Harmonizing functional connectivity reduces scanner effects in community detection.
Harmonizing functional connectivity reduces scanner effects in community detection.
复制标题
协调功能连通性会降低扫描仪在社区检测中的影响。
DOI：
10.1016/j.neuroimage.2022.119198
复制
发表时间：
2022-08-01
期刊：
影响因子：
5.7
通讯作者：
Shou H
中科院分区：
文献类型：
--
作者：
Chen AA;Srinivasan D;Pomponio R;Fan Y;Nasrallah IM;Resnick SM;Beason-Held LL;Davatzikos C;Satterthwaite TD;Bassett DS;Shinohara RT;Shou H
从功能磁共振成像（fMRI）数据构建的图上的社区检测导致了对大脑功能组织的重要见解。对大脑群落结构的大型研究通常包括在不同研究中使用多个扫描仪获得的图像。扫描仪中的差异会将可变性引入下游结果，这些差异通常称为扫描仪效应。这种效应以前已经被证明会显著影响常见的网络指标。在这项研究中，我们确定了数据驱动的社区检测结果和相关的网络指标的扫描仪效应。我们评估了一种常用的协调方法，并提出了新的方法来协调功能连接，利用现有的知识网络结构以及数据中的协方差模式。最后，我们证明了我们的新方法减少了社区结构和网络指标中的扫描器效应。我们的研究结果突出了大脑功能组织研究中的扫描仪效应，并提供了额外的工具来解决这些不必要的影响。这些发现和方法可以纳入未来的功能连接研究，可能会防止虚假的发现和提高结果的可靠性。
Community detection on graphs constructed from functional magnetic resonance imaging (fMRI) data has led to important insights into brain functional organization. Large studies of brain community structure often include images acquired on multiple scanners across different studies. Differences in scanner can introduce variability into the downstream results, and these differences are often referred to as scanner effects. Such effects have been previously shown to significantly impact common network metrics. In this study, we identify scanner effects in data-driven community detection results and related network metrics. We assess a commonly employed harmonization method and propose new methodology for harmonizing functional connectivity that leverage existing knowledge about network structure as well as patterns of covariance in the data. Finally, we demonstrate that our new methods reduce scanner effects in community structure and network metrics. Our results highlight scanner effects in studies of brain functional organization and provide additional tools to address these unwanted effects. These findings and methods can be incorporated into future functional connectivity studies, potentially preventing spurious findings and improving reliability of results.
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DOI：
10.1109/jbhi.2018.2863202
发表时间：
2019-01-01
影响因子：
7.7
作者：
Dragomir, Andrei;Vrahatis, Aristidis G.;Bezerianos, Anastasios
通讯作者：
Bezerianos, Anastasios
影响因子：
5.7
作者：
Alexander-Bloch, Aaron;Lambiotte, Renaud;Roberts, Ben;Giedd, Jay;Gogtay, Nitin;Bullmore, Edward T.
通讯作者：
Bullmore, Edward T.
影响因子：
4.8
作者：
Chen AA;Beer JC;Tustison NJ;Cook PA;Shinohara RT;Shou H;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
影响因子：
3.7
作者：
FLURY, BN
通讯作者：
FLURY, BN
影响因子：
4.3
作者：
Feis RA;Smith SM;Filippini N;Douaud G;Dopper EG;Heise V;Trachtenberg AJ;van Swieten JC;van Buchem MA;Rombouts SA;Mackay CE
通讯作者：
Mackay CE