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

项目总结 口腔癌的发病率位居第七，约有51,540例新病例和10,030例癌症- 相关死亡预计将在2018年发生。虽然有多种治疗方法可用，但死亡率 高于大多数癌症的五年发病率约为50%。使用频率最高的 治疗方法，舌叶切除术，涉及手术切除肿瘤和周围组织，以及 移植组织的添加，通常随后是放射治疗。虽然舌癌及其治疗已经 语言衰弱效应，不同程度切除重建对形成的影响 关于言语中的功能单位，人们仍然知之甚少。为了产生可理解的语音，各种 舌的局部肌肉群--即功能单位--在一种 高度协调的时尚。因此，理解功能单元的形成对于 语音产生可以为正常的、病态的和适应的运动控制提供实质性的见解 舌癌对照和患者新的治疗、手术和康复策略 战略。术前手术和治疗计划以及术后的关键挑战之一 舌癌的手术评估是制定客观、定量的评估指标的难点，也是目前舌癌评估的难点。 评估其功能结果的可预测性。为了解决这一问题，在本提案中，有三种综合方法 将在体内用于舌头运动的语音过程中无缝识别和关联的功能单位 量化方法：多模式磁共振成像方法、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，从而允许我们(1)测试关于时空的假设 以协调的方式建立肌肉协调的基础，以及(2)制定客观的量化措施，这些措施是 了解复杂的生物力学系统以及预测功能结果所需的 经过各种重建方法后。由PI和团队发布的第一份概念证明研究报告 使用稀疏非负矩阵因式分解框架识别语音任务的功能单元，在 其中来自标记MRI的位移的大小和角度被用作我们的输入量。使用 在这些进展到位后，我们将进一步结合扩散磁共振成像和运动的肌肉纤维解剖 从标记的MRI跟踪到我们的框架中，以产生具有生理和解剖学意义的功能 单位。此外，我们将创建一种全新的集成方式，将功能单元直接关联到 舌肌解剖，通过多模式深度学习技术学习关节表示，以及链接 进行生物力学模拟。此外，将利用3D和4D地图集来识别客观和 基于我们的功能单元分析的量化措施。总而言之，成功实施 我们的综合框架将确定可用于舌部运动研究的功能单元， 手术计划，以及一系列语言相关疾病的诊断、预后和康复。

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.