Fine-scale computations for adaptive processing in the human brain

Adapting to the environment statistics by reducing brain responses to repetitive sensory information is key for efficient information processing. Yet, the fine-scale computations that support this adaptive processing in the human brain remain largely unknown. Here, we capitalise on the sub-millimetr...

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Main Authors: Elisa Zamboni, Valentin G Kemper, Nuno Reis Goncalves, Ke Jia, Vasilis M Karlaftis, Samuel J Bell, Joseph Giorgio, Reuben Rideaux, Rainer Goebel, Zoe Kourtzi
Format: Article
Language:English
Published: eLife Sciences Publications Ltd 2020-11-01
Series:eLife
Subjects:
Online Access:https://elifesciences.org/articles/57637
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spelling doaj-88ea0564512a48aba0eed8f68a6dd4962021-05-05T21:42:19ZengeLife Sciences Publications LtdeLife2050-084X2020-11-01910.7554/eLife.57637Fine-scale computations for adaptive processing in the human brainElisa Zamboni0https://orcid.org/0000-0001-9200-8031Valentin G Kemper1Nuno Reis Goncalves2Ke Jia3Vasilis M Karlaftis4https://orcid.org/0000-0003-1285-1593Samuel J Bell5Joseph Giorgio6Reuben Rideaux7https://orcid.org/0000-0001-8416-005XRainer Goebel8Zoe Kourtzi9https://orcid.org/0000-0001-9441-7832Department of Psychology, University of Cambridge, Cambridge, United KingdomDepartment of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands; Department of Cognitive Neuroscience, Maastricht Brain Imaging Center, Maastricht University, Maastricht, NetherlandsDepartment of Psychology, University of Cambridge, Cambridge, United KingdomDepartment of Psychology, University of Cambridge, Cambridge, United KingdomDepartment of Psychology, University of Cambridge, Cambridge, United KingdomDepartment of Psychology, University of Cambridge, Cambridge, United KingdomDepartment of Psychology, University of Cambridge, Cambridge, United KingdomDepartment of Psychology, University of Cambridge, Cambridge, United KingdomDepartment of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands; Department of Cognitive Neuroscience, Maastricht Brain Imaging Center, Maastricht University, Maastricht, NetherlandsDepartment of Psychology, University of Cambridge, Cambridge, United KingdomAdapting to the environment statistics by reducing brain responses to repetitive sensory information is key for efficient information processing. Yet, the fine-scale computations that support this adaptive processing in the human brain remain largely unknown. Here, we capitalise on the sub-millimetre resolution of ultra-high field imaging to examine functional magnetic resonance imaging signals across cortical depth and discern competing hypotheses about the brain mechanisms (feedforward vs. feedback) that mediate adaptive processing. We demonstrate layer-specific suppressive processing within visual cortex, as indicated by stronger BOLD decrease in superficial and middle than deeper layers for gratings that were repeatedly presented at the same orientation. Further, we show altered functional connectivity for adaptation: enhanced feedforward connectivity from V1 to higher visual areas, short-range feedback connectivity between V1 and V2, and long-range feedback occipito-parietal connectivity. Our findings provide evidence for a circuit of local recurrent and feedback interactions that mediate rapid brain plasticity for adaptive information processing.https://elifesciences.org/articles/57637visual cortexadaptationfMRIlayerlaminarfunctional connectivity
collection DOAJ
language English
format Article
sources DOAJ
author Elisa Zamboni
Valentin G Kemper
Nuno Reis Goncalves
Ke Jia
Vasilis M Karlaftis
Samuel J Bell
Joseph Giorgio
Reuben Rideaux
Rainer Goebel
Zoe Kourtzi
spellingShingle Elisa Zamboni
Valentin G Kemper
Nuno Reis Goncalves
Ke Jia
Vasilis M Karlaftis
Samuel J Bell
Joseph Giorgio
Reuben Rideaux
Rainer Goebel
Zoe Kourtzi
Fine-scale computations for adaptive processing in the human brain
eLife
visual cortex
adaptation
fMRI
layer
laminar
functional connectivity
author_facet Elisa Zamboni
Valentin G Kemper
Nuno Reis Goncalves
Ke Jia
Vasilis M Karlaftis
Samuel J Bell
Joseph Giorgio
Reuben Rideaux
Rainer Goebel
Zoe Kourtzi
author_sort Elisa Zamboni
title Fine-scale computations for adaptive processing in the human brain
title_short Fine-scale computations for adaptive processing in the human brain
title_full Fine-scale computations for adaptive processing in the human brain
title_fullStr Fine-scale computations for adaptive processing in the human brain
title_full_unstemmed Fine-scale computations for adaptive processing in the human brain
title_sort fine-scale computations for adaptive processing in the human brain
publisher eLife Sciences Publications Ltd
series eLife
issn 2050-084X
publishDate 2020-11-01
description Adapting to the environment statistics by reducing brain responses to repetitive sensory information is key for efficient information processing. Yet, the fine-scale computations that support this adaptive processing in the human brain remain largely unknown. Here, we capitalise on the sub-millimetre resolution of ultra-high field imaging to examine functional magnetic resonance imaging signals across cortical depth and discern competing hypotheses about the brain mechanisms (feedforward vs. feedback) that mediate adaptive processing. We demonstrate layer-specific suppressive processing within visual cortex, as indicated by stronger BOLD decrease in superficial and middle than deeper layers for gratings that were repeatedly presented at the same orientation. Further, we show altered functional connectivity for adaptation: enhanced feedforward connectivity from V1 to higher visual areas, short-range feedback connectivity between V1 and V2, and long-range feedback occipito-parietal connectivity. Our findings provide evidence for a circuit of local recurrent and feedback interactions that mediate rapid brain plasticity for adaptive information processing.
topic visual cortex
adaptation
fMRI
layer
laminar
functional connectivity
url https://elifesciences.org/articles/57637
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