An improved high-throughput lipid extraction method for the analysis of human brain lipids.

PubMed

Abbott, Sarah K; Jenner, Andrew M; Mitchell, Todd W; Brown, Simon H J; Halliday, Glenda M; Garner, Brett

2013-03-01

We have developed a protocol suitable for high-throughput lipidomic analysis of human brain samples. The traditional Folch extraction (using chloroform and glass-glass homogenization) was compared to a high-throughput method combining methyl-tert-butyl ether (MTBE) extraction with mechanical homogenization utilizing ceramic beads. This high-throughput method significantly reduced sample handling time and increased efficiency compared to glass-glass homogenizing. Furthermore, replacing chloroform with MTBE is safer (less carcinogenic/toxic), with lipids dissolving in the upper phase, allowing for easier pipetting and the potential for automation (i.e., robotics). Both methods were applied to the analysis of human occipital cortex. Lipid species (including ceramides, sphingomyelins, choline glycerophospholipids, ethanolamine glycerophospholipids and phosphatidylserines) were analyzed via electrospray ionization mass spectrometry and sterol species were analyzed using gas chromatography mass spectrometry. No differences in lipid species composition were evident when the lipid extraction protocols were compared, indicating that MTBE extraction with mechanical bead homogenization provides an improved method for the lipidomic profiling of human brain tissue.

[A Feature Extraction Method for Brain Computer Interface Based on Multivariate Empirical Mode Decomposition].

PubMed

Wang, Jinjia; Liu, Yuan

2015-04-01

This paper presents a feature extraction method based on multivariate empirical mode decomposition (MEMD) combining with the power spectrum feature, and the method aims at the non-stationary electroencephalogram (EEG) or magnetoencephalogram (MEG) signal in brain-computer interface (BCI) system. Firstly, we utilized MEMD algorithm to decompose multichannel brain signals into a series of multiple intrinsic mode function (IMF), which was proximate stationary and with multi-scale. Then we extracted and reduced the power characteristic from each IMF to a lower dimensions using principal component analysis (PCA). Finally, we classified the motor imagery tasks by linear discriminant analysis classifier. The experimental verification showed that the correct recognition rates of the two-class and four-class tasks of the BCI competition III and competition IV reached 92.0% and 46.2%, respectively, which were superior to the winner of the BCI competition. The experimental proved that the proposed method was reasonably effective and stable and it would provide a new way for feature extraction.

Segmentation of brain volume based on 3D region growing by integrating intensity and edge for image-guided surgery

NASA Astrophysics Data System (ADS)

Tsagaan, Baigalmaa; Abe, Keiichi; Goto, Masahiro; Yamamoto, Seiji; Terakawa, Susumu

2006-03-01

This paper presents a segmentation method of brain tissues from MR images, invented for our image-guided neurosurgery system under development. Our goal is to segment brain tissues for creating biomechanical model. The proposed segmentation method is based on 3-D region growing and outperforms conventional approaches by stepwise usage of intensity similarities between voxels in conjunction with edge information. Since the intensity and the edge information are complementary to each other in the region-based segmentation, we use them twice by performing a coarse-to-fine extraction. First, the edge information in an appropriate neighborhood of the voxel being considered is examined to constrain the region growing. The expanded region of the first extraction result is then used as the domain for the next processing. The intensity and the edge information of the current voxel only are utilized in the final extraction. Before segmentation, the intensity parameters of the brain tissues as well as partial volume effect are estimated by using expectation-maximization (EM) algorithm in order to provide an accurate data interpretation into the extraction. We tested the proposed method on T1-weighted MR images of brain and evaluated the segmentation effectiveness comparing the results with ground truths. Also, the generated meshes from the segmented brain volume by using mesh generating software are shown in this paper.

Estimation of effective brain connectivity with dual Kalman filter and EEG source localization methods.

PubMed

Rajabioun, Mehdi; Nasrabadi, Ali Motie; Shamsollahi, Mohammad Bagher

2017-09-01

Effective connectivity is one of the most important considerations in brain functional mapping via EEG. It demonstrates the effects of a particular active brain region on others. In this paper, a new method is proposed which is based on dual Kalman filter. In this method, firstly by using a brain active localization method (standardized low resolution brain electromagnetic tomography) and applying it to EEG signal, active regions are extracted, and appropriate time model (multivariate autoregressive model) is fitted to extracted brain active sources for evaluating the activity and time dependence between sources. Then, dual Kalman filter is used to estimate model parameters or effective connectivity between active regions. The advantage of this method is the estimation of different brain parts activity simultaneously with the calculation of effective connectivity between active regions. By combining dual Kalman filter with brain source localization methods, in addition to the connectivity estimation between parts, source activity is updated during the time. The proposed method performance has been evaluated firstly by applying it to simulated EEG signals with interacting connectivity simulation between active parts. Noisy simulated signals with different signal to noise ratios are used for evaluating method sensitivity to noise and comparing proposed method performance with other methods. Then the method is applied to real signals and the estimation error during a sweeping window is calculated. By comparing proposed method results in different simulation (simulated and real signals), proposed method gives acceptable results with least mean square error in noisy or real conditions.

Approximation-based common principal component for feature extraction in multi-class brain-computer interfaces.

PubMed

Hoang, Tuan; Tran, Dat; Huang, Xu

2013-01-01

Common Spatial Pattern (CSP) is a state-of-the-art method for feature extraction in Brain-Computer Interface (BCI) systems. However it is designed for 2-class BCI classification problems. Current extensions of this method to multiple classes based on subspace union and covariance matrix similarity do not provide a high performance. This paper presents a new approach to solving multi-class BCI classification problems by forming a subspace resembled from original subspaces and the proposed method for this approach is called Approximation-based Common Principal Component (ACPC). We perform experiments on Dataset 2a used in BCI Competition IV to evaluate the proposed method. This dataset was designed for motor imagery classification with 4 classes. Preliminary experiments show that the proposed ACPC feature extraction method when combining with Support Vector Machines outperforms CSP-based feature extraction methods on the experimental dataset.

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

PubMed Central

2017-01-01

Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain–computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method. PMID:28558002

Real time system design of motor imagery brain-computer interface based on multi band CSP and SVM

NASA Astrophysics Data System (ADS)

Zhao, Li; Li, Xiaoqin; Bian, Yan

2018-04-01

Motion imagery (MT) is an effective method to promote the recovery of limbs in patients after stroke. Though an online MT brain computer interface (BCT) system, which apply MT, can enhance the patient's participation and accelerate their recovery process. The traditional method deals with the electroencephalogram (EEG) induced by MT by common spatial pattern (CSP), which is used to extract information from a frequency band. Tn order to further improve the classification accuracy of the system, information of two characteristic frequency bands is extracted. The effectiveness of the proposed feature extraction method is verified by off-line analysis of competition data and the analysis of online system.

Brain extraction from normal and pathological images: A joint PCA/Image-Reconstruction approach.

PubMed

Han, Xu; Kwitt, Roland; Aylward, Stephen; Bakas, Spyridon; Menze, Bjoern; Asturias, Alexander; Vespa, Paul; Van Horn, John; Niethammer, Marc

2018-08-01

Brain extraction from 3D medical images is a common pre-processing step. A variety of approaches exist, but they are frequently only designed to perform brain extraction from images without strong pathologies. Extracting the brain from images exhibiting strong pathologies, for example, the presence of a brain tumor or of a traumatic brain injury (TBI), is challenging. In such cases, tissue appearance may substantially deviate from normal tissue appearance and hence violates algorithmic assumptions for standard approaches to brain extraction; consequently, the brain may not be correctly extracted. This paper proposes a brain extraction approach which can explicitly account for pathologies by jointly modeling normal tissue appearance and pathologies. Specifically, our model uses a three-part image decomposition: (1) normal tissue appearance is captured by principal component analysis (PCA), (2) pathologies are captured via a total variation term, and (3) the skull and surrounding tissue is captured by a sparsity term. Due to its convexity, the resulting decomposition model allows for efficient optimization. Decomposition and image registration steps are alternated to allow statistical modeling of normal tissue appearance in a fixed atlas coordinate system. As a beneficial side effect, the decomposition model allows for the identification of potentially pathological areas and the reconstruction of a quasi-normal image in atlas space. We demonstrate the effectiveness of our approach on four datasets: the publicly available IBSR and LPBA40 datasets which show normal image appearance, the BRATS dataset containing images with brain tumors, and a dataset containing clinical TBI images. We compare the performance with other popular brain extraction models: ROBEX, BEaST, MASS, BET, BSE and a recently proposed deep learning approach. Our model performs better than these competing approaches on all four datasets. Specifically, our model achieves the best median (97.11) and mean (96.88) Dice scores over all datasets. The two best performing competitors, ROBEX and MASS, achieve scores of 96.23/95.62 and 96.67/94.25 respectively. Hence, our approach is an effective method for high quality brain extraction for a wide variety of images. Copyright © 2018 Elsevier Inc. All rights reserved.

Method and apparatus for extraction of low-frequency artifacts from brain waves for alertness detection

DOEpatents

Clapp, Ned E.; Hively, Lee M.

1997-01-01

Methods and apparatus automatically detect alertness in humans by monitoring and analyzing brain wave signals. Steps include: acquiring the brain wave (EEG or MEG) data from the subject, digitizing the data, separating artifact data from raw data, and comparing trends in f-data to alertness indicators, providing notification of inadequate alertness.

Integrated feature extraction and selection for neuroimage classification

NASA Astrophysics Data System (ADS)

Fan, Yong; Shen, Dinggang

2009-02-01

Feature extraction and selection are of great importance in neuroimage classification for identifying informative features and reducing feature dimensionality, which are generally implemented as two separate steps. This paper presents an integrated feature extraction and selection algorithm with two iterative steps: constrained subspace learning based feature extraction and support vector machine (SVM) based feature selection. The subspace learning based feature extraction focuses on the brain regions with higher possibility of being affected by the disease under study, while the possibility of brain regions being affected by disease is estimated by the SVM based feature selection, in conjunction with SVM classification. This algorithm can not only take into account the inter-correlation among different brain regions, but also overcome the limitation of traditional subspace learning based feature extraction methods. To achieve robust performance and optimal selection of parameters involved in feature extraction, selection, and classification, a bootstrapping strategy is used to generate multiple versions of training and testing sets for parameter optimization, according to the classification performance measured by the area under the ROC (receiver operating characteristic) curve. The integrated feature extraction and selection method is applied to a structural MR image based Alzheimer's disease (AD) study with 98 non-demented and 100 demented subjects. Cross-validation results indicate that the proposed algorithm can improve performance of the traditional subspace learning based classification.

Nonlocal Intracranial Cavity Extraction

PubMed Central

Manjón, José V.; Eskildsen, Simon F.; Coupé, Pierrick; Romero, José E.; Collins, D. Louis; Robles, Montserrat

2014-01-01

Automatic and accurate methods to estimate normalized regional brain volumes from MRI data are valuable tools which may help to obtain an objective diagnosis and followup of many neurological diseases. To estimate such regional brain volumes, the intracranial cavity volume (ICV) is often used for normalization. However, the high variability of brain shape and size due to normal intersubject variability, normal changes occurring over the lifespan, and abnormal changes due to disease makes the ICV estimation problem challenging. In this paper, we present a new approach to perform ICV extraction based on the use of a library of prelabeled brain images to capture the large variability of brain shapes. To this end, an improved nonlocal label fusion scheme based on BEaST technique is proposed to increase the accuracy of the ICV estimation. The proposed method is compared with recent state-of-the-art methods and the results demonstrate an improved performance both in terms of accuracy and reproducibility while maintaining a reduced computational burden. PMID:25328511

Method and apparatus for extraction of low-frequency artifacts from brain waves for alertness detection

DOEpatents

Clapp, N.E.; Hively, L.M.

1997-05-06

Methods and apparatus automatically detect alertness in humans by monitoring and analyzing brain wave signals. Steps include: acquiring the brain wave (EEG or MEG) data from the subject, digitizing the data, separating artifact data from raw data, and comparing trends in f-data to alertness indicators, providing notification of inadequate alertness. 4 figs.

A Method for Automatic Extracting Intracranial Region in MR Brain Image

NASA Astrophysics Data System (ADS)

Kurokawa, Keiji; Miura, Shin; Nishida, Makoto; Kageyama, Yoichi; Namura, Ikuro

It is well known that temporal lobe in MR brain image is in use for estimating the grade of Alzheimer-type dementia. It is difficult to use only region of temporal lobe for estimating the grade of Alzheimer-type dementia. From the standpoint for supporting the medical specialists, this paper proposes a data processing approach on the automatic extraction of the intracranial region from the MR brain image. The method is able to eliminate the cranium region with the laplacian histogram method and the brainstem with the feature points which are related to the observations given by a medical specialist. In order to examine the usefulness of the proposed approach, the percentage of the temporal lobe in the intracranial region was calculated. As a result, the percentage of temporal lobe in the intracranial region on the process of the grade was in agreement with the visual sense standards of temporal lobe atrophy given by the medical specialist. It became clear that intracranial region extracted by the proposed method was good for estimating the grade of Alzheimer-type dementia.

Distribution of the hallucinogens N,N-dimethyltryptamine and 5-methoxy-N,N-dimethyltryptamine in rat brain following intraperitoneal injection: application of a new solid-phase extraction LC-APcI-MS-MS-isotope dilution method.

PubMed

Barker, S A; Littlefield-Chabaud, M A; David, C

2001-02-10

A method for the solid-phase extraction (SPE) and liquid chromatographic-atmospheric pressure chemical ionization-mass spectrometric-mass spectrometric-isotope dilution (LC-APcI-MS-MS-ID) analysis of the indole hallucinogens N,N-dimethyltryptamine (DMT) and 5-methoxy DMT (or O-methyl bufotenin, OMB) from rat brain tissue is reported. Rats were administered DMT or OMB by the intraperitoneal route at a dose of 5 mg/kg and sacrificed 15 min post treatment. Brains were dissected into discrete areas and analyzed by the methods described as a demonstration of the procedure's applicability. The synthesis and use of two new deuterated internal standards for these purposes are also reported.

Brain's tumor image processing using shearlet transform

NASA Astrophysics Data System (ADS)

Cadena, Luis; Espinosa, Nikolai; Cadena, Franklin; Korneeva, Anna; Kruglyakov, Alexey; Legalov, Alexander; Romanenko, Alexey; Zotin, Alexander

2017-09-01

Brain tumor detection is well known research area for medical and computer scientists. In last decades there has been much research done on tumor detection, segmentation, and classification. Medical imaging plays a central role in the diagnosis of brain tumors and nowadays uses methods non-invasive, high-resolution techniques, especially magnetic resonance imaging and computed tomography scans. Edge detection is a fundamental tool in image processing, particularly in the areas of feature detection and feature extraction, which aim at identifying points in a digital image at which the image has discontinuities. Shearlets is the most successful frameworks for the efficient representation of multidimensional data, capturing edges and other anisotropic features which frequently dominate multidimensional phenomena. The paper proposes an improved brain tumor detection method by automatically detecting tumor location in MR images, its features are extracted by new shearlet transform.

Precise and accurate assay of pregnenolone and five other neurosteroids in monkey brain tissue by LC-MS/MS.

PubMed

Dury, Alain Y; Ke, Yuyong; Labrie, Fernand

2016-09-01

A series of steroids present in the brain have been named "neurosteroids" following the possibility of their role in the central nervous system impairments such as anxiety disorders, depression, premenstrual dysphoric disorder (PMDD), addiction, or even neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. Study of their potential role requires a sensitive and accurate assay of their concentration in the monkey brain, the closest model to the human. We have thus developed a robust, precise and accurate liquid chromatography-tandem mass spectrometry method for the assay of pregnenolone, pregnanolone, epipregnanolone, allopregnanolone, epiallopregnanolone, and androsterone in the cynomolgus monkey brain. The extraction method includes a thorough sample cleanup using protein precipitation and phospholipid removal, followed by hexane liquid-liquid extraction and a Girard T ketone-specific derivatization. This method opens the possibility of investigating the potential implication of these six steroids in the most suitable animal model for neurosteroid-related research. Copyright © 2016 Elsevier Inc. All rights reserved.

Using Fractal and Local Binary Pattern Features for Classification of ECOG Motor Imagery Tasks Obtained from the Right Brain Hemisphere.

PubMed

Xu, Fangzhou; Zhou, Weidong; Zhen, Yilin; Yuan, Qi; Wu, Qi

2016-09-01

The feature extraction and classification of brain signal is very significant in brain-computer interface (BCI). In this study, we describe an algorithm for motor imagery (MI) classification of electrocorticogram (ECoG)-based BCI. The proposed approach employs multi-resolution fractal measures and local binary pattern (LBP) operators to form a combined feature for characterizing an ECoG epoch recording from the right hemisphere of the brain. A classifier is trained by using the gradient boosting in conjunction with ordinary least squares (OLS) method. The fractal intercept, lacunarity and LBP features are extracted to classify imagined movements of either the left small finger or the tongue. Experimental results on dataset I of BCI competition III demonstrate the superior performance of our method. The cross-validation accuracy and accuracy is 90.6% and 95%, respectively. Furthermore, the low computational burden of this method makes it a promising candidate for real-time BCI systems.

Immunomodulatory effect of Hawthorn extract in an experimental stroke model

PubMed Central

2010-01-01

Background Recently, we reported a neuroprotective effect for Hawthorn (Crataegus oxyacantha) ethanolic extract in middle cerebral artery occlusion-(MCAO) induced stroke in rats. The present study sheds more light on the extract's mechanism of neuroprotection, especially its immunomodulatory effect. Methods After 15 days of treatment with Hawthorn extract [100 mg/kg, pretreatment (oral)], male Sprague Dawley rats underwent transient MCAO for 75 mins followed by reperfusion (either 3 or 24 hrs). We measured pro-inflammatory cytokines (IL-1β, TNF-α, IL-6), ICAM-1, IL-10 and pSTAT-3 expression in the brain by appropriate methods. We also looked at the cytotoxic T cell sub-population among leukocytes (FACS) and inflammatory cell activation and recruitment in brain (using a myeloperoxidase activity assay) after ischemia and reperfusion (I/R). Apoptosis (TUNEL), and Bcl-xL- and Foxp3- (Treg marker) positive cells in the ipsilateral hemisphere of the brain were analyzed separately using immunofluorescence. Results Our results indicate that occlusion followed by 3 hrs of reperfusion increased pro-inflammatory cytokine and ICAM-1 gene expressions in the ipsilateral hemisphere, and that Hawthorn pre-treatment significantly (p ≤ 0.01) lowered these levels. Furthermore, such pre-treatment was able to increase IL-10 levels and Foxp3-positive cells in brain after 24 hrs of reperfusion. The increase in cytotoxic T cell population in vehicle rats after 24 hrs of reperfusion was decreased by at least 40% with Hawthorn pretreatment. In addition, there was a decrease in inflammatory cell activation and infiltration in pretreated brain. Hawthorn pretreatment elevated pSTAT-3 levels in brain after I/R. We also observed an increase in Bcl-xL-positive cells, which in turn may have influenced the reduction in TUNEL-positive cells compared to vehicle-treated brain. Conclusions In summary, Hawthorn extract helped alleviate pro-inflammatory immune responses associated with I/R-induced injury, boosted IL-10 levels, and increased Foxp3-positive Tregs in the brain, which may have aided in suppression of activated inflammatory cells. Such treatment also minimizes apoptotic cell death by influencing STAT-3 phosphorylation and Bcl-xL expression in the brain. Taken together, the immunomodulatory effect of Hawthorn extract may play a critical role in the neuroprotection observed in this MCAO-induced stroke model. PMID:21192826

Knowledge-Guided Robust MRI Brain Extraction for Diverse Large-Scale Neuroimaging Studies on Humans and Non-Human Primates

PubMed Central

Wang, Yaping; Nie, Jingxin; Yap, Pew-Thian; Li, Gang; Shi, Feng; Geng, Xiujuan; Guo, Lei; Shen, Dinggang

2014-01-01

Accurate and robust brain extraction is a critical step in most neuroimaging analysis pipelines. In particular, for the large-scale multi-site neuroimaging studies involving a significant number of subjects with diverse age and diagnostic groups, accurate and robust extraction of the brain automatically and consistently is highly desirable. In this paper, we introduce population-specific probability maps to guide the brain extraction of diverse subject groups, including both healthy and diseased adult human populations, both developing and aging human populations, as well as non-human primates. Specifically, the proposed method combines an atlas-based approach, for coarse skull-stripping, with a deformable-surface-based approach that is guided by local intensity information and population-specific prior information learned from a set of real brain images for more localized refinement. Comprehensive quantitative evaluations were performed on the diverse large-scale populations of ADNI dataset with over 800 subjects (55∼90 years of age, multi-site, various diagnosis groups), OASIS dataset with over 400 subjects (18∼96 years of age, wide age range, various diagnosis groups), and NIH pediatrics dataset with 150 subjects (5∼18 years of age, multi-site, wide age range as a complementary age group to the adult dataset). The results demonstrate that our method consistently yields the best overall results across almost the entire human life span, with only a single set of parameters. To demonstrate its capability to work on non-human primates, the proposed method is further evaluated using a rhesus macaque dataset with 20 subjects. Quantitative comparisons with popularly used state-of-the-art methods, including BET, Two-pass BET, BET-B, BSE, HWA, ROBEX and AFNI, demonstrate that the proposed method performs favorably with superior performance on all testing datasets, indicating its robustness and effectiveness. PMID:24489639

Performance analysis of unsupervised optimal fuzzy clustering algorithm for MRI brain tumor segmentation.

PubMed

Blessy, S A Praylin Selva; Sulochana, C Helen

2015-01-01

Segmentation of brain tumor from Magnetic Resonance Imaging (MRI) becomes very complicated due to the structural complexities of human brain and the presence of intensity inhomogeneities. To propose a method that effectively segments brain tumor from MR images and to evaluate the performance of unsupervised optimal fuzzy clustering (UOFC) algorithm for segmentation of brain tumor from MR images. Segmentation is done by preprocessing the MR image to standardize intensity inhomogeneities followed by feature extraction, feature fusion and clustering. Different validation measures are used to evaluate the performance of the proposed method using different clustering algorithms. The proposed method using UOFC algorithm produces high sensitivity (96%) and low specificity (4%) compared to other clustering methods. Validation results clearly show that the proposed method with UOFC algorithm effectively segments brain tumor from MR images.

On characterizing population commonalities and subject variations in brain networks.

PubMed

Ghanbari, Yasser; Bloy, Luke; Tunc, Birkan; Shankar, Varsha; Roberts, Timothy P L; Edgar, J Christopher; Schultz, Robert T; Verma, Ragini

2017-05-01

Brain networks based on resting state connectivity as well as inter-regional anatomical pathways obtained using diffusion imaging have provided insight into pathology and development. Such work has underscored the need for methods that can extract sub-networks that can accurately capture the connectivity patterns of the underlying population while simultaneously describing the variation of sub-networks at the subject level. We have designed a multi-layer graph clustering method that extracts clusters of nodes, called 'network hubs', which display higher levels of connectivity within the cluster than to the rest of the brain. The method determines an atlas of network hubs that describes the population, as well as weights that characterize subject-wise variation in terms of within- and between-hub connectivity. This lowers the dimensionality of brain networks, thereby providing a representation amenable to statistical analyses. The applicability of the proposed technique is demonstrated by extracting an atlas of network hubs for a population of typically developing controls (TDCs) as well as children with autism spectrum disorder (ASD), and using the structural and functional networks of a population to determine the subject-level variation of these hubs and their inter-connectivity. These hubs are then used to compare ASD and TDCs. Our method is generalizable to any population whose connectivity (structural or functional) can be captured via non-negative network graphs. Copyright © 2015 Elsevier B.V. All rights reserved.

User-customized brain computer interfaces using Bayesian optimization

NASA Astrophysics Data System (ADS)

Bashashati, Hossein; Ward, Rabab K.; Bashashati, Ali

2016-04-01

Objective. The brain characteristics of different people are not the same. Brain computer interfaces (BCIs) should thus be customized for each individual person. In motor-imagery based synchronous BCIs, a number of parameters (referred to as hyper-parameters) including the EEG frequency bands, the channels and the time intervals from which the features are extracted should be pre-determined based on each subject’s brain characteristics. Approach. To determine the hyper-parameter values, previous work has relied on manual or semi-automatic methods that are not applicable to high-dimensional search spaces. In this paper, we propose a fully automatic, scalable and computationally inexpensive algorithm that uses Bayesian optimization to tune these hyper-parameters. We then build different classifiers trained on the sets of hyper-parameter values proposed by the Bayesian optimization. A final classifier aggregates the results of the different classifiers. Main Results. We have applied our method to 21 subjects from three BCI competition datasets. We have conducted rigorous statistical tests, and have shown the positive impact of hyper-parameter optimization in improving the accuracy of BCIs. Furthermore, We have compared our results to those reported in the literature. Significance. Unlike the best reported results in the literature, which are based on more sophisticated feature extraction and classification methods, and rely on prestudies to determine the hyper-parameter values, our method has the advantage of being fully automated, uses less sophisticated feature extraction and classification methods, and yields similar or superior results compared to the best performing designs in the literature.

Machine Learning Classification Combining Multiple Features of A Hyper-Network of fMRI Data in Alzheimer's Disease

PubMed Central

Guo, Hao; Zhang, Fan; Chen, Junjie; Xu, Yong; Xiang, Jie

2017-01-01

Exploring functional interactions among various brain regions is helpful for understanding the pathological underpinnings of neurological disorders. Brain networks provide an important representation of those functional interactions, and thus are widely applied in the diagnosis and classification of neurodegenerative diseases. Many mental disorders involve a sharp decline in cognitive ability as a major symptom, which can be caused by abnormal connectivity patterns among several brain regions. However, conventional functional connectivity networks are usually constructed based on pairwise correlations among different brain regions. This approach ignores higher-order relationships, and cannot effectively characterize the high-order interactions of many brain regions working together. Recent neuroscience research suggests that higher-order relationships between brain regions are important for brain network analysis. Hyper-networks have been proposed that can effectively represent the interactions among brain regions. However, this method extracts the local properties of brain regions as features, but ignores the global topology information, which affects the evaluation of network topology and reduces the performance of the classifier. This problem can be compensated by a subgraph feature-based method, but it is not sensitive to change in a single brain region. Considering that both of these feature extraction methods result in the loss of information, we propose a novel machine learning classification method that combines multiple features of a hyper-network based on functional magnetic resonance imaging in Alzheimer's disease. The method combines the brain region features and subgraph features, and then uses a multi-kernel SVM for classification. This retains not only the global topological information, but also the sensitivity to change in a single brain region. To certify the proposed method, 28 normal control subjects and 38 Alzheimer's disease patients were selected to participate in an experiment. The proposed method achieved satisfactory classification accuracy, with an average of 91.60%. The abnormal brain regions included the bilateral precuneus, right parahippocampal gyrus\\hippocampus, right posterior cingulate gyrus, and other regions that are known to be important in Alzheimer's disease. Machine learning classification combining multiple features of a hyper-network of functional magnetic resonance imaging data in Alzheimer's disease obtains better classification performance. PMID:29209156

Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach

PubMed Central

Sohrabpour, Abbas; Ye, Shuai; Worrell, Gregory A.; Zhang, Wenbo

2016-01-01

Objective Combined source imaging techniques and directional connectivity analysis can provide useful information about the underlying brain networks in a non-invasive fashion. Source imaging techniques have been used successfully to either determine the source of activity or to extract source time-courses for Granger causality analysis, previously. In this work, we utilize source imaging algorithms to both find the network nodes (regions of interest) and then extract the activation time series for further Granger causality analysis. The aim of this work is to find network nodes objectively from noninvasive electromagnetic signals, extract activation time-courses and apply Granger analysis on the extracted series to study brain networks under realistic conditions. Methods Source imaging methods are used to identify network nodes and extract time-courses and then Granger causality analysis is applied to delineate the directional functional connectivity of underlying brain networks. Computer simulations studies where the underlying network (nodes and connectivity pattern) is known were performed; additionally, this approach has been evaluated in partial epilepsy patients to study epilepsy networks from inter-ictal and ictal signals recorded by EEG and/or MEG. Results Localization errors of network nodes are less than 5 mm and normalized connectivity errors of ~20% in estimating underlying brain networks in simulation studies. Additionally, two focal epilepsy patients were studied and the identified nodes driving the epileptic network were concordant with clinical findings from intracranial recordings or surgical resection. Conclusion Our study indicates that combined source imaging algorithms with Granger causality analysis can identify underlying networks precisely (both in terms of network nodes location and internodal connectivity). Significance The combined source imaging and Granger analysis technique is an effective tool for studying normal or pathological brain conditions. PMID:27740473

Glioma grading using cell nuclei morphologic features in digital pathology images

NASA Astrophysics Data System (ADS)

Reza, Syed M. S.; Iftekharuddin, Khan M.

2016-03-01

This work proposes a computationally efficient cell nuclei morphologic feature analysis technique to characterize the brain gliomas in tissue slide images. In this work, our contributions are two-fold: 1) obtain an optimized cell nuclei segmentation method based on the pros and cons of the existing techniques in literature, 2) extract representative features by k-mean clustering of nuclei morphologic features to include area, perimeter, eccentricity, and major axis length. This clustering based representative feature extraction avoids shortcomings of extensive tile [1] [2] and nuclear score [3] based methods for brain glioma grading in pathology images. Multilayer perceptron (MLP) is used to classify extracted features into two tumor types: glioblastoma multiforme (GBM) and low grade glioma (LGG). Quantitative scores such as precision, recall, and accuracy are obtained using 66 clinical patients' images from The Cancer Genome Atlas (TCGA) [4] dataset. On an average ~94% accuracy from 10 fold crossvalidation confirms the efficacy of the proposed method.

Validated Automatic Brain Extraction of Head CT Images

PubMed Central

Muschelli, John; Ullman, Natalie L.; Mould, W. Andrew; Vespa, Paul; Hanley, Daniel F.; Crainiceanu, Ciprian M.

2015-01-01

Background X-ray Computed Tomography (CT) imaging of the brain is commonly used in diagnostic settings. Although CT scans are primarily used in clinical practice, they are increasingly used in research. A fundamental processing step in brain imaging research is brain extraction – the process of separating the brain tissue from all other tissues. Methods for brain extraction have either been 1) validated but not fully automated, or 2) fully automated and informally proposed, but never formally validated. Aim To systematically analyze and validate the performance of FSL's brain extraction tool (BET) on head CT images of patients with intracranial hemorrhage. This was done by comparing the manual gold standard with the results of several versions of automatic brain extraction and by estimating the reliability of automated segmentation of longitudinal scans. The effects of the choice of BET parameters and data smoothing is studied and reported. Methods All images were thresholded using a 0 – 100 Hounsfield units (HU) range. In one variant of the pipeline, data were smoothed using a 3-dimensional Gaussian kernel (σ = 1mm3) and re-thresholded to 0 – 100 HU; in the other, data were not smoothed. BET was applied using 1 of 3 fractional intensity (FI) thresholds: 0.01, 0.1, or 0.35 and any holes in the brain mask were filled. For validation against a manual segmentation, 36 images from patients with intracranial hemorrhage were selected from 19 different centers from the MISTIE (Minimally Invasive Surgery plus recombinant-tissue plasminogen activator for Intracerebral Evacuation) stroke trial. Intracranial masks of the brain were manually created by one expert CT reader. The resulting brain tissue masks were quantitatively compared to the manual segmentations using sensitivity, specificity, accuracy, and the Dice Similarity Index (DSI). Brain extraction performance across smoothing and FI thresholds was compared using the Wilcoxon signed-rank test. The intracranial volume (ICV) of each scan was estimated by multiplying the number of voxels in the brain mask by the dimensions of each voxel for that scan. From this, we calculated the ICV ratio comparing manual and automated segmentation: ICVautomatedICVmanual. To estimate the performance in a large number of scans, brain masks were generated from the 6 BET pipelines for 1095 longitudinal scans from 129 patients. Failure rates were estimated from visual inspection. ICV of each scan was estimated and and an intraclass correlation (ICC) was estimated using a one-way ANOVA. Results Smoothing images improves brain extraction results using BET for all measures except specificity (all p < 0.01, uncorrected), irrespective of the FI threshold. Using an FI of 0.01 or 0.1 performed better than 0.35. Thus, all reported results refer only to smoothed data using an FI of 0.01 or 0.1. Using an FI of 0.01 had a higher median sensitivity (0.9901) than an FI of 0.1 (0.9884, median difference: 0.0014, p < 0.001), accuracy (0.9971 vs. 0.9971; median difference: 0.0001, p < 0.001), and DSI (0.9895 vs. 0.9894; median difference: 0.0004, p < 0.001) and lower specificity (0.9981 vs. 0.9982; median difference: −0.0001, p < 0.001). These measures are all very high indicating that a range of FI values may produce visually indistinguishable brain extractions. Using smoothed data and an FI of 0.01, the mean (SD) ICV ratio was 1.002 (0.008); the mean being close to 1 indicates the ICV estimates are similar for automated and manual segmentation. In the 1095 longitudinal scans, this pipeline had a low failure rate (5.2%) and the ICC estimate was high (0.929, 95% CI: 0.91, 0.945) for successfully extracted brains. Conclusion BET performs well at brain extraction on thresholded, 1mm3 smoothed CT images with an FI of 0.01 or 0.1. Smoothing before applying BET is an important step not previously discussed in the literature. Analysis code is provided. PMID:25862260

A mathematical theory of shape and neuro-fuzzy methodology-based diagnostic analysis: a comparative study on early detection and treatment planning of brain cancer.

