Beyond lesion-language mapping in aphasia: A novel imaging-based prediction model

超越失语症的病变语言映射:一种新颖的基于成像的预测模型

基本信息

  • 批准号:
    10292884
  • 负责人:
  • 金额:
    --
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2018
  • 资助国家:
    美国
  • 起止时间:
    2018-10-01 至 2021-09-30
  • 项目状态:
    已结题

项目摘要

Response to post-stroke aphasia language rehabilitation is variable, mainly because there are few predictors that can help identify individualized treatment options. Imaging techniques, such as Voxel-based Lesion Symptom Mapping (VLSM) have been useful in linking specific brain areas to language behavior; however, further development is required to optimize use of structural and functional information in guiding individualized treatment for our Veterans with aphasia. This CDA1 addresses this gap through development of a novel technique that improves prediction of language behavior using anatomical measure of gliosis+ as well as physiological measures such as Cerebral Blood Flow (CBF) and Glucose Extraction Fraction (GEF). In the first aim, we test the sensitivity of our novel anatomical measure, gliosis+, in relating to confrontation naming of nouns and verbs, thereby advancing current VLSM techniques. Our approach to testing the working hypothesis will be to recruit patients with aphasia on whom we will test confrontation naming of nouns and verbs and acquire high-resolution structural MRI scans. The study of nouns and verbs has multiple avenues of clinical significance, including (1) anomia is the most pervasive deficit in aphasia, (2) noun and verb naming can be differentially impaired in aphasia, and (3) nouns and verbs are often targets in impairment-based treatment. We plan to determine the structure-behavior association with three different methodologies (binary lesion maps, continuous T1w signal maps, gliosis + maps), and statistically compare the amount of behavioral variance that each VLSM technique accounts for. After completing Aim 1, it is our expectation that we will have identified which VLSM methodology accounts for the most behavioral variance. In the second aim, we develop a novel methodology called Voxel-based Lesion and Physiology Symptom Mapping (VLPSM) by combining gliosis+ VLSM with regional CBF to improve the predictive capability of structure-behavior association maps, and expand them to structure-function-behavior association maps. We will then compare VLPSM to VLSM, and determine which methodology accounts for more variance in language behavior, with the expectation that combining structural and physiological information will account for more language behavior variance across subjects than when considering structural information alone. After completing Aim 2, it is our expectation that we will have a robust and novel method of mapping language behavior to brain areas utilizing information from both structure and physiology. In the third aim, we will develop regional GEF measures on a Magnetic Resonance Imaging (MRI) system, which can be used as a better marker of neuronal health in VLPSM. Our approach will be to develop the theory of the pulse sequence, ensure proper MRI signal formation via Bloch equation modeling, simulate different conditions of the MRI signal formation, and implement the pulse sequence on a 3T Siemens Prisma platform. We will perform extensive quality and reproducibility tests on phantoms. If our target sensitivity, spatial resolution, and reproducibility are not met, we will identify the limiting factors and overcome them through improved pulse sequence design and potentially improvements in RF hardware. Finally, we will test the novel GEF technique on neurologically healthy young and old subjects to determine reproducibility and sensitivity to physiological changes due to age, and compare these results with CBF measures. After completing Aim 3, it is our expectation that we will have a fully developed methodology to non-invasively measure regional glucose extraction fraction in the brain. The over-arching objective of this application is to create a map that contains both anatomical and physiological correlates of language behavior, highlighting key target brain areas for rehabilitation. The long- term goal of this line of research is to determine how VLPSM can be best used to provide clinicians with individualized maps to predict aphasia treatment outcome, thereby assisting in treatment choices.
对中风后失语症语言康复的反应是可变的,主要是因为很少有 这些预测因素可以帮助确定个性化的治疗方案。成像技术,例如基于体素的 病变症状图(VLSM)在将特定脑区与语言行为联系起来方面很有用; 然而,还需要进一步的发展,以优化结构和功能信息的使用, 为患有失语症的退伍军人提供个性化治疗。本CDA 1通过制定 一种新的技术,可以通过神经胶质增生+的解剖测量来改善语言行为的预测, 生理测量,如脑血流量(CBF)和葡萄糖提取分数(GEF)。 在第一个目标中,我们测试了我们新的解剖学测量,神经胶质增生+, 名词和动词的对抗命名,从而推进当前VLSM技术。我们的测试方法 工作假设将招募失语症患者,我们将测试他们的对抗命名, 名词和动词,并获得高分辨率的结构MRI扫描。对名词和动词的研究具有多重意义。 临床意义的途径,包括(1)命名障碍是失语症中最普遍的缺陷,(2)名词和动词 命名可以在失语症中不同程度地受损;(3)名词和动词通常是基于损伤的 治疗我们计划用三种不同的方法(二进制)来确定结构-行为关联 病变图、连续T1 w信号图、神经胶质增生+图),并统计学比较行为神经元的数量。 每个VLSM技术都考虑到了方差。在完成目标1后,我们期望 确定了VLSM方法解释了最多的行为差异。 在第二个目标中,我们开发了一种新的方法,称为基于体素的病变和生理症状 通过将胶质细胞增生+ VLSM与区域CBF相结合来提高VLPSM的预测能力, 结构-行为关联图,并将其扩展为结构-功能-行为关联图。我们将 然后将VLPSM与VLSM进行比较,确定哪种方法在语言中的差异更大 行为,并期望结合结构和生理信息将占更多 语言行为的差异比单独考虑结构信息时要大。后 完成目标2,我们期望我们将有一个强大的和新颖的方法映射语言 利用来自结构和生理学的信息将行为转移到大脑区域。 在第三个目标中,我们将制定关于磁共振成像系统的区域全环基金措施, 其可用作VLPSM中神经元健康的更好标记物。我们的方法是发展理论 通过Bloch方程建模确保适当的MRI信号形成,模拟不同的 MRI信号形成的条件,并在3 T Siemens Prisma平台上实现脉冲序列。 我们将对幻影进行广泛的质量和再现性测试。如果我们的目标灵敏度,空间 分辨率和再现性,我们将确定限制因素,并通过以下措施克服它们: 改进的脉冲序列设计和RF硬件的潜在改进。最后,我们将测试小说 在神经健康的年轻和老年受试者中使用GEF技术,以确定重复性和对 生理变化,由于年龄,并比较这些结果与CBF措施。完成目标3后, 我们期望我们将有一个充分发展的方法来非侵入性地测量区域葡萄糖, 脑中的提取分数。 本应用程序的总体目标是创建一个包含解剖结构和 语言行为的生理相关性,突出康复的关键目标大脑区域。很长的- 这一系列研究的长期目标是确定如何最好地使用VLPSM为临床医生提供 个性化地图,以预测失语症的治疗结果,从而帮助治疗选择。

