NeuroImage, 99:477-486, 2014. PMCID: PMC4151353
Linking structural neuroimaging data from multiple modalities to cognitive performance is an important challenge for cognitive neuroscience. In this study we examined the relationship between verbal ﬂuency perfor-mance and neuroanatomy in 54 patients with frontotemporal degeneration (FTD) and 15 age-matched controls, all of whom had T1- and diffusion-weighted imaging. Our goal was to incorporate measures of both gray matter (voxel-based cortical thickness) and white matter (fractional anisotropy) into a single statistical model that relates to behavioral performance. We ﬁrst used eigenanatomy to deﬁne data-driven regions of interest (DD-ROIs) for both gray matter and white matter. Eigenanatomy is a multivariate dimensionality reduction approach that identiﬁes spatially smooth, unsigned principal components that explain the maximal amount of variance across subjects. We then used a statistical model selection procedure to see which of these DD-ROIs best modeled performance on verbal ﬂuency tasks hypothesized to rely on distinct components of a large-scale neural network that support language: category ﬂuency requires a semantic-guided search and is hypothesized to rely primarily on temporal cortices that support lexical-semantic representations; letter-guided ﬂuency requires a strategic mental search and is hypothesized to require executive resources to support a more demanding search process, which depends on prefrontal cortex in addition to temporal network components that support lexical representations. We observed that both types of verbal ﬂuency performance are best described by a network that includes a combination of gray matter and white matter. For category ﬂuency, the identiﬁed regions included bilateral temporal cortex and a white matter region in-cluding left inferior longitudinal fasciculus and frontal–occipital fasciculus. For letter ﬂuency, a left tempo-ral lobe region was also selected, and also regions of frontal cortex. These results are consistent with our hypothesized neuroanatomical models of language processing and its breakdown in FTD. We conclude that clustering the data with eigenanatomy before performing linear regression is a promising tool for mul-timodal data analysis.