PubMed

Kar, Subrata; Majumder, D Dutta

2017-08-01

Investigation of brain cancer can detect the abnormal growth of tissue in the brain using computed tomography (CT) scans and magnetic resonance (MR) images of patients. The proposed method classifies brain cancer on shape-based feature extraction as either benign or malignant. The authors used input variables such as shape distance (SD) and shape similarity measure (SSM) in fuzzy tools, and used fuzzy rules to evaluate the risk status as an output variable. We presented a classifier neural network system (NNS), namely Levenberg-Marquardt (LM), which is a feed-forward back-propagation learning algorithm used to train the NN for the status of brain cancer, if any, and which achieved satisfactory performance with 100% accuracy. The proposed methodology is divided into three phases. First, we find the region of interest (ROI) in the brain to detect the tumors using CT and MR images. Second, we extract the shape-based features, like SD and SSM, and grade the brain tumors as benign or malignant with the concept of SD function and SSM as shape-based parameters. Third, we classify the brain cancers using neuro-fuzzy tools. In this experiment, we used a 16-sample database with SSM (μ) values and classified the benignancy or malignancy of the brain tumor lesions using the neuro-fuzzy system (NFS). We have developed a fuzzy expert system (FES) and NFS for early detection of brain cancer from CT and MR images. In this experiment, shape-based features, such as SD and SSM, were extracted from the ROI of brain tumor lesions. These shape-based features were considered as input variables and, using fuzzy rules, we were able to evaluate brain cancer risk values for each case. We used an NNS with LM, a feed-forward back-propagation learning algorithm, as a classifier for the diagnosis of brain cancer and achieved satisfactory performance with 100% accuracy. The proposed network was trained with MR image datasets of 16 cases. The 16 cases were fed to the ANN with 2 input neurons, one hidden layer of 10 neurons and 2 output neurons. Of the 16-sample database, 10 datasets for training, 3 datasets for validation, and 3 datasets for testing were used in the ANN classification system. From the SSM (µ) confusion matrix, the number of output datasets of true positive, false positive, true negative and false negative was 6, 0, 10, and 0, respectively. The sensitivity, specificity and accuracy were each equal to 100%. The method of diagnosing brain cancer presented in this study is a successful model to assist doctors in the screening and treatment of brain cancer patients. The presented FES successfully identified the presence of brain cancer in CT and MR images using the extracted shape-based features and the use of NFS for the identification of brain cancer in the early stages. From the analysis and diagnosis of the disease, the doctors can decide the stage of cancer and take the necessary steps for more accurate treatment. Here, we have presented an investigation and comparison study of the shape-based feature extraction method with the use of NFS for classifying brain tumors as showing normal or abnormal patterns. The results have proved that the shape-based features with the use of NFS can achieve a satisfactory performance with 100% accuracy. We intend to extend this methodology for the early detection of cancer in other regions such as the prostate region and human cervix.

Amateur boxing and risk of chronic traumatic brain injury: systematic review of observational studies

PubMed Central

Knowles, Charles H; Whyte, Greg P

2007-01-01

Objective To evaluate the risk of chronic traumatic brain injury from amateur boxing. Setting Secondary research performed by combination of sport physicians and clinical academics. Design, data sources, and methods Systematic review of observational studies in which chronic traumatic brain injury was defined as any abnormality on clinical neurological examination, psychometric testing, neuroimaging studies, and electroencephalography. Studies were identified through database (1950 to date) and bibliographic searches without language restrictions. Two reviewers extracted study characteristics, quality, and data, with adherence to a protocol developed from a widely recommended method for systematic review of observational studies (MOOSE). Results 36 papers had relevant extractable data (from a detailed evaluation of 93 studies of 943 identified from the initial search). Quality of evidence was generally poor. The best quality studies were those with a cohort design and those that used psychometric tests. These yielded the most negative results: only four of 17 (24%) better quality studies found any indication of chronic traumatic brain injury in a minority of boxers studied. Conclusion There is no strong evidence to associate chronic traumatic brain injury with amateur boxing. PMID:17916811

Amateur boxing and risk of chronic traumatic brain injury: systematic review of observational studies.

PubMed

Loosemore, Mike; Knowles, Charles H; Whyte, Greg P

2007-10-20

To evaluate the risk of chronic traumatic brain injury from amateur boxing. Secondary research performed by combination of sport physicians and clinical academics. DESIGN, DATA SOURCES, AND METHODS: Systematic review of observational studies in which chronic traumatic brain injury was defined as any abnormality on clinical neurological examination, psychometric testing, neuroimaging studies, and electroencephalography. Studies were identified through database (1950 to date) and bibliographic searches without language restrictions. Two reviewers extracted study characteristics, quality, and data, with adherence to a protocol developed from a widely recommended method for systematic review of observational studies (MOOSE). 36 papers had relevant extractable data (from a detailed evaluation of 93 studies of 943 identified from the initial search). Quality of evidence was generally poor. The best quality studies were those with a cohort design and those that used psychometric tests. These yielded the most negative results: only four of 17 (24%) better quality studies found any indication of chronic traumatic brain injury in a minority of boxers studied. There is no strong evidence to associate chronic traumatic brain injury with amateur boxing.

Molecular weights and metabolism of rat brain proteins

PubMed Central

Vrba, R.; Cannon, Wendy

1970-01-01

1. Rats were injected with [U-14C]glucose and after various intervals extracts of whole brain proteins (and in some cases proteins from liver, blood and heart) were prepared by high-speed centrifugation of homogenates in 0.9% sodium chloride or 0.5% sodium deoxycholate. 2. The extracts were subjected to gel filtration on columns of Sephadex G-200 equilibrated with 0.9% sodium chloride or 0.5% sodium deoxycholate. 3. Extracts prepared with both solvents displayed on gel filtration a continuous range of proteins of approximate molecular weights ranging from less than 2×104 to more than 8×105. 4. The relative amount of the large proteins (mol.wt.>8×105) was conspicuously higher in brain and liver than in blood. 5. At 15min after the injection of [U-14C]glucose the smaller protein molecules (mol.wt.<2×104) were significantly radioactive, whereas no 14C could be detected in the larger (mol.wt.>2×104) protein molecules. The labelling of all protein samples was similar within 4h after injection of [U-14C]glucose. Fractionation of brain proteins into distinctly different groups by the methods used in the present work yielded protein samples with a specific radioactivity comparable with that of total brain protein. 6. No evidence could be obtained by the methods used in the present and previous work to indicate the presence of a significant amount of `metabolically inert protein' in the brain. 7. It is concluded that: (a) most or all of the brain proteins are in a dynamic state of equilibrium between continuous catabolism and anabolism; (b) the continuous conversion of glucose into protein is an important part of the maintenance of this equilibrium and of the homoeostasis of brain proteins in vivo. PMID:5435499

Brain tumour classification and abnormality detection using neuro-fuzzy technique and Otsu thresholding.

PubMed

Renjith, Arokia; Manjula, P; Mohan Kumar, P

2015-01-01

Brain tumour is one of the main causes for an increase in transience among children and adults. This paper proposes an improved method based on Magnetic Resonance Imaging (MRI) brain image classification and image segmentation approach. Automated classification is encouraged by the need of high accuracy when dealing with a human life. The detection of the brain tumour is a challenging problem, due to high diversity in tumour appearance and ambiguous tumour boundaries. MRI images are chosen for detection of brain tumours, as they are used in soft tissue determinations. First of all, image pre-processing is used to enhance the image quality. Second, dual-tree complex wavelet transform multi-scale decomposition is used to analyse texture of an image. Feature extraction extracts features from an image using gray-level co-occurrence matrix (GLCM). Then, the Neuro-Fuzzy technique is used to classify the stages of brain tumour as benign, malignant or normal based on texture features. Finally, tumour location is detected using Otsu thresholding. The classifier performance is evaluated based on classification accuracies. The simulated results show that the proposed classifier provides better accuracy than previous method.

Protective effect of extract of Cordyceps sinensis in middle cerebral artery occlusion-induced focal cerebral ischemia in rats

PubMed Central

2010-01-01

Background Ischemic hypoxic brain injury often causes irreversible brain damage. The lack of effective and widely applicable pharmacological treatments for ischemic stroke patients may explain a growing interest in traditional medicines. From the point of view of "self-medication" or "preventive medicine," Cordyceps sinensis was used in the prevention of cerebral ischemia in this paper. Methods The right middle cerebral artery occlusion model was used in the study. The effects of Cordyceps sinensis (Caterpillar fungus) extract on mortality rate, neurobehavior, grip strength, lactate dehydrogenase, glutathione content, Lipid Peroxidation, glutathione peroxidase activity, glutathione reductase activity, catalase activity, Na+K+ATPase activity and glutathione S transferase activity in a rat model were studied respectively. Results Cordyceps sinensis extract significantly improved the outcome in rats after cerebral ischemia and reperfusion in terms of neurobehavioral function. At the same time, supplementation of Cordyceps sinensis extract significantly boosted the defense mechanism against cerebral ischemia by increasing antioxidants activity related to lesion pathogenesis. Restoration of the antioxidant homeostasis in the brain after reperfusion may have helped the brain recover from ischemic injury. Conclusions These experimental results suggest that complement Cordyceps sinensis extract is protective after cerebral ischemia in specific way. The administration of Cordyceps sinensis extract significantly reduced focal cerebral ischemic/reperfusion injury. The defense mechanism against cerebral ischemia was by increasing antioxidants activity related to lesion pathogenesis. PMID:20955613

Effect of Ginkgo biloba extract on apoptosis of brain tissues in rats with acute cerebral infarction and related gene expression.

PubMed

Wu, C; Zhao, X; Zhang, X; Liu, S; Zhao, H; Chen, Y

2015-06-11

We investigated the effect of Ginkgo biloba extract on apoptosis of brain tissues in rats with acute cerebral infarction and apoptosis-related gene expression. Rat models of acute cerebral infarction were constructed using the suture method, and randomly divided into the control group, model, and treatment groups. In the treatment group, 4 mg/kg G. biloba extract was intravenously injected into the rat tail vein. Phosphate-buffered saline solution was injected in the model group. Seventy-two hours after treatment, rats were euthanized, and brain tissues were removed to analyze the changes in caspase-3, B-cell lymphoma 2 (Bcl-2), and Bcl-2-associated X protein (Bax) mRNA and protein levels, and variation in brain tissue cells' apoptosis indices was measured. Compared with the control group, the model and treatment groups showed significantly upregulated caspase-3, Bcl-2, and Bax mRNA and protein levels in brain tissues, but remarkably downregulated Bcl-2 mRNA and protein levels (P < 0.05). After treatment, in treatment group brain tissues, caspase-3 and Bax mRNA and protein levels were significantly lower than those in the model group, while Bcl-2 mRNA and protein levels were higher than that in the model group (P < 0.05). The model and treatment groups showed increased cell apoptosis indices of brain tissues compared to the control group; after treatment, the apoptosis index in the treatment group was significantly downregulated compared with that in the model group (P < 0.05). In conclusion, G. biloba extract significantly reduced apoptosis in rat brain tissue cells with acute cerebral infarction and thus protected brain tissues.

Detection of brain tumor margins using optical coherence tomography

NASA Astrophysics Data System (ADS)

Juarez-Chambi, Ronald M.; Kut, Carmen; Rico-Jimenez, Jesus; Campos-Delgado, Daniel U.; Quinones-Hinojosa, Alfredo; Li, Xingde; Jo, Javier

2018-02-01

In brain cancer surgery, it is critical to achieve extensive resection without compromising adjacent healthy, noncancerous regions. Various technological advances have made major contributions in imaging, including intraoperative magnetic imaging (MRI) and computed tomography (CT). However, these technologies have pros and cons in providing quantitative, real-time and three-dimensional (3D) continuous guidance in brain cancer detection. Optical Coherence Tomography (OCT) is a non-invasive, label-free, cost-effective technique capable of imaging tissue in three dimensions and real time. The purpose of this study is to reliably and efficiently discriminate between non-cancer and cancerinfiltrated brain regions using OCT images. To this end, a mathematical model for quantitative evaluation known as the Blind End-Member and Abundances Extraction method (BEAE). This BEAE method is a constrained optimization technique which extracts spatial information from volumetric OCT images. Using this novel method, we are able to discriminate between cancerous and non-cancerous tissues and using logistic regression as a classifier for automatic brain tumor margin detection. Using this technique, we are able to achieve excellent performance using an extensive cross-validation of the training dataset (sensitivity 92.91% and specificity 98.15%) and again using an independent, blinded validation dataset (sensitivity 92.91% and specificity 86.36%). In summary, BEAE is well-suited to differentiate brain tissue which could support the guiding surgery process for tissue resection.

Detection of brain tumor margins using optical coherence tomography

NASA Astrophysics Data System (ADS)

Juarez-Chambi, Ronald M.; Kut, Carmen; Rico-Jimenez, Jesus; Campos-Delgado, Daniel U.; Quinones-Hinojosa, Alfredo; Li, Xingde; Jo, Javier

2018-02-01

In brain cancer surgery, it is critical to achieve extensive resection without compromising adjacent healthy, non-cancerous regions. Various technological advances have made major contributions in imaging, including intraoperative magnetic imaging (MRI) and computed tomography (CT). However, these technologies have pros and cons in providing quantitative, real-time and three-dimensional (3D) continuous guidance in brain cancer detection. Optical Coherence Tomography (OCT) is a non-invasive, label-free, cost-effective technique capable of imaging tissue in three dimensions and real time. The purpose of this study is to reliably and efficiently discriminate between non-cancer and cancer-infiltrated brain regions using OCT images. To this end, a mathematical model for quantitative evaluation known as the Blind End- Member and Abundances Extraction method (BEAE). This BEAE method is a constrained optimization technique which extracts spatial information from volumetric OCT images. Using this novel method, we are able to discriminate between cancerous and non-cancerous tissues and using logistic regression as a classifier for automatic brain tumor margin detection. Using this technique, we are able to achieve excellent performance using an extensive cross-validation of the training dataset (sensitivity 92.91% and specificity 98.15%) and again using an independent, blinded validation dataset (sensitivity 92.91% and specificity 86.36%). In summary, BEAE is well-suited to differentiate brain tissue which could support the guiding surgery process for tissue resection.

Development and validation of a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the quantification of tigecycline in rat brain tissues.

PubMed

Munyeza, Chiedza F; Shobo, Adeola; Baijnath, Sooraj; Bratkowska, Dominika; Naiker, Suhashni; Bester, Linda A; Singh, Sanil D; Maguire, Glenn E M; Kruger, Hendrik G; Naicker, Tricia; Govender, Thavendran

2016-06-01

Tigecycline (TIG), a derivative of minocycline, is the first in the novel class of glycylcyclines and is currently indicated for the treatment of complicated skin structure and intra-abdominal infections. A selective, accurate and reversed-phase high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method was developed for the determination of TIG in rat brain tissues. Sample preparation was based on protein precipitation and solid phase extraction using Supel-Select HLB (30 mg/1 mL) cartridges. The samples were separated on a YMC Triart C18 column (150 mm x 3.0 mm. 3.0 µm) using gradient elution. Positive electrospray ionization (ESI+) was used for the detection mechanism with the multiple reaction monitoring (MRM) mode. The method was validated over the concentration range of 150-1200 ng/mL for rat brain tissue. The precision and accuracy for all brain analyses were within the acceptable limit. The mean extraction recovery in rat brain was 83.6%. This validated method was successfully applied to a pharmacokinetic study in female Sprague Dawley rats, which were given a dose of 25 mg/kg TIG intraperitoneally at various time-points. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Classification of MR brain images by combination of multi-CNNs for AD diagnosis

NASA Astrophysics Data System (ADS)

Cheng, Danni; Liu, Manhua; Fu, Jianliang; Wang, Yaping

2017-07-01

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder with progressive impairment of memory and cognitive functions. Its early diagnosis is crucial for development of future treatment. Magnetic resonance images (MRI) play important role to help understand the brain anatomical changes related to AD. Conventional methods extract the hand-crafted features such as gray matter volumes and cortical thickness and train a classifier to distinguish AD from other groups. Different from these methods, this paper proposes to construct multiple deep 3D convolutional neural networks (3D-CNNs) to learn the various features from local brain images which are combined to make the final classification for AD diagnosis. First, a number of local image patches are extracted from the whole brain image and a 3D-CNN is built upon each local patch to transform the local image into more compact high-level features. Then, the upper convolution and fully connected layers are fine-tuned to combine the multiple 3D-CNNs for image classification. The proposed method can automatically learn the generic features from imaging data for classification. Our method is evaluated using T1-weighted structural MR brain images on 428 subjects including 199 AD patients and 229 normal controls (NC) from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an accuracy of 87.15% and an AUC (area under the ROC curve) of 92.26% for AD classification, demonstrating the promising classification performances.

Structure constrained semi-nonnegative matrix factorization for EEG-based motor imagery classification.

PubMed

Lu, Na; Li, Tengfei; Pan, Jinjin; Ren, Xiaodong; Feng, Zuren; Miao, Hongyu

2015-05-01

Electroencephalogram (EEG) provides a non-invasive approach to measure the electrical activities of brain neurons and has long been employed for the development of brain-computer interface (BCI). For this purpose, various patterns/features of EEG data need to be extracted and associated with specific events like cue-paced motor imagery. However, this is a challenging task since EEG data are usually non-stationary time series with a low signal-to-noise ratio. In this study, we propose a novel method, called structure constrained semi-nonnegative matrix factorization (SCS-NMF), to extract the key patterns of EEG data in time domain by imposing the mean envelopes of event-related potentials (ERPs) as constraints on the semi-NMF procedure. The proposed method is applicable to general EEG time series, and the extracted temporal features by SCS-NMF can also be combined with other features in frequency domain to improve the performance of motor imagery classification. Real data experiments have been performed using the SCS-NMF approach for motor imagery classification, and the results clearly suggest the superiority of the proposed method. Comparison experiments have also been conducted. The compared methods include ICA, PCA, Semi-NMF, Wavelets, EMD and CSP, which further verified the effectivity of SCS-NMF. The SCS-NMF method could obtain better or competitive performance over the state of the art methods, which provides a novel solution for brain pattern analysis from the perspective of structure constraint. Copyright © 2015 Elsevier Ltd. All rights reserved.

A surrogate analyte method to determine D-serine in mouse brain using liquid chromatography-tandem mass spectrometry.

PubMed

Kinoshita, Kohnosuke; Jingu, Shigeji; Yamaguchi, Jun-ichi

2013-01-15

A bioanalytical method for determining endogenous d-serine levels in the mouse brain using a surrogate analyte and liquid chromatography-tandem mass spectrometry (LC-MS/MS) was developed. [2,3,3-(2)H]D-serine and [(15)N]D-serine were used as a surrogate analyte and an internal standard, respectively. The surrogate analyte was spiked into brain homogenate to yield calibration standards and quality control (QC) samples. Both endogenous and surrogate analytes were extracted using protein precipitation followed by solid phase extraction. Enantiomeric separation was achieved on a chiral crown ether column with an analysis time of only 6 min without any derivatization. The column eluent was introduced into an electrospray interface of a triple-quadrupole mass spectrometer. The calibration range was 1.00 to 300 nmol/g, and the method showed acceptable accuracy and precision at all QC concentration levels from a validation point of view. In addition, the brain d-serine levels of normal mice determined using this method were the same as those obtained by a standard addition method, which is time-consuming but is often used for the accurate measurement of endogenous substances. Thus, this surrogate analyte method should be applicable to the measurement of d-serine levels as a potential biomarker for monitoring certain effects of drug candidates on the central nervous system. Copyright © 2012 Elsevier Inc. All rights reserved.

Extraction efficiency and implications for absolute quantitation of propranolol in mouse brain, liver and kidney thin tissue sections using droplet-based liquid microjunction surface sampling-HPLC ESI-MS/MS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kertesz, Vilmos; Weiskittel, Taylor M.; Vavek, Marissa

Currently, absolute quantitation aspects of droplet-based surface sampling for thin tissue analysis using a fully automated autosampler/HPLC-ESI-MS/MS system are not fully evaluated. Knowledge of extraction efficiency and its reproducibility is required to judge the potential of the method for absolute quantitation of analytes from thin tissue sections. Methods: Adjacent thin tissue sections of propranolol dosed mouse brain (10- μm-thick), kidney (10- μm-thick) and liver (8-, 10-, 16- and 24- μm-thick) were obtained. Absolute concentration of propranolol was determined in tissue punches from serial sections using standard bulk tissue extraction protocols and subsequent HPLC separations and tandem mass spectrometric analysis. Thesemore » values were used to determine propranolol extraction efficiency from the tissues with the droplet-based surface sampling approach. Results: Extraction efficiency of propranolol using 10- μm-thick brain, kidney and liver thin tissues using droplet-based surface sampling varied between ~45-63%. Extraction efficiency decreased from ~65% to ~36% with liver thickness increasing from 8 μm to 24 μm. Randomly selecting half of the samples as standards, precision and accuracy of propranolol concentrations obtained for the other half of samples as quality control metrics were determined. Resulting precision ( ±15%) and accuracy ( ±3%) values, respectively, were within acceptable limits. In conclusion, comparative quantitation of adjacent mouse thin tissue sections of different organs and of various thicknesses by droplet-based surface sampling and by bulk extraction of tissue punches showed that extraction efficiency was incomplete using the former method, and that it depended on the organ and tissue thickness. However, once extraction efficiency was determined and applied, the droplet-based approach provided the required quantitation accuracy and precision for assay validations. Furthermore, this means that once the extraction efficiency was calibrated for a given tissue type and drug, the droplet-based approach provides a non-labor intensive and high-throughput means to acquire spatially resolved quantitative analysis of multiple samples of the same type.« less

Extraction efficiency and implications for absolute quantitation of propranolol in mouse brain, liver and kidney thin tissue sections using droplet-based liquid microjunction surface sampling-HPLC ESI-MS/MS

DOE PAGES

Kertesz, Vilmos; Weiskittel, Taylor M.; Vavek, Marissa; ...

2016-06-22

Currently, absolute quantitation aspects of droplet-based surface sampling for thin tissue analysis using a fully automated autosampler/HPLC-ESI-MS/MS system are not fully evaluated. Knowledge of extraction efficiency and its reproducibility is required to judge the potential of the method for absolute quantitation of analytes from thin tissue sections. Methods: Adjacent thin tissue sections of propranolol dosed mouse brain (10- μm-thick), kidney (10- μm-thick) and liver (8-, 10-, 16- and 24- μm-thick) were obtained. Absolute concentration of propranolol was determined in tissue punches from serial sections using standard bulk tissue extraction protocols and subsequent HPLC separations and tandem mass spectrometric analysis. Thesemore » values were used to determine propranolol extraction efficiency from the tissues with the droplet-based surface sampling approach. Results: Extraction efficiency of propranolol using 10- μm-thick brain, kidney and liver thin tissues using droplet-based surface sampling varied between ~45-63%. Extraction efficiency decreased from ~65% to ~36% with liver thickness increasing from 8 μm to 24 μm. Randomly selecting half of the samples as standards, precision and accuracy of propranolol concentrations obtained for the other half of samples as quality control metrics were determined. Resulting precision ( ±15%) and accuracy ( ±3%) values, respectively, were within acceptable limits. In conclusion, comparative quantitation of adjacent mouse thin tissue sections of different organs and of various thicknesses by droplet-based surface sampling and by bulk extraction of tissue punches showed that extraction efficiency was incomplete using the former method, and that it depended on the organ and tissue thickness. However, once extraction efficiency was determined and applied, the droplet-based approach provided the required quantitation accuracy and precision for assay validations. Furthermore, this means that once the extraction efficiency was calibrated for a given tissue type and drug, the droplet-based approach provides a non-labor intensive and high-throughput means to acquire spatially resolved quantitative analysis of multiple samples of the same type.« less

High-throughput analysis of sulfatides in cerebrospinal fluid using automated extraction and UPLC-MS/MS.

PubMed

Blomqvist, Maria; Borén, Jan; Zetterberg, Henrik; Blennow, Kaj; Månsson, Jan-Eric; Ståhlman, Marcus

2017-07-01

Sulfatides (STs) are a group of glycosphingolipids that are highly expressed in brain. Due to their importance for normal brain function and their potential involvement in neurological diseases, development of accurate and sensitive methods for their determination is needed. Here we describe a high-throughput oriented and quantitative method for the determination of STs in cerebrospinal fluid (CSF). The STs were extracted using a fully automated liquid/liquid extraction method and quantified using ultra-performance liquid chromatography coupled to tandem mass spectrometry. With the high sensitivity of the developed method, quantification of 20 ST species from only 100 μl of CSF was performed. Validation of the method showed that the STs were extracted with high recovery (90%) and could be determined with low inter- and intra-day variation. Our method was applied to a patient cohort of subjects with an Alzheimer's disease biomarker profile. Although the total ST levels were unaltered compared with an age-matched control group, we show that the ratio of hydroxylated/nonhydroxylated STs was increased in the patient cohort. In conclusion, we believe that the fast, sensitive, and accurate method described in this study is a powerful new tool for the determination of STs in clinical as well as preclinical settings. Copyright © 2017 by the American Society for Biochemistry and Molecular Biology, Inc.

Mapping brain activity in gradient-echo functional MRI using principal component analysis

NASA Astrophysics Data System (ADS)

Khosla, Deepak; Singh, Manbir; Don, Manuel

1997-05-01

The detection of sites of brain activation in functional MRI has been a topic of immense research interest and many technique shave been proposed to this end. Recently, principal component analysis (PCA) has been applied to extract the activated regions and their time course of activation. This method is based on the assumption that the activation is orthogonal to other signal variations such as brain motion, physiological oscillations and other uncorrelated noises. A distinct advantage of this method is that it does not require any knowledge of the time course of the true stimulus paradigm. This technique is well suited to EPI image sequences where the sampling rate is high enough to capture the effects of physiological oscillations. In this work, we propose and apply tow methods that are based on PCA to conventional gradient-echo images and investigate their usefulness as tools to extract reliable information on brain activation. The first method is a conventional technique where a single image sequence with alternating on and off stages is subject to a principal component analysis. The second method is a PCA-based approach called the common spatial factor analysis technique (CSF). As the name suggests, this method relies on common spatial factors between the above fMRI image sequence and a background fMRI. We have applied these methods to identify active brain ares during visual stimulation and motor tasks. The results from these methods are compared to those obtained by using the standard cross-correlation technique. We found good agreement in the areas identified as active across all three techniques. The results suggest that PCA and CSF methods have good potential in detecting the true stimulus correlated changes in the presence of other interfering signals.

Simultaneous quantification of nicotine, opioids, cocaine, and metabolites in human fetal postmortem brain by liquid chromatography tandem mass spectrometry

PubMed Central

Shakleya, Diaa M.

2011-01-01

A validated method for simultaneous LCMSMS quantification of nicotine, cocaine, 6-acetylmorphine (6AM), codeine, and metabolites in 100 mg fetal human brain was developed and validated. After homogenization and solid-phase extraction, analytes were resolved on a Hydro-RP analytical column with gradient elution. Empirically determined linearity was from 5–5,000 pg/mg for cocaine and benzoylecgonine (BE), 25–5,000 pg/mg for cotinine, ecgonine methyl ester (EME) and 6AM, 50–5000 pg/mg for trans-3-hydroxycotinine (OH-cotinine) and codeine, and 250–5,000 pg/mg for nicotine. Potential endogenous and exogenous interferences were resolved. Intra- and inter-assay analytical recoveries were ≥92%, intra- and inter-day and total assay imprecision were ≤14% RSD and extraction efficiencies were ≥67.2% with ≤83% matrix effect. Method applicability was demonstrated with a postmortem fetal brain containing 40 pg/mg cotinine, 65 pg/mg OH-cotinine, 13 pg/mg cocaine, 34 pg/mg EME, and 525 pg/mg BE. This validated method is useful for determination of nicotine, opioid, and cocaine biomarkers in brain. PMID:19229524

A Semisupervised Support Vector Machines Algorithm for BCI Systems

PubMed Central

Qin, Jianzhao; Li, Yuanqing; Sun, Wei

2007-01-01

As an emerging technology, brain-computer interfaces (BCIs) bring us new communication interfaces which translate brain activities into control signals for devices like computers, robots, and so forth. In this study, we propose a semisupervised support vector machine (SVM) algorithm for brain-computer interface (BCI) systems, aiming at reducing the time-consuming training process. In this algorithm, we apply a semisupervised SVM for translating the features extracted from the electrical recordings of brain into control signals. This SVM classifier is built from a small labeled data set and a large unlabeled data set. Meanwhile, to reduce the time for training semisupervised SVM, we propose a batch-mode incremental learning method, which can also be easily applied to the online BCI systems. Additionally, it is suggested in many studies that common spatial pattern (CSP) is very effective in discriminating two different brain states. However, CSP needs a sufficient labeled data set. In order to overcome the drawback of CSP, we suggest a two-stage feature extraction method for the semisupervised learning algorithm. We apply our algorithm to two BCI experimental data sets. The offline data analysis results demonstrate the effectiveness of our algorithm. PMID:18368141

Protective role of Scoparia dulcis plant extract on brain antioxidant status and lipidperoxidation in STZ diabetic male Wistar rats

PubMed Central

Pari, Leelavinothan; Latha, Muniappan

2004-01-01

Background The aim of the study was to investigate the effect of aqueous extract of Scoparia dulcis on the occurrence of oxidative stress in the brain of rats during diabetes by measuring the extent of oxidative damage as well as the status of the antioxidant defense system. Methods Aqueous extract of Scoparia dulcis plant was administered orally (200 mg/kg body weight) and the effect of extract on blood glucose, plasma insulin and the levels of thiobarbituric acid reactive substances (TBARS), hydroperoxides, superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx), glutathione-S-transferase (GST) and reduced glutathione (GSH) were estimated in streptozotocin (STZ) induced diabetic rats. Glibenclamide was used as standard reference drug. Results A significant increase in the activities of plasma insulin, superoxide dismutase, catalase, glutathione peroxidase, glutathione-S-transferase and reduced glutathione was observed in brain on treatment with 200 mg/kg body weight of Scoparia dulcis plant extract (SPEt) and glibenclamide for 6 weeks. Both the treated groups showed significant decrease in TBARS and hydroperoxides formation in brain, suggesting its role in protection against lipidperoxidation induced membrane damage. Conclusions Since the study of induction of the antioxidant enzymes is considered to be a reliable marker for evaluating the antiperoxidative efficacy of the medicinal plant, these findings suggest a possible antiperoxidative role for Scoparia dulcis plant extract. Hence, in addition to antidiabetic effect, Scoparia dulcis possess antioxidant potential that may be used for therapeutic purposes. PMID:15522116

Novel Features for Brain-Computer Interfaces

PubMed Central

Woon, W. L.; Cichocki, A.

2007-01-01

While conventional approaches of BCI feature extraction are based on the power spectrum, we have tried using nonlinear features for classifying BCI data. In this paper, we report our test results and findings, which indicate that the proposed method is a potentially useful addition to current feature extraction techniques. PMID:18364991

Brain vascular image segmentation based on fuzzy local information C-means clustering

NASA Astrophysics Data System (ADS)

Hu, Chaoen; Liu, Xia; Liang, Xiao; Hui, Hui; Yang, Xin; Tian, Jie

2017-02-01

Light sheet fluorescence microscopy (LSFM) is a powerful optical resolution fluorescence microscopy technique which enables to observe the mouse brain vascular network in cellular resolution. However, micro-vessel structures are intensity inhomogeneity in LSFM images, which make an inconvenience for extracting line structures. In this work, we developed a vascular image segmentation method by enhancing vessel details which should be useful for estimating statistics like micro-vessel density. Since the eigenvalues of hessian matrix and its sign describes different geometric structure in images, which enable to construct vascular similarity function and enhance line signals, the main idea of our method is to cluster the pixel values of the enhanced image. Our method contained three steps: 1) calculate the multiscale gradients and the differences between eigenvalues of Hessian matrix. 2) In order to generate the enhanced microvessels structures, a feed forward neural network was trained by 2.26 million pixels for dealing with the correlations between multi-scale gradients and the differences between eigenvalues. 3) The fuzzy local information c-means clustering (FLICM) was used to cluster the pixel values in enhance line signals. To verify the feasibility and effectiveness of this method, mouse brain vascular images have been acquired by a commercial light-sheet microscope in our lab. The experiment of the segmentation method showed that dice similarity coefficient can reach up to 85%. The results illustrated that our approach extracting line structures of blood vessels dramatically improves the vascular image and enable to accurately extract blood vessels in LSFM images.

