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-scale. While we acknowledge the importance of big data, we 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.
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: Using 7Tesla fMRI for laminar profiling of imagined and veridical sensory representations in visual cortex.
- Main PIs: Fracasso, Goense, Gross, Gunamony, Harvey, Ince, Muckli, Matias Palva, Satu Palva, Philiastides, Robertson, Rousselet, Schyns, Thut, Uhlhaas
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. Example Highlight: Developing EEG/MEG-guided neuronavigated transcranial stimulation to enhance efficacy and specificity of these non-invasive interventions.
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.
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 £11.8M in competitive awards (BBSRC, Wellcome, MRC) including two ERC Consolidator Award (Muckli, £1.2 'Brain Reading of contextual feedback and predictions’ and Kayser, £1.4 ‘Multisensory Integration'), three Wellcome Trust Investigator Awards (Thut and Gross, £2M, 'State-dependent decoding and driving of human Brain Oscillations’ and Schyns, 'Brain algorithmics: reverse engineering dynamic information processing in brain networks from MEG time series’, £1.3M), and a 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.