Brain Structural Networks Associated with Intelligence and Visuomotor Ability.

Increasing evidence indicates that multiple structures in the brain are associated with intelligence and cognitive function at the network level. The association between the grey matter (GM) structural network and intelligence and cognition is not well understood. We applied a multivariate approach to identify the pattern of GM and link the structural network to intelligence and cognitive functions. Structural magnetic resonance imaging was acquired from 92 healthy individuals. Source-based morphometry analysis was applied to the imaging data to extract GM structural covariance. We assessed the intelligence, verbal fluency, processing speed, and executive functioning of the participants and further investigated the correlations of the GM structural networks with intelligence and cognitive functions. Six GM structural networks were identified. The cerebello-parietal component and the frontal component were significantly associated with intelligence. The parietal and frontal regions were each distinctively associated with intelligence by maintaining structural networks with the cerebellum and the temporal region, respectively. The cerebellar component was associated with visuomotor ability. Our results support the parieto-frontal integration theory of intelligence by demonstrating how each core region for intelligence works in concert with other regions. In addition, we revealed how the cerebellum is associated with intelligence and cognitive functions.

Progressive changes in hippocampal resting-state connectivity across cognitive impairment: a cross-sectional study from normal to Alzheimer disease.

We investigate the changes in functional connectivity of the left and right hippocampus by comparing the resting-state low-frequency fluctuations in the blood oxygen level-dependent signal from these regions with relation to Alzheimer disease (AD) progression. AD patients were divided into subgroups based on the clinical dementia rating (CDR) scores. Patients with amnestic mild cognitive impairment (aMCI) were also analyzed as an intermediate stage between normal controls and AD. We found that the total functional connectivity of both the right and left hippocampus was maintained during aMCI and the early stages of AD and that it decreased in the later stages of AD. However, when total functional connectivity was broken down into specific regions of the brain, we observed increased or decreased connectivity to specific regions beginning with aMCI. Direct correlation analysis in seeding the left hippocampus revealed a significant decrease in the functional connectivity with the posterior cingulate cortex region and lateral parietal areas, and an increase in functional connectivity in and the anterior cingulate cortex beginning with aMCI. In this study, we were able to quantify the deterioration of resting-state hippocampal connectivity with disease severity and formation of compensatory recruitment in the early stages of AD.

Independent component analysis of localized resting-state functional magnetic resonance imaging reveals specific motor subnetworks.

Recent studies have shown that blood oxygen level-dependent low-frequency (<0.1 Hz) fluctuations (LFFs) during a resting-state exhibit a high degree of correlation with other regions that share cognitive function. Initial studies of resting-state network mapping have focused primarily on major networks such as the default mode network, primary motor, somatosensory, visual, and auditory networks. However, more specific or subnetworks, including those associated with specific motor functions, have yet to be properly addressed. We performed independent component analysis (ICA) in a specific target region of the brain, a process we name, "localized ICA." We demonstrated that when ICA is applied to localized fMRI data, it can be used to distinguish resting-state LFFs associated with specific motor functions (e.g., finger tapping, foot movement, or bilateral lip pulsing) in the primary motor cortex. These ICA components generated from localized data can then be used as functional regions of interest to map whole-brain connectivity. In addition, this method can be used to visualize inter-regional connectivity by expanding the localized region and identifying components that show connectivity between the two regions.