Publications | Dipanjan Roy

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†: Corresponding author    **: Reviews/Opinion/Perspective/Commentary    #: Joint authorship

[76] Aging increases orbitofrontal neural volatility during affective inference. Majumdar, G., Yazin, F., Banerjee, A., & Roy, D.† (2026). Cerebral Cortex, 36(4), bhag053.

[75] MIRA-Net: A Cross-Cohort Representation Learning Framework for Parkinson’s Disease Classification Using Acoustic and Beta-Band MEG Biomarkers. Akhila, N., Ekbal, A., & Roy, D.† (2026). medRxiv. DOI

[74] Competition in the brain: a conserved principle with lifespan consequences. Roy, D.†, & Banerjee, A. (2026). Nature Communications Biology.

[73] When the inner clock fades: Interoceptive decline and consolidation of phase resetting in cortical rhythms by cardiac events underlie healthy lifespan ageing. Saluja, K., Roy, D., & Banerjee, A. (2026). bioRxiv. DOI (Accepted in Imaging Neuroscience)

[72] Differential role of beta band activity in a dual-task working memory paradigm under internally vs. externally directed cognition. Yadav, A., Banerjee, A., & Roy, D.† (2026). Frontiers in Human Neuroscience, 20, 1791453.

[71] A mechanistic whole brain model to capture simultaneous EEG-fMRI data. Bandyopadhyay, A., Chakravarthy, V. S.†, & Roy, D.† (2026). Cerebral Cortex, 36(1), bhag002.

[70] 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). Nature Communications Biology, 8(1), 1251.

[69] Prestimulus periodic and aperiodic neural activity shapes McGurk perception. Singh, V. A. V., Kumar, V. G., Banerjee, A., & Roy, D. (2025). DOI (Accepted in eNeuro)

[68] Neural signatures of prioritization and facilitation in retrieving repeated items in Visual Working Memory. Narvaria, A. S., Banerjee, A., & Roy, D. (Accepted in Frontiers in Human Neuroscience, 2025) Link

[67] 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 Neurotypicals. Bhavna, K., Ghosh, N., Banerjee, R., & Roy, D. DOI (Accepted in Network Neuroscience, MIT Press, 2025)

[66] 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), bhae496.

[65] Synergistic control of axon regeneration and functional recovery by let-7 miRNA and Insulin signaling (IIS) pathways. Ravivarma, S., Behera, S., Roy, D., & Ghosh-Roy, A. (2025). Accepted in Journal of Biosciences.

[64] Aging distorts the representation of emotions by amplifying prefrontal variability. Majumdar, G., Yazin, F., Banerjee, A., & Roy, D. (2025). bioRxiv (Revision stage). Link

[63] Directional connectivity in prestimulus large-scale functional networks underpins McGurk perception. Singh, V. A. V., Kumar, V. G., Banerjee, A., & Roy, D. (2025). (Under preparation)

[62] Characterization of the temporal stability of ToM and pain functional brain networks carry distinct developmental signatures during naturalistic viewing. Bhavna, K., Ghosh, N., Banerjee, R., & Roy, D. (2024). Scientific Reports, 14, 22479. DOI

[61] Differential Neural Correlates of EEG Mediate the Impact of Internally and Externally Directed Attention in a Dual-task Working Memory Paradigm. Yadav, A., Banerjee, A., & Roy, D. Proceedings of the Annual Meeting of the Cognitive Science Society, 46 (2024).

[60] Explainable Deep-Learning Framework: Decoding brain States and Prediction of Individual Performance in False-Belief Task at Early Childhood Stage. Bhavna, K., Akhtar, A., Banerjee, R., & Roy, D. Frontiers in Neuroinformatics, 18, 1392661.

[59] End-to-End Explainable AI: Derived Theory-of-Mind Fingerprints to Distinguish Between Autistic and Typically Developing and Social Symptom Severity. Bhavna, K., Banerjee, R., & Roy, D. (2024). bioRxiv, 2023-01. Link

[58] Diet-induced miRNAs regulate adult neurogenesis and functional activity of nascent neurons in the hypothalamus. Srinivasan, B., Samaddar, S., Roy, D., & Banerjee, S. (2024). DOI (In preparation)

[57] 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.

[56] 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.

