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Relating brain anatomy and cognitive ability using a multivariate multimodal framework.

ABSTRACT: 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 performance 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 including left inferior longitudinal fasciculus and frontal-occipital fasciculus. For letter fluency, a left temporal 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 multimodal data analysis.

SUBMITTER: Cook PA

PROVIDER: S-EPMC4151353 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Relating brain anatomy and cognitive ability using a multivariate multimodal framework.

Cook Philip A PA McMillan Corey T CT Avants Brian B BB Peelle Jonathan E JE Gee James C JC Grossman Murray M

NeuroImage 20140513

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 performance 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 ...[more]

PMID: 24830834

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Project description:BACKGROUND:Cognitive and structural brain abnormalities range from mild to severe in psychosis. The relationships of specific cognitive functions to specific brain structures across the psychosis spectrum is less certain. METHODS:Participants (n = 678) with bipolar, schizoaffective, or schizophrenia psychoses and healthy control subjects were recruited via the Bipolar-Schizophrenia Network for Intermediate Phenotypes. The Schizo-Bipolar Scale was used to create a psychosis continuum (from purely affective to purely nonaffective). Canonical correlation between 14 cognitive measures and structural brain measures (gray matter volume, cortical thickness, cortical surface area, and local gyrification indices) for 68 neocortical regions yielded constructs that defined shared cognition-brain structure relationships. Canonical discriminant analysis was used to integrate these constructs and efficiently summarize cognition-brain structure relationships across the psychosis continuum. RESULTS:General cognition was associated with larger gray matter volumes and thicker cortices but smaller cortical surface area in frontoparietal regions. Working memory was associated with larger volume and surface area in frontotemporal regions. Faster response speed was associated with thicker frontal cortices. Constructs that captured general cognitive ability and working memory and their relationship to cortical volumes primarily defined an ordered psychosis spectrum (purely affective, least abnormal through purely nonaffective, and most abnormal). A construct that captured general cognitive ability and its relationship to cortical surface area differentiated purely affective cases from other groups. CONCLUSIONS:General cognition and working memory with cortical volume deviations characterized more nonaffective psychoses. Alternatively, affective psychosis cases with general cognitive deficits had deviations in cortical surface area, perhaps accounting for heterogeneous findings across previous studies.

Dataset Information

Relating brain anatomy and cognitive ability using a multivariate multimodal framework.

Publications

Relating brain anatomy and cognitive ability using a multivariate multimodal framework.

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