Individualized spatial topology in functional neuroimaging

项目摘要。神经成像已经做好准备，将在 理解人类行为的神经生理基础，由于 结合改进的分析技术和成像数据的质量。这些 技术的进步使研究人员能够开发出人群水平的多变量模型 功能性大脑代表潜在的行为、表现、临床状态和 预后和其他结果。基于群体的模型可以识别大脑的模式 可以预测行为并解码新的心理状态的活动，或称“签名” 个人，生产可概括的知识和高度可重复性的地图。这些 签名可以捕获具有较大效果大小的行为，并且可以使用和测试 跨研究小组。然而，此类签名的潜力受到以下因素的限制 神经解剖学限制，特别是脑功能解剖学中的个体差异。 为了绕过这个问题，当前的模型要么只适用于个人 参与者，严重限制了概括性，或迫使参与者的数据进入解剖学 不尊重单个功能拓扑的参考空间(地图集) 边界。在这里，我们试图通过开发新的拓扑结构来克服这一缺点 受试者间对齐的模型，将参与者的功能脑图登记到 彼此之间。这将提高有效的空间分辨率，更重要的是允许我们 显式分析功能图的空间拓扑结构 不同人和不同心理状态的激活位置和形状的差异。 我们将使用专门设计的实验(n=120)来测试和验证这些方法。 这包括两种类型的自然主义叙事体验(电影和音频故事) 以及来自三个功能域(疼痛、情感和认知)的任务。这些任务包括 在设计时考虑到几个限制，包括：(1)系统地覆盖 认知、情绪和感觉任务在刺激属性(例如，刺激)上匹配 持续时间)；以及(2)每个任务内的多个任务需求级别，以允许参数 需求水平的建模和预测。我们将把我们的新方法与现有的方法进行比较 基于样本外效应大小的行为预测和重测方法 可靠性。我们将向其他用户提供分析方法、软件和数据集 研究人员，以及一个功能参考空间图书馆，为多个心理学 各州。

Project Summary. Neuroimaging is poised to take a substantial leap forward in understanding the neurophysiological underpinnings of human behavior, due to a combination of improved analytic techniques and the quality of imaging data. These advances are allowing researchers to develop population-level multivariate models of the functional brain representations underlying behavior, performance, clinical status and prognosis, and other outcomes. Population-based models can identify patterns of brain activity, or `signatures', that can predict behavior and decode mental states in new individuals, producing generalizable knowledge and highly reproducible maps. These signatures can capture behavior with large effect sizes, and can be used and tested across research groups. However, the potential of such signatures is limited by neuroanatomical constraints, in particular individual variation in functional brain anatomy. To circumvent this problem, current models are either applied only to individual participants, severely limiting generalizability, or force participants' data into anatomical reference spaces (atlases) that do not respect individual functional topology and boundaries. Here we seek to overcome this shortcoming by developing new topological models for inter-subject alignment, which register participants' functional brain maps to one another. This will increase effective spatial resolution, and more importantly allow us to explicitly analyze the spatial topology of functional maps make inferences on differences in activation location and shape across persons and psychological states. We will test and validate the methods using a purpose-designed experiment (n = 120) that includes two types of naturalistic narrative experiences (movies and audio stories) and tasks from three functional domains (pain, emotion, and cognition). The tasks are designed with several constraints in mind, including: (1) systematic coverage of cognitive, emotional, and sensory tasks matched in stimulus properties (e.g., stimulus duration); and (2) multiple levels of task demand within each task, to permit parametric modeling and prediction of demand levels. We will compare our new methods to existing methods based on out-of-sample effect sizes in predicting behavior and test-retest reliability. We will make the analytic methods, software, and dataset available to other researchers, along with a library of functional reference spaces for multiple psychological states.