[55] Emotion dynamics as hierarchical Bayesian inference in time. Majumdar, G., Yazin, F., Banerjee, A., & Roy, D. (2023). Cerebral Cortex, 33(7), 3750-3772.

[54] Stability of sensorimotor network sculpts the dynamic repertoire of resting state over lifespan. Sastry, N. C., Roy, D., & Banerjee, A. (2023). Cerebral Cortex, 33(4), 1246-1262.

[53] Effective networks mediate right hemispheric dominance of human 40 Hz auditory steady-state response. Kumar, N., Jaiswal, A., Roy, D., & Banerjee, A. (2023). Neuropsychologia, 108559.

[52] Temporal structure of neural processes coupling sensory, motor and cognitive functions of the brain, volume II. Gupta, D. S., Banerjee, A., Piras, F., & Roy, D. (2023). Frontiers in Computational Neuroscience, 17.

[51] Whole Brain network models: From Physics to Bedside. Pathak, A., Roy, D., & Banerjee, A. (2022). Frontiers in Computational Neuroscience. DOI

[50] Characterizing the Dynamic Reorganization in Healthy Ageing and Classification of Brain Age. Dash, A., Bapi, R. S., Roy, D., & Vinod, P. K. (2022). 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1-7. IEEE.

[49] Hippocampus Maintains a Coherent Map Under Reward Feature–Landmark Cue Conflict. Nair, I. R., Bhasin, G., & Roy, D. (2022). Frontiers in Neural Circuits, 31.

[48] 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), 1-12.

[47] Aperiodic and periodic components of ongoing oscillatory brain dynamics link distinct functional aspects of cognition across adult lifespan. Thuwal, K., Banerjee, A., & Roy, D. (2021). eNeuro, 8(5).

[46] Contextual Prediction Errors Reorganize Episodic Memories in Time. Yazin, F., Das, M., Banerjee, A., & Roy, D.† (2021). Scientific Reports, 11(1), 12364. DOI

[45] Multi-scale dynamic mean field model (MDMF) relates resting-state brain dynamics with local cortical excitatory-inhibitory neurotransmitter homeostasis. Naskar, A., Vattikonda, A., Deco, G., Roy, D., & Banerjee, A. (2021). Network Neuroscience, 1-55.

[44] 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

[43] Psychophysical data to study the brain network mechanisms involved in reorienting attention to salient events during goal directed visual discrimination and search tasks. Ghosh, P., Roy, D., & Banerjee, A. (2021). Data in Brief, 36, 107020.

[42] Atypical core-periphery brain dynamics in autism: Implications for symptom severity. Roy, D.†, & Uddin, L. Q. (2021). Network Neuroscience, 1-27.

[41] Reconfiguration of directed functional connectivity among neurocognitive networks with ageing: Considering the role of thalamo-cortical interactions. Das, M., Singh, V., Uddin, L. Q., Banerjee, A., & Roy, D.† (2021). Cerebral Cortex, 31(4), 1970-1986.

[40] Biophysical mechanisms governing large-scale brain network dynamics underlying individual-specific variability of perception. Kumar, V. G., Dutta, S., Talwar, S., Roy, D.†, & Banerjee, A.† (2020). European Journal of Neuroscience, 52(7), 3746-3762.

[39] Lifespan associated changes in global patterns of coherent communication. Sahoo, B., Pathak, A., Deco, G., Banerjee, A., & Roy, D.† (2020). NeuroImage, 216, 116824.

[38] Empirical Mode Decomposition Algorithms for Classification of Single-Channel EEG Manifesting McGurk Effect. Pal, A. K., Roy, D., Kumar, G. V., Chatterjee, B., Sharma, L. N., Banerjee, A., & Gupta, C. N. (2020). International Conference Series on Intelligent Human-Computer Interaction (IHCI 2019), LNCS 11886, pp. 49-60. Springer. DOI

[37] Large-scale functional integration, rather than functional dissociation along dorsal and ventral streams, underlies visual perception and action. Ray, D., Hazare, N., Roy, D., & Banerjee, A. (2020). Journal of Cognitive Neuroscience, 1-15.

