The purpose of this study was to demonstrate the potential alterations in structural network properties related to physical activity (PA) in healthy elderly. We recruited 76 elderly individuals with normal cognition from Samsung Medical Center in Seoul, Korea. All participants underwent the Cambridge Neuropsychological Test Automated Battery and 3.0T brain magnetic resonance imaging (MRI). Participants were subdivided into quartiles according to the International Physical Activity Questionnaire scores, which represents the amount of PA. Through graph theory based analyses, we compared global and local network topologies according to PA quartile. The higher PA group demonstrated better performance in speed processing compared to the lower PA group. Regional nodal strength also significantly increased in the higher PA group, which involved the bilateral middle frontal, bilateral inferior parietal, right medial orbitofrontal, right superior, and middle temporal gyri. These results were further replicated when the highest and the lowest quartile groups were compared in terms of regional nodal strengths and local efficiency. Our findings that the regional nodal strengths associated with the attentional network were increased in the higher PA group suggest the preventive effects of PA on age-related cognitive decline, especially in attention.

UNLABELLED: The purpose of this study was to investigate if multi-domain cognitive training, especially robot-assisted training, alters cortical thickness in the brains of elderly participants. A controlled trial was conducted with 85 volunteers without cognitive impairment who were 60 years old or older. Participants were first randomized into two groups. One group consisted of 48 participants who would receive cognitive training and 37 who would not receive training. The cognitive training group was randomly divided into two groups, 24 who received traditional cognitive training and 24 who received robot-assisted cognitive training. The training for both groups consisted of daily 90-min-session, five days a week for a total of 12 weeks. The primary outcome was the changes in cortical thickness. When compared to the control group, both groups who underwent cognitive training demonstrated attenuation of age related cortical thinning in the frontotemporal association cortices. When the robot and the traditional interventions were directly compared, the robot group showed less cortical thinning in the anterior cingulate cortices. Our results suggest that cognitive training can mitigate age-associated structural brain changes in the elderly. TRIAL REGISTRATION: ClnicalTrials.gov NCT01596205.

BACKGROUND: We tested our hypothesis that the white matter network might mediate the effect of amyloid and small vessel disease (SVD) on cortical thickness and/or cognition. METHODS: We prospectively recruited 232 patients with cognitive impairment. Amyloid was assessed using Pittsburgh compound B-PET. SVD was quantified as white matter hyperintensity volume and lacune number. The regional white matter network connectivity was measured as regional nodal efficiency by applying graph theoretical analysis to diffusion tensor imaging data. We measured cortical thickness and performed neuropsychological tests. RESULTS: SVD burden was associated with decreased nodal efficiency in the bilateral frontal, lateral temporal, lateral parietal, and occipital regions. Path analyses showed that the frontal nodal efficiency mediated the effect of SVD on the frontal atrophy and frontal-executive dysfunction. The temporoparietal nodal efficiency mediated the effect of SVD on the temporoparietal atrophy and memory dysfunction. However, Pittsburgh compound B retention ratio affected cortical atrophy and cognitive impairment without being mediated by nodal efficiency. CONCLUSIONS: We suggest that a disrupted white matter network mediates the effect of SVD, but not amyloid, on specific patterns of cortical atrophy and/or cognitive impairment. Therefore, our findings provide insight to better understand how amyloid and SVD burden can give rise to brain atrophy or cognitive impairment in specific patterns.

There is growing evidence that the human brain is a large scale complex network. The structural network is reported to be disrupted in cognitively impaired patients. However, there have been few studies evaluating the effects of amyloid and small vessel disease (SVD) markers, the common causes of cognitive impairment, on structural networks. Thus, we evaluated the association between amyloid and SVD burdens and structural networks using diffusion tensor imaging (DTI). Furthermore, we determined if network parameters predict cognitive impairments. Graph theoretical analysis was applied to DTI data from 232 cognitively impaired patients with varying degrees of amyloid and SVD burdens. All patients underwent Pittsburgh compound-B (PiB) PET to detect amyloid burden, MRI to detect markers of SVD, including the volume of white matter hyperintensities and the number of lacunes, and detailed neuropsychological testing. The whole-brain network was assessed by network parameters of integration (shortest path length, global efficiency) and segregation (clustering coefficient, transitivity, modularity). PiB retention ratio was not associated with any white matter network parameters. Greater white matter hyperintensity volumes or lacunae numbers were significantly associated with decreased network integration (increased shortest path length, decreased global efficiency) and increased network segregation (increased clustering coefficient, increased transitivity, increased modularity). Decreased network integration or increased network segregation were associated with poor performances in attention, language, visuospatial, memory, and frontal-executive functions. Our results suggest that SVD alters white matter network integration and segregation, which further predicts cognitive dysfunction.

BACKGROUND AND PURPOSE: Recent studies have demonstrated an association between increased insulin secretion and cognitive impairment. However, there is no previous study that directly evaluates the association between increased insulin secretion and cortical thickness to our knowledge. Therefore, our aim was to evaluate the effect of hyperinsulinemia, as measured by C-peptide level, on cortical thickness in a large sample of cognitively normal individuals. METHODS: Cortical thickness was measured in 1093 patients who visited the Samsung Medical Health Promotion Center and underwent brain magnetic resonance imaging (MRI) and a blood test to measure C-peptide concentration. Automated surface-based analyses of the MRI data were used to measure cortical thickness. C-peptide levels were divided into quartiles for comparison. Patients in the first to third quartiles were used as the reference category. RESULTS: Patients in the highest quartile group (Q4) of C-peptide levels showed cortical thinning, predominantly in both medial temporal lobes, the right inferior temporal gyrus, both medial prefrontal lobes and the right superior parietal lobule, compared with the lower quartile groups (Q1-Q3) after controlling for age, gender, body mass index, history of hypertension, hyperlipidemia, previous stroke, cardiovascular disease and fasting glucose level. CONCLUSIONS: A higher C-peptide level is associated with regional cortical thinning, even in cognitively normal individuals.

