Dynamic Inverse Solutions for Multimodal Imaging

描述（申请人提供）：近年来，研究脑功能的成像技术发展迅速。目前许多成像中心的一个重要研究重点是开发功能神经成像工具，利用功能磁共振成像（fMRI）、漫射光学断层扫描（DOT）、脑电图（EEG）和脑磁图（MEG）测量的各种组合来进行多模态图像融合。这需要进行实验，同时或按顺序从两个或多个模式进行成像，以便来自不同来源的信息可以最佳地组合。同时使用两种或两种以上的成像模式提供了在不同空间和时间尺度上跟踪大脑活动动态的令人兴奋的前景。为了响应> PAR-04-023，我们建议在麻省总医院的Athinoula a . Martinos生物医学成像中心建立生物工程研究合作伙伴关系，开发用于融合两种或多种模式成像测量的计算资源。利用EEG、MEG、fMRI和DOT，该合作伙伴关系将基于这些成像模式的生物物理学、生理学和解剖学，开发一个综合的状态空间计算框架。该计算框架的模型组件将通过一系列跨模态实验进行识别和验证。高速超级计算资源将用于设计和测试模拟和实际多模态实验成像数据的状态空间数据分析算法。作为该伙伴关系的一部分开发的数据分析算法以及在跨模态实验中收集的数据将免费传播给脑成像界。该项目的长期目标是为脑成像界提供一个统一的计算框架，用于结合两种或多种成像模式的测量，可用于实时研究和患者的临床管理。脑功能的实时分析对于理解正常脑功能的动态变化，以及这些动态变化在癫痫、阿尔茨海默病、中风和帕金森病等病理条件下的变化，以及在睡眠、麻醉和重症监护病房治疗的患者中监测脑功能具有重要意义。该合作伙伴关系由dr。博阿斯，邦马萨，布朗和哈玛莱宁。总的来说，他们是fMRI （All）、EEG和MEG （Bonmassar、Brown和Hamalainen）和DOT （Boas）多模态组合、跨膜实验、分析和临床应用方面的专家。

DESCRIPTION (provided by applicant): In recent years, there has been rapid progress in the development of imaging technologies to study brain function. An important focus of current research in many imaging centers is the development of functional neural imaging tools to carry out multimodal image fusion using various combinations of functional magnetic resonance imaging (fMRI), diffuse optical tomography (DOT), electroencephalography (EEG) and magnetoencephalography (MEG) measurements. This requires conducting experiments in which imaging is performed from two or more modalities simultaneously or in sequence so that the information from the different sources can be optimally combined. Using two or more imaging modalities simultaneously offers the exciting prospect of tracking the dynamics of brain activity on different spatial and time-scales. In response to > PAR-04-023, we propose to form a Bioengineering Research Partnership at the Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital to develop computational resources for fusing imaging measurements from two or more modalities. Using EEG, MEG, fMRI and DOT, this Partnership will develop an integrated state-space, computational framework based on the biophysics, physiology and anatomy of these imaging modalities. The model components for this computational framework will be identified and validated through a series of cross-modality experiments. High-speed supercomputing resources will be used to design and test the state-space data analysis algorithms on simulated and actual multimodal experimental imaging data. The data analysis algorithms developed as part of this Partnership and the data collected in the cross-modality experiments will be freely disseminated to the brain imaging community. The long-term goals of this project are to provide the brain imaging community with a unified computational framework for combining measurements from two or more imaging modalities that can be used in both real-time research studies and clinical management of patients. Real-time analysis of brain function will have important implications for understanding the dynamics of normal brain function, how these dynamics change in pathological conditions such as epilepsy, Alzheimer's disease, stroke and Parkinson's disease and for monitoring brain function during sleep, under anesthesia and in patients treated in the intensive care unit. The partnership is lead by Drs. Boas, Bonmassar, Brown, and Hamalainen. Collectively, they are experts in the multi-modal combination of fMRI (All), EEG and MEG (Bonmassar, Brown, and Hamalainen), and DOT (Boas), spannin experiments, analysis, and clinical application.