Abrupt hippocampal remapping signals resolution of memory interference.
TL;DR: It is shown that activity patterns in human CA3/dentate gyrus exhibit an abrupt, temporally-specific decorrelation of highly similar memory representations that is precisely coupled with behavioral expressions of successful learning, establishing a critical link between hippocampal remapping and episodic memory interference and providing insight into why remapping occurs.
Abstract: Remapping refers to a decorrelation of hippocampal representations of similar spatial environments. While it has been speculated that remapping may contribute to the resolution of episodic memory interference in humans, direct evidence is surprisingly limited. We tested this idea using high-resolution, pattern-based fMRI analyses. Here we show that activity patterns in human CA3/dentate gyrus exhibit an abrupt, temporally-specific decorrelation of highly similar memory representations that is precisely coupled with behavioral expressions of successful learning. The magnitude of this learning-related decorrelation was predicted by the amount of pattern overlap during initial stages of learning, with greater initial overlap leading to stronger decorrelation. Finally, we show that remapped activity patterns carry relatively more information about learned episodic associations compared to competing associations, further validating the learning-related significance of remapping. Collectively, these findings establish a critical link between hippocampal remapping and episodic memory interference and provide insight into why remapping occurs. When two memories are similar, their encoding and retrieval can be disrupted by each other. Here the authors show that memory interference is resolved through abrupt remapping of activity patterns in the human hippocampal CA3 and dentate gyrus.
Citations
More filters
TL;DR: For instance, this article found evidence for pattern separation (representational orthogonalization) in hippocampal subfield CA2/3/DG and repulsion in CA1 (differentiation beyond orthogonality).
Abstract: When we remember a city that we have visited, we retrieve places related to finding our goal but also non-target locations within this environment. Yet, understanding how the human brain implements the neural computations underlying holistic retrieval remains unsolved, particularly for shared aspects of environments. Here, human participants learned and retrieved details from three partially overlapping environments while undergoing high-resolution functional magnetic resonance imaging (fMRI). Our findings show reinstatement of stores even when they are not related to a specific trial probe, providing evidence for holistic environmental retrieval. For stores shared between cities, we find evidence for pattern separation (representational orthogonalization) in hippocampal subfield CA2/3/DG and repulsion in CA1 (differentiation beyond orthogonalization). Additionally, our findings demonstrate that medial prefrontal cortex (mPFC) stores representations of the common spatial structure, termed schema, across environments. Together, our findings suggest how unique and common elements of multiple spatial environments are accessed computationally and neurally.
15 citations
TL;DR: In this paper , the authors describe the rapid, substantial, and continuous transformation of memory representation during the encoding, maintenance, consolidation, and retrieval of both single and multiple events, as well as event sequences.
6 citations
TL;DR: In this paper , the authors propose the "cortico-hippocampal pattern separation" (CHiPS) framework, which asserts that brain regions involved in cognitive control play a significant role in pattern separation.
Abstract: Pattern separation, or the process by which highly similar stimuli or experiences in memory are represented by non-overlapping neural ensembles, has typically been ascribed to processes supported by the hippocampus. Converging evidence from a wide range of studies, however, suggests that pattern separation is a multistage process supported by a network of brain regions. Based on this evidence, considered together with related findings from the interference resolution literature, we propose the 'cortico-hippocampal pattern separation' (CHiPS) framework, which asserts that brain regions involved in cognitive control play a significant role in pattern separation. Particularly, these regions may contribute to pattern separation by (1) resolving interference in sensory regions that project to the hippocampus, thus regulating its cortical input, or (2) directly modulating hippocampal processes in accordance with task demands. Considering recent interest in how hippocampal operations are modulated by goal states likely represented and regulated by extra-hippocampal regions, we argue that pattern separation is similarly supported by neocortical-hippocampal interactions.
5 citations
TL;DR: In this article , it was shown that the hippocampus plays a critical role in memory leaks and these may drive, and be driven, by cortical circuits, and that the hippocampal contribution is likely achieved in concert with cortical areas.
4 citations
TL;DR: This review paper showcases observations from existing studies that demonstrate superior navigation/spatial memory performance in late adulthood, explores possible cognitive correlates and neurophysiological mechanisms underlying these preserved spatial abilities, and discusses the potential link between the superior navigators inLate adulthood and SuperAgers (older adults with superior episodic memory).
