Sources and functional consequences of individual differences in human functional brain networks related to controlled behavior

项目摘要 使用功能磁共振可以非侵入性地测量人脑的大规模网络 成像(FMRI)。虽然之前的大多数工作都集中在功能网络的群组描述上，但最近 研究结果表明，对高抽样率的单一参与者的研究可以揭示大脑的新方面 特定于个人的组织。在这里，我们关注的是个体功能不正常的非典型位置 网络与组不匹配，我们称之为网络变体。初步数据表明，网络 所有个体都有变种，但在位置、编号和网络分配上有所不同。变种是 最常与大脑系统联系在一起，与目标导向的“受控”加工有关。这一观察结果 耐人寻味，因为控制功能的个体差异已知是巨大的和可遗传的，并且在 极端病例可能是精神分裂症等疾病病理的核心贡献。基于这些 初步发现，我们开发了一个模型，在该模型中，我们认为稳定因素(例如，遗传、长期 经验)重新划分皮质区域的功能优先级，导致创建网络变体、更改任务 激活和行为。我们的目标是通过检查变异的来源和后果来测试这个模型。 鉴于变体与受控任务相关的区域最相关，我们将测试的重点放在控制上- 相关激活和行为。我们将检验以下假设：(目标1)变异代表稳定， 大脑组织中个体差异的可遗传、内表型(目标2)变异与个体有关 控制任务中大脑激活的差异，以及(目标3)变量与个体行为的差异有关 在控制任务中。在目标1中，我们建议通过测量变异的稳定性来解决变异的类似特征的性质 跨州，以及不相关个体、同卵双胞胎和同卵双胞胎之间的变异模式的相似性。在……里面 目的2，我们建议使用一种精确的fMRI方法来测量一系列对照的不同激活- 相关任务上下文。最后，在目标3中，我们建议检查变异是否与 与控制相关的行为。这一提议是创新的：它采用了尖端的方法来可靠地表征 单个个体的网络，用于研究大脑网络的非典型组成部分(而不是群体描述) 并为了解大脑组织中潜在的个体差异的可能机制提供了新的窗口， 激活和行为。这一建议将影响(1)基础科学，扩大我们对 大脑网络中的个体变异性及其与大脑功能和行为的关系，以及(2)翻译 研究，通过为研究发现的极端形式的个体控制差异奠定基础 精神病理学，未来有可能在个性化医学中发挥作用。因此，本提案涉及RDoC 通过调查(1)多个层次(大脑组织、生理和行为)的个体差异来实现目标， (2)个体差异的遗传和环境来源；(3)潜在的维度生物标志物 与精神病理学有关的个体差异。

Project Summary Large-scale networks of the human brain can be measured non-invasively using functional Magnetic Resonance Imaging (fMRI). While most previous work has focused on group descriptions of functional networks, recent findings suggest that the study of highly-sampled single participants can reveal novel aspects of brain organization specific to an individual. Here, we focus on atypical locations where an individual’s functional networks do not match the group, which we call network variants. Preliminary data demonstrates that network variants are present across all individuals, but differ in location, number, and network assignment. Variants are most often associated with systems of the brain linked to goal-directed “controlled” processing. This observation is intriguing, given that individual differences in control functions are known to be large and heritable, and in extreme cases can be central contributions to pathology in disorders such as schizophrenia. Based on these preliminary findings, we develop a model, wherein we suggest that stable factors (e.g., genetics, long-term experience) reprioritize the functions of cortical areas, leading to the creation of network variants, altered task activations, and behavior. Our goal is to test this model by examining the sources and consequences of variants. Given that variants are most associated with regions related to controlled tasks, we focus our tests on control- related activations and behavior. We will test the following hypotheses: (Aim 1) variants represent stable, heritable, endophenotypes for individual differences in brain organization, (Aim 2) variants relate to individual differences in brain activations in control tasks, and (Aim 3) variants relate to individual differences in behavior in control tasks. In Aim 1 we propose addressing the trait-like nature of variants by measuring variant stability across states, and the similarity of variant patterns across unrelated individuals, mono-, and dizygotic twins. In Aim 2, we propose using a precision fMRI approach to measure variant activations across a range of control- related task contexts. Finally, in Aim 3 we propose examining whether variants are related to differences in control-related behavior. This proposal is innovative: it adopts cutting-edge methods for reliably characterizing networks in single individuals to study atypical components of brain networks (rather than group descriptions) and provides a new window into possible mechanisms underlying individual differences in brain organization, activations, and behavior. This proposal will impact (1) basic science, by expanding our understanding of individual variability in brain networks and their relationship to brain function and behavior, and (2) translational research, by laying groundwork for the study of extreme forms of individual differences in control found in psychopathology, potentially with future utility in personalized medicine. Thus, this proposal addresses RDoC goals by investigating (1) individual differences at multiple levels (brain organization, physiology, and behavior), (2) genetic and environmental sources for individual differences, and (3) potential biomarkers of dimensional individual differences linked to psychopathology.