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Interactions between Memory and New Learning: Insights from fMRI Multivoxel Pattern Analysis

机译:记忆与新学习之间的相互作用:fMRI Multivoxel模式分析的见解

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Introduction Declarative memory—long-term memory for events and facts—is a key form of cognition that depends on distributed neural coding. Given the rich, multifaceted nature of life events, their neural representations (episodic memory “engrams”) typically incorporate a broad set of cortical and subcortical regions whose coding properties underlie event features (Paller and Wagner, 2002 ; Rugg et al., 2002 ; Tulving, 2002 ; Schacter et al., 2007 ). With continued experience, representations of individual events may form a foundation for more generalized semantic knowledge about the world (van Kesteren et al., 2012 ). A fundamental theoretical question is how existing memories interact with encoding of new experiences to enable formation of integrated knowledge structures. The distributed nature of memory content in the brain, both locally (i.e., across neurons within a region) and across relevant cortical and subcortical regions, creates challenges for measurement of mnemonic content across various stages of memory encoding and retrieval. By combining non-invasive imaging techniques (e.g., functional magnetic resonance imaging–fMRI) with multivariate pattern-analyses (MVPA), such representational content can be decoded from distributed patterns of brain activity (Polyn et al., 2005 ; Norman et al., 2006 ; Rissman and Wagner, 2012 ). Moreover, quantitative measures of mnemonic representations can be related to behavioral performance measures, thus informing mechanistic models of memory. At a macroscopic level, mnemonic representations of events are distributed across perceptual, motor, affective, and associative brain regions (Tulving and Markowitsch, 1997 ). Episodic memory retrieval entails the reinstatement or reconstruction of information encoded in memory (for reviews see Danker and Anderson, 2010 ; Ben-Yakov et al., 2015 ). MVPA provides a means of measuring distributed neural representations, and quantifying reinstatement processes (Norman et al., 2006 ; Rissman and Wagner, 2012 ). Importantly, a myriad of externally and internally generated retrieval cues can drive reinstatement of existing memory traces during encoding of related information. Such reinstatement may support the formation of more generalized knowledge through integration of new with old memories (Shohamy and Wagner, 2008 ; Preston and Eichenbaum, 2013 ). As such, elements of new memories that overlap with prior experiences can trigger reinstatement and integration processes allowing for extension and strengthening of existing associative knowledge structures, or “schemas” (Tse et al., 2007 ; van Kesteren et al., 2012 ). The medial temporal lobe (MTL)—with the hippocampus at its core—is the most prominently studied region in memory research (Burgess et al., 2002 ; Squire et al., 2004 ; Eichenbaum et al., 2007 ). The hippocampus serves as an integrative hub for the binding of disparate neocortical representations of event features into unified memories (Eichenbaum et al., 2004 ; Andersen, 2007 ). Through creating flexibly addressable memory traces that link to the driving cortical representations of event content, the hippocampus can support subsequent reactivation of a remembered event's feature representations in the neocortex during retrieval. MVPA techniques