Akhter, A., Banerjee, R., & Roy, D.(2024). Frontiers in Neuroinformatics , 18 , 1392661.
- Characterization of the temporal stability of ToM and pain functional brain networks carry distinct developmental signatures during naturalistic viewing. Bhavna, Km, Niniva Ghosh, Romi Banerjee, and Dipanjan Roy. Scientific Reports 14, no. 1 (2024): 22479.
Atypical brain network dynamics in neurodevelopmental disorders
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:
A lightweight, end-to-end explainable, and generalized attention-based graph neural network model trained on high-order spatiotemporal organization of dynamic functional connectivity to classify autistics from typically developing Bhavna, Km, Niniva Ghosh, Romi Banerjee, and Dipanjan Roy _Network Neuroscience: 1-29. (2025).
Explainable deep-learning framework: decoding brain states and prediction of individual performance in false-belief task at early childhood stage.Bhavna, K., Akhter, A., Banerjee, R., &Roy, D._Frontiers in Neuroinformatics, 18, 1392661 (2024)
Altered global modular organization of intrinsic functional connectivity in autism arises from atypical node‐level processing. Sigar, P., Uddin, L. Q., & Roy, D. (2023). Autism Research , 16(1), 66-83.
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 Connectivity _http://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 Roy , Brahmdeep 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 **
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:
Structural-and-dynamical similarity predicts compensatory brain areas driving the post-lesion functional recovery mechanism. Chakraborty, P., Saha, S., Deco, G., Banerjee, A., & Roy, D. (2023). Cerebral Cortex Communications , 4(3), tgad012.
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 and Deep Neural Networks (DNNs)
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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.
Emotion dynamics as hierarchical Bayesian inference in time. Majumdar, G., Yazin, F., Banerjee, A., & Roy, D. (2023). Cerebral Cortex , 33(7), 3750-3772.
Effective networks mediate right hemispheric dominance of human 40 Hz auditory steady-state response. Kumar, N., Jaiswal, A., Roy, D., & Banerjee, A. (2023). Neuropsychologia , 184 , 108559.
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 naturalistic episodic memories in time. Yazin, F., Das, M., Banerjee, A., & Roy, D. (2021). Scientific reports , 11(1), 12364.
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 neurostimulations, feedback and modulation of neurophysiological responses during multisensory perceptual processing and oscillatory BCI models **
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.
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selected papers:
Predicting Response to McGurk Illusion Based on Periodic and Aperiodic Prestimulus EEG Activity (2024) Vinsea A V Singh, Vinodh G Kumar, Arpan Banerjee, Dipanjan Roy
doi: https://doi.org/10.1101/2022.01.20.477172 https://www.biorxiv.org/content/10.1101/2022.01.20.477172v2
_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.