Influence of local and remote white matter conductivity anisotropy for a thalamic source on EEG/MEG field and return current computation

nverse methods are used to reconstruct current sources in the human brain by means of Electroencephalogra- phy (EEG) and Magnetoencephalography (MEG) measure- ments of event related fields or epileptic seizures. There exists a persistent uncertainty regarding the influence of anisotropy of the white matter compartment on neural source reconstruc- tion. In this paper, we study the sensitivity to anisotropy of the EEG/MEG forward problem for a thalamic source in a high resolution finite element volume conductor. The influence of anisotropy on computed fields will be presented by both high resolution visualization of fields and return current flow and topography and magnitude error measures. We pay particular attention to the influence of local conductivity changes in the neighborhood of the source. The combination of simulation and visualization provides deep insight into the effect of white matter conductivity anisotropy. We found that for both EEG and MEG formulations, the local presence of electrical anisotropy in the tissue surroun- ding the source substantially compromised the forward field computation, and correspondingly, the inverse source recons- truction. The degree of error resulting from the uncompen- sated presence of tissue anisotropy depended strongly on the proximity of the anisotropy to the source; remote anisotropy had a much weaker influence than anisotropic tissue that included the source

White matter brain structure predicts language performance and learning success

Individual differences in the ability to process language have long been discussed. Much of the neural basis of these, however, is yet unknown. Here we investigated the relationship between long-range white matter connectivity of the brain, as revealed by diffusion tractography, and the ability to process syntactically complex sentences in the participants' native language as well as the improvement thereof by multiday training. We identified specific network motifs by singular value decomposition that indeed related white matter structural connectivity to individual language processing performance. First, for two such motifs, one in the left and one in the right hemisphere, their individual prevalence significantly predicted the individual language performance, suggesting an anatomical predisposition for the individual ability to process syntactically complex sentences. Both motifs comprise a number of cortical regions, but seem to be dominated by areas known for the involvement in working memory rather than the classical language network itself. Second, we identified another left hemispheric network motif, whose change of prevalence over the training period significantly correlated with the individual change in performance, thus reflecting training induced white matter plasticity. This motif comprises diverse cortical areas including regions known for their involvement in language processing, working memory and motor functions. The present findings suggest that individual differences in language processing and learning can be explained, in part, by individual differences in the brain's white matter structure. Brain structure may be a crucial factor to be considered when discussing variations in human cognitive performance, more generally

Sex-Dependent Influences of Obesity on Cerebral White Matter Investigated by Diffusion-Tensor Imaging

Several studies have shown that obesity is associated with changes in human brain function and structure. Since women are more susceptible to obesity than men, it seems plausible that neural correlates may also be different. However, this has not been demonstrated so far. To address this issue, we systematically investigated the brain's white matter (WM) structure in 23 lean to obese women (mean age 25.5 y, std 5.1 y; mean body mass index (BMI) 29.5 kg/m(2), std 7.3 kg/m(2)) and 26 lean to obese men (mean age 27.1 y, std 5.0 y; mean BMI 28.8 kg/m(2), std 6.8 kg/m(2)) with diffusion-weighted magnetic resonance imaging (MRI). There was no significant age (p > 0.2) or BMI (p > 0.7) difference between female and male participants. Using tract-based spatial statistics, we correlated several diffusion parameters including the apparent diffusion coefficient, fractional anisotropy (FA), as well as axial (lambda(parallel to)) and radial diffusivity (lambda(perpendicular to)) with BMI and serum leptin levels. In female and male subjects, the putative axon marker lambda(parallel to) was consistently reduced throughout the corpus callosum, particularly in the splenium (r = -0.62, p < 0.005). This suggests that obesity may be associated with axonal degeneration. Only in women, the putative myelin marker lambda(perpendicular to) significantly increased with increasing BMI (r = 0.57, p < 0.005) and serum leptin levels (r = 0.62, p < 0.005) predominantly in the genu of the corpus callosum, suggesting additional myelin degeneration. Comparable structural changes were reported for the aging brain, which may point to accelerated aging of WM structure in obese subjects. In conclusion, we demonstrate structural WM changes related to an elevated body weight, but with differences between men and women. Future studies on obesity-related functional and structural brain changes should therefore account for sex-related differences

Groupwise Structural Parcellation of the Cortex: A Sound Approach Based on Logistic Models

International audienceCurrent theories hold that brain function is highly related with long-range physical connections through axonal bundles, namely extrinsic connectivity. However, obtaining a groupwise cortical parcella-tion based on extrinsic connectivity remains challenging. Current par-cellation methods are computationally expensive; need tuning of several parameters or rely on ad-hoc constraints. Furthermore, none of these methods present a model for the cortical extrinsic connectivity. To tackle these problems, we propose a parsimonious model for the extrinsic con-nectivity and an efficient parcellation technique based on clustering of tractograms. Our technique allows the creation of single subject and groupwise parcellations of the whole cortex. The parcellations obtained with our technique are in agreement with anatomical and functional par-cellations in the literature. In particular, the motor and sensory cortex are subdivided in agreement with the human homunculus of Penfield. We illustrate this by comparing our resulting parcels with an anatomical atlas and the motor strip mapping included in the Human Connectome Project data

Brain structure and function: a multidisciplinary pipeline to study hominoid brain evolution

To decipher the evolution of the hominoid brain and its functions, it is essential to conduct comparative studies in primates, including our closest living relatives. However, strong ethical concerns preclude in vivo neuroimaging of great apes. We propose a responsible and multidisciplinary alternative approach that links behavior to brain anatomy in non-human primates from diverse ecological backgrounds. The brains of primates observed in the wild or in captivity are extracted and fixed shortly after natural death, and then studied using advanced MRI neuroimaging and histology to reveal macro- and microstructures. By linking detailed neuroanatomy with observed behavior within and across primate species, our approach provides new perspectives on brain evolution. Combined with endocranial brain imprints extracted from computed tomographic scans of the skulls these data provide a framework for decoding evolutionary changes in hominin fossils. This approach is poised to become a key resource for investigating the evolution and functional differentiation of hominoid brains