Prediction of subsequent recognition performance using brain activity in the medial temporal lobe

Watanabe, Takamitsu; Hirose, Satoshi; Wada, Hiroyuki; Katsura, Masaki; Chikazoe, Junichi; Jimura, Koji; Imai, Yoshio; Machida, Tatsuya; Shirouzu, Ichiro; Miyashita, Yasushi; Konishi, Seiki

doi:10.1016/j.neuroimage.2010.10.066

Cited by 26 publications

(38 citation statements)

References 63 publications

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“…Is it necessary to consider attentional states to predict memory from the hippocampus during encoding? To assess the selectivity of our findings to the state-template match variable, we examined two additional variables that have been linked to encoding in prior subsequent memory studies: (i) patterns of activity without respect to attentional state (37)(38)(39), and (ii) the overall level of univariate activity (5,23,40).…”

Section: Predicting Memory From the Attentional State Of The Hippocampusmentioning

confidence: 99%

Attention promotes episodic encoding by stabilizing hippocampal representations

Aly

Turk‐Browne

2016

Proc. Natl. Acad. Sci. U.S.A.

176

157

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Attention influences what is later remembered, but little is known about how this occurs in the brain. We hypothesized that behavioral goals modulate the attentional state of the hippocampus to prioritize goal-relevant aspects of experience for encoding. Participants viewed rooms with paintings, attending to room layouts or painting styles on different trials during high-resolution functional MRI. We identified template activity patterns in each hippocampal subfield that corresponded to the attentional state induced by each task. Participants then incidentally encoded new rooms with art while attending to the layout or painting style, and memory was subsequently tested. We found that when task-relevant information was better remembered, the hippocampus was more likely to have been in the correct attentional state during encoding. This effect was specific to the hippocampus, and not found in medial temporal lobe cortex, category-selective areas of the visual system, or elsewhere in the brain. These findings provide mechanistic insight into how attention transforms percepts into memories.long-term memory | selective attention | hippocampal subfields | medial temporal lobe | representational stability W hy do we remember some things and not others? Consider a recent experience, such as the last movie complex you visited, flight you took, or restaurant at which you ate. More information was available to your senses than was stored in memory, such as the theater number of the movie, the faces of other passengers, and the color of the napkins. The selective nature of memory is adaptive, because encoding carries a cost: newly stored memories can interfere with existing ones and with our ability to learn new information in the future. What is the mechanism by which information gets selected for encoding?Attention offers a means of prioritizing information in the environment that is most relevant to behavioral goals. Attended information, in turn, has stronger control over behavior and is represented more robustly in the brain (1, 2). If attention gates which information we perceive and act upon, then it may also determine what information we remember. Indeed, attention during encoding affects both subsequent behavioral expressions of memory (3) and the extent to which activity levels in the brain predict memory formation (4-7). Although these findings suggest that attention modulates processes related to memory, how it does so is unclear.According to biased competition and other theories of attention (1, 8), task-relevant stimuli are more robustly represented in sensory systems, and thus fare better in competition with taskirrelevant stimuli for downstream processing. Indeed, there is extensive evidence that attention enhances overall activity in visual areas that represent attended vs. unattended features and locations (2, 9). Moreover, attention modulates cortical areas of the medial temporal lobe that provide input to the hippocampus (10-12).Attention can also modulate the hippocampus itself. Specifically, there is g...

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Section: Predicting Memory From the Attentional State Of The Hippocampusmentioning

confidence: 99%

Attention promotes episodic encoding by stabilizing hippocampal representations

Aly

Turk‐Browne

2016

Proc. Natl. Acad. Sci. U.S.A.

176

157

View full text Add to dashboard Cite

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“…Methods from pattern recognition have been used for this prediction problem [19–21]. In a recent fMRI study, MVPA has been used to predict subsequent memory performance for 19 participants according to the period of encoding phonogram stimuli [19]. The analysis consisted of 3 stages.…”

Section: Related Workmentioning

confidence: 99%

“…This approach predicted SWMT trial performance with 84% accuracy in healthy adults and 74% accuracy in schizophrenia adults. Overall, the related work mentioned in [19–21] all used methods from pattern recognition to extract information from data and predict memory performance for each stimulus.…”

Section: Related Workmentioning

confidence: 99%

Remembered or Forgotten?—An EEG-Based Computational Prediction Approach

et al. 2016

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Prediction of memory performance (remembered or forgotten) has various potential applications not only for knowledge learning but also for disease diagnosis. Recently, subsequent memory effects (SMEs)—the statistical differences in electroencephalography (EEG) signals before or during learning between subsequently remembered and forgotten events—have been found. This finding indicates that EEG signals convey the information relevant to memory performance. In this paper, based on SMEs we propose a computational approach to predict memory performance of an event from EEG signals. We devise a convolutional neural network for EEG, called ConvEEGNN, to predict subsequently remembered and forgotten events from EEG recorded during memory process. With the ConvEEGNN, prediction of memory performance can be achieved by integrating two main stages: feature extraction and classification. To verify the proposed approach, we employ an auditory memory task to collect EEG signals from scalp electrodes. For ConvEEGNN, the average prediction accuracy was 72.07% by using EEG data from pre-stimulus and during-stimulus periods, outperforming other approaches. It was observed that signals from pre-stimulus period and those from during-stimulus period had comparable contributions to memory performance. Furthermore, the connection weights of ConvEEGNN network can reveal prominent channels, which are consistent with the distribution of SME studied previously.

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“…For example, following in the tradition of numerous fMRI studies that have used univariate analyses to examine the relationship between encoding activity in frontoparietal and MTL regions and later memory behavior (for recent meta-analyses of such studies, see Kim 2011; Uncapher & Wagner 2009), Watanabe and colleagues (2011) demonstrated that multi-voxel patterns within MTL are predictive of whether visually presented pseudowords will be subsequently recognized or forgotten. From these data alone, it is unclear whether the predictive value of the classifier was driven by diagnostic information contained within distributed activity patterns per se, or whether it capitalized on the fact that many MTL voxels tended to show greater BOLD signal on trials associated with later recognition.…”

Section: Distributed Representations In Episodic Memorymentioning

confidence: 99%

Distributed Representations in Memory: Insights from Functional Brain Imaging

Rissman

Wagner

2012

Annu. Rev. Psychol.

263

217

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Forging new memories for facts and events, holding critical details in mind on a moment-to-moment basis, and retrieving knowledge in the service of current goals all depend on a complex interplay between neural ensembles throughout the brain. Over the past decade, researchers have increasingly leveraged powerful analytical tools (e.g., multi-voxel pattern analysis) to decode the information represented within distributed fMRI activity patterns. In this review, we discuss how these methods can sensitively index neural representations of perceptual and semantic content, and how leverage on the engagement of distributed representations provides unique insights into distinct aspects of memory-guided behavior. We emphasize that, in addition to characterizing the contents of memories, analyses of distributed patterns shed light on the processes that influence how information is encoded, maintained, or retrieved, and thus inform memory theory. We conclude by highlighting open questions about memory that can be addressed through distributed pattern analyses.

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Prediction of subsequent recognition performance using brain activity in the medial temporal lobe

Cited by 26 publications

References 63 publications

Attention promotes episodic encoding by stabilizing hippocampal representations

Attention promotes episodic encoding by stabilizing hippocampal representations

Remembered or Forgotten?—An EEG-Based Computational Prediction Approach

Distributed Representations in Memory: Insights from Functional Brain Imaging

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