Study Of Brain Diseases Research Articles

A multiparametric and multiscale approach to automated segmentation of brain veins.

Cerebral vein analysis provides a fundamental tool to study brain diseases such as neurodegenerative disorders or traumatic brain injuries. In order to assess the vascular anatomy, manual segmentation approaches can be used but are observer-dependent and time-consuming. In the present work, a fully automated cerebral vein segmentation method is proposed, based on a multiscale and multiparametric approach. The combined investigation of the R2(*)- and a Vesselness probability-map was used to obtain a fast and highly reliable classification of venous voxels. A semiquantitative analysis showed that our approach outperformed the previous state-of-the-art algorithm both in sensitivity and specificity. Inclusion of this tool within a parametric brain framework may therefore pave the way for a quantitative study of the intracranial venous system.

Hydrocortisone enhances the barrier properties of HBMEC/ciβ, a brain microvascular endothelial cell line, through mesenchymal-to-endothelial transition-like effects.

BackgroundBecause in vitro blood–brain barrier (BBB) models are important tools for studying brain diseases and drug development, we recently established a new line of conditionally immortalized human brain microvascular endothelial cells (HBMEC/ciβ) for use in such models. Since one of the most important functional features of the BBB is its strong intercellular adhesion, in this study, we aimed at improving HBMEC/ciβ barrier properties by means of culture media modifications, thus enhancing their use for future BBB studies. In addition, we simultaneously attempted to obtain insights on related mechanistic properties.MethodsSeveral types of culture media were prepared in an effort to identify the medium most suitable for culturing HBMEC/ciβ. The barrier properties of HBMEC/ciβ were examined by determining Na+-fluorescein permeability and transendothelial electric resistance (TEER). Endothelial marker mRNA expression levels were determined by quantitative real-time polymerase chain reaction. Adherens junction (AJ) formation was examined by immunocytochemistry. Cell migration ability was analyzed by scratch assay. Furthermore, cellular lipid composition was examined by liquid chromatography-time-of-flight mass spectrometry.ResultsOur initial screening tests showed that addition of hydrocortisone (HC) to the basal medium significantly reduced the Na+-fluorescein permeability and increased the TEER of HBMEC/ciβ monolayers. It was also found that, while AJ proteins were diffused in the cytoplasm of HBMEC/ciβ cultured without HC, those expressed in cells cultured with HC were primarily localized at the cell border. Furthermore, this facilitation of AJ formation by HC was in concert with increased endothelial marker mRNA levels and increased ether-type phosphatidylethanolamine levels, while cell migration was retarded in the presence of HC.ConclusionsOur results show that HC supplementation to the basal medium significantly enhances the barrier properties of HBMEC/ciβ. This was associated with a marked phenotypic alteration in HBMEC/ciβ through orchestration of various signaling pathways. Taken together, it appears that overall effects of HC on HBMEC/ciβ could be summarized as facilitating endothelial differentiation characteristics while concurrently retarding mesenchymal characteristics.Electronic supplementary materialThe online version of this article (doi:10.1186/s12987-015-0003-0) contains supplementary material, which is available to authorized users.

Disruption of resting functional connectivity in Alzheimer's patients and at-risk subjects.

The resting brain exhibits continuous intrinsic activity, which is correlated between groups of regions forming resting state networks. Evaluating resting connectivity is a popular approach for studying brain diseases. Several hundred studies are now available that address integrity of resting connectivity in patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI), as well as preclinical at-risk subjects. Most studies focus on the default mode network, a system of specific brain areas showing strong connected resting activity that attenuates during goal-directed behavior. The extent of intrinsic brain activity tends to be strongly correlated with cognitive processes and is specifically disrupted in AD and MCI patients and at-risk subjects, with changes seeming to evolve during the transition between the disease stages. In this study, we review the current findings in default mode network and other resting state network studies in AD and MCI patients and at-risk subjects as assessed by resting state functional magnetic resonance imaging.

Brain waves for automatic biometric-based user recognition

Brain signals have been investigated within the medical field for more than a century to study brain diseases like epilepsy, spinal cord injuries, Alzheimer's, Parkinson's, schizophrenia, and stroke among others. They are also used in both brain computer and brain machine interface systems with assistance, rehabilitative, and entertainment applications. Despite the broad interest in clinical applications, the use of brain signals has been only recently investigated by the scientific community as a biometric characteristic to be used in automatic people recognition systems. However, brain signals present some peculiarities, not shared by the most commonly used biometrics, such as face, iris, and fingerprints, with reference to privacy compliance, robustness against spoofing attacks, possibility to perform continuous identification, intrinsic liveness detection, and universality. These peculiarities make the use of brain signals appealing. On the other hand, there are many challenges which need to be properly addressed. The understanding of the level of uniqueness and permanence of brain responses, the design of elicitation protocols, and the invasiveness of the acquisition process are only few of the challenges which need to be tackled. In this paper, we further speculate on those issues, which represent an obstacle toward the deployment of biometric systems based on the analysis of brain activity in real life applications and intend to provide a critical and comprehensive review of state-of-the-art methods for electroencephalogram-based automatic user recognition, also reporting neurophysiological evidences related to the performed claims.

