Capturing Autobiographical Memory Formation in Real World Spaces Using Multimodal Recordings

Overview

The goal of this observational study is to develop novel methods for integrating multimodal data streams with invasive neural recordings to study autobiographical memory (AM) formation in individuals with implanted neurostimulation devices (e.g., NeuroPace RNS) for epilepsy treatment. The main questions it aims to answer are: How does the brain encode and retrieve real-world autobiographical memories? Can multimodal data integration enhance our understanding of memory-related cognitive and neural mechanisms? Participants will: * Use a smartphone-based recording application (CAPTURE app) to collect real-world data. * Have their wearable sensor data (e.g., audio-visual, accelerometry, GPS, autonomic physiology, eye tracking) synchronized with invasive neural recordings. Researchers will analyze these multimodal data streams to develop new analytic approaches for studying memory formation in naturalistic settings, with the long-term goal of informing neuromodulation-based memory enhancement treatments for individuals with memory disorders.

This observational study aims to develop and validate novel methodologies for integrating multimodal data streams with invasive neural recordings in individuals with implanted neurostimulation devices (e.g., NeuroPace RNS) undergoing treatment for epilepsy. The study focuses on understanding the neural mechanisms underlying autobiographical memory (AM) formation and retrieval in real-world settings. By leveraging real-time, ecologically valid data collection, this research seeks to bridge the gap between controlled laboratory-based memory studies and naturalistic memory processes. Study Design and Data Collection Participants with implanted neurostimulation devices will use a smartphone-based application (CAPTURE) to log real-world experiences while wearing additional sensors to record behavioral, physiological, and environmental data. The study integrates multiple data modalities, including: * Neural recordings from implanted neurostimulation devices * Wearable sensor data, including: * Audio-visual recordings * GPS location tracking * Accelerometry for movement analysis * Autonomic physiology measurements (e.g., heart rate, skin conductance) * Eye-tracking data By synchronizing these data streams, researchers will establish a high-fidelity representation of autobiographical memory encoding and retrieval processes in daily life. Analytic Methods and Data Integration This study aims to develop computational frameworks for real-time data alignment and analysis. The primary methodological goals include: * Temporal synchronization of multimodal data with neural recordings * Automated feature extraction from behavioral and physiological data * Neural signal processing techniques to identify patterns of activity associated with memory formation and retrieval * Development of machine learning models to predict successful memory encoding and recall based on neural and behavioral signals * These analytic advancements will enable researchers to assess memory-related brain activity in naturalistic environments, providing insights into the neural correlates of real-world autobiographical memory. Potential Impact: The findings from this study have significant implications for neuroscience, cognitive psychology, and clinical applications. By establishing methods for naturalistic neural recording and analysis, this research paves the way for neuromodulation-based memory enhancement therapies for individuals with memory disorders, including those with epilepsy, traumatic brain injury, and neurodegenerative diseases. The study also advances real-world cognitive neuroscience, enabling a deeper understanding of how the brain supports memory outside of laboratory settings.