Brooks, P. P. | bioRxiv Science

bioRxiv2020

Narratives: fMRI data for evaluating models of naturalistic language comprehension

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.

neuroscience↗

bioRxiv2018

Targeted memory reactivation during sleep elicits neural signals related to learning content

Reactivation of learning-related neural activity patterns is thought to drive memory stabilization. However, finding reliable, non-invasive, content-specific indicators of reactivation remains a central challenge. Here, we attempted to decode the content of reactivated memories in the electroencephalogram (EEG) during sleep. During encoding, human participants learned to associate spatial locations of visual objects with left- or right-hand movements, and each object was accompanied by an inherently related sound. During subsequent slow-wave sleep within an afternoon nap, we presented half of the sound cues that were associated (during wake) with left- and right-hand movements before bringing participants back for a final post-nap test. We trained a classifier on sleep EEG data (focusing on lateralized EEG features that discriminated left- vs. right-sided trials during wake) to predict learning content when we reactivated the memories during sleep. Discrimination performance was significantly above chance and predicted subsequent memory, supporting the idea that reactivation leads to memory stabilization. Moreover, these lateralized signals increased with post-cue spindle power, demonstrating that reactivation has a strong relationship with spindles. These results show that lateralized activity related to individual memories can be decoded from sleep EEG, providing an effective indicator of offline reactivation.

neuroscience↗