Relating brain anatomy and cognitive ability using a multivariate multimodal framework.
Relating brain anatomy and cognitive ability using a multivariate multimodal framework.
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DOI：
10.1016/j.neuroimage.2014.05.008
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发表时间：
2014-10-01
期刊：
影响因子：
5.7
通讯作者：
Grossman M
中科院分区：
文献类型：
--
作者：
Cook PA;McMillan CT;Avants BB;Peelle JE;Gee JC;Grossman M
将多模态的结构神经成像数据与认知表现联系起来是认知神经科学的重要挑战。在这项研究中，我们检查了54名额颞叶变性（FTD）患者和15名年龄匹配的对照组的语言流畅性表现与神经解剖学之间的关系，所有这些患者都进行了T1和弥散加权成像。我们的目标是将灰质（基于体素的皮质厚度）和白质（分数各向异性）纳入一个与行为表现相关的统计模型中。我们首先使用特征解剖来定义灰质和白质的数据驱动感兴趣区域（dd - roi）。特征解剖是一种多变量降维方法，用于识别空间平滑、无符号的主成分，这些主成分可以解释受试者之间的最大方差。然后，我们使用统计模型选择程序来查看这些dd - roi中哪一个在言语流畅性任务中表现最好，这些任务假设依赖于支持语言的大规模神经网络的不同组成部分：类别流畅性需要语义引导搜索，并且假设主要依赖于支持词汇语义表示的颞叶皮层；字母引导的流利需要战略性的心理搜索，并且假设需要执行资源来支持更苛刻的搜索过程，这取决于前额叶皮层以及支持词汇表征的时间网络组件。我们观察到，这两种类型的语言流利表现都是由一个包括灰质和白质组合的网络来描述的。对于类别流畅性，确定的区域包括双侧颞叶皮层和白质区域，包括左下纵束和额枕束。对于字母流畅性，左颞叶区域和额叶皮质区域也被选中。这些结果与我们假设的语言处理及其在FTD中崩溃的神经解剖学模型一致。我们得出结论，在进行线性回归之前用特征解剖聚类数据是一种很有前途的多模态数据分析工具。
Linking structural neuroimaging data from multiple modalities to cognitive performance is an important challenge for cognitive neuroscience. In this study we examined the relationship between verbal fluency performance and neuroanatomy in 54 patients with frontotemporal degeneration (FTD) and 15 age-matched controls, all of whom had T1- and diffusion-weighted imaging. Our goal was to incorporate measures of both gray matter (voxel-based cortical thickness) and white matter (fractional anisotropy) into a single statistical model that relates to behavioral performance. We first used eigenanatomy to define data-driven regions of interest (DD-ROIs) for both gray matter and white matter. Eigenanatomy is a multivariate dimensionality reduction approach that identifies spatially smooth, unsigned principal components that explain the maximal amount of variance across subjects. We then used a statistical model selection procedure to see which of these DD-ROIs best modeled performance on verbal fluency tasks hypothesized to rely on distinct components of a large-scale neural network that support language: category fluency requires a semantic-guided search and is hypothesized to rely primarily on temporal cortices that support lexical-semantic representations; letter-guided fluency requires a strategic mental search and is hypothesized to require executive resources to support a more demanding search process, which depends on prefrontal cortex in addition to temporal network components that support lexical representations. We observed that both types of verbal fluency performance are best described by a network that includes a combination of gray matter and white matter. For category fluency, the identified regions included bilateral temporal cortex and a white matter region including left inferior longitudinal fasciculus and frontal–occipital fasciculus. For letter fluency, a left temporal lobe region was also selected, and also regions of frontal cortex. These results are consistent with our hypothesized neuroanatomical models of language processing and its breakdown in FTD. We conclude that clustering the data with eigenanatomy before performing linear regression is a promising tool for multimodal data analysis.
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影响因子：
3.7
作者：
Lillo P;Mioshi E;Burrell JR;Kiernan MC;Hodges JR;Hornberger M
通讯作者：
Hornberger M
DOI：
10.1007/978-3-642-33454-2_26
发表时间：
2012
期刊：
LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
影响因子：
--
作者：
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通讯作者：
Gee, James C.
影响因子：
14.5
作者：
Kloeppel, Stefan;Stonnington, Cynthia M.;Chu, Carlton;Draganski, Bogdan;Scahill, Rachael I.;Rohrer, Jonathan D.;Fox, Nick C.;Jack, Clifford R., Jr.;Ashburner, John;Frackowiak, Richard S. J.
通讯作者：
Frackowiak, Richard S. J.
影响因子：
4.2
作者：
Mahoney CJ;Malone IB;Ridgway GR;Buckley AH;Downey LE;Golden HL;Ryan NS;Ourselin S;Schott JM;Rossor MN;Fox NC;Warren JD
通讯作者：
Warren JD
影响因子：
14.5
作者：
Grossman, M;McMillan, C;Gee, J
通讯作者：
Gee, J