Multifractal texture estimation for detection and segmentation of brain tumors.

PubMed

Islam, Atiq; Reza, Syed M S; Iftekharuddin, Khan M

2013-11-01

A stochastic model for characterizing tumor texture in brain magnetic resonance (MR) images is proposed. The efficacy of the model is demonstrated in patient-independent brain tumor texture feature extraction and tumor segmentation in magnetic resonance images (MRIs). Due to complex appearance in MRI, brain tumor texture is formulated using a multiresolution-fractal model known as multifractional Brownian motion (mBm). Detailed mathematical derivation for mBm model and corresponding novel algorithm to extract spatially varying multifractal features are proposed. A multifractal feature-based brain tumor segmentation method is developed next. To evaluate efficacy, tumor segmentation performance using proposed multifractal feature is compared with that using Gabor-like multiscale texton feature. Furthermore, novel patient-independent tumor segmentation scheme is proposed by extending the well-known AdaBoost algorithm. The modification of AdaBoost algorithm involves assigning weights to component classifiers based on their ability to classify difficult samples and confidence in such classification. Experimental results for 14 patients with over 300 MRIs show the efficacy of the proposed technique in automatic segmentation of tumors in brain MRIs. Finally, comparison with other state-of-the art brain tumor segmentation works with publicly available low-grade glioma BRATS2012 dataset show that our segmentation results are more consistent and on the average outperforms these methods for the patients where ground truth is made available.

Multifractal Texture Estimation for Detection and Segmentation of Brain Tumors

PubMed Central

Islam, Atiq; Reza, Syed M. S.

2016-01-01

A stochastic model for characterizing tumor texture in brain magnetic resonance (MR) images is proposed. The efficacy of the model is demonstrated in patient-independent brain tumor texture feature extraction and tumor segmentation in magnetic resonance images (MRIs). Due to complex appearance in MRI, brain tumor texture is formulated using a multiresolution-fractal model known as multifractional Brownian motion (mBm). Detailed mathematical derivation for mBm model and corresponding novel algorithm to extract spatially varying multifractal features are proposed. A multifractal feature-based brain tumor segmentation method is developed next. To evaluate efficacy, tumor segmentation performance using proposed multifractal feature is compared with that using Gabor-like multiscale texton feature. Furthermore, novel patient-independent tumor segmentation scheme is proposed by extending the well-known AdaBoost algorithm. The modification of AdaBoost algorithm involves assigning weights to component classifiers based on their ability to classify difficult samples and confidence in such classification. Experimental results for 14 patients with over 300 MRIs show the efficacy of the proposed technique in automatic segmentation of tumors in brain MRIs. Finally, comparison with other state-of-the art brain tumor segmentation works with publicly available low-grade glioma BRATS2012 dataset show that our segmentation results are more consistent and on the average outperforms these methods for the patients where ground truth is made available. PMID:23807424

Activation of Macrophages in vitro by Phospholipids from Brain of Katsuwonus pelamis (Skipjack Tuna).

PubMed

Lu, Hang; Zhang, Li; Zhao, Hui; Li, Jingjing; You, Hailin; Jiang, Lu; Hu, Jianen

2018-03-01

The biological activities of phospholipids (PLs) have attracted people's attention, especially marine phospholipids with omega-3 polyunsaturated fatty acids DHA and EPA. In this study, we investigated the immunity activation of macrophages in vitro by phospholipids from skipjack brain. The phospholipids were extracted with hexane and ethanol ultrasonication instead of the traditional method of methanol and chloroform. The content of phospholipids from Skipjack brain was 19.59 g/kg by the method (the ratio of hexane and ethanol 2:1, 40 min, 35°C, 1:9 of the ratio of material to solvent, ultrasonic power 300W, ultrasonic extraction 2 times). The RAW264.7 macrophages were stimulated by the phospholipids from the Skipjack, by which the volume, viability and phagocytosis of macrophages were increased. The concentration of NO and the activity of SOD of the cells were also enhanced. The gene expressions of IL-1β, IL-6, iNOS and TNF-α mRNA assayed by RT-PCR were up-regulated. Phospholipids from brain of Skipjack Tuna could activate macrophages immunity which displayed to induce pro-inflammatroy cytokines mRNA expression.

A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface

PubMed Central

Yaacoub, Charles; Mhanna, Georges; Rihana, Sandy

2017-01-01

Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves left/right hand movement identification of a motor imagery brain-computer interface, based on genetic algorithms and artificial neural networks used as classifiers. Raw electroencephalography signals are first preprocessed using appropriate filtering. Feature extraction is carried out afterwards, based on spectral and temporal signal components, and thus a feature vector is constructed. As various features might be inaccurate and mislead the classifier, thus degrading the overall system performance, the proposed approach identifies a subset of features from a large feature space, such that the classifier error rate is reduced. Experimental results show that the proposed method is able to reduce the number of features to as low as 0.5% (i.e., the number of ignored features can reach 99.5%) while improving the accuracy, sensitivity, specificity, and precision of the classifier. PMID:28124985

A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface.

PubMed

Yaacoub, Charles; Mhanna, Georges; Rihana, Sandy

2017-01-23

Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves left/right hand movement identification of a motor imagery brain-computer interface, based on genetic algorithms and artificial neural networks used as classifiers. Raw electroencephalography signals are first preprocessed using appropriate filtering. Feature extraction is carried out afterwards, based on spectral and temporal signal components, and thus a feature vector is constructed. As various features might be inaccurate and mislead the classifier, thus degrading the overall system performance, the proposed approach identifies a subset of features from a large feature space, such that the classifier error rate is reduced. Experimental results show that the proposed method is able to reduce the number of features to as low as 0.5% (i.e., the number of ignored features can reach 99.5%) while improving the accuracy, sensitivity, specificity, and precision of the classifier.

A Learning-Based Wrapper Method to Correct Systematic Errors in Automatic Image Segmentation: Consistently Improved Performance in Hippocampus, Cortex and Brain Segmentation

PubMed Central

Wang, Hongzhi; Das, Sandhitsu R.; Suh, Jung Wook; Altinay, Murat; Pluta, John; Craige, Caryne; Avants, Brian; Yushkevich, Paul A.

2011-01-01

We propose a simple but generally applicable approach to improving the accuracy of automatic image segmentation algorithms relative to manual segmentations. The approach is based on the hypothesis that a large fraction of the errors produced by automatic segmentation are systematic, i.e., occur consistently from subject to subject, and serves as a wrapper method around a given host segmentation method. The wrapper method attempts to learn the intensity, spatial and contextual patterns associated with systematic segmentation errors produced by the host method on training data for which manual segmentations are available. The method then attempts to correct such errors in segmentations produced by the host method on new images. One practical use of the proposed wrapper method is to adapt existing segmentation tools, without explicit modification, to imaging data and segmentation protocols that are different from those on which the tools were trained and tuned. An open-source implementation of the proposed wrapper method is provided, and can be applied to a wide range of image segmentation problems. The wrapper method is evaluated with four host brain MRI segmentation methods: hippocampus segmentation using FreeSurfer (Fischl et al., 2002); hippocampus segmentation using multi-atlas label fusion (Artaechevarria et al., 2009); brain extraction using BET (Smith, 2002); and brain tissue segmentation using FAST (Zhang et al., 2001). The wrapper method generates 72%, 14%, 29% and 21% fewer erroneously segmented voxels than the respective host segmentation methods. In the hippocampus segmentation experiment with multi-atlas label fusion as the host method, the average Dice overlap between reference segmentations and segmentations produced by the wrapper method is 0.908 for normal controls and 0.893 for patients with mild cognitive impairment. Average Dice overlaps of 0.964, 0.905 and 0.951 are obtained for brain extraction, white matter segmentation and gray matter segmentation, respectively. PMID:21237273

The antiepileptic effect of Centella asiatica on the activities of Na+/K+, Mg2+ and Ca2+-ATPases in rat brain during pentylenetetrazol–induced epilepsy

PubMed Central

G., Visweswari; K., Siva Prasad; V., Lokanatha; Rajendra, W.

2010-01-01

Background: To study the anticonvulsant effect of different extracts of Centella asiatica (CA) in male albino rats with reference to Na+/K+, Mg2+ and Ca2+-ATPase activities. Materials and Methods: Male Wistar rats (150±25 g b.w.) were divided into seven groups of six each i.e. (a) control rats treated with saline, (b) pentylenetetrazol (PTZ)-induced epileptic group (60 mg/kg, i.p.), (c) epileptic group pretreated with n-hexane extract (n-HE), (d) epileptic group pretreated with chloroform extract (CE), (e) epileptic group pretreated with ethyl acetate extract (EAE), (f) epileptic group pretreated with n-butanol extract (n-BE), and (g) epileptic group pretreated with aqueous extract (AE). Results: The activities of three ATPases were decreased in different regions of brain during PTZ-induced epilepsy and were increased in epileptic rats pretreated with different extracts of CA except AE. Conclusion: The extracts of C. asiatica, except AE, possess anticonvulsant and neuroprotective activity and thus can be used for effective management in treatment of epileptic seizures. PMID:20711371

Immunomodulatory effect of Hawthorn extract in an experimental stroke model.

PubMed

Elango, Chinnasamy; Devaraj, Sivasithambaram Niranjali

2010-12-30

Recently, we reported a neuroprotective effect for Hawthorn (Crataegus oxyacantha) ethanolic extract in middle cerebral artery occlusion-(MCAO) induced stroke in rats. The present study sheds more light on the extract's mechanism of neuroprotection, especially its immunomodulatory effect. After 15 days of treatment with Hawthorn extract [100 mg/kg, pretreatment (oral)], male Sprague Dawley rats underwent transient MCAO for 75 mins followed by reperfusion (either 3 or 24 hrs). We measured pro-inflammatory cytokines (IL-1β, TNF-α, IL-6), ICAM-1, IL-10 and pSTAT-3 expression in the brain by appropriate methods. We also looked at the cytotoxic T cell sub-population among leukocytes (FACS) and inflammatory cell activation and recruitment in brain (using a myeloperoxidase activity assay) after ischemia and reperfusion (I/R). Apoptosis (TUNEL), and Bcl-xL- and Foxp3- (T(reg) marker) positive cells in the ipsilateral hemisphere of the brain were analyzed separately using immunofluorescence. Our results indicate that occlusion followed by 3 hrs of reperfusion increased pro-inflammatory cytokine and ICAM-1 gene expressions in the ipsilateral hemisphere, and that Hawthorn pre-treatment significantly (p ≤ 0.01) lowered these levels. Furthermore, such pre-treatment was able to increase IL-10 levels and Foxp3-positive cells in brain after 24 hrs of reperfusion. The increase in cytotoxic T cell population in vehicle rats after 24 hrs of reperfusion was decreased by at least 40% with Hawthorn pretreatment. In addition, there was a decrease in inflammatory cell activation and infiltration in pretreated brain. Hawthorn pretreatment elevated pSTAT-3 levels in brain after I/R. We also observed an increase in Bcl-xL-positive cells, which in turn may have influenced the reduction in TUNEL-positive cells compared to vehicle-treated brain. In summary, Hawthorn extract helped alleviate pro-inflammatory immune responses associated with I/R-induced injury, boosted IL-10 levels, and increased Foxp3-positive T(regs) in the brain, which may have aided in suppression of activated inflammatory cells. Such treatment also minimizes apoptotic cell death by influencing STAT-3 phosphorylation and Bcl-xL expression in the brain. Taken together, the immunomodulatory effect of Hawthorn extract may play a critical role in the neuroprotection observed in this MCAO-induced stroke model.

Development of an assisting detection system for early infarct diagnosis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sim, K. S.; Nia, M. E.; Ee, C. S.

2015-04-24

In this paper, a detection assisting system for early infarct detection is developed. This new developed method is used to assist the medical practitioners to diagnose infarct from computed tomography images of brain. Using this assisting system, the infarct could be diagnosed at earlier stages. The non-contrast computed tomography (NCCT) brain images are the data set used for this system. Detection module extracts the pixel data from NCCT brain images, and produces the colourized version of images. The proposed method showed great potential in detecting infarct, and helps medical practitioners to make earlier and better diagnoses.

Pomegranate extract protects against cerebral ischemia/reperfusion injury and preserves brain DNA integrity in rats.

PubMed

Ahmed, Maha A E; El Morsy, Engy M; Ahmed, Amany A E

2014-08-21

Interruption to blood flow causes ischemia and infarction of brain tissues with consequent neuronal damage and brain dysfunction. Pomegranate extract is well tolerated, and safely consumed all over the world. Interestingly, pomegranate extract has shown remarkable antioxidant and anti-inflammatory effects in experimental models. Many investigators consider natural extracts as novel therapies for neurodegenerative disorders. Therefore, this study was carried out to investigate the protective effects of standardized pomegranate extract against cerebral ischemia/reperfusion-induced brain injury in rats. Adult male albino rats were randomly divided into sham-operated control group, ischemia/reperfusion (I/R) group, and two other groups that received standardized pomegranate extract at two dose levels (250, 500 mg/kg) for 15 days prior to ischemia/reperfusion (PMG250+I/R, and PMG500+I/R groups). After I/R or sham operation, all rats were sacrificed and brains were harvested for subsequent biochemical analysis. Results showed reduction in brain contents of MDA (malondialdehyde), and NO (nitric oxide), in addition to enhancement of SOD (superoxide dismutase), GPX (glutathione peroxidase), and GRD (glutathione reductase) activities in rats treated with pomegranate extract prior to cerebral I/R. Moreover, pomegranate extract decreased brain levels of NF-κB p65 (nuclear factor kappa B p65), TNF-α (tumor necrosis factor-alpha), caspase-3 and increased brain levels of IL-10 (interleukin-10), and cerebral ATP (adenosine triphosphate) production. Comet assay showed less brain DNA (deoxyribonucleic acid) damage in rats protected with pomegranate extract. The present study showed, for the first time, that pre-administration of pomegranate extract to rats, can offer a significant dose-dependent neuroprotective activity against cerebral I/R brain injury and DNA damage via antioxidant, anti-inflammatory, anti-apoptotic and ATP-replenishing effects. Copyright © 2014 Elsevier Inc. All rights reserved.

Onion extract structural changes during in vitro digestion and its potential antioxidant effect on brain lipids obtained from low- and high-fat-fed mice.

PubMed

Hur, S J; Lee, S J; Kim, D H; Chun, S C; Lee, S K

2013-12-01

This study investigated the effects of onion (Allium cepa, L.) extract on the antioxidant activity of lipids in low-and high-fat-fed mouse brain lipids and its structural change during in vitro human digestion. The onion extracts were passed through an in vitro human digestion model that simulated the composition of the mouth, stomach, and small intestine juice. The brain lipids were collected from low- and high-fat-fed mouse brain and then incubated with the in vitro-digested onion extracts to determine the lipid oxidation. The results confirmed that the main phenolics of onion extract were kaempferol, myricetin, quercetin, and quercitrin. The quercetin content increased with digestion of the onion extract. Antioxidant activity was strongly influenced by in vitro human digestion of both onion extract and quercetin standard. After digestion by the small intestine, the antioxidant activity values were dramatically increased, whereas the antioxidant activity was less influenced by digestion in the stomach for both onion extract and quercetin standard. The inhibitory effect of lipid oxidation of onion extract in mouse brain lipids increased after digestion in the stomach. The inhibitory effect of lipid oxidation of onion extract was higher in the high-fat-fed mouse brain lipids than that in the low-fat-fed mouse brain lipids. The major study finding is that the antioxidative effect of onion extract may be higher in high-fat-fed mouse brain lipids than that in low-fat-fed mouse brain lipids. Thus, dietary onion may have important applications as a natural antioxidant agent in a high-fat diet.

Artifact suppression and analysis of brain activities with electroencephalography signals.

PubMed

Rashed-Al-Mahfuz, Md; Islam, Md Rabiul; Hirose, Keikichi; Molla, Md Khademul Islam

2013-06-05

Brain-computer interface is a communication system that connects the brain with computer (or other devices) but is not dependent on the normal output of the brain (i.e., peripheral nerve and muscle). Electro-oculogram is a dominant artifact which has a significant negative influence on further analysis of real electroencephalography data. This paper presented a data adaptive technique for artifact suppression and brain wave extraction from electroencephalography signals to detect regional brain activities. Empirical mode decomposition based adaptive thresholding approach was employed here to suppress the electro-oculogram artifact. Fractional Gaussian noise was used to determine the threshold level derived from the analysis data without any training. The purified electroencephalography signal was composed of the brain waves also called rhythmic components which represent the brain activities. The rhythmic components were extracted from each electroencephalography channel using adaptive wiener filter with the original scale. The regional brain activities were mapped on the basis of the spatial distribution of rhythmic components, and the results showed that different regions of the brain are activated in response to different stimuli. This research analyzed the activities of a single rhythmic component, alpha with respect to different motor imaginations. The experimental results showed that the proposed method is very efficient in artifact suppression and identifying individual motor imagery based on the activities of alpha component.

Isolation and purification of monosialotetrahexosylgangliosides from pig brain by extraction and liquid chromatography.

PubMed

Bian, Liujiao; Yang, Jianting; Sun, Yu

2015-10-01

Monosialotetrahexosylganglioside (GM1), one of glycosphingolipids containing sialic acid, plays particularly important role in fighting against paralysis, dementia and other diseases caused by brain and nerve damage. In this work, a simple and highly efficient method with high yield was developed for isolation and purification of GM1 from pig brain. The method consisted of an extraction by chloroform-methanol-water and a two-step chromatographic separation by DEAE-Sepharose Fast Flow anion-exchange medium and Sephacryl S-100 HR size-exclusion medium. The purified GM1 was proved to be homogeneous and had a purity of >98.0% by high-performance anion-exchange and size-exclusion chromatography. The molecular weight was 30.0 kDa by high-performance size-exclusion chromatography and 1546.9 Da by electrospray ionization mass spectrometry. The chromogenic reaction by resorcinol-hydrochloric acid solution indicated that the purified GM1 showed a specific chromogenic reaction of sialic acid. Through this isolation and purification program, ~1.0 mg of pure GM1 could be captured from 500 g wet pig brain tissue and the yield of GM1 was around 0.022%, which was higher than the yields by other methods. The method may provide an alternative for isolation and purification of GM1 in other biological tissues. Copyright © 2015 John Wiley & Sons, Ltd.

Large-scale extraction of brain connectivity from the neuroscientific literature

PubMed Central

Richardet, Renaud; Chappelier, Jean-Cédric; Telefont, Martin; Hill, Sean

2015-01-01

Motivation: In neuroscience, as in many other scientific domains, the primary form of knowledge dissemination is through published articles. One challenge for modern neuroinformatics is finding methods to make the knowledge from the tremendous backlog of publications accessible for search, analysis and the integration of such data into computational models. A key example of this is metascale brain connectivity, where results are not reported in a normalized repository. Instead, these experimental results are published in natural language, scattered among individual scientific publications. This lack of normalization and centralization hinders the large-scale integration of brain connectivity results. In this article, we present text-mining models to extract and aggregate brain connectivity results from 13.2 million PubMed abstracts and 630 216 full-text publications related to neuroscience. The brain regions are identified with three different named entity recognizers (NERs) and then normalized against two atlases: the Allen Brain Atlas (ABA) and the atlas from the Brain Architecture Management System (BAMS). We then use three different extractors to assess inter-region connectivity. Results: NERs and connectivity extractors are evaluated against a manually annotated corpus. The complete in litero extraction models are also evaluated against in vivo connectivity data from ABA with an estimated precision of 78%. The resulting database contains over 4 million brain region mentions and over 100 000 (ABA) and 122 000 (BAMS) potential brain region connections. This database drastically accelerates connectivity literature review, by providing a centralized repository of connectivity data to neuroscientists. Availability and implementation: The resulting models are publicly available at github.com/BlueBrain/bluima. Contact: renaud.richardet@epfl.ch Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25609795

Determination of selected neurotoxic insecticides in small amounts of animal tissue utilizing a newly constructed mini-extractor.

PubMed

Seifertová, Marta; Čechová, Eliška; Llansola, Marta; Felipo, Vicente; Vykoukalová, Martina; Kočan, Anton

2017-10-01

We developed a simple analytical method for the simultaneous determination of representatives of various groups of neurotoxic insecticides (carbaryl, chlorpyrifos, cypermethrin, and α-endosulfan and β-endosulfan and their metabolite endosulfan sulfate) in limited amounts of animal tissues containing different amounts of lipids. Selected tissues (rodent fat, liver, and brain) were extracted in a special in-house-designed mini-extractor constructed on the basis of the Soxhlet and Twisselmann extractors. A dried tissue sample placed in a small cartridge was extracted, while the nascent extract was simultaneously filtered through a layer of sodium sulfate. The extraction was followed by combined clean-up, including gel permeation chromatography (in case of high lipid content), ultrasonication, and solid-phase extraction chromatography using C 18 on silica and aluminum oxide. Gas chromatography coupled with high-resolution mass spectrometry was used for analyte separation, detection, and quantification. Average recoveries for individual insecticides ranged from 82 to 111%. Expanded measurement uncertainties were generally lower than 35%. The developed method was successfully applied to rat tissue samples obtained from an animal model dealing with insecticide exposure during brain development. This method may also be applied to the analytical treatment of small amounts of various types of animal and human tissue samples. A significant advantage achieved using this method is high sample throughput due to the simultaneous treatment of many samples. Graphical abstract Optimized workflow for the determination of selected insecticides in small amounts of animal tissue including newly developed mini-extractor.

Neuronal nuclei isolation from human postmortem brain tissue.

PubMed

Matevossian, Anouch; Akbarian, Schahram

2008-10-01

Neurons in the human brain become postmitotic largely during prenatal development, and thus maintain their nuclei throughout the full lifespan. However, little is known about changes in neuronal chromatin and nuclear organization during the course of development and aging, or in chronic neuropsychiatric disease. However, to date most chromatin and DNA based assays (other than FISH) lack single cell resolution. To this end, the considerable cellular heterogeneity of brain tissue poses a significant limitation, because typically various subpopulations of neurons are intermingled with different types of glia and other non-neuronal cells. One possible solution would be to grow cell-type specific cultures, but most CNS cells, including neurons, are ex vivo sustainable, at best, for only a few weeks and thus would provide an incomplete model for epigenetic mechanisms potentially operating across the full lifespan. Here, we provide a protocol to extract and purify nuclei from frozen (never fixed) human postmortem brain. The method involves extraction of nuclei in hypotonic lysis buffer, followed by ultracentrifugation and immunotagging with anti-NeuN antibody. Labeled neuronal nuclei are then collected separately using fluorescence-activated sorting. This method should be applicable to any brain region in a wide range of species and suitable for chromatin immunoprecipitation studies with site- and modification-specific anti-histone antibodies, and for DNA methylation and other assays.

A multi-atlas based method for automated anatomical Macaca fascicularis brain MRI segmentation and PET kinetic extraction.

PubMed

Ballanger, Bénédicte; Tremblay, Léon; Sgambato-Faure, Véronique; Beaudoin-Gobert, Maude; Lavenne, Franck; Le Bars, Didier; Costes, Nicolas

2013-08-15

MRI templates and digital atlases are needed for automated and reproducible quantitative analysis of non-human primate PET studies. Segmenting brain images via multiple atlases outperforms single-atlas labelling in humans. We present a set of atlases manually delineated on brain MRI scans of the monkey Macaca fascicularis. We use this multi-atlas dataset to evaluate two automated methods in terms of accuracy, robustness and reliability in segmenting brain structures on MRI and extracting regional PET measures. Twelve individual Macaca fascicularis high-resolution 3DT1 MR images were acquired. Four individual atlases were created by manually drawing 42 anatomical structures, including cortical and sub-cortical structures, white matter regions, and ventricles. To create the MRI template, we first chose one MRI to define a reference space, and then performed a two-step iterative procedure: affine registration of individual MRIs to the reference MRI, followed by averaging of the twelve resampled MRIs. Automated segmentation in native space was obtained in two ways: 1) Maximum probability atlases were created by decision fusion of two to four individual atlases in the reference space, and transformation back into the individual native space (MAXPROB)(.) 2) One to four individual atlases were registered directly to the individual native space, and combined by decision fusion (PROPAG). Accuracy was evaluated by computing the Dice similarity index and the volume difference. The robustness and reproducibility of PET regional measurements obtained via automated segmentation was evaluated on four co-registered MRI/PET datasets, which included test-retest data. Dice indices were always over 0.7 and reached maximal values of 0.9 for PROPAG with all four individual atlases. There was no significant mean volume bias. The standard deviation of the bias decreased significantly when increasing the number of individual atlases. MAXPROB performed better when increasing the number of atlases used. When all four atlases were used for the MAXPROB creation, the accuracy of morphometric segmentation approached that of the PROPAG method. PET measures extracted either via automatic methods or via the manually defined regions were strongly correlated, with no significant regional differences between methods. Intra-class correlation coefficients for test-retest data were over 0.87. Compared to single atlas extractions, multi-atlas methods improve the accuracy of region definition. They also perform comparably to manually defined regions for PET quantification. Multiple atlases of Macaca fascicularis brains are now available and allow reproducible and simplified analyses. Copyright © 2013 Elsevier Inc. All rights reserved.

Brain Volume Estimation Enhancement by Morphological Image Processing Tools.

PubMed

Zeinali, R; Keshtkar, A; Zamani, A; Gharehaghaji, N

2017-12-01

Volume estimation of brain is important for many neurological applications. It is necessary in measuring brain growth and changes in brain in normal/abnormal patients. Thus, accurate brain volume measurement is very important. Magnetic resonance imaging (MRI) is the method of choice for volume quantification due to excellent levels of image resolution and between-tissue contrast. Stereology method is a good method for estimating volume but it requires to segment enough MRI slices and have a good resolution. In this study, it is desired to enhance stereology method for volume estimation of brain using less MRI slices with less resolution. In this study, a program for calculating volume using stereology method has been introduced. After morphologic method, dilation was applied and the stereology method enhanced. For the evaluation of this method, we used T1-wighted MR images from digital phantom in BrainWeb which had ground truth. The volume of 20 normal brain extracted from BrainWeb, was calculated. The volumes of white matter, gray matter and cerebrospinal fluid with given dimension were estimated correctly. Volume calculation from Stereology method in different cases was made. In three cases, Root Mean Square Error (RMSE) was measured. Case I with T=5, d=5, Case II with T=10, D=10 and Case III with T=20, d=20 (T=slice thickness, d=resolution as stereology parameters). By comparing these results of two methods, it is obvious that RMSE values for our proposed method are smaller than Stereology method. Using morphological operation, dilation allows to enhance the estimation volume method, Stereology. In the case with less MRI slices and less test points, this method works much better compared to Stereology method.

Brain medical image diagnosis based on corners with importance-values.

PubMed

Gao, Linlin; Pan, Haiwei; Li, Qing; Xie, Xiaoqin; Zhang, Zhiqiang; Han, Jinming; Zhai, Xiao

2017-11-21

Brain disorders are one of the top causes of human death. Generally, neurologists analyze brain medical images for diagnosis. In the image analysis field, corners are one of the most important features, which makes corner detection and matching studies essential. However, existing corner detection studies do not consider the domain information of brain. This leads to many useless corners and the loss of significant information. Regarding corner matching, the uncertainty and structure of brain are not employed in existing methods. Moreover, most corner matching studies are used for 3D image registration. They are inapplicable for 2D brain image diagnosis because of the different mechanisms. To address these problems, we propose a novel corner-based brain medical image classification method. Specifically, we automatically extract multilayer texture images (MTIs) which embody diagnostic information from neurologists. Moreover, we present a corner matching method utilizing the uncertainty and structure of brain medical images and a bipartite graph model. Finally, we propose a similarity calculation method for diagnosis. Brain CT and MRI image sets are utilized to evaluate the proposed method. First, classifiers are trained in N-fold cross-validation analysis to produce the best θ and K. Then independent brain image sets are tested to evaluate the classifiers. Moreover, the classifiers are also compared with advanced brain image classification studies. For the brain CT image set, the proposed classifier outperforms the comparison methods by at least 8% on accuracy and 2.4% on F1-score. Regarding the brain MRI image set, the proposed classifier is superior to the comparison methods by more than 7.3% on accuracy and 4.9% on F1-score. Results also demonstrate that the proposed method is robust to different intensity ranges of brain medical image. In this study, we develop a robust corner-based brain medical image classifier. Specifically, we propose a corner detection method utilizing the diagnostic information from neurologists and a corner matching method based on the uncertainty and structure of brain medical images. Additionally, we present a similarity calculation method for brain image classification. Experimental results on two brain image sets show the proposed corner-based brain medical image classifier outperforms the state-of-the-art studies.

A brain-region-based meta-analysis method utilizing the Apriori algorithm.

PubMed

Niu, Zhendong; Nie, Yaoxin; Zhou, Qian; Zhu, Linlin; Wei, Jieyao

2016-05-18

Brain network connectivity modeling is a crucial method for studying the brain's cognitive functions. Meta-analyses can unearth reliable results from individual studies. Meta-analytic connectivity modeling is a connectivity analysis method based on regions of interest (ROIs) which showed that meta-analyses could be used to discover brain network connectivity. In this paper, we propose a new meta-analysis method that can be used to find network connectivity models based on the Apriori algorithm, which has the potential to derive brain network connectivity models from activation information in the literature, without requiring ROIs. This method first extracts activation information from experimental studies that use cognitive tasks of the same category, and then maps the activation information to corresponding brain areas by using the automatic anatomical label atlas, after which the activation rate of these brain areas is calculated. Finally, using these brain areas, a potential brain network connectivity model is calculated based on the Apriori algorithm. The present study used this method to conduct a mining analysis on the citations in a language review article by Price (Neuroimage 62(2):816-847, 2012). The results showed that the obtained network connectivity model was consistent with that reported by Price. The proposed method is helpful to find brain network connectivity by mining the co-activation relationships among brain regions. Furthermore, results of the co-activation relationship analysis can be used as a priori knowledge for the corresponding dynamic causal modeling analysis, possibly achieving a significant dimension-reducing effect, thus increasing the efficiency of the dynamic causal modeling analysis.

Complex network inference from P300 signals: Decoding brain state under visual stimulus for able-bodied and disabled subjects

NASA Astrophysics Data System (ADS)

Gao, Zhong-Ke; Cai, Qing; Dong, Na; Zhang, Shan-Shan; Bo, Yun; Zhang, Jie

2016-10-01

Distinguishing brain cognitive behavior underlying disabled and able-bodied subjects constitutes a challenging problem of significant importance. Complex network has established itself as a powerful tool for exploring functional brain networks, which sheds light on the inner workings of the human brain. Most existing works in constructing brain network focus on phase-synchronization measures between regional neural activities. In contrast, we propose a novel approach for inferring functional networks from P300 event-related potentials by integrating time and frequency domain information extracted from each channel signal, which we show to be efficient in subsequent pattern recognition. In particular, we construct brain network by regarding each channel signal as a node and determining the edges in terms of correlation of the extracted feature vectors. A six-choice P300 paradigm with six different images is used in testing our new approach, involving one able-bodied subject and three disabled subjects suffering from multiple sclerosis, cerebral palsy, traumatic brain and spinal-cord injury, respectively. We then exploit global efficiency, local efficiency and small-world indices from the derived brain networks to assess the network topological structure associated with different target images. The findings suggest that our method allows identifying brain cognitive behaviors related to visual stimulus between able-bodied and disabled subjects.

Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach.

PubMed

Sohrabpour, Abbas; Ye, Shuai; Worrell, Gregory A; Zhang, Wenbo; He, Bin

2016-12-01

Combined source-imaging techniques and directional connectivity analysis can provide useful information about the underlying brain networks in a noninvasive fashion. Source-imaging techniques have been used successfully to either determine the source of activity or to extract source time-courses for Granger causality analysis, previously. In this work, we utilize source-imaging algorithms to both find the network nodes [regions of interest (ROI)] and then extract the activation time series for further Granger causality analysis. The aim of this work is to find network nodes objectively from noninvasive electromagnetic signals, extract activation time-courses, and apply Granger analysis on the extracted series to study brain networks under realistic conditions. Source-imaging methods are used to identify network nodes and extract time-courses and then Granger causality analysis is applied to delineate the directional functional connectivity of underlying brain networks. Computer simulations studies where the underlying network (nodes and connectivity pattern) is known were performed; additionally, this approach has been evaluated in partial epilepsy patients to study epilepsy networks from interictal and ictal signals recorded by EEG and/or Magnetoencephalography (MEG). Localization errors of network nodes are less than 5 mm and normalized connectivity errors of ∼20% in estimating underlying brain networks in simulation studies. Additionally, two focal epilepsy patients were studied and the identified nodes driving the epileptic network were concordant with clinical findings from intracranial recordings or surgical resection. Our study indicates that combined source-imaging algorithms with Granger causality analysis can identify underlying networks precisely (both in terms of network nodes location and internodal connectivity). The combined source imaging and Granger analysis technique is an effective tool for studying normal or pathological brain conditions.

Utilizing gamma band to improve mental task based brain-computer interface design.