Abstract: Normal aging is typically associated with declines in navigation and spatial memory abilities. However, increased interindividual variability in performance across various navigation/spatial memory tasks is also evident with advancing age. In this review paper, we shed the spotlight on those older individuals who exhibit exceptional, sometimes even youth-like navigational/spatial memory abilities. Importantly, we (1) showcase observations from existing studies that demonstrate superior navigation/spatial memory performance in late adulthood, (2) explore possible cognitive correlates and neurophysiological mechanisms underlying these preserved spatial abilities, and (3) discuss the potential link between the superior navigators in late adulthood and SuperAgers (older adults with superior episodic memory). In the closing section, given the lack of studies that directly focus on this subpopulation, we highlight several important directions that future studies could look into to better understand the cognitive characteristics of older superior navigators and the factors enabling such successful cognitive aging.
3 citations
References
More filters
9,425 citations
Book•
01 Jan 1978
TL;DR: The amnesic syndrome is presented as an extension of the theory to humans and the role of operators in the locale system is examined.
Abstract: Table of Contents: Chapter 1 - Remembrance of places past: a history of theories of space / Chapter 2 - Spatial behaviour / Chapter 3 - Anatomy / Chapter 4 - Physiology / Chapter 5 - Introduction to the lesion review / Chapter 6 - Exploration / Chapter 7 - Discrimination and maze learning / Chapter 8 - Aversively motivated behaviour / Chapter 9 - Operants: the limited role of the locale system / Chapter 10 - Reactions to reward change / Chapter 11 - Maintenance behaviours / Chapter 12 - Stimulation studies / Chapter 13 - Long-term memory / Chapter 14 - An extension of the theory to humans / Chapter 15 - The amnesic syndrome
8,313 citations
TL;DR: The authors propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations.
Abstract: The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limitation-no spatial information is taken into account. This causes the FM model to work only on well-defined images with low levels of noise; unfortunately, this is often not the the case due to artifacts such as partial volume effect and bias field distortion. Under these conditions, FM model-based methods produce unreliable results. Here, the authors propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown that the FM model is a degenerate version of the HMRF model. The advantage of the HMRF model derives from the way in which the spatial information is encoded through the mutual influences of neighboring sites. Although MRF modeling has been employed in MR image segmentation by other researchers, most reported methods are limited to using MRF as a general prior in an FM model-based approach. To fit the HMRF model, an EM algorithm is used. The authors show that by incorporating both the HMRF model and the EM algorithm into a HMRF-EM framework, an accurate and robust segmentation can be achieved. More importantly, the HMRF-EM framework can easily be combined with other techniques. As an example, the authors show how the bias field correction algorithm of Guillemaud and Brady (1997) can be incorporated into this framework to achieve a three-dimensional fully automated approach for brain MR image segmentation.
6,335 citations
TL;DR: This study indicates that SyN, with cross-correlation, is a reliable method for normalizing and making anatomical measurements in volumetric MRI of patients and at-risk elderly individuals.
4,233 citations
TL;DR: A variant of the popular nonparametric nonuniform intensity normalization (N3) algorithm is proposed for bias field correction with the substitution of a recently developed fast and robust B-spline approximation routine and a modified hierarchical optimization scheme for improved bias field Correction over the original N3 algorithm.
Abstract: A variant of the popular nonparametric nonuniform intensity normalization (N3) algorithm is proposed for bias field correction. Given the superb performance of N3 and its public availability, it has been the subject of several evaluation studies. These studies have demonstrated the importance of certain parameters associated with the B-spline least-squares fitting. We propose the substitution of a recently developed fast and robust B-spline approximation routine and a modified hierarchical optimization scheme for improved bias field correction over the original N3 algorithm. Similar to the N3 algorithm, we also make the source code, testing, and technical documentation of our contribution, which we denote as ?N4ITK,? available to the public through the Insight Toolkit of the National Institutes of Health. Performance assessment is demonstrated using simulated data from the publicly available Brainweb database, hyperpolarized 3He lung image data, and 9.4T postmortem hippocampus data.
4,090 citations