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Identification of a Functional Connectome for Long-Term Fear Memory in Mice

Overview of attention for article published in PLoS Computational Biology, January 2013
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Title
Identification of a Functional Connectome for Long-Term Fear Memory in Mice
Published in
PLoS Computational Biology, January 2013
DOI 10.1371/journal.pcbi.1002853
Pubmed ID
Authors

Anne L. Wheeler, Cátia M. Teixeira, Afra H. Wang, Xuejian Xiong, Natasa Kovacevic, Jason P. Lerch, Anthony R. McIntosh, John Parkinson, Paul W. Frankland

Abstract

Long-term memories are thought to depend upon the coordinated activation of a broad network of cortical and subcortical brain regions. However, the distributed nature of this representation has made it challenging to define the neural elements of the memory trace, and lesion and electrophysiological approaches provide only a narrow window into what is appreciated a much more global network. Here we used a global mapping approach to identify networks of brain regions activated following recall of long-term fear memories in mice. Analysis of Fos expression across 84 brain regions allowed us to identify regions that were co-active following memory recall. These analyses revealed that the functional organization of long-term fear memories depends on memory age and is altered in mutant mice that exhibit premature forgetting. Most importantly, these analyses indicate that long-term memory recall engages a network that has a distinct thalamic-hippocampal-cortical signature. This network is concurrently integrated and segregated and therefore has small-world properties, and contains hub-like regions in the prefrontal cortex and thalamus that may play privileged roles in memory expression.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 402 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 1%
Canada 2 <1%
United Kingdom 2 <1%
France 1 <1%
Ireland 1 <1%
Turkey 1 <1%
Malaysia 1 <1%
Germany 1 <1%
Brazil 1 <1%
Other 5 1%
Unknown 381 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 96 24%
Researcher 82 20%
Student > Master 45 11%
Student > Bachelor 34 8%
Student > Doctoral Student 21 5%
Other 69 17%
Unknown 55 14%
Readers by discipline Count As %
Neuroscience 121 30%
Agricultural and Biological Sciences 110 27%
Psychology 24 6%
Medicine and Dentistry 17 4%
Computer Science 16 4%
Other 46 11%
Unknown 68 17%