After the landmark studies reporting changes in the cerebral metabolic rate of glucose (CMR Glc ) in excess of those in oxygen (CMR O2 ) during physiological stimulation, several studies have examined the fate of the extra carbon taken up by the brain, reporting a wide range of changes in brain lactate from 20% to 250%. The present study reports functional magnetic resonance spectroscopy measurements at 7 Tesla using the enhanced sensitivity to study a small cohort (n 5 6). Small increases in lactate (19% 6 4%, P < 0.05) and glutamate (4% 6 1%, P < 0.001) were seen within the first 2 min of activation. With the exception of glucose (12% 6 5%, P < 0.001), no other metabolite concentration changes beyond experimental error were significantly observed. Therefore, the present study confirms that lactate and glutamate changes during physiological stimulation are small (i.e. below 20%) and shows that the increased sensitivity allows reproduction of previous results with fewer subjects. In addition, the initial rate of glutamate and lactate concentration increases implies an increase in CMR O2 that is slightly below that of CMR Glc during the first 1-2 min of activation. V V C 2013 Wiley Periodicals, Inc.

Brain white matter connections have become a focus of major interest with important maturational processes occurring in newborns. To study the complex microstructural developmental changes in-vivo, it is imperative that non-invasive neuroimaging approaches are developed for this age-group. Multi-b-value diffusion weighted imaging data were acquired in 13 newborns, and the biophysical compartment diffusion models CHARMED-light and NODDI, providing new microstructural parameters such as intra-neurite volume fraction (νin) and neurite orientation dispersion index (ODI), were developed for newborn data. Comparative analysis was performed and twenty ROIs in the white matter were investigated. Diffusion tensor imaging and both biophysical compartment models highlighted the compact and oriented structure of the corpus-callosum with the highest FA and νin values and the smallest ODI values. We could clearly differentiate, using the FA, νin and ODI, the posterior and anterior internal capsule representing similar cellular structure but with different maturation (i.e. partially myelinated and absence of myelin, respectively). Late maturing regions (external capsule and periventricular crossroads of pathways) had lower νin values, but displayed significant differences in ODI. The compartmented models CHARMED-light and NODDI bring new indices corroborating the cellular architectures, with the lowest νin, reflecting the late maturation of areas with thin non-myelinated fibers, and with highest ODI indicating the presence of fiber crossings and fanning. The application of biophysical compartment diffusion models adds new insights to the brain white matter development in vivo.

Focal epilepsy is increasingly recognized as the result of an altered brain network, both on the structural and functional levels and the characterization of these widespread brain alterations is crucial for our understanding of the clinical manifestation of seizure and cognitive deficits as well as for the management of candidates to epilepsy surgery.Tractography based on Diffusion Tensor Imaging allows non-invasive mapping of white matter tracts in vivo. Recently, diffusion spectrum imaging (DSI), based on an increased number of diffusion directions and intensities, has improved the sensitivity of tractography, notably with respect to the problem of fiber crossing and recent developments allow acquisition times compatible with clinical application.We used DSI and parcellation of the gray matter in regions of interest to build whole-brain connectivity matrices describing the mutual connections between cortical and subcortical regions in patients with focal epilepsy and healthy controls. In addition, the high angular and radial resolution of DSI allowed us to evaluate also some of the biophysical compartment models, to better understand the cause of the changes in diffusion anisotropy.Global connectivity, hub architecture and regional connectivity patterns were altered in TLE patients and showed different characteristics in RTLE vs LTLE with stronger abnormalities in RTLE. The microstructural analysis suggested that disturbed axonal density contributed more than fiber orientation to the connectivity changes affecting the temporal lobes whereas fiber orientation changes were more involved in extratemporal lobe changes. Our study provides further structural evidence that RTLE and LTLE are not symmetrical entities and DSI-based imaging could help investigate the microstructural correlate of these imaging abnormalities.

A two-compartment model of diffusion in white matter, which accounts for intra- and extra-axonal spaces, is associated with two plausible mathematical scenarios: either the intra-axonal axial diffusivity D is higher than the extra-axonal D (Branch 1), or the opposite, i.e. D < D (Branch 2). This duality calls for an independent validation of compartment axial diffusivities, to determine which of the two cases holds. The aim of the present study was to use an intracerebroventricular injection of a gadolinium-based contrast agent to selectively reduce the extracellular water signal in the rat brain, and compare diffusion metrics in the genu of the corpus callosum before and after gadolinium infusion. The diffusion metrics considered were diffusion and kurtosis tensor metrics, as well as compartment-specific estimates of the WMTI-Watson two-compartment model. A strong decrease in genu T and T relaxation times post-Gd was observed (p < 0.001), as well as an increase of 48% in radial kurtosis (p < 0.05), which implies that the relative fraction of extracellular water signal was selectively decreased. This was further supported by a significant increase in intra-axonal water fraction as estimated from the two-compartment model, for both branches (p < 0.01 for Branch 1, p < 0.05 for Branch 2). However, pre-Gd estimates of axon dispersion in Branch 1 agreed better with literature than those of Branch 2. Furthermore, comparison of post-Gd changes in diffusivity and dispersion between data and simulations further supported Branch 1 as the biologically plausible solution, i.e. D > D. This result is fully consistent with other recent measurements of compartment axial diffusivities that used entirely different approaches, such as diffusion tensor encoding.