Cognitive Brain Dynamics Lab IITJodhpur

Research Areas

Aging, Brain health, and neurocompensatory mechanisms 

At the core of our work is a fundamental question: how do large-scale brain networks — shaped by the brain’s physical and anatomical architecture — give rise to the rich complexity of human cognition?

To answer this, we harness the complementary strengths of modern neuroimaging — EEG, MEG, fMRI, and DTI/DWI — capturing brain activity across both resting states and goal-directed tasks. Our approach rests on three interlocking pillars:

  • Neuroimaging — mapping the brain’s structure and function with high spatial and temporal resolution
  • Computational & Data-Driven Methods — leveraging signal processing, graph theory, machine learning, and deep learning to extract meaningful patterns from complex neural data
  • Behavioural Experiments — grounded in established neurocognitive and neuropsychological theory, linking brain dynamics to observable human behaviour

Perception, Attention, and Memory Across the Lifespan

A central theme of our research is understanding how multisensory perception and attentional control jointly shape memory processing — and how cognitive aging disrupts this balance. We investigate age-related changes in perceptual integration, attentional variability, working memory, and episodic memory, seeking to characterise how the aging brain adapts — or struggles to adapt — to the demands of everyday cognition.

Neurodynamical Modelling of the Aging Brain

We employ neurodynamical computational modelling to probe how biophysical and physiological parameters — including neural noise, conduction delays, and brain states — influence the relationship between structural and functional connectivity in the aging brain. This includes a rigorous examination of large-scale network properties such as modularity, small-world and scale-free topology, and centrality, alongside dynamic measures of synchrony, coherence, metastability, and flexibility.

Multisensory Brain Connectivity — In Space and Time

Using carefully designed unisensory (visual, auditory, somatosensory) and multisensory (visual–auditory, visual–tactile) fMRI and EEG paradigms, we map the brain networks and region-specific interactions that underlie multisensory processing. A key goal is fingerprinting multisensory brain connectivity — identifying individual-level signatures of how the brain integrates information across the senses, across both space and time.

Methods at the Frontier

Underpinning all of this is a commitment to methodological innovation. We develop and apply advanced analytical frameworks drawing from dynamical systems theory, mathematical modelling, graph theory, and modern machine learning and deep learning, enabling us to trace lifespan-associated changes in brain connectivity and dynamics with precision and depth.

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 2022 International 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 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

Decoding the Social Brain in Childhood Neuroimaging × Machine Learning × Developmental Neuroscience

We are mapping the developing brain’s social circuitry — using cutting-edge neuroimaging and machine learning to decode how children process the social world, and what happens when that development goes off course.

selected papers:

Can AI Read a Child’s Mind? Decoding Social Brain States with Explainable Deep Learning

One of the earliest milestones in a child’s development is the ability to understand that others hold beliefs and perspectives different from their own — known as Theory of Mind (ToM). We developed an explainable deep learning framework to decode brain states during the false-belief task in young children, predicting individual performance while revealing which neural features drive it — offering a transparent window into the developing social brain and new pathways for identifying atypical social cognitive development early.

Bhavna, K., Akhter, A., Banerjee, R., & Roy, D. (2024). Explainable deep-learning framework: decoding brain states and prediction of individual performance in false-belief task at early childhood stage. Frontiers in Neuroinformatics, 18, 1392661.

Atypical Brain Network Dynamics in Neurodevelopmental Disorders

The brain is never truly at rest. Even in the absence of external stimuli, large-scale neural oscillations pulse continuously — shaped by the brain’s intrinsic anatomy and the dense web of connections linking its modules across and between hemispheres. In typical development, these spontaneous rhythms interact fluidly with evoked responses during perception, learning, and memory. In neurodevelopmental disorders, this dynamic balance is disrupted — and understanding how and why is at the heart of our research.

We measure neurophysiological processes and behavioural responses non-invasively using fMRI, EEG, and MEG, capturing both hemodynamic signatures and rapid oscillatory dynamics across multiple timescales. Our focus is on pinpointing the specific alterations in cognitive response that characterise neurodevelopmental conditions, and tracing these back to their underlying neuronal mechanisms — from cortical connectivity and lesion effects to learning and memory consolidation.

To do this rigorously, we develop mathematical and computational frameworks tailored to track how brain networks reconfigure over time. In close collaboration with experts in human behaviour, we deploy these tools to investigate perceptual learning, visual processing, and psychiatric disease — working toward a mechanistic, clinically grounded understanding of atypical brain dynamics.

 

 

 

 

 

 

 

 

 

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.

