Brain Health and Aphasia Recovery
大脑健康和失语症恢复
基本信息
- 批准号:10390288
- 负责人:
- 金额:$ 16.57万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2016
- 资助国家:美国
- 起止时间:2016-04-01 至 2026-03-31
- 项目状态:未结题
- 来源:
- 关键词:AcuteAddressAffectAgingAnatomyAphasiaBehavioralBlood VesselsBrainBrain InjuriesBrain regionCharacteristicsChronicClinicalCognitiveCollaborationsDataDiffusion Magnetic Resonance ImagingElderlyExhibitsFiberGoalsGuidelinesHumanImageImpairmentIndividualInjuryInternationalLanguageLanguage DisordersLeadLesionLinguisticsLiteratureLocationMachine LearningMeasuresMediatingMediationMedicalMetabolicMethodsMicrovascular DysfunctionModelingNervous System TraumaNeurobiologyNeurologicNeuronsOutcomePathway interactionsPredispositionRecoveryReportingResearchResidual stateSemanticsSeveritiesStrokeSymptomsTestingVascular blood supplyWorkacute symptomaphasia recoverybasebrain healthbrain magnetic resonance imagingbrain tissuecardiovascular healthcardiovascular risk factorcognitive reserveconnectomeexperienceinnovationlanguage impairmentlexicalloss of functionmultimodal neuroimagingmultimodalitynetwork architectureneural network architectureneuroimagingnovelpersistent symptomphonologypost strokepreservationresiliencestroke recoverystroke-induced aphasiasynergismwhite matter
项目摘要
Abstract
Language impairments can vary considerably between individuals with aphasia. Our neurobiological
models based on the stroke lesion can only partly explain the aphasic symptoms. We hypothesize that the
integrity of the residual brain tissue outside the stroke lesion is an important, but not yet fully appreciated,
determinant of aphasia severity and recovery.
It is well recognized that cardiovascular risk factors lead to cumulative widespread brain damage
through small vessel disease (SVD). Outside the aphasia literature, SVD has been strongly associated with
poor cognitive reserve and reduced resiliency to various forms of neurological injury. Stroke survivors with
aphasia typically have cardiovascular risk factors and they commonly exhibit SVD. However, the impact of
SVD is not usually taken into account in our models of recovery, even though the residual brain tissue is
responsible for overcoming the loss of function. It follows that higher degrees of SVD outside the lesion may
lead to worse aphasic symptoms and less chances of recovery due to reduced capacity to compensate for the
stroke injury. Our goal is to directly test this hypothesis.
We propose to evaluate how aphasia is shaped by the stroke lesion in combination with residual brain
integrity. Neuroimaging (brain MRI) is ideally suited to address this problem. SVD is composed of
microangiopathic ischemic changes and microhemorrhages. The ischemic changes from SVD can be
measured through white matter hyper intensities using T2-weighted and T2-FLAIR images, and the
microhemorrhages can be assessed using susceptibility-weighted images. SVD preferentially affects white
matter and diffusion MRI can provide additional measures of white matter microstructural integrity and their
relationship with the whole brain neuronal networks architecture (the brain connectome).
Using our experience with post-stroke lesion symptom mapping, white matter and connectome imaging
we propose a comprehensive study of the neurobiology and impact of SVD in aphasia. Our project will build on
international guidelines for SVD assessment (The STandards for ReportIng Vascular changes on
nEuroimaging - STRIVE) and it will develop an innovative multimodal machine learning approach to fully
assess brain integrity.
Brain integrity and language measures will be assessed in the context of chronic (Project 1) and acute
(Project 2) aphasia recovery. The behavioral and linguistic assessments will be guided by Project 4. With the
neuroimaging core, we will develop and distribute a multimodal neuroimaging approach to quantify the severity
and location of SVD.
Specific Aim 1 will longitudinally assess the independent impact of SVD, controlling for the brain lesion,
on acute and chronic symptoms, as well as acute and chronic language recovery. Specific Aim 2 will evaluate
the mechanisms by which SVD leads to language impairments by assessing the impact of SVD and stroke
lesions on connectome neural network architecture, loss of associative long-range white matter fibers and its
relationship with semantic, lexical-semantic, lexical-phonological, phonological/phonetic deficits.
