Structural Information Framework for Brain Mapping

描述（由申请人提供）：在过去的几十年里，用于研究活体人类大脑功能的技术激增，其中一个特别受益的神经科学领域是语言研究。诸如PET、EEG、MEG、单细胞记录和皮层刺激映射（CSM）等方法正在提供对语言功能的越来越多的了解。然而，每种方法都显示了功能激活的不同方面，常常导致看似矛盾的测量结果。我们需要的是整合和可视化这些不同形式的数据的方法，作为语言功能计算模型的第一步。然而，为了进行这些比较，一个实验室自己收集所有数据是不可能的，也是多余的，目前很难从其他实验室获得可比数据，这既是因为解剖变异和语言任务协议的非标准化等技术因素，由于涉及隐私和知识产权的社会学因素。在本申请中，我们通过扩展和推广我们现有的方法来满足这些需求。开发用于围绕神经解剖学框架为单个实验室组织神经外科语言数据。我们将1）开发一个时空实验管理系统（STEMS），处理复杂的多分辨率脑图数据，2）开发人类和计算机与数据库交互的方法，包括时空结果的三维可视化，和3）开发方法的对等共享独立维护的时空实验管理系统。我们的假设是，这种“自下而上”为各个实验室开发STEMS，然后在对等数据库中将它们互连的方法，是实现人脑项目开发原始神经科学数据的互操作数据库的目标的可行方法。我们将通过构建至少两个这样的STEMS来测试这一假设，并评估它们在生成神经科学结果方面的效用，无论是单独还是组合，如果没有它们，这些结果就不容易生成。

DESCRIPTION (provided by applicant): The past decades have seen a proliferation of techniques for studying the functioning of the living human brain, and one area of neuroscience that has particularly benefited is the study of language. Methods such as PET, EEG, MEG, single cell recording and cortical stimulation mapping (CSM) are providing increasing insight about language function. However, each method shows a different aspect of functional activation, often leading to seemingly contradictory measurements. What is needed are methods for integrating and visualizing these diverse forms of data as a first step towards computational models of language function. Yet it is impossible and redundant for a single lab to collect all the data themselves in order to make these comparisons, and it is currently very difficult to obtain comparable data from other labs, both because of technical factors such as anatomical variation and non-standardization of language task protocols, and because of sociological factors relating to privacy and intellectual property.In this application we address these needs by extending and generalizing methods we have developed to organize neurosurgical language data for a single lab around a neuroanatomical framework. We will 1) develop a spatio-temporal experiment management system (STEMS) that handles complex multi-resolution brain map data, 2) develop methods for both humans and computers to interact with the database, including 3-D visualization of spatio-temporal results, and 3) develop methods for peer-to-peer sharing of independently maintained spatio-temporal experiment management systems.Our hypothesis is that this "bottom-up" approach of developing STEMSs for individual labs, then interconnecting them in a peer-to-peer database, is a viable approach to achieving a goal of the Human Brain Project to develop interoperable databases of raw neuroscience data. We will test this hypothesis by building at least two such STEMSs, and assessing their utility, both alone and in combination, in generating neuroscience results that would not have been easily generated without them.