Cited by 223Neuroscience and Music PerceptionNeural dynamics and brain functionFunctional Brain Connectivity Studies

Repetition suppression and plasticity in the human brain

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Garrido, Marta I., Kilner, James M., Kiebel, Stefan J., Stephan, Klaas E., Baldeweg, Torsten, Friston, Karl J.·2009·NeuroImage

Abstract

The suppression of neuronal responses to a repeated event is a ubiquitous phenomenon in neuroscience. However, the underlying mechanisms remain largely unexplored. The aim of this study was to examine the temporal evolution of experience-dependent changes in connectivity induced by repeated stimuli. We recorded event-related potentials (ERPs) during frequency changes of a repeating tone. Bayesian inversion of dynamic causal models (DCM) of ERPs revealed systematic repetition-dependent changes in both intrinsic and extrinsic connections, within a hierarchical cortical network. Critically, these changes occurred very quickly, over inter-stimulus intervals that implicate short-term synaptic plasticity. Furthermore, intrinsic (within-source) connections showed biphasic changes that were much faster than changes in extrinsic (between-source) connections, which decreased monotonically with repetition. This study shows that auditory perceptual learning is associated with repetition-dependent plasticity in the human brain. It is remarkable that distinct changes in intrinsic and extrinsic connections could be quantified so reliably and non-invasively using EEG.

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Crossref · 10.1016/j.neuroimage.2009.06.034

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  • The changing magnitude of the mismatch response over successive tone repetitions is considered to index the precision (certainty) of prediction error.

    “Specifically, the changing magnitude of the mismatch response over successive tone repetitions (Figure 1A) is considered to index the precision (certainty) of prediction error. 4”

    From Alzheimer's disease and its progression reduce pyramidal cell gain and connectivity (2025) · cites this paper as [4]

    The Results (Repetition effects; page unavailable) report that the mismatch negativity diminishes with tone repetition, and the Introduction interprets it as signaling prediction error. But the Discussion (Implications; page unavailable) associates the estimated precision of prediction error with changes in intrinsic connectivity, which fall and then recover; it does not establish that the changing magnitude of the mismatch response itself indexes precision. (LLM, conf=0.86)

    Experimental — accuracy not yet measured
  • Neural circuits automatically learn and adapt to changing stimulus frequency.

    “neural circuits automatically learn and adapt to changing stimulus frequency.”
  • This learning reflects short-term plasticity and can be achieved by iterative updating of top-down predictions by bottom-up prediction errors.

    “This learning reflects short‐term plasticity and can be achieved by iterative updating of top–down predictions by bottom–up prediction errors. 4”
  • Six of the eight regions are based on prior studies that established a frontotemporal network responsible for generating the mismatch negativity response.

    “Six of these regions are based on a series of prior studies of mismatch negativity generation with healthy adults and those with non‐AD dementia, 4 , 18 , 24 , 25 which established a frontotemporal network (primary auditory, superior temporal, and inferior frontal regions) responsible for generating the mismatch negativity response.”
  • The second set of models with both exponential and phasic change embody the hypothesis of predictive coding for sequential neural responses to stimuli.

    “The second set of models with both exponential and phasic change embody the hypothesis of predictive coding for sequential neural responses to stimuli, allowing extrinsic connections to show stimulus‐specific adaptation with exponential decrease with repetition; while intrinsic connections show a phasic response, reducing after the deviant tone and recovering with subsequent tones thereby refle”

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References 60 · Cited by 223

Authors
Garrido, M. I., Kilner, J. M., Kiebel, S. J., Stephan, K. E., Baldeweg, T., & Friston, K. J.
Affiliations
  • Wellcome Centre for Human Neuroimaging (GB)
  • Max Planck Society (DE)
  • University College London (GB)
Corresponding author
Garrido, Marta I.
Venue
NeuroImage
Publisher
Elsevier BV
Published
2009-10
Volume
48
Issue
1
Pages
269-279
ISSN
1053-8119 (ISSN-L)
OpenAlex
W1995468513
Cited by
223
References
60
Citations by year
2026: 10 · 2025: 10 · 2024: 20 · 2023: 18 · 2022: 18 · 2021: 16 · 2020: 16 · 2019: 13 · 2018: 18 · 2017: 14 · 2016: 8 · 2015: 7 · 2014: 12 · 2013: 12 · 2012: 10
FWCI
4.94
Type
journal article
Language
en
Open access
Locations
  • NeuroImage (publishedVersion)
  • PubMed (publishedVersion)
  • Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
  • PubMed Central (acceptedVersion)
  • Max Planck Digital Library (submittedVersion)
  • UCL Discovery (University College London) (submittedVersion)
  • MPG.PuRe (Max Planck Society) (submittedVersion)
  • Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
  • Zurich Open Repository and Archive (University of Zurich)
Funders
Wellcome Trust (056750, 088130)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neuroscience and Music Perception
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords
repetition suppression, short-term plasticity, auditory perceptual learning, dynamic causal modeling, Bayesian inversion, event-related potentials, auditory cortex
MeSH terms
Acoustic Stimulation, Adaptation, Psychological, Adult, Algorithms, Auditory Cortex, Auditory Perception, Bayes Theorem, Brain, Electroencephalography, Evoked Potentials, Auditory, Female, Humans, Learning, Male, Models, Neurological, Neuronal Plasticity, Temporal Lobe, Young Adult

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