Gladwin Thomas E, Vink Matthijs, Mars Roger B
Military Mental Health Research Centre, Ministry of Defense, P.O. Box 90.000, 3509AA Utrecht, The Netherlands; Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands.
Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands; Departments of Experimental and Developmental Psychology, Faculty of Social Sciences, Utrecht University, Utrecht, The Netherlands.
MethodsX. 2016 Jul 7;3:477-82. doi: 10.1016/j.mex.2016.06.002. eCollection 2016.
Cluster-based analysis methods in neuroimaging provide control of whole-brain false positive rates without the need to conservatively correct for the number of voxels and the associated false negative results. The current method defines clusters based purely on shapes in the landscape of activation, instead of requiring the choice of a statistical threshold that may strongly affect results. Statistical significance is determined using permutation testing, combining both size and height of activation. A method is proposed for dealing with relatively small local peaks. Simulations confirm the method controls the false positive rate and correctly identifies regions of activation. The method is also illustrated using real data. •A landscape-based method to define clusters in neuroimaging data avoids the need to pre-specify a threshold to define clusters.•The implementation of the method works as expected, based on simulated and real data.•The recursive method used for defining clusters, the method used for combining clusters, and the definition of the "value" of a cluster may be of interest for future variations.
神经成像中基于聚类的分析方法可控制全脑假阳性率，无需因体素数量及相关假阴性结果而进行保守校正。当前方法纯粹基于激活态势中的形状来定义聚类，而非需要选择可能强烈影响结果的统计阈值。使用置换检验确定统计显著性，同时结合激活的大小和高度。提出了一种处理相对较小局部峰值的方法。模拟结果证实该方法可控制假阳性率并正确识别激活区域。还使用真实数据对该方法进行了说明。
•一种基于态势的方法可在神经成像数据中定义聚类，无需预先指定阈值来定义聚类。
•基于模拟和真实数据，该方法的实现按预期运行。
•用于定义聚类的递归方法、用于合并聚类的方法以及聚类“值”的定义可能对未来的变体有意义。
