Integrating complementary learning principles in aphasia rehabilitation via adaptive modeling

通过适应性建模将补充学习原则融入失语症康复中

• 批准号: 10366326
• 负责人: William Streicher Evans
• 金额: $ 62.54万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-15 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10366326
• 关键词: Address; Affect; Aftercare; Algorithms; Anomia; Aphasia; Back; Brain Injuries; Clinical; Clinical Trials; Combined Modality Therapy; Complex; Computer software; Computers; Data; Data Set; Development; Equilibrium; Frustration; Future; Gaussian model; Hour; Individual; Intervention; Language Disorders; Lead; Learning; Literature; Memory; Modeling; Names; Nature; Neuronal Plasticity; Outcome; Outcome Study; Participant; Patients; Performance; Persons; Population; Quality of life; Randomized; Reaction Time; Research; Retrieval; Schedule; Speech; Speed; Stroke; System; Testing; Time; Training; adaptive learning; aphasia rehabilitation; base; clinical practice; design; effective intervention; flexibility; improved; improved outcome; individual patient; innovation; interest; neglect; next generation; novel; open source; personalized medicine; processing speed; standard care; telehealth; theories; treatment research

Aphasia is a language disorder commonly caused by stroke and other acquired brain injuries that affects over two million people in the US and has a large negative effect on quality of life. Anomia (i.e., word-finding difficulty) is a primary frustration for people with aphasia, and naming treatments for anomia are both widely researched and commonly used in clinical practice. For naming treatments to make a meaningful impact on the lives of people with aphasia, they must produce durable gains in word-finding which generalize beyond the treatment context. However, most theoretically-motivated naming treatment research fails to address the long- term retention of trained words and their generalization to connected speech, limiting their clinical impact. Prevailing learning theory suggests that “desirable difficulty” improves treatment retention and generalization. The current proposal therefore seeks to improve the durability and context generalization of computer-based naming treatment by incorporating model-based algorithms to adaptively maintain desirable difficulty. We will test two distinct models in parallel clinical trials. Our central premise is that these models will facilitate a balance between what have historically been framed as contrasting learning approaches: errorless learning vs. effortful retrieval (Study 1) and massed vs. distributed practice (Study 2). Instead, our models will integrate these approaches by replacing extreme static contrasts with continuous task components which can be adaptively modified based on ongoing patient performance. Study 1 will adaptively balance effort and accuracy using speeded naming deadlines based on a model we have developed which characterizes individuals’ speed-accuracy tradeoffs in picture naming over time. Study 2 will manipulate trial spacing using an adaptive scheduling and memory decay model built into widely available, open-source flashcard software. In both studies, we predict that when compared to matched traditional non-adaptive treatment conditions, our adaptive conditions will produce more successful retention of trained words 3 and 6 months post-treatment on naming probes (Aims 1a, 2a), and better context generalization to connected speech when tested on complex scene descriptions containing untrained exemplars of trained words (Aims 1b, 2b). We also predict that adaptive trial spacing in Study 2 will successfully train many more words than is possible in current standard care. In addition, data generated in Studies 1 and 2 will be used to develop the next generation of adaptive timing models (Aims 1c and 2c), spurring future innovations in personalized medicine. Successful clinical trial outcomes will demonstrate that adaptive computer-based naming treatments provide a novel way to produce large, durable, and generalizable treatment gains, and positive Study 2 findings could be immediately implemented in clinical practice at scale using free open-source software. Successful modeling outcomes will lead to even more effective interventions and lay the groundwork for a transformative research agenda that could ultimately lead to comprehensive adaptive learning systems for aphasia rehabilitation.

失语症是一种通常由中风和其他产生的脑损伤引起的语言障碍 在美国有200万人，对生活质量有很大的负面影响。异构体（即单词调查 困难）是失语症患者的主要沮丧 研究并常用于临床实践。命名治疗对 失语症的生活中的生活，他们必须在单词调查中产生持久的收益，从而超出 治疗环境。但是，大多数神经动机的命名治疗研究都无法解决长期的 培训单词的任期保留及其对连接语音的概括，从而限制了它们的临床影响。 流行的学习理论表明，“理想的困难”改善了治疗的保留率和 概括。因此，当前的建议旨在提高耐用性和上下文的概括 通过增加基于模型的算法来适应理想的算法，以计算机为基础的命名处理 难的。我们将在平行临床试验中测试两个不同的模型。我们的主要前提是这些模型将 促进历史上框架作为对比的学习方法之间的平衡：毫无疑问 学习与努力检索（研究1）和批量与分布式实践（研究2）。相反，我们的模型将 通过替换连续的任务组件替换极端静态对比来整合了这些方法 根据持续的患者表现进行自适应修改。研究1将适应平衡努力和 基于我们开发的模型，使用加速命名截止日期的准确性 随着时间的流逝，个人的速度准确性权衡在图片中命名。研究2将使用 自适应调度和内存衰减模型内置在广泛可用的开源抽认卡软件中。 在这两项研究中，我们都预测，与匹配的传统非自适应治疗相比 条件，我们的适应性条件将使训练有素的单词3和6个月更成功地保留 有关命名问题的治疗后（目标1A，2A），以及更好的上下文概括，以与连接的语音进行更好的概括 在复杂的场景描述上进行了测试，其中包含未经训练的训练词的典范（Aims 1b，2b）。我们也是 预测研究2中的自适应试验间距将成功训练比当前可能更多的单词 标准护理。此外，研究1和2中产生的数据将用于开发下一代 自适应定时模型（AIMS 1C和2C），促进了个性化医学的未来创新。 成功的临床试验结果将证明，基于计算机的命名治疗可提供 产生大型，耐用且可概括的治疗收益的新型方法，而积极的研究2可能是 立即使用免费的开源软件在大规模临床实践中实施。成功的建模 结果将导致更有效的干预措施，并为变革性研究奠定基础 议程最终可能导致全面的自适应学习系统进行失语症康复。