Cortical surface-based threshold-free cluster enhancement and cortexwise mediation.

TitleCortical surface-based threshold-free cluster enhancement and cortexwise mediation.
Publication TypeJournal Article
Year of Publication2017
AuthorsLett TA, Waller L, Tost H, Veer IM, Nazeri A, Erk S, Brandl EJ, Charlet K, Beck A, Vollstädt-Klein S, Jorde A, Kiefer F, Heinz A, Meyer-Lindenberg A, Chakravarty MM, Walter H
JournalHum Brain Mapp
Date Published2017 Jun

Threshold-free cluster enhancement (TFCE) is a sensitive means to incorporate spatial neighborhood information in neuroimaging studies without using arbitrary thresholds. The majority of methods have applied TFCE to voxelwise data. The need to understand the relationship among multiple variables and imaging modalities has become critical. We propose a new method of applying TFCE to vertexwise statistical images as well as cortexwise (either voxel- or vertexwise) mediation analysis. Here we present TFCE_mediation, a toolbox that can be used for cortexwise multiple regression analysis with TFCE, and additionally cortexwise mediation using TFCE. The toolbox is open source and publicly available ( We validated TFCE_mediation in healthy controls from two independent multimodal neuroimaging samples (N = 199 and N = 183). We found a consistent structure-function relationship between surface area and the first independent component (IC1) of the N-back task, that white matter fractional anisotropy is strongly associated with IC1 N-back, and that our voxel-based results are essentially identical to FSL randomise using TFCE (all PFWE <0.05). Using cortexwise mediation, we showed that the relationship between white matter FA and IC1 N-back is mediated by surface area in the right superior frontal cortex (PFWE  < 0.05). We also demonstrated that the same mediation model is present using vertexwise mediation (PFWE  < 0.05). In conclusion, cortexwise analysis with TFCE provides an effective analysis of multimodal neuroimaging data. Furthermore, cortexwise mediation analysis may identify or explain a mechanism that underlies an observed relationship among a predictor, intermediary, and dependent variables in which one of these variables is assessed at a whole-brain scale. Hum Brain Mapp 38:2795-2807, 2017. © 2017 Wiley Periodicals, Inc.

Alternate JournalHum Brain Mapp
PubMed ID28317230