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

NeuroDataRR：通过静息态 fMRI 预测智力：分区、管道和模型

• 批准号: 1840756
• 负责人: Ralph Adolphs
• 金额: $ 57.9万
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1840756&HistoricalAwards=false
• 关键词: NeuroDataRR; Predicting; intelligence; resting; state

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.

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预测，在一定程度上，从大脑区域之间的连接模式，在休息与功能磁共振成像（rs-fMRI）的个体差异的智力。 然而，目前还不清楚这些发现的可靠性如何，它们的复制效果如何，以及它们在多大程度上推广到其他样本。 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内容的理解;例如，是否存在特定的大脑区域，或者数据处理的特定方式？ 为了解决这些问题，该项目将开始与一个成功的初步发现，预测智力从rs-fMRI与特定的方法，在一个大的数据集（人类连接组计划数据，HCP）。 在这一初步发现的基础上，一系列的研究目标将研究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，也将对有关智力神经基础的重要问题提供初步答案。所有工作将使用开放科学的做法，包括但不限于预注册和数据共享的数据和software.This项目利用最近成功预测一般智力（g）从静息态功能磁共振成像（rs-fMRI）数据在人类连接组项目数据集（HCP）。 其主要目的是调查这一发现的可靠性、可重复性和可推广性。第一个目标是量化大脑对齐，rs-fMRI去噪，大脑分割和模型学习策略对HCP中rs-fMRI智能预测的影响。 该目标将量化该处理决策树中关键交叉点的选择如何影响最终的预测结果。这项研究将提供一个有价值的清单，可能的处理管道，不同的参数选择的差异，旨在产生一个单一的“最佳”组合的分析选择，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形理论的总结功能和外部驱动的大脑状态，以提高对HCP的智力预测。 从rs-或task-fMRI得到的特征是否能产生更好的预测？从每个范例中单独构建的模型，以及针对不同任务的模型，是否指向共享的解剖区域？结合这两种范式的特征，我们能得到的最佳预测是什么？ 为了研究所得结果的普遍性，将在三个独立的数据集上复制结果：增强型Nathan Kline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）。这些预先注册的研究将量化大脑预测智力的可靠性（针对不同的受试者样本和不同的MRI采集方法）。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

No strong evidence that social network index is associated with gray matter volume from a data-driven investigation.

• DOI: 10.1016/j.cortex.2020.01.021
• 发表时间: 2020-04
• 期刊: Cortex; a journal devoted to the study of the nervous system and behavior
• 作者: Lin C;Keles U;Tyszka JM;Gallo M;Paul L;Adolphs R
• 通讯作者: Adolphs R

Intrinsic Functional Connectivity of the Brain in Adults with a Single Cerebral Hemisphere

• DOI: 10.1016/j.celrep.2019.10.067
• 发表时间: 2019-11-19
• 期刊: CELL REPORTS
• 影响因子: 8.8
• 作者: Kliemann, Dorit;Adolphs, Ralph;Paul, Lynn K.
• 通讯作者: Paul, Lynn K.

Personality beyond taxonomy

超越分类的人格

• DOI: 10.1038/s41562-020-00989-3
• 发表时间: 2020
• 期刊: Nature Human Behaviour
• 影响因子: 29.9
• 作者: Dubois, Julien;Eberhardt, Frederick;Paul, Lynn K.;Adolphs, Ralph
• 通讯作者: Adolphs, Ralph