[36] Identification and Classification of Hubs in microRNA Target Gene Networks in Human Neural Stem/Progenitor Cells following Japanese Encephalitis Virus Infection. Mukherjee, S., Akbar, I., Bhagat, R., Hazra, B., Bhattacharyya, A., Seth, P., Roy, D.†, & Basu, A.† (2019). mSphere, 4(5), e00588-19.

[35] Atypical flexibility in dynamic functional connectivity quantifies the severity in autism spectrum disorder. Harlalka, V., Bapi, R. S., Vinod, P. K., & Roy, D. (2019). Frontiers in Human Neuroscience. DOI

[34] Generative framework for dimensionality reduction of large scale network of non-linear dynamical systems driven by external input. Dutta, S., Roy, D., & Banerjee, A. (2019). New Journal of Physics, 21.

[33] Resting-State Dynamics Meets Anatomical Structure: Temporal Multiple Kernel Learning (tMKL) Model. Surampudi, S. G., Mishra, J., Bapi, R. S., Deco, G., Sharma, A., & Roy, D. (2019). NeuroImage, 184, 609-620. DOI

[32] Age, disease and their interaction effects on the intrinsic connectivity of children and adolescents in Autism Spectrum Disorder using functional connectomics. Harlalka, V., et al. (2018). Brain Connectivity, 8(7). DOI

[31] Integrative network analysis reveals the cell-type-specific changes in the hippocampus of young, aging and Alzheimer’s disease. Lanke, V., Moolamalla, S. T. R., Roy, D., & Vinod, P. K. Frontiers in Aging Neuroscience. DOI

[30] Multiple Kernel Learning Model for Relating Structural and Functional Connectivity in the Brain. Surampudi, S. G., Naik, S., Surampudi, R. B., Jirsa, V. K., Sharma, A., & Roy, D. (2018). Scientific Reports, 8(1), 3265.

[29] Distinct Neurobehavioral Mechanisms for Expectancy Violation and Value Updating. Das, M., & Ray, D. (2018). Journal of Neuroscience, 38(1), 26-28. DOI

[28] Segregation and Integration of cortical information processing underlying cross-modal perception. Kumar, V. G., Kumar, N., Roy, D., & Banerjee, A. (2017). Multisensory Research. DOI

[27] The neural substrate of group mental health: Insights from a multi-brain reference frame in functional neuroimaging. Ray, D., Roy, D., Sindhu, B., Sharan, P., & Banerjee, A. (2017). Frontiers in Psychology.

[26] Metastability in Senescence. Naik, S., Bapi, R. S., Banerjee, A., Deco, G., & Roy, D. (2017). Trends in Cognitive Sciences. DOI

[25] Metastability of Cortical BOLD Signals in Maturation and Senescence. Naik, S., Oota, S., Banerjee, A., Roy, D., & Bapi, R. S. (2017). IEEE International Joint Conference on Neural Networks (IJCNN). DOI

[24] Combining Multiscale Diffusion Kernels for Learning the Structural and Functional Brain Connectivity. Surampudi, S. G., Naik, S., Sharma, A., Bapi, R. S., & Roy, D. (2016). Neural Information Processing Systems (NIPS 2016), Barcelona. DOI

[23] Large-scale functional brain networks underlying temporal integration of audio-visual speech perception: An EEG study. Kumar, V. G., Halder, T., Jaiswal, A. K., Mukherjee, A., Roy, D., & Banerjee, A. Frontiers in Psychology. DOI

[22] Does the regulation of local excitation-inhibition balance aid in recovery of functional connectivity? A computational account. Vattikonda, A., Bapi, R., Banerjee, A., Deco, G., & Roy, D. (2016). NeuroImage, 136, 57-67. DOI

[21] Neurophysiological Investigation of Context Modulation based on Musical Stimulus. Mehrotra, S., Shukla, A., & Roy, D. International Conference on Music Perception and Cognition (ICMPC14), July 2016, San Francisco.

[20] Promises and pitfalls of relating alteration of white matter pathways causing improvement in Cognitive performance. Roy, D., & Pammi, V. S. C. Cognitive Neuroscience. DOI

[19] Near-infrared spectroscopy (NIRS) - electroencephalography (EEG) based brain-state dependent electrotherapy (BSDE) to facilitate post-stroke neurorehabilitation: inhibition–excitation balance hypothesis. Dagar, S., Bapi, R., Raychoudhury, S., Dutta, A., & Roy, D. Frontiers in Neurology, 7:123. DOI

[18] Inferring network properties of cortical neurons with synaptic coupling and parameter dispersion. Roy, D., & Jirsa, V. In Neural Masses and Fields: Modeling the Dynamics of Brain Activity (ed. K. Friston), Frontiers in Computational Neuroscience, 2015.