PubMed

Palaniappan, Ramaswamy

2006-09-01

A common method for designing brain-computer Interface (BCI) is to use electroencephalogram (EEG) signals extracted during mental tasks. In these BCI designs, features from EEG such as power and asymmetry ratios from delta, theta, alpha, and beta bands have been used in classifying different mental tasks. In this paper, the performance of the mental task based BCI design is improved by using spectral power and asymmetry ratios from gamma (24-37 Hz) band in addition to the lower frequency bands. In the experimental study, EEG signals extracted during five mental tasks from four subjects were used. Elman neural network (ENN) trained by the resilient backpropagation algorithm was used to classify the power and asymmetry ratios from EEG into different combinations of two mental tasks. The results indicated that ((1) the classification performance and training time of the BCI design were improved through the use of additional gamma band features; (2) classification performances were nearly invariant to the number of ENN hidden units or feature extraction method.

Validation of ethnopharmacology of ayurvedic sarasvata ghrita and comparative evaluation of its neuroprotective effect with modern alcoholic and lipid based extracts in β-amyloid induced memory impairment.

PubMed

Shelar, Madhuri; Nanaware, Sadhana; Arulmozhi, S; Lohidasan, Sathiyanarayanan; Mahadik, Kakasaheb

2018-06-12

Sarasvata ghrita (SG), a polyherbal formulation from ayurveda, an ancient medicinal system of India, has been used to improve intelligence and memory, treat speech delay, speaking difficulties and low digestion power in children. Study aimed to validate the ethno use of SG in memory enhancement through systematic scientific protocol. The effect of SG and modern extracts of ingredients of SG was compared on cognitive function and neuroprotection in amyloid-β peptide 25-35(Aβ25-35) induced memory impairment in wistar rats. Further the underlying mechanism for neuroprotective activity was investigated. SG was prepared as per traditional method, ethanolic extract (EE) was prepared by conventional method and lipid based extract was prepared by modern extraction method. All extracts were standardised by newly developed HPLC method with respect to marker compounds. SG, EE and LE were administered orally to male Wistar rats at doses of 100,200 and 400 mg/kg Body Weight by feeding needle for a period of 21 days after the intracerebroventricular administration of Aβ25-35 bilaterally. Spatial memory of rats was tested using Morris water maze (MWM) and Radial arm maze (RAM) test. The possible underlying mechanisms for the cognitive improvement exhibited by SG, EE and LE was investigated through ex-vivo brain antioxidant effect, monoamine level estimation, acetylcholine esterase (AchE) inhibitory effect and Brain-derived neurotropic factor (BDNF) levels estimation. SG, EE and LE were analyzed by HPLC method, results showed that EE extract has high percent of selected phytoconstituents as compared with SG and LE. SG and LE decrease escape latency and searching distance in a dose dependant manner during MWM test. In case of RAM significant decrease in number of errors and increase in number of correct choices indicate an elevation in retention and recall aspects of learning and memory after administration of SG an LE. SG and LE extract can efficiently prevent accumulation of β-amyloid plaque in hippocampus region. There was increase in SOD, GSH, CAT and NO level and decrease in MDA levels in SG and LE administered animals. SG and LE have found to exhibit AchE inhibitiory activity and significant dose-dependant increase in BDNF level in the plasma. SG and LE significantly increased the levels of noradrenaline, dopamine and 5-hydroxytryptamine in the brain. The study validated the neuroprotective activity of SG. The study concludes the extraction efficiency of SG for selected phytoconstituents is less than modern methods. However the neuroprotective activity of SG and LE was found to be greater than EE. Copyright © 2018. Published by Elsevier B.V.

SIGNALING PATHWAYS REGULATED BY BRASSICACEAE EXTRACT INHIBIT THE FORMATION OF ADVANCED GLYCATED END PRODUCTS IN RAT BRAIN

PubMed Central

Al-Malki, Abdulrahman L.; Barbour, Elie K.; EA, Huwait; Moselhy, Said S.; ALZahrani, Anas Hassan Saeed; Kumosani, Taha A.

2017-01-01

Background: The goal of this study was identification signaling molecules mediated the formation of AGEs in brain of rats injected with CdCl2 and the role of camel whey proteins and Brassicaceae extract on formation of AGEs in brain. Methods: Ninety male rats were randomly grouped into five groups; Normal control (GpI) and the other rats (groups II-V) were received a single dose of cadmium chloride i.p (5 μg/kg/b.w) for induction of neurodegeneration. Rats in groups III-V were treated daily with whey protein (1g/kg b.w) or Brassicaceae extract (1mg/kg b.w) or combined respectively for 12 weeks. Results: It was found that whey protein combined with Brassicaceae extract prevented the formation of AGEs and enhance the antioxidant activity compared with untreated group (p <0.001). Serum tumor necrosis factor (TNF-α) and interleukine (IL-6) levels were significantly decreased (p<0.01) in rats treated with whey protein and Brassicaceae extract formation compared with untreated. The combined treatment showed a better impact than individual ones (p<0.001). The level of cAMP but not cGMP were lowered in combined treatment than individual (p<0.01). Conclusion: It can be postulated that Whey protein + Brassicaceae extract formation could have potential benefits in the prevention of the onset and progression of neuropathy in patients. PMID:28573240

[Extraction of evoked related potentials by using the combination of independent component analysis and wavelet analysis].

PubMed

Zou, Ling; Chen, Shuyue; Sun, Yuqiang; Ma, Zhenghua

2010-08-01

In this paper we present a new method of combining Independent Component Analysis (ICA) and Wavelet de-noising algorithm to extract Evoked Related Potentials (ERPs). First, the extended Infomax-ICA algorithm is used to analyze EEG signals and obtain the independent components (Ics); Then, the Wave Shrink (WS) method is applied to the demixed Ics as an intermediate step; the EEG data were rebuilt by using the inverse ICA based on the new Ics; the ERPs were extracted by using de-noised EEG data after being averaged several trials. The experimental results showed that the combined method and ICA method could remove eye artifacts and muscle artifacts mixed in the ERPs, while the combined method could retain the brain neural activity mixed in the noise Ics and could extract the weak ERPs efficiently from strong background artifacts.

Patient-specific model-based segmentation of brain tumors in 3D intraoperative ultrasound images.

PubMed

Ilunga-Mbuyamba, Elisee; Avina-Cervantes, Juan Gabriel; Lindner, Dirk; Arlt, Felix; Ituna-Yudonago, Jean Fulbert; Chalopin, Claire

2018-03-01

Intraoperative ultrasound (iUS) imaging is commonly used to support brain tumor operation. The tumor segmentation in the iUS images is a difficult task and still under improvement because of the low signal-to-noise ratio. The success of automatic methods is also limited due to the high noise sensibility. Therefore, an alternative brain tumor segmentation method in 3D-iUS data using a tumor model obtained from magnetic resonance (MR) data for local MR-iUS registration is presented in this paper. The aim is to enhance the visualization of the brain tumor contours in iUS. A multistep approach is proposed. First, a region of interest (ROI) based on the specific patient tumor model is defined. Second, hyperechogenic structures, mainly tumor tissues, are extracted from the ROI of both modalities by using automatic thresholding techniques. Third, the registration is performed over the extracted binary sub-volumes using a similarity measure based on gradient values, and rigid and affine transformations. Finally, the tumor model is aligned with the 3D-iUS data, and its contours are represented. Experiments were successfully conducted on a dataset of 33 patients. The method was evaluated by comparing the tumor segmentation with expert manual delineations using two binary metrics: contour mean distance and Dice index. The proposed segmentation method using local and binary registration was compared with two grayscale-based approaches. The outcomes showed that our approach reached better results in terms of computational time and accuracy than the comparative methods. The proposed approach requires limited interaction and reduced computation time, making it relevant for intraoperative use. Experimental results and evaluations were performed offline. The developed tool could be useful for brain tumor resection supporting neurosurgeons to improve tumor border visualization in the iUS volumes.

Quantification of petroleum-type hydrocarbons in avian tissue

USGS Publications Warehouse

Gay, M.L.; Belisle, A.A.; Patton, J.F.

1980-01-01

Methods were developed for the analysis of 16 hydrocarbons in avian tissue. Mechanical extraction with pentane was followed by clean-up on Florisil and Silicar. Residues were determined by gas—liquid chromatography and gas—liquid, chromatography—mass spectrometry. The method was applied to the analysis of liver, kidney, fat, and brain tissue of mallard ducks (Anas platyrhynchos) fed a mixture of hydrocarbons. Measurable concentrations of all compounds analyzed were present in all tissues except brain. Highest concentrations were in fat.

Improving Functional MRI Registration Using Whole-Brain Functional Correlation Tensors.

PubMed

Zhou, Yujia; Yap, Pew-Thian; Zhang, Han; Zhang, Lichi; Feng, Qianjin; Shen, Dinggang

2017-09-01

Population studies of brain function with resting-state functional magnetic resonance imaging (rs-fMRI) largely rely on the accurate inter-subject registration of functional areas. This is typically achieved through registration of the corresponding T1-weighted MR images with more structural details. However, accumulating evidence has suggested that such strategy cannot well-align functional regions which are not necessarily confined by the anatomical boundaries defined by the T1-weighted MR images. To mitigate this problem, various registration algorithms based directly on rs-fMRI data have been developed, most of which have utilized functional connectivity (FC) as features for registration. However, most of the FC-based registration methods usually extract the functional features only from the thin and highly curved cortical grey matter (GM), posing a great challenge in accurately estimating the whole-brain deformation field. In this paper, we demonstrate that the additional useful functional features can be extracted from brain regions beyond the GM, particularly, white-matter (WM) based on rs-fMRI, for improving the overall functional registration. Specifically, we quantify the local anisotropic correlation patterns of the blood oxygenation level-dependent (BOLD) signals, modeled by functional correlation tensors (FCTs), in both GM and WM. Functional registration is then performed based on multiple components of the whole-brain FCTs using a multichannel Large Deformation Diffeomorphic Metric Mapping (mLDDMM) algorithm. Experimental results show that our proposed method achieves superior functional registration performance, compared with other conventional registration methods.

Learning a common dictionary for subject-transfer decoding with resting calibration.

PubMed

Morioka, Hiroshi; Kanemura, Atsunori; Hirayama, Jun-ichiro; Shikauchi, Manabu; Ogawa, Takeshi; Ikeda, Shigeyuki; Kawanabe, Motoaki; Ishii, Shin

2015-05-01

Brain signals measured over a series of experiments have inherent variability because of different physical and mental conditions among multiple subjects and sessions. Such variability complicates the analysis of data from multiple subjects and sessions in a consistent way, and degrades the performance of subject-transfer decoding in a brain-machine interface (BMI). To accommodate the variability in brain signals, we propose 1) a method for extracting spatial bases (or a dictionary) shared by multiple subjects, by employing a signal-processing technique of dictionary learning modified to compensate for variations between subjects and sessions, and 2) an approach to subject-transfer decoding that uses the resting-state activity of a previously unseen target subject as calibration data for compensating for variations, eliminating the need for a standard calibration based on task sessions. Applying our methodology to a dataset of electroencephalography (EEG) recordings during a selective visual-spatial attention task from multiple subjects and sessions, where the variability compensation was essential for reducing the redundancy of the dictionary, we found that the extracted common brain activities were reasonable in the light of neuroscience knowledge. The applicability to subject-transfer decoding was confirmed by improved performance over existing decoding methods. These results suggest that analyzing multisubject brain activities on common bases by the proposed method enables information sharing across subjects with low-burden resting calibration, and is effective for practical use of BMI in variable environments. Copyright © 2015 Elsevier Inc. All rights reserved.

Quantitative measurement of intact alpha-synuclein proteoforms from post-mortem control and Parkinson's disease brain tissue by intact protein mass spectrometry.

PubMed

Kellie, John F; Higgs, Richard E; Ryder, John W; Major, Anthony; Beach, Thomas G; Adler, Charles H; Merchant, Kalpana; Knierman, Michael D

2014-07-23

A robust top down proteomics method is presented for profiling alpha-synuclein species from autopsied human frontal cortex brain tissue from Parkinson's cases and controls. The method was used to test the hypothesis that pathology associated brain tissue will have a different profile of post-translationally modified alpha-synuclein than the control samples. Validation of the sample processing steps, mass spectrometry based measurements, and data processing steps were performed. The intact protein quantitation method features extraction and integration of m/z data from each charge state of a detected alpha-synuclein species and fitting of the data to a simple linear model which accounts for concentration and charge state variability. The quantitation method was validated with serial dilutions of intact protein standards. Using the method on the human brain samples, several previously unreported modifications in alpha-synuclein were identified. Low levels of phosphorylated alpha synuclein were detected in brain tissue fractions enriched for Lewy body pathology and were marginally significant between PD cases and controls (p = 0.03).

Composite technique for regional neurochemical studies: measurement of energy and neurotransmitter metabolites in single tissue sample.

PubMed

Djuricic, B M; Ueki, Y; Spatz, M

1985-06-01

A combined method is described for the determination of various metabolites from a single tissue sample of the brain. It comprises a quick inactivation of cerebral enzymes by microwave irradiation, easy separation of the desired brain regions, and perchloric acid extraction of tissue substances, which are assayed either by specific enzymatic techniques or by HPLC with electrochemical detection. The obtained values of most energy and neurotransmitter metabolites in the brain are in agreement with those reported using other methods. However, this technique, in contrast to the brain freezing in vitro or freeze-blowing, provides a more efficient procedure for rapid arrest of cerebral metabolism even in the deep brain structures and is therefore suitable for detection of early changes particularly those occurring in experimental pathological conditions such as ischemia.

Toward On-Demand Deep Brain Stimulation Using Online Parkinson's Disease Prediction Driven by Dynamic Detection.

PubMed

Mohammed, Ameer; Zamani, Majid; Bayford, Richard; Demosthenous, Andreas

2017-12-01

In Parkinson's disease (PD), on-demand deep brain stimulation is required so that stimulation is regulated to reduce side effects resulting from continuous stimulation and PD exacerbation due to untimely stimulation. Also, the progressive nature of PD necessitates the use of dynamic detection schemes that can track the nonlinearities in PD. This paper proposes the use of dynamic feature extraction and dynamic pattern classification to achieve dynamic PD detection taking into account the demand for high accuracy, low computation, and real-time detection. The dynamic feature extraction and dynamic pattern classification are selected by evaluating a subset of feature extraction, dimensionality reduction, and classification algorithms that have been used in brain-machine interfaces. A novel dimensionality reduction technique, the maximum ratio method (MRM) is proposed, which provides the most efficient performance. In terms of accuracy and complexity for hardware implementation, a combination having discrete wavelet transform for feature extraction, MRM for dimensionality reduction, and dynamic k-nearest neighbor for classification was chosen as the most efficient. It achieves a classification accuracy of 99.29%, an F1-score of 97.90%, and a choice probability of 99.86%.

Sex genes for genomic analysis in human brain: internal controls for comparison of probe level data extraction.

PubMed Central

Galfalvy, Hanga C; Erraji-Benchekroun, Loubna; Smyrniotopoulos, Peggy; Pavlidis, Paul; Ellis, Steven P; Mann, J John; Sibille, Etienne; Arango, Victoria

2003-01-01

Background Genomic studies of complex tissues pose unique analytical challenges for assessment of data quality, performance of statistical methods used for data extraction, and detection of differentially expressed genes. Ideally, to assess the accuracy of gene expression analysis methods, one needs a set of genes which are known to be differentially expressed in the samples and which can be used as a "gold standard". We introduce the idea of using sex-chromosome genes as an alternative to spiked-in control genes or simulations for assessment of microarray data and analysis methods. Results Expression of sex-chromosome genes were used as true internal biological controls to compare alternate probe-level data extraction algorithms (Microarray Suite 5.0 [MAS5.0], Model Based Expression Index [MBEI] and Robust Multi-array Average [RMA]), to assess microarray data quality and to establish some statistical guidelines for analyzing large-scale gene expression. These approaches were implemented on a large new dataset of human brain samples. RMA-generated gene expression values were markedly less variable and more reliable than MAS5.0 and MBEI-derived values. A statistical technique controlling the false discovery rate was applied to adjust for multiple testing, as an alternative to the Bonferroni method, and showed no evidence of false negative results. Fourteen probesets, representing nine Y- and two X-chromosome linked genes, displayed significant sex differences in brain prefrontal cortex gene expression. Conclusion In this study, we have demonstrated the use of sex genes as true biological internal controls for genomic analysis of complex tissues, and suggested analytical guidelines for testing alternate oligonucleotide microarray data extraction protocols and for adjusting multiple statistical analysis of differentially expressed genes. Our results also provided evidence for sex differences in gene expression in the brain prefrontal cortex, supporting the notion of a putative direct role of sex-chromosome genes in differentiation and maintenance of sexual dimorphism of the central nervous system. Importantly, these analytical approaches are applicable to all microarray studies that include male and female human or animal subjects. PMID:12962547

Sex genes for genomic analysis in human brain: internal controls for comparison of probe level data extraction.

PubMed

Galfalvy, Hanga C; Erraji-Benchekroun, Loubna; Smyrniotopoulos, Peggy; Pavlidis, Paul; Ellis, Steven P; Mann, J John; Sibille, Etienne; Arango, Victoria

2003-09-08

Genomic studies of complex tissues pose unique analytical challenges for assessment of data quality, performance of statistical methods used for data extraction, and detection of differentially expressed genes. Ideally, to assess the accuracy of gene expression analysis methods, one needs a set of genes which are known to be differentially expressed in the samples and which can be used as a "gold standard". We introduce the idea of using sex-chromosome genes as an alternative to spiked-in control genes or simulations for assessment of microarray data and analysis methods. Expression of sex-chromosome genes were used as true internal biological controls to compare alternate probe-level data extraction algorithms (Microarray Suite 5.0 [MAS5.0], Model Based Expression Index [MBEI] and Robust Multi-array Average [RMA]), to assess microarray data quality and to establish some statistical guidelines for analyzing large-scale gene expression. These approaches were implemented on a large new dataset of human brain samples. RMA-generated gene expression values were markedly less variable and more reliable than MAS5.0 and MBEI-derived values. A statistical technique controlling the false discovery rate was applied to adjust for multiple testing, as an alternative to the Bonferroni method, and showed no evidence of false negative results. Fourteen probesets, representing nine Y- and two X-chromosome linked genes, displayed significant sex differences in brain prefrontal cortex gene expression. In this study, we have demonstrated the use of sex genes as true biological internal controls for genomic analysis of complex tissues, and suggested analytical guidelines for testing alternate oligonucleotide microarray data extraction protocols and for adjusting multiple statistical analysis of differentially expressed genes. Our results also provided evidence for sex differences in gene expression in the brain prefrontal cortex, supporting the notion of a putative direct role of sex-chromosome genes in differentiation and maintenance of sexual dimorphism of the central nervous system. Importantly, these analytical approaches are applicable to all microarray studies that include male and female human or animal subjects.

Comparison of continuously acquired resting state and extracted analogues from active tasks

PubMed Central

Ganger, Sebastian; Hahn, Andreas; Küblböck, Martin; Kranz, Georg S.; Spies, Marie; Vanicek, Thomas; Seiger, René; Sladky, Ronald; Windischberger, Christian; Kasper, Siegfried

2015-01-01

Abstract Functional connectivity analysis of brain networks has become an important tool for investigation of human brain function. Although functional connectivity computations are usually based on resting‐state data, the application to task‐specific fMRI has received growing attention. Three major methods for extraction of resting‐state data from task‐related signal have been proposed (1) usage of unmanipulated task data for functional connectivity; (2) regression against task effects, subsequently using the residuals; and (3) concatenation of baseline blocks located in‐between task blocks. Despite widespread application in current research, consensus on which method best resembles resting‐state seems to be missing. We, therefore, evaluated these techniques in a sample of 26 healthy controls measured at 7 Tesla. In addition to continuous resting‐state, two different task paradigms were assessed (emotion discrimination and right finger‐tapping) and five well‐described networks were analyzed (default mode, thalamus, cuneus, sensorimotor, and auditory). Investigating the similarity to continuous resting‐state (Dice, Intraclass correlation coefficient (ICC), R 2) showed that regression against task effects yields functional connectivity networks most alike to resting‐state. However, all methods exhibited significant differences when compared to continuous resting‐state and similarity metrics were lower than test‐retest of two resting‐state scans. Omitting global signal regression did not change these findings. Visually, the networks are highly similar, but through further investigation marked differences can be found. Therefore, our data does not support referring to resting‐state when extracting signals from task designs, although functional connectivity computed from task‐specific data may indeed yield interesting information. Hum Brain Mapp 36:4053–4063, 2015. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. PMID:26178250

In vitro and in vivo studies of Allium sativum extract against deltamethrin-induced oxidative stress in rats brain and kidney.

PubMed

Ncir, Marwa; Saoudi, Mongi; Sellami, Hanen; Rahmouni, Fatma; Lahyani, Amina; Makni Ayadi, Fatma; El Feki, Abdelfattah; Allagui, Mohamed Salah

2017-09-18

The present study investigated the in vitro and the in vivo antioxidant capacities of Allium sativum (garlic) extract against deltamethrin-induced oxidative damage in rat's brain and kidney. The in vitro result showed that highest extraction yield was achieved with methanol (20.08%). Among the tested extracts, the methanol extract exhibited the highest total phenolic, flavonoids contents and antioxidant activity. The in vivo results showed that deltamethrin treatment caused an increase of the acetylcholinesterase level (AChE) in brain and plasma, the brain and kidney conjugated dienes and lipid peroxidation (LPO) levels as compared to control group. The antioxidant enzymes results showed that deltamethrin treatment induced a significantly decrease (p < 0.01) in brain and kidney antioxidant enzymes as catalase (CAT), superoxide dismutase (SOD) and glutathione peroxidase (GPx) to control group. The co-administration of garlic extract reduced the toxic effects in brain and kidney tissues induced by deltamethrin.

A robust, efficient and flexible method for staining myelinated axons in blocks of brain tissue.

PubMed

Wahlsten, Douglas; Colbourne, Frederick; Pleus, Richard

2003-03-15

Previous studies have demonstrated the utility of the gold chloride method for en bloc staining of a bisected brain in mice and rats. The present study explores several variations in the method, assesses its reliability, and extends the limits of its application. We conclude that the method is very efficient, highly robust, sufficiently accurate for most purposes, and adaptable to many morphometric measures. We obtained acceptable staining of commissures in every brain, despite a wide variety of fixation methods. One-half could be stained 24 h after the brain was extracted and the other half could be stained months later. When staining failed because of an exhausted solution, the brain could be stained successfully in fresh solution. Relatively small changes were found in the sizes of commissures several weeks after initial fixation or staining. A half brain stained to reveal the mid-sagittal section could then be sectioned coronally and stained again in either gold chloride for myelin or cresyl violet for Nissl substance. Uncertainty, arising from pixelation of digitized images was far less than errors arising from human judgments about the histological limits of major commissures. Useful data for morphometric analysis were obtained by scanning the surface of a gold chloride stained block of brain with an inexpensive flatbed scanner.

Segmentation and texture analysis of structural biomarkers using neighborhood-clustering-based level set in MRI of the schizophrenic brain.

PubMed

Latha, Manohar; Kavitha, Ganesan

2018-02-03

Schizophrenia (SZ) is a psychiatric disorder that especially affects individuals during their adolescence. There is a need to study the subanatomical regions of SZ brain on magnetic resonance images (MRI) based on morphometry. In this work, an attempt was made to analyze alterations in structure and texture patterns in images of the SZ brain using the level-set method and Laws texture features. T1-weighted MRI of the brain from Center of Biomedical Research Excellence (COBRE) database were considered for analysis. Segmentation was carried out using the level-set method. Geometrical and Laws texture features were extracted from the segmented brain stem, corpus callosum, cerebellum, and ventricle regions to analyze pattern changes in SZ. The level-set method segmented multiple brain regions, with higher similarity and correlation values compared with an optimized method. The geometric features obtained from regions of the corpus callosum and ventricle showed significant variation (p < 0.00001) between normal and SZ brain. Laws texture feature identified a heterogeneous appearance in the brain stem, corpus callosum and ventricular regions, and features from the brain stem were correlated with Positive and Negative Syndrome Scale (PANSS) score (p < 0.005). A framework of geometric and Laws texture features obtained from brain subregions can be used as a supplement for diagnosis of psychiatric disorders.

Low-power hardware implementation of movement decoding for brain computer interface with reduced-resolution discrete cosine transform.

PubMed

Minho Won; Albalawi, Hassan; Xin Li; Thomas, Donald E

2014-01-01

This paper describes a low-power hardware implementation for movement decoding of brain computer interface. Our proposed hardware design is facilitated by two novel ideas: (i) an efficient feature extraction method based on reduced-resolution discrete cosine transform (DCT), and (ii) a new hardware architecture of dual look-up table to perform discrete cosine transform without explicit multiplication. The proposed hardware implementation has been validated for movement decoding of electrocorticography (ECoG) signal by using a Xilinx FPGA Zynq-7000 board. It achieves more than 56× energy reduction over a reference design using band-pass filters for feature extraction.

Synthetic Minority Oversampling Technique and Fractal Dimension for Identifying Multiple Sclerosis

NASA Astrophysics Data System (ADS)

Zhang, Yu-Dong; Zhang, Yin; Phillips, Preetha; Dong, Zhengchao; Wang, Shuihua

Multiple sclerosis (MS) is a severe brain disease. Early detection can provide timely treatment. Fractal dimension can provide statistical index of pattern changes with scale at a given brain image. In this study, our team used susceptibility weighted imaging technique to obtain 676 MS slices and 880 healthy slices. We used synthetic minority oversampling technique to process the unbalanced dataset. Then, we used Canny edge detector to extract distinguishing edges. The Minkowski-Bouligand dimension was a fractal dimension estimation method and used to extract features from edges. Single hidden layer neural network was used as the classifier. Finally, we proposed a three-segment representation biogeography-based optimization to train the classifier. Our method achieved a sensitivity of 97.78±1.29%, a specificity of 97.82±1.60% and an accuracy of 97.80±1.40%. The proposed method is superior to seven state-of-the-art methods in terms of sensitivity and accuracy.

A simple procedure for the extraction of DNA from long-term formalin-preserved brain tissues for the detection of EBV by PCR.

PubMed

Hassani, Asma; Khan, Gulfaraz

2015-12-01

Long-term formalin fixed brain tissues are potentially an important source of material for molecular studies. Ironically, very few protocols have been published describing DNA extraction from such material for use in PCR analysis. In our attempt to investigate the role of Epstein-Barr virus (EBV) in the pathogenesis of multiple sclerosis (MS), extracting PCR quality DNA from brain samples fixed in formalin for 2-22 years, proved to be very difficult and challenging. As expected, DNA extracted from these samples was not only of poor quality and quantity, but more importantly, it was frequently found to be non-amplifiable due to the presence of PCR inhibitors. Here, we describe a simple and reproducible procedure for extracting DNA using a modified proteinase K and phenol-chloroform methodology. Central to this protocol is the thorough pre-digestion washing of the tissues in PBS, extensive digestion with proteinase K in low SDS containing buffer, and using low NaCl concentration during DNA precipitation. The optimized protocol was used in extracting DNA from meninges of 26 MS and 6 non-MS cases. Although the quality of DNA from these samples was generally poor, small size amplicons (100-200 nucleotides) of the house-keeping gene, β-globin could be reliably amplified from all the cases. PCR for EBV revealed positivity in 35% (9/26) MS cases, but 0/6 non-MS cases. These findings indicate that the method described here is suitable for PCR detection of viral sequences in long-term formalin persevered brain tissues. Our findings also support a possible role for EBV in the pathogenesis of MS. Copyright © 2015 Elsevier Inc. All rights reserved.

Novel active contour model based on multi-variate local Gaussian distribution for local segmentation of MR brain images

NASA Astrophysics Data System (ADS)

Zheng, Qiang; Li, Honglun; Fan, Baode; Wu, Shuanhu; Xu, Jindong

2017-12-01

Active contour model (ACM) has been one of the most widely utilized methods in magnetic resonance (MR) brain image segmentation because of its ability of capturing topology changes. However, most of the existing ACMs only consider single-slice information in MR brain image data, i.e., the information used in ACMs based segmentation method is extracted only from one slice of MR brain image, which cannot take full advantage of the adjacent slice images' information, and cannot satisfy the local segmentation of MR brain images. In this paper, a novel ACM is proposed to solve the problem discussed above, which is based on multi-variate local Gaussian distribution and combines the adjacent slice images' information in MR brain image data to satisfy segmentation. The segmentation is finally achieved through maximizing the likelihood estimation. Experiments demonstrate the advantages of the proposed ACM over the single-slice ACM in local segmentation of MR brain image series.

Multiple feature extraction and classification of electroencephalograph signal for Alzheimers' with spectrum and bispectrum

NASA Astrophysics Data System (ADS)

Wang, Ruofan; Wang, Jiang; Li, Shunan; Yu, Haitao; Deng, Bin; Wei, Xile

2015-01-01

In this paper, we have combined experimental neurophysiologic recording and statistical analysis to investigate the nonlinear characteristic and the cognitive function of the brain. Spectrum and bispectrum analyses are proposed to extract multiple effective features of electroencephalograph (EEG) signals from Alzheimer's disease (AD) patients and further applied to distinguish AD patients from the normal controls. Spectral analysis based on autoregressive Burg method is first used to quantify the power distribution of EEG series in the frequency domain. Compared to the control group, the relative power spectral density of AD group is significantly higher in the theta frequency band, while lower in the alpha frequency bands. In addition, median frequency of spectrum is decreased, and spectral entropy ratio of these two frequency bands undergoes drastic changes at the P3 electrode in the central-parietal brain region, implying that the electrophysiological behavior in AD brain is much slower and less irregular. In order to explore the nonlinear high order information, bispectral analysis which measures the complexity of phase-coupling is further applied to P3 electrode in the whole frequency band. It is demonstrated that less bispectral peaks appear and the amplitudes of peaks fall, suggesting a decrease of non-Gaussianity and nonlinearity of EEG in ADs. Notably, the application of this method to five brain regions shows higher concentration of the weighted center of bispectrum and lower complexity reflecting phase-coupling by bispectral entropy. Based on spectrum and bispectrum analyses, six efficient features are extracted and then applied to discriminate AD from the normal in the five brain regions. The classification results indicate that all these features could differentiate AD patients from the normal controls with a maximum accuracy of 90.2%. Particularly, different brain regions are sensitive to different features. Moreover, the optimal combination of features obtained by discriminant analysis may improve the classification accuracy. These results demonstrate the great promise for scape EEG spectral and bispectral features as a potential effective method for detection of AD, which may facilitate our understanding of the pathological mechanism of the disease.

Multiple feature extraction and classification of electroencephalograph signal for Alzheimers' with spectrum and bispectrum.

PubMed

Wang, Ruofan; Wang, Jiang; Li, Shunan; Yu, Haitao; Deng, Bin; Wei, Xile

2015-01-01

In this paper, we have combined experimental neurophysiologic recording and statistical analysis to investigate the nonlinear characteristic and the cognitive function of the brain. Spectrum and bispectrum analyses are proposed to extract multiple effective features of electroencephalograph (EEG) signals from Alzheimer's disease (AD) patients and further applied to distinguish AD patients from the normal controls. Spectral analysis based on autoregressive Burg method is first used to quantify the power distribution of EEG series in the frequency domain. Compared to the control group, the relative power spectral density of AD group is significantly higher in the theta frequency band, while lower in the alpha frequency bands. In addition, median frequency of spectrum is decreased, and spectral entropy ratio of these two frequency bands undergoes drastic changes at the P3 electrode in the central-parietal brain region, implying that the electrophysiological behavior in AD brain is much slower and less irregular. In order to explore the nonlinear high order information, bispectral analysis which measures the complexity of phase-coupling is further applied to P3 electrode in the whole frequency band. It is demonstrated that less bispectral peaks appear and the amplitudes of peaks fall, suggesting a decrease of non-Gaussianity and nonlinearity of EEG in ADs. Notably, the application of this method to five brain regions shows higher concentration of the weighted center of bispectrum and lower complexity reflecting phase-coupling by bispectral entropy. Based on spectrum and bispectrum analyses, six efficient features are extracted and then applied to discriminate AD from the normal in the five brain regions. The classification results indicate that all these features could differentiate AD patients from the normal controls with a maximum accuracy of 90.2%. Particularly, different brain regions are sensitive to different features. Moreover, the optimal combination of features obtained by discriminant analysis may improve the classification accuracy. These results demonstrate the great promise for scape EEG spectral and bispectral features as a potential effective method for detection of AD, which may facilitate our understanding of the pathological mechanism of the disease.

Directionality analysis on functional magnetic resonance imaging during motor task using Granger causality.

