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

与受控行为相关的人类功能性大脑网络个体差异的来源和后果

• 批准号: 10382083
• 负责人: Caterina Gratton
• 金额: $ 17.03万
• 依托单位: NORTHWESTERN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10382083
• 关键词: Address; Administrative Supplement; Adult; Affect; Age; Award; Behavior; Behavior Control; Behavioral; Brain; Brain region; Data; Data Collection; Functional Magnetic Resonance Imaging; Goals; Heterogeneity; Human; Individual; Individual Differences; Link; Location; Longevity; Magnetic Resonance Imaging; Measurement; Measures; Mental Depression; Mental disorders; Methods; National Institute of Mental Health; Neurobiology; Neuropsychology; Parents; Participant; Pattern; Performance; Population; Property; Quality of life; Rest; Sampling; Schizophrenia; Source; Symptoms; Testing; Time; Variant; age group; behavior measurement; cognitive control; data acquisition; denoising; endophenotype; executive function; falls; improved; middle age; preservation; trait; young adult

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.

项目摘要 人类大脑的大规模网络可以使用功能性磁共振成像（fMRI）进行非侵入性测量。虽然以前的大多数工作都集中在功能网络的群体描述上，但最近的研究结果表明，对高样本单个参与者的研究可以揭示个体大脑组织的新方面。在这里，我们专注于非典型的位置，个人的功能网络不匹配的组，我们称之为网络变体。初步数据表明，网络变体存在于所有个体中，但在位置，数量和网络分配方面有所不同。变异体通常与大脑中与目标导向的“受控”处理有关的系统有关。这一观察结果很有趣，因为控制功能的个体差异很大，而且是可遗传的，在极端情况下，这种差异可能是精神分裂症等疾病病理学的核心因素。基于这些初步发现，我们开发了一个模型，其中我们建议稳定因素（例如，遗传学，长期经验）重新确定皮层区域功能的优先级，导致网络变体的创建，改变任务激活和行为。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。鉴于变异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。我们将测试以下假设：（目的1）变异代表大脑组织中个体差异的稳定的、可遗传的内表型，（目的2）变异与控制任务中大脑激活的个体差异有关，（目的3）变异与控制任务中行为的个体差异有关。在目标1中，我们提出通过测量跨状态的变异稳定性以及不相关个体、单卵双胞胎和双卵双胞胎之间的变异模式的相似性来解决变异的性状样性质。在目标2中，我们提出使用精确的fMRI方法来测量跨一系列控制相关的任务上下文的变体激活。最后，在目标3中，我们建议检查变体是否与控制相关行为的差异有关。这一建议具有创新性：它采用了尖端的方法来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），并提供了一个新的窗口，以了解大脑组织，激活和行为中个体差异的可能机制。这项提议将影响（1）基础科学，通过扩大我们对大脑网络中个体差异及其与大脑功能和行为关系的理解，以及（2）转化研究，通过为研究精神病理学中发现的控制中的极端形式的个体差异奠定基础，可能在未来的个性化医疗中发挥作用。因此，该提案通过调查（1）多个水平（大脑组织，生理学和行为）的个体差异，（2）个体差异的遗传和环境来源，以及（3）与精神病理学相关的维度个体差异的潜在生物标志物来解决RDoC目标。