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Many experiences unfold predictably over time. Memory for these temporal regularities enables anticipation of events multiple steps into the future. Because temporally predictable events repeat over days, weeks, and years, we must maintain — and potentially transform — memories of temporal structure to support adaptive behavior. We explored how individuals build durable models of temporal regularities to guide multi-step anticipation. Healthy young adults (Experiment 1: N = 99, Experiment 2: N = 204) learned sequences of scene images that were predictable at the category level and contained incidental perceptual details. Individuals then anticipated upcoming scene categories multiple steps into the future, immediately and at a delay. Consolidation increased the efficiency of anticipation, particularly for events further in the future, but diminished access to perceptual features. Further, maintaining a link-based model of the sequence after consolidation improved anticipation accuracy. Consolidation may therefore promote efficient and durable models of temporal structure, thus facilitating anticipation of future events.

Our ability to shift from one emotion to the next allows us to adapt our behaviors to a constantly-changing and often uncertain environment. Although previous studies have identified cortical and subcortical regions involved in affective responding, no studies have asked whether and how these regions track and represent transitionsbetweendifferent emotional states and modulate their responses based on the recent emotional context. To this end, we commissioned new musical pieces designed to systematically move participants through different emotional states during fMRI. Using a combination of data-driven (Hidden Markov Modeling) and hypothesis-driven methods, we show that spatiotemporal patterns of activation along the temporoparietal axis reflect transitions between music-evoked emotions. Furthermore, self-reported emotions and the subsequent neural response patterns were sensitive to the emotional context in which the music was heard. The findings highlight the role of temporal and parietal brain regions in not only processing low-level auditory signals, but in linking changes in these signals with our on-going, contextually-dependent emotional responses.

The “Narratives” collection aggregates a variety of functional MRI datasets collected while human subjects listened to naturalistic spoken stories. The current release includes 345 subjects, 891 functional scans, and 27 diverse stories of varying duration totaling ~4.6 hours of unique stimuli (~43,000 words). This data collection is well-suited for naturalistic neuroimaging analysis, and is intended to serve as a benchmark for models of language and narrative comprehension. We provide standardized MRI data accompanied by rich metadata, preprocessed versions of the data ready for immediate use, and the spoken story stimuli with time-stamped phoneme- and word-level transcripts. All code and data are publicly available with full provenance in keeping with current best practices in transparent and reproducible neuroimaging., Measurement(s)functional brain measurementTechnology Type(s)functional magnetic resonance imagingFactor Type(s)story • native languageSample Characteristic - OrganismHomo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14818587

Functional magnetic resonance imaging (fMRI) offers a rich source of data for studying the neural basis of cognition. Here, we describe the Brain Imaging Analysis Kit (BrainIAK), an open-source, free Python package that provides computationally optimized solutions to key problems in advanced fMRI analysis. A variety of techniques are presently included in BrainIAK: intersubject correlation (ISC) and intersubject functional connectivity (ISFC), functional alignment via the shared response model (SRM), full correlation matrix analysis (FCMA), a Bayesian version of representational similarity analysis (BRSA), event segmentation using hidden Markov models, topographic factor analysis (TFA), inverted encoding models (IEMs), an fMRI data simulator that uses noise characteristics from real data (fmrisim), and some emerging methods. These techniques have been optimized to leverage the efficiencies of high-performance compute (HPC) clusters, and the same code can be seamlessly transferred from a laptop to a cluster. For each of the aforementioned techniques, we describe the data analysis problem that the technique is meant to solve and how it solves that problem; we also include an example Jupyter notebook for each technique and an annotated bibliography of papers that have used and/or described that technique. In addition to the sections describing various analysis techniques in BrainIAK, we have included sections describing the future applications of BrainIAK to real-time fMRI, tutorials that we have developed and shared online to facilitate learning the techniques in BrainIAK, computational innovations in BrainIAK, and how to contribute to BrainIAK. We hope that this manuscript helps readers to understand how BrainIAK might be useful in their research.