Reprogramming of adult human neural stem cells into induced pluripotent stem cells

Background Since an effective method for generating induced pluripotent stem cells (iPSCs) from human neural stem cells (hNSCs) can offer us a promising tool for studying brain diseases, here we reported direct reprogramming of adult hNSCs into iPSCs by retroviral transduction of four defined factors. Methods NSCs were successfully isolated and cultured from the hippocampus tissue of epilepsy patients. When combined with four factors (OCT3/4, SOX2, KLF4, and c-MYC), iPSCs colonies were successfully obtained. Results Morphological characterization and specific genetic expression confirmed that these hNSCs-derived iPSCs showed embryonic stem cells-like properties, which include the ability to differentiate into all three germ layers both in vitro and in vivo. Conclusion Our method would be useful for generating human iPSCs from NSCs and provide an important tool for studying neurological diseases.

Connectomics Sheds New Light on Alzheimer’s Disease

Connectomics Sheds New Light on Alzheimer’s Disease

Neurons from reprogrammed cells

‘Diseases in a dish’ are starting to show intriguing symptoms. Neurons made from the reprogrammed cells of individuals with schizophrenia form relatively few connections, but these increase upon exposure to a known antipsychotic1. Neurons made from individuals with Parkinson’s disease are more sensitive to oxidative stress2 and show mitochondrial deficits3. Those made from individuals with spinal muscular atrophy die quickly in culture4. Those from people with Alzheimer’s make higher concentrations of amyloid-b than those from people without the disease5 (Table 1). Not so long ago, these kinds of experiments would have been inconceivable. Compared with other organs, brain tissue is particularly hard to collect from patient biopsies. Neuroscientists have long had to make difficult choices between studying nonhuman cells or non-neural cells. But in 2007, researchers showed that human cells could be reprogrammed into a pluripotent state by adding genes active in the earliest stages of the embryo; the resultant cells could subsequently differentiate into all the major types of cells, including neurons. As reprogramming technology became more robust, academic hospitals around the world set up core facilities to generate cell lines from diagnosed patients. A cornucopia of induced pluripotent stem (iPS) cells has been derived from individuals diagnosed with myriad diseases. Thanks to reprogramming, onceprecious neural cells can now, in principle, be made in unlimited supplies. In addition, every cell line is genetically matched to someone with a known medical history. Human embryonic stem (hES) cells can also, in principle, supply endless quantities of cells. However, because making iPS cells is relatively easy and provides cells from a known individual for studying disease or developing therapies, much work on hES cells now involves using them as controls for comparison with iPS cells. The potential of iPS cells has generated a steady drumbeat of publications. But, like all exciting technologies, the road from proof of principle to routine practice is arduous. Researchers hoping to use reprogrammed cells to study brain disease first have to deal with a host of less glamorous tasks: reducing experimental variation across cell lines, pro-

N-Acetylcysteine Normalizes Neurochemical Changes in the Glutathione-Deficient Schizophrenia Mouse Model During Development

Glutathione (GSH) is the major cellular redox-regulator and antioxidant. Redox-imbalance due to genetically impaired GSH synthesis is among the risk factors for schizophrenia. Here we used a mouse model with chronic GSH deficit induced by knockout (KO) of the key GSH-synthesizing enzyme, glutamate-cysteine ligase modulatory subunit (GCLM). With high-resolution magnetic resonance spectroscopy at 14.1 T, we determined the neurochemical profile of GCLM-KO, heterozygous, and wild-type mice in anterior cortex throughout development in a longitudinal study design. Chronic GSH deficit was accompanied by an elevation of glutamine (Gln), glutamate (Glu), Gln/Glu, N-acetylaspartate, myo-Inositol, lactate, and alanine. Changes were predominantly present at prepubertal ages (postnatal days 20 and 30). Treatment with N-acetylcysteine from gestation on normalized most neurochemical alterations to wild-type level. Changes observed in GCLM-KO anterior cortex, notably the increase in Gln, Glu, and Gln/Glu, were similar to those reported in early schizophrenia, emphasizing the link between redox imbalance and the disease and validating the model. The data also highlight the prepubertal period as a sensitive time for redox-related neurochemical changes and demonstrate beneficial effects of early N-acetylcysteine treatment. Moreover, the data demonstrate the translational value of magnetic resonance spectroscopy to study brain disease in preclinical models.

Induced pluripotent stem cells (iPSCs) and neurological disease modeling: progress and promises

The systematic generation of neurons from patients with neurological disorders can provide important insights into disease pathology, progression and mechanism. This review will discuss recent progress in modeling neurodegenerative and neurodevelopmental diseases using induced pluripotent stem cells (iPSCs) and highlight some of the current challenges in the field. Combined with other technologies previously used to study brain disease, iPSC modeling has the promise to influence modern medicine on several fronts: early diagnosis, drug development and effective treatment.

