The Centre for Cognitive Neuroimaging (CCNi)

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.

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 468 papers between 2008 and 2017, with more than 16,032 citations.

Sample Publications:

  • 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 Communication, 8: 15808. 
  • H Park, RAA Ince, PG Schyns, G Thut, J Gross (2015) Frontal Top-Down Signals Increase Coupling of Auditory Low-Frequency Oscillations to Continuous Speech in Human Listeners, Current Biology, 25(12), 1649-53.
  • 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 (20), 2690-2695.
  • P Vetter, M-H Grosbras, L Muckli (2013) TMS over V5 disrupts motion prediction. Cerebral Cortex, 25(4), 1052-1059.
  • PJ Uhlhaas W Singer (2012) Neuronal dynamics and neuropsychiatric disorders: toward a translational paradigm for dysfunctional large-scale networks. Neuron, 75 (6). 963-980.