Schematic prior knowledge can scaffold the construction of event memories during perception and also provide structured cues to guide memory search during retrieval. We measured the activation of story-specific and schematic representations using fMRI while participants were presented with 16 stories and then recalled each of the narratives, and related these activations to memory for specific story details. We predicted that schema representations in medial prefrontal cortex (mPFC) would be correlated with successful recall of story details. In keeping with this prediction, an anterior mPFC region showed a significant correlation between activation of schema representations at encoding and subsequent behavioral recall performance; however, this mPFC region was not implicated in schema representation during retrieval. More generally, our analyses revealed largely distinct brain networks at encoding and retrieval in which schema activation was related to successful recall. These results provide new insight into when and where event knowledge can support narrative memory.Our day-to-day experiences are incredibly complex, so how does the brain remember them? Cognitive scientists have shown that memories rely on knowledge of common events that we have experienced before. Think about going to a restaurant: you arrive, you find a table, you order food, and then you eat. This kind of predictable sequence is called a schema. When humans make memories, our brains use schemas like these as scaffolding. They take a basic pattern constructed from past experience and fill it in with the specific details of an event. When memories are recalled, our brains use schemas as step-by-step guides to remember the events in the right order. Most research so far on how the brain uses schemas for memory has involved showing participants pictures or words and then testing their memory by asking ‘true or false’ questions. This revealed that a brain area called the medial prefrontal cortex plays an important role in creating and retrieving memories for items related to a schema. But, studies have not yet assessed exactly how the brain uses schemas to understand and remember a long, realistic event that unfolds over several minutes. To answer this question, Masís-Obando et al. scanned people’s brains while they watched or listened to clips of two familiar experiences: eating at a restaurant or catching a flight at an airport. Then, the participants were scanned while they tried to retell each story in their own words. The volunteers were graded based on how many details they recalled. The scans showed that when volunteers’ medial prefrontal cortex kept track of the schema throughout the whole time that an event was happening, they were more likely to score well on the memory test. But it wasn’t necessary for medial prefrontal cortex to hold the schema in mind when remembering the story. Instead, a different set of brain regions maintained schema information during successful remembering. This study reveals new information about how memories and schemas work that could help explain why people develop problems making or recalling memories in diseases such as Alzheimer’s. The findings could also be used to help people make experiences or stories more memorable.

Recent fMRI studies of event segmentation have found that default mode regions represent high-level event structure during movie watching. In these regions, neural patterns are relatively stable during events and shift at event boundaries. Music, like narratives, contains hierarchical event structure (e.g., sections are composed of phrases). Here, we tested the hypothesis that brain activity patterns in default mode regions reflect the high-level event structure of music. We used fMRI to record brain activity from 25 participants (male and female) as they listened to a continuous playlist of 16 musical excerpts, and additionally collected annotations for these excerpts by asking a separate group of participants to mark when meaningful changes occurred in each one. We then identified temporal boundaries between stable patterns of brain activity using a hidden Markov model and compared the location of the model boundaries to the location of the human annotations. We identified multiple brain regions with significant matches to the observer-identified boundaries, including auditory cortex, medial prefrontal cortex, parietal cortex, and angular gyrus. From these results, we conclude that both higher-order and sensory areas contain information relating to the high-level event structure of music. Moreover, the higher-order areas in this study overlap with areas found in previous studies of event perception in movies and audio narratives, including regions in the default mode network.Significance StatementListening to music requires the brain to track dynamics at multiple hierarchical timescales. In our study, we had fMRI participants listen to real-world music (classical and jazz pieces) and then used an unsupervised learning algorithm (a hidden Markov model) to model the high-level event structure of music within participants’ brain data. This approach revealed that default mode brain regions involved in representing the high-level event structure of narratives are also involved in representing the high-level event structure of music. These findings provide converging support for the hypothesis that these regions play a domain-general role in processing events occurring over long timescales.

The “Narratives” collection aggregates a variety of functional MRI datasets collected while human subjects listened to naturalistic spoken stories. The current release includes 345 subjects, 891 functional scans, and 27 diverse stories of varying duration totaling ~4.6 hours of unique stimuli (~43,000 words). This data collection is well-suited for naturalistic neuroimaging analysis, and is intended to serve as a benchmark for models of language and narrative comprehension. We provide standardized MRI data accompanied by rich metadata, preprocessed versions of the data ready for immediate use, and the spoken story stimuli with time-stamped phoneme- and word-level transcripts. All code and data are publicly available with full provenance in keeping with current best practices in transparent and reproducible neuroimaging.

