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

Arjen Alink; Hunar Abdulrahman; Richard N Henson

doi:10.1038/s41467-018-05957-0

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

Standard

Forward models demonstrate that repetition suppression is best modelled by local neural scaling. / Alink, Arjen; Abdulrahman, Hunar; Henson, Richard N.

in: NAT COMMUN, Jahrgang 9, Nr. 1, 21.09.2018, S. 3854.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung

Harvard

Alink, A, Abdulrahman, H & Henson, RN 2018, 'Forward models demonstrate that repetition suppression is best modelled by local neural scaling', NAT COMMUN, Jg. 9, Nr. 1, S. 3854. https://doi.org/10.1038/s41467-018-05957-0

APA

Alink, A., Abdulrahman, H., & Henson, R. N. (2018). Forward models demonstrate that repetition suppression is best modelled by local neural scaling. NAT COMMUN, 9(1), 3854. https://doi.org/10.1038/s41467-018-05957-0

Vancouver

Alink A, Abdulrahman H, Henson RN. Forward models demonstrate that repetition suppression is best modelled by local neural scaling. NAT COMMUN. 2018 Sep 21;9(1):3854. https://doi.org/10.1038/s41467-018-05957-0

Bibtex

@article{fdf470996a7944a7a85d65523e356b71,

title = "Forward models demonstrate that repetition suppression is best modelled by local neural scaling",

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 emphasise the importance of formal modelling for bridging neuronal and fMRI levels of investigation.",

keywords = "Adult, Computer Simulation, Female, Humans, Magnetic Resonance Imaging, Male, Models, Neurological, Neurons, Young Adult, Journal Article, Research Support, Non-U.S. Gov't",

author = "Arjen Alink and Hunar Abdulrahman and Henson, {Richard N}",

year = "2018",

month = sep,

day = "21",

doi = "10.1038/s41467-018-05957-0",

language = "English",

volume = "9",

pages = "3854",

journal = "NAT COMMUN",

issn = "2041-1723",

publisher = "NATURE PUBLISHING GROUP",

number = "1",

}

RIS

TY - JOUR

T1 - Forward models demonstrate that repetition suppression is best modelled by local neural scaling

AU - Alink, Arjen

AU - Abdulrahman, Hunar

AU - Henson, Richard N

PY - 2018/9/21

Y1 - 2018/9/21

N2 - 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 emphasise the importance of formal modelling for bridging neuronal and fMRI levels of investigation.

AB - 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 emphasise the importance of formal modelling for bridging neuronal and fMRI levels of investigation.

KW - Adult

KW - Computer Simulation

KW - Female

KW - Humans

KW - Magnetic Resonance Imaging

KW - Male

KW - Models, Neurological

KW - Neurons

KW - Young Adult

KW - Journal Article

KW - Research Support, Non-U.S. Gov't

U2 - 10.1038/s41467-018-05957-0

DO - 10.1038/s41467-018-05957-0

M3 - SCORING: Journal article

C2 - 30242150

VL - 9

SP - 3854

JO - NAT COMMUN

JF - NAT COMMUN

SN - 2041-1723

IS - 1

ER -