Cognitive Neuroimaging

Artist's impression of neurons firing in the brain

The Centre for Cognitive Neuroimaging (CCNi) brings together a multidisciplinary group of neuroscientists whose mission is to understand how the human brain gives rise to complex cognitive functions, in health and disease. Researchers at the CCNi approach this question from an integrated cognitive, neuroscientific and computational perspective. Our research covers topics in vision, sensory-motor integration, attention, predictive processing, memory & plasticity, decision making, and social interaction.

We develop and apply cutting-edge neuroimaging tools and sophisticated analysis techniques that help us unravel how information about the outside world is represented and communicated in the human brain. We study this information flow at multiple levels, from single neurons to fine-grained regional activity patterns to complex temporal dynamics and behaviour. Many investigators at the CCNi have expertise in multi-modal imaging and the fusion of different modalities (EEG-fMRI, TMS-EEG). The CCNi also has world-leading expertise in non-invasive brain stimulation, and we develop novel tools to enhance the precision of these stimulation methods. In collaboration with our colleagues at the Centre for Neuroscience, GEMRIC, ICE and other national and international partners, we conduct cross-scale, cross-species neuroimaging studies, and explore novel ways to apply basic science to clinical contexts.

We acknowledge the importance of big data, but also emphasize the need for understanding reproducibility and variability at the individual level. Our four areas of specialization (outlined below) reflect this integrative, multi-scale approach to neuroimaging.

CORE MEMBERS

Daube, Fracasso, Hanslmayr, Harvey, Ince, Muckli, Philiastides, Robertson, Rousselet, Sampaio-Baptista, Schyns, Svanera, Thut, Wimber

AFFLIATE MEMBERS

Cross, Jack, Pollick, Sereno

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Computational cognitive neuroimaging

We use computational approaches to unravel how the fundamental building blocks of cognition (such as accumulation and integration of information, sensory and memory representations, prediction) are implemented in brain activity. Many researchers at the CCNi use sophisticated behavioural tasks, psychophysical sampling techniques, information theoretical approaches and computational modelling to map information content to brain activity and behaviour.

High-resolution cognitive neuroimaging

We use fMRI (3T and 7T) to image neural motif of sensory and cognitive processes with high spatial precision, and often combine fMRI with simultaneous EEG to infuse temporal dynamics into the fMRI patterns. We leverage machine learning tools and advanced data analytics to understand cognitive functions at the level of neural representations, including the fine-grained patterns emerging in different cortical layers that can be distinguished with fMRI recordings at high (7T) field strengths.

Fast-scale brain dynamics

Electrophysiological recordings allow us to track neural activity with high temporal precision, in quasi real-time. Researchers at the CCNi use a combination of EEG, MEG and intracranial EEG, with a strong emphasis on how synchronized and desynchronized brain activity patterns orchestrate the circuit dynamics that underly complex cognitive functions. Intracranial EEG recordings in epileptic patients offer unique insights into the local computations carried out by single neurons and larger neural assemblies in the human brain. We study these computations in sensory regions, the hippocampus and associated neocortical brain areas, with a strong focus on understanding visual and auditory perception as well as memory and plasticity.

Neuroimaging-guided brain stimulation

Using insights from computational and cognitive neuroimaging, we refine conventional non-invasive (transcranial) brain stimulation techniques into more powerful tools for a more precise targeting of brain activity and associated functions. These methods also allow us to induce states of synchronized activity and probe the resulting effects on behaviour. Real-time fMRI/EEG neurofeedback is used at the CCNi to modulate task-specific connectivity and cognition. The results from these brain stimulation experiments allow us to establish a solid causal footing for the neural mechanisms underlying cognition.  