Learning about temporal structure is adaptive because it enables the generation of expectations. We examined how the brain uses experience in structured environments to anticipate upcoming events. During fMRI (functional magnetic resonance imaging), individuals watched a 90 s movie clip six times. Using a hidden Markov model applied to searchlights across the whole brain, we identified temporal shifts between activity patterns evoked by the first vs. repeated viewings of the movie clip. In many regions throughout the cortex, neural activity patterns for repeated viewings shifted to precede those of initial viewing by up to 15 s. This anticipation varied hierarchically in a posterior (less anticipation) to anterior (more anticipation) fashion. We also identified specific regions in which the timing of the brain’s event boundaries was related to those of human-labeled event boundaries, with the timing of this relationship shifting on repeated viewings. With repeated viewing, the brain’s event boundaries came to precede human-annotated boundaries by 1–4 s on average. Together, these results demonstrate a hierarchy of anticipatory signals in the human brain and link them to subjective experiences of events., eLife digest Anticipating future events is essential. It allows individuals to plan and prepare what they will do seconds, minutes, or hours in the future. But how the brain can predict future events in both the short-term and long-term is not yet clear. Researchers know that the brain processes images or other sensory information in stages. For example, visual features are processed from lines to shapes to objects, and eventually scenes. This staged approach allows the brain to create representations of many parts of the world simultaneously. A similar hierarchy may be at play in anticipation. Different parts of the brain may track what is happening now, and what could happen in the next few seconds and minutes. This would provide a way for the brain to forecast upcoming events in the immediate, near, and more distant future at the same time. Now, Lee et al. show that the regions in the back of the brain anticipate the immediate future, while longer-term predictions are made in brain regions near the front. In the experiments, study participants watched a 90-second clip of the movie ‘The Grand Budapest Hotel’ six times while undergoing functional magnetic resonance imaging (fMRI). Then, Lee et al. used computer modeling to compare the brain activity captured by fMRI during successive viewings. This allowed the researchers to watch participants’ brain activity moment-by-moment. As the participants repeatedly watched the movie clip, their brains began to anticipate what was coming next. Regions near the back of the brain like the visual cortex anticipated events in the next 1 to 4 seconds. Areas in the middle of the brain anticipated 5 to 8 seconds in the future. The front of brain anticipated 8 to 15 seconds into the future. Lee et al. show that many parts of the brain work together to predict the near and more distant future. More research is needed to understand how this information translates into actions. Learning more may help scientists understand how diseases or injuries affect people’s ability to plan and respond to future events.

BackgroundWith advances in methods for collecting and analyzing fMRI data, there is a concurrent need to understand how to reliably evaluate and optimally use these methods. Simulations of fMRI data can aid in both the evaluation of complex designs and the analysis of data.New MethodWe present fmrisim, a new Python package for standardized, realistic simulation of fMRI data. This package is part of BrainIAK: a recently released open-source Python toolbox for advanced neuroimaging analyses. We describe how to use fmrisim to extract noise properties from real fMRI data and then create a synthetic dataset with matched noise properties and a user-specified signal.ResultsWe validate the noise generated by fmrisim to show that it can approximate the noise properties of real data. We further show how fmrisim can help researchers find the optimal design in terms of power.Comparison with other methodsfmrisim ports the functionality of other packages to the Python platform while extending what is available in order to make it seamless to simulate realistic fMRI data.ConclusionsThe fmrisim package holds promise for improving the design of fMRI experiments, which may facilitate both the pre-registration of such experiments as well as the analysis of fMRI data.Highlightsfmrisim can simulate fMRI data matched to the noise properties of real fMRI.This can help researchers investigate the power of their fMRI designs.This also facilitates open science by making it easy to pre-register analysis pipelines.

