The availability of high-quality RNA-sequencing and genotyping data of post-mortem brain collections from consortia such as CommonMind Consortium (CMC) and the Accelerating Medicines Partnership for Alzheimer’s Disease (AMP-AD) Consortium enable the generation of a large-scale brain cis-eQTL meta-analysis. Here we generate cerebral cortical eQTL from 1433 samples available from four cohorts (identifying >4.1 million significant eQTL for >18,000 genes), as well as cerebellar eQTL from 261 samples (identifying 874,836 significant eQTL for >10,000 genes). We find substantially improved power in the meta-analysis over individual cohort analyses, particularly in comparison to the Genotype-Tissue Expression (GTEx) Project eQTL. Additionally, we observed differences in eQTL patterns between cerebral and cerebellar brain regions. We provide these brain eQTL as a resource for use by the research community. As a proof of principle for their utility, we apply a colocalization analysis to identify genes underlying the GWAS association peaks for schizophrenia and identify a potentially novel gene colocalization with lncRNA RP11-677M14.2 (posterior probability of colocalization 0.975).

No abstract

Benign epilepsy with centrotemporal spikes (BECT) is the most common childhood idiopathic focal epilepsy syndrome, which characterized with white‐matter abnormalities in the rolandic cortex. Although diffusion tensor imaging research could characterize white‐matter structural architecture, it cannot detect neural activity or white‐matter functions. Recent studies demonstrated the functional organization of white‐matter by using functional magnetic resonance imaging (fMRI), suggesting that it is feasible to investigate white‐matter dysfunctions in BECT. Resting‐state fMRI data were collected from 24 new‐onset drug‐naive (unmedicated [NMED]), 21 medicated (MED) BECT patients, and 27 healthy controls (HC). Several white‐matter functional networks were obtained using a clustering analysis on voxel‐by‐voxel correlation profiles. Subsequently, conventional functional connectivity (FC) was calculated in four frequency sub‐bands (Slow‐5:0.01–0.027, Slow‐4:0.027–0.073, Slow‐3:0.073–0.198, and Slow‐2:0.198–0.25 Hz). We also employed a functional covariance connectivity (FCC) to estimate the covariant relationship between two white‐matter networks based on their correlations with multiple gray‐matter regions. Compared with HC, the NMED showed increased FC and/or FCC in rolandic network (RN) and precentral/postcentral network, and decreased FC and/or FCC in dorsal frontal network, while these alterations were not observed in the MED group. Moreover, the changes exhibited frequency‐specific properties. Specifically, only two alterations were shared in at least two frequency bands. Most of these alterations were observed in the frequency bands of Slow‐3 and Slow‐4. This study provided further support on the existence of white‐matter functional networks which exhibited frequency‐specific properties, and extended abnormalities of rolandic area from the perspective of white‐matter dysfunction in BECT.

No abstract

Objective: To observe the real-time microarchitecture changes of the alveolar bone and root resorption during orthodontic treatment. Materials and Methods: A 10 g force was delivered to move the maxillary left first molars mesially in twenty 10-week-old rats for 14 days. The first molar and adjacent alveolar bone were scanned using in vivo microcomputed tomography at the following time points: days 0, 3, 7, and 14. Microarchitecture parameters, including bone volume fraction, structure model index, trabecular thickness, trabecular number, and trabecular separation of alveolar bone, were measured on the compression and tension side. The total root volume was measured, and the resorption crater volume at each time point was calculated. Univariate repeated measures analysis of variance with Bonferroni corrections were performed to compare the differences in each parameter between time points with significance level at P , .05. Results: From day 3 to day 7, bone volume fraction, structure model index, trabecular thickness, and trabecular separation decreased significantly on the compression side, but the same parameters increased significantly on the tension side from day 7 to day 14. Root resorption volume of the mesial root increased significantly on day 7 of orthodontic loading. Conclusions: Real-time root and bone resorption during orthodontic movement can be observed in 3 dimensions using in vivo micro-CT. Alveolar bone resorption and root resorption were observed mostly in the apical third on day 7 on the compression side; bone formation was observed on day 14 on the tension side during orthodontic tooth movement. (Angle Orthod. 2013;83:402-409.)