Infrastructure & Funding

The CCNi was created in 2008 as part of significant investment from the University of Glasgow, Scottish Research Infrastructure Fund (SRIF) and Wolfson in state-of-the-art, multi-modal neuroimaging technologies to support research in cognitive neuroscience. The high-field 7T MRI scanner, housed in the ICE, was co-funded in 2016 by the MRC, the European Regional Development Fund, and the Sackler Trust. In 2020, the Wellcome Trust awarded a Multi-User Equipment Grant of £1M (PI: Schyns, Co-Is: Thut, Palva, Palva, Ince, Philiastides, Ulhaas) for a new state-of-the-art MEG-TRIUX-neo system. The centre is part of a bigger multidisciplinary platform, crossing fundamental, social and cognitive neuroscience with links to the ICE and the SOCIAL AI Lab in the ARC.

Selected Publications

 

  • Arnulfo, G., Wang, S. H., Myrov, V., Toselli, B., Hirvonen, J., Fato, M. M., Nobili, L., Cardinale, F., Rubino, A., Zhigalov, A., Palva, S., & Palva, J. M. (2020). Long-range phase synchronization of high-frequency oscillations in human cortex. Nature Communications, 11(1), 5363. https://doi.org/10.1038/s41467-020-18975-8
  • Benwell, C. S. Y., Coldea, A., Harvey, M., & Thut, G. (2022). Low pre-stimulus EEG alpha power amplifies visual awareness but not visual sensitivity. The European Journal of Neuroscience, 55(11–12), 3125–3140. https://doi.org/10.1111/ejn.15166
  • Bieniek, M. M., Bennett, P. J., Sekuler, A. B., & Rousselet, G. A. (2016). A robust and representative lower bound on object processing speed in humans. European Journal of Neuroscience, 44(2), 1804–1814. https://doi.org/10.1111/ejn.13100
  • Bontempi, D., Benini, S., Signoroni, A., Svanera, M., & Muckli, L. (2020). CEREBRUM: A fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner MRI. Medical Image Analysis, 62, 101688. https://doi.org/10.1016/j.media.2020.101688
  • Breton, J., & Robertson, E. M. (2017). Dual enhancement mechanisms for overnight motor memory consolidation. Nature Human Behaviour, 1(6), 0111. https://doi.org/10.1038/s41562-017-0111
  • Clouter, A., Shapiro, K. L., & Hanslmayr, S. (2017). Theta Phase Synchronization Is the Glue that Binds Human Associative Memory. Current Biology: CB, 27(20), 3143-3148.e6. https://doi.org/10.1016/j.cub.2017.09.001
  • Daube, C., Ince, R. A. A., & Gross, J. (2019). Simple Acoustic Features Can Explain Phoneme-Based Predictions of Cortical Responses to Speech. Current Biology: CB, 29(12), 1924-1937.e9. https://doi.org/10.1016/j.cub.2019.04.067
  • Daube, C., Xu, T., Zhan, J., Webb, A., Ince, R. A. A., Garrod, O. G. B., & Schyns, P. G. (2021). Grounding deep neural network predictions of human categorization behavior in understandable functional features: The case of face identity. Patterns (New York, N.Y.), 2(10), 100348. https://doi.org/10.1016/j.patter.2021.100348
  • Di Gregorio, F., Trajkovic, J., Roperti, C., Marcantoni, E., Di Luzio, P., Avenanti, A., Thut, G., & Romei, V. (2022). Tuning alpha rhythms to shape conscious visual perception. Current Biology: CB, 32(5), 988-998.e6. https://doi.org/10.1016/j.cub.2022.01.003