Segmentation of continuous experience into discrete events is driven by rapid fluctuations in encoding stability at context shifts (i.e., event boundaries), yet the mechanisms underlying the online formation of event memories are poorly understood. We investigated the neural per-time point spatial similarity patterns of the scalp electrophysiological (EEG) activity of 30 human participants (male and female) watching a 50 min movie and found that event boundaries triggered the rapid reinstatement of the just-encoded movie event EEG patterns. We also found that the onset of memory reinstatement at boundary onset was accompanied by a left-lateralized anterior negative ERP effect, which likely reflects the detection of a shift in the narrative structure of the movie. A data-driven approach based on Hidden Markov modeling allowed us to detect event boundaries as shifts between stable patterns of brain EEG activity during encoding, and to identify their reactivation during a free recall task. These results provide the first neurophysiological underpinnings for how the memory systems segment a continuous long stream of experience into episodic events.SIGNIFICANCE OF STATEMENTMemory for specific episodic events are the building blocks of our autobiographical memory. However, it is still unclear how the memory systems structure the unfolding experience into discrete event units that can be understood and remembered at the long-term. Here, we show that the detection of context shifts, or event boundaries, during a 50 min movie viewing triggers the rapid memory reactivation of the just-encoded event to promote its successful encoding into long-term memory. By finding that memory reactivation, a neural mechanism critical for episodic memory formation and consolidation, takes place under these ecologically valid experimental circumstances, our results provide valuable insights into how the brain shapes the ongoing experience into episodic memories in the real-life.

How do we know that a kitchen is a kitchen by looking? Traditional models posit that scene categorization is achieved through recognizing necessary and sufficient features and objects, yet there is little consensus about what these may be. However, scene categories should reflect how we use visual information. Therefore, we test the hypothesis that scene categories reflect functions, or the possibilities for actions within a scene. Our approach is to compare human categorization patterns with predictions made by both functions and alternative models. We collected a large-scale scene category distance matrix (5 million trials) by asking observers to simply decide whether 2 images were from the same or different categories. Using the actions from the American Time Use Survey, we mapped actions onto each scene (1.4 million trials). We found a strong relationship between ranked category distance and functional distance (r = .50, or 66% of the maximum possible correlation). The function model outperformed alternative models of object-based distance (r = .33), visual features from a convolutional neural network (r = .39), lexical distance (r = .27), and models of visual features. Using hierarchical linear regression, we found that functions captured 85.5% of overall explained variance, with nearly half of the explained variance captured only by functions, implying that the predictive power of alternative models was because of their shared variance with the function-based model. These results challenge the dominant school of thought that visual features and objects are sufficient for scene categorization, suggesting instead that a scene's category may be determined by the scene's function.

Understanding movies and stories requires maintaining a high-level situation model that abstracts away from perceptual details to describe the location, characters, actions, and causal relationships of the currently unfolding event. These models are built not only from information present in the current narrative, but also from prior knowledge about schematic event scripts, which describe typical event sequences encountered throughout a lifetime. We analyzed fMRI data from 44 human subjects presented with sixteen three-minute stories, consisting of four schematic events drawn from two different scripts (eating at a restaurant or going through the airport). Aside from this shared script structure, the stories varied widely in terms of their characters and storylines, and were presented in two highly dissimilar formats (audiovisual clips or spoken narration). One group was presented with the stories in an intact temporal sequence, while a separate control group was presented with the same events in scrambled order. Regions including the posterior medial cortex, medial prefrontal cortex (mPFC), and superior frontal gyrus exhibited schematic event patterns that generalized across stories, subjects, and modalities. Patterns in mPFC were also sensitive to overall script structure, with temporally scrambled events evoking weaker schematic representations. Using a Hidden Markov Model, patterns in these regions can predict the script (restaurant vs. airport) of unlabeled data with high accuracy, and can be used to temporally align multiple stories with a shared script. These results extend work on the perception of controlled, artificial schemas in human and animal experiments to naturalistic perception of complex narrative stimuli.Significance StatementIn almost all situations we encounter in our daily lives, we are able to draw on our schematic knowledge about what typically happens in the world to better perceive and mentally represent our ongoing experiences. In contrast to previous studies that investigated schematic cognition using simple, artificial associations, we measured brain activity from subjects watching movies and listening to stories depicting restaurant or airport experiences. Our results reveal a network of brain regions that is sensitive to the shared temporal structure of these naturalistic situations. These regions abstract away from the particular details of each story, activating a representation of the general type of situation being perceived.