PubMed

Anwar, A R; Muthalib, M; Perrey, S; Galka, A; Granert, O; Wolff, S; Deuschl, G; Raethjen, J; Heute, U; Muthuraman, M

2012-01-01

Directionality analysis of signals originating from different parts of brain during motor tasks has gained a lot of interest. Since brain activity can be recorded over time, methods of time series analysis can be applied to medical time series as well. Granger Causality is a method to find a causal relationship between time series. Such causality can be referred to as a directional connection and is not necessarily bidirectional. The aim of this study is to differentiate between different motor tasks on the basis of activation maps and also to understand the nature of connections present between different parts of the brain. In this paper, three different motor tasks (finger tapping, simple finger sequencing, and complex finger sequencing) are analyzed. Time series for each task were extracted from functional magnetic resonance imaging (fMRI) data, which have a very good spatial resolution and can look into the sub-cortical regions of the brain. Activation maps based on fMRI images show that, in case of complex finger sequencing, most parts of the brain are active, unlike finger tapping during which only limited regions show activity. Directionality analysis on time series extracted from contralateral motor cortex (CMC), supplementary motor area (SMA), and cerebellum (CER) show bidirectional connections between these parts of the brain. In case of simple finger sequencing and complex finger sequencing, the strongest connections originate from SMA and CMC, while connections originating from CER in either direction are the weakest ones in magnitude during all paradigms.

Transpulmonary hypothermia: a novel method of rapid brain cooling through augmented heat extraction from the lungs.

PubMed

Kumar, Matthew M; Goldberg, Andrew D; Kashiouris, Markos; Keenan, Lawrence R; Rabinstein, Alejandro A; Afessa, Bekele; Johnson, Larry D; Atkinson, John L D; Nayagam, Vedha

2014-10-01

Delay in instituting neuroprotective measures after cardiac arrest increases death and decreases neuronal recovery. Current hypothermia methods are slow, ineffective, unreliable, or highly invasive. We report the feasibility of rapid hypothermia induction in swine through augmented heat extraction from the lungs. Twenty-four domestic crossbred pigs (weight, 50-55kg) were ventilated with room air. Intraparenchymal brain temperature and core temperatures from pulmonary artery, lower esophagus, bladder, rectum, nasopharynx, and tympanum were recorded. In eight animals, ventilation was switched to cooled helium-oxygen mixture (heliox) and perfluorocarbon (PFC) aerosol and continued for 90min or until target brain temperature of 32°C was reached. Eight animals received body-surface cooling with water-circulating blankets; eight control animals continued to be ventilated with room air. Brain and core temperatures declined rapidly with cooled heliox-PFC ventilation. The brain reached target temperature within the study period (mean [SD], 66 [7.6]min) in only the transpulmonary cooling group. Cardiopulmonary functions and poststudy histopathological examination of the lungs were normal. Transpulmonary cooling is novel, rapid, minimally invasive, and an effective technique to induce therapeutic hypothermia. High thermal conductivity of helium and vaporization of PFC produces rapid cooling of alveolar gases. The thinness and large surface area of alveolar membrane facilitate rapid cooling of the pulmonary circulation. Because of differences in thermogenesis, blood flow, insulation, and exposure to the external environment, the brain cools at a different rate than other organs. Transpulmonary hypothermia was significantly faster than body surface cooling in reaching target brain temperature. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Locally adaptive MR intensity models and MRF-based segmentation of multiple sclerosis lesions

NASA Astrophysics Data System (ADS)

Galimzianova, Alfiia; Lesjak, Žiga; Likar, Boštjan; Pernuš, Franjo; Špiclin, Žiga

2015-03-01

Neuroimaging biomarkers are an important paraclinical tool used to characterize a number of neurological diseases, however, their extraction requires accurate and reliable segmentation of normal and pathological brain structures. For MR images of healthy brains the intensity models of normal-appearing brain tissue (NABT) in combination with Markov random field (MRF) models are known to give reliable and smooth NABT segmentation. However, the presence of pathology, MR intensity bias and natural tissue-dependent intensity variability altogether represent difficult challenges for a reliable estimation of NABT intensity model based on MR images. In this paper, we propose a novel method for segmentation of normal and pathological structures in brain MR images of multiple sclerosis (MS) patients that is based on locally-adaptive NABT model, a robust method for the estimation of model parameters and a MRF-based segmentation framework. Experiments on multi-sequence brain MR images of 27 MS patients show that, compared to whole-brain model and compared to the widely used Expectation-Maximization Segmentation (EMS) method, the locally-adaptive NABT model increases the accuracy of MS lesion segmentation.

Analysis of multiple quaternary ammonium compounds in the brain using tandem capillary column separation and high resolution mass spectrometric detection.

PubMed

Falasca, Sara; Petruzziello, Filomena; Kretz, Robert; Rainer, Gregor; Zhang, Xiaozhe

2012-06-08

Endogenous quaternary ammonium compounds are involved in various physiological processes in the central nervous system. In the present study, eleven quaternary ammonium compounds, including acetylcholine, choline, carnitine, acetylcarnitine and seven other acylcarnitines of low polarity, were analyzed from brain extracts using a two dimension capillary liquid chromatography-Fourier transform mass spectrometry method. To deal with their large difference in hydrophobicities, tandem coupling between reversed phase and hydrophilic interaction chromatography columns was used to separate all the targeted quaternary ammonium compounds. Using high accuracy mass spectrometry in selected ion monitoring mode, all the compounds could be detected from each brain sample with high selectivity. The developed method was applied for the relative quantification of these quaternary ammonium compounds in three different brain regions of tree shrews: prefrontal cortex, striatum, and hippocampus. The comparative analysis showed that quaternary ammonium compounds were differentially distributed across the three brain areas. The analytical method proved to be highly sensitive and reliable for simultaneous determination of all the targeted analytes from brain samples. Copyright © 2012 Elsevier B.V. All rights reserved.

Validated UPLC-MS/MS method for determination of moclobemide in human brain cell supernatant and its application to bidirectional transport study.

PubMed

Li-Bo, Dai; Miao, Yan; Huan-De, Li; Ping-Fei, Fang; Feng, Wang; Yang, Deng

2013-09-01

A simple and sensitive analytical method based on ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) has been developed for determination of moclobemide in human brain cell monolayer as an in vitro model of blood-brain barrier. Brucine was employed as the internal standard. Moclobemide and internal standard were extracted from cell supernatant by ethyl acetate after alkalinizing with sodium hydroxide. The UPLC separation was performed on an Acquity UPLC(TM) BEH C18 column (50 × 2.1 mm, 1.7 µm, Waters, USA) with a mobile phase consisting of methanol-water (29.5:70.5, v/v); the water in the mobile phase contained 0.05% ammonium acetate and 0.1% formic acid. Detection of the analytes was achieved using positive ion electrospray via multiple reaction monitoring mode. The mass transitions were m/z 269.16 → 182.01 for moclobemide and m/z 395.24 → 324.15 for brucine. The extraction recovery was 83.0-83.4% and the lower limit of quantitation (LLOQ) was 1.0 ng/mL for moclobemide. The method was validated from LLOQ to 1980 ng/mL with a coefficient of determination greater than 0.999. Intra- and inter-day accuracies of the method at three concentrations ranged from 89.1 to 100.9% for moclobemide with precision of 1.1-9.6%. This validated method was successfully applied to bidirectional transport study of moclobemide blood-brain barrier permeability. Copyright © 2013 John Wiley & Sons, Ltd.

Causal Structure of Brain Physiology after Brain Injury from Subarachnoid Hemorrhage.

PubMed

Claassen, Jan; Rahman, Shah Atiqur; Huang, Yuxiao; Frey, Hans-Peter; Schmidt, J Michael; Albers, David; Falo, Cristina Maria; Park, Soojin; Agarwal, Sachin; Connolly, E Sander; Kleinberg, Samantha

2016-01-01

High frequency physiologic data are routinely generated for intensive care patients. While massive amounts of data make it difficult for clinicians to extract meaningful signals, these data could provide insight into the state of critically ill patients and guide interventions. We develop uniquely customized computational methods to uncover the causal structure within systemic and brain physiologic measures recorded in a neurological intensive care unit after subarachnoid hemorrhage. While the data have many missing values, poor signal-to-noise ratio, and are composed from a heterogeneous patient population, our advanced imputation and causal inference techniques enable physiologic models to be learned for individuals. Our analyses confirm that complex physiologic relationships including demand and supply of oxygen underlie brain oxygen measurements and that mechanisms for brain swelling early after injury may differ from those that develop in a delayed fashion. These inference methods will enable wider use of ICU data to understand patient physiology.

Emotion Discrimination Using Spatially Compact Regions of Interest Extracted from Imaging EEG Activity

PubMed Central

Padilla-Buritica, Jorge I.; Martinez-Vargas, Juan D.; Castellanos-Dominguez, German

2016-01-01

Lately, research on computational models of emotion had been getting much attention due to their potential for understanding the mechanisms of emotions and their promising broad range of applications that potentially bridge the gap between human and machine interactions. We propose a new method for emotion classification that relies on features extracted from those active brain areas that are most likely related to emotions. To this end, we carry out the selection of spatially compact regions of interest that are computed using the brain neural activity reconstructed from Electroencephalography data. Throughout this study, we consider three representative feature extraction methods widely applied to emotion detection tasks, including Power spectral density, Wavelet, and Hjorth parameters. Further feature selection is carried out using principal component analysis. For validation purpose, these features are used to feed a support vector machine classifier that is trained under the leave-one-out cross-validation strategy. Obtained results on real affective data show that incorporation of the proposed training method in combination with the enhanced spatial resolution provided by the source estimation allows improving the performed accuracy of discrimination in most of the considered emotions, namely: dominance, valence, and liking. PMID:27489541

Lycium barbarum Extracts Protect the Brain from Blood-Brain Barrier Disruption and Cerebral Edema in Experimental Stroke

PubMed Central

Yang, Di; Li, Suk-Yee; Yeung, Chung-Man; Chang, Raymond Chuen-Chung; So, Kwok-Fai; Wong, David; Lo, Amy C. Y.

2012-01-01

Background and Purpose Ischemic stroke is a destructive cerebrovascular disease and a leading cause of death. Yet, no ideal neuroprotective agents are available, leaving prevention an attractive alternative. The extracts from the fruits of Lycium barbarum (LBP), a Chinese anti-aging medicine and food supplement, showed neuroprotective function in the retina when given prophylactically. We aim to evaluate the protective effects of LBP pre-treatment in an experimental stroke model. Methods C57BL/6N male mice were first fed with either vehicle (PBS) or LBP (1 or 10 mg/kg) daily for 7 days. Mice were then subjected to 2-hour transient middle cerebral artery occlusion (MCAO) by the intraluminal method followed by 22-hour reperfusion upon filament removal. Mice were evaluated for neurological deficits just before sacrifice. Brains were harvested for infarct size estimation, water content measurement, immunohistochemical analysis, and Western blot experiments. Evans blue (EB) extravasation was determined to assess blood-brain barrier (BBB) disruption after MCAO. Results LBP pre-treatment significantly improved neurological deficits as well as decreased infarct size, hemispheric swelling, and water content. Fewer apoptotic cells were identified in LBP-treated brains by TUNEL assay. Reduced EB extravasation, fewer IgG-leaky vessels, and up-regulation of occludin expression were also observed in LBP-treated brains. Moreover, immunoreactivity for aquaporin-4 and glial fibrillary acidic protein were significantly decreased in LBP-treated brains. Conclusions Seven-day oral LBP pre-treatment effectively improved neurological deficits, decreased infarct size and cerebral edema as well as protected the brain from BBB disruption, aquaporin-4 up-regulation, and glial activation. The present study suggests that LBP may be used as a prophylactic neuroprotectant in patients at high risk for ischemic stroke. PMID:22438957

An Active Contour Model Based on Adaptive Threshold for Extraction of Cerebral Vascular Structures.

PubMed

Wang, Jiaxin; Zhao, Shifeng; Liu, Zifeng; Tian, Yun; Duan, Fuqing; Pan, Yutong

2016-01-01

Cerebral vessel segmentation is essential and helpful for the clinical diagnosis and the related research. However, automatic segmentation of brain vessels remains challenging because of the variable vessel shape and high complex of vessel geometry. This study proposes a new active contour model (ACM) implemented by the level-set method for segmenting vessels from TOF-MRA data. The energy function of the new model, combining both region intensity and boundary information, is composed of two region terms, one boundary term and one penalty term. The global threshold representing the lower gray boundary of the target object by maximum intensity projection (MIP) is defined in the first-region term, and it is used to guide the segmentation of the thick vessels. In the second term, a dynamic intensity threshold is employed to extract the tiny vessels. The boundary term is used to drive the contours to evolve towards the boundaries with high gradients. The penalty term is used to avoid reinitialization of the level-set function. Experimental results on 10 clinical brain data sets demonstrate that our method is not only able to achieve better Dice Similarity Coefficient than the global threshold based method and localized hybrid level-set method but also able to extract whole cerebral vessel trees, including the thin vessels.

Age- and brain region-dependent α-synuclein oligomerization is attributed to alterations in intrinsic enzymes regulating α-synuclein phosphorylation in aging monkey brains.

PubMed

Chen, Min; Yang, Weiwei; Li, Xin; Li, Xuran; Wang, Peng; Yue, Feng; Yang, Hui; Chan, Piu; Yu, Shun

2016-02-23

We previously reported that the levels of α-syn oligomers, which play pivotal pathogenic roles in age-related Parkinson's disease (PD) and dementia with Lewy bodies, increase heterogeneously in the aging brain. Here, we show that exogenous α-syn incubated with brain extracts from older cynomolgus monkeys and in Lewy body pathology (LBP)-susceptible brain regions (striatum and hippocampus) forms higher amounts of phosphorylated and oligomeric α-syn than that in extracts from younger monkeys and LBP-insusceptible brain regions (cerebellum and occipital cortex). The increased α-syn phosphorylation and oligomerization in the brain extracts from older monkeys and in LBP-susceptible brain regions were associated with higher levels of polo-like kinase 2 (PLK2), an enzyme promoting α-syn phosphorylation, and lower activity of protein phosphatase 2A (PP2A), an enzyme inhibiting α-syn phosphorylation, in these brain extracts. Further, the extent of the age- and brain-dependent increase in α-syn phosphorylation and oligomerization was reduced by inhibition of PLK2 and activation of PP2A. Inversely, phosphorylated α-syn oligomers reduced the activity of PP2A and showed potent cytotoxicity. In addition, the activity of GCase and the levels of ceramide, a product of GCase shown to activate PP2A, were lower in brain extracts from older monkeys and in LBP-susceptible brain regions. Our results suggest a role for altered intrinsic metabolic enzymes in age- and brain region-dependent α-syn oligomerization in aging brains.

Rough-Fuzzy Clustering and Unsupervised Feature Selection for Wavelet Based MR Image Segmentation

PubMed Central

Maji, Pradipta; Roy, Shaswati

2015-01-01

Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR) images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices. PMID:25848961

Therapeutic effect of methanolic extract of Laportea aestuans (L.) Chew, on oxidative stress in the brain of male Wistar rats

NASA Astrophysics Data System (ADS)

Elizabeth, Omotosho Omolola; Olawumi, Ogunlade Oladipupo

2018-04-01

The aim of this study was to assess the effect of diclofenac-induced oxidative stress in the brain of Wistar rats. The experiment was carried out using thirty-six rats. Six groups contained six rats in each. The first group being the control group received 1ml of gum acacia which is the vehicle. Groups 2 to 6 were induced with oxidative stress by oral administration of 40 mg/kg body weight of diclofenac and pretreated as follows: group 2 received only diclofenac, group 3 with 200 mg/kg body weight of methanolic extract of Laportea aestuans (L.) Chew, group 4 with 400 mg/kg body weight of Laportea aestuans extract, group 5 with 800 mg/kg body weight of Laportea aestuans and group 6 with 50 mg/kg body weight of cimetidine. The pretreatment was carried out for a period of seven days after which oxidative stress was induced. The animals were thereafter sacrificed and brain was excised. Antioxidant enzymes and molecules such as superoxide dismutase, catalase, glutathione, levels of malondialdehyde and protein carbonyl were assayed by standard methods. The results showed significant increases in glutathione level and activities of catalase, superoxide dismutase and significant decrease in lipid peroxidation and protein carbonyl in groups 3 to 5 when compared to group 2. This shows that the methanolic extract of Laportea aestuans has a protective effect on the brain against oxidative stress.

In vitro and In vivo Antioxidant Evaluation and Estimation of Total Phenolic, Flavonoidal Content of Mimosa pudica L

PubMed Central

Patro, Ganesh; Bhattamisra, Subrat Kumar; Mohanty, Bijay Kumar; Sahoo, Himanshu Bhusan

2016-01-01

Objective: Mimosa pudica Linn. (Mimosaceae) is traditionally used as a folk medicine to treat various ailments including convulsions, alopecia, diarrhea, dysentery, insomnia, tumor, wound healing, snake bite, etc., Here, the study was aimed to evaluate the antioxidant potential of M. pudica leaves extract against 2, 2-diphenyl-1-picrylhydrazyl (DPPH) (in vitro) and its modulatory effect on rat brain enzymes. Materials and Methods: Total phenolic, flavonoid contents, and in vitro antioxidant potential against DPPH radical were evaluated from various extracts of M. pudica leaves. In addition, ethyl acetate extract of Mimosa pudica leaves (EAMP) in doses of 100, 200, and 400 mg/kg/day were administered orally for 7 consecutive days to albino rats and evaluated for the oxidative stress markers as thiobarbituric acid reactive substances (TBARS), superoxide dismutase (SOD), catalase (CAT), and glutathione (GSH) from rat brain homogenate. Results: The ethyl acetate extract showed the highest total phenolic content and total flavonoid content among other extracts of M. pudica leaves. The percentage inhibition and IC50 value of all the extracts were followed dose-dependency and found significant (P < 0.01) as compared to standard (ascorbic acid). The oxidative stress markers as SOD, CAT, and GSH were increased significantly (P < 0.01) at 200 and 400 mg/kg of EAMP treated animals and decreased significantly the TBARS level at 400 mg/kg of EAMP as compared to control group. Conclusion: These results revealed that the ethyl acetate extract of M. pudica exhibits both in vitro antioxidant activity against DPPH and in vivo antioxidant activity by modulating brain enzymes in the rat. This could be further correlated with its potential to neuroprotective activity due to the presence of flavonoids and phenolic contents in the extract. SUMMARY Total phenolic, flavonoid contents and in-vitro antioxidant potential were evaluated from various extracts of M. pudica leaves. Again, in-vivo antioxidant evaluation from brain homogenate on oxidative stress markers as TBARS, SOD, CAT and GSH from rat was investigated. Our findings revealed that M. pudica possesses both in-vitro and in-vivo antioxidant activity due to presence of phenolics and flavonoids. PMID:26941532

Real-time system for extracting and monitoring the cerebral functional component during fNIRS measurements

NASA Astrophysics Data System (ADS)

Yamada, Toru; Ohashi, Mitsuo; Umeyama, Shinji

2015-12-01

Functional near-infrared spectroscopy (fNIRS) can non-invasively detect hemodynamic changes associated with cerebral neural activation in human subjects. However, its signal is often affected by changes in the optical characteristics of tissues in the head other than brain. To conduct fNIRS measurements precisely and efficiently, the extraction and realtime monitoring of the cerebral functional component is crucial. We previously developed methods for extracting the cerebral functional component—the multidistance optode arrangement (MD) method and the hemodynamic modality separation (HMS) method. In this study, we implemented these methods in a software used with the fNIRS system OEG- 17APD (Spectratech, Japan), and realized a real-time display of the extracted results. When using this system for human subject experiments, the baselines obtained with the MD and HMS methods were highly stabilized, whereas originally, the fNIRS signal fluctuated significantly when the subject moved. Through a functional experiment with repetitive single-sided hand clasping tasks, the extracted signals showed distinctively higher reproducibility than that obtained in the conventional measurements.

The effect of lead exposure on fatty acid composition in mouse brain analyzed using pseudo-catalytic derivatization.

PubMed

Jung, Jong-Min; Lee, Jechan; Kim, Ki-Hyun; Jang, In Geon; Song, Jae Gwang; Kang, Kyeongjin; Tack, Filip M G; Oh, Jeong-Ik; Kwon, Eilhann E; Kim, Hyung-Wook

2017-03-01

We performed toxicological study of mice exposed to lead by quantifying fatty acids in brain of the mice. This study suggests that the introduced analytical method had an extremely high tolerance against impurities such as water and extractives; thus, it led to the enhanced resolution in visualizing the spectrum of fatty acid profiles in animal brain. Furthermore, one of the biggest technical advantages achieved in this study was the quantitation of fatty acid methyl ester profiles of mouse brain using a trace amount of sample (e.g., 100 μL mixture). Methanol was screened as the most effective extraction solvent for mouse brain. The behavioral test of the mice before and after lead exposure was conducted to see the effect of lead exposure on fatty acid composition of the mice' brain. The lead exposure led to changes in disease-related behavior of the mice. Also, the lead exposure induced significant alterations of fatty acid profile (C16:0, C 18:0, and C 18:1) in brain of the mice, implicated in pathology of psychiatric diseases. The alteration of fatty acid profile of brain of the mice suggests that the derivatizing technique can be applicable to most research fields associated with the environmental neurotoxins with better resolution in a short time, as compared to the current protocols for lipid analysis. Copyright © 2016 Elsevier Ltd. All rights reserved.

Functional Brain Networks: Does the Choice of Dependency Estimator and Binarization Method Matter?

NASA Astrophysics Data System (ADS)

Jalili, Mahdi

2016-07-01

The human brain can be modelled as a complex networked structure with brain regions as individual nodes and their anatomical/functional links as edges. Functional brain networks are constructed by first extracting weighted connectivity matrices, and then binarizing them to minimize the noise level. Different methods have been used to estimate the dependency values between the nodes and to obtain a binary network from a weighted connectivity matrix. In this work we study topological properties of EEG-based functional networks in Alzheimer’s Disease (AD). To estimate the connectivity strength between two time series, we use Pearson correlation, coherence, phase order parameter and synchronization likelihood. In order to binarize the weighted connectivity matrices, we use Minimum Spanning Tree (MST), Minimum Connected Component (MCC), uniform threshold and density-preserving methods. We find that the detected AD-related abnormalities highly depend on the methods used for dependency estimation and binarization. Topological properties of networks constructed using coherence method and MCC binarization show more significant differences between AD and healthy subjects than the other methods. These results might explain contradictory results reported in the literature for network properties specific to AD symptoms. The analysis method should be seriously taken into account in the interpretation of network-based analysis of brain signals.

Assessment of Mexican Arnica (Heterotheca inuloides Cass) and Rosemary (Rosmarinus officinalis) Extracts on Dopamine and Selected Biomarkers of Oxidative Stress in Stomach and Brain of Salmonella typhimurium Infected rats

PubMed Central

Guzmàn, David Calderón; Herrera, Maribel Ortiz; Brizuela, Norma Osnaya; Mejía, Gerardo Barragàn; García, Ernestina Hernàndez; Olguín, Hugo Juàrez; Peraza, Armando Valenzuela; Ruíz, Norma Labra; Del Angel, Daniel Santamaría

2017-01-01

Background: The effects of some natural products on dopamine (DA) and 5-hydroxyindole acetic acid (5-HIAA) in brain of infected models are still unclear. Objective: The purpose of this study was to measure the effect of Mexican arnica/rosemary (MAR) water extract and oseltamivir on both biogenic amines and some oxidative biomarkers in the brain and stomach of young rats under infection condition. Methods: Female Wistar rats (weight 80 g) in the presence of MAR or absence (no-MAR) were treated as follows: group 1, buffer solution (controls); oseltamivir (100 mg/kg), group 2; culture of Salmonella typhimurium (S.Typh) (1 × 106 colony-forming units/rat) group 3; oseltamivir (100 mg/kg) + S.Typh (same dose) group 4. Drug and extracts were administered intraperitoneally every 24 h for 5 days, and S.Typh was given orally on days 1 and 3. On the fifth day, blood was collected to measure glucose and hemoglobin. The brains and stomachs were obtained to measure levels of DA, 5-HIAA, glutathione (GSH), TBARS, H2O2, and total ATPase activity using validated methods. Results: DA levels increased in MAR group treated with oseltamivir alone but decreased in no-MAR group treated with oseltamivir plus S.Typh. 5-HIAA, GSH, and H2O2 decreased in this last group, and ATPase activity increased in MAR group treated with oseltamivir plus S.Typh. TBARS (lipid peroxidation) increased in MAR group that received oseltamivir alone. Most of the biomarkers were not altered significantly in the stomach. Conclusion: MAR extract alters DA and metabolism of 5-HIAA in the brain of young animals infected. Antioxidant capacity may be involved in these effects. SUMMARY The purpose of this study was to measure the effect of Mexican arnica/rosemary water extract and oseltamivir on both biogenic amines and some oxidative biomarkers in the brain and stomach of young rats under infection condition. Results: Mexican arnica and rosemary extract alter dopamine and metabolism of 5-HIAA in the brain of young animals infected. Antioxidant capacity may be involved in these effects. Abbreviations used: AS: Automated system, ATP: Adenosine triphosphate, CNS: Central nervous system, CFU: Colony-forming unit, DA: Dopamine EDTA: Ethylenediaminetetraacetic acid, 5-HIAA: Äcido 5-hidroxindolacético (serotonina), GABA: γ-aminobutyric acid, GSH: Glutathione, H2O2: Hidrogen peroxide, HCLO4: Perchloric acid, iNOS: Inducible nitric oxide synthase, LPS: Lipopolysaccharides, MAR: Arnica/Rosemary, NaCl: Sodium Chloride, NOGSH: nitrosoglutathione, NOS: Nitric oxide, OPT: Ortho-phtaldialdehyde, Pbs: Phosphate buffered saline, pH: potential of Hydrogen, Pi: Inorganic phosphate, ROS: Reactive oxygen species, RNSs: Reactive nitrogen species Tba: Thiobarbaturic acid, TBARS: Thiobarbituric aid reactive, Tca: Trichloroacetic, Tris-HCL: Tris hydrochloride, TSA: Trypticasein Soya Agar PMID:28539708

Stress does not increase blood–brain barrier permeability in mice

PubMed Central

Roszkowski, Martin

2016-01-01

Several studies have reported that exposure to acute psychophysiological stressors can lead to an increase in blood–brain barrier permeability, but these findings remain controversial and disputed. We thoroughly examined this issue by assessing the effect of several well-established paradigms of acute stress and chronic stress on blood–brain barrier permeability in several brain areas of adult mice. Using cerebral extraction ratio for the small molecule tracer sodium fluorescein (NaF, 376 Da) as a sensitive measure of blood–brain barrier permeability, we find that neither acute swim nor restraint stress lead to increased cerebral extraction ratio. Daily 6-h restraint stress for 21 days, a model for the severe detrimental impact of chronic stress on brain function, also does not alter cerebral extraction ratio. In contrast, we find that cold forced swim and cold restraint stress both lead to a transient, pronounced decrease of cerebral extraction ratio in hippocampus and cortex, suggesting that body temperature can be an important confounding factor in studies of blood–brain barrier permeability. To additionally assess if stress could change blood–brain barrier permeability for macromolecules, we measured cerebral extraction ratio for fluorescein isothiocyanate-dextran (70 kDa). We find that neither acute restraint nor cold swim stress affected blood–brain barrier permeability for macromolecules, thus corroborating our findings that various stressors do not increase blood–brain barrier permeability. PMID:27146513

HPLC determination of strychnine and brucine in rat tissues and the distribution study of processed semen strychni.

PubMed

Chen, Jun; Hou, Ting; Fang, Yun; Chen, Zhi-peng; Liu, Xiao; Cai, Hao; Lu, Tu-lin; Yan, Guo-jun; Cai, Bao-chang

2011-01-01

A simple and low-cost HPLC method with UV absorbance detection was developed and validated to simultaneously determine strychnine and brucine, the most abundant alkaloids in the processed Semen Strychni, in rat tissues (kidney, liver, spleen, lung, heart, stomach, small intestine, brain and plasma). The tissue samples were treated with a simple liquid-liquid extraction prior to HPLC. The LOQs were in the range of 0.039-0.050 µg/ml for different tissue or plasma samples. The extraction recoveries varied from 71.63 to 98.79%. The linear range was 0.05-2 µg/ml with correlation coefficient of over 0.991. The intra- and inter-day precision was less than 15%. Then the method was used to measure the tissue distribution of strychnine and brucine after intravenous administration of 1 mg/kg crude alkaloids fraction (CAF) extracted from the processed Semen Strychni. The results revealed that strychnine and brucine possessed similar tissue distribution characterization. The highest level was observed in kidney, while the lowest level was found in brain. It was indicated that kidney might be the primary excretion organ of prototype strychnine and brucine. It was also deduced that strychnine and brucine had difficulty in crossing the blood-brain barrier. Furthermore, no long-term accumulation of strychnine and brucine was found in rat tissues.

Protective Effects of Crocus Sativus L. Extract and Crocin against Chronic-Stress Induced Oxidative Damage of Brain, Liver and Kidneys in Rats

PubMed Central

Bandegi, Ahmad Reza; Rashidy-Pour, Ali; Vafaei, Abbas Ali; Ghadrdoost, Behshid

2014-01-01

Purpose: Chronic stress has been reported to induce oxidative damage of the brain. A few studies have shown that Crocus Sativus L., commonly known as saffron and its active constituent crocin may have a protective effect against oxidative stress. The present work was designed to study the protective effects of saffron extract and crocin on chronic – stress induced oxidative stress damage of the brain, liver and kidneys. Methods: Rats were injected with a daily dose of saffron extract (30 mg/kg, IP) or crocin (30 mg/kg, IP) during a period of 21 days following chronic restraint stress (6 h/day). In order to determine the changes of the oxidative stress parameters following chronic stress, the levels of the lipid peroxidation product, malondialdehyde (MDA), the total antioxidant reactivity (TAR), as well as antioxidant enzyme activities glutathione peroxidase (GPx), glutathione reductase (GR) and superoxide dismutase (SOD) were measured in the brain, liver and kidneys tissues after the end of chronic stress. Results: In the stressed animals that receiving of saline, levels of MDA, and the activities of GPx, GR, and SOD were significantly higher (P<0.0001) and the TAR capacity were significantly lower than those of the non-stressed animals (P<0.0001). Both saffron extract and crocin were able to reverse these changes in the stressed animals as compared with the control groups (P<0.05). Conclusion: These observations indicate that saffron and its active constituent crocin can prevent chronic stress–induced oxidative stress damage of the brain, liver and kidneys and suggest that these substances may be useful against oxidative stress. PMID:25671180

Zingiber zerumbet L. (Smith) extract alleviates the ethanol-induced brain damage via its antioxidant activity.

PubMed

Hamid, Asmah; Ibrahim, Farah Wahida; Ming, Teoh Hooi; Nasrom, Mohd Nazir; Eusoff, Norelina; Husain, Khairana; Abdul Latif, Mazlyzam

2018-03-20

Zingiber zerumbet (L.) Smith belongs to the Zingiberaceae family that is widely distributed throughout the tropics, particularly in Southeast Asia. It is locally known as 'Lempoyang' and traditionally used to treat fever, constipation and to relieve pain. It is also known to possess antioxidant and anti-inflammatory activities. Based on these antioxidant and anti-inflammatory activities, this study was conducted to investigate the effects of ethyl-acetate extract of Z. zerumbet rhizomes against ethanol-induced brain damage in male Wistar rats. Twenty-four male Wistar rats were divided into four groups which consist of normal, 1.8 g/kg ethanol (40% v/v), 200 mg/kg Z. zerumbet extract plus ethanol and 400 mg/kg Z. zerumbet plus ethanol. The extract of Z. zerumbet was given once daily by oral gavage, 30 min prior to ethanol exposure via intraperitoneal route for 14 consecutive days. The rats were then sacrificed. Blood and brain homogenate were subjected to biochemical tests and part of the brain tissue was sectioned for histological analysis. Treatment with ethyl-acetate Z. zerumbet extract at 200 mg/kg and 400 mg/kg significantly reduced the level of malondialdehyde (MDA) and protein carbonyl (p < 0.05) in the brain homogenate. Both doses of extracts also significantly increased the level of serum superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GPx) activities as well as glutathione (GSH) level (p < 0.05). However, administration of ethyl-acetate Z. zerumbet extract at 400 mg/kg showed better protective effects on the ethanol-induced brain damage as shown with higher levels of SOD, CAT, GPx and GSH in the brain homogenate as compared to 200 mg/kg dose. Histological observation of the cerebellum and cerebral cortex showed that the extract prevented the loss of Purkinje cells and retained the number and the shape of the cells. Ethyl-acetate extract of Z. zerumbet has protective effects against ethanol-induced brain damage and this is mediated through its antioxidant properties. Z. zerumbet extract protects against ethanol-induced brain damage via its antioxidant properties.

