The mental context in which we experience an event plays a fundamental role in how we organize our memories of an event (e.g. in relation to other events) and, in turn, how we retrieve those memories later. Because we use contextual representations to retrieve information pertaining to our past, processes that alter our representations of context can enhance or diminish our capacity to retrieve particular memories. We designed a functional magnetic resonance imaging (fMRI) experiment to test the hypothesis that people can intentionally forget previously experienced events by changing their mental representations of contextual information associated with those events. We had human participants study two lists of words, manipulating whether they were told to forget (or remember) the first list prior to studying the second list. We used pattern classifiers to track neural patterns that reflected contextual information associated with the first list and found that, consistent with the notion of contextual change, the activation of the first-list contextual representation was lower following a forget instruction than a remember instruction. Further, the magnitude of this neural signature of contextual change was negatively correlated with participants’ abilities to later recall items from the first list.

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 pFC, 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.

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.

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, mPFC, 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 stimuli with long-timescale dependencies.

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In recent years, the push to embrace naturalistic stimuli over artificial designs has enriched what we know about the neural underpinnings of human attention, memory, and communication in real life. Previous work using natural stories scrambled (at the word, sentence, and paragraph level) has revealed a hierarchy of brain regions that organize natural acoustic input at these different timescales. While this approach has advanced our understanding of language processing, many fewer studies to date have explored the neural underpinnings of music perception, let alone music production, in naturalistic settings. In our novel paradigm, we asked expert pianists to play musical pieces, scrambled at different timescales (measure, phrase, section) on a non-ferromagnetic piano keyboard inside the fMRI scanner. This dataset provides unprecedented access to expert musicians’ brains starting from their first exposure to a novel piece and continuing over the course of learning to play it. We found distinct patterns of tuning to musical timescales across several clusters of brain regions (e.g., sensory/motor, parietal, and frontal/memory). We also found that musical predictability impacts functional connectivity between auditory, motor, and higher-order regions during performance. Finally, we applied several machine learning analyses to understand how the brain dynamically represents acoustic and musical features.