Literature DB >> 19261714

Dynamic causal modeling of the response to frequency deviants.

Marta I Garrido1, James M Kilner, Stefan J Kiebel, Karl J Friston.   

Abstract

This article describes the use of dynamic causal modeling to test hypotheses about the genesis of evoked responses. Specifically, we consider the mismatch negativity (MMN), a well-characterized response to deviant sounds and one of the most widely studied evoked responses. There have been several mechanistic accounts of how the MMN might arise. It has been suggested that the MMN results from a comparison between sensory input and a memory trace of previous input, although others have argued that local adaptation, due to stimulus repetition, is sufficient to explain the MMN. Thus the precise mechanisms underlying the generation of the MMN remain unclear. This study tests some biologically plausible spatiotemporal dipole models that rest on changes in extrinsic top-down connections (that enable comparison) and intrinsic changes (that model adaptation). Dynamic causal modeling suggested that responses to deviants are best explained by changes in effective connectivity both within and between cortical sources in a hierarchical network of distributed sources. Our model comparison suggests that both adaptation and memory comparison operate in concert to produce the early (N1 enhancement) and late (MMN) parts of the response to frequency deviants. We consider these mechanisms in the light of predictive coding and hierarchical inference in the brain.

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Year:  2009        PMID: 19261714      PMCID: PMC2681422          DOI: 10.1152/jn.90291.2008

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  58 in total

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2.  Dynamic sensory updating in the auditory system.

Authors:  E Sussman; I Winkler
Journal:  Brain Res Cogn Brain Res       Date:  2001-12

3.  Electric brain response to sound repetition in humans: an index of long-term-memory - trace formation?

Authors:  Risto Näätänen; Teemu Rinne
Journal:  Neurosci Lett       Date:  2002-01-18       Impact factor: 3.046

4.  Is there pre-attentive memory-based comparison of pitch?

Authors:  T Jacobsen; E Schröger
Journal:  Psychophysiology       Date:  2001-07       Impact factor: 4.016

5.  Preattentive extraction of abstract feature conjunctions from auditory stimulation as reflected by the mismatch negativity (MMN).

Authors:  P Paavilainen; J Simola; M Jaramillo; R Näätänen; I Winkler
Journal:  Psychophysiology       Date:  2001-03       Impact factor: 4.016

6.  On the functional role of temporal and frontal cortex activation in passive detection of auditory deviance.

Authors:  Chun-Yu Tse; Trevor B Penney
Journal:  Neuroimage       Date:  2008-04-04       Impact factor: 6.556

7.  The functional anatomy of the MMN: a DCM study of the roving paradigm.

Authors:  Marta I Garrido; Karl J Friston; Stefan J Kiebel; Klaas E Stephan; Torsten Baldeweg; James M Kilner
Journal:  Neuroimage       Date:  2008-05-20       Impact factor: 6.556

8.  Identifying neural drivers with functional MRI: an electrophysiological validation.

Authors:  Olivier David; Isabelle Guillemain; Sandrine Saillet; Sebastien Reyt; Colin Deransart; Christoph Segebarth; Antoine Depaulis
Journal:  PLoS Biol       Date:  2008-12-23       Impact factor: 8.029

9.  Hierarchical models in the brain.

Authors:  Karl Friston
Journal:  PLoS Comput Biol       Date:  2008-11-07       Impact factor: 4.475

10.  Dynamic causal modelling of evoked potentials: a reproducibility study.

Authors:  Marta I Garrido; James M Kilner; Stefan J Kiebel; Klaas E Stephan; Karl J Friston
Journal:  Neuroimage       Date:  2007-03-27       Impact factor: 6.556

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  72 in total

1.  Evidence for a hierarchy of predictions and prediction errors in human cortex.

Authors:  Catherine Wacongne; Etienne Labyt; Virginie van Wassenhove; Tristan Bekinschtein; Lionel Naccache; Stanislas Dehaene
Journal:  Proc Natl Acad Sci U S A       Date:  2011-12-06       Impact factor: 11.205

2.  Updating representations of temporal intervals.

Authors:  James Danckert; Britt Anderson
Journal:  Exp Brain Res       Date:  2015-08-25       Impact factor: 1.972

3.  Expectation and attention in hierarchical auditory prediction.

Authors:  Srivas Chennu; Valdas Noreika; David Gueorguiev; Alejandro Blenkmann; Silvia Kochen; Agustín Ibáñez; Adrian M Owen; Tristan A Bekinschtein
Journal:  J Neurosci       Date:  2013-07-03       Impact factor: 6.167

4.  Functional features of crossmodal mismatch responses.

Authors:  Chen Zhao; Elia Valentini; Li Hu
Journal:  Exp Brain Res       Date:  2014-11-15       Impact factor: 1.972

5.  Predictions Shape Confidence in Right Inferior Frontal Gyrus.

Authors:  Maxine T Sherman; Anil K Seth; Ryota Kanai
Journal:  J Neurosci       Date:  2016-10-05       Impact factor: 6.167

6.  Predictive coding as a model of cognition.

Authors:  M W Spratling
Journal:  Cogn Process       Date:  2016-04-27

7.  Mismatch negativity encoding of prediction errors predicts S-ketamine-induced cognitive impairments.

Authors:  André Schmidt; Rosilla Bachmann; Michael Kometer; Philipp A Csomor; Klaas E Stephan; Erich Seifritz; Franz X Vollenweider
Journal:  Neuropsychopharmacology       Date:  2011-10-26       Impact factor: 7.853

8.  The Unpredictive Brain Under Threat: A Neurocomputational Account of Anxious Hypervigilance.

Authors:  Brian R Cornwell; Marta I Garrido; Cassie Overstreet; Daniel S Pine; Christian Grillon
Journal:  Biol Psychiatry       Date:  2017-07-06       Impact factor: 13.382

9.  Brain responses in humans reveal ideal observer-like sensitivity to complex acoustic patterns.

Authors:  Nicolas Barascud; Marcus T Pearce; Timothy D Griffiths; Karl J Friston; Maria Chait
Journal:  Proc Natl Acad Sci U S A       Date:  2016-01-19       Impact factor: 11.205

10.  Distinguishing Neural Adaptation and Predictive Coding Hypotheses in Auditory Change Detection.

Authors:  Renée M Symonds; Wei Wei Lee; Adam Kohn; Odelia Schwartz; Sarah Witkowski; Elyse S Sussman
Journal:  Brain Topogr       Date:  2016-10-17       Impact factor: 3.020

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