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.

Human communication is remarkably versatile, enabling teachers to share highly abstracted and novel information with their students. What neural processes enable such transfer of information across brains during naturalistic teaching and learning? Here, we show that during lectures, wherein information transmission is unidirectional and flows from the teacher to the student, the student's brain mirrors the teacher's brain and that this neural coupling is correlated with learning outcomes. A teacher was scanned in fMRI giving an oral lecture with slides on a scientific topic followed by a review lecture. Students were then scanned watching either the intact lecture and review (N = 20) or a temporally scrambled version of the lecture (N = 20).Using intersubject correlation (ISC), we observed widespread teacher-student neural coupling spanning sensory cortex and language regions along the superior temporal sulcus as well as higher-level regions including posterior medial cortex (PMC), superior parietal lobule (SPL), and dorsolateral and dorsomedial prefrontal cortex. Teacher-student alignment in higher-level areas was not observed when learning was disrupted by temporally scrambling the lecture. Moreover, teacher-student coupling in PMC was significantly correlated with learning outcomes: the more closely the student's brain mirrored the teacher's brain, the more the student improved between behavioral pre-learning and post-learning assessments. Together, these results suggest that the alignment of neural responses between teacher and students may underlie effective communication of complex information across brains in classroom settings. 3 Significance statementHow is technical, non-narrative information communicated from one brain to another during teaching and learning? In this fMRI study, we show that the DMN activity of teachers and students are coupled during naturalistic teaching. This teacher-student neural coupling emerges only during intact learning and is correlated with learning outcomes. Together, these findings suggest that teacher-student neural alignment underlies effective communication during teaching.

The Effective Medium Approximation (EMA) is a usual method for estimating the dielectric response of a mixture (in our case polycrystalline silicon). From the n and k spectra (calculated with this method) it is possible to evaluate the value of the optical bandgap of this material by models of Tauc and Cody taking into account that the material is noncrystalline.

Shared information content is represented across brains in idiosyncratic functional topographies. Hyperalignment addresses these idiosyncrasies by using neural responses to project individuals’ brain data into a common model space while maintaining the geometric relationships between distinct patterns of activity or connectivity. The dimensions of this common model capture functional profiles that are shared across individuals such as cortical response profiles collected during a common time-locked stimulus presentation (e.g. movie viewing) or functional connectivity profiles. Hyperalignment can use either response-based or connectivity-based input data to derive transformations that project individuals’ neural data from anatomical space into the common model space. Previously, only response or connectivity profiles were used in the derivation of these transformations. In this study, we developed a new hyperalignment algorithm, hybrid hyperalignment, that derives transformations based on both response-based and connectivity-based information. We used three different movie-viewing fMRI datasets to test the performance of our new algorithm. Hybrid hyperalignment derives a single common model space that aligns response-based information as well as or better than response hyperalignment while simultaneously aligning connectivity-based information better than connectivity hyperalignment. These results suggest that a single common information space can encode both shared cortical response and functional connectivity profiles across individuals.

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