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The ability to form associations between a multitude of events is the hallmark of episodic memory. Computational models have espoused the importance of the hippocampus as convergence zone, binding different aspects of an episode into a coherent representation, by integrating information from multiple brain regions. However, evidence for this long-held hypothesis is limited, since previous work has largely focused on representational and network properties of the hippocampus in isolation. Here we identify the hippocampus as mnemonic convergence zone, using a combination of multivariate pattern and graph-theoretical network analyses of functional magnetic resonance imaging data from humans performing an associative memory task. We observe overlap of conjunctive coding and hub-like network attributes in the hippocampus. These results provide evidence for mnemonic convergence in the hippocampus, underlying the integration of distributed information into episodic memory representations., The ability to form associations between events is the hallmark of episodic memory and is thought to involve the hippocampus. Here the authors use a combination of multivariate pattern and graph theoretical network analyses of functional imaging data in humans, and show conjunctive coding and hub-like network attributes 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 STATEMENTNarratives, 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.

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.

SummaryOur 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.

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.

Contains fulltext : 122909.pdf (Publisher’s version ) (Open Access) 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.

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.

Visual short-term memory (VSTM) capacity is often assessed using change detection tasks, and individual differences in performance have been shown to predict cognitive aptitudes across a range of domains in children and adults. We recently showed that intelligence correlates with an attentional component necessary for change detection rather than with memory capacity per se (Cusack, Lehmann, Veldsman, & Mitchell, 2009). It remained unclear, however, whether different attentional strategies during change detection have most impact during the encoding or maintenance of information. Here we present recent findings from our laboratory supporting the hypothesis that attentional selection during encoding dominates individual differences in change detection measures of visual short-term memory. In a first study, we unpredictably varied whether short-term memory was probed using change detection or whole report, encouraging participants to adopt the same encoding strategy throughout the tasks. Change detection performance of lower-IQ individuals improved. In a second study, we found that deficits in top-down attentional selectivity can be alleviated in participants with low change detection performance by providing helpful grouping information during encoding. Finally, a meta-analysis of neuroimaging data from 112 participants performing a variety of VSTM tasks showed that performance correlates with activity in several parietal and frontal regions during the encoding but not the maintenance phase. Taken together, these results support the notion that encoding strategy and not short-term memory capacity itself largely determines individual differences in visual change detection performance.

The everyday act of speaking involves the complex processes of speech motor control. One important feature of such control is regulation of articulation when auditory concomitants of speech do not correspond to the intended motor gesture. While theoretical accounts of speech monitoring posit multiple functional components required for detection of errors in speech planning (e.g., Levelt, 1983), neuroimaging studies generally indicate either single brain regions sensitive to speech production errors, or small, discrete networks. Here we demonstrate that the complex system controlling speech is supported by a complex neural network that is involved in linguistic, motoric and sensory processing. With the aid of novel real-time acoustic analyses and representational similarity analyses of fMRI signals, our data show functionally differentiated networks underlying auditory feedback control of speech.