dcSBM: A federated constrained source-based morphometry approach for multivariate brain structure mapping.
dcSBM: A federated constrained source-based morphometry approach for multivariate brain structure mapping.
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DOI：
10.1002/hbm.26483
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发表时间：
2023-12-01
影响因子：
4.8
通讯作者：
Calhoun, Vince D.
中科院分区：
文献类型：
--
作者：
Saha, Debbrata K.;Silva, Rogers F.;Baker, Bradley T.;Saha, Rekha;Calhoun, Vince D.
The examination of multivariate brain morphometry patterns has gained attention in recent years, especially for their powerful exploratory capabilities in the study of differences between patients and controls. Among the many existing methods and tools for the analysis of brain anatomy based on structural magnetic resonance imaging data, data‐driven source‐based morphometry (SBM) focuses on the exploratory detection of such patterns. Here, we implement a semi‐blind extension of SBM, called constrained source‐based morphometry (constrained SBM), which enables the extraction of maximally independent reference‐alike sources using the constrained independent component analysis (ICA) approach. To do this, we combine SBM with a set of reference components covering the full brain, derived from a large independent data set (UKBiobank), to provide a fully automated SBM framework. This also allows us to implement a federated version of constrained SBM (cSBM) to allow analysis of data that is not locally accessible. In our proposed decentralized constrained source‐based morphometry (dcSBM), the original data never leaves the local site. Each site operates constrained ICA on its private local data using a common distributed computation platform. Next, an aggregator/master node aggregates the results estimated from each local site and applies statistical analysis to estimate the significance of the sources. Finally, we utilize two additional multisite patient data sets to validate our model by comparing the resulting group difference estimates from both cSBM and dcSBM. While centralized source‐based morphometry (SBM) is effective when data are locally accessible, it cannot operate on data that is decentralized. To address this limitation, we have introduced decentralized constrained source‐based morphometry (dcSBM) and our extensive analysis demonstrates that the proposed dcSBM can operate on decentralized data and achieve results that are almost identical to those of centralized SBM.
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影响因子：
4.8
作者：
Lin, Qiu-Hua;Liu, Jingyu;Zheng, Yong-Rui;Liang, Hualou;Calhoun, Vince D.
通讯作者：
Calhoun, Vince D.
NeuroMark：基于自动化和自适应 ICA 的管道，用于识别脑部疾病的可重复功能磁共振成像标记。
DOI：
10.1016/j.nicl.2020.102375
发表时间：
2020
期刊：
NeuroImage. Clinical
影响因子：
--
作者：
Du Y;Fu Z;Sui J;Gao S;Xing Y;Lin D;Salman M;Abrol A;Rahaman MA;Chen J;Hong LE;Kochunov P;Osuch EA;Calhoun VD;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
影响因子：
3.5
作者：
Gazula H;Baker BT;Damaraju E;Plis SM;Panta SR;Silva RF;Calhoun VD
通讯作者：
Calhoun VD
用于 fMRI 数据分析的群体信息引导 ICA
影响因子：
5.7
作者：
Du, Yuhui;Fan, Yong
通讯作者：
Fan, Yong
影响因子：
3.7
作者：
Pearlson GD;Liu J;Calhoun VD
通讯作者：
Calhoun VD