Age- and gender-related changes in the normal human brain using hybrid diffusion imaging (HYDI)

Diffusion tensor imaging has been widely used to study brain diseases, disorders, development, and aging. However, few studies have explored the effects of aging on diffusion imaging measures with higher b values. Further, the water diffusion in biological tissues appears biexponential, although this also has not been explored with aging. In this study, hybrid diffusion imaging (HYDI) was used to study 52 healthy subjects with an age range from 18 to 72years. The HYDI diffusion-encoding scheme consisted of five concentric q-space shells with b values ranging from 0 to 9375s/mm2. Quantitative diffusion measures were investigated as a function of age and gender using both region-of-interest (whole-brain white matter, genu and splenium of corpus callosum, posterior limb of the internal capsule) and whole-brain voxel-based analyses. Diffusion measures included measures of the diffusion probability density function (zero displacement probability and mean-squared displacement), biexponential diffusion (i.e., volume fractions of fast/slow diffusion compartments and fast/slow diffusivities), and DTI measures (fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity). The biexponential volume fraction, the fast diffusivity, and the axial diffusivity measures (f1, D1, and Da) were found to be more sensitive to normal aging than the restricted, slow and radial diffusion measures (P0, D2, and Dr). The biexponential volume fraction, f1, showed the most widespread age dependence in the voxel-based analyses, although both FA and mean diffusivity did show changes in frontal white matter regions that may be associated with age-related decline.

Quantitative proteomic profiling of membrane proteins from the mouse brain cortex, hippocampus, and cerebellum using the HysTag reagent: Mapping of neurotransmitter receptors and ion channels

Analysis of the brain proteome and studying brain diseases through clinical biopsies and animal disease models require methods of quantitative proteomics that are sensitive and allow identification and quantification of low abundant membrane proteins from minute amount of tissue. Taking advantage of recently developed methods for isolation of membrane proteins from 10–20 mg brain tissue [Nielsen, P.Aa., Olsen, J.V., Podtelejnokov, A.V., Andersen, J.R., Mann, M., Wiśniewski, J.R., 2005. Proteomic mapping of brain plasma membrane proteins. Mol. Cell. Proteomics 4, 402--408] and the HysTag-quantification method [Olsen, J.V., Andersen, J.R., Nielsen, P.Aa., Nielsen, M.L., Figeys, D., Mann, M., Wiśniewski, J.R., 2004. HysTag---A novel proteomic qualification tool applied to differential analysis of membrane proteins from distinct areas of mouse brain. Mol. Cell. Proteomics 3, 82--92] we performed quantitative proteomic analysis of three functionally distinct compartments of mouse brain: cortex, hippocampus, and cerebellum. In total, 976 unique peptides corresponding to 555 unique proteins were quantified. Up to 20-fold differences in the levels of some proteins between brain areas were measured. For many quantified proteins – as for glutamate receptors, calcium channel subunits, and ATP-ases – an excellent correlation between our proteomic data and previously published mRNA expression levels or intensity of immunostaining was found. Our results clearly demonstrate differences in levels of membrane proteins mapped in distinct brain compartments and offer a technology that allows in depth study of brain membrane proteomes, such as mouse models of neurological diseases.

Brain anatomical feature detection by solving partial differential equations on general manifolds

One important problem in human brain mapping research is to locate the important anatomical features. Anatomical features on the cortical surface are usually represented by landmark curves, called sulci/gyri curves. These landmark curves are important information for neuroscientists to study brain disease and to match different cortical surfaces. Manual labelling of these landmark curves is time-consuming, especially when large sets of data have to be analyzed. In this paper, we present algorithms to automatically detect and match landmark curves on cortical surfaces to get an optimized brain conformal parametrization. First, we propose an algorithm to obtain a hypothesized landmark region/curves using the Chan-Vese segmentation method, which solves a Partial Differential Equation (PDE) on a manifold with global conformal parameterization. This is done by segmentating the high mean curvature region. Second, we propose an automatic landmark curve tracing method based on the principal directions of the local Weingarten matrix. Based on the global conformal parametrization of a cortical surface, our method adjusts the landmark curves iteratively on the spherical or rectangular parameter domain of the cortical surface along its principal direction field, using umbilic points of the surface as anchors. The landmark curves can then be mapped back onto the cortical surface. Experimental results show that the landmark curves detected by our algorithm closely resemble these manually labeled curves. Next, we applied these automatically labeled landmark curves to generate an optimized conformal parametrization of the cortical surface, in the sense that homologous features across subjects are caused to lie at the same parameter locations in a conformal grid. Experimental results show that our method can effectively help in automatically matching cortical surfaces across subjects.