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

项目摘要 使用功能磁共振成像(FMRI)可以非侵入性地测量人脑的大规模网络。虽然之前的大多数工作都集中在对功能网络的群体描述上，但最近的研究结果表明，对高抽样的单个参与者的研究可以揭示特定于个人的大脑组织的新方面。在这里，我们关注的是个体的功能网络与群体不匹配的非典型位置，我们称之为网络变体。初步数据显示，网络变体存在于所有个体中，但在位置、数量和网络分配上有所不同。变体最常与大脑系统联系在一起，这些系统与目标导向的“受控”加工有关。这一观察结果很耐人寻味，因为众所周知，控制功能的个体差异是巨大的和可遗传的，在极端情况下，可能是精神分裂症等疾病的病理学的核心贡献。基于这些初步发现，我们开发了一个模型，在该模型中，我们认为稳定的因素(例如，遗传、长期经验)重新排列了皮质区域功能的优先顺序，导致了网络变体的创建、任务激活的改变和行为。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。鉴于变量与受控任务相关的区域最相关，我们将测试重点放在与控制相关的激活和行为上。我们将检验以下假设：(目标1)变量代表稳定的、可遗传的、反映大脑组织个体差异的内表型，(目标2)变量与控制任务中大脑激活的个体差异有关，以及(目标3)变量与控制任务中的行为个体差异有关。在目标1中，我们建议通过测量跨州的变异稳定性以及不相关的个体、同卵双胞胎和异卵双胞胎的变异模式的相似性来解决变异的性状性质。在目标2中，我们建议使用精确的fMRI方法来测量一系列与控制相关的任务上下文中的不同激活。最后，在目标3中，我们建议研究变量是否与控制相关行为的差异有关。这一建议是创新的：它采用尖端方法可靠地表征单个个体的网络，以研究大脑网络的非典型组成部分(而不是群体描述)，并为揭示大脑组织、激活和行为中个体差异的可能机制提供了一个新的窗口。这一提议将影响(1)基础科学，通过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的理解，以及(2)翻译研究，通过为研究在精神病理学中发现的极端形式的个体控制差异奠定基础，并可能在未来的个性化医学中发挥作用。因此，这项建议通过调查(1)多个水平上的个体差异(大脑组织、生理和行为)，(2)个体差异的遗传和环境来源，以及(3)与精神病理学有关的维度个体差异的潜在生物标记物，来解决RDoC的目标。

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.