A holistic approach to identifying functional units of tongue motion during speech

识别言语过程中舌头运动功能单位的整体方法

• 批准号: 10376818
• 负责人: Jonghye Woo
• 金额: $ 47.01万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-20 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10376818
• 关键词: 3-Dimensional; Acoustics; Address; Affect; Aftercare; Anatomy; Atlases; Behavior; Biomechanics; Cessation of life; Clinical; Complex; Computing Methodologies; Data; Death Rate; Deglutition; Diagnosis; Diffusion Magnetic Resonance Imaging; Disease; Elements; Evaluation; Excision; Exhibits; Fiber; Geometry; Glossectomy; Goals; Grouping; Impairment; Incidence; Joints; Knowledge; Learning; Link; Magnetic Resonance Imaging; Malignant Neoplasms; Maps; Measurement; Measures; Methods; Modeling; Motion; Muscle; Muscle Fibers; Operative Surgical Procedures; Outcome; Pathologic; Patients; Physiological; Postoperative Period; Predictive Value; Procedures; Production; Prognosis; Proxy; Publishing; Radiation therapy; Rehabilitation therapy; Research; Resolution; Speech; Speech Intelligibility; Standardization; Structure; Surface; System; Techniques; Testing; Time; Tissue Grafts; Tissues; Tongue; Weight; Work; base; biomechanical model; clinical practice; deep learning; functional outcomes; holistic approach; improved; in vivo; insight; malignant mouth neoplasm; malignant tongue neoplasm; motor control; multimodality; muscular structure; novel; novel therapeutics; outcome prediction; reconstruction; rehabilitation strategy; signal processing; spatiotemporal; success; tool; treatment planning; treatment strategy; tumor

PROJECT SUMMARY Oral cancers have the seventh highest incidence, with roughly 51,540 new cases and 10,030 cancer- related deaths expected to occur in 2018. Although a variety of treatment methods are available, the death rate is higher than that for most cancers with five-year rates of about 50 percent. The most frequently used treatment method, glossectomy surgery, involves the surgical removal of tumors and surrounding tissues, and the addition of grafted tissues, often followed by radiotherapy. Although tongue cancer and its treatment have debilitating effects on speech, the impact of varying degrees of resection and reconstruction on the formation of functional units in speech has remained poorly understood. In order to produce intelligible speech, a variety of local muscle groupings of the tongue—i.e., functional units—emerge and recede rapidly and nimbly in a highly coordinated fashion. Therefore, understanding the formation of functional units that are critical for speech production can provide substantial insights into normal, pathological, and adapted motor control strategies in controls and patients with tongue cancer for novel therapeutic, surgical, and rehabilitative strategies. One of the critical challenges in pre-operative surgical and treatment planning, as well as in post- operative evaluation for tongue cancer is the difficulty in developing objective and quantitative measures and in evaluating their functional outcome predictability. To address this, in this proposal, three integrated approaches will be used in in vivo tongue motion during speech to seamlessly identify the functional units and associated quantitative measures: multimodal MRI methods, multimodal deep learning, and biomechanical simulations. This will provide a convergent approach, thereby allowing us to (1) test hypotheses about the spatiotemporal basis of muscle coordination in a consilient way, and (2) develop objective quantitative measures that are required for understanding the complex biomechanical system as well as for predicting the functional outcomes after various reconstruction methods. The first proof of concept study published by the PI and the team identified the functional units of speech tasks using the sparse non-negative matrix factorization framework, in which the magnitude and angle of displacements from tagged MRI were used as our input quantities. With these advances in place, we will further incorporate muscle fiber anatomy from diffusion MRI and motion tracking from tagged MRI into our framework to yield physiologically and anatomically meaningful functional units. In addition, we will create a completely novel and integrated way of directly relating the functional units to tongue muscle anatomy, learning joint representation via a multimodal deep learning technique, and linking them to biomechanical simulations. Furthermore, 3D and 4D atlases will be utilized to identify objective and quantitative measures based on our functional units analysis. Taken together, the successful implementation of our integrated framework will identify functional units that can be used for research on tongue motion, for surgical planning, and for diagnosis, prognosis, and rehabilitation in a range of speech-related disorders.

项目摘要 口服癌症的事件是第七高的事件，大约51,540例新病例和10,030例癌症 - 预计将在2018年发生相关死亡。尽管有多种治疗方法，但死亡率 对于大多数癌症，五年率约为50％的癌症。最常用的 治疗方法，刻骨切除术手术，涉及肿瘤和周围组织的手术切除，以及 加入移植组织，通常进行放疗。尽管舌癌及其治疗有 对言语的影响使人衰弱的影响，不同程度的切除和重建对形成的影响 语音中功能单位的理解仍然很差。为了产生可理解的演讲， 舌头的局部肌肉分组（即功能单位）在一个 高度协调的时尚。因此，了解至关重要的功能单元的形成 语音产生可以提供对正常，病理和适应性运动控制的大量见解 对照和舌癌患者进行新治疗，外科和康复的策略 策略。术前手术和治疗计划以及后的关键挑战之一 舌癌的手术评估是开发客观和定量措施的困难以及 评估其功能结果可预测性。为了解决这个问题，在此提案中，三种综合方法 语音期间将用于体内舌头运动，以无缝识别功能单元和相关的功能单位 定量测量：多模式MRI方法，多模式深度学习和生物力学模拟。 这将提供一种收敛的方法，从而使我们能够（1）关于时空的测试假设 肌肉协调的基础是一致的，（2）制定客观定量措施 了解复杂的生物力学系统以及预测功能结果所必需的 经过各种重建方法。 PI和团队发表的第一个概念研究证明 使用稀疏的非负矩阵分解框架确定了语音任务的功能单位， 标记的MRI的位移幅度和角度用作我们的输入量。和 这些进步已到位，我们将进一步结合扩散MRI和运动中的肌肉纤维解剖结构 从标记的MRI跟踪到我们的框架，以产生身体和解剖学上有意义的功能 单位。此外，我们将创建一种完全新颖而整合的方式，将功能单元与 舌肌解剖学，通过多模式深度学习技术学习联合表示并链接 它们进行生物力学模拟。此外，将利用3D和4D地图集来确定目标和 基于我们的功能单元分析的定量度量。总之，成功实施 我们的集成框架将确定可用于研究舌运动的功能单元，因为 手术计划，以及一系列与语音有关的疾病中的诊断，预后和康复。