  • Fracasso, A., Dumoulin, S. O., & Petridou, N. (2021). Point-spread function of the BOLD response across columns and cortical depth in human extra-striate cortex. Progress in Neurobiology, 207, 102187. https://doi.org/10.1016/j.pneurobio.2021.102187
  • Franzen, L., Delis, I., De Sousa, G., Kayser, C., & Philiastides, M. G. (2020). Auditory information enhances post-sensory visual evidence during rapid multisensory decision-making. Nature Communications, 11(1), 5440. https://doi.org/10.1038/s41467-020-19306-7
  • Griffiths, B. J., Parish, G., Roux, F., Michelmann, S., van der Plas, M., Kolibius, L. D., Chelvarajah, R., Rollings, D. T., Sawlani, V., Hamer, H., Gollwitzer, S., Kreiselmeyer, G., Staresina, B., Wimber, M., & Hanslmayr, S. (2019). Directional coupling of slow and fast hippocampal gamma with neocortical alpha/beta oscillations in human episodic memory. Proceedings of the National Academy of Sciences of the United States of America, 116(43), 21834–21842. https://doi.org/10.1073/pnas.1914180116
  • Hanslmayr, S., Staresina, B. P., & Bowman, H. (2016). Oscillations and Episodic Memory: Addressing the Synchronization/Desynchronization Conundrum. Trends in Neurosciences, 39(1), 16–25. https://doi.org/10.1016/j.tins.2015.11.004
  • Harvey, B. M., Dumoulin, S. O., Fracasso, A., & Paul, J. M. (2020). A Network of Topographic Maps in Human Association Cortex Hierarchically Transforms Visual Timing-Selective Responses. Current Biology: CB, 30(8), 1424-1434.e6. https://doi.org/10.1016/j.cub.2020.01.090
  • Ince, R. A. A., Giordano, B. L., Kayser, C., Rousselet, G. A., Gross, J., & Schyns, P. G. (2017). A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula. Human Brain Mapping, 38(3), 1541–1573. https://doi.org/10.1002/hbm.23471
  • Ince, R. A. A., Kay, J. W., & Schyns, P. G. (2022). Within-participant statistics for cognitive science. Trends in Cognitive Sciences, 26(8), 626–630. https://doi.org/10.1016/j.tics.2022.05.008
  • Ince, R. A., Paton, A. T., Kay, J. W., & Schyns, P. G. (2021). Bayesian inference of population prevalence. ELife, 10, e62461. https://doi.org/10.7554/eLife.62461
  • Jack, R. E., Garrod, O. G. B., & Schyns, P. G. (2014). Dynamic facial expressions of emotion transmit an evolving hierarchy of signals over time. Current Biology: CB, 24(2), 187–192. https://doi.org/10.1016/j.cub.2013.11.064
  • Jack, R. E., Garrod, O. G. B., Yu, H., Caldara, R., & Schyns, P. G. (2012). Facial expressions of emotion are not culturally universal. Proceedings of the National Academy of Sciences of the United States of America, 109(19), 7241–7244. https://doi.org/10.1073/pnas.1200155109
  • Jack, R. E., & Schyns, P. G. (2017). Toward a Social Psychophysics of Face Communication. Annual Review of Psychology, 68, 269–297. https://doi.org/10.1146/annurev-psych-010416-044242
