Discovery Science of Human Brain Function Across the Lifespan

人类大脑整个生命周期功能的发现科学

• 批准号: 8733854
• 负责人: Michael Peter Milham
• 金额: $ 13.99万
• 依托单位: NATHAN S. KLINE INSTITUTE FOR PSYCH RES
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-20 至 2015-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8733854
• 关键词: Achievement; Adolescent; Adult; Age; Architecture; Behavioral; Brain; Brain imaging; Child; Cognitive; Commit; Communities; County; Data; Data Collection; Data Set; Development; Distant; Elderly; Enrollment; Functional Magnetic Resonance Imaging; Funding; Goals; Grant; Growth; Human; Image; Individual; Institutes; International; Investigation; Learning; Longevity; Magnetic Resonance Imaging; Measures; Mental disorders; Methods; Metric; Modeling; Molecular Genetics; National Institute of Mental Health; Participant; Pathology; Phase; Phenotype; Process; Psychopathology; Recruitment Activity; Research Personnel; Resources; Rest; Sample Size; Sampling; Schedule; Science; Scientist; Site; Structure; Techniques; Technology; Testing; Update; Variant; Work; age effect; age related; base; brain behavior; data mining; data sharing; density; experience; indexing; innovation; neuroimaging; pediatrician; prospective; prototype; psychologic; relating to nervous system; repository; sex; success; tool

DESCRIPTION (provided by applicant): Faced with the challenge of charting brain function and the origins of mental illness across the lifespan, the functional neuroimaging community is following the precedent set by molecular genetics in turning to discovery science. Once a distant goal, the 1000 Functional Connectomes Project (FCP) demonstrated the feasibility of conducting discovery science for human brain function. Capitalizing on the ease of data-sharing with resting state fMRI (R-fMRI), the FCP pooled datasets from over 1200 individuals independently collected at sites throughout the world. The FCP dataset immediately demonstrated the power of large-scale R-fMRI investigations, revealing widespread differences in the brain's intrinsic functional architecture related to sex and age that are not easily detected with typical sample sizes (e.g., n = 20-100). The FCP effort represented the inauguration of effective, open data-sharing, with researchers who once struggled to obtain 20-30 datasets for analyses suddenly granted access to over 1200 datasets for methods innovation and data mining. Building on the initial success of the FCP, we recently called for the establishment of prospective data-sharing, using the pilot NKI-Rockland Sample, a phenotypically rich, life-span sample, as our prototype. With more than 300 phenotypic variables obtained across 26 psychiatric, behavioral, and cognitive assessment tools, the NKI- Rockland Sample exemplifies they type of data collection model ideally suited to allow neuroimaging methods to employ the data mining and discovery approach applied successfully in molecular genetics. Neuroimagers can now identify brain-behavior relationships and delineate their dynamic trajectories across the lifespan. Having established the feasibility of generating phenotypically rich datasets and openly sharing them with the scientific community on a prospective basis, the proposed work aims to generate a more carefully constructed lifespan sample, larger in scale, maximally representative of the community and sampled using appropriate strategies for the delineation of normative trajectories for metrics of the brain's intrinsic functional architecture. Specifically, we propose to recruit 1000 participants (ages 6-85) over a four-year period, densely sampling early developmental and advanced aging periods, where age-related gradients of changes are maximal and model-fitting techniques are most prone to artifactual results. Recruitment and enrollment strategies will be carefully controlled to maximize the community representativeness of the sample and minimize biases commonly encountered with opportunistic recruiting. Seventy percent of the collected datasets will be randomly selected for discovery, while the remaining 30% will serve to rigorously test hypotheses generated during the discovery phase. Consistent with the model established for the pilot NKI-Rockland Sample, data generated as part of this proposal will be shared prospectively, on a weekly basis, via the FCP and the associated International Neuroimaging Data-sharing Initiative (INDI).

描述（由申请人提供）：面对绘制大脑功能的挑战和整个生命周期中精神疾病的起源，功能性神经影像社区正在遵循分子遗传学在转向发现科学方面的先例。一旦一个遥远的目标，1000个功能连接求解项目（FCP）证明了对人脑功能进行发现科学的可行性。利用静止状态fMRI（R-FMRI）的数据共享的便利性，FCP汇总了来自全世界现场独立收集的1200多个人的数据集。 FCP数据集立即证明了大规模R-FMRI研究的力量，揭示了与性别和年龄相关的大脑固有功能结构的广泛差异，而这些功能和年龄与典型样本量不易被检测到（例如n = 20-100）。 FCP的工作代表了有效，开放数据共享的就职典礼，他们曾经努力获得20-30个数据集进行分析的研究人员突然允许访问超过1200个数据集以进行方法创新和数据挖掘。在FCP的最初成功的基础上，我们最近呼吁建立前瞻性数据共享，使用Pilot Nki-Rockland样本（一种表型丰富的寿命样本）作为我们的原型。 Nki-Rockland样本在26个精神病，行为和认知评估工具中获得了300多个表型变量，nki-Rockland样本典范它们类型的数据收集模型非常适合允许神经影像学方法采用数据挖掘和在分子遗传学中成功应用的数据挖掘和发现方法。神经模子现在可以识别脑行为关系，并在整个生命周期中描述其动态轨迹。在建立了生成表型富含数据集并在预期的基础上与科学界公开共享的可行性，拟议的工作旨在生成更精心构建的寿命样本，规模较大，最大程度地代表了社区，并使用适当的策略对规范性轨迹进行了适当的策略来对规范性轨迹进行规范性轨迹，以实现大脑的脑构建型脑构造。具体而言，我们建议在四年期间招募1000名参与者（6-85岁），并密集地采样早期的发育和高级衰老期，其中与年龄相关的变化梯度是最大和模型拟合技术，最容易易于人工结果。将仔细控制招聘和入学策略，以最大程度地控制样本的社区代表性，并最大程度地减少机会招聘通常遇到的偏见。 70％的收集数据集将随机选择进行发现，而其余的30％将用于在发现阶段生成的严格检验假设。与为NKI-Rockland样本建立的模型一致，作为该提案的一部分生成的数据将每周通过FCP和相关的国际神经影像学计划（INDI）预期共享。