The human neocortex has numerous specialized functional areas whose formation is poorly understood. Here, we describe a 15-base pair deletion mutation in a regulatory element of GPR56 that selectively disrupts human cortex surrounding the Sylvian fissure bilaterally including "Broca's area," the primary language area, by disrupting regional GPR56 expression and blocking RFX transcription factor binding. GPR56 encodes a heterotrimeric guanine nucleotide-binding protein (G protein)-coupled receptor required for normal cortical development and is expressed in cortical progenitor cells. GPR56 expression levels regulate progenitor proliferation. GPR56 splice forms are highly variable between mice and humans, and the regulatory element of gyrencephalic mammals directs restricted lateral cortical expression. Our data reveal a mechanism by which control of GPR56 expression pattern by multiple alternative promoters can influence stem cell proliferation, gyral patterning, and, potentially, neocortex evolution.

Polymicrogyria (PMG) is a cortical malformation characterized by multiple small gyri and altered cortical lamination, which may be associated with disrupted white matter connectivity. However, little is known about the topological patterns of white matter networks in PMG. We examined structural connectivity and network topology using individual primary gyral pattern-based nodes in PMG patients, overcoming the limitations of an atlas-based approach. Structural networks were constructed from structural and diffusion magnetic resonance images in 25 typically developing and 14 PMG subjects. The connectivity analysis for different fiber groups divided based on gyral topology revealed severely reduced connectivity between neighboring primary gyri (short U-fibers) in PMG, which was highly correlated with the regional involvement and extent of abnormal gyral folding. The patients also showed significantly reduced connectivity between distant gyri (long association fibers) and between the two cortical hemispheres. In relation to these results, gyral node-based graph theoretical analysis revealed significantly altered topological organization of the network (lower clustering and higher modularity) and disrupted network hub architecture in cortical association areas involved in cognitive and language functions in PMG patients. Furthermore, the network segregation in PMG patients decreased with the extent of PMG and the degree of language impairment. Our approach provides the first detailed findings and interpretations on altered cortical network topology in PMG related to abnormal cortical structure and brain function, and shows the potential for an individualized method to characterize network properties and alterations in connections that are associated with malformations of cortical development.

A number of imaging studies have reported neuroanatomical correlates of human intelligence with various morphological characteristics of the cerebral cortex. However, it is not yet clear whether these morphological properties of the cerebral cortex account for human intelligence. We assumed that the complex structure of the cerebral cortex could be explained effectively considering cortical thickness, surface area, sulcal depth and absolute mean curvature together. In 78 young healthy adults (age range: 17-27, male/female: 39/39), we used the full-scale intelligence quotient (FSIQ) and the cortical measurements calculated in native space from each subject to determine how much combining various cortical measures explained human intelligence. Since each cortical measure is thought to be not independent but highly inter-related, we applied partial least square (PLS) regression, which is one of the most promising multivariate analysis approaches, to overcome multicollinearity among cortical measures. Our results showed that 30% of FSIQ was explained by the first latent variable extracted from PLS regression analysis. Although it is difficult to relate the first derived latent variable with specific anatomy, we found that cortical thickness measures had a substantial impact on the PLS model supporting the most significant factor accounting for FSIQ. Our results presented here strongly suggest that the new predictor combining different morphometric properties of complex cortical structure is well suited for predicting human intelligence.

Early-onset Alzheimer's disease (EOAD) has been shown to progress more rapidly than late-onset Alzheimer's disease (LOAD). However, no studies have compared the topography of brain volume reduction over time. The purpose of this 3-year longitudinal study was to compare EOAD and LOAD in terms of their rates of decline in cognitive testing and topography of cortical thinning. We prospectively recruited 36 patients with AD (14 EOAD and 22 LOAD) and 14 normal controls. All subjects were assessed with neuropsychological tests and with magnetic resonance imaging at baseline, Year 1, and Year 3. The EOAD group showed more rapid decline than the LOAD group in attention, language, and frontal-executive tests. The EOAD group also showed more rapid cortical thinning in widespread association cortices. In contrast, the LOAD group presented more rapid cortical thinning than the EOAD group only in the left parahippocampal gyrus. Our study suggests that patients with EOAD show more rapid cortical atrophy than patients with LOAD, which accounts for faster cognitive decline on neuropsychological tests.

Polymicrogyria (PMG) is a malformation of cortical development characterized by an irregular gyral pattern and its diagnosis and severity have been qualitatively judged by visual inspection of imaging features. We aimed to provide a quantitative description of abnormal sulcal patterns for individual PMG brains using our sulcal graph-based analysis and examined the association with language impairment. The sulcal graphs were constructed from magnetic resonance images in 26 typical developing and 18 PMG subjects and the similarity between sulcal graphs was computed by using their geometric and topological features. The similarities between typical and PMG groups were significantly lower than the similarities measured within the typical group. Furthermore, more lobar regions were determined to be abnormal in most patients when compared with the visual diagnosis of PMG involvement, suggesting that PMG may have more global effects on cortical folding than previously expected. Among the PMG, the group with intact language development showed sulcal patterns more closely matched with the typical than the impaired group in the left parietal lobe. Our approach shows the potential to provide a quantitative means for detecting the severity and extent of involvement of cortical malformation and a greater understanding of genotype-phenotype and clinical-imaging features correlations.