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

个体认知能力的差异最终源于大脑功能的差异。最近的研究已经能够从功能磁共振成像（rs-fMRI）在休息时获得的大脑区域之间的连接模式，在一定程度上预测个体的智力差异。然而，目前尚不清楚这些发现的可靠性、可重复性以及在多大程度上可以推广到其他样本。这些问题也限制了我们对驱动这些预测的神经成像数据的理解；例如，是否有特定的大脑区域，或者处理数据的特定方式产生影响？为了解决这些问题，该项目将从一个成功的初步发现开始，在一个大数据集（人类连接组项目数据，HCP）中，用一种特定的方法预测rs-fMRI的智力。基于这一初步发现，一系列的研究目标将调查不同类型的分析如何产生不同的结果，这些发现在统计上的可靠性如何，它们的重复性如何，以及它们如何推广到HCP以外的数据库。这些发现对所有在这一领域工作的科学家来说都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。所有工作将采用开放科学实践，包括但不限于数据和软件的预注册和数据共享。该项目利用了人类连接组项目数据集（HCP）中静息状态功能磁共振成像（rs-fMRI）数据预测一般智力(g)的最新成功。其主要目的是调查这一发现的可靠性、可重复性和普遍性。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智力预测的影响。目的是量化该处理决策树中关键交叉点的选择如何影响最终预测结果。这项调查将为可能的处理管道提供有价值的清单，以及不同参数选择的差异；以产生单一的“最佳”分析选择组合为目标；并探索大脑的哪些解剖区域和网络可以最好地预测智力。第二个目标是增加图形理论总结特征和外部驱动的大脑状态，以提高HCP对智力的预测。从rs- fmri或任务- fmri中得到的特征能产生更好的预测吗？从每个范例中独立建立的模型，针对不同的任务，是否指向共享的解剖区域？结合两种范式的特征，我们能得到的最佳预测是什么？为了调查所获得结果的普遍性，结果将在三个独立的数据集上进行复制：增强内森克莱恩研究所-罗克兰样本（NKI-RS；目标1000名参与者，6-85岁），美国国立卫生研究院青少年大脑认知发展数据集（ABCD；目标10000名参与者，9-10岁），以及剑桥老龄化和神经科学中心（Cam-CAN； 700名参与者，18-88岁）。这些预先注册的研究将量化智力的大脑预测因子（对不同的受试者样本和不同的MRI获取方法）的稳健性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.