Hybrid Clustering And Boundary Value Refinement for Tumor Segmentation using Brain MRI

NASA Astrophysics Data System (ADS)

Gupta, Anjali; Pahuja, Gunjan

2017-08-01

The method of brain tumor segmentation is the separation of tumor area from Brain Magnetic Resonance (MR) images. There are number of methods already exist for segmentation of brain tumor efficiently. However it’s tedious task to identify the brain tumor from MR images. The segmentation process is extraction of different tumor tissues such as active, tumor, necrosis, and edema from the normal brain tissues such as gray matter (GM), white matter (WM), as well as cerebrospinal fluid (CSF). As per the survey study, most of time the brain tumors are detected easily from brain MR image using region based approach but required level of accuracy, abnormalities classification is not predictable. The segmentation of brain tumor consists of many stages. Manually segmenting the tumor from brain MR images is very time consuming hence there exist many challenges in manual segmentation. In this research paper, our main goal is to present the hybrid clustering which consists of Fuzzy C-Means Clustering (for accurate tumor detection) and level set method(for handling complex shapes) for the detection of exact shape of tumor in minimal computational time. using this approach we observe that for a certain set of images 0.9412 sec of time is taken to detect tumor which is very less in comparison to recent existing algorithm i.e. Hybrid clustering (Fuzzy C-Means and K Means clustering).

HPLC-MS-MS Determination of ZCZ-011, A Novel Pharmacological Tool for Investigation of the Cannabinoid Receptor in Mouse Brain Using Clean Screen FASt™ Column Extraction.

PubMed

Poklis, Justin L; Clay, Deborah J; Ignatowska-Jankowska, Bogna M; Zanato, Chiara; Ross, Ruth A; Greig, Iain R; Abdullah, Rehab A; Mustafa, Mohammed A; Lichtman, Aron H; Poklis, Alphonse

2015-06-01

A high-performance liquid chromatography tandem mass spectrometry method was developed for the detection and quantification of 6-methyl-3-(2-nitro-1-(thiophen-2-yl)propyl)-2-phenyl-1H-indole (ZCZ-011) using 2-phenylindole as the internal standard (ISTD). ZCZ-011 was synthesized as a possible positive allosteric modulator with the CB1 cannabinoid receptor. The analytical method employs a rapid extraction technique using Clean Screen FASt™ columns with a Positive Pressure Manifold. FASt™ columns were originally developed for urine drug analysis but we have successfully adapted them to the extraction of brain tissue. Chromatographic separation was performed on a Restek Allure Biphenyl 5 µ, 100 × 3.2 mm column (Bellefonte, PA). The mobile phase consisted of 1:9 deionized water with 10 mmol ammonium acetate and 0.1% formic acid-methanol. The following transition ions (m/z) were monitored for ZCZ-011: 363 > 207 and 363 > 110 and for the ISTD: 194 > 165 and 194 > 89. The FASt™ columns lowered and stabilized the ion suppression over the linear range of the assay (40-4,000 ng/g). The method was evaluated for recovery, ion suppression, accuracy/bias, intraday and interday precision, bench-top stability, freeze-thaw and post-preparative stability. The method was successfully applied to brain tissue from C57BL/6J mice that received intraperitoneal (i.p.) injections with 40 mg/kg of ZCZ-011 or vehicle. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Comparison of EEG-Features and Classification Methods for Motor Imagery in Patients with Disorders of Consciousness

PubMed Central

Höller, Yvonne; Bergmann, Jürgen; Thomschewski, Aljoscha; Kronbichler, Martin; Höller, Peter; Crone, Julia S.; Schmid, Elisabeth V.; Butz, Kevin; Nardone, Raffaele; Trinka, Eugen

2013-01-01

Current research aims at identifying voluntary brain activation in patients who are behaviorally diagnosed as being unconscious, but are able to perform commands by modulating their brain activity patterns. This involves machine learning techniques and feature extraction methods such as applied in brain computer interfaces. In this study, we try to answer the question if features/classification methods which show advantages in healthy participants are also accurate when applied to data of patients with disorders of consciousness. A sample of healthy participants (N = 22), patients in a minimally conscious state (MCS; N = 5), and with unresponsive wakefulness syndrome (UWS; N = 9) was examined with a motor imagery task which involved imagery of moving both hands and an instruction to hold both hands firm. We extracted a set of 20 features from the electroencephalogram and used linear discriminant analysis, k-nearest neighbor classification, and support vector machines (SVM) as classification methods. In healthy participants, the best classification accuracies were seen with coherences (mean = .79; range = .53−.94) and power spectra (mean = .69; range = .40−.85). The coherence patterns in healthy participants did not match the expectation of central modulated -rhythm. Instead, coherence involved mainly frontal regions. In healthy participants, the best classification tool was SVM. Five patients had at least one feature-classifier outcome with p0.05 (none of which were coherence or power spectra), though none remained significant after false-discovery rate correction for multiple comparisons. The present work suggests the use of coherences in patients with disorders of consciousness because they show high reliability among healthy subjects and patient groups. However, feature extraction and classification is a challenging task in unresponsive patients because there is no ground truth to validate the results. PMID:24282545

The Effects of Capillary Transit Time Heterogeneity (CTH) on the Cerebral Uptake of Glucose and Glucose Analogs: Application to FDG and Comparison to Oxygen Uptake

PubMed Central

Angleys, Hugo; Jespersen, Sune N.; Østergaard, Leif

2016-01-01

Glucose is the brain's principal source of ATP, but the extent to which cerebral glucose consumption (CMRglc) is coupled with its oxygen consumption (CMRO2) remains unclear. Measurements of the brain's oxygen-glucose index OGI = CMRO2/CMRglc suggest that its oxygen uptake largely suffices for oxidative phosphorylation. Nevertheless, during functional activation and in some disease states, brain tissue seemingly produces lactate although cerebral blood flow (CBF) delivers sufficient oxygen, so-called aerobic glycolysis. OGI measurements, in turn, are method-dependent in that estimates based on glucose analog uptake depend on the so-called lumped constant (LC) to arrive at CMRglc. Capillary transit time heterogeneity (CTH), which is believed to change during functional activation and in some disease states, affects the extraction efficacy of oxygen from blood. We developed a three-compartment model of glucose extraction to examine whether CTH also affects glucose extraction into brain tissue. We then combined this model with our previous model of oxygen extraction to examine whether differential glucose and oxygen extraction might favor non-oxidative glucose metabolism under certain conditions. Our model predicts that glucose uptake is largely unaffected by changes in its plasma concentration, while changes in CBF and CTH affect glucose and oxygen uptake to different extents. Accordingly, functional hyperemia facilitates glucose uptake more than oxygen uptake, favoring aerobic glycolysis during enhanced energy demands. Applying our model to glucose analogs, we observe that LC depends on physiological state, with a risk of overestimating relative increases in CMRglc during functional activation by as much as 50%. PMID:27790110

Hawthorn extract reduces infarct volume and improves neurological score by reducing oxidative stress in rat brain following middle cerebral artery occlusion.

PubMed

Elango, Chinnasamy; Jayachandaran, Kasevan Sawaminathan; Niranjali Devaraj, S

2009-12-01

In our present investigation the neuroprotective effect of alcoholic extract of Hawthorn (Crataegus oxycantha) was evaluated against middle cerebral artery occlusion induced ischemia/reperfusion injury in rats. Male Sprague-Dawley rats were pretreated with 100 mg/kg body weight of the extract by oral gavage for 15 days. The middle cerebral artery was then occluded for 75 min followed by 24 h of reperfusion. The pretreated rats showed significantly improved neurological behavior with reduced brain infarct when compared to vehicle control rats. The glutathione level in brain was found to be significantly (p<0.05) low in vehicle control rats after 24 h of reperfusion when compared to sham operated animals. However, in Hawthorn extract pretreated rats the levels were found to be close to that of sham. Malondialdehyde levels in brain of sham and pretreated group were found to be significantly lower than the non-treated vehicle group (p<0.05). The nitric oxide levels in brain were measured and found to be significantly (p<0.05) higher in vehicle than in sham or extract treated rats. Our results suggest that Hawthorn extract which is a well known prophylactic for cardiac conditions may very well protect the brain against ischemia-reperfusion. The reduced brain damage and improved neurological behavior after 24 h of reperfusion in Hawthorn extract pretreated group may be attributed to its antioxidant property which restores glutathione levels, circumvents the increase in lipid peroxidation and nitric oxide levels thereby reducing peroxynitrite formation and free radical induced brain damage.

Kinetic Modeling of PET Data Without Blood Sampling

NASA Astrophysics Data System (ADS)

Bentourkia, M.

2005-06-01

In positron emission tomography (PET) imaging, application of kinetic modeling always requires an input curve (IC) together with the PET data. The IC can be obtained by means of external blood sampling or, in the case of cardiac studies, by means of a region-of-interest (ROI) drawn on the blood pool. It is, however, very unsuitable to withdraw and to analyze blood samples, and in small animals, these operations become difficult, while ICs determined from ROIs are generally contaminated by emissions from neighboring sites, or they are underestimated because of partial volume effect. In this paper, we report a new method to extract kinetic parameters from dynamic PET studies without a priori knowledge of the IC. The method is applied in human brain data measured with fluorodeoxyglucose (FDG) human-brain and in cardiac-rat perfusion studies with /sup 13/N-ammonia and /sup 11/C-acetate. The tissue blood volume (TBV), usually fitted together with the rate constants, is extracted simultaneously with the tissue time activity curves for cardiac studies, while for brain gray matter, TBV is known to be about 4% to 7%. The shape of IC is obtained by means of factor analysis from an ROI drawn around a cardiac tissue or a brain artery. The results show a good correlation (p<0.05) between the cerebral metabolic rate of glucose, myocardial blood flow, and oxygen consumption obtained with the new method in comparison to the usual method. In conclusion, it is possible to apply kinetic modeling without any blood sampling, which significantly simplifies PET acquisition and data analysis.

Brain Tumor Segmentation Using Deep Belief Networks and Pathological Knowledge.

PubMed

Zhan, Tianming; Chen, Yi; Hong, Xunning; Lu, Zhenyu; Chen, Yunjie

2017-01-01

In this paper, we propose an automatic brain tumor segmentation method based on Deep Belief Networks (DBNs) and pathological knowledge. The proposed method is targeted against gliomas (both low and high grade) obtained in multi-sequence magnetic resonance images (MRIs). Firstly, a novel deep architecture is proposed to combine the multi-sequences intensities feature extraction with classification to get the classification probabilities of each voxel. Then, graph cut based optimization is executed on the classification probabilities to strengthen the spatial relationships of voxels. At last, pathological knowledge of gliomas is applied to remove some false positives. Our method was validated in the Brain Tumor Segmentation Challenge 2012 and 2013 databases (BRATS 2012, 2013). The performance of segmentation results demonstrates our proposal providing a competitive solution with stateof- the-art methods. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Extracting the inclination angle of nerve fibers within the human brain with 3D-PLI independent of system properties

NASA Astrophysics Data System (ADS)

Reckfort, Julia; Wiese, Hendrik; Dohmen, Melanie; Grässel, David; Pietrzyk, Uwe; Zilles, Karl; Amunts, Katrin; Axer, Markus

2013-09-01

The neuroimaging technique 3D-polarized light imaging (3D-PLI) has opened up new avenues to study the complex nerve fiber architecture of the human brain at sub-millimeter spatial resolution. This polarimetry technique is applicable to histological sections of postmortem brains utilizing the birefringence of nerve fibers caused by the regular arrangement of lipids and proteins in the myelin sheaths surrounding axons. 3D-PLI provides a three-dimensional description of the anatomical wiring scheme defined by the in-section direction angle and the out-of-section inclination angle. To date, 3D-PLI is the only available method that allows bridging the microscopic and the macroscopic description of the fiber architecture of the human brain. Here we introduce a new approach to retrieve the inclination angle of the fibers independently of the properties of the used polarimeters. This is relevant because the image resolution and the signal transmission inuence the measured birefringent signal (retardation) significantly. The image resolution was determined using the USAF- 1951 testchart applying the Rayleigh criterion. The signal transmission was measured by elliptical polarizers applying the Michelson contrast and histological slices of the optic tract of a postmortem brain. Based on these results, a modified retardation-inclination transfer function was proposed to extract the fiber inclination. The comparison of the actual and the inclination angles calculated with the theoretically proposed and the modified transfer function revealed a significant improvement in the extraction of the fiber inclinations.

DSA Image Blood Vessel Skeleton Extraction Based on Anti-concentration Diffusion and Level Set Method

NASA Astrophysics Data System (ADS)

Xu, Jing; Wu, Jian; Feng, Daming; Cui, Zhiming

Serious types of vascular diseases such as carotid stenosis, aneurysm and vascular malformation may lead to brain stroke, which are the third leading cause of death and the number one cause of disability. In the clinical practice of diagnosis and treatment of cerebral vascular diseases, how to do effective detection and description of the vascular structure of two-dimensional angiography sequence image that is blood vessel skeleton extraction has been a difficult study for a long time. This paper mainly discussed two-dimensional image of blood vessel skeleton extraction based on the level set method, first do the preprocessing to the DSA image, namely uses anti-concentration diffusion model for the effective enhancement and uses improved Otsu local threshold segmentation technology based on regional division for the image binarization, then vascular skeleton extraction based on GMM (Group marching method) with fast sweeping theory was actualized. Experiments show that our approach not only improved the time complexity, but also make a good extraction results.

Mind Reading and Writing: The Future of Neurotechnology.

PubMed

Roelfsema, Pieter R; Denys, Damiaan; Klink, P Christiaan

2018-05-02

Recent advances in neuroscience and technology have made it possible to record from large assemblies of neurons and to decode their activity to extract information. At the same time, available methods to stimulate the brain and influence ongoing processing are also rapidly expanding. These developments pave the way for advanced neurotechnological applications that directly read from, and write to, the human brain. While such technologies are still primarily used in restricted therapeutic contexts, this may change in the future once their performance has improved and they become more widely applicable. Here, we provide an overview of methods to interface with the brain, speculate about potential applications, and discuss important issues associated with a neurotechnologically assisted future. Copyright © 2018 Elsevier Ltd. All rights reserved.

Analysis of Brain Recurrence

NASA Astrophysics Data System (ADS)

Frilot, Clifton; Kim, Paul Y.; Carrubba, Simona; McCarty, David E.; Chesson, Andrew L.; Marino, Andrew A.

Analysis of Brain Recurrence (ABR) is a method for extracting physiologically significant information from the electroencephalogram (EEG), a non-stationary electrical output of the brain, the ultimate complex dynamical system. ABR permits quantification of temporal patterns in the EEG produced by the non-autonomous differential laws that govern brain metabolism. In the context of appropriate experimental and statistical designs, ABR is ideally suited to the task of interpreting the EEG. Present applications of ABR include discovery of a human magnetic sense, increased mechanistic understanding of neuronal membrane processes, diagnosis of degenerative neurological disease, detection of changes in brain metabolism caused by weak environmental electromagnetic fields, objective characterization of the quality of human sleep, and evaluation of sleep disorders. ABR has important beneficial implications for the development of clinical and experimental neuroscience.

Application of wavelet transformation and adaptive neighborhood based modified backpropagation (ANMBP) for classification of brain cancer

NASA Astrophysics Data System (ADS)

Werdiningsih, Indah; Zaman, Badrus; Nuqoba, Barry

2017-08-01

This paper presents classification of brain cancer using wavelet transformation and Adaptive Neighborhood Based Modified Backpropagation (ANMBP). Three stages of the processes, namely features extraction, features reduction, and classification process. Wavelet transformation is used for feature extraction and ANMBP is used for classification process. The result of features extraction is feature vectors. Features reduction used 100 energy values per feature and 10 energy values per feature. Classifications of brain cancer are normal, alzheimer, glioma, and carcinoma. Based on simulation results, 10 energy values per feature can be used to classify brain cancer correctly. The correct classification rate of proposed system is 95 %. This research demonstrated that wavelet transformation can be used for features extraction and ANMBP can be used for classification of brain cancer.

Metabolomics studies in brain tissue: A review.

PubMed

Gonzalez-Riano, Carolina; Garcia, Antonia; Barbas, Coral

2016-10-25

Brain is still an organ with a composition to be discovered but beyond that, mental disorders and especially all diseases that curse with dementia are devastating for the patient, the family and the society. Metabolomics can offer an alternative tool for unveiling new insights in the discovery of new treatments and biomarkers of mental disorders. Until now, most of metabolomic studies have been based on biofluids: serum/plasma or urine, because brain tissue accessibility is limited to animal models or post mortem studies, but even so it is crucial for understanding the pathological processes. Metabolomics studies of brain tissue imply several challenges due to sample extraction, along with brain heterogeneity, sample storage, and sample treatment for a wide coverage of metabolites with a wide range of concentrations of many lipophilic and some polar compounds. In this review, the current analytical practices for target and non-targeted metabolomics are described and discussed with emphasis on critical aspects: sample treatment (quenching, homogenization, filtration, centrifugation and extraction), analytical methods, as well as findings considering the used strategies. Besides that, the altered analytes in the different brain regions have been associated with their corresponding pathways to obtain a global overview of their dysregulation, trying to establish the link between altered biological pathways and pathophysiological conditions. Copyright © 2016 Elsevier B.V. All rights reserved.

NMR imaging and spectroscopy of the mammalian central nervous system after heavy ion radiation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Richards, T.

NMR imaging, NMR spectroscopic, and histopathologic techniques were used to study the proton relaxation time and related biochemical changes in the central nervous system after helium beam in vivo irradiation of the rodent brain. The spectroscopic observations reported in this dissertation were made possible by development of methods for measuring the NMR parameters of the rodent brain in vivo and in vitro. The methods include (1) depth selective spectroscopy using an optimization of rf pulse energy based on a priori knowledge of N-acetyl aspartate and lipid spectra of the normal brain, (2) phase-encoded proton spectroscopy of the living rodent usingmore » a surface coil, and (3) dual aqueous and organic tissue extraction technique for spectroscopy. Radiation induced increases were observed in lipid and p-choline peaks of the proton spectrum, in vivo. Proton NMR spectroscopy measurements on brain extracts (aqueous and organic solvents) were made to observe chemical changes that could not be seen in vivo. Radiation-induced changes were observed in lactate, GABA, glutamate, and p-choline peak areas of the aqueous fraction spectra. In the organic fraction, decreases were observed in peak area ratios of the terminal-methyl peaks, the N-methyl groups of choline, and at a peak at 2.84 ppM (phosphatidyl ethanolamine and phosphatidyl serine resonances) relative to TMS. With histology and Evans blue injections, blood-brain barrier alternations were seen as early as 4 days after irradiation. 83 references, 53 figures.« less

Pathological brain detection based on wavelet entropy and Hu moment invariants.

PubMed

Zhang, Yudong; Wang, Shuihua; Sun, Ping; Phillips, Preetha

2015-01-01

With the aim of developing an accurate pathological brain detection system, we proposed a novel automatic computer-aided diagnosis (CAD) to detect pathological brains from normal brains obtained by magnetic resonance imaging (MRI) scanning. The problem still remained a challenge for technicians and clinicians, since MR imaging generated an exceptionally large information dataset. A new two-step approach was proposed in this study. We used wavelet entropy (WE) and Hu moment invariants (HMI) for feature extraction, and the generalized eigenvalue proximal support vector machine (GEPSVM) for classification. To further enhance classification accuracy, the popular radial basis function (RBF) kernel was employed. The 10 runs of k-fold stratified cross validation result showed that the proposed "WE + HMI + GEPSVM + RBF" method was superior to existing methods w.r.t. classification accuracy. It obtained the average classification accuracies of 100%, 100%, and 99.45% over Dataset-66, Dataset-160, and Dataset-255, respectively. The proposed method is effective and can be applied to realistic use.

An adaptive singular spectrum analysis method for extracting brain rhythms of electroencephalography

PubMed Central

Hu, Hai; Guo, Shengxin; Liu, Ran

2017-01-01

Artifacts removal and rhythms extraction from electroencephalography (EEG) signals are important for portable and wearable EEG recording devices. Incorporating a novel grouping rule, we proposed an adaptive singular spectrum analysis (SSA) method for artifacts removal and rhythms extraction. Based on the EEG signal amplitude, the grouping rule determines adaptively the first one or two SSA reconstructed components as artifacts and removes them. The remaining reconstructed components are then grouped based on their peak frequencies in the Fourier transform to extract the desired rhythms. The grouping rule thus enables SSA to be adaptive to EEG signals containing different levels of artifacts and rhythms. The simulated EEG data based on the Markov Process Amplitude (MPA) EEG model and the experimental EEG data in the eyes-open and eyes-closed states were used to verify the adaptive SSA method. Results showed a better performance in artifacts removal and rhythms extraction, compared with the wavelet decomposition (WDec) and another two recently reported SSA methods. Features of the extracted alpha rhythms using adaptive SSA were calculated to distinguish between the eyes-open and eyes-closed states. Results showed a higher accuracy (95.8%) than those of the WDec method (79.2%) and the infinite impulse response (IIR) filtering method (83.3%). PMID:28674650

Evaluation of metabolites extraction strategies for identifying different brain regions and their relationship with alcohol preference and gender difference using NMR metabolomics.

PubMed

Wang, Jie; Zeng, Hao-Long; Du, Hongying; Liu, Zeyuan; Cheng, Ji; Liu, Taotao; Hu, Ting; Kamal, Ghulam Mustafa; Li, Xihai; Liu, Huili; Xu, Fuqiang

2018-03-01

Metabolomics generate a profile of small molecules from cellular/tissue metabolism, which could directly reflect the mechanisms of complex networks of biochemical reactions. Traditional metabolomics methods, such as OPLS-DA, PLS-DA are mainly used for binary class discrimination. Multiple groups are always involved in the biological system, especially for brain research. Multiple brain regions are involved in the neuronal study of brain metabolic dysfunctions such as alcoholism, Alzheimer's disease, etc. In the current study, 10 different brain regions were utilized for comparative studies between alcohol preferring and non-preferring rats, male and female rats respectively. As many classes are involved (ten different regions and four types of animals), traditional metabolomics methods are no longer efficient for showing differentiation. Here, a novel strategy based on the decision tree algorithm was employed for successfully constructing different classification models to screen out the major characteristics of ten brain regions at the same time. Subsequently, this method was also utilized to select the major effective brain regions related to alcohol preference and gender difference. Compared with the traditional multivariate statistical methods, the decision tree could construct acceptable and understandable classification models for multi-class data analysis. Therefore, the current technology could also be applied to other general metabolomics studies involving multi class data. Copyright © 2017 Elsevier B.V. All rights reserved.

In vivo and In vitro neurochemical-based assessments of wastewater effluents from the Maumee River area of concern.

PubMed

Arini, Adeline; Cavallin, Jenna E; Berninger, Jason P; Marfil-Vega, Ruth; Mills, Marc; Villeneuve, Daniel L; Basu, Niladri

2016-04-01

Wastewater treatment plant (WWTP) effluents contain potentially neuroactive chemicals though few methods are available to screen for the presence of such agents. Here, two parallel approaches (in vivo and in vitro) were used to assess WWTP exposure-related changes to neurochemistry. First, fathead minnows (FHM, Pimephales promelas) were caged for four days along a WWTP discharge zone into the Maumee River (Ohio, USA). Grab water samples were collected and extracts obtained for the detection of alkylphenols, bisphenol A (BPA) and steroid hormones. Second, the extracts were then used as a source of in vitro exposure to brain tissues from FHM and four additional species relevant to the Great Lakes ecosystem (rainbow trout (RT), river otter (RO), bald eagle (BE) and human (HU)). The ability of the wastewater (in vivo) or extracts (in vitro) to interact with enzymes (monoamine oxidase (MAO) and glutamine synthetase (GS)) and receptors (dopamine (D2) and N-methyl-D-aspartate receptor (NMDA)) involved in dopamine and glutamate-dependent neurotransmission were examined on brain homogenates. In vivo exposure of FHM led to significant decreases of NMDA receptor binding in females (24-42%), and increases of MAO activity in males (2.8- to 3.2-fold). In vitro, alkylphenol-targeted extracts significantly inhibited D2 (66% in FHM) and NMDA (24-54% in HU and RT) receptor binding, and induced MAO activity in RT, RO, and BE brains. Steroid hormone-targeted extracts inhibited GS activity in all species except FHM. BPA-targeted extracts caused a MAO inhibition in FHM, RT and BE brains. Using both in vivo and in vitro approaches, this study shows that WWTP effluents contain agents that can interact with neurochemicals important in reproduction and other neurological functions. Additional work is needed to better resolve in vitro to in vivo extrapolations (IVIVE) as well as cross-species differences. Copyright © 2016 Elsevier Ltd. All rights reserved.

Brain-computer interface analysis of a dynamic visuo-motor task.

PubMed

Logar, Vito; Belič, Aleš

2011-01-01

The area of brain-computer interfaces (BCIs) represents one of the more interesting fields in neurophysiological research, since it investigates the development of the machines that perform different transformations of the brain's "thoughts" to certain pre-defined actions. Experimental studies have reported some successful implementations of BCIs; however, much of the field still remains unexplored. According to some recent reports the phase coding of informational content is an important mechanism in the brain's function and cognition, and has the potential to explain various mechanisms of the brain's data transfer, but it has yet to be scrutinized in the context of brain-computer interface. Therefore, if the mechanism of phase coding is plausible, one should be able to extract the phase-coded content, carried by brain signals, using appropriate signal-processing methods. In our previous studies we have shown that by using a phase-demodulation-based signal-processing approach it is possible to decode some relevant information on the current motor action in the brain from electroencephalographic (EEG) data. In this paper the authors would like to present a continuation of their previous work on the brain-information-decoding analysis of visuo-motor (VM) tasks. The present study shows that EEG data measured during more complex, dynamic visuo-motor (dVM) tasks carries enough information about the currently performed motor action to be successfully extracted by using the appropriate signal-processing and identification methods. The aim of this paper is therefore to present a mathematical model, which by means of the EEG measurements as its inputs predicts the course of the wrist movements as applied by each subject during the task in simulated or real time (BCI analysis). However, several modifications to the existing methodology are needed to achieve optimal decoding results and a real-time, data-processing ability. The information extracted from the EEG could, therefore, be further used for the development of a closed-loop, non-invasive, brain-computer interface. For the case of this study two types of measurements were performed, i.e., the electroencephalographic (EEG) signals and the wrist movements were measured simultaneously, during the subject's performance of a dynamic visuo-motor task. Wrist-movement predictions were computed by using the EEG data-processing methodology of double brain-rhythm filtering, double phase demodulation and double principal component analyses (PCA), each with a separate set of parameters. For the movement-prediction model a fuzzy inference system was used. The results have shown that the EEG signals measured during the dVM tasks carry enough information about the subjects' wrist movements for them to be successfully decoded using the presented methodology. Reasonably high values of the correlation coefficients suggest that the validation of the proposed approach is satisfactory. Moreover, since the causality of the rhythm filtering and the PCA transformation has been achieved, we have shown that these methods can also be used in a real-time, brain-computer interface. The study revealed that using non-causal, optimized methods yields better prediction results in comparison with the causal, non-optimized methodology; however, taking into account that the causality of these methods allows real-time processing, the minor decrease in prediction quality is acceptable. The study suggests that the methodology that was proposed in our previous studies is also valid for identifying the EEG-coded content during dVM tasks, albeit with various modifications, which allow better prediction results and real-time data processing. The results have shown that wrist movements can be predicted in simulated or real time; however, the results of the non-causal, optimized methodology (simulated) are slightly better. Nevertheless, the study has revealed that these methods should be suitable for use in the development of a non-invasive, brain-computer interface. Copyright © 2010 Elsevier B.V. All rights reserved.

Toxicology of 2,4-dichlorophenoxyacetic acid (2,4-D) and its determination in serum and brain tissue using gas chromatography-electron-capture detection.

PubMed

Oliveira, G H; Palermo-Neto, J

1995-01-01

A gas-liquid chromatographic method with an electron-capture detector was applied for 2,4-dichlorophenoxyacetic acid (2,4-D) determination in the serum and brain tissue of rats acutely intoxicated with the dimethylamine salt of 2,4-D. After extraction with ethyl ether, 2,4-D derivatization was performed using 2-chloroethanol and BCI3. The average recovery values found for serum and brain tissue were 98.5 +/- 4.8 and 93.3 +/- 7.5, respectively. The sensitivity limit of the method was 250 ng/mL for serum and 300 ng/g for brain tissue. The toxic effects of 2,4-D in rats were observed within one-half hour after its oral administration. Results suggest that the toxic mechanism of 2,4-D is related to an action on the central nervous system.

Effects of Treating Old Rats with an Aqueous Agaricus blazei Extract on Oxidative and Functional Parameters of the Brain Tissue and Brain Mitochondria

PubMed Central

de Sá-Nakanishi, Anacharis B.; Soares, Andréia A.; de Oliveira, Andrea Luiza; Fernando Comar, Jurandir; Peralta, Rosane M.; Bracht, Adelar

2014-01-01

Dysfunction of the mitochondrial respiratory chain and increased oxidative stress is a striking phenomenon in the brain of aged individuals. For this reason there has been a constant search for drugs and natural products able to prevent or at least to mitigate these problems. In the present study the effects of an aqueous extract of Agaricus blazei, a medicinal mushroom, on the oxidative state and on the functionality of mitochondria from the brain of old rats (21 months) were conducted. The extract was administered intragastrically during 21 days at doses of 200 mg/kg. The administration of the A. blazei extract was protective to the brain of old rats against oxidative stress by decreasing the lipid peroxidation levels and the reactive oxygen species content and by increasing the nonenzymic and enzymic antioxidant capacities. Administration of the A. blazei extract also increased the activity of several mitochondrial respiratory enzymes and, depending on the substrate, the mitochondrial coupled respiration. PMID:24876914

Effects of treating old rats with an aqueous Agaricus blazei extract on oxidative and functional parameters of the brain tissue and brain mitochondria.

PubMed

de Sá-Nakanishi, Anacharis B; Soares, Andréia A; de Oliveira, Andrea Luiza; Comar, Jurandir Fernando; Peralta, Rosane M; Bracht, Adelar

2014-01-01

Dysfunction of the mitochondrial respiratory chain and increased oxidative stress is a striking phenomenon in the brain of aged individuals. For this reason there has been a constant search for drugs and natural products able to prevent or at least to mitigate these problems. In the present study the effects of an aqueous extract of Agaricus blazei, a medicinal mushroom, on the oxidative state and on the functionality of mitochondria from the brain of old rats (21 months) were conducted. The extract was administered intragastrically during 21 days at doses of 200 mg/kg. The administration of the A. blazei extract was protective to the brain of old rats against oxidative stress by decreasing the lipid peroxidation levels and the reactive oxygen species content and by increasing the nonenzymic and enzymic antioxidant capacities. Administration of the A. blazei extract also increased the activity of several mitochondrial respiratory enzymes and, depending on the substrate, the mitochondrial coupled respiration.

Subject-based feature extraction by using fisher WPD-CSP in brain-computer interfaces.

PubMed

Yang, Banghua; Li, Huarong; Wang, Qian; Zhang, Yunyuan

2016-06-01

Feature extraction of electroencephalogram (EEG) plays a vital role in brain-computer interfaces (BCIs). In recent years, common spatial pattern (CSP) has been proven to be an effective feature extraction method. However, the traditional CSP has disadvantages of requiring a lot of input channels and the lack of frequency information. In order to remedy the defects of CSP, wavelet packet decomposition (WPD) and CSP are combined to extract effective features. But WPD-CSP method considers less about extracting specific features that are fitted for the specific subject. So a subject-based feature extraction method using fisher WPD-CSP is proposed in this paper. The idea of proposed method is to adapt fisher WPD-CSP to each subject separately. It mainly includes the following six steps: (1) original EEG signals from all channels are decomposed into a series of sub-bands using WPD; (2) average power values of obtained sub-bands are computed; (3) the specified sub-bands with larger values of fisher distance according to average power are selected for that particular subject; (4) each selected sub-band is reconstructed to be regarded as a new EEG channel; (5) all new EEG channels are used as input of the CSP and a six-dimensional feature vector is obtained by the CSP. The subject-based feature extraction model is so formed; (6) the probabilistic neural network (PNN) is used as the classifier and the classification accuracy is obtained. Data from six subjects are processed by the subject-based fisher WPD-CSP, the non-subject-based fisher WPD-CSP and WPD-CSP, respectively. Compared with non-subject-based fisher WPD-CSP and WPD-CSP, the results show that the proposed method yields better performance (sensitivity: 88.7±0.9%, and specificity: 91±1%) and the classification accuracy from subject-based fisher WPD-CSP is increased by 6-12% and 14%, respectively. The proposed subject-based fisher WPD-CSP method can not only remedy disadvantages of CSP by WPD but also discriminate helpless sub-bands for each subject and make remaining fewer sub-bands keep better separability by fisher distance, which leads to a higher classification accuracy than WPD-CSP method. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

High-resolution in vivo Wistar rodent brain atlas based on T1 weighted image

NASA Astrophysics Data System (ADS)

Huang, Su; Lu, Zhongkang; Huang, Weimin; Seramani, Sankar; Ramasamy, Boominathan; Sekar, Sakthivel; Guan, Cuntai; Bhakoo, Kishore

2016-03-01

Image based atlases for rats brain have a significant impact on pre-clinical research. In this project we acquired T1-weighted images from Wistar rodent brains with fine 59μm isotropical resolution for generation of the atlas template image. By applying post-process procedures using a semi-automatic brain extraction method, we delineated the brain tissues from source data. Furthermore, we applied a symmetric group-wise normalization method to generate an optimized template of T1 image of rodent brain, then aligned our template to the Waxholm Space. In addition, we defined several simple and explicit landmarks to corresponding our template with the well known Paxinos stereotaxic reference system. Anchoring at the origin of the Waxholm Space, we applied piece-wise linear transformation method to map the voxels of the template into the coordinates system in Paxinos' stereotoxic coordinates to facilitate the labelling task. We also cross-referenced our data with both published rodent brain atlas and image atlases available online, methodologically labelling the template to produce a Wistar brain atlas identifying more than 130 structures. Particular attention was paid to the cortex and cerebellum, as these areas encompass the most researched aspects of brain functions. Moreover, we adopted the structure hierarchy and naming nomenclature common to various atlases, so that the names and hierarchy structure presented in the atlas are readily recognised for easy use. It is believed the atlas will present a useful tool in rodent brain functional and pharmaceutical studies.