  • Jack, R. E., Sun, W., Delis, I., Garrod, O. G. B., & Schyns, P. G. (2016). Four not six: Revealing culturally common facial expressions of emotion. Journal of Experimental Psychology. General, 145(6), 708–730. https://doi.org/10.1037/xge0000162
  • Jaworska, K., Yan, Y., van Rijsbergen, N. J., Ince, R. A. A., & Schyns, P. G. (2022). Different computations over the same inputs produce selective behavior in algorithmic brain networks. ELife, 11, e73651. https://doi.org/10.7554/eLife.73651
  • Jaworska, K., Yi, F., Ince, R. A. A., Rijsbergen, N. J., Schyns, P. G., & Rousselet, G. A. (2020). Healthy aging delays the neural processing of face features relevant for behavior by 40 ms. Human Brain Mapping, 41(5), 1212–1225. https://doi.org/10.1002/hbm.24869
  • Kanel, D., Al-Wasity, S., Stefanov, K., & Pollick, F. E. (2019). Empathy to emotional voices and the use of real-time fMRI to enhance activation of the anterior insula. NeuroImage, 198, 53–62. https://doi.org/10.1016/j.neuroimage.2019.05.021
  • Kerrén, C., Linde-Domingo, J., Hanslmayr, S., & Wimber, M. (2018). An Optimal Oscillatory Phase for Pattern Reactivation during Memory Retrieval. Current Biology: CB, 28(21), 3383-3392.e6. https://doi.org/10.1016/j.cub.2018.08.065
  • Lakatos, P., Gross, J., & Thut, G. (2019). A New Unifying Account of the Roles of Neuronal Entrainment. Current Biology: CB, 29(18), R890–R905. https://doi.org/10.1016/j.cub.2019.07.075
  • Learmonth, G., Benwell, C. S. Y., Märker, G., Dascalu, D., Checketts, M., Santosh, C., Barber, M., Walters, M., Muir, K. W., & Harvey, M. (2021). Non-invasive brain stimulation in Stroke patients (NIBS): A prospective randomized open blinded end-point (PROBE) feasibility trial using transcranial direct current stimulation (tDCS) in post-stroke hemispatial neglect. Neuropsychological Rehabilitation, 31(8), 1163–1189. https://doi.org/10.1080/09602011.2020.1767161
  • Li, G., McGill, M., Brewster, S., Chen, C. P., Anguera, J. A., Gazzaley, A., & Pollick, F. (2022). Multimodal Biosensing for Vestibular Network-Based Cybersickness Detection. IEEE Journal of Biomedical and Health Informatics, 26(6), 2469–2480. https://doi.org/10.1109/JBHI.2021.3134024
  • Lifanov, J., Linde-Domingo, J., & Wimber, M. (2021). Feature-specific reaction times reveal a semanticisation of memories over time and with repeated remembering. Nature Communications, 12(1), 3177. https://doi.org/10.1038/s41467-021-23288-5
  • Linde-Domingo, J., Treder, M. S., Kerrén, C., & Wimber, M. (2019). Evidence that neural information flow is reversed between object perception and object reconstruction from memory. Nature Communications, 10(1), 179. https://doi.org/10.1038/s41467-018-08080-2
  • Morgan, A. T., Petro, L. S., & Muckli, L. (2019). Scene Representations Conveyed by Cortical Feedback to Early Visual Cortex Can Be Described by Line Drawings. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 39(47), 9410–9423. https://doi.org/10.1523/JNEUROSCI.0852-19.2019
  • Muckli, L., De Martino, F., Vizioli, L., Petro, L. S., Smith, F. W., Ugurbil, K., Goebel, R., & Yacoub, E. (2015). Contextual Feedback to Superficial Layers of V1. Current Biology: CB, 25(20), 2690–2695. https://doi.org/10.1016/j.cub.2015.08.057
