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General fluid-type intelligence (gF) reflects abstract reasoning and problem solving abilities, and is an important predictor for lifetime trajectories of cognition, and physical and mental health. Structural and functional neuroimaging studies have demonstrated the role of parieto-frontal gray matter, but the white matter (WM) underpinnings of gF and the contribution of individual gF components to gF–WM relationship still need to be explored.
The aim of this study was to characterize, in a sample of 100 healthy middle-aged and old subjects (mean=63.8years), the relationship between gF and indices of WM structure obtained from diffusion tensor magnetic resonance imaging (DT-MRI) (fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD)). gF was estimated by principal component analysis including measures of episodic memory, reasoning, and processing speed. Tract-based spatial statistics and permutation-based inference statistics were used to test the association between gF and WM indices, while controlling for the effect of age and sex. We hypothesized a positive relationship between gF and WM structure. Based on previous studies, we further hypothesized that this relationship was heavily influenced by the processing speed component of gF.
We found a robust relationship between gF and DT-MRI measures of FA, RD and MD in all major WM tracts. Higher gF score was related to higher degree of WM integrity, in middle-aged as well as old individuals. Thus, the distributed relationship between gF and indices of WM microstructure is consistent with the notion that gF reflects efficient signaling between cortical areas. Furthermore, analysis of relationships between WM measures and gF components revealed an association with information processing speed and reasoning ability, but not with episodic memory. Thus, although all subcomponents loaded high on gF factor, the speed-related components were most strongly associated with DT-MRI-derived measures. These results suggest that DT-MRI can be used to parse gF.
•General fluid-type intelligence (gF) derived by principal component analysis•Strong association between gF and diffusion measures of major WM tracts•gF shares common WM anatomy with information processing speed•Relevance of DT-MRI based parcellation of gF