Coding of Event Nodes and Narrative Context in the Hippocampus.

Narratives may provide a general context, unrestricted by space and time, which can be used to organize episodic memories into networks of related events. However, it is not clear how narrative contexts are represented in the brain. Here we test the novel hypothesis that the formation of narrative-based contextual representations in humans relies on the same hippocampal mechanisms that enable formation of spatiotemporal contexts in rodents. Participants watched a movie consisting of two interleaved narratives while we monitored their brain activity using fMRI. We used representational similarity analysis, a type of multivariate pattern analysis, which uses across-voxel correlations as a proxy for neural-pattern similarity, to examine whether the patterns of neural activity can be used to differentiate between narratives and recurring narrative elements, such as people and locations. We demonstrate that the neural activity patterns in the hippocampus differentiate between event nodes (people and locations) and narratives (different stories) and that these narrative-context representations diverge gradually over time akin to remapping-induced spatial maps represented by rodent place cells. SIGNIFICANCE STATEMENT: Narratives, especially in movie format, are very engaging and can be used to investigate neural mechanisms underlying cognitive functions in more naturalistic settings than that of traditional paradigms. Narratives also provide a more general context, unrestricted by space and time, that can be used to organize memories into networks of related events. For this reason, narratives are ideally suited to engage neural mechanisms underlying episodic memory formation. In this study, participants watched a movie with two interleaved narratives while their brain activity was monitored using fMRI. We show that the hippocampus, which is involved in formation of spatiotemporal contexts in episodic memory, also represents gradually diverging narrative contexts as well as narrative elements, such as people and locations.

Hippocampal Attractor Dynamics Predict Memory-Based Decision Making.

Memories are thought to be retrieved by attractor dynamics if a given input is sufficiently similar to a stored attractor state [1-5]. The hippocampus, a region crucial for spatial navigation [6-12] and episodic memory [13-18], has been associated with attractor-based computations [5, 9], receiving support from the way rodent place cells "remap" nonlinearly between spatial representations [19-22]. In humans, nonlinear response patterns have been reported in perceptual categorization tasks [23-25]; however, it remains elusive whether human memory retrieval is driven by attractor dynamics and what neural mechanisms might underpin them. To test this, we used a virtual reality [7, 11, 26-28] task where participants learned object-location associations within two distinct virtual reality environments. Participants were subsequently exposed to four novel intermediate environments, generated by linearly morphing the background landscapes of the familiar environments, while tracking fMRI activity. We show that linear changes in environmental context cause linear changes in activity patterns in sensory cortex but cause dynamic, nonlinear changes in both hippocampal activity pattern and remembered locations. Furthermore, the sigmoidal response in the hippocampus scaled with the strength of the sigmoidal pattern in spatial memory. These results indicate that mnemonic decisions in an ambiguous novel context relate to putative attractor dynamics in the hippocampus, which support the dynamic remapping of memories.

Insight reconfigures hippocampal-prefrontal memories.

Our memories are remarkably dynamic and allow us to reinterpret the past once new information comes to light. Gaining novel insights can lead to mental reorganization of previously unrelated events, thus linking them into narratives. The hippocampus and medial prefrontal cortex (mPFC) support integration of partially overlapping events, but the neural mechanisms underlying the reorganization of memories for the formation of coherent narratives remain elusive. Here, we combine fMRI with The Sims 3 videos of life-like animated events, which could either be integrated into narratives or not. We show that insight triggers the emergence of de novo mnemonic representations of the narratives and is associated with increased neural similarity between linked event representations in the posterior hippocampus, mPFC, and autobiographical-memory network. Simultaneously, events irrelevant to the newly established memory of the narrative were pruned out. This process was accompanied by increased neural dissimilarity between non-linked event representations in the posterior hippocampus and mPFC and was additionally signaled by a mismatch response in the anterior hippocampus. Our results demonstrate that insight leads to neural reconfiguration of representational networks within a memory space and have implications for knowledge acquisition in educational settings.

Automatic analysis (aa): efficient neuroimaging workflows and parallel processing using Matlab and XML.

