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.

失语症是一种语言障碍，通常由中风和其他后天性脑损伤引起， 美国有200万人，对生活质量有很大的负面影响。失范（即，找词 困难）是失语症患者的主要挫折，命名治疗失名症的方法都广泛存在。 研究和临床实践中常用。对于命名治疗，以产生有意义的影响， 失语症患者的生活，他们必须在单词发现方面产生持久的收益， 治疗背景。然而，大多数理论上的命名治疗研究未能解决长期的- 训练词的术语保留及其对连接语音的概括，限制了其临床影响。 流行的学习理论表明，“理想的难度”提高了治疗的保留率， 一般化因此，目前的建议旨在提高持久性和上下文泛化， 通过结合基于模型的算法以自适应地保持期望的 困难我们将在平行临床试验中测试两种不同的模型。我们的核心前提是，这些模型将 促进历史上被视为对立的学习方法之间的平衡：无误 学习与努力检索（研究1）和集中与分布式实践（研究2）。相反，我们的模型将 通过用连续的任务组成部分取代极端的静态对比来整合这些方法， 基于正在进行的患者表现自适应地修改。研究1将自适应地平衡努力， 准确性使用加速命名的最后期限的基础上，我们已经开发的模型，其特点是 随着时间的推移，个人在图片命名中的速度-准确性权衡。研究2将使用以下方法操纵试验间隔： 自适应调度和内存衰减模型内置于广泛使用的开源闪存卡软件中。 在这两项研究中，我们预测，与匹配的传统非适应性治疗相比， 条件下，我们的适应条件将产生更成功的保留训练的话3和6个月 命名探针的后处理（目标1a，2a），以及当 在复杂场景描述中进行测试，其中包含训练单词的未训练样本（目标1b，2b）。我们也 预测研究2中的自适应试验间距将成功训练比目前更多的单词。 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（目标1c和2c），刺激个性化医疗的未来创新。 成功的临床试验结果将证明，自适应计算机命名治疗提供了一个 一种产生大的、持久的和可推广的治疗收益的新方法，研究2的积极结果可能是 立即在临床实践中使用免费开源软件大规模实施。造模成功 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适应性学习系统的失语症康复。