Literature DB >> 18463251

Functional magnetic resonance imaging of delay and trace eyeblink conditioning in the primary visual cortex of the rabbit.

Michael J Miller1, Craig Weiss, Xiaomu Song, Gheorghe Iordanescu, John F Disterhoft, Alice M Wyrwicz.   

Abstract

The primary sensory cortices have been shown in recent years to undergo experience- and learning-related plasticity under a variety of experimental circumstances. In this study, we used functional magnetic resonance imaging (fMRI) in parallel with both delay and trace eyeblink conditioning to image the learning-related functional activation within the primary visual cortex (V1) of awake, behaving rabbits. We expected that the differing level of forebrain dependence between these two conditioning paradigms should produce a differential blood oxygenation level-dependent (BOLD) functional response in V1. Our results showed a significant expansion of activated volume within V1, particularly early in learning, after training with the more cognitively demanding trace paradigm. In contrast, the simpler delay paradigm produced an increase in the magnitude of the BOLD response in activated voxels, but no significant change in activated volume. No accompanying learning-related changes were observed in the primary somatosensory cortex, which mediates the unconditioned stimulus. These results suggest that the recruitment of additional neurons within V1 is necessary to support the more demanding memory imposed by the trace interval. To our knowledge, this work is the first functional imaging study to compare directly trace and delay eyeblink conditioning in an animal model.

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Year:  2008        PMID: 18463251      PMCID: PMC2682544          DOI: 10.1523/JNEUROSCI.5622-07.2008

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  55 in total

1.  Cortical involvement in acquisition and extinction of trace eyeblink conditioning.

Authors:  A P Weible; M D McEchron; J F Disterhoft
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2.  Involvement of cerebral cortical structures in the classical conditioning of eyelid responses in rabbits.

Authors:  A Gruart; S Morcuende; S Martínez; J M Delgado-García
Journal:  Neuroscience       Date:  2000       Impact factor: 3.590

Review 3.  Imaging learning and memory: classical conditioning.

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5.  Associative retuning in the thalamic source of input to the amygdala and auditory cortex: receptive field plasticity in the medial division of the medial geniculate body.

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Journal:  Behav Neurosci       Date:  1992-02       Impact factor: 1.912

6.  Changes in the distributed temporal response properties of SI cortical neurons reflect improvements in performance on a temporally based tactile discrimination task.

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7.  Cortical barrel lesions impair whisker-CS trace eyeblink conditioning.

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8.  Lateralization and behavioral correlation of changes in regional cerebral blood flow with classical conditioning of the human eyeblink response.

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Review 9.  Mammalian brain substrates of aversive classical conditioning.

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Journal:  Annu Rev Psychol       Date:  1993       Impact factor: 24.137

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Authors:  J R Moyer; R A Deyo; J F Disterhoft
Journal:  Behav Neurosci       Date:  1990-04       Impact factor: 1.912

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  14 in total

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Review 2.  The impact of hippocampal lesions on trace-eyeblink conditioning and forebrain-cerebellar interactions.

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Journal:  Behav Neurosci       Date:  2015-08       Impact factor: 1.912

Review 3.  Towards a unified model of pavlovian conditioning: short review of trace conditioning models.

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4.  Spatially regularized machine learning for task and resting-state fMRI.

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5.  Learning-related neuronal activity in the ventral lateral geniculate nucleus during associative cerebellar learning.

Authors:  Alireza Kashef; Matthew M Campolattaro; John H Freeman
Journal:  J Neurophysiol       Date:  2014-08-13       Impact factor: 2.714

6.  A SVM-based quantitative fMRI method for resting-state functional network detection.

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Journal:  Magn Reson Imaging       Date:  2014-04-13       Impact factor: 2.546

7.  Visual cortical contributions to associative cerebellar learning.

Authors:  Adam B Steinmetz; Thomas C Harmon; John H Freeman
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8.  The rabbit as a behavioral model system for magnetic resonance imaging.

Authors:  Craig Weiss; Daniel Procissi; John M Power; John F Disterhoft
Journal:  J Neurosci Methods       Date:  2017-05-26       Impact factor: 2.390

9.  Learning strategy refinement reverses early sensory cortical map expansion but not behavior: Support for a theory of directed cortical substrates of learning and memory.

Authors:  Gabriel A Elias; Kasia M Bieszczad; Norman M Weinberger
Journal:  Neurobiol Learn Mem       Date:  2015-10-24       Impact factor: 2.877

10.  Unsupervised spatiotemporal fMRI data analysis using support vector machines.

Authors:  Xiaomu Song; Alice M Wyrwicz
Journal:  Neuroimage       Date:  2009-03-31       Impact factor: 6.556

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