Characterizing cross‐subject spatial interaction patterns in functional magnetic resonance imaging studies: A two‐stage point‐process model. Issue 6 (12th July 2017)

• Record Type: Journal Article
• Authors: Lee, Adél; Särkkä, Aila; Madhyastha, Tara M.; Grabowski, Thomas J.
• Is Part Of: Biometrical journal. Volume 59:Issue 6(2017:Nov.)
• Page Start: 1352
• Page End: 1381
• Publication Date: 2017-07-12
• Subjects: Cross‐subject interaction -- Geyer saturation model -- Misalignment in cross‐subject functional signals -- Multisubject fMRI studies -- Spatial point process model; Biometry -- Periodicals; Medical statistics -- Periodicals; 570.15195
• DOI: 10.1002/bimj.201600212
• Languages: English
• ISSNs: 0323-3847

Abstract

Abstract: We develop a two‐stage spatial point process model that introduces new characterizations of activation patterns in multisubject functional Magnetic Resonance Imaging (fMRI) studies. Conventionally multisubject fMRI methods rely on combining information across subjects one voxel at a time in order to identify locations of peak activation in the brain. The two‐stage model that we develop here addresses shortcomings of standard methods by explicitly modeling the spatial structure of functional signals and recognizing that corresponding cross‐subject functional signals can be spatially misaligned. In our first stage analysis, we introduce a marked spatial point process model that captures the spatial features of the functional response and identifies a configuration of activation units for each subject. The locations of these activation units are used as input for the second stage model. The point process model of the second stage analysis is developed to characterize multisubject activation patterns by estimating the strength of cross‐subject interactions at different spatial ranges. The model uses spatial neighborhoods to account for the cross‐subject spatial misalignment in corresponding functional units. We applied our methods to an fMRI study of 21 individuals who performed an attention test. We identified four brain regions that are involved in the test and found that our model results agree well with our understanding of how these regions engage with the tasks performed during the attention test. Our results highlighted that cross‐subject interactions are stronger in brain areas that have a more specific function in performing the experimental tasks than in other areas.