Inherent correlations between visual and semantic features in real-world scenes make it difficult to determine how different scene properties contribute to neural representations. Here, we assessed the contributions of multiple properties to scene representation by partitioning the variance explained in human behavioral and brain measurements by three feature models whose inter-correlations were minimizeda priorithrough stimulus preselection. Behavioral assessments of scene similarity reflected unique contributions from a functional feature model indicating potential actions in scenes as well as high-level visual features from a deep neural network (DNN). In contrast, similarity of cortical responses in scene-selective areas was uniquely explained by mid- and high-level DNN features only, while an object label model did not contribute uniquely to either domain. The striking dissociation between functional and DNN features in their contribution to behavioral and brain representations of scenes indicates that scene-selective cortex represents only a subset of behaviorally relevant scene information.

Visual Abstract, A number of regions in the human brain are known to be involved in processing natural scenes, but the field has lacked a unifying framework for understanding how these different regions are organized and interact. We provide evidence from functional connectivity and meta-analyses for a new organizational principle, in which scene processing relies upon two distinct networks that split the classically defined parahippocampal place area (PPA). The first network of strongly connected regions consists of the occipital place area/transverse occipital sulcus and posterior PPA, which contain retinotopic maps and are not strongly coupled to the hippocampus at rest. The second network consists of the caudal inferior parietal lobule, retrosplenial complex, and anterior PPA, which connect to the hippocampus (especially anterior hippocampus), and are implicated in both visual and nonvisual tasks, including episodic memory and navigation. We propose that these two distinct networks capture the primary functional division among scene-processing regions, between those that process visual features from the current view of a scene and those that connect information from a current scene view with a much broader temporal and spatial context. This new framework for understanding the neural substrates of scene-processing bridges results from many lines of research, and makes specific functional predictions.

Peripherally presented stimuli evoke stronger activity in scene-processing regions than foveally presented stimuli, suggesting that scene understanding is driven largely by peripheral information. We used functional MRI to investigate whether functional connectivity evoked during natural perception of audiovisual movies reflects this peripheral bias. For each scene-sensitive region--the parahippocampal place area (PPA), retrosplenial cortex, and occipital place area--we computed two measures: the extent to which its activity could be predicted by V1 activity (connectivity strength) and the eccentricities within V1 to which it was most closely related (connectivity profile). Scene regions were most related to peripheral voxels in V1, but the detailed nature of this connectivity varied within and between these regions. The retrosplenial cortex showed the most consistent peripheral bias but was less predictable from V1 activity, while the occipital place area was related to a wider range of eccentricities and was strongly coupled to V1. We divided the PPA along its posterior-anterior axis into retinotopic maps PHC1, PHC2, and anterior PPA, and found that a peripheral bias was detectable throughout all subregions, though the anterior PPA showed a less consistent relationship to eccentricity and a substantially weaker overall relationship to V1. We also observed an opposite foveal bias in object-perception regions including the lateral occipital complex and fusiform face area. These results show a fine-scale relationship between eccentricity biases and functional correlation during natural perception, giving new insight into the structure of the scene-perception network.

Several research groups have shown how to correlate fMRI responses to the meanings of presented stimuli. This paper presents new methods for doing so when only a natural language annotation is available as the description of the stimulus. We study fMRI data gathered from subjects watching an episode of BBCs Sherlock [1], and learn bidirectional mappings between fMRI responses and natural language representations. We show how to leverage data from multiple subjects watching the same movie to improve the accuracy of the mappings, allowing us to succeed at a scene classification task with 72% accuracy (random guessing would give 4%) and at a scene ranking task with average rank in the top 4% (random guessing would give 50%). The key ingredients are (a) the use of the Shared Response Model (SRM) and its variant SRM-ICA [2, 3] to aggregate fMRI data from multiple subjects, both of which are shown to be superior to standard PCA in producing low-dimensional representations for the tasks in this paper; (b) a sentence embedding technique adapted from the natural language processing (NLP) literature [4] that produces semantic vector representation of the annotations; (c) using previous timestep information in the featurization of the predictor data., Comment: 19 pages, 9 figures, in submission to NeuroImage. Prior version presented at MLINI-2016 workshop, 2016 (arXiv:1701.01437) and ICML 2016 Workshop on Multi-view Representation Learning