Recent years have seen neuroimaging data sets becoming richer, with larger cohorts of participants, a greater variety of acquisition techniques, and increasingly complex analyses. These advances have made data analysis pipelines complicated to set up and run (increasing the risk of human error) and time consuming to execute (restricting what analyses are attempted). Here we present an open-source framework, automatic analysis (aa), to address these concerns. Human efficiency is increased by making code modular and reusable, and managing its execution with a processing engine that tracks what has been completed and what needs to be (re)done. Analysis is accelerated by optional parallel processing of independent tasks on cluster or cloud computing resources. A pipeline comprises a series of modules that each perform a specific task. The processing engine keeps track of the data, calculating a map of upstream and downstream dependencies for each module. Existing modules are available for many analysis tasks, such as SPM-based fMRI preprocessing, individual and group level statistics, voxel-based morphometry, tractography, and multi-voxel pattern analyses (MVPA). However, aa also allows for full customization, and encourages efficient management of code: new modules may be written with only a small code overhead. aa has been used by more than 50 researchers in hundreds of neuroimaging studies comprising thousands of subjects. It has been found to be robust, fast, and efficient, for simple-single subject studies up to multimodal pipelines on hundreds of subjects. It is attractive to both novice and experienced users. aa can reduce the amount of time neuroimaging laboratories spend performing analyses and reduce errors, expanding the range of scientific questions it is practical to address.

Strength of retinotopic representation of visual memories is modulated by strategy.

Human visual cortex shows retinotopic organization during both perception and attention, but whether this remains true for visual short-term memory (VSTM) is uncertain. In 2 functional magnetic resonance imaging experiments, we separated retinotopic activation during perception, attention, and VSTM maintenance. The 2 experiments differed in whether spatial encoding of the VSTM stimuli and prospective attention to the locations of the remembered items was encouraged or discouraged. Using multivoxel pattern analysis to extract a measure of spatial coding in early visual cortex, we saw sensory and attentional retinotopic coding in both experiments. However, significant spatial coding during memory maintenance was only seen where a spatial strategy was encouraged. Furthermore, individual differences in attentional spatial coding predicted performance in both experiments, while individual differences in maintenance spatial coding predicted performance in neither. We conclude that retinotopic coding in the early visual cortex during VSTM maintenance is not obligatory, that attentional processes during stimulus perception modulate memory performance, and that different attentional strategies are used depending on the task in hand.

Multivoxel patterns reveal functionally differentiated networks underlying auditory feedback processing of speech.

The everyday act of speaking involves the complex processes of speech motor control. An important component of control is monitoring, detection, and processing of errors when auditory feedback does not correspond to the intended motor gesture. Here we show, using fMRI and converging operations within a multivoxel pattern analysis framework, that this sensorimotor process is supported by functionally differentiated brain networks. During scanning, a real-time speech-tracking system was used to deliver two acoustically different types of distorted auditory feedback or unaltered feedback while human participants were vocalizing monosyllabic words, and to present the same auditory stimuli while participants were passively listening. Whole-brain analysis of neural-pattern similarity revealed three functional networks that were differentially sensitive to distorted auditory feedback during vocalization, compared with during passive listening. One network of regions appears to encode an "error signal" regardless of acoustic features of the error: this network, including right angular gyrus, right supplementary motor area, and bilateral cerebellum, yielded consistent neural patterns across acoustically different, distorted feedback types, only during articulation (not during passive listening). In contrast, a frontotemporal network appears sensitive to the speech features of auditory stimuli during passive listening; this preference for speech features was diminished when the same stimuli were presented as auditory concomitants of vocalization. A third network, showing a distinct functional pattern from the other two, appears to capture aspects of both neural response profiles. Together, our findings suggest that auditory feedback processing during speech motor control may rely on multiple, interactive, functionally differentiated neural systems.

Stimulus-specific suppression preserves information in auditory short-term memory.

Philosophers and scientists have puzzled for millennia over how perceptual information is stored in short-term memory. Some have suggested that early sensory representations are involved, but their precise role has remained unclear. The current study asks whether auditory cortex shows sustained frequency-specific activation while sounds are maintained in short-term memory using high-resolution functional MRI (fMRI). Investigating short-term memory representations within regions of human auditory cortex with fMRI has been difficult because of their small size and high anatomical variability between subjects. However, we overcame these constraints by using multivoxel pattern analysis. It clearly revealed frequency-specific activity during the encoding phase of a change detection task, and the degree of this frequency-specific activation was positively related to performance in the task. Although the sounds had to be maintained in memory, activity in auditory cortex was significantly suppressed. Strikingly, patterns of activity in this maintenance period correlated negatively with the patterns evoked by the same frequencies during encoding. Furthermore, individuals who used a rehearsal strategy to remember the sounds showed reduced frequency-specific suppression during the maintenance period. Although negative activations are often disregarded in fMRI research, our findings imply that decreases in blood oxygenation level-dependent response carry important stimulus-specific information and can be related to cognitive processes. We hypothesize that, during auditory change detection, frequency-specific suppression protects short-term memory representations from being overwritten by inhibiting the encoding of interfering sounds.