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Convergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalography.

Published version
Peer-reviewed

Repository DOI


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Authors

Phillips, Holly N 
Blenkmann, Alejandro 
Hughes, Laura E 
Kochen, Silvia 
Bekinschtein, Tristan A 

Abstract

We propose that sensory inputs are processed in terms of optimised predictions and prediction error signals within hierarchical neurocognitive models. The combination of non-invasive brain imaging and generative network models has provided support for hierarchical frontotemporal interactions in oddball tasks, including recent identification of a temporal expectancy signal acting on prefrontal cortex. However, these studies are limited by the need to invert magnetoencephalographic or electroencephalographic sensor signals to localise activity from cortical 'nodes' in the network, or to infer neural responses from indirect measures such as the fMRI BOLD signal. To overcome this limitation, we examined frontotemporal interactions estimated from direct cortical recordings from two human participants with cortical electrode grids (electrocorticography - ECoG). Their frontotemporal network dynamics were compared to those identified by magnetoencephalography (MEG) in forty healthy adults. All participants performed the same auditory oddball task with standard tones interspersed with five deviant tone types. We normalised post-operative electrode locations to standardised anatomic space, to compare across modalities, and inverted the MEG to cortical sources using the estimated lead field from subject-specific head models. A mismatch negativity signal in frontal and temporal cortex was identified in all subjects. Generative models of the electrocorticographic and magnetoencephalographic data were separately compared using the free-energy estimate of the model evidence. Model comparison confirmed the same critical features of hierarchical frontotemporal networks in each patient as in the group-wise MEG analysis. These features included bilateral, feedforward and feedback frontotemporal modulated connectivity, in addition to an asymmetric expectancy driving input on left frontal cortex. The invasive ECoG provides an important step in construct validation of the use of neural generative models of MEG, which in turn enables generalisation to larger populations. Together, they give convergent evidence for the hierarchical interactions in frontotemporal networks for expectation and processing of sensory inputs.

Description

Keywords

Cognition, Dynamic causal modelling, Electrocorticography, Magnetoencephalography, Mismatch negativity, Adult, Brain, Brain Mapping, Electrocorticography, Female, Humans, Magnetoencephalography, Male, Models, Neurological, Nerve Net, Young Adult

Journal Title

Cortex

Conference Name

Journal ISSN

0010-9452
1973-8102

Volume Title

82

Publisher

Elsevier BV
Sponsorship
Medical Research Council (G1000183)
Biotechnology and Biological Sciences Research Council (BB/H008217/1)
Wellcome Trust (103838/Z/14/Z)
James S McDonnell Foundation (220020289)
Medical Research Council (MC_U105597119)
MRC (1233633)
Wellcome Trust (093875/Z/10/Z)
Medical Research Council (MC-A060-5PQ30 and a Doctoral Training award to HNP), Wellcome Trust (103838 Senior Research Fellowship to JBR, LEH, Biomedical Research Fellowship WT093811MA to TAB), the James F. McDonnell Foundation 21st Century Science Initiative: Understanding Human Cognition. The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) research was supported by the Biotechnology and Biological Sciences Research Council (grant number BB/H008217/1). PIDC 53/2012, PICT 0775/2012 and UNAJ investiga 2014 to AB and SK.