Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification.
Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification.
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DOI：
10.1002/hbm.23711
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发表时间：
2017-10
影响因子：
4.8
通讯作者：
Shen D
中科院分区：
文献类型：
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作者：
Chen X;Zhang H;Zhang L;Shen C;Lee SW;Shen D
静息状态功能磁共振成像（RS-fMRI）提取的脑功能连通性（FC）已成为诊断各种神经退行性疾病的常用方法，包括阿尔茨海默病（AD）及其前体期、轻度认知障碍（MCI）。目前的研究主要基于反映同步神经活动的血氧水平依赖（BOLD）信号的时间共变来构建脑灰质（GM）区域之间的FC网络。然而，很少有人研究在白质（WM）内检测到的FC能否为诊断提供有用的信息。基于RS-fMRI计算的功能相关张量（FCT）用于表征WM中局部FC的结构模式，本文提出了一种基于GM区域之间和WM区域内FC传递的信息的MCI分类方法。具体来说，在WM中，首先基于FCT计算基于张量的度量（例如，分数各向异性[FA]，类似于基于弥散张量成像[DTI]计算的度量），然后沿着连接每对大脑GM区域的每个主要WM纤维束进行汇总。这可以捕获WM中的功能信息，其网络结构与为GM构建的FC网络相似，仅基于相同的RS-fMRI数据。此外，采用滑动窗口方法将体素方向的BOLD信号划分为多个短的重叠段。然后，分别根据GM和WM中的BOLD信号段计算每对脑区之间的FC和FCT。通过这种方式，我们的方法可以生成动态FC和动态FCT，从而更好地捕获GM和WM中的功能信息，并通过我们开发的特征提取、选择和集成学习算法将它们进一步整合在一起。实验结果表明，动态FCT可以为WM提供有价值的功能信息；将其与GM中的动态FC相结合，即使单独使用RS-fMRI数据，也可以显著提高MCI受试者的诊断准确性。
Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for diagnosing various neurodegenerative diseases, including Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Current studies mainly construct the FC networks between grey matter (GM) regions of the brain based on temporal co-variations of the blood oxygenation level-dependent (BOLD) signals, which reflects the synchronized neural activities. However, it was rarely investigated whether the FC detected within the white matter (WM) could provide useful information for diagnosis. Motivated by the recently proposed functional correlation tensors (FCT) computed from RS-fMRI and used to characterize the structured pattern of local FC in the WM, we propose in this paper a novel MCI classification method based on the information conveyed by both the FC between the GM regions and that within the WM regions. Specifically, in the WM, the tensor-based metrics (e.g., fractional anisotropy [FA], similar to the metric calculated based on diffusion tensor imaging [DTI]) are first calculated based on the FCT and then summarized along each of the major WM fiber tracts connecting each pair of the brain GM regions. This could capture the functional information in the WM, in a similar network structure as the FC network constructed for the GM, based only on the same RS-fMRI data. Moreover, a sliding window approach is further used to partition the voxel-wise BOLD signal into multiple short overlapping segments. Then, both the FC and FCT between each pair of the brain regions can be calculated based on the BOLD signal segments in the GM and WM, respectively. In such a way, our method can generate dynamic FC and dynamic FCT to better capture functional information in both GM and WM and further integrate them together by using our developed feature extraction, selection, and ensemble learning algorithms. The experimental results verify that the dynamic FCT can provide valuable functional information in the WM; by combining it with the dynamic FC in the GM, the diagnosis accuracy for MCI subjects can be significantly improved even using RS-fMRI data alone.
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影响因子：
3.7
作者：
Adriaanse SM;Binnewijzend MA;Ossenkoppele R;Tijms BM;van der Flier WM;Koene T;Smits LL;Wink AM;Scheltens P;van Berckel BN;Barkhof F
通讯作者：
Barkhof F
DOI：
10.1073/pnas.0504136102
发表时间：
2005-07-05
影响因子：
11.1
作者：
Fox, MD;Snyder, AZ;Raichle, ME
通讯作者：
Raichle, ME
影响因子：
4.8
作者：
Chen X;Zhang H;Gao Y;Wee CY;Li G;Shen D;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
影响因子：
14
作者：
Brookmeyer, Ron;Johnson, Elizabeth;Arrighi, H. Michael
通讯作者：
Arrighi, H. Michael
影响因子：
14
作者：
通讯作者：
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