Limbic Tract Integrity Contributes to Pattern Separation Performance Across the Lifespan
Limbic Tract Integrity Contributes to Pattern Separation Performance Across the Lifespan
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DOI：
10.1093/cercor/bhu093
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发表时间：
2015-09-01
期刊：
影响因子：
3.7
通讯作者：
Stark, Craig E. L.
中科院分区：
文献类型：
--
作者：
Bennett, Ilana J.;Huffman, Derek J.;Stark, Craig E. L.
人们认为，离散事件的准确记忆依赖于模式分离来正交化相似事件的表示。之前，我们报道了模式分离的行为指数与海马体（齿状回，CA3）的活动以及为海马体提供输入的穿通路径的完整性相关。然而，如果海马体作为更广泛的神经网络的一部分运行，模式分离也可能与连接海马体和分布式大脑区域的边缘束（穹窿、扣带束和钩束）的完整性有关。在这项研究中，健康成年人（20-89 岁）接受了弥散张量成像，并完成了行为模式分离任务-对象版本 (BPS-O) 和雷伊听觉言语学习测试 (RAVLT)。在控制了大脑衰老的整体影响后，探索性骨骼和目标纤维束成像分析表明，穹窿完整性（分数各向异性、平均扩散率和径向扩散率；但不是模式）与模式分离（使用 BPS-O 和 RAVLT 任务测量）显着相关，但与识别记忆无关。这些数据表明，海马体的断开，通过个体和年龄相关的边缘束完整性差异，有助于模式分离性能。扩展了我们早期的工作，这些结果也支持了这样的观点：模式分离依赖于互连海马体的广泛神经网络。
Accurate memory for discrete events is thought to rely on pattern separation to orthogonalize the representations of similar events. Previously, we reported that a behavioral index of pattern separation was correlated with activity in the hippocampus (dentate gyrus, CA3) and with integrity of the perforant path, which provides input to the hippocampus. If the hippocampus operates as part of a broader neural network, however, pattern separation would likely also relate to integrity of limbic tracts (fornix, cingulum bundle, and uncinate fasciculus) that connect the hippocampus to distributed brain regions. In this study, healthy adults (20-89 years) underwent diffusion tensor imaging and completed the Behavioral Pattern Separation Task-Object Version (BPS-O) and Rey Auditory Verbal Learning Test (RAVLT). After controlling for global effects of brain aging, exploratory skeleton-wise and targeted tractography analyses revealed that fornix integrity (fractional anisotropy, mean diffusivity, and radial diffusivity; but not mode) was significantly related to pattern separation (measured using BPS-O and RAVLT tasks), but not to recognition memory. These data suggest that hippocampal disconnection, via individual-and age-related differences in limbic tract integrity, contributes to pattern separation performance. Extending our earlier work, these results also support the notion that pattern separation relies on broad neural networks interconnecting the hippocampus.