Murugesan Sugeerth, Bouchard Kristofer, Brown Jesse A, Hamann Bernd, Seeley William W, Trujillo Andrew, Weber Gunther H
IEEE/ACM Trans Comput Biol Bioinform. 2017 Jul-Aug;14(4):805-818. doi: 10.1109/TCBB.2016.2564970. Epub 2016 May 9.
We present Brain Modulyzer, an interactive visual exploration tool for functional magnetic resonance imaging (fMRI) brain scans, aimed at analyzing the correlation between different brain regions when resting or when performing mental tasks. Brain Modulyzer combines multiple coordinated views-such as heat maps, node link diagrams and anatomical views-using brushing and linking to provide an anatomical context for brain connectivity data. Integrating methods from graph theory and analysis, e.g., community detection and derived graph measures, makes it possible to explore the modular and hierarchical organization of functional brain networks. Providing immediate feedback by displaying analysis results instantaneously while changing parameters gives neuroscientists a powerful means to comprehend complex brain structure more effectively and efficiently and supports forming hypotheses that can then be validated via statistical analysis. To demonstrate the utility of our tool, we present two case studies-exploring progressive supranuclear palsy, as well as memory encoding and retrieval.
我们展示了Brain Modulyzer,这是一种用于功能磁共振成像(fMRI)脑部扫描的交互式视觉探索工具,旨在分析静息或执行心理任务时不同脑区之间的相关性。Brain Modulyzer结合了多个协同视图,如热图、节点链接图和解剖视图,通过刷选和链接为脑连接数据提供解剖学背景。整合来自图论和分析的方法,例如社区检测和派生图度量,使得探索功能性脑网络的模块化和层次化组织成为可能。通过在更改参数时即时显示分析结果来提供即时反馈,为神经科学家提供了一种更有效、高效地理解复杂脑结构的强大手段,并支持形成可通过统计分析进行验证的假设。为了证明我们工具的实用性,我们展示了两个案例研究——探索进行性核上性麻痹以及记忆编码和检索。