The Brain Ages Optimally to Model Its Environment: Evidence from Sensory Learning over the Adult Lifespan
The Brain Ages Optimally to Model Its Environment: Evidence from Sensory Learning over the Adult Lifespan
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DOI：
10.1371/journal.pcbi.1003422
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发表时间：
2014-01-01
影响因子：
4.3
通讯作者：
Friston, Karl J.
中科院分区：
文献类型：
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作者：
Moran, Rosalyn J.;Symmonds, Mkael;Friston, Karl J.
衰老的大脑表现为神经功能的逐渐丧失，伴随着神经元可塑性、感觉学习和记忆的细微变化。神经生理学上，衰老减弱了诱发反应，包括失配负性（MMN）。这伴随着皮层反应从感觉（后）区域向执行（前）区域的转移，这被解释为认知衰退的代偿反应。理论神经生物学为所有这些效应提供了一个更简单的解释——从贝叶斯的角度来看，随着大脑逐渐优化以模拟其世界，其复杂性将降低。这种复杂性降低的必然结果是贝叶斯更新或感官学习的衰减。在这里，我们用脑磁图记录了一大批年龄在30岁到90岁之间的人类受试者的错配负性，证实了这一假设。采用动态因果模型分析这些非侵入性记录背后的突触机制，我们发现突触连接变化的选择性年龄相关衰减是快速感觉学习的基础。相比之下，基线突触连接强度在过去几十年里一直很强。我们的研究结果表明，感官体验的终生积累优化了大脑的功能结构，从而实现了对世界的有效和概括的预测。
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.