The Centre for Cognitive Neuroimaging (CCNi)
Overview and Mission: The Center for Cognitive Neuroimaging represents a multidisciplinary effort for developing and applying new Cognitive Neuroimaging Techniques for studies into the Human Brain. Our mission is to unravel how information is communicated and represented in the brain across the meso- and macro-scales. We acknowledge the importance of big data, but also emphasize the need for understanding reproducibility and variability at the individual level. To this end, we build on our three areas of specialization (outlined below) in an integrative approach, with the aim to enhance precision of non-invasive neuroimaging and brain stimulation.
Computational Cognitive Neuroimaging: We are using computational approaches to unravel how the fundamental building blocks of cognition (such as accumulation and integration of information, sensory and memory representations, prediction etc.) are coded in brain activity. Example Highlight: Using psychophysical subsampling techniques and information theoretical approaches to map information content to brain activity at the individual level.
- Main PIs: Gross, Hanslmayr, Jack, Muckli, Ince, Matias Palva, Satu Palva, Philiastides, Rousselet, Schyns, Wimber
High-Resolution Cognitive Neuroimaging: We are using MEG/EEG and fMRI (including 7Tesla) to image neural motives of sensory and cognitive processes with high temporal and spatial precision (emphases on oscillatory brain activity, laminar imaging, functional network- and circuit-dynamics). Example Highlight: Leveraging machine learning and advanced data analytics for inferring cortical and subcortical network dynamics during decision making and learning via EEG-fMRI fusion (at 3T and 7T).
- Main PIs: Fracasso, Goense, Gross, Gunamony, Hanslmayr, Harvey, Ince, Muckli, Matias Palva, Satu Palva, Philiastides, Robertson, Rousselet, Schyns, Thut, Uhlhaas, Wimber
NeuroImaging-guided Brain Stimulation: Using insight from Computational and Cognitive Neuroimaging, we aim to develop conventional non-invasive (transcranial) brain stimulation techniques into more powerful tools for a more precise targeting of brain activity and associated functions. We are also using real-time fMRI/EEG Neurofeedback. Example Highlight: Developing EEG/MEG-guided neuronavigated transcranial stimulation to enhance efficacy and specificity of these non-invasive interventions.
- Main PIs: Hanslmayr, Harvey, Satu Palva, Matias Palva, Robertson, Sampaio-Baptista, Thut, Uhlhaas, Wimber
To achieve the above, many CCNi PIs have expertise in multimodal imaging (fMRI-EEG, EEG-TMS) and in the development of sophisticated analysis techniques. While many research projects are on the working of the healthy human brain, an important part of our research has a translational component. Example Highlight: Brain Imaging Consortium project on Prodromal Schizophrenia.
Our research covers topics in vision, memory & cognition, social signalling, decision making, plasticity, and disorders/disease. In collaboration with our colleagues at the Centre for Neuroscience, GEMRIC, ICE and other national and international partners, we are running Cross-scale, Cross-species Neuroimaging studies. Example Highlight: Multiscale multimethod brain imaging to investigate how contextual predictive information amplifies and dis-amplifies different neuronal components (‘apical amplification’). Comparing layer-specific fMRI in humans, with layer dependent calcium imaging in rodent.
History: 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.
Grant Highlights: Since opening in 2008, present and past CCNi researchers have gained £19.8M in competitive awards (Wellcome, RCUK, ERC, HBP). These include three ERC Consolidator Awards (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'), 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’), and a multisite MRC research grant (Uhlhaas, £0.8M, 'Prodromal Schizophrenia brain imaging research')
Publication highlights: The CCNi researchers have published more than 497 papers between 2008 and 2019, with more than 16,523 citations.
G Shajan, M Kozlov, J Hoffmann, R Turner, K Scheffler, R Pohmann (2014) A 16-Channel Dual-Row Transmit Array in Combination with a 31-Element Receive Array for Human Brain Imaging at 9.4 T. Magnetic Resonance in Medicine, 71, 870–879.
L Muckli, F De Martino, L Vizioli, LS Petro, FW Smith, K Ugurbil, R Goebel (2015) Contextual Feedback to Superficial Layers of V1, Current Biology, 25, 2690-2695.
J Breton J, EM Robertson (2017) Dual enhancement mechanisms for overnight motor memory consolidation. Nature Human Behaviour, 1, 1-7.
MA Pisauro, E Fouragnan, C Retzler, MG Philiastides (2017) Neural correlates of evidence accumulation during value-based decisions revealed via simultaneous EEG-fMRI. Nature Communications, 8, 15808.
RA Ince, BL Giordano, C Kayser, GA Rousselet, J Gross, PG Schyns (2017) A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula. Human Brain Mapping, 38, 1541-1573.
MS Nieuwland, S Politzer-Ahles, E Heyselaar, K Segaert, E Darley, N Kazanina, S Von Grebmer Zu Wolfsthurn, F Bartolozzi, V Kogan, A Ito, D Mézière, DJ Barr, GA Rousselet, HJ Ferguson, S Busch-Moreno, X Fu, J Tuomainen, E Kulakova, EM Husband, DI Donaldson, Z Kohút, SA Rueschemeyer, F Huettig (2018) Large-scale replication study reveals a limit on probabilistic prediction in language comprehension. Elife, 7, e33468.
H Park, RA Ince, PG Schyns, G Thut, J Gross (2018) Representational interactions during audiovisual speech entrainment: Redundancy in left posterior superior temporal gyrus and synergy in left motor cortex. PLoS Biology 16, e2006558.
T Grent-'t-Jong, D Rivolta, J Gross, R Gajwani, SM Lawrie, M Schwannauer, T Heidegger, M Wibral, W Singer, A Sauer, B Scheller, PJ Uhlhaas (2018) Acute ketamine dysregulates task-related gamma-band oscillations in thalamo-cortical circuits in schizophrenia. Brain, 141, 2511-2526.
JH Fabius, A Fracasso, TC Nijboer, S Van der Stigchel (2019) Time course of spatiotopic updating across saccades. Proceedings of the National Academy of Sciences, 116, 2027-2032.
J Zhan, OGB Garrod, N van Rijsbergen, PG Schyns (2019) Modelling face memory reveals task-generalizable representations. Nature Human Behaviour 3, 817-826.
Our Centre houses a cutting-edge neuroimaging platform, including:
- a 3-T MRI-scanner (Siemens Tim Trio),
- a whole-head MEG system (4D),
- multichannel EEG labs (n=3), and
- specialised Brain Stimulation labs (n=3).
All labs feature Eye tracking and many behavioural testing options (visual, tactile, auditory stimulation). The Brain Stimulation labs are equipped with state-of-the-art TMS, multichannel tES (tACS, tDCS, tRNS), and devices for anatomical MRI-based neuronavigation and MEP recordings. In addition, our facilities allow for the combination of fMRI, MEG or TMS/tES with simultaneous EEG acquisition (including 7TfMRI-EEG).
The computing facilities are outstanding, featuring ~ 6Ptb of Data storage and computing clusters (including dedicated parallel Matlab clusters with 5000 cores, 5Tb of RAM,).
The centre is part of a bigger multidisciplinary platform, crossing fundamental, social and cognitive neuroscience with links to nearby 7T-fMRI Scanner (Siemens Terra) at ICE.
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