Proton T 1 relaxation times of metabolites in the human brain have not previously been published at 7 T. In this study, T 1 values of CH 3 and CH 2 group of N-acetylaspartate and total creatine as well as nine other brain metabolites were measured in occipital white matter and gray matter at 7 T using an inversion-recovery technique combined with a newly implemented semi-adiabatic spin-echo full-intensity acquired localized spectroscopy sequence (echo time 5 12 ms). The mean T 1 values of metabolites in occipital white matter and gray matter ranged from 0.9 to 2.2 s. Among them, the T 1 of glutathione, scylloinositol, taurine, phosphorylethanolamine, and N-acetylaspartylglutamate were determined for the first time in the human brain. Significant differences in T 1 between white matter and gray matter were found for water (228%), total choline (214%), N-acetylaspartylglutamate (229%), N-acetylaspartate (14%), and glutamate (18%). An increasing trend in T 1 was observed when compared with previously reported values of N-acetylaspartate (CH 3 ), total creatine (CH 3 ), and total choline at 3 T. However, for N-acetylaspartate (CH 3 ), total creatine, and total choline, no substantial differences compared to previously reported values at 9.4 T were discernible. The T 1 values reported here will be useful for the quantification of metabolites and signal-to-noise optimization in human brain at 7 T. Magn Reson Med 69:931-936, 2013. V C 2012 Wiley Periodicals, Inc.

GCLC high-risk genotypes are associated with low [GSHmPFC], highlighting that GCLC polymorphisms should be considered in pathology studies of cerebral GSH. Low brain GSH levels are related to low peripheral oxidation status in controls but with high oxidation status in patients, pointing to a dysregulated GSH homeostasis in early psychosis patients. GCLC polymorphisms and disease associated correlations between brain GSH and Glu levels may allow patients stratification.

The function of the neocortex is fundamentally determined by its repeating microcircuit motif, but also by its rich, hierarchical, interregional structure with a highly specific laminar architecture. The last decade has seen the emergence of extensive new data sets on anatomy and connectivity at the whole brain scale, providing promising new directions for studies of cortical function that take into account the inseparability of whole-brain and microcircuit architectures. Here, we present a data-driven computational model of the anatomy of non-barrel primary somatosensory cortex of juvenile rat, which integrates whole-brain scale data while providing cellular and subcellular specificity. This multiscale integration was achieved by building the morphologically detailed model of cortical circuitry embedded within a volumetric, digital brain atlas. The model consists of 4.2 million morphologically detailed neurons belonging to 60 different morphological types, placed in the nonbarrel subregions of the Paxinos and Watson atlas. They are connected by 13.2 billion synapses determined by axo-dendritic overlap, comprising local connectivity and long-range connectivity defined by topographic mappings between subregions and laminar axonal projection profiles, both parameterized by whole brain data sets. Additionally, we incorporated core- and matrix-type thalamocortical projection systems, associated with sensory and higher-order extrinsic inputs, respectively. An analysis of the modeled synaptic connectivity revealed a highly nonrandom topology with substantial structural differences but also synergy between local and long-range connectivity. Long-range connections featured a more divergent structure with a comparatively small group of neurons serving as hubs to distribute excitation to far away locations. Taken together with analyses at different spatial granularities, these results support the notion that local and interregional connectivity exist on a spectrum of scales, rather than as separate and distinct networks, as is commonly assumed. Finally, we predicted how the emergence of primary sensory cortical maps is constrained by the anatomy of thalamo-cortical projections. A subvolume of the model comprising 211,712 neurons in the front limb, jaw, and dysgranular zone has been made freely and openly available to the community.

In connectomics, the study of the network structure of connected neurons, great advances are being made on two different scales: that of macro- and meso-scale connectomics, studying the connectivity between populations of neurons, and that of micro-scale connectomics, studying connectivity between individual neurons. We combine these two complementary views of connectomics to build a first draft statistical model of the micro-connectome of a whole mouse neocortex based on available data on region-to-region connectivity and individual whole-brain axon reconstructions. This process reveals a targeting principle that allows us to predict the innervation logic of individual axons from meso-scale data. The resulting connectome recreates biological trends of targeting on all scales and predicts that an established principle of scale invariant topological organization of connectivity can be extended down to the level of individual neurons. It can serve as a powerful null model and as a substrate for whole-brain simulations.

Respiratory motion correction by registration of individual echo-planar images leads to clearly reduced signal variations in renal DTI for both triggered and particularly non-triggered scans. Secondarily, the results suggest that respiratory-triggering still seems advantageous.