项目成果

期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
ASL MRI informs blood flow to chronic stroke lesions in patients with aphasia.
  • DOI:
    10.3389/fphys.2023.1240992
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    4
  • 作者:
  • 通讯作者:
Not All Lesioned Tissue Is Equal: Identifying Pericavitational Areas in Chronic Stroke With Tissue Integrity Gradation via T2w T1w Ratio.
  • DOI:
    10.3389/fnins.2021.665707
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    4.3
  • 作者:
    Krishnamurthy LC;Krishnamurthy V;Rodriguez AD;McGregor KM;Glassman CN;Champion GS;Rocha N;Harnish SM;Belagaje SR;Kundu S;Crosson BA
  • 通讯作者:
    Crosson BA
Rostral anterior cingulate connectivity in older adults with subthreshold depressive symptoms: A preliminary study.
  • DOI:
    10.1016/j.nbas.2022.100059
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Gradone, Andrew M.;Champion, Gabriell;Mcgregor, Keith M.;Nocera, Joe R.;Barber, Sarah J.;Krishnamurthy, Lisa C.;Dotson, Vonetta M.
  • 通讯作者:
    Dotson, Vonetta M.
Relationships between frontal metabolites and Alzheimer's disease biomarkers in cognitively normal older adults.
  • DOI:
    10.1016/j.neurobiolaging.2021.09.016
  • 发表时间:
    2022-01
  • 期刊:
  • 影响因子:
    4.2
  • 作者:
    Hone-Blanchet, Antoine;Bohsali, Anastasia;Krishnamurthy, Lisa C.;Shahid, Salman;Lin, Qixiang;Zhao, Liping;Loring, David;Goldstein, Felicia;John, Samantha E.;Fleischer, Candace C.;Levey, Allan;Lah, James;Qiu, Deqiang;Crosson, Bruce
  • 通讯作者:
    Crosson, Bruce
Functional Magnetic Resonance Imaging Evaluation of Auricular Percutaneous Electrical Neural Field Stimulation for Fibromyalgia: Protocol for a Feasibility Study.
  • DOI:
    10.2196/resprot.8692
  • 发表时间:
    2018-02-06
  • 期刊:
  • 影响因子:
    1.7
  • 作者:
    Gebre M;Woodbury A;Napadow V;Krishnamurthy V;Krishnamurthy LC;Sniecinski R;Crosson B
  • 通讯作者:
    Crosson B
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Lisa C. Krishnamurthy其他文献

Proceedings #45: Effects of Active and Sham tDCS on Lexical Decision in Three Persons with Aphasia
  • DOI:
    10.1016/j.brs.2018.12.214
  • 发表时间:
    2019-03-01
  • 期刊:
  • 影响因子:
  • 作者:
    Rachael M. Harrington;Simone R. Roberts;Lisa C. Krishnamurthy;Venkatagiri Krishnamurthy;Amy D. Rodriguez;Keith M. McGregor;Marcus Meinzer;Bruce Crosson
  • 通讯作者:
    Bruce Crosson

Lisa C. Krishnamurthy的其他文献

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{{ truncateString('Lisa C. Krishnamurthy', 18)}}的其他基金

Effects of Acute Exercise on Functional Magnetic Resonance Spectroscopy Measures of GABA in Aging and Chronic Stroke
急性运动对衰老和慢性中风中 GABA 功能磁共振波谱测量的影响
  • 批准号:
    10311114
  • 财政年份:
    2021
  • 资助金额:
    --
  • 项目类别:

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