具有静止状态fMRI的连通映射
Connectopic mapping with resting-state fMRI
摘要：
大脑区域通常是拓扑连接的：一个大脑区域内的附近位置与另一个区域内的附近位置相连。绘制这些连接拓扑图，或者简称为“连接”，对于理解信息在大脑中是如何处理的至关重要。在这里，我们提出了原则性的，完全数据驱动的方法映射connectopies使用功能性磁共振成像（fMRI）的数据在休息时通过结合光谱嵌入的体素连接的“指纹”与一种新的方法来空间统计推断。我们将该方法应用于人类初级运动皮层和视觉皮层，并表明它可以跟踪生物学上合理的，重叠的连接在个体受试者遵循这些区域的躯体和视网膜的地图。作为一种通用的机制，以执行推理的connectopies，新的空间统计方法，使严格的统计测试的假设有关的细粒度的空间配置文件的功能连接和配置文件是否是不同的科目之间或实验条件。结合的框架提供了一个基本的替代现有的方法来调查功能连接在大脑中，从体素或种子对明智的功能协会的特征，对一个完整的，多变量的空间地形特征。
英文摘要：
Brain regions are often topographically connected: nearby locations within one brain area connect with nearby locations in another area. Mapping these connection topographies, or 'connectopies' in short, is crucial for understanding how information is processed in the brain. Here, we propose principled, fully data-driven methods for mapping connectopies using functional magnetic resonance imaging (fMRI) data acquired at rest by combining spectral embedding of voxel-wise connectivity 'fingerprints' with a novel approach to spatial statistical inference. We applied the approach in human primary motor and visual cortex, and show that it can trace biologically plausible, overlapping connectopies in individual subjects that follow these regions' somatotopic and retinotopic maps. As a generic mechanism to perform inference over connectopies, the new spatial statistics approach enables rigorous statistical testing of hypotheses regarding the fine-grained spatial profile of functional connectivity and whether that profile is different between subjects or between experimental conditions. The combined framework offers a fundamental alternative to existing approaches to investigating functional connectivity in the brain, from voxel- or seed-pair wise characterizations of functional association, towards a full, multivariate characterization of spatial topography.
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