  • Mutanen, T. P., Bracco, M., & Robertson, E. M. (2020). A Common Task Structure Links Together the Fate of Different Types of Memories. Current Biology: CB, 30(11), 2139-2145.e5. https://doi.org/10.1016/j.cub.2020.03.043
  • Palva, J. M., Zhigalov, A., Hirvonen, J., Korhonen, O., Linkenkaer-Hansen, K., & Palva, S. (2013). Neuronal long-range temporal correlations and avalanche dynamics are correlated with behavioral scaling laws. Proceedings of the National Academy of Sciences of the United States of America, 110(9), 3585–3590. https://doi.org/10.1073/pnas.1216855110
  • Palva, S., & Palva, J. M. (2018). Roles of Brain Criticality and Multiscale Oscillations in Temporal Predictions for Sensorimotor Processing. Trends in Neurosciences, 41(10), 729–743. https://doi.org/10.1016/j.tins.2018.08.008
  • Petrini, K., McAleer, P., Neary, C., Gillard, J., & Pollick, F. E. (2014). Experience in judging intent to harm modulates parahippocampal activity: An fMRI study with experienced CCTV operators. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior, 57, 74–91. https://doi.org/10.1016/j.cortex.2014.02.026
  • Philiastides, M. G., Tu, T., & Sajda, P. (2021). Inferring Macroscale Brain Dynamics via Fusion of Simultaneous EEG-fMRI. Annual Review of Neuroscience, 44, 315–334. https://doi.org/10.1146/annurev-neuro-100220-093239
  • Queirazza, F., Fouragnan, E., Steele, J. D., Cavanagh, J., & Philiastides, M. G. (2019). Neural correlates of weighted reward prediction error during reinforcement learning classify response to cognitive behavioral therapy in depression. Science Advances, 5(7), eaav4962. https://doi.org/10.1126/sciadv.aav4962
  • Robertson, E. M. (2022). Memory leaks: Information shared across memory systems. Trends in Cognitive Sciences, 26(7), 544–554. https://doi.org/10.1016/j.tics.2022.03.010
  • Rossit, S., Benwell, C. S. Y., Szymanek, L., Learmonth, G., McKernan-Ward, L., Corrigan, E., Muir, K., Reeves, I., Duncan, G., Birschel, P., Roberts, M., Livingstone, K., Jackson, H., Castle, P., & Harvey, M. (2019). Efficacy of home-based visuomotor feedback training in stroke patients with chronic hemispatial neglect. Neuropsychological Rehabilitation, 29(2), 251–272. https://doi.org/10.1080/09602011.2016.1273119
  • Rousselet, G. A., & Wilcox, R. R. (2020). Reaction Times and other Skewed Distributions. Meta-Psychology, 4. https://doi.org/10.15626/MP.2019.1630
  • Sampaio-Baptista, C., & Johansen-Berg, H. (2017). White Matter Plasticity in the Adult Brain. Neuron, 96(6), 1239–1251. https://doi.org/10.1016/j.neuron.2017.11.026
  • Sampaio-Baptista, C., Neyedli, H. F., Sanders, Z.-B., Diosi, K., Havard, D., Huang, Y., Andersson, J. L. R., Lühr, M., Goebel, R., & Johansen-Berg, H. (2021). FMRI neurofeedback in the motor system elicits bidirectional changes in activity and in white matter structure in the adult human brain. Cell Reports, 37(4), 109890. https://doi.org/10.1016/j.celrep.2021.109890
