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Diffusion tensor (DT) imaging and related multifiber reconstruction algorithms allow the study of in vivo microstructure and, by means of tractography, structural connectivity. Although reconstruction algorithms are promising imaging tools, high-quality diffusion-weighted imaging (DWI) datasets for verification and validation of postprocessing and analysis methods are lacking. Clinical in vivo DWI is limited by, for example, physiological noise and low signal-to-noise ratio. Here, we performed a series of DWI measurements on postmortem pig brains, which resemble the human brain in neuroanatomical complexity, to establish an ex vivo imaging pipeline for generating high-quality DWI datasets. Perfusion fixation ensured that tissue characteristics were comparable to in vivo conditions. There were three main results: (i) heat conduction and unstable tissue mechanics accounted for time-varying artefacts in the DWI dataset, which were present for up to 15 h after positioning brain tissue in the scanner; (ii) using fitted DT, q-ball, and persistent angular structure magnetic resonance imaging algorithms, any b-value between ∼2,000 and ∼8,000 s/mm(2) , with an optimal value around 4,000 s/mm(2) , allowed for consistent reconstruction of fiber directions; (iii) diffusivity measures in the postmortem brain tissue were stable over a 3-year period. On the basis of these results, we established an optimized ex vivo pipeline for high-quality and high-resolution DWI. The pipeline produces DWI data sets with a high level of tissue structure detail showing for example two parallel horizontal rims in the cerebral cortex and multiple rims in the hippocampus. We conclude that high-quality ex vivo DWI can be used to validate fiber reconstruction algorithms and to complement histological studies.

The degree to which individual variation in brain structure in humans is genetically or environmentally determined is as yet not well understood. We studied the brains of 54 monozygotic (33 male, 21 female) and 58 dizygotic (17 male, 20 female, 21 opposite sex) pairs of twins and 34 of their full siblings (19 male, 15 female) by means of high resolution magnetic resonance imaging scans. Structural equation modeling was used to quantify the genetic and environmental contributions to phenotypic (co)variance in whole brain, gray and white matter volume of the cerebrum, lateral ventricle volume and associated variables such as intracranial volume and height. Because the cerebral cortex makes up more that two-thirds of the brain mass and almost three-quarters of its synapses, our data predominantly concerns the telencephalon. Genetic factors accounted for most of the individual differences in whole brain (90%), gray (82%) and white (88%) matter volume. Individual differences in lateral ventricle volume were best explained by a model containing common (58%) and unique (42%) environmental factors, indicating genes to be of no or minor influence. In our sample, genetic or environmental influences were not different for males and females. The same genes influenced brain volumes and intracranial volume and almost completely explained their high phenotypic correlation. Genes influencing gray and white matter overlapped to a large extent and completely determined their phenotypic correlation. The high heritability estimates that were found indicate that brain volumes may be useful as intermediate phenotypes in behavioral genetic research.

Variation in gray matter (GM) and white matter (WM) volume of the adult human brain is primarily genetically determined. Moreover, total brain volume is positively correlated with general intelligence, and both share a common genetic origin. However, although genetic effects on morphology of specific GM areas in the brain have been studied, the heritability of focal WM is unknown. Similarly, it is unresolved whether there is a common genetic origin of focal GM and WM structures with intelligence. We explored the genetic influence on focal GM and WM densities in magnetic resonance brain images of 54 monozygotic and 58 dizygotic twin pairs and 34 of their siblings.

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Abstract■ During childhood and adolescence, ongoing white matter maturation in the fronto-parietal cortices and connecting fiber tracts is measurable with diffusion-weighted imaging. Important questions remain, however, about the links between these changes and developing cognitive functions. Spatial working memory (SWM) performance improves significantly throughout the childhood years, and several lines of evidence implicate the left fronto-parietal cortices and connecting fiber tracts in SWM processing. Here we report results from a study of 76 typically developing children, 7 to 13 years of age. We hypothesized that better SWM performance would be associated with increased fractional anisotropy (FA) in a left fronto-parietal network composed of the superior longitudinal fasciculus (SLF), the regional white matter underlying the dorsolateral pFC, and the posterior parietal cortex. As hypothesized, we observed a significant association between higher FA in the left fronto-parietal network and better SWM skills, and the effect was independent of age. This association was mainly accounted for by variability in left SLF FA and remained significant when FA measures from global fiber tracts or right SLF were included in the model. Further, the effect of FA in left SLF appeared to be mediated primarily by decreasing perpendicular diffusivity. Such associations could be related to individual differences among children in the architecture of fronto-parietal connections and/or to differences in the pace of fiber tract development. Further studies are needed to determine the contributions of intrinsic and experiential factors to the development of functionally significant individual differences in fiber tract structure. ■

Smaller intracranial volumes in the monozygotic patients and their cotwins suggest that increased genetic risk to develop schizophrenia is related to reduced brain growth early in life. The additional reduction in whole-brain volume found in the patients suggests that the manifestation of the disorder is related to (neurodegenerative) processes that are most likely nongenetic in origin.