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Diagnosing Mild TBI in VA and Active Duty Military Patients using MEG and DTI

Diagnosing Mild TBI in VA and Active Duty Military Patients using MEG and DTI
使用 MEG 和 DTI 诊断 VA 和现役军人患者的轻度 TBI
批准号:
8590197
负责人:
MINGXIONG HUANG
金额:
$0.0万
依托单位国家:
美国
项目类别:
财政年份:
2011
资助国家:
美国
项目状态:
已结题
起止时间:
2011-10-01 至 2015-09-30
关键词:
AccelerationAccountingAcuteAddressAffectAfghanistanAnisotropyAnteriorAreaBackBehavioralBiological MarkersBlast CellBloodBlood capillariesBlunt TraumaBrainBrain ConcussionCharacteristicsClinicalCognitiveCognitive deficitsConflict (Psychology)Corpus CallosumCraniocerebral TraumaDataDecelerationDevicesDiagnosisDiagnosticDiffuse Axonal InjuryDiffusion Magnetic Resonance ImagingDorsalElectromagneticsEmotionalEvaluationFiberFrequenciesFunctional ImagingFunctional Magnetic Resonance ImagingFunctional disorderGenerationsGlasgow Coma ScaleGoalsHealthImageImage AnalysisImaging TechniquesImpaired cognitionImpairmentIndividualInferiorInjuryInterventionIraqLateralLeadLeftLesionLinkLobeLocationLungMagnetic Resonance ImagingMagnetismMagnetoencephalographyMeasurementMeasuresMedialMemory impairmentMethodsMilitary PersonnelModalityMonitorNatureNerve FibersNervous System TraumaNeuraxisNeurologicNeuronal InjuryNeuronsNoiseOrganOutcomeParietalParietal LobePathologyPatientsPatternPerformancePhysiologicalPost-Concussion SyndromePrefrontal CortexProbabilityReportingResearchResidual stateResolutionRestSensitivity and SpecificityShort-Term MemorySignal TransductionSlow-Wave SleepSoftware ToolsSoldierSourceSportsStretchingStructureSurfaceSymptomsTBI PatientsTechniquesTemporal LobeTestingTissuesTraumaTraumatic Brain InjuryVisual CortexWarbasebrain tissuecapillarycombatcraniumdiffusion anisotropyexperiencefrontal lobegray matterimaging modalityinjuredmemory encodingmemory retrievalmillimetermillisecondnerve injuryneuroimagingneurophysiologyneuropsychologicalnovelpatient populationpost interventionpsychologicresponsesuccesstoolwhite matter

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中文摘要
翻译
描述(由申请人提供): 轻度创伤性脑损伤(TBI)是VA和现役军人患者最重要的健康问题之一。创伤性脑损伤的诊断和监测是创伤性脑损伤研究的重点。轻度(和一些中度)TBI可能难以诊断,因为损伤通常在常规急性神经成像技术上不可见(例如,CT和MRI)。此外,传统的神经影像学技术对TBI引起的生理变化的敏感性有限,并且对长期结果的预测效用较差。我们的初步研究表明,创伤损伤的神经组织产生低频电磁信号,功能连接性降低,扩散各向异性降低。该研究将使用包括脑磁图(MEG)和扩散张量成像(DTI)在内的综合多模态神经成像方法来诊断和监测轻度TBI。这种方法有可能达到更高的灵敏度和特异性比传统的成像技术在检测VA和现役军人的轻度TBI患者的细微神经元损伤。在拟议的研究中有三个具体目标:具体目标1将通过检测传统神经成像方法不可见的神经元损伤(损伤部位以及受影响的神经元网络),研究整合MEG-DTI方法在VA和现役mTBI患者中的诊断价值(例如,CT和MRI)。我们的初步数据表明,病理性MEG慢波,减少MEG功能连接,并减少DTI各向异性的轴突损伤的特点,由于组织剪切和拉伸在mTBI,具有显着更好的敏感性比CT/MRI诊断个别轻度TBI患者。具体目标2研究了现役和VA轻度TBI患者使用N-back工作记忆(WM)MEG任务的认知障碍的神经生理学基础。本申请的具体目的3将研究在VA和现役mTBI患者中的脑震荡后症状、通过神经心理学检查测量的认知缺陷与利用MEG和DTI的神经成像测量之间的关系。为了实现这些目标,我们建议开发新的成像分析工具:频域维斯塔尔,用于准确定位病理性MEG慢波;双核Beamformer,用于在信噪比差的条件下使用MEG可靠地获得功能连接减少的神经元网络;以及一个平台,用于整合灰质中的功能性MEG结果与白质纤维束中的结构性DTI结果。所提出的方法的成功将不仅大大提高我们通过检测细微的神经损伤(例如,基因座和网络),使用传统的神经成像技术是不可见的,而且还将提供神经成像工具和软件,其可以潜在地用作VA和现役TBI患者的新型神经药理学和/或神经心理学治疗的干预前和干预后评估期间的客观评价方法。
英文摘要
DESCRIPTION (provided by applicant): Mild traumatic brain injury (TBI) represents one of the most significant health issues in VA and active duty military patients. Diagnosing and monitoring of TBI are major focuses of VA research. Mild (and some moderate) TBI can be difficult to diagnose because the injuries are generally not visible on conventional acute neuroimaging techniques (e.g., CT and MRI). Furthermore, conventional neuroimaging techniques have limited sensitivity to the physiological alterations due to TBI, and poor