General fluid-type intelligence is related to indices of white matter structure in middle-aged and old adults.

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=63.8 years), 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.

Increased hippocampal default mode synchronization during rest in middle-aged and elderly APOE ε4 carriers: relationships with memory performance.

The apolipoprotein (APOE) ε4 allele is a strong genetic risk factor for Alzheimer's disease (AD). Intrinsic fluctuations of brain activity measured by fMRI during rest may be sensitive to AD-related neuropathology. In particular, functional connectivity of the default-mode network (DMN) has gained recent attention as a possible biomarker of disease processes and associated memory decline in AD. Here, we tested the hypothesis of APOE-related alterations in DMN functional connectivity in 95 healthy individuals between 50 and 80 years of age, including 33 carriers of the ε4 allele. Based on previous studies, we hypothesized increased hippocampal DMN synchronization in APOE ε4 carriers. This was supported using independent component analysis in combination with a dual-regression approach for analysis of resting state data. Whole-brain analysis suggested effects also in other areas, including the posterior cingulate cortex, parietal cortex, and parahippocampal regions. DMN synchronization showed a negative correlation with performance on a test of memory functioning, suggesting a neurocognitive significance of the brain activity patterns during rest. Our findings indicate that increased genetic vulnerability for AD is reflected in increased hippocampal DMN synchronization during rest several years before clinical manifestation. We propose that the results reflect ε4-related failure in hippocampal decoupling, which might elevate the total hippocampal metabolic burden and increase the risk of cognitive decline and AD. The results provide an important confirmation of specific genotype effects on intrinsic fluctuations and support the use of functional connectivity indices as imaging-derived endophenotypes in the emerging field of imaging genetics.