Cerebral vessels segmentation for light-sheet microscopy image using convolutional neural networks

NASA Astrophysics Data System (ADS)

Hu, Chaoen; Hui, Hui; Wang, Shuo; Dong, Di; Liu, Xia; Yang, Xin; Tian, Jie

2017-03-01

Cerebral vessel segmentation is an important step in image analysis for brain function and brain disease studies. To extract all the cerebrovascular patterns, including arteries and capillaries, some filter-based methods are used to segment vessels. However, the design of accurate and robust vessel segmentation algorithms is still challenging, due to the variety and complexity of images, especially in cerebral blood vessel segmentation. In this work, we addressed a problem of automatic and robust segmentation of cerebral micro-vessels structures in cerebrovascular images acquired by light-sheet microscope for mouse. To segment micro-vessels in large-scale image data, we proposed a convolutional neural networks (CNNs) architecture trained by 1.58 million pixels with manual label. Three convolutional layers and one fully connected layer were used in the CNNs model. We extracted a patch of size 32x32 pixels in each acquired brain vessel image as training data set to feed into CNNs for classification. This network was trained to output the probability that the center pixel of input patch belongs to vessel structures. To build the CNNs architecture, a series of mouse brain vascular images acquired from a commercial light sheet fluorescence microscopy (LSFM) system were used for training the model. The experimental results demonstrated that our approach is a promising method for effectively segmenting micro-vessels structures in cerebrovascular images with vessel-dense, nonuniform gray-level and long-scale contrast regions.

A Hybrid Hierarchical Approach for Brain Tissue Segmentation by Combining Brain Atlas and Least Square Support Vector Machine

PubMed Central

Kasiri, Keyvan; Kazemi, Kamran; Dehghani, Mohammad Javad; Helfroush, Mohammad Sadegh

2013-01-01

In this paper, we present a new semi-automatic brain tissue segmentation method based on a hybrid hierarchical approach that combines a brain atlas as a priori information and a least-square support vector machine (LS-SVM). The method consists of three steps. In the first two steps, the skull is removed and the cerebrospinal fluid (CSF) is extracted. These two steps are performed using the toolbox FMRIB's automated segmentation tool integrated in the FSL software (FSL-FAST) developed in Oxford Centre for functional MRI of the brain (FMRIB). Then, in the third step, the LS-SVM is used to segment grey matter (GM) and white matter (WM). The training samples for LS-SVM are selected from the registered brain atlas. The voxel intensities and spatial positions are selected as the two feature groups for training and test. SVM as a powerful discriminator is able to handle nonlinear classification problems; however, it cannot provide posterior probability. Thus, we use a sigmoid function to map the SVM output into probabilities. The proposed method is used to segment CSF, GM and WM from the simulated magnetic resonance imaging (MRI) using Brainweb MRI simulator and real data provided by Internet Brain Segmentation Repository. The semi-automatically segmented brain tissues were evaluated by comparing to the corresponding ground truth. The Dice and Jaccard similarity coefficients, sensitivity and specificity were calculated for the quantitative validation of the results. The quantitative results show that the proposed method segments brain tissues accurately with respect to corresponding ground truth. PMID:24696800

Hyperforin modifies neuronal membrane properties in vivo.

PubMed

Eckert, Gunter P; Keller, Jan-Henning; Jourdan, Claudia; Karas, Michael; Volmer, Dietrich A; Schubert-Zsilavecz, Manfred; Müller, Walter E

2004-09-02

Hyperforin, the major active constituent of St. John Wort (SJW) extract, affects several neurotransmitter systems in the brain putatively by modulation of the physical state of neuronal membranes. Accordingly, we tested the effects of SJW extract and of hyperforin on the properties of murine brain membrane fluidity. Oral administration of SJW extract and of hyperforin sodium salt results in significant hyperforin brain levels. Treatment of mice with hyperforin leads to decreased annular- and bulk fluidity and increased acyl-chain flexibility of brain membranes. All hyperforin related changes of membrane properties were significantly correlated with the corresponding hyperforin brain levels. Our data emphasises a membrane interaction of hyperforin that possibly contributes to its pharmacological effects.

Characterization of the Distance Relationship Between Localized Serotonin Receptors and Glia Cells on Fluorescence Microscopy Images of Brain Tissue.

PubMed

Jacak, Jaroslaw; Schaller, Susanne; Borgmann, Daniela; Winkler, Stephan M

2015-08-01

We here present two new methods for the characterization of fluorescent localization microscopy images obtained from immunostained brain tissue sections. Direct stochastic optical reconstruction microscopy images of 5-HT1A serotonin receptors and glial fibrillary acidic proteins in healthy cryopreserved brain tissues are analyzed. In detail, we here present two image processing methods for characterizing differences in receptor distribution on glial cells and their distribution on neural cells: One variant relies on skeleton extraction and adaptive thresholding, the other on k-means based discrete layer segmentation. Experimental results show that both methods can be applied for distinguishing classes of images with respect to serotonin receptor distribution. Quantification of nanoscopic changes in relative protein expression on particular cell types can be used to analyze degeneration in tissues caused by diseases or medical treatment.

A new feature extraction method and classification of early stage Parkinsonian rats with and without DBS treatment.

PubMed

Iravani, B; Towhidkhah, F; Roghani, M

2014-12-01

Parkinson Disease (PD) is one of the most common neural disorders worldwide. Different treatments such as medication and deep brain stimulation (DBS) have been proposed to minimize and control Parkinson's symptoms. DBS has been recognized as an effective approach to decrease most movement disorders of PD. In this study, a new method is proposed for feature extraction and separation of treated and untreated Parkinsonan rats. For this purpose, unilateral intrastriatal 6-hydroxydopamine (6-OHDA, 12.5 μg/5 μl of saline-ascorbate)-lesioned rats were treated with DBS. We performed a behavioral experiment and video tracked traveled trajectories of rats. Then, we investigated the effect of deep brain stimulation of subthalamus nucleus on their behavioral movements. Time, frequency and chaotic features of traveled trajectories were extracted. These features provide the ability to quantify the behavioral movements of Parkinsonian rats. The results showed that the traveled trajectories of untreated were more convoluted with the different time/frequency response. Compared to the traditional features used before to quantify the animals' behavior, the new features improved classification accuracy up to 80 % for untreated and treated rats.

Robust electroencephalogram phase estimation with applications in brain-computer interface systems.

PubMed

Seraj, Esmaeil; Sameni, Reza

2017-03-01

In this study, a robust method is developed for frequency-specific electroencephalogram (EEG) phase extraction using the analytic representation of the EEG. Based on recent theoretical findings in this area, it is shown that some of the phase variations-previously associated to the brain response-are systematic side-effects of the methods used for EEG phase calculation, especially during low analytical amplitude segments of the EEG. With this insight, the proposed method generates randomized ensembles of the EEG phase using minor perturbations in the zero-pole loci of narrow-band filters, followed by phase estimation using the signal's analytical form and ensemble averaging over the randomized ensembles to obtain a robust EEG phase and frequency. This Monte Carlo estimation method is shown to be very robust to noise and minor changes of the filter parameters and reduces the effect of fake EEG phase jumps, which do not have a cerebral origin. As proof of concept, the proposed method is used for extracting EEG phase features for a brain computer interface (BCI) application. The results show significant improvement in classification rates using rather simple phase-related features and a standard K-nearest neighbors and random forest classifiers, over a standard BCI dataset. The average performance was improved between 4-7% (in absence of additive noise) and 8-12% (in presence of additive noise). The significance of these improvements was statistically confirmed by a paired sample t-test, with 0.01 and 0.03 p-values, respectively. The proposed method for EEG phase calculation is very generic and may be applied to other EEG phase-based studies.

In situ enzymatic activity of transglutaminase isoforms on brain tissue sections of rodents: A new approach to monitor differences in post-translational protein modifications during neurodegeneration.

PubMed

Schulze-Krebs, Anja; Canneva, Fabio; Schnepf, Rebecca; Dobner, Julia; Dieterich, Walburga; von Hörsten, Stephan

2016-01-15

Mammalian transglutaminases (TGs) catalyze the irreversible post-translational modifications of proteins, the most prominent of which is the calcium-dependent formation of covalent acyl transfers between the γ-carboxamide group of glutamine and the ε-amino-group of lysine (GGEL-linkage). In the central nervous system, at least four TG isoforms are present and some of them are differentially expressed under pathological conditions in human patients. However, the precise TG-isoform-dependent enzymatic activities in the brain as well as their anatomical distribution are unknown. Specificity of the used biotinylated peptides was analyzed using an in vitro assay. Isoform-specific TG activity was evaluated in in vitro and in situ studies, using brain extracts and native brain tissue obtained from rodents. Our method allowed us to reveal in vitro and in situ TG-isoform-dependent enzymatic activity in brain extracts and tissue of rats and mice, with a specific focus on TG6. In situ activity of this isoform varied between BACHD mice in comparison to their wt controls. TG isozyme-specific activity can be detected by isoform-specific biotinylated peptides in brain tissue sections of rodents to reveal differences in the anatomical and/or subcellular distribution of TG activity. Our findings yield the basis for a broader application of this method for the screening of pathological expression and activity of TGs in a variety of animal models of human diseases, as in the case of neurodegenerative conditions such as Huntington׳s, Parkinson׳s and Alzheimer׳s, where protein modification is involved as a key mechanism of disease progression. Copyright © 2015 Elsevier B.V. All rights reserved.

Ethanolic extracts of Alstonia Scholaris and Bacopa Monniera possess neuroleptic activity due to anti-dopaminergic effect

PubMed Central

Jash, Rajiv; Chowdary, K. Appana

2014-01-01

Background: An increased inclination has been observed for the use of herbal drugs in chronic and incurable diseases. Treatment of psychiatric diseases like schizophrenia is largely palliative and more importantly, a prominent adverse effect prevails with the majority of anti-psychotic drugs, which are the extrapyramidal motor disorders. Existing anti-psychotic drug therapy is not so promising, and their adverse effect is a matter of concern for continuing the therapy for long duration. Objective: This experimental study was done to evaluate the neuroleptic activity of the ethanolic extracts of two plants Alstonia Scholaris and Bacopa Monnieri with different anti-psychotic animal models with a view that these plant extracts shall have no or at least reduced adverse effect so that it can be used for long duration. Materials and Methods: Two doses of both the extracts (100 and 200 mg/kg) and also standard drug haloperidol (0.2 mg/kg) were administered to their respective groups once daily with 5 different animal models. After that, the concentration of the dopamine neurotransmitter was estimated in two different regions of the brain viz. frontal cortex and striatum. Results: The result of the study indicated a significant reduction of amphetamine-induced stereotype and conditioned avoidance response for both the extracts compared with the control group, but both did not have any significant effect in phencyclidine-induced locomotor activity and social interaction activity. However, both the extracts showed minor signs of catalepsy compared to the control group. The study also revealed that the neuroleptic effect was due to the reduction of the dopamine concentration in the frontal cortex region of the rat brain. The results largely pointed out the fact that both the extract may be having the property to alleviate the positive symptoms of schizophrenia by reducing the dopamine levels of dopaminergic neurons of the brain. Conclusion: The estimation of dopamine in the two major regions of brain indicated the alteration of dopamine levels was the reason for the anti-psychotic activity as demonstrated by the different animal models. PMID:24497742

Estimating individual contribution from group-based structural correlation networks.

PubMed

Saggar, Manish; Hosseini, S M Hadi; Bruno, Jennifer L; Quintin, Eve-Marie; Raman, Mira M; Kesler, Shelli R; Reiss, Allan L

2015-10-15

Coordinated variations in brain morphology (e.g., cortical thickness) across individuals have been widely used to infer large-scale population brain networks. These structural correlation networks (SCNs) have been shown to reflect synchronized maturational changes in connected brain regions. Further, evidence suggests that SCNs, to some extent, reflect both anatomical and functional connectivity and hence provide a complementary measure of brain connectivity in addition to diffusion weighted networks and resting-state functional networks. Although widely used to study between-group differences in network properties, SCNs are inferred only at the group-level using brain morphology data from a set of participants, thereby not providing any knowledge regarding how the observed differences in SCNs are associated with individual behavioral, cognitive and disorder states. In the present study, we introduce two novel distance-based approaches to extract information regarding individual differences from the group-level SCNs. We applied the proposed approaches to a moderately large dataset (n=100) consisting of individuals with fragile X syndrome (FXS; n=50) and age-matched typically developing individuals (TD; n=50). We tested the stability of proposed approaches using permutation analysis. Lastly, to test the efficacy of our method, individual contributions extracted from the group-level SCNs were examined for associations with intelligence scores and genetic data. The extracted individual contributions were stable and were significantly related to both genetic and intelligence estimates, in both typically developing individuals and participants with FXS. We anticipate that the approaches developed in this work could be used as a putative biomarker for altered connectivity in individuals with neurodevelopmental disorders. Copyright © 2015 Elsevier Inc. All rights reserved.

Comparison of continuously acquired resting state and extracted analogues from active tasks.

PubMed

Ganger, Sebastian; Hahn, Andreas; Küblböck, Martin; Kranz, Georg S; Spies, Marie; Vanicek, Thomas; Seiger, René; Sladky, Ronald; Windischberger, Christian; Kasper, Siegfried; Lanzenberger, Rupert

2015-10-01

Functional connectivity analysis of brain networks has become an important tool for investigation of human brain function. Although functional connectivity computations are usually based on resting-state data, the application to task-specific fMRI has received growing attention. Three major methods for extraction of resting-state data from task-related signal have been proposed (1) usage of unmanipulated task data for functional connectivity; (2) regression against task effects, subsequently using the residuals; and (3) concatenation of baseline blocks located in-between task blocks. Despite widespread application in current research, consensus on which method best resembles resting-state seems to be missing. We, therefore, evaluated these techniques in a sample of 26 healthy controls measured at 7 Tesla. In addition to continuous resting-state, two different task paradigms were assessed (emotion discrimination and right finger-tapping) and five well-described networks were analyzed (default mode, thalamus, cuneus, sensorimotor, and auditory). Investigating the similarity to continuous resting-state (Dice, Intraclass correlation coefficient (ICC), R(2) ) showed that regression against task effects yields functional connectivity networks most alike to resting-state. However, all methods exhibited significant differences when compared to continuous resting-state and similarity metrics were lower than test-retest of two resting-state scans. Omitting global signal regression did not change these findings. Visually, the networks are highly similar, but through further investigation marked differences can be found. Therefore, our data does not support referring to resting-state when extracting signals from task designs, although functional connectivity computed from task-specific data may indeed yield interesting information. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Occurrence of specific environmental risk factors in brain tissues of sudden infant death and sudden intrauterine unexpected death victims assessed with gas chromatography-tandem mass spectrometry.

PubMed

Termopoli, Veronica; Famiglini, Giorgio; Palma, Pierangela; Magrini, Laura; Cappiello, Achille

2015-03-01

Sudden infant death syndrome (SIDS) and sudden intrauterine unexpected death syndrome (SIUDS) are an unresolved teaser in the social-medical and health setting of modern medicine and are the result of multifactorial interactions. Recently, prenatal exposure to environmental contaminants has been associated with negative pregnancy outcomes, and verification of their presence in fetal and newborn tissues is of crucial importance. A gas chromatography-tandem mass spectrometry (MS/MS) method, using a triple quadrupole analyzer, is proposed to assess the presence of 20 organochlorine pesticides, two organophosphate pesticides, one carbamate (boscalid), and a phenol (bisphenol A) in human brain tissues. Samples were collected during autopsies of infants and fetuses that died suddenly without any evident cause. The method involves a liquid-solid extraction using n-hexane as the extraction solvent. The extracts were purified with Florisil cartridges prior to the final determination. Recovery experiments using lamb brain spiked at three different concentrations in the range of 1-50 ng g(-1) were performed, with recoveries ranging from 79 to 106%. Intraday and interday repeatability were evaluated, and relative standard deviations lower than 10% and 18%, respectively, were obtained. The selectivity and sensitivity achieved in multiple reaction monitoring mode allowed us to achieve quantification and confirmation in a real matrix at levels as low as 0.2-0.6 ng g(-1). Two MS/MS transitions were acquired for each analyte, using the Q/q ratio as the confirmatory parameter. This method was applied to the analysis of 14 cerebral cortex samples (ten SIUDS and four SIDS cases), and confirmed the presence of several selected compounds.

Functional brain networks in Alzheimer's disease: EEG analysis based on limited penetrable visibility graph and phase space method

NASA Astrophysics Data System (ADS)

Wang, Jiang; Yang, Chen; Wang, Ruofan; Yu, Haitao; Cao, Yibin; Liu, Jing

2016-10-01

In this paper, EEG series are applied to construct functional connections with the correlation between different regions in order to investigate the nonlinear characteristic and the cognitive function of the brain with Alzheimer's disease (AD). First, limited penetrable visibility graph (LPVG) and phase space method map single EEG series into networks, and investigate the underlying chaotic system dynamics of AD brain. Topological properties of the networks are extracted, such as average path length and clustering coefficient. It is found that the network topology of AD in several local brain regions are different from that of the control group with no statistically significant difference existing all over the brain. Furthermore, in order to detect the abnormality of AD brain as a whole, functional connections among different brain regions are reconstructed based on similarity of clustering coefficient sequence (CCSS) of EEG series in the four frequency bands (delta, theta, alpha, and beta), which exhibit obvious small-world properties. Graph analysis demonstrates that for both methodologies, the functional connections between regions of AD brain decrease, particularly in the alpha frequency band. AD causes the graph index complexity of the functional network decreased, the small-world properties weakened, and the vulnerability increased. The obtained results show that the brain functional network constructed by LPVG and phase space method might be more effective to distinguish AD from the normal control than the analysis of single series, which is helpful for revealing the underlying pathological mechanism of the disease.

Studies on brain biogenic amines in methanolic extract of Cuscuta reflexa Roxb. and Corchorus olitorius Linn. seed treated mice.

PubMed

Gupta, Malaya; Mazumder, Upal Kanti; Pal, Dilipkumar; Bhattacharya, Shiladitya; Chakrabarty, Sumit

2003-01-01

The methanolic extract of both Cuscuta reflexa stem and Corchorus olitorius seed showed marked protection against convulsion induced by chemoconvulsive agents in mice. The catecholamines contained were significantly increased in the processed extract treated mice. The amount of GABA, which is most likely to be involved in seizure activity, was increased significantly in mice brain after a six week treatment. Results of the present study revealed that both the processed extracts showed a significant anticonvulsive property by altering the level of catecholamines and brain amino acids in mice.

Neuroprotective effect of Feronia limonia on ischemia reperfusion induced brain injury in rats

PubMed Central

Rakhunde, Purushottam B.; Saher, Sana; Ali, Syed Ayaz

2014-01-01

Objectives: Brain stroke is a leading cause of death without effective treatment. Feronia limonia have potent antioxidant activity and can be proved as neuroprotective against ischemia-reperfusion induced brain injury. Materials and Methods: We studied the effect of methanolic extract of F. limonia fruit (250 mg/kg, 500 mg/kg body weight, p.o.) and Vitamin E as reference standard drug on 30 min induced ischemia, followed by reperfusion by testing the neurobehavioral tests such as neurodeficit score, rota rod test, hanging wire test, beam walk test and elevated plus maze. The biochemical parameters, which were measured in animals brain were catalase, superoxide dismutase (SOD), malondialdehyde and nitric oxide in control and treated rats. Results: The methanolic extract of F. limonia fruit (250 mg/kg, 500 mg/kg body weight, p.o.) treated groups showed a statistically significant improvement in the neurobehavioral parameters such as motor performance (neurological status, significant increase in grasping ability, forelimb strength improvement in balance and co-ordination). The biochemical parameters in the brains of rats showed a significant reduction in the total nitrite (P < 0.01) and lipid peroxidation (P < 0.01), also a significant enhanced activity of enzymatic antioxidants such as catalase (P < 0.01) and SOD (P < 0.05). Conclusion: These observations suggest the neuroprotective and antioxidant activity of F. limonia and Vitamin E on ischemia reperfusion induced brain injury and may require further evaluation. PMID:25538333

Functional MRI registration with tissue-specific patch-based functional correlation tensors.

PubMed

Zhou, Yujia; Zhang, Han; Zhang, Lichi; Cao, Xiaohuan; Yang, Ru; Feng, Qianjin; Yap, Pew-Thian; Shen, Dinggang

2018-06-01

Population studies of brain function with resting-state functional magnetic resonance imaging (rs-fMRI) rely on accurate intersubject registration of functional areas. This is typically achieved through registration using high-resolution structural images with more spatial details and better tissue contrast. However, accumulating evidence has suggested that such strategy cannot align functional regions well because functional areas are not necessarily consistent with anatomical structures. To alleviate this problem, a number of registration algorithms based directly on rs-fMRI data have been developed, most of which utilize functional connectivity (FC) features for registration. However, most of these methods usually extract functional features only from the thin and highly curved cortical grey matter (GM), posing great challenges to accurate estimation of whole-brain deformation fields. In this article, we demonstrate that additional useful functional features can also be extracted from the whole brain, not restricted to the GM, particularly the white-matter (WM), for improving the overall functional registration. Specifically, we quantify local anisotropic correlation patterns of the blood oxygenation level-dependent (BOLD) signals using tissue-specific patch-based functional correlation tensors (ts-PFCTs) in both GM and WM. Functional registration is then performed by integrating the features from different tissues using the multi-channel large deformation diffeomorphic metric mapping (mLDDMM) algorithm. Experimental results show that our method achieves superior functional registration performance, compared with conventional registration methods. © 2018 Wiley Periodicals, Inc.

Computer-aided diagnostic method for classification of Alzheimer's disease with atrophic image features on MR images

NASA Astrophysics Data System (ADS)

Arimura, Hidetaka; Yoshiura, Takashi; Kumazawa, Seiji; Tanaka, Kazuhiro; Koga, Hiroshi; Mihara, Futoshi; Honda, Hiroshi; Sakai, Shuji; Toyofuku, Fukai; Higashida, Yoshiharu

2008-03-01

Our goal for this study was to attempt to develop a computer-aided diagnostic (CAD) method for classification of Alzheimer's disease (AD) with atrophic image features derived from specific anatomical regions in three-dimensional (3-D) T1-weighted magnetic resonance (MR) images. Specific regions related to the cerebral atrophy of AD were white matter and gray matter regions, and CSF regions in this study. Cerebral cortical gray matter regions were determined by extracting a brain and white matter regions based on a level set based method, whose speed function depended on gradient vectors in an original image and pixel values in grown regions. The CSF regions in cerebral sulci and lateral ventricles were extracted by wrapping the brain tightly with a zero level set determined from a level set function. Volumes of the specific regions and the cortical thickness were determined as atrophic image features. Average cortical thickness was calculated in 32 subregions, which were obtained by dividing each brain region. Finally, AD patients were classified by using a support vector machine, which was trained by the image features of AD and non-AD cases. We applied our CAD method to MR images of whole brains obtained from 29 clinically diagnosed AD cases and 25 non-AD cases. As a result, the area under a receiver operating characteristic (ROC) curve obtained by our computerized method was 0.901 based on a leave-one-out test in identification of AD cases among 54 cases including 8 AD patients at early stages. The accuracy for discrimination between 29 AD patients and 25 non-AD subjects was 0.840, which was determined at the point where the sensitivity was the same as the specificity on the ROC curve. This result showed that our CAD method based on atrophic image features may be promising for detecting AD patients by using 3-D MR images.

Wireless brain-machine interface using EEG and EOG: brain wave classification and robot control

NASA Astrophysics Data System (ADS)

Oh, Sechang; Kumar, Prashanth S.; Kwon, Hyeokjun; Varadan, Vijay K.

2012-04-01

A brain-machine interface (BMI) links a user's brain activity directly to an external device. It enables a person to control devices using only thought. Hence, it has gained significant interest in the design of assistive devices and systems for people with disabilities. In addition, BMI has also been proposed to replace humans with robots in the performance of dangerous tasks like explosives handling/diffusing, hazardous materials handling, fire fighting etc. There are mainly two types of BMI based on the measurement method of brain activity; invasive and non-invasive. Invasive BMI can provide pristine signals but it is expensive and surgery may lead to undesirable side effects. Recent advances in non-invasive BMI have opened the possibility of generating robust control signals from noisy brain activity signals like EEG and EOG. A practical implementation of a non-invasive BMI such as robot control requires: acquisition of brain signals with a robust wearable unit, noise filtering and signal processing, identification and extraction of relevant brain wave features and finally, an algorithm to determine control signals based on the wave features. In this work, we developed a wireless brain-machine interface with a small platform and established a BMI that can be used to control the movement of a robot by using the extracted features of the EEG and EOG signals. The system records and classifies EEG as alpha, beta, delta, and theta waves. The classified brain waves are then used to define the level of attention. The acceleration and deceleration or stopping of the robot is controlled based on the attention level of the wearer. In addition, the left and right movements of eye ball control the direction of the robot.

Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer’s disease

PubMed Central

Zhan, Liang; Zhou, Jiayu; Wang, Yalin; Jin, Yan; Jahanshad, Neda; Prasad, Gautam; Nir, Talia M.; Leonardo, Cassandra D.; Ye, Jieping; Thompson, Paul M.; for the Alzheimer’s Disease Neuroimaging Initiative

2015-01-01

Alzheimer’s disease (AD) involves a gradual breakdown of brain connectivity, and network analyses offer a promising new approach to track and understand disease progression. Even so, our ability to detect degenerative changes in brain networks depends on the methods used. Here we compared several tractography and feature extraction methods to see which ones gave best diagnostic classification for 202 people with AD, mild cognitive impairment or normal cognition, scanned with 41-gradient diffusion-weighted magnetic resonance imaging as part of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) project. We computed brain networks based on whole brain tractography with nine different methods – four of them tensor-based deterministic (FACT, RK2, SL, and TL), two orientation distribution function (ODF)-based deterministic (FACT, RK2), two ODF-based probabilistic approaches (Hough and PICo), and one “ball-and-stick” approach (Probtrackx). Brain networks derived from different tractography algorithms did not differ in terms of classification performance on ADNI, but performing principal components analysis on networks helped classification in some cases. Small differences may still be detectable in a truly vast cohort, but these experiments help assess the relative advantages of different tractography algorithms, and different post-processing choices, when used for classification. PMID:25926791

Association between abnormal brain functional connectivity in children and psychopathology: A study based on graph theory and machine learning.

PubMed

Sato, João Ricardo; Biazoli, Claudinei Eduardo; Salum, Giovanni Abrahão; Gadelha, Ary; Crossley, Nicolas; Vieira, Gilson; Zugman, André; Picon, Felipe Almeida; Pan, Pedro Mario; Hoexter, Marcelo Queiroz; Amaro, Edson; Anés, Mauricio; Moura, Luciana Monteiro; Del'Aquilla, Marco Antonio Gomes; Mcguire, Philip; Rohde, Luis Augusto; Miguel, Euripedes Constantino; Jackowski, Andrea Parolin; Bressan, Rodrigo Affonseca

2018-03-01

One of the major challenges facing psychiatry is how to incorporate biological measures in the classification of mental health disorders. Many of these disorders affect brain development and its connectivity. In this study, we propose a novel method for assessing brain networks based on the combination of a graph theory measure (eigenvector centrality) and a one-class support vector machine (OC-SVM). We applied this approach to resting-state fMRI data from 622 children and adolescents. Eigenvector centrality (EVC) of nodes from positive- and negative-task networks were extracted from each subject and used as input to an OC-SVM to label individual brain networks as typical or atypical. We hypothesised that classification of these subjects regarding the pattern of brain connectivity would predict the level of psychopathology. Subjects with atypical brain network organisation had higher levels of psychopathology (p < 0.001). There was a greater EVC in the typical group at the bilateral posterior cingulate and bilateral posterior temporal cortices; and significant decreases in EVC at left temporal pole. The combination of graph theory methods and an OC-SVM is a promising method to characterise neurodevelopment, and may be useful to understand the deviations leading to mental disorders.

Individual Morphological Brain Network Construction Based on Multivariate Euclidean Distances Between Brain Regions.

PubMed

Yu, Kaixin; Wang, Xuetong; Li, Qiongling; Zhang, Xiaohui; Li, Xinwei; Li, Shuyu

2018-01-01

Morphological brain network plays a key role in investigating abnormalities in neurological diseases such as mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, most of the morphological brain network construction methods only considered a single morphological feature. Each type of morphological feature has specific neurological and genetic underpinnings. A combination of morphological features has been proven to have better diagnostic performance compared with a single feature, which suggests that an individual morphological brain network based on multiple morphological features would be beneficial in disease diagnosis. Here, we proposed a novel method to construct individual morphological brain networks for two datasets by calculating the exponential function of multivariate Euclidean distance as the evaluation of similarity between two regions. The first dataset included 24 healthy subjects who were scanned twice within a 3-month period. The topological properties of these brain networks were analyzed and compared with previous studies that used different methods and modalities. Small world property was observed in all of the subjects, and the high reproducibility indicated the robustness of our method. The second dataset included 170 patients with MCI (86 stable MCI and 84 progressive MCI cases) and 169 normal controls (NC). The edge features extracted from the individual morphological brain networks were used to distinguish MCI from NC and separate MCI subgroups (progressive vs. stable) through the support vector machine in order to validate our method. The results showed that our method achieved an accuracy of 79.65% (MCI vs. NC) and 70.59% (stable MCI vs. progressive MCI) in a one-dimension situation. In a multiple-dimension situation, our method improved the classification performance with an accuracy of 80.53% (MCI vs. NC) and 77.06% (stable MCI vs. progressive MCI) compared with the method using a single feature. The results indicated that our method could effectively construct an individual morphological brain network based on multiple morphological features and could accurately discriminate MCI from NC and stable MCI from progressive MCI, and may provide a valuable tool for the investigation of individual morphological brain networks.

Evidences for the involvement of monoaminergic and GABAergic systems in antidepressant-like activity of garlic extract in mice

PubMed Central

Dhingra, Dinesh; Kumar, Vaibhav

2008-01-01

Objectives: The present study was undertaken to investigate the effect of the ethanolic extract of Allium sativum L. (Family: Lilliaceae), commonly known as garlic, on depression in mice. Materials and Methods: Ethanolic extract of garlic (25, 50 and 100 mg/kg) was administered orally for 14 successive days to young Swiss albino mice of either sex and antidepressant-like activity was evaluated employing tail suspension test (TST) and forced swim test (FST). The efficacy of the extract was compared with standard antidepressant drugs like fluoxetine and imipramine. The mechanism of action of the extract was investigated by co-administration of prazosin (α1-adrenoceptor antagonist), sulpiride (selective D2-receptor antagonist), baclofen (GABAB agonist) and p-CPA (serotonin antagonist) separately with the extract and by studying the effect of the extract on brain MAO-A and MAO-B levels. Results: Garlic extract (25, 50 and 100 mg/kg) significantly decreased immobility time in a dose-dependent manner in both TST and FST, indicating significant antidepressant-like activity. The efficacy of the extract was found to be comparable to fluoxetine (20 mg/kg p.o.) and imipramine (15 mg/kg p.o.) in both TST and FST. The extract did not show any significant effect on the locomotor activity of the mice. Prazosin, sulpiride, baclofen and p-CPA significantly attenuated the extract-induced antidepressant-like effect in TST. Garlic extract (100 mg/kg) administered orally for 14 successive days significantly decreased brain MAO-A and MAO-B levels, as compared to the control group. Conclusion: Garlic extract showed significant antidepressant-like activity probably by inhibiting MAO-A and MAO-B levels and through interaction with adrenergic, dopaminergic, serotonergic and GABAergic systems. PMID:20040952

Localization of synchronous cortical neural sources.

