A human brain atlas derived via n-cut parcellation of resting-state and task-based fMRI data.
A human brain atlas derived via n-cut parcellation of resting-state and task-based fMRI data.
复制标题
DOI：
10.1016/j.mri.2015.10.036
复制
发表时间：
2016-02
影响因子：
2.5
通讯作者：
Bush KA
中科院分区：
文献类型：
--
作者：
James GA;Hazaroglu O;Bush KA
功能磁共振成像的发展导致了人脑图谱的发展，这些图谱是通过将静息状态的连接模式划分为功能独立的感兴趣区(ROI)而产生的。到目前为止，所有的功能图谱都是从静息状态的fMRI数据中得出的。但考虑到区域之间的功能连接因任务而异，我们假设，与仅从静息状态派生的图谱相比，同时包含静息状态和基于任务的fMRI数据的图谱将产生对任务相关区域具有更精细描述的图谱。为了验证这一假设，我们从29名参加认知连接项目的健康成年参与者那里获得了分离地图集，该项目旨在通过绘制大脑-行为关系的规范差异图来改进功能磁共振到临床决策的转换。参与者接受了基于休息状态和任务的功能磁共振成像，涉及九个认知领域：运动、视觉空间、注意力、语言、记忆、情感处理、决策、工作记忆和执行功能。使用(1)所有参与者的功能数据(任务)或(2)单一的静息状态扫描(REST)的空间受限的n割分割派生的脑地图集。为了比较的目的，还从随机分割中得出了一份地图集(随机)。比较了两种方法：(1)对组的平均边缘权重进行分割(Mean)，以及(2)两阶段方法，即先分割单个边缘权重，然后再分割平均二值化边缘(两阶段)。由此得到的任务和休息地图集之间的相似性(平均贾卡德指数JI=0.72-0.85)明显高于随机地图集(JI=0.59-0.63；经Bonferroni校正后均为p<；0.001)。两阶段法的任务和休息图谱相似性最大(JI=0.85)，已被证明比平均法更稳健；这些图谱也比随机图谱(r=0.64-0.72)更好地再现了休息和执行n-back工作记忆任务时左侧背外侧前额叶皮质的体素种子图(r=0.75-0.80)，进一步验证了它们的实用性。我们预计控制高级认知的区域(如额叶和前颞叶)在任务和休息地图集之间显示出最大的差异；与预期相反，这些区域在地图集之间具有最大的相似性。我们的发现表明，分离基于任务的和静息状态的fMRI数据得到的图谱具有很高的可比性，现有的静息状态图谱适合于基于任务的分析。我们介绍了一个解剖标记的fMRI衍生的全脑人类图谱，用于未来的认知连接组分析。
The growth of functional MRI has led to development of human brain atlases derived by parcellating resting-state connectivity patterns into functionally independent regions of interest (ROIs). All functional atlases to date have been derived from resting-state fMRI data. But given that functional connectivity between regions varies with task, we hypothesized that an atlas incorporating both resting-state and task-based fMRI data would produce an atlas with finer characterization of task-relevant regions than an atlas derived from resting-state alone. To test this hypothesis, we derived parcellation atlases from twenty-nine healthy adult participants enrolled in the Cognitive Connectome project, an initiative to improve functional MRI’s translation into clinical decision-making by mapping normative variance in brain-behavior relationships. Participants underwent resting-state and task-based fMRI spanning nine cognitive domains: motor, visuospatial, attention, language, memory, affective processing, decision-making, working memory, and executive function. Spatially constrained n-cut parcellation derived brain atlases using (1) all participants’ functional data (Task) or (2) a single resting-state scan (Rest). An atlas was also derived from random parcellation for comparison purposes (Random). Two methods were compared: (1) a parcellation applied to the group’s mean edge weights (mean), and (2) a two-stage approach with parcellation of individual edge weights followed by parcellation of mean binarized edges (two-stage). The resulting Task and Rest atlases had significantly greater similarity with each other (mean Jaccard indices JI= 0.72–0.85) than with the Random atlases (JI=0.59–0.63; all p<0.001 after Bonferroni correction). Task and Rest atlas similarity was greatest for the two-stage method (JI=0.85), which has been shown as more robust than the mean method; these atlases also better reproduced voxelwise seed maps of the left dorsolateral prefrontal cortex during rest and performing the n-back working memory task (r=0.75–0.80) than the Random atlases (r=0.64–0.72), further validating their utility. We expected regions governing higher-order cognition (such as frontal and anterior temporal lobes) to show greatest difference between Task and Rest atlases; contrary to expectations, these areas had greatest similarity between atlases. Our findings indicate that atlases derived from parcellation of task-based and resting-state fMRI data are highly comparable, and existing resting-state atlases are suitable for task-based analyses. We introduce an anatomically labeled fMRI-derived whole-brain human atlas for future Cognitive Connectome analyses.