Brain Biomarkers of Response to Treatment for Apraxia of Speech

描述（由申请人提供）： 已经开发了许多治疗方法来治疗获得性言语失用症（AOS）的发音缺陷。一种这样的干预是声音产生治疗（SPT），它采用由言语建模/重复，图形提示，整体刺激和发音位置指令组成的反应-视情况而定的层次结构。通常发现诸如SPT的干预可以改善患者的言语准确性，但程度不同。在目前的研究中，我们将测试的假设，病变部位发挥了作用的变异性的SPT反应。我们将使用最先进的MRI成像和后处理技术，以更好地了解哪些患者最能从AOS治疗中受益，以及治疗效果背后的神经变化。具体而言，我们将使用基于体素的病变症状映射来识别最能预测治疗反应程度的病变部位，并使用先进的扩散成像技术来映射治疗前后白色物质束完整性的变化。为此，我们将在4年内从VA北方加州患者人群中招募30名AOS患者，使用严格的入选和排除标准。参与者将在VA Martinez校园的3 T MRI扫描仪上进行扫描。将在三个时间点收集结构MRI和弥散成像序列：治疗基线期之前和之后（以测量治疗前的扫描-再扫描可靠性）以及治疗后再次收集（以测量与治疗相关的神经变化）。治疗将由熟悉该患者人群并接受过发声治疗方案培训的有执照的言语语言病理学家提供。患者将接受三种不同声音的训练（例如，辅音、元音），在预测试中产生错误率>70%。测试刺激将包括训练和未训练的声音，以测试概括性。为了识别预测SPT治疗反应的神经元区域，我们将对标准化病变重建进行基于体素的病变症状映射分析，自变量为病变状态（病变与否），因变量为发声准确性的变化，对训练和未训练的项目进行单独分析。根据我们以前的工作和相关文献，我们预测，SPT反应的大小（效应量）的治疗和未治疗的项目将受到病变涉及左侧上级中央前回的小脑。接下来，扩散成像衍生纤维束成像将用于重建八个 主要的白色物质路径，并且将计算每个路径中感兴趣的特定区域的分数各向异性值。对训练和未训练刺激的治疗反应程度预计与小脑中央前回上级周围的白色物质中的各向异性分数值相关，左侧弓形、上级纵向和内侧胼胝体下束，因为这些束已经涉及流畅性和运动性语言障碍的研究。为了测量与SPT协议相关的神经可塑性，我们将计算八个纤维束中每一个的扫描2到3的各向异性分数值的差异。我们的预测再次集中在文献中直接与言语产生相关的神经束，弓状束、上级纵束和内侧胼胝体下束，以及极囊的纤维。简而言之，目前的建议是重要的，因为它解决了文献中关于前瞻性地识别特定灰色和 白色物质与预测AOS治疗反应的相关性，以及这种治疗的神经机制。拟议的项目将提高我们对语音语言治疗机制的科学理解，并为临床医生提供关键信息，以改善患者护理。

DESCRIPTION (provided by applicant): A number of treatments have been developed to treat articulatory deficits in acquired apraxia of speech (AOS). One such intervention is sound production treatment (SPT), which employs a response-contingent hierarchy made up of verbal modeling/repetition, graphic cueing, integral stimulation, and articulatory placement instruction. Interventions such as SPT are generally found to improve patients' speech accuracy but to varying degrees. In the current study, we will test the hypothesis that lesion site plays a role in the variability of SPT response. We will use state-of-the-art MRI imaging and post-processing techniques to better understand which patients are best able to benefit from AOS treatment, as well as the neural changes underlying treatment effects. Specifically, we will use voxel-based lesion symptom mapping to identify lesion sites most predictive of the degree of treatment response and advanced diffusion imaging techniques to map changes in the integrity of white matter tracts from pre- to post-treatment. To do this, we will recruit 30 individuals with AOS from the VA Northern California patient populations over the course of four years, using strict inclusion and exclusion criteria. Participants will be scanned on a 3T MRI scanner on the VA Martinez campus. Structural MRI and diffusion imaging sequences will be collected at three time points: before and after the baseline phase of treatment (to measure scan-rescan reliability prior to treatment) and again following treatment (to measure the neural changes associated with treatment). Treatment will be delivered by licensed speech-language pathologists familiar with this patient population and trained on the sound production treatment protocol. Patients will be trained on three different sounds (e.g., consonant, vowel sounds) that produce error rates >70% on pre-testing. Test stimuli will include both trained and untrained sounds to test for generalizability. To identify brin regions predictive of response to the SPT treatment, we will run voxel-based lesion symptom mapping analyses on normalized lesion reconstructions, with the independent variable being lesion status (lesioned or not) and the dependent variable, the change in sound production accuracy, with separate analyses for trained and untrained items. Based on our previous work and the relevant literature, we predict that the size of the SPT response (effect size) for both treated and untreated items will be affected by lesions involving the left superior pre-central gyrus of the insula. Next, diffusion imaging-derived tractography will be used to reconstruct eight major white matter pathways, and fractional anisotropy values will be calculated for specific regions of interest in each pathway. The degree of treatment response to both trained and untrained stimuli is predicted to correlate with fractional anisotropy values in the white matter surrounding the superior pre-central gyrus of the insula, the left arcuate, superior longitudinal, and medial subcallosal fasciculi, as these tracts have been implicated in studies of fluency and motor speech disorders. To measure neuroplasticity associated with the SPT protocol, we will calculate the difference in fractional anisotropy values from Scan 2 to 3 for each of the eight fiber tracts. Our predictions again focus on those tracts that have been directly associated with speech production in the literature, the arcuate, superior longitudinal, and medial subcallosal fasciculi, and fibers of the extreme capsule. In short, the current proposal is significant in thatit addresses a large gap in the literature with respect to prospectively identifying specific grey and white matter correlates that predict AOS treatment response, as well as the neural mechanisms underlying such treatment. The proposed project will improve our scientific understanding of the mechanisms underlying speech-language treatment and provide clinicians with critical information that they can use to improve patient care.