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Forward models demonstrate that repetition suppression is best modelled by local neural scaling


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Authors

Alink, Arjen 
Abdulrahman, A 
Henson, RN 

Abstract

Inferring neural mechanisms from functional magnetic resonance imaging (fMRI) is challenging because the fMRI signal integrates over millions of neurons. One approach is to compare computational models that map neural activity to fMRI responses, to see which best predicts fMRI data. We use this approach to compare four possible neural mechanisms of fMRI adaptation to repeated stimuli (scaling, sharpening, repulsive shifting and attractive shifting), acting across three domains (global, local and remote). Six features of fMRI repetition effects are identified, both univariate and multivariate, from two independent fMRI experiments. After searching over parameter values, only the local scaling model can simultaneously fit all data features from both experiments. Thus fMRI stimulus repetition effects are best captured by down-scaling neuronal tuning curves in proportion to the difference between the stimulus and neuronal preference. These results emphasize the importance of formal modelling for bridging neuronal and fMRI levels of investigation.

Description

Keywords

Adult, Computer Simulation, Female, Humans, Magnetic Resonance Imaging, Male, Models, Neurological, Neurons, Young Adult

Journal Title

Nature Communications

Conference Name

Journal ISSN

2041-1723
2041-1723

Volume Title

9

Publisher

Springer Nature
Sponsorship
MRC (unknown)
Medical Research Council (MC_UU_00005/8)
This work was supported by British Academy postdoctoral fellowship and a Marie Curie fellowship (753441) to A.A., a Cambridge University international scholarship and IDB merit scholarship award to H.A., and Medical Research Council programme grant (SUAG/010 RG91365) to R.N.H.