  • Sampaio-Baptista, C., Sanders, Z.-B., & Johansen-Berg, H. (2018). Structural Plasticity in Adulthood with Motor Learning and Stroke Rehabilitation. Annual Review of Neuroscience, 41, 25–40. https://doi.org/10.1146/annurev-neuro-080317-062015
  • Siebenhühner, F., Wang, S. H., Arnulfo, G., Lampinen, A., Nobili, L., Palva, J. M., & Palva, S. (2020). Genuine cross-frequency coupling networks in human resting-state electrophysiological recordings. PLOS Biology, 18(5), e3000685. https://doi.org/10.1371/journal.pbio.3000685
  • Siebenhühner, F., Wang, S. H., Palva, J. M., & Palva, S. (2016). Cross-frequency synchronization connects networks of fast and slow oscillations during visual working memory maintenance. ELife, 5, e13451. https://doi.org/10.7554/eLife.13451
  • Svanera, M., Morgan, A. T., Petro, L. S., & Muckli, L. (2021). A self-supervised deep neural network for image completion resembles early visual cortex fMRI activity patterns for occluded scenes. Journal of Vision, 21(7), 5. https://doi.org/10.1167/jov.21.7.5
  • Svanera, M., Savardi, M., Benini, S., Signoroni, A., Raz, G., Hendler, T., Muckli, L., Goebel, R., & Valente, G. (2019). Transfer learning of deep neural network representations for fMRI decoding. Journal of Neuroscience Methods, 328, 108319. https://doi.org/10.1016/j.jneumeth.2019.108319
  • ter Wal, M., Linde-Domingo, J., Lifanov, J., Roux, F., Kolibius, L. D., Gollwitzer, S., Lang, J., Hamer, H., Rollings, D., Sawlani, V., Chelvarajah, R., Staresina, B., Hanslmayr, S., & Wimber, M. (2021). Theta rhythmicity governs human behavior and hippocampal signals during memory-dependent tasks. Nature Communications, 12(1), 7048. https://doi.org/10.1038/s41467-021-27323-3
  • van Bree, S., Melcón, M., Kolibius, L. D., Kerrén, C., Wimber, M., & Hanslmayr, S. (2022). The brain time toolbox, a software library to retune electrophysiology data to brain dynamics. Nature Human Behaviour. https://doi.org/10.1038/s41562-022-01386-8
  • Veniero, D., Gross, J., Morand, S., Duecker, F., Sack, A. T., & Thut, G. (2021). Top-down control of visual cortex by the frontal eye fields through oscillatory realignment. Nature Communications, 12(1), 1757. https://doi.org/10.1038/s41467-021-21979-7
  • Vetter, P., Bola, Ł., Reich, L., Bennett, M., Muckli, L., & Amedi, A. (2020). Decoding Natural Sounds in Early “Visual” Cortex of Congenitally Blind Individuals. Current Biology, 30(15), 3039-3044.e2. https://doi.org/10.1016/j.cub.2020.05.071
  • Zhan, J., Garrod, O. G. B., van Rijsbergen, N., & Schyns, P. G. (2019). Modelling face memory reveals task-generalizable representations. Nature Human Behaviour, 3(8), 817–826. https://doi.org/10.1038/s41562-019-0625-3
  • Zikidi, K., Gajwani, R., Gross, J., Gumley, A. I., Lawrie, S. M., Schwannauer, M., Schultze-Lutter, F., Fracasso, A., & Uhlhaas, P. J. (2020). Grey-matter abnormalities in clinical high-risk participants for psychosis. Schizophrenia Research, 226, 120–128. https://doi.org/10.1016/j.schres.2019.08.034

