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Sources and Functional 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大脑网络个体差异的来源和功能后果

基本信息

批准号：
10636946
负责人：
Caterina Gratton
金额：
$ 57.95万
依托单位：
FLORIDA STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10636946
关键词：
Address Adopted Affect Area Attention deficit hyperactivity disorder Basic Science Behavior Behavior Control Brain Cognition Complex Cues Data Dimensions Disease Dizygotic Twins Ensure Exhibits Functional Magnetic Resonance Imaging Future Genetic Goals Heritability Human Individual Individual Differences Life Link Location Maintenance Measurement Measures Mental Depression Mental disorders Methods Modeling Nature Participant Pathology Pattern Performance Phenotype Physiology Population Psychopathology Research Research Domain Criteria Rest Sampling Schizophrenia Source System Testing Time Translational Research Twin Multiple Birth Update Variant Work cognitive control connectome endophenotype executive function experience indexing individual variation innovation network architecture neural neuromechanism novel personalized approach personalized medicine potential biomarker response trait translational applications

项目摘要

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.

项目摘要使用功能性磁共振可以非侵入性地测量人类大脑的大规模网络成像（fMRI）。虽然大多数以前的工作都集中在功能网络的组描述，研究结果表明，对高采样的单个参与者的研究可以揭示大脑的新方面，组织具体到个人。在这里，我们专注于非典型的地方，个人的功能网络不匹配组，我们称之为网络变体。初步数据显示，变异存在于所有个体中，但在位置、数量和网络分配上不同。变体大多数情况下与大脑系统有关，这些系统与目标导向的“受控”处理有关。该观察结果是有趣的，鉴于控制功能的个体差异是已知的大和遗传，极端病例可能是精神分裂症等疾病病理学的主要贡献。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。鉴于变体与受控任务相关的区域最相关，我们将测试重点放在控制上- 相关的激活和行为。我们将测试以下假设：（目标1）变体代表稳定，脑组织中个体差异的可遗传的内表型，（目标2）与个体相关的变异控制任务中大脑激活的差异，以及（目标3）与行为个体差异相关的变体在控制任务。在目标1中，我们提出通过测量变体稳定性来解决变体的性状样性质跨州，以及不相关个体，单卵双胞胎和双卵双胞胎之间变异模式的相似性。在目标2，我们建议使用精确的fMRI方法来测量控制范围内的变体激活，相关的任务上下文。最后，在目标3中，我们建议检查变体是否与控制相关的行为。该提案具有创新性：它采用先进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，以了解大脑组织中个体差异的潜在机制，激活和行为。这一建议将影响（1）基础科学，通过扩大我们对大脑网络的个体差异及其与大脑功能和行为的关系，以及（2）翻译研究，通过为研究控制中发现的极端形式的个体差异奠定基础，精神病理学，未来可能用于个性化医疗。因此，本提案涉及RDoC 通过调查（1）多层次（大脑组织，生理和行为）的个体差异， (2)个体差异的遗传和环境来源，和（3）潜在的生物标志物的尺寸与精神病理学有关的个体差异