[17] Using the Virtual Brain to Reveal the Role of Oscillations and Plasticity in Shaping Brain’s Dynamical Landscape. Roy, D., Sigala, R., Breakspear, M., McIntosh, A. R., Jirsa, V. K., Deco, G., & Ritter, P. Brain Connectivity, 2014. DOI

[16] The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models. Sigala, R., Haufe, S., Roy, D., Dinse, H. R., & Ritter, P. (2014). Frontiers in Computational Neuroscience. DOI

[15] Influence of network timescale on network dynamics with fast synapses and parameter dispersion for a mean field model composed of bursting units. Roy, D., & Jirsa, V. K. (2013). Frontiers in Computational Neuroscience, 7:20. DOI

[14] Afferent specificity, feature-specific connectivity influence orientation selectivity: A computational study in mouse primary visual cortex. Roy, D., Tjandra, Y., Mergenthaler, K., Petravicz, J., Runyan, C. A., Wilson, N. R., Sur, M., & Obermayer, K. (2013). arXiv preprint, 1301.0996.

[13] Changes in V1 orientation tuning when blocking astrocytic glutamate transporters: models for extra- and intra-synaptic mechanisms. Mergenthaler, K.*, Roy, D.*, Petravicz, J., Sur, M., & Obermayer, K. (2013). BMC Neuroscience, 14(Suppl 1), P298. (*equal contribution)

[12] Brain state-dependent post-inhibitory rebound in entorhinal cortex interneurons. Adhikari, M. H., Roy, D., Quilchini, P. P., Jirsa, V. K., & Bernard, C. (2012). Journal of Neuroscience, 32(19), 6501-6510.

[11] Phase description of Neural oscillators with global electric and synaptic coupling. Roy, D., Ghosh, A., & Jirsa, V. K. (2011). Physical Review E, 83, 051909.

[10] A simple model for bursting dynamics. Ghosh, A., Roy, D., & Jirsa, V. K. (2009). Physical Review E, 80, 041930.

[9] The great oxidation of Earth’s atmosphere. Roy, D., Musielak, Z. E., & Cuntz, M. (2010). Proceedings of the International Astronomical Union, 5, 680-681.

[8] Search for supersymmetry using final states with one lepton, jets, and missing transverse momentum with the ATLAS detector in √s=7 TeV pp collisions. ATLAS Collaboration (2011). Physical Review Letters, 106, 131802.

[7] Charged-particle multiplicities in pp interactions at √s=900 GeV measured with the ATLAS detector at the LHC. ATLAS Collaboration (2010). Physics Letters B, 688(1), 21-42.

[6] The great oxidation of Earth’s Atmosphere: Contesting the Yoyo model via transition stability analysis. Cuntz, M., Roy, D., & Musielak, Z. E. Astrophysical Journal Letters, 706, L178-L182.

[5] Method to derive Lagrangian and Hamiltonian for a nonlinear dynamical system with variable coefficients. Musielak, Z. E., Roy, D., & Swift, L. D. (2008). Chaos, Solitons & Fractals, 38(3), 894-902.

[4] Standard and nonstandard Lagrangians for dissipative dynamical systems with variable coefficients. Musielak, Z. E., & Roy, D. (2007). Journal of Physics A: Mathematical and Theoretical, 41, 055205.

[3] Generalized Lorenz models and their routes to chaos. III. Energy-conserving horizontal and vertical mode truncations. Roy, D., & Musielak, Z. (2007). Chaos, Solitons & Fractals, 33(3), 1064-1070.

[2] Generalized Lorenz models and their routes to chaos. I. Energy-conserving vertical mode truncations. Roy, D., & Musielak, Z. E. (2007). Chaos, Solitons & Fractals, 32(3), 1038-1052.

[1] Generalized Lorenz models and their routes to chaos. II. Energy-conserving horizontal mode truncations. Roy, D., & Musielak, Z. E. (2007). Chaos, Solitons & Fractals, 31(3), 747-756.