Structural Information Framework for Brain Mapping

描述（由申请人提供）：在过去的几十年里，研究活人大脑功能的技术激增，神经科学的一个领域特别受益于语言研究。PET、EEG、MEG、单细胞记录和皮质刺激图谱（CSM）等方法为语言功能研究提供了越来越多的线索。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，作为迈向语言功能计算模型的第一步。然而，为了进行这些比较，单个实验室自己收集所有数据是不可能的，也是多余的，而且目前很难从其他实验室获得可比数据，这既是由于解剖学变异和语言任务协议的非标准化等技术因素，也是由于与隐私和知识产权有关的社会学因素。在这个应用程序中，我们通过扩展和推广我们开发的方法来满足这些需求，这些方法可以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)开发处理复杂多分辨率脑图数据的时空实验管理系统（stem）， 2)开发人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，以及3)开发独立维护的时空实验管理系统的点对点共享方法。我们的假设是，这种“自下而上”的方法，即为单个实验室开发stems，然后将它们连接在一个点对点数据库中，是实现人类大脑计划目标的可行方法，即开发原始神经科学数据的可互操作数据库。我们将通过构建至少两个这样的stems来验证这一假设，并评估它们单独和组合在产生神经科学结果方面的效用，这些结果如果没有它们就不会轻易产生。

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.