Multimodal Hyperspectral Imaging of Brain Activity and Connectivity
Multimodal Hyperspectral Imaging of Brain Activity and Connectivity
批准号:
9250811
负责人:
Zhongming Liu
金额:
$51.97万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-08-01 至 2019-04-30
关键词:
AnatomyAreaBehaviorBiological MarkersBrainBrain DiseasesBrain MappingBrain imagingBrain regionClinical ResearchCodeCognitionColorComputer SimulationCouplingDataDiagnosisDiffusion Magnetic Resonance ImagingElectrocorticogramElectroencephalographyElectrophysiology (science)EpilepsyEvaluationFingerprintFrequenciesFunctional Magnetic Resonance ImagingGoalsHumanImageImaging DeviceImaging TechniquesImplantImplanted ElectrodesIndividualJointsMagnetic Resonance ImagingMagnetoencephalographyMapsMeasuresMental HealthMental disordersMethodologyMethodsModelingMultimodal ImagingNeuronsNeurosciencesOperative Surgical ProceduresOutcomePatientsPatternPerceptionPreventionResearchResolutionSamplingSignal TransductionStructureTailTechniquesTestingTimeVariantbaseconnectomeexpectationimaging approachimaging modalityimprovedmental disorder diagnosismental disorder preventionmultimodalityneural circuitneural patterningneuroimagingneuroinformaticsnon-invasive imagingnovelpublic health relevancerelating to nervous systemspatiotemporaltemporal measurementtooltractographyvirtual
中文摘要
描述(申请人提供):大量大脑区域之间的动态相互作用产生了人类所有的感知、认知和行为。人们越来越认识到,大多数精神障碍是由分布式神经回路的中断引起的,其结构和功能仍然鲜为人知。因此,绘制人脑网络的解剖和动力学图对于我们理解各种人类行为和精神疾病的机制至关重要。然而,现有神经记录和成像技术的技术限制阻碍了这一领域的重大进展。到目前为止,还没有一种单一的非侵入性神经成像技术能够提供全脑神经元相互作用的完整时空模式。迫切需要建立新的具有高空间和时间分辨率的非侵入性成像方法,以揭示正常和疾病大脑中的神经回路动力学。为了满足这一关键需求,我们建议建立和验证一种新的多模式高光谱成像(MHI)技术,该技术基于功能磁共振成像(FMRI)和脑电(EEG)的同时采集和联合分析,以允许在整个脑动力学频谱上以特定频率的高分辨率映射大脑活动和连接。这项与扩散磁共振成像(DMRI)相结合的独特技术将立即可用于创建显著丰富的人脑连接体,该连接体不仅将描绘解剖特定大脑区域之间的详细连接,还将为每个区域和每个连接分配颜色编码的“光谱特征”,表明它们在全脑神经回路的不同神经元时间尺度上参与分布式网络活动的不同程度。为了达到这一目标,我们建议实现三个具体目标。1)我们将基于虚拟大脑(TVB)这一模拟全脑网络动力学的神经信息平台,通过逼真的计算模拟来开发和优化MHI。2)我们将结合MHI和dMRI,创建一种光谱颜色编码的人类连接体,既需要结构连接,也需要功能连接。3)我们将验证MHI成像的皮质活性和连接性,并与使用植入硬膜下格栅进行神经外科手术的同一组癫痫患者的皮层脑电(ECoG)直接测量的结果进行比较。这项拟议研究的结果将提供一种新的成像工具,揭示准确评估大脑功能和识别诊断精神障碍的生物标记物的网络基础。该项目将对描绘大脑的结构和功能连通性产生显著的积极影响,为更好地了解和诊断精神健康铺平道路,并显著有助于精神障碍的治疗和预防。
英文摘要
DESCRIPTION (provided by applicant): Dynamic interactions among large sets of brain regions produce all human perception, cognition and behavior. It is increasingly recognized that most mental disorders are caused by disruptions of distributed neural circuits, the structure and function of which still remain poorly known. Therefore, mapping the anatomy and dynamics of human brain networks is critical for us to understand the mechanisms underlying a variety of human behaviors and mental illness. However, significant progress in this area is hindered by technical limitations of existing neural recording and imaging techniques. To date, there is no single non-invasive neuroimaging technique ca- pable of providing a complete spatiotemporal pattern of whole-brain neuronal interactions. There is a critical need to establish new non-invasive imaging methods with high spatial and temporal resolution to uncover neural circuit dynamics in normal vs. diseased brains. To meet this critical need, we propose to establish and validate a novel multimodal hyperspectral imaging (MHI) technique, based on simultaneous acquisition and joint analysis of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), to permit high- resolution mapping of brain activity and connectivity at specific frequencies over the full spectrum of brain dynamics. This unique technique combined with diffusion MRI (dMRI) will be immediately usable to create a significantly enriched human brain connectome that will not only depict detailed connections among anatomically specific brain regions, but also assign to each region and each connection color-coded "spectral signatures" indicating their differential degrees of involvement in distributed network activities over various neuronal time scales across whole-brain neural circuits. To achieve this objective, we propose to accomplish three specific aims. 1) We will develop and optimize MHI through realistic computation simulations based on the virtual brain (TVB), a neuroinformatic platform to simulate the whole-brain network dynamics. 2) We will combine MHI and dMRI tractography to create a spectrally color-coded human connectome that entails both structural and functional connectivity. 3) We will validate the cortical activity and connectivity imaged with MHI against those directly measured with electrocorticography (ECoG) from the same group of epilepsy patients undergoing neuro- surgical evaluation with implanted subdural grids. The outcome from the proposed research will provide a new imaging tool to uncover the network basis for accurately assessing brain functions and identifying biomarkers for diagnosis of mental disorders. This project will have a significantly positive impact in delineating the brain's structural and functional connectivity, paving the way for better understanding and diagnosis of mental health, and significantly aid treatment and prevention of mental disorders.
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