PubMed

Zerouali, Younes; Herry, Christophe L; Jemel, Boutheina; Lina, Jean-Marc

2013-03-01

Neural synchronization is a key mechanism to a wide variety of brain functions, such as cognition, perception, or memory. High temporal resolution achieved by EEG recordings allows the study of the dynamical properties of synchronous patterns of activity at a very fine temporal scale but with very low spatial resolution. Spatial resolution can be improved by retrieving the neural sources of EEG signal, thus solving the so-called inverse problem. Although many methods have been proposed to solve the inverse problem and localize brain activity, few of them target the synchronous brain regions. In this paper, we propose a novel algorithm aimed at localizing specifically synchronous brain regions and reconstructing the time course of their activity. Using multivariate wavelet ridge analysis, we extract signals capturing the synchronous events buried in the EEG and then solve the inverse problem on these signals. Using simulated data, we compare results of source reconstruction accuracy achieved by our method to a standard source reconstruction approach. We show that the proposed method performs better across a wide range of noise levels and source configurations. In addition, we applied our method on real dataset and identified successfully cortical areas involved in the functional network underlying visual face perception. We conclude that the proposed approach allows an accurate localization of synchronous brain regions and a robust estimation of their activity.

Large scale preparation and crystallization of neuron-specific enolase.

PubMed

Ishioka, N; Isobe, T; Kadoya, T; Okuyama, T; Nakajima, T

1984-03-01

A simple method has been developed for the large scale purification of neuron-specific enolase [EC 4.2.1.11]. The method consists of ammonium sulfate fractionation of brain extract, and two subsequent column chromatography steps on DEAE Sephadex A-50. The chromatography was performed on a short (25 cm height) and thick (8.5 cm inside diameter) column unit that was specially devised for the large scale preparation. The purified enolase was crystallized in 0.05 M imidazole-HCl buffer containing 1.6 M ammonium sulfate (pH 6.39), with a yield of 0.9 g/kg of bovine brain tissue.

Comparison of spike-sorting algorithms for future hardware implementation.

PubMed

Gibson, Sarah; Judy, Jack W; Markovic, Dejan

2008-01-01

Applications such as brain-machine interfaces require hardware spike sorting in order to (1) obtain single-unit activity and (2) perform data reduction for wireless transmission of data. Such systems must be low-power, low-area, high-accuracy, automatic, and able to operate in real time. Several detection and feature extraction algorithms for spike sorting are described briefly and evaluated in terms of accuracy versus computational complexity. The nonlinear energy operator method is chosen as the optimal spike detection algorithm, being most robust over noise and relatively simple. The discrete derivatives method [1] is chosen as the optimal feature extraction method, maintaining high accuracy across SNRs with a complexity orders of magnitude less than that of traditional methods such as PCA.

Non-causal spike filtering improves decoding of movement intention for intracortical BCIs

PubMed Central

Masse, Nicolas Y.; Jarosiewicz, Beata; Simeral, John D.; Bacher, Daniel; Stavisky, Sergey D.; Cash, Sydney S.; Oakley, Erin M.; Berhanu, Etsub; Eskandar, Emad; Friehs, Gerhard; Hochberg, Leigh R.; Donoghue, John P.

2014-01-01

Background Multiple types of neural signals are available for controlling assistive devices through brain-computer interfaces (BCIs). Intracortically-recorded spiking neural signals are attractive for BCIs because they can in principle provide greater fidelity of encoded information compared to electrocorticographic (ECoG) signals and electroencephalograms (EEGs). Recent reports show that the information content of these spiking neural signals can be reliably extracted simply by causally band-pass filtering the recorded extracellular voltage signals and then applying a spike detection threshold, without relying on “sorting” action potentials. New method We show that replacing the causal filter with an equivalent non-causal filter increases the information content extracted from the extracellular spiking signal and improves decoding of intended movement direction. This method can be used for real-time BCI applications by using a 4 ms lag between recording and filtering neural signals. Results Across 18 sessions from two people with tetraplegia enrolled in the BrainGate2 pilot clinical trial, we found that threshold crossing events extracted using this non-causal filtering method were significantly more informative of each participant’s intended cursor kinematics compared to threshold crossing events derived from causally filtered signals. This new method decreased the mean angular error between the intended and decoded cursor direction by 9.7° for participant S3, who was implanted 5.4 years prior to this study, and by 3.5° for participant T2, who was implanted 3 months prior to this study. Conclusions Non-causally filtering neural signals prior to extracting threshold crossing events may be a simple yet effective way to condition intracortically recorded neural activity for direct control of external devices through BCIs. PMID:25128256

(1)H NMR-based metabonomics revealed protective effect of Naodesheng bioactive extract on ischemic stroke rats.

PubMed

Luo, Lan; Zhen, Lifeng; Xu, Yatao; Yang, Yongxia; Feng, Suxiang; Wang, Shumei; Liang, Shengwang

2016-06-20

Stroke is a leading cause of death and disability in the world. However, current therapies are limited. Naodesheng, a widely used traditional Chinese medicine prescription, has shown a good clinical curative effect on ischemic stroke. Also, Naodesheng has been suggested to have neuroprotective effect on focal cerebral ischemia rats, but the underlying molecular mechanism remains unclear. The present study was designed to evaluate the effect of Naodesheng bioactive extract on the metabolic changes in brain tissue, plasma and urine induced by cerebral ischemia perfusion injury, and explore the possible metabolic mechanisms by using a (1)H NMR-based metabonomics approach. A middle cerebral artery occlusion rat model was established and confirmed by the experiments of neurobehavioral abnormality evaluation, brain tissue TTC staining and pathological examination. The metabolic changes in brain tissue, plasma and urine were then assessed by a (1)H NMR technique combined with multivariate statistical analysis method. These NMR data showed that cerebral ischemia reperfusion induced great metabolic disorders in brain tissue, plasma and urine metabolisms. However, Naodesheng bioactive extract could reverse most of the imbalanced metabolites. Meanwhile, it was found that both the medium and high dosages of Naodesheng bioactive extract were more effective on the metabolic changes than the low dosage, consistent with histopathological assessments. These results revealed that Naodesheng had protective effect on ischemic stroke rats and the underlying mechanisms involved multiple metabolic pathways, including energy metabolism, amino acid metabolism, oxidative stress and inflammatory injury. The present study could provide evidence that metabonomics revealed its capacity to evaluate the holistic efficacy of traditional Chinese medicine and explore the underlying mechanisms. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Segmentation, feature extraction, and multiclass brain tumor classification.

PubMed

Sachdeva, Jainy; Kumar, Vinod; Gupta, Indra; Khandelwal, Niranjan; Ahuja, Chirag Kamal

2013-12-01

Multiclass brain tumor classification is performed by using a diversified dataset of 428 post-contrast T1-weighted MR images from 55 patients. These images are of primary brain tumors namely astrocytoma (AS), glioblastoma multiforme (GBM), childhood tumor-medulloblastoma (MED), meningioma (MEN), secondary tumor-metastatic (MET), and normal regions (NR). Eight hundred fifty-six regions of interest (SROIs) are extracted by a content-based active contour model. Two hundred eighteen intensity and texture features are extracted from these SROIs. In this study, principal component analysis (PCA) is used for reduction of dimensionality of the feature space. These six classes are then classified by artificial neural network (ANN). Hence, this approach is named as PCA-ANN approach. Three sets of experiments have been performed. In the first experiment, classification accuracy by ANN approach is performed. In the second experiment, PCA-ANN approach with random sub-sampling has been used in which the SROIs from the same patient may get repeated during testing. It is observed that the classification accuracy has increased from 77 to 91 %. PCA-ANN has delivered high accuracy for each class: AS-90.74 %, GBM-88.46 %, MED-85 %, MEN-90.70 %, MET-96.67 %, and NR-93.78 %. In the third experiment, to remove bias and to test the robustness of the proposed system, data is partitioned in a manner such that the SROIs from the same patient are not common for training and testing sets. In this case also, the proposed system has performed well by delivering an overall accuracy of 85.23 %. The individual class accuracy for each class is: AS-86.15 %, GBM-65.1 %, MED-63.36 %, MEN-91.5 %, MET-65.21 %, and NR-93.3 %. A computer-aided diagnostic system comprising of developed methods for segmentation, feature extraction, and classification of brain tumors can be beneficial to radiologists for precise localization, diagnosis, and interpretation of brain tumors on MR images.

Semi-automatic segmentation of brain tumors using population and individual information.

PubMed

Wu, Yao; Yang, Wei; Jiang, Jun; Li, Shuanqian; Feng, Qianjin; Chen, Wufan

2013-08-01

Efficient segmentation of tumors in medical images is of great practical importance in early diagnosis and radiation plan. This paper proposes a novel semi-automatic segmentation method based on population and individual statistical information to segment brain tumors in magnetic resonance (MR) images. First, high-dimensional image features are extracted. Neighborhood components analysis is proposed to learn two optimal distance metrics, which contain population and patient-specific information, respectively. The probability of each pixel belonging to the foreground (tumor) and the background is estimated by the k-nearest neighborhood classifier under the learned optimal distance metrics. A cost function for segmentation is constructed through these probabilities and is optimized using graph cuts. Finally, some morphological operations are performed to improve the achieved segmentation results. Our dataset consists of 137 brain MR images, including 68 for training and 69 for testing. The proposed method overcomes segmentation difficulties caused by the uneven gray level distribution of the tumors and even can get satisfactory results if the tumors have fuzzy edges. Experimental results demonstrate that the proposed method is robust to brain tumor segmentation.

Alternative method of removing otoliths from sturgeon

USGS Publications Warehouse

Chalupnicki, Marc A.; Dittman, Dawn E.

2016-01-01

Extracting the otoliths (ear bones) from fish that have very thick skulls can be difficult and very time consuming. The common practice of making a transverse vertical incision on the top of the skull with a hand or electrical saw may damage the otolith if not performed correctly. Sturgeons (Acipenseridae) are one family in particular that have a very large and thick skull. A new laboratory method entering the brain cavity from the ventral side of the fish to expose the otoliths was easier than other otolith extraction methods found in the literature. Methods reviewed in the literature are designed for the field and are more efficient at processing large quantities of fish quickly. However, this new technique was designed to be more suited for a laboratory setting when time is not pressing and successful extraction from each specimen is critical. The success of finding and removing otoliths using this technique is very high and does not compromise the structure in any manner. This alternative technique is applicable to other similar fish species for extracting the otoliths.

Discrete Wavelet Transform-Based Whole-Spectral and Subspectral Analysis for Improved Brain Tumor Clustering Using Single Voxel MR Spectroscopy.

PubMed

Yang, Guang; Nawaz, Tahir; Barrick, Thomas R; Howe, Franklyn A; Slabaugh, Greg

2015-12-01

Many approaches have been considered for automatic grading of brain tumors by means of pattern recognition with magnetic resonance spectroscopy (MRS). Providing an improved technique which can assist clinicians in accurately identifying brain tumor grades is our main objective. The proposed technique, which is based on the discrete wavelet transform (DWT) of whole-spectral or subspectral information of key metabolites, combined with unsupervised learning, inspects the separability of the extracted wavelet features from the MRS signal to aid the clustering. In total, we included 134 short echo time single voxel MRS spectra (SV MRS) in our study that cover normal controls, low grade and high grade tumors. The combination of DWT-based whole-spectral or subspectral analysis and unsupervised clustering achieved an overall clustering accuracy of 94.8% and a balanced error rate of 7.8%. To the best of our knowledge, it is the first study using DWT combined with unsupervised learning to cluster brain SV MRS. Instead of dimensionality reduction on SV MRS or feature selection using model fitting, our study provides an alternative method of extracting features to obtain promising clustering results.

Robust skull stripping using multiple MR image contrasts insensitive to pathology.

PubMed

Roy, Snehashis; Butman, John A; Pham, Dzung L

2017-02-01

Automatic skull-stripping or brain extraction of magnetic resonance (MR) images is often a fundamental step in many neuroimage processing pipelines. The accuracy of subsequent image processing relies on the accuracy of the skull-stripping. Although many automated stripping methods have been proposed in the past, it is still an active area of research particularly in the context of brain pathology. Most stripping methods are validated on T 1 -w MR images of normal brains, especially because high resolution T 1 -w sequences are widely acquired and ground truth manual brain mask segmentations are publicly available for normal brains. However, different MR acquisition protocols can provide complementary information about the brain tissues, which can be exploited for better distinction between brain, cerebrospinal fluid, and unwanted tissues such as skull, dura, marrow, or fat. This is especially true in the presence of pathology, where hemorrhages or other types of lesions can have similar intensities as skull in a T 1 -w image. In this paper, we propose a sparse patch based Multi-cONtrast brain STRipping method (MONSTR), 2 where non-local patch information from one or more atlases, which contain multiple MR sequences and reference delineations of brain masks, are combined to generate a target brain mask. We compared MONSTR with four state-of-the-art, publicly available methods: BEaST, SPECTRE, ROBEX, and OptiBET. We evaluated the performance of these methods on 6 datasets consisting of both healthy subjects and patients with various pathologies. Three datasets (ADNI, MRBrainS, NAMIC) are publicly available, consisting of 44 healthy volunteers and 10 patients with schizophrenia. Other three in-house datasets, comprising 87 subjects in total, consisted of patients with mild to severe traumatic brain injury, brain tumors, and various movement disorders. A combination of T 1 -w, T 2 -w were used to skull-strip these datasets. We show significant improvement in stripping over the competing methods on both healthy and pathological brains. We also show that our multi-contrast framework is robust and maintains accurate performance across different types of acquisitions and scanners, even when using normal brains as atlases to strip pathological brains, demonstrating that our algorithm is applicable even when reference segmentations of pathological brains are not available to be used as atlases. Copyright © 2016 Elsevier Inc. All rights reserved.

Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates

NASA Astrophysics Data System (ADS)

Jamal, Wasifa; Das, Saptarshi; Oprescu, Ioana-Anastasia; Maharatna, Koushik; Apicella, Fabio; Sicca, Federico

2014-08-01

Objective. The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. Approach. Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. Main results. The leave one out cross-validation of the classification algorithm gives 94.7% accuracy as the best performance with corresponding sensitivity and specificity values as 85.7% and 100% respectively. Significance. The proposed method gives high classification accuracies and outperforms other contemporary research results. The effectiveness of the proposed method for classification of autistic and typical children suggests the possibility of using it on a larger population to validate it for clinical practice.

Reversal of P-glycoprotein overexpression by Ginkgo biloba extract in the brains of pentylenetetrazole-kindled and phenytoin-treated mice.

PubMed

Zhang, Ce; Fan, Qing; Chen, Shu-Liang; Ma, Hui

2015-08-01

The purpose of this study was to investigate the combined effects of Ginkgo biloba extract and phenytoin (PHT) sodium as a dose regimen simulating the clinical treatment of patients with epilepsy, on P-glycoprotein (P-GP) overexpression in a pentylenetetrazole-kindled mouse model of epilepsy. Epilepsy was induced by intraperitoneal administration of pentylenetetrazole (40 mg/kg) for 7 days followed by intragastric administration of PHT (40 mg/kg) for 14 days. Thirty mice that developed seizures were randomly divided into three groups and administered PHT as well as the following treatments: saline (negative control); verapamil (20 mg/kg, positive control); and G. biloba (30 mg/kg). Seizure severity was recorded 30 minutes after treatment on Day 4 of drug administration, after which the mice were euthanized, and their brains isolated. Western blots and immunohistochemistry were performed to analyze the expression of P-GP and caspase-3, respectively, in the brain tissue. High-performance liquid chromatography was used to measure the concentrations of PHT in the brains of the treated mice. After 4 consecutive days of treatment, the seizure severity in the mice in the G. biloba extract group was more significantly reduced than the seizure severity in the saline control group, and a significant difference was observed between the G. biloba extract and verapamil control groups (p < 0.05). P-GP expression in the brain more significantly decreased in the mice treated with G. biloba extract and verapamil than it did in the saline-treated control group (p < 0.05). Compared with the saline-treated control group, the mice treated with G. biloba extract and verapamil showed significantly increased brain PHT concentrations (p < 0.05). Furthermore, caspase-3 expression in the brain tissue of the G. biloba extract group was significantly lower than that in the vehicle control group (p < 0.05); this finding demonstrated the neuroprotective effects of G. biloba. Therefore, this study showed that treatment with G. biloba extract in combination with PHT prevented the upregulation of P-GP expression in mice. Moreover, G. biloba extract decreased seizure severity in pentylenetetrazole-kindled/PHT-treated mice through a mechanism that might be related to the reduction of P-GP expression in the brain. Copyright © 2015. Published by Elsevier Taiwan.

Towards quantification of toxicity of lithium ion battery electrolytes - development and validation of a liquid-liquid extraction GC-MS method for the determination of organic carbonates in cell culture materials.

PubMed

Strehlau, Jenny; Weber, Till; Lürenbaum, Constantin; Bornhorst, Julia; Galla, Hans-Joachim; Schwerdtle, Tanja; Winter, Martin; Nowak, Sascha

2017-10-01

A novel method based on liquid-liquid extraction with subsequent gas chromatography separation and mass spectrometric detection (GC-MS) for the quantification of organic carbonates in cell culture materials is presented. Method parameters including the choice of extraction solvent, of extraction method and of extraction time were optimised and the method was validated. The setup allowed for determination within a linear range of more than two orders of magnitude. The limits of detection (LODs) were between 0.0002 and 0.002 mmol/L and the repeatability precisions were in the range of 1.5-12.9%. It could be shown that no matrix effects were present and recovery rates between 98 and 104% were achieved. The methodology was applied to cell culture models incubated with commercial lithium ion battery (LIB) electrolytes to gain more insight into the potential toxic effects of these compounds. The stability of the organic carbonates in cell culture medium after incubation was studied. In a porcine model of the blood-cerebrospinal fluid (CSF) barrier, it could be shown that a transfer of organic carbonates into the brain facing compartment took place. Graphical abstract Schematic setup for the investigation of toxicity of lithium ion battery electrolytes.

A MISO-ARX-Based Method for Single-Trial Evoked Potential Extraction.

PubMed

Yu, Nannan; Wu, Lingling; Zou, Dexuan; Chen, Ying; Lu, Hanbing

2017-01-01

In this paper, we propose a novel method for solving the single-trial evoked potential (EP) estimation problem. In this method, the single-trial EP is considered as a complex containing many components, which may originate from different functional brain sites; these components can be distinguished according to their respective latencies and amplitudes and are extracted simultaneously by multiple-input single-output autoregressive modeling with exogenous input (MISO-ARX). The extraction process is performed in three stages: first, we use a reference EP as a template and decompose it into a set of components, which serve as subtemplates for the remaining steps. Then, a dictionary is constructed with these subtemplates, and EPs are preliminarily extracted by sparse coding in order to roughly estimate the latency of each component. Finally, the single-trial measurement is parametrically modeled by MISO-ARX while characterizing spontaneous electroencephalographic activity as an autoregression model driven by white noise and with each component of the EP modeled by autoregressive-moving-average filtering of the subtemplates. Once optimized, all components of the EP can be extracted. Compared with ARX, our method has greater tracking capabilities of specific components of the EP complex as each component is modeled individually in MISO-ARX. We provide exhaustive experimental results to show the effectiveness and feasibility of our method.

Compact point-detection fluorescence spectroscopy system for quantifying intrinsic fluorescence redox ratio in brain cancer diagnostics

NASA Astrophysics Data System (ADS)

Liu, Quan; Grant, Gerald; Li, Jianjun; Zhang, Yan; Hu, Fangyao; Li, Shuqin; Wilson, Christy; Chen, Kui; Bigner, Darell; Vo-Dinh, Tuan

2011-03-01

We report the development of a compact point-detection fluorescence spectroscopy system and two data analysis methods to quantify the intrinsic fluorescence redox ratio and diagnose brain cancer in an orthotopic brain tumor rat model. Our system employs one compact cw diode laser (407 nm) to excite two primary endogenous fluorophores, reduced nicotinamide adenine dinucleotide, and flavin adenine dinucleotide. The spectra were first analyzed using a spectral filtering modulation method developed previously to derive the intrinsic fluorescence redox ratio, which has the advantages of insensitivty to optical coupling and rapid data acquisition and analysis. This method represents a convenient and rapid alternative for achieving intrinsic fluorescence-based redox measurements as compared to those complicated model-based methods. It is worth noting that the method can also extract total hemoglobin concentration at the same time but only if the emission path length of fluorescence light, which depends on the illumination and collection geometry of the optical probe, is long enough so that the effect of absorption on fluorescence intensity due to hemoglobin is significant. Then a multivariate method was used to statistically classify normal tissues and tumors. Although the first method offers quantitative tissue metabolism information, the second method provides high overall classification accuracy. The two methods provide complementary capabilities for understanding cancer development and noninvasively diagnosing brain cancer. The results of our study suggest that this portable system can be potentially used to demarcate the elusive boundary between a brain tumor and the surrounding normal tissue during surgical resection.

A novel approach to segmentation and measurement of medical image using level set methods.

PubMed

Chen, Yao-Tien

2017-06-01

The study proposes a novel approach for segmentation and visualization plus value-added surface area and volume measurements for brain medical image analysis. The proposed method contains edge detection and Bayesian based level set segmentation, surface and volume rendering, and surface area and volume measurements for 3D objects of interest (i.e., brain tumor, brain tissue, or whole brain). Two extensions based on edge detection and Bayesian level set are first used to segment 3D objects. Ray casting and a modified marching cubes algorithm are then adopted to facilitate volume and surface visualization of medical-image dataset. To provide physicians with more useful information for diagnosis, the surface area and volume of an examined 3D object are calculated by the techniques of linear algebra and surface integration. Experiment results are finally reported in terms of 3D object extraction, surface and volume rendering, and surface area and volume measurements for medical image analysis. Copyright © 2017 Elsevier Inc. All rights reserved.

PROTECTIVE EFFECT OF MORINGA PEREGRINA LEAVES EXTRACT ON ACETAMINOPHEN -INDUCED LIVER TOXICITY IN ALBINO RATS

PubMed Central

Azim, Samy Abdelfatah Abdel; Abdelrahem, Mohamed Taha; Said, Mostafa Mohamed; khattab, Alshaimaa

2017-01-01

Background: Acetaminophen is a common antipyretic drug but at overdose can cause severe hepatotoxicity that may further develop into liver failure and hepatic centrilobular necrosis in experimental animals and humans. This study was undertaken to assess the ameliorative role of Moringa peregrina leaves extract against acetaminophen toxicity in rats. Materials and methods: Induction of hepatotoxicity was done by chronic oral administration of acetaminophen (750 mg/kg bwt) for 4 weeks. To study the possible hepatoprotective effect, Moringa peregrina leaves extract (200 mg/kg bwt) or Silymarin (50 mg/kg bwt) was administered orally, for 4 weeks, along with acetaminophen. Results: acetaminophen significantly increased serum liver enzymes and caused oxidative stress, evidenced by significantly increased tissue malondialdehyde, glutathione peroxidase, hepatic DNA fragmentation, and significant decrease of glutathione and antioxidant enzymes in liver, blood and brain. On the other hand, administration of Moringa peregrina leaves extract reversed acetaminophen-related toxic effects through: powerful malondialdehyde suppression, glutathione peroxidase normalization and stimulation of the cellular antioxidants synthesis represented by significant increase of glutathione, catalase and superoxide dismutase in liver, blood and brain, besides, DNA fragmentation was significantly decreased in the liver tissue. Conclusion: acetaminophen induced oxidative damage can be improved by Moringa peregrina leaves extract-treatment, due to its antioxidant potential. PMID:28573237

Nootropic activity of lipid-based extract of Bacopa monniera Linn. compared with traditional preparation and extracts.

PubMed

Lohidasan, Sathiyanarayanan; Paradkar, Anant R; Mahadik, Kakasaheb R

2009-11-01

The aim was to design an alternative solvent-free extraction method using the hydrophilic lipid Gelucire (polyethylene glycol glycerides) for herbal extraction and to confirm the efficacy of extraction using biological screening. Bacopa monniera Linn. (BM) was selected for the study. Conventional methanolic extract (MEBM), Ayurvedic ghrita (AGBM) and lipid extracts (LEBM) were prepared and standardised by high-performance thin-layer chromatography (HPTLC). Nootropic activity in rats was evaluated using the two-trial Y-maze test and the anterograde amnesia induced by scopolamine (1 mg/kg i.p.) determined by the conditioned avoidance response. The extracts were administered daily at doses of 100, 200 and 400 mg/kg orally. At the end of the conditioned avoidance response test, brain monoamine levels were estimated by HPLC. The LEBM, MEBM and AGBM contained 3.56%, 4.10% and 0.005% bacoside A, respectively. Significantly greater spatial recognition was observed with LEBM (P < 0.001 at 400 and 200 mg/kg) and MEBM (P < 0.001 at 400 mg/kg, P < 0.01 at 200 mg/kg) than AGBM. The conditioned avoidance response was significantly higher in the groups treated with high doses of LEBM and MEBM than AGBM. There were significant decreases in brain noradrenaline (P < 0.001) and 5-hydroxytryptamine (P < 0.01) levels and an increase in dopamine levels (P < 0.05) in the LEBM-treated groups compared with the stress control group. The proposed LEBM is solvent free, does not have the shortcomings associated with conventional extraction, and had comparable nootropic activity to the MEBM.

Lion's Mane, Hericium erinaceus and Tiger Milk, Lignosus rhinocerotis (Higher Basidiomycetes) Medicinal Mushrooms Stimulate Neurite Outgrowth in Dissociated Cells of Brain, Spinal Cord, and Retina: An In Vitro Study.

PubMed

Samberkar, Snehlata; Gandhi, Sivasangkary; Naidu, Murali; Wong, Kah-Hui; Raman, Jegadeesh; Sabaratnam, Vikineswary

2015-01-01

Neurodegenerative disease is defined as a deterioration of the nervous system in the intellectual and cognitive capabilities. Statistics show that more than 80-90 million individuals age 65 and above in 2050 may be affected by neurodegenerative conditions like Alzheimer's and Parkinson's disease. Studies have shown that out of 2000 different types of edible and/or medicinal mushrooms, only a few countable mushrooms have been selected until now for neurohealth activity. Hericium erinaceus is one of the well-established medicinal mushrooms for neuronal health. It has been documented for its regenerative capability in peripheral nerve. Another mushroom used as traditional medicine is Lignosus rhinocerotis, which has been used for various illnesses. It has been documented for its neurite outgrowth potential in PC12 cells. Based on the regenerative capabilities of both the mushrooms, priority was given to select them for our study. The aim of this study was to investigate the potential of H. erinaceus and L. rhinocerotis to stimulate neurite outgrowth in dissociated cells of brain, spinal cord, and retina from chick embryo when compared to brain derived neurotrophic factor (BDNF). Neurite outgrowth activity was confirmed by the immu-nofluorescence method in all tissue samples. Treatment with different concentrations of extracts resulted in neuronal differentiation and neuronal elongation. H. erinaceus extract at 50 µg/mL triggered neurite outgrowth at 20.47%, 22.47%, and 21.70% in brain, spinal cord, and retinal cells. L. rhinocerotis sclerotium extract at 50 µg/mL induced maximum neurite outgrowth of 20.77% and 24.73% in brain and spinal cord, whereas 20.77% of neurite outgrowth was observed in retinal cells at 25 µg/mL, respectively.

Quantitative determination of free D-Asp, L-Asp and N-methyl-D-aspartate in mouse brain tissues by chiral separation and Multiple Reaction Monitoring tandem mass spectrometry.

PubMed

Fontanarosa, Carolina; Pane, Francesca; Sepe, Nunzio; Pinto, Gabriella; Trifuoggi, Marco; Squillace, Marta; Errico, Francesco; Usiello, Alessandro; Pucci, Piero; Amoresano, Angela

2017-01-01

Several studies have suggested that free d-Asp has a crucial role in N-methyl d-Asp receptor-mediated neurotransmission playing very important functions in physiological and pathological processes. This paper describes the development of an analytical procedure for the direct and simultaneous determination of free d-Asp, l-Asp and N-methyl d-Asp in specimens of different mouse brain tissues using chiral LC-MS/MS in Multiple Reaction Monitoring scan mode. After comparing three procedures and different buffers and extraction solvents, a simple preparation procedure was selected the analytes of extraction. The method was validated by analyzing l-Asp, d-Asp and N-methyl d-Asp recovery at different spiked concentrations (50, 100 and 200 pg/μl) yielding satisfactory recoveries (75-110%), and good repeatability. Limits of detection (LOD) resulted to be 0.52 pg/μl for d-Asp, 0.46 pg/μl for l-Asp and 0.54 pg/μl for NMDA, respectively. Limits of quantification (LOQ) were 1.57 pg/μl for d-Asp, 1.41 pg/μl for l-Asp and 1.64 pg/μl for NMDA, respectively. Different concentration levels were used for constructing the calibration curves which showed good linearity. The validated method was then successfully applied to the simultaneous detection of d-Asp, l-Asp and NMDA in mouse brain tissues. The concurrent, sensitive, fast, and reproducible measurement of these metabolites in brain tissues will be useful to correlate the amount of free d-Asp with relevant neurological processes, making the LC-MS/MS MRM method well suited, not only for research work but also for clinical analyses.

Feature Selection in Order to Extract Multiple Sclerosis Lesions Automatically in 3D Brain Magnetic Resonance Images Using Combination of Support Vector Machine and Genetic Algorithm.

PubMed

Khotanlou, Hassan; Afrasiabi, Mahlagha

2012-10-01

This paper presents a new feature selection approach for automatically extracting multiple sclerosis (MS) lesions in three-dimensional (3D) magnetic resonance (MR) images. Presented method is applicable to different types of MS lesions. In this method, T1, T2, and fluid attenuated inversion recovery (FLAIR) images are firstly preprocessed. In the next phase, effective features to extract MS lesions are selected by using a genetic algorithm (GA). The fitness function of the GA is the Similarity Index (SI) of a support vector machine (SVM) classifier. The results obtained on different types of lesions have been evaluated by comparison with manual segmentations. This algorithm is evaluated on 15 real 3D MR images using several measures. As a result, the SI between MS regions determined by the proposed method and radiologists was 87% on average. Experiments and comparisons with other methods show the effectiveness and the efficiency of the proposed approach.

Automatic MRI 2D brain segmentation using graph searching technique.

PubMed

Pedoia, Valentina; Binaghi, Elisabetta

2013-09-01

Accurate and efficient segmentation of the whole brain in magnetic resonance (MR) images is a key task in many neuroscience and medical studies either because the whole brain is the final anatomical structure of interest or because the automatic extraction facilitates further analysis. The problem of segmenting brain MRI images has been extensively addressed by many researchers. Despite the relevant achievements obtained, automated segmentation of brain MRI imagery is still a challenging problem whose solution has to cope with critical aspects such as anatomical variability and pathological deformation. In the present paper, we describe and experimentally evaluate a method for segmenting brain from MRI images basing on two-dimensional graph searching principles for border detection. The segmentation of the whole brain over the entire volume is accomplished slice by slice, automatically detecting frames including eyes. The method is fully automatic and easily reproducible by computing the internal main parameters directly from the image data. The segmentation procedure is conceived as a tool of general applicability, although design requirements are especially commensurate with the accuracy required in clinical tasks such as surgical planning and post-surgical assessment. Several experiments were performed to assess the performance of the algorithm on a varied set of MRI images obtaining good results in terms of accuracy and stability. Copyright © 2012 John Wiley & Sons, Ltd.

[UPLC-MS/MS determination of content of three iridoids of xingnaojing oral preparation in rat brains and study on their brain pharmacokinetics].

PubMed

Xu, Pan; Du, Shou-Ying; Lu, Yang; Bai, Jie; Liu, Hui-Min; Du, Qiu; Chen, Zhen-Zhen; Wang, Zhen

2014-06-01

To establish a UPLC-MS/MS method for the simultaneous determination of geniposide, genipin 1-O-beta-D-gentiobioside and geniposidic acid in rat brains and study the brain pharmacokinetics of the three iridoid glycosides in stroke rat after the oral administration of Xingnaojing. In this experiment, brain samples were precipitated with protein for twice. Acquity BEH C18 column was adopted, with acetonitrile-0.1% formic acid-water as the mobile phase for gradient elution. ESI source was adopted for mass spectra; multiple reaction monitoring (MRM) was conducted to detect negative ions. The time for sample analysis was 3.5 min. the results showed good linear relations among the three iridoid glycosides, with the extraction recovery between 99.6% and 114.3%, good intra- and inter-day precisions and accuracies and stability in line with the requirements. The t1/2 and MRT in the three components were similar in brains of stroke rats. Geniposide and genipin 1-O-beta-D-gentiobioside showed double peaks; where as geniposidic acid showed a single peak. In conclusion, the method is so specific, sensitive, accurate and reliable that it can be used to study the brain pharmacokinetics of Xingnaojing oral preparation.

A three-dimensional histological atlas of the human basal ganglia. II. Atlas deformation strategy and evaluation in deep brain stimulation for Parkinson disease.

PubMed

Bardinet, Eric; Bhattacharjee, Manik; Dormont, Didier; Pidoux, Bernard; Malandain, Grégoire; Schüpbach, Michael; Ayache, Nicholas; Cornu, Philippe; Agid, Yves; Yelnik, Jérôme

2009-02-01

The localization of any given target in the brain has become a challenging issue because of the increased use of deep brain stimulation to treat Parkinson disease, dystonia, and nonmotor diseases (for example, Tourette syndrome, obsessive compulsive disorders, and depression). The aim of this study was to develop an automated