 

Bridging Brain Theory, Computation, and Experiment — From Neural Dynamics to Psychiatric Disease

The human brain is among the most complex systems in nature — generating rich, ever-changing spatiotemporal signals that underlie everything we think, feel, and do. Understanding these signals requires more than measurement alone; it demands theory.

We develop and apply mathematical frameworks rooted in neural mass models — describing population-level firing rates and time-dependent regional dynamics — to simulate and predict how the brain behaves as a system. These theory-driven predictions, grounded in biological constraints, serve as a powerful engine for experimental design — guiding the construction of novel sensory stimuli that probe brain dynamics across multiple spatial and temporal scales.

Mapping the Brain’s Structural and Functional Architecture

Using a rich suite of non-invasive neuroimaging techniques — fMRI, MEG, MRI, DTI, and DSI — we examine how the brain’s structural scaffold gives rise to its functional dynamics. Our goal is to uncover the fundamental organisational principles that govern both anatomy and function, including:

  • The topology of brain networks and how modular organisation supports specific cognitive functions
  • The spatial and temporal scaling of network architecture
  • The adaptability of brain networks in response to rising cognitive demands and in the context of learning

Computational Neuropsychiatry — When Networks Break Down

We are deeply invested in the emerging field of computational neuropsychiatry, where converging evidence from brain disorders illuminates the principles of healthy brain function. Our focus spans schizophrenia and Parkinsonian disease — conditions characterized by disrupted connectivity patterns, widespread wiring inefficiency, and compromised neurochemical balance. Together, these disruptions cascade across scales, profoundly altering whole-brain network dynamics. By modelling these breakdowns computationally, we aim to uncover the mechanistic roots of psychiatric and neurological disease.

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 the Brain Builds Cognition — Tracing the Path from Sensation and Perception to Attention, Emotion, Prediction, Learning, and Memory

Neural oscillations are fundamental to the coordination of cognitive processes — integrating perception, attention, and action across distributed brain networks. In neuropsychiatric conditions, disruptions to these oscillatory dynamics impair long-range communication between brain regions, with direct consequences for cognition and behaviour. Our research employs network-based computational methods to characterise these disruptions and identify mechanistic targets for therapeutic intervention.

A parallel and complementary focus concerns the brain’s operation near criticality — a dynamical regime at the boundary of order and chaos associated with optimal information processing and adaptability. We investigate the signatures of criticality through the analysis of long-range spatiotemporal correlations and neuronal avalanche dynamics, as well as behaviourally relevant oscillatory bursts such as gamma-band activity during attentional engagement.

To formalise these observations, we develop drift-diffusion models grounded in statistical physics, providing a unified theoretical framework for understanding how neural population dynamics emerge from and feed back into the brain’s large-scale network architecture.

  • Prestimulus Periodic and Aperiodic Neural Activity Shapes McGurk Perception. Singh, Vinsea AV, Vinodh G. Kumar, Arpan Banerjee, and Dipanjan Roy. eNeuro (2025).
  • 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.
  • Vinodh G. 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.

Neurostimulation and Closed-Loop Feedback for Multisensory Speech Processing and Oscillatory BCI Models

Figure_1

How do we hear a voice and simultaneously read lips — and why does the brain sometimes fuse these signals into a unified percept, even when they conflict? Multisensory integration is one of the brain’s most elegant and enigmatic capabilities, and understanding how it can be selectively enhanced or disrupted lies at the heart of this research theme.

We combine neurostimulation techniques — including transcranial magnetic stimulation (TMS) and transcranial alternating current stimulation (tACS) — with closed-loop feedback paradigms to actively shape neural responses during multisensory speech processing. Rather than simply observing the brain, we intervene — probing causal relationships between oscillatory dynamics and perceptual outcomes, and testing whether targeted stimulation can steer the brain toward more precise or flexible sensory integration.

A central focus is the role of neural oscillations — particularly gamma and beta rhythms — in binding auditory and visual streams of speech into coherent perception. We investigate how prestimulus brain states set the stage for what we ultimately hear and see, and how disrupting or enhancing these states shifts perceptual experience.

This work extends naturally into the design of oscillatory brain-computer interface (BCI) models — frameworks that exploit the brain’s rhythmic architecture to build more responsive, adaptive, and biologically grounded human-machine communication systems, with direct implications for assistive technology and neurorehabilitation.

  • 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:

  • Science and Engineering Research Board (SERB) Core Research Grant, Govt. of India
  • Department of Biotechnology (DBT) Flagship project on Common Mental Health and Brain Mapping, Department of Biotechnology (DBT), Govt. of India
  • Department of Science and Technology (DST) Cognitive Science Research Initiative, Govt. of India
  • Department of Science and Technology (DST)
  • NBRC and IIT Jodhpur core