Digital Memory Augmentation based on how the brain works (i2i Phase 1)

基于大脑工作方式的数字记忆增强（i2i 第一阶段）

• 批准号: 577502-2022
• 负责人: Barense, MorganMD
• 金额: $ 9.11万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Idea to Innovation
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=764223
• 关键词: Digital; Memory; Augmentation; based; how

Memory loss is the most common and debilitating cognitive impairment resulting from Alzheimer's disease (AD) and from the mild cognitive impairment (MCI) which often precedes AD. Currently 564,000 Canadians live with AD or a related dementia, costing $33 billion annually (rising to $293 billion by 2040). Memory loss diminishes one's sense of identity and autonomy, leading to a vicious cycle of social isolation and depression,which in turn exacerbates memory loss. Digital Memory Augmentation (DMA) is an approach to mitigate memory loss in which portable devices capture information about everyday episodes, allowing for later review and recollection. DMA has been shown to produce memory benefits - even more so than common strategies such as keeping a diary. However, prior DMA approaches have not been informed by our understanding ofbrain function or key learning principles.This project aims to optimize and field test a new kind of DMA platform that optimally reduces memory impairment in aging and dementia. This is the first DMA tool designed based on our understanding of how the brain internally "reminds" itself of prior events. It mimics important functions of the hippocampus - a brain area damaged in MCI/AD - by replaying and reorganizing movies of an individual's life to stabilize and retrieve memories. The proposed work will enhance the replay algorithm to improve efficacy and user enjoyment. Specifically, we will use field-leading machine vision tools so that the replay algorithm groups related events into meaningful and coherent narratives that can retrieved on demand to reveal memories relevant to any given situation. We predict that grouping related events into meaningful narratives that are pertinent to the currentsituation will not only enhance memory benefits, but also lead to increased user satisfaction and engagement. We will test our enhanced platform with key stakeholders, including community-dwelling older adults as well as residents and caregivers at a residential living facility.

记忆丧失是由阿尔茨海默病(AD)和通常先于AD的轻度认知损害(MCI)引起的最常见和最衰弱的认知损害。目前有56.4万加拿大人患有阿尔茨海默病或相关的痴呆症，每年花费330亿美元(到2040年上升到2930亿美元)。记忆丧失会削弱一个人的认同感和自主感，导致社会孤立和抑郁的恶性循环，这反过来又会加剧记忆丧失。数字记忆增强(DMA)是一种减轻记忆损失的方法，在这种方法中，便携式设备捕获关于日常事件的信息，以便以后查看和回忆。DMA已被证明对记忆有好处--甚至比写日记等常见策略更有好处。然而，以前的DMA方法并不是基于我们对大脑功能或关键学习原理的理解。本项目旨在优化和现场测试一种新型的DMA平台，以最佳地减少衰老和痴呆症的记忆损害。这是第一个基于我们对大脑如何在内部“提醒”自己先前事件的理解而设计的DMA工具。它通过重播和重组个人生活的电影来模拟海马体的重要功能，以稳定和恢复记忆。海马体是MCI/AD中受损的大脑区域。所提出的工作将对重播算法进行改进，以提高效率和用户体验。具体地说，我们将使用领域领先的机器视觉工具，以便重播算法将相关事件分组为有意义和连贯的叙事，这些叙事可以按需检索，以揭示与任何给定情况相关的记忆。我们预测，将相关事件分组为与当前情况相关的有意义的叙述不仅会增强记忆力，还会导致用户满意度和参与度的提高。我们将与主要利益攸关方一起测试我们增强的平台，包括社区居住的老年人以及居住在住宅生活设施的居民和照顾者。