NeuroDataRR: Predicting intelligence from resting-state fMRI: parcellation, pipelines and models

认知能力的个体差异最终源于大脑功能的差异。最近的研究已经能够从通过功能磁共振成像(rs-fmri)获得的静态大脑区域之间的连接模式在某种程度上预测个体智力的差异。然而，目前还不清楚这些发现的可靠性有多高，它们的重复性有多好，以及它们在多大程度上适用于其他样本。这些问题也限制了我们对神经成像数据中驱动这些预测的因素的理解；例如，是否有特定的大脑区域，或数据处理的特定方式产生了影响？为了解决这些问题，这个项目将从一个成功的初步发现开始，在一个大型数据集(人类连接项目数据，HCP)中，用一种特定的方法预测来自RS-fMRI的情报。在这一初步发现的基础上，一系列研究目标将调查不同类型的分析如何产生不同的结果，这些发现在统计上有多可靠，它们的重复性如何，以及它们如何推广到HCP以外的数据库。这些发现将对所有从事这一领域工作的科学家具有很高的方法论价值，也将为有关智力的神经基础的重要问题提供初步答案。所有工作都将使用开放科学做法，包括但不限于数据和软件的预注册和数据共享。该项目利用了最近在从人类连接项目数据集(HCP)的静息功能磁共振(RS-FMRI)数据预测一般智力(G)方面的成功。它的主要目的是调查这一发现的可靠性、重复性和普适性。第一个目标将量化大脑对齐、rs-fmri去噪、大脑分割和模型学习策略对hcp中rs-fmri预测智力的影响。其目的是量化这一处理决策树中关键交叉口的选择如何影响最终的预测结果。这项研究将提供一份关于可能的处理管道以及不同参数选择所产生的差异的宝贵清单；目的是产生一个分析选择的单一“最佳”组合；并探索哪些解剖大脑区域和网络可以最好地预测智力。第二个目标将增加图论总结特征和外部驱动的大脑状态，以提高对HCP中智力的预测。从RS-或任务-fMRI派生的特征能产生更好的预测吗？对于不同的任务，独立于每个范例构建的模型是否指向共享的解剖区域？结合这两种范式的特征，我们能得到的最好预测是什么？为了调查所获得的结果的普适性，结果将在三个独立的数据集上重复：增强型内森-克莱恩研究所-罗克兰样本(NKI-RS；目标1000名参与者，6-85岁)；NIH青少年大脑认知发展数据集(ABCD；目标10,000名参与者，9-10岁)和剑桥老龄化和神经科学中心(CaM-CAN；700名参与者，18-88岁)。这些预先登记的研究将量化大脑对智力的预测能力(针对不同的受试者样本和不同的MRI获取方法)。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Individual differences in cognitive abilities ultimately derive from differences in brain function. Recent studies have been able to predict, to some degree, individual differences in intelligence from the connectivity pattern among brain regions obtained at rest with functional magnetic resonance imaging (rs-fMRI). However, it remains unclear how reliable these findings are, how well they replicate, and to what extent they generalize to other samples. These questions have also limited our understand of what it is in the neuroimaging data that drives these predictions; for example, are there specific brain regions, or specific ways that the data are processed that make a difference? To address these questions this project will begin with a successful initial finding, predicting intelligence from rs-fMRI with a particular approach, in a large data set (the Human Connectome Project data, HCP). Building from this initial finding, a series of research aims will then investigate how different kinds of analyses might yield different results, how statistically reliable the findings are, how well they replicate, and how they generalize to databases other than the HCP. These findings will be high methodological value to all scientists working in this field and will also yield initial answers to important questions regarding the neural basis of intelligence. All work will use open-science practices including but not limited to pre-registration and data sharing of data and software.This project capitalizes on recent success in predicting general intelligence (g) from resting-state fMRI (rs-fMRI) data in the Human Connectome Project dataset (HCP). Its principal aims are to investigate the reliability, reproducibility, and generalizability of this finding. A first aim will quantify the effect of brain alignment, rs-fMRI denoising, brain parcellation, and model-learning strategy on the prediction of intelligence from rs-fMRI in the HCP. The aim will quantify how choices at key intersections in this processing decision tree affect final prediction results. This investigation will provide a valuable inventory of possible processing pipelines, and the difference that different parameter choices make; aim to yield a single "best" combination of analytical choices; and explore which anatomical brain regions, and networks, can best predict intelligence. A second aim will add graph-theoretical summary features and externally driven brain states to improve the prediction of intelligence in the HCP. Do features derived from rs- or task-fMRI yield substantially better predictions? Do models built separately from each paradigm, and for different tasks, point to shared anatomical regions? Combining features from both paradigms, what is the best prediction we can obtain? To investigate the generalizability of results obtained, the results will be replicated across three independent datasets: the Enhanced Nathan Kline Institute - Rockland Sample (NKI-RS; target 1000 participants, 6-85 year-olds), the NIH Adolescent Brain Cognitive Development dataset (ABCD; target 10,000 participants, 9-10 year-olds), and the Cambridge Center for Ageing and Neuroscience (Cam-CAN; 700 participants, 18-88 year-olds). These pre-registered studies will quantify how robust are the brain predictors of intelligence (to different subject samples, and different MRI acquisition methods).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.