Adjusting the neuroimaging statistical inferences for nonstationarity.

Journal article

In neuroimaging cluster-based inference has generally been found to be more powerful than voxel-wise inference. However standard cluster-based methods assume stationarity (constant smoothness), while under nonstationarity clusters are larger in smooth regions just by chance, making false positive risk spatially variant. Hayasaka et al. proposed a Random Field Theory (RFT) based nonstationarity adjustment for cluster inference and validated the method in terms of controlling the overall family...

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Authors: Salimi-Khorshidi, G; Smith, S; Nichols, T

• Journal: Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
• Volume: 12
• Issue: Pt 1
• Pages: 992-999
• Publication date: 2009-01-01
• Language: English