can index expressions of distributed memory representations and processes in MTL as they unfold, as well as probe reinstatement and integration processes in content-selective cortical regions (Polyn et al., 2005 ; Johnson et al., 2009 ; Staresina et al., 2012 ; Gordon et al., 2014 ; Sigman et al., 2014 ). Beyond the MTL, other cortical areas have been posited to contribute to across-event integration. In particular, the integration of associated memories is thought to also depend on computations within the medial prefrontal cortex (mPFC), a prefrontal region intimately connected with the hippocampus and suggested to be involved in the building of knowledge structures (van Kesteren et al., 2012 ; Preston and Eichenbaum, 2013 ). Recent evidence from direct neuronal recordings in non-human models of memory has linked hippocampus and mPFC population coding to the expression of schema knowledge (McKenzie and Eichenbaum, 2011 ; McKenzie et al., 2014 ; Richards et al., 2014 ). In humans, MVPA provides a powerful means to assess how mPFC and the hippocampus underlie integration of newly learned experiences with existing memories, and critically, to link this integration process with cortical reinstatement (Dudai and Eisenberg, 2004 ; Kuhl et al., 2010 ; Nadel et al., 2012 ). Here we review how MVPA, applied to fMRI-data, is leveraged to address fundamental questions about reinstatement and subsequent integration of memory representations in the human brain. We discuss a framework in which reinstatement of prior knowledge during new learning can facilitate formation of integrated knowledge across experiences, highlight evidence for potentially disruptive effects of such processes on o
机译:简介声明性记忆(对事件和事实的长期记忆)是认知的一种关键形式,它依赖于分布式神经编码。考虑到生活事件的丰富性,多面性,它们的神经表示(周期性记忆“枚举”)通常包含大量的皮质和皮质下区域,它们的编码特性是事件特征的基础(Paller和Wagner,2002; Rugg等人,2002; Norman等,2002)。 Tulving,2002; Schacter等,2007)。凭借不断的经验,单个事件的表示可能会构成关于世界的更广义语义知识的基础(van Kesteren等,2012)。一个基本的理论问题是,现有的记忆如何与新经验的编码相互作用,以形成整合的知识结构。大脑中局部(即,在一个区域内的整个神经元之间)以及在相关皮层和皮层下区域的大脑中记忆内容的分布特性,在记忆编码和检索的各个阶段对记忆内容的测量提出了挑战。通过将非侵入性成像技术(例如功能磁共振成像–fMRI)与多元模式分析(MVPA)相结合,可以从大脑活动的分布式模式中解码出此类代表性内容(Polyn等,2005; Norman等。 ,2006; Rissman和Wagner,2012)。此外,助记符表示的定量度量可以与行为表现度量相关,从而通知记忆的机械模型。在宏观层面上,事件的助记符表示分布在感知,运动,情感和联想的大脑区域(Tulving和Markowitsch,1997年)。情景记忆检索需要恢复或重建记忆中编码的信息(有关评论,请参阅Danker和Anderson,2010; Ben-Yakov等,2015)。 MVPA提供了一种测量分布式神经表示和量化恢复过程的方法(Norman等,2006; Rissman和Wagner,2012)。重要的是,在相关信息的编码过程中,无数个内部和外部生成的检索线索可以驱动现有内存跟踪的恢复。通过将新的记忆与旧的记忆结合起来,这种恢复可能会支持形成更广泛的知识(Shohamy和Wagner,2008; Preston和Eichenbaum,2013)。因此,与先前经验重叠的新记忆元素可以触发恢复和整合过程,从而扩展和加强现有的关联知识结构或“方案”(Tse等,2007; van Kesteren等,2012)。以海马体为核心的颞颞叶(MTL)是记忆研究中最突出的研究区域(Burgess等,2002; Squire等,2004; Eichenbaum等,2007)。海马作为整合枢纽,将事件特征的不同新皮层表示结合到统一的记忆中(Eichenbaum等,2004; Andersen,2007)。通过创建链接到事件内容的驱动皮质表示的灵活可寻址内存迹线,海马可以在检索过程中支持新皮质中记忆事件的特征表示的后续重新激活。 MVPA技术可以在MTL展开时索引分布的内存表示形式和过程中的表达,以及在内容选择皮质区域中进行探针恢复和整合过程的表达(Polyn等,2005; Johnson等,2009; Staresina等。 ,2012; Gordon等人,2014; Sigman等人,2014)。除了MTL,其他皮质区域也被认为有助于跨事件整合。特别是,相关记忆的整合还被认为还取决于内侧前额叶皮层(mPFC)内的计算,内侧前额叶皮层与海马紧密相连,并建议参与知识结构的构建(van Kesteren等, 2012年; Preston和Eichenbaum,2013年)。非人类记忆模型中直接神经元录音的最新证据已将海马和mPFC群体编码与图式知识的表达联系起来(McKenzie和Eichenbaum,2011; McKenzie等,2014; Richards等,2014)。在人类中,MVPA提供了一种强有力的手段来评估mPFC和海马如何将新学习的经验与现有记忆整合在一起,并至关重要地将这种整合过程与皮质修复联系起来(Dudai和Eisenberg,2004; Kuhl等人,2010; Daniel and Eisenberg,2010)。 Nadel等人,2012年)。在这里,我们回顾了如何将适用于fMRI数据的MVPA用来解决有关恢复和随后整合人脑中记忆表示的基本问题。我们讨论了一个框架,在该框架中,在新学习过程中恢复现有知识可以促进跨经验整合知识的形成,重点说明此类过程对o可能造成破坏性影响的证据

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