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The Brain Ages Optimally to Model Its Environment: Evidence from Sensory Learning over the Adult Lifespan

Overview of attention for article published in PLoS Computational Biology, January 2014
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Title
The Brain Ages Optimally to Model Its Environment: Evidence from Sensory Learning over the Adult Lifespan
Published in
PLoS Computational Biology, January 2014
DOI 10.1371/journal.pcbi.1003422
Pubmed ID
Authors

Rosalyn J. Moran, Mkael Symmonds, Raymond J. Dolan, Karl J. Friston

Abstract

The aging brain shows a progressive loss of neuropil, which is accompanied by subtle changes in neuronal plasticity, sensory learning and memory. Neurophysiologically, aging attenuates evoked responses--including the mismatch negativity (MMN). This is accompanied by a shift in cortical responsivity from sensory (posterior) regions to executive (anterior) regions, which has been interpreted as a compensatory response for cognitive decline. Theoretical neurobiology offers a simpler explanation for all of these effects--from a Bayesian perspective, as the brain is progressively optimized to model its world, its complexity will decrease. A corollary of this complexity reduction is an attenuation of Bayesian updating or sensory learning. Here we confirmed this hypothesis using magnetoencephalographic recordings of the mismatch negativity elicited in a large cohort of human subjects, in their third to ninth decade. Employing dynamic causal modeling to assay the synaptic mechanisms underlying these non-invasive recordings, we found a selective age-related attenuation of synaptic connectivity changes that underpin rapid sensory learning. In contrast, baseline synaptic connectivity strengths were consistently strong over the decades. Our findings suggest that the lifetime accrual of sensory experience optimizes functional brain architectures to enable efficient and generalizable predictions of the world.

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Geographical breakdown

Country Count As %
United States 3 1%
United Kingdom 3 1%
France 2 <1%
Switzerland 1 <1%
Austria 1 <1%
Hungary 1 <1%
Germany 1 <1%
Australia 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 197 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 46 22%
Researcher 46 22%
Student > Master 22 10%
Student > Doctoral Student 14 7%
Professor 13 6%
Other 49 23%
Unknown 22 10%
Readers by discipline Count As %
Psychology 62 29%
Neuroscience 43 20%
Agricultural and Biological Sciences 19 9%
Computer Science 13 6%
Medicine and Dentistry 10 5%
Other 28 13%
Unknown 37 17%