Enriched white matter connectivity networks for accurate identification of MCI patients.
Enriched white matter connectivity networks for accurate identification of MCI patients.
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DOI：
10.1016/j.neuroimage.2010.10.026
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发表时间：
2011-02-01
期刊：
影响因子：
5.7
通讯作者：
Shen D
中科院分区：
文献类型：
--
作者：
Wee CY;Yap PT;Li W;Denny K;Browndyke JN;Potter GG;Welsh-Bohmer KA;Wang L;Shen D
轻度认知障碍(MCI)通常是阿尔茨海默病(AD)的前驱症状，常被认为是AD早期诊断和治疗干预的良好靶点。最近出现的可靠的网络表征技术使在全脑连接水平上理解神经疾病成为可能。因此，我们提出了一种有效的基于网络的多变量分类算法，使用来自白质(WM)连接网络的测量集合来准确地识别MCI患者和正常对照。对WM连接的丰富描述利用六个生理参数，即纤维计数、分数各向异性(FA)、平均扩散系数(MD)和主扩散系数(λ1、λ2、λ3)，为每个对象产生六个连接网络，以考虑连接拓扑和连接的生物物理属性。在将大脑划分为90个感兴趣区(ROI)后，这些特性可以针对每一对具有共同穿越纤维的区域进行量化。为了建立MCI分类器，提取每个感兴趣区相对于剩余感兴趣区的聚类系数作为分类特征。然后根据它们相对于临床标签的皮尔逊相关性对这些特征进行排序，并进一步筛选以使用基于支持向量机的特征选择算法来选择最具区分性的特征子集。最后，使用选择的特征子集来训练支持向量机(SVM)。分类准确率通过留一法交叉验证进行评估，以确保性能的泛化。我们对WM连接的丰富描述给出的分类准确率为88.9%，比使用任何单个生理参数的简单WM连接描述至少提高了14.8%。对泛化性能的交叉验证估计显示，接收器工作特性曲线下的面积为0.929，表明良好的诊断能力。还发现，根据选定的特征，前额叶皮质、眶前皮质、顶叶和岛区的部分区域提供了最具区分性的分类特征，这与以前研究报告的结果一致。我们的MCI分类框架，特别是对WM连接的丰富描述，使我们能够准确地及早发现大脑异常，这对于潜在AD患者的治疗管理至关重要。
Mild cognitive impairment (MCI), often a prodromal phase of Alzheimer’s disease (AD), is frequently considered to be good target for early diagnosis and therapeutic interventions of AD. Recent emergence of reliable network characterization techniques has made it possible to understand neurological disorders at a whole-brain connectivity level. Accordingly, we propose an effective network-based multivariate classification algorithm, using a collection of measures derived from white-matter (WM) connectivity networks, to accurately identify MCI patients from normal controls. An enriched description of WM connections, utilizing six physiological parameters, i.e., fiber count, fractional anisotropy (FA), mean diffusivity (MD), and principal diffusivities (λ1, λ2, λ3), results in six connectivity networks for each subject to account for the connection topology and the biophysical properties of the connections. Upon parcellating the brain into 90 regions-of-interest (ROIs), these properties can be quantified for each pair of regions with common traversing fibers. For building an MCI classifier, clustering coefficient of each ROI in relation to the remaining ROIs is extracted as feature for classification. These features are then ranked according to their Pearson correlation with respect to the clinical labels, and are further sieved to select the most discriminant subset of features using a SVM-based feature selection algorithm. Finally, support vector machines (SVMs) are trained using the selected subset of features. Classification accuracy was evaluated via leave-one-out cross-validation to ensure generalization of performance. The classification accuracy given by our enriched description of WM connections is 88.9%, which is an increase of at least 14.8% from that using simple WM connectivity description with any single physiological parameter. A cross-validation estimation of the generalization performance shows an area of 0.929 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power. It was also found, based on the selected features, that portions of the prefrontal cortex, orbitofrontal cortex, parietal lobe and insula regions provided the most discriminant features for classification, in line with results reported in previous studies. Our MCI classification framework, especially the enriched description of WM connections, allows accurate early detection of brain abnormalities, which is of paramount importance for treatment management of potential AD patients.
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DOI：
10.1523/jneurosci.1929-08.2008
发表时间：
2008-09-10
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Bassett DS;Bullmore E;Verchinski BA;Mattay VS;Weinberger DR;Meyer-Lindenberg A
通讯作者：
Meyer-Lindenberg A
影响因子：
3.7
作者：
Hagmann, Patric;Kurant, Maciej;Gigandet, Xavier;Thiran, Patrick;Wedeen, Van J.;Meuli, Reto;Thiran, Jean-Philippe
通讯作者：
Thiran, Jean-Philippe
DOI：
10.1109/tim.2004.834070
发表时间：
2004-12-01
影响因子：
5.6
作者：
Malhi, A;Gao, RX
通讯作者：
Gao, RX
影响因子：
4.4
作者：
Rose, Stephen E.;Janke, Andrew L.;Chalk, Jonathan B.
通讯作者：
Chalk, Jonathan B.
DOI：
10.1073/pnas.0308627101
发表时间：
2004-03-30
影响因子：
11.1
作者：
Greicius, MD;Srivastava, G;Menon, V
通讯作者：
Menon, V