Classification of Decomposed Neural Data in Memory Networks and LLM-Based Stimuli Processing

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Schumacher, Shella Keilholz, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7222077/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Apr, 2026 Read the published version in Brain Imaging and Behavior → Version 1 posted 6 You are reading this latest preprint version Abstract Naturalistic paradigms, where participants are exposed to real-world stimuli (e.g., narratives) are an important technique in memory research. They may provide a more complete assay of human memory processes and their underlying mechanisms. In these procedures, stimuli are continuous and do not have well-defined scene boundaries. Defining scene boundaries based on narrative events such as plot twists or character developments is crucial as these moments are known to influence memory recall. Aligning neural activity with such boundaries helps in studying the dynamics of memory recall during narrative experiences. However, segmenting narratives based on memory-driven cues is difficult due to the continuous flow of events. To overcome this, we developed an automatic scene segmentation technique using large language models (LLMs). The LLMs segment narratives into meaningful scenes offering a consistent unbiased method for recall-based segmentation. These segments are then used to analyze brain dynamics from fMRI data. In our study, 180 participants listened to four different stories while undergoing fMRI. Functional connectivity (FC) was computed based on the LLM-derived scene segments. For memory recall analysis, classification models were trained using participants’ recall scores as labels. To further understand the neural basis of memory, FC matrices were decomposed into shared (low-rank) and individual (idiosyncratic) components. Classification results demonstrate that LLM-based segmentation effectively defines scene boundaries and that memory recall is not solely tied to common or idiosyncratic activity. This approach offers a robust framework for exploring brain-behavior relationships in naturalistic memory research. Functional connectivity (FC) Memory low-30 rank approximation Large Language Model (LLM) Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryFileNarrativeMemoryPatterns.pdf Cite Share Download PDF Status: Published Journal Publication published 18 Apr, 2026 Read the published version in Brain Imaging and Behavior → Version 1 posted Editorial decision: Accepted 08 Apr, 2026 Reviews received at journal 29 Jan, 2026 Reviewers agreed at journal 29 Dec, 2025 Reviewers invited by journal 29 Dec, 2025 Submission checks completed at journal 15 Dec, 2025 First submitted to journal 11 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7222077","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":571383777,"identity":"56db4c79-1d31-44dd-814e-67c5e96bccc6","order_by":0,"name":"Muhammad Shahzaib","email":"","orcid":"","institution":"Institute of Space Technology","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Shahzaib","suffix":""},{"id":571383779,"identity":"fa7ae37f-d6be-4dbb-b533-133796ca091e","order_by":1,"name":"Salma Zainab Farooq","email":"","orcid":"","institution":"Institute of Space Technology","correspondingAuthor":false,"prefix":"","firstName":"Salma","middleName":"Zainab","lastName":"Farooq","suffix":""},{"id":571383780,"identity":"de223ee5-cedd-4894-af71-b4c9e6e65480","order_by":2,"name":"Eric H. 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