抽象的
失语症患者之间的语言障碍可能存在很大差异。我们的神经生物学
基于中风病变的模型只能部分解释失语症状。我们假设
中风病灶外残留脑组织的完整性很重要,但尚未得到充分认识,
失语严重程度和恢复的决定因素。
众所周知,心血管危险因素会导致累积性广泛脑损伤
通过小血管疾病(SVD)。在失语症文献之外,SVD 与以下疾病密切相关:
认知储备能力差,对各种形式的神经损伤的弹性降低。中风幸存者
失语症通常有心血管危险因素,并且通常表现出 SVD。然而,影响
在我们的恢复模型中通常不考虑 SVD,尽管残余脑组织是
负责克服功能丧失。由此可见,病灶外的 SVD 程度可能较高
由于代偿能力降低,导致失语症症状更严重,恢复机会更小
中风损伤。我们的目标是直接检验这个假设。
我们建议结合脑残评估中风病变如何形成失语症
正直。神经影像(脑部 MRI)非常适合解决这个问题。 SVD 的组成为
微血管病性缺血性改变和微出血。 SVD 引起的缺血性改变可以是
使用 T2 加权和 T2-FLAIR 图像通过白质高强度测量,以及
可以使用磁敏感加权图像来评估微出血。 SVD 优先影响白色
物质和扩散 MRI 可以提供白质微结构完整性及其相关性的额外测量
与全脑神经元网络结构(大脑连接组)的关系。
利用我们在中风后病变症状图谱、白质和连接组成像方面的经验
我们建议对神经生物学和 SVD 对失语症的影响进行全面研究。我们的项目将建立在
SVD 评估国际指南(报告血管变化标准
nEuroimaging - STRIVE),它将开发一种创新的多模式机器学习方法,以充分
评估大脑完整性。
大脑完整性和语言测量将在慢性(项目 1)和急性的背景下进行评估
(项目2)失语恢复。行为和语言评估将以项目 4 为指导。
神经影像核心,我们将开发和分发多模式神经影像方法来量化严重程度
和 SVD 的位置。
具体目标 1 将纵向评估 SVD 的独立影响,控制脑损伤,
关于急性和慢性症状,以及急性和慢性语言恢复。具体目标 2 将评估
通过评估 SVD 和中风的影响,了解 SVD 导致语言障碍的机制
连接组神经网络结构的损伤、关联性长程白质纤维的丧失及其
与语义、词汇语义、词汇语音、语音/语音缺陷的关系。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Leonardo F Bonilha其他文献
Leonardo F Bonilha的其他文献
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{{ truncateString('Leonardo F Bonilha', 18)}}的其他基金
Speech Entrainment for Aphasia Recovery (SpARc)
失语症恢复的言语诱导 (SpARc)
- 批准号:
9811129 - 财政年份:2019
- 资助金额:
$ 16.57万 - 项目类别:
Speech Entrainment for Aphasia Recovery (SpARc)
失语症恢复的言语诱导 (SpARc)
- 批准号:
10241330 - 财政年份:2019
- 资助金额:
$ 16.57万 - 项目类别:
Predicting Epilepsy Surgery Outcomes Using Neural Network Architecture
使用神经网络架构预测癫痫手术结果
- 批准号:
10649724 - 财政年份:2019
- 资助金额:
$ 16.57万 - 项目类别:
Speech Entrainment for Aphasia Recovery (SpARc)
失语症恢复的言语诱导 (SpARc)
- 批准号:
10470912 - 财政年份:2019
- 资助金额:
$ 16.57万 - 项目类别:
Speech Entrainment for Aphasia Recovery (SpARc)
失语症恢复的言语诱导 (SpARc)
- 批准号:
10005301 - 财政年份:2019
- 资助金额:
$ 16.57万 - 项目类别:
Predicting Epilepsy Surgery Outcomes Using Neural Network Architecture
使用神经网络架构预测癫痫手术结果
- 批准号:
10619937 - 财政年份:2019
- 资助金额:
$ 16.57万 - 项目类别:
Predicting Epilepsy Surgery Outcomes Using Neural Network Architecture
使用神经网络架构预测癫痫手术结果
- 批准号:
10158551 - 财政年份:2019
- 资助金额:
$ 16.57万 - 项目类别:
Prediction of seizure lateralization and postoperative outcome through the use of deep learning applied to multi-site MRI/DTI data: An ENIGMA-Epilepsy study
通过将深度学习应用于多部位 MRI/DTI 数据来预测癫痫偏侧化和术后结果:ENIGMA-癫痫研究
- 批准号:
9751025 - 财政年份:2019
- 资助金额:
$ 16.57万 - 项目类别:
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