predictive utility for long-term outcome. Our preliminary study shows that neuronal tissues injured by trauma generate low frequency electromagnetic signals, decreased functional connectivity, and reduction of diffusion anisotropy. The proposed study will use integrated multi-modality neuroimaging approach involving Magnetoencephalography (MEG) and diffusion tensor imaging (DTI) in diagnosing and monitoring mild TBI. This approach has the potential of attaining higher sensitivity and specificity than conventional imaging techniques in detecting subtle neuronal injuries in mild TBI patients in VA and active duty military patients. There are three specific aims in the proposed study: Specific Aim 1 will investigate the diagnostic value of the integrated MEG-DTI approach in VA and active duty patients with mTBI by detecting neuronal injuries (loci of the injury as well as affected neuronal networks) not visible with conventional neuroimaging methods (e.g., CT and MRI). Our preliminary data show that pathological MEG slow-waves, reduced MEG functional connectivity, and reduced DTI anisotropy are characteristics of axonal injury due to tissue shearing and stretching in mTBI, with markedly better sensitivity than CT/MRI in diagnosing individual mild TBI patients. Specific Aim 2 studies the neurophysiological basis of the cognitive impairments using N-back working memory (WM) MEG task in active duty and VA patients with mild TBI. Specific Aim 3 of the present application will study the relationship between post-concussive symptoms, cognitive deficits as measured by neuropsychological exams, and the neuroimaging measurements with MEG and DTI in VA and active duty patients with mTBI. To achieve these aims, we propose to develop new imaging analysis tools: frequency-domain VESTAL for accurately localizing pathological MEG slow-waves; Dual-core Beamformer for reliably obtaining the neuronal networks with reduced functional connectivity using MEG under the condition of poor signal to noise ratio; and a platform for integrating the functional MEG findings in the gray-matter with structural DTI findings in the white-matter fiber tracts. The success of the proposed approach will not only greatly enhance our ability to diagnose mild TBI by detecting subtle neural injuries (e.g., loci and networks) that are invisible using conventional neuroimaging techniques, but also will provide the neuroimaging tools and software which can potentially be used as an objective evaluation method during pre- and post-intervention assessments of novel neuropharmacological and/or neuropsychological treatments for VA and active duty patients with TBI.
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Diagnostic Machine Learning Algorithm to Identify MEG Features of Mild TBI and Comorbid PTSD
  • 批准号:
    10651625
  • 项目类别:
  • 资助金额:
    $0.0万
  • 财政年份:
    2020
  • 负责人:
    MINGXIONG HUANG
  • 依托单位:
Diagnostic Machine Learning Algorithm to Identify MEG Features of Mild TBI and Comorbid PTSD
  • 批准号:
    10398791
  • 项目类别:
  • 资助金额:
    $0.0万
  • 财政年份:
    2020
  • 负责人:
    MINGXIONG HUANG
  • 依托单位:
Diagnostic Machine Learning Algorithm to Identify MEG Features of Mild TBI and Comorbid PTSD
  • 批准号:
    9888520
  • 项目类别:
  • 资助金额:
    $0.0万
  • 财政年份:
    2020
  • 负责人:
    MINGXIONG HUANG
  • 依托单位:
Passive electrical neurofeedback treatment of mTBI: MEG and Behavioral Outcomes
  • 批准号:
    10189733
  • 项目类别:
  • 资助金额:
    $0.0万
  • 财政年份:
    2018
  • 负责人:
    MINGXIONG HUANG
  • 依托单位:
海外基金