Postersession 3
Poster #: 27
Topic: Computational models
Friday, Sep 11, 2015
15:30-17:00
1st floor
Hierarchical frontotemporal networks in MMN: dynamic causal modelling of MEG supported by human intracranial EEG
1Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
2Institute of Cellular Biology and Neuroscience, CONICET - University of Buenos Aires, Buenos Aires, Argentina
3Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
4Department of Psychology, University of Cambridge, Cambridge, United Kingdom
holly.phillips@mrc-cbu.cam.ac.uk
Neurocomputational models (Lieder et al. 2013) and dynamic causal modelling (DCM) of the MMN in EEG (Garrido et al. 2009) and MEG (Hughes et al. 2013) indicate a hierarchy of feedback sensory prediction and feedforward prediction errors, between primary auditory cortex (A1), superior temporal gyrus (STG) and prefrontal cortex (PFC). However, such DCM for non-invasive M/EEG relies on optimisation of the forward model. We compared MEG and intracranial EEG (iEEG) recordings to see whether this frontotemporal message passing is evident across imaging modalities.
We used an auditory MMN paradigm (Näätänen et al. 2004), alternating standard tones with deviant tones, differing by frequency, intensity, location, duration or a silent gap. We recorded MEG data in healthy adults and electrocorticography (ECoG) in patient candidates for epilepsy surgery, covering temporal and frontal cortices. We compared 12 dynamic causal models of networks among A1, STG and PFC and used Bayesian model selection to compare models.
Results show matching models across the modalities, with evidence for modulated frontotemporal feedforward and feedback connections and internally-generated inputs to PFC. We show ECoG and MEG are complimentary methods that balance generalisation to larger populations (MEG) against precise anatomical localisation with direct recording of cortical field potentials (ECoG). They provide convergent evidence for the hierarchical interactions in frontotemporal networks and evidence for an internal input influencing prefrontal cortex.