Discovering functional connectivity between and within brain regions is a key concern in neuroscience. Due to the noise inherent in fMRI data, it is challenging to characterize the properties of individual voxels, and current methods are unable to flexibly analyze voxel-level connectivity differences. We propose a new functional connectivity method which incorporates a spatial smoothness constraint using regularized optimization, enabling the discovery of voxel-level interactions between brain regions from the small datasets characteristic of fMRI experiments. We validate our method in two separate experiments, demonstrating that we can learn coherent connectivity maps that are consistent with known results. First, we examine the functional connectivity between early visual areas V1 and VP, confirming that this connectivity structure preserves retinotopic mapping. Then, we show that two category-selective regions in ventral cortex - the Parahippocampal Place Area (PPA) and the Fusiform Face Area (FFA) - exhibit an expected peripheral versus foveal bias in their connectivity with visual area hV4. These results show that our approach is powerful, widely applicable, and capable of uncovering complex connectivity patterns with only a small amount of input data.

SummaryDuring realistic, continuous perception, humans automatically segment experiences into discrete events. Using a novel model of neural event dynamics, we investigate how cortical structures generate event representations during continuous narratives, and how these events are stored and retrieved from long-term memory. Our data-driven approach enables identification of event boundaries and event correspondences across datasets without human-generated stimulus annotations, and reveals that different regions segment narratives at different timescales. We also provide the first direct evidence that narrative event boundaries in high-order areas (overlapping the default mode network) trigger encoding processes in the hippocampus, and that this encoding activity predicts pattern reinstatement during recall. Finally, we demonstrate that these areas represent abstract, multimodal situation models, and show anticipatory event reinstatement as subjects listen to a familiar narrative. Our results provide strong evidence that brain activity is naturally structured into semantically meaningful events, which are stored in and retrieved from long-term memory.

Understanding human–object interactions is critical for extracting meaning from everyday visual scenes and requires integrating complex relationships between human pose and object identity into a new percept. To understand how the brain builds these representations, we conducted 2 fMRI experiments in which subjects viewed humans interacting with objects, noninteracting human–object pairs, and isolated humans and objects. A number of visual regions process features of human–object interactions, including object identity information in the lateral occipital complex (LOC) and parahippocampal place area (PPA), and human pose information in the extrastriate body area (EBA) and posterior superior temporal sulcus (pSTS). Representations of human–object interactions in some regions, such as the posterior PPA (retinotopic maps PHC1 and PHC2) are well predicted by a simple linear combination of the response to object and pose information. Other regions, however, especially pSTS, exhibit representations for human–object interaction categories that are not predicted by their individual components, indicating that they encode human–object interactions as more than the sum of their parts. These results reveal the distributed networks underlying the emergent representation of human–object interactions necessary for social perception.

A common goal in biological sciences is to model a complex web of connections using a small number of interacting units. We present a general approach for dividing up elements in a spatial map based on their connectivity properties, allowing for the discovery of local regions underlying large-scale connectivity matrices. Our method is specifically designed to respect spatial layout and identify locally-connected clusters, corresponding to plausible coherent units such as strings of adjacent DNA base pairs, subregions of the brain, animal communities, or geographic ecosystems. Instead of using approximate greedy clustering, our nonparametric Bayesian model infers a precise parcellation using collapsed Gibbs sampling. We utilize an infinite clustering prior that intrinsically incorporates spatial constraints, allowing the model to search directly in the space of spatially-coherent parcellations. After showing results on synthetic datasets, we apply our method to both functional and structural connectivity data from the human brain. We find that our parcellation is substantially more effective than previous approaches at summarizing the brain's connectivity structure using a small number of clusters, produces better generalization to individual subject data, and reveals functional parcels related to known retinotopic maps in visual cortex. Additionally, we demonstrate the generality of our method by applying the same model to human migration data within the United States. This analysis reveals that migration behavior is generally influenced by state borders, but also identifies regional communities which cut across state lines. Our parcellation approach has a wide range of potential applications in understanding the spatial structure of complex biological networks.