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The brain ages optimally to model its environment: evidence from sensory learning over the adult lifespan

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.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1371/journal.pcbi.1003422

Authors

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Institution:
University of Oxford
Division:
MSD
Department:
Clinical Neurosciences
Role:
Author


Publisher:
Public Library of Science
Journal:
PLoS Computational Biology More from this journal
Volume:
10
Issue:
1
Pages:
e1003422
Publication date:
2014-01-23
Acceptance date:
2013-11-01
DOI:
EISSN:
1553-7358
ISSN:
1553-734X


Language:
English
Keywords:
Pubs id:
447974
UUID:
uuid:081d49ff-5547-4bbb-a921-28226f637466
Local pid:
pubs:447974
Source identifiers:
447974
Deposit date:
2014-02-13
ARK identifier:

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