Selected Grants

  • Leverhulme Trust (2021-24). Modulating sleep with learning to enhance learning, £300,000; PI: Robertson.
  • ERC Consolidator Grant (2021-26). Dynamic Network Reconstruction of Human Perceptual and Reward Learning via Multimodal Data Fusion. €2M, PI: Philiastides.
  • MRC (2021-24). Multi-scale brain network mechanisms of working memory and short-term memory. £654,000. PI: S Palva, Co-I: Thut, Co-I: M Palva.
  • MRC (2021-24). Causal roles of neural synchrony in signal transmission and cognition in the human brain. £536,000, PI: Thut, Co-I: S Palva, Co-I: M Palva.
  • BBSRC (2021-24). Layer-specific cortical feedback dynamics - Human Ultra-High Resolution functional Brain Imaging for Predictive Brain Functions. £765,472, PI: Muckli.
  • UKRI Centre for Doctoral Training (2018-2027). Socially intelligent artificial intelligence. £4.9M, Lead (Psychology & Neuroscience): Harvey.
  • Air Force Office of Scientific Research (2022-23). Identifying the leak between memory systems, £100,000, PI: Robertson.
  • ERC Starting Grant (2018-24). Computing the Face Syntax of Social Communication. £1,878,815, PI: Jack
  • ViAjeRo Project (Subproject of ERC Advanced Grant, 2019-23). Using brain-based techniques of mitigating cybersickness to improve passenger experience with XR. £401,000, PI: Pollick
  • ESRC (2018-2022). TIME - Gluing Cross-Modal Memories via Synchronisation. £522,803, PI: Hanslmayr
  • ERC Starting Grant (2017-2023). STREAM – The Spatio-Temporal Representational Architecture of Memory. €1.5M, PI: Wimber.
  • ERC Consolidator Grant (2015-2022). Neural Oscillations – A Code for Memory. €1,890,000, PI: Hanslmayr.
  • Canadian Institute of Health Research (2019-2024). Combined effects of acute exercise and sleep restriction on cognition. £330,000; Collaborator: Robertson (PI: Dang-Vu).
  • MURI ONR/EPSRC (2015-2022). Understanding scenes and events through joint parsing, cognitive reasoning and lifelong learning. £730,000 (from £5M partnership with US: UCLA, CMU, MIT, Stanford and Yale; UK: Oxford, Birmingham, Glasgow and Reading), PI: Schyns. Versus Arthritis Charity (2021-23). Probing the Rheumatoid Arthritis Brain to Elucidate Central Pain Pathways. £206’507, Co-I: Thut (PI: N Basu).
  • Wellcome Trust Multi-User Equipment Grant (2020-25). State of the art MEG-TRIUX-neo for advancing multi-modal neuroimaging research in Scotland. £1M, PI: Schyns (PI), Co-I: Thut, Co-I: S Palva, Co-I: M Palva, Co-I: Ince, Co-I: M Philiastides, Co-I: P Uhlhaas.     
  • Templeton World Charity Foundation (2021-2022, subproject). Adversarial testing of predictive processing and integrated information theories of consciousness. Collaborator: Muckli.     
  • Human Brain Project (2020-2023, subproject). SGA_3 Apical dendritic amplification for cognitive performance: data-driven models and experimental validation. PI: Muckli.
  • Wellcome Senior Investigator Grant (2015-2020). Brain algorithmics: Reverse engineering dynamic information processing in brain networks from MEG time series. £1.3M, PI: Schyns.
  • ESRC Future Research Leaders Award (2012-15), £170,260, Mapping the Cultural Landscape of Emotions for Social Interaction, PI: Jack.                                                                                       
  • Open Research Area (ESRC/NOW; UK/NL, 2012-15). Data-driven Analysis of the Dynamics of Information-acquisition Over Time During Social Judgement. £326,026, Co-I: Jack (with R Dotsch, D Wigboldus, & P G Schyns).            
  • AHRC (2008-10). Watching dance: Kinesthetic empathy. £695,423, PI: Pollick.

Highlights

Grant Highlights: Since opening in 2008, CCNi researchers have gained >£22M in competitive awards (Wellcome, RCUK, ERC, HBP). These include four ERC Consolidator Awards (Hanslmayr, €2M, ‘Neural oscillations – a code for memory’, Philiastides, €2M ‘Dynamic Network Reconstruction of Human Perceptual and Reward Learning’, Muckli, €1.5M 'Brain Reading of contextual feedback and predictions’ and Kayser, £1.4M ‘Multisensory Integration'), two ERC Starting Grants (Jack, €1.9M ‘Computing the Face Syntax of Social Communication’, Wimber, €1.5M, ‘The Spatio-Temporal Representational Architecture of Memory’), funding from the Human Brain Project (HPB) (Muckli, €2.2M phase 1-3 ‘Multiscale multimethod brain imaging of contextual predictive coding’), three Wellcome Trust Investigator Awards (Thut and Gross, £2M, 'State-dependent decoding and driving of human Brain Oscillations’ and Schyns, £1.3M 'Brain algorithmics: reverse engineering dynamic information processing in brain networks from MEG time series’), a multisite MRC research grant (Uhlhaas, £0.8M, 'Prodromal Schizophrenia brain imaging research') and two individual MRC grants (Thut, £0.5M and S. Palva, £0.6M), as well as funding from the BBSRC (Muckli, £0.8M) and the Leverhulme Trust (Robertson, £0.3M).

Publication highlights: The CCNi researchers have published more than 497 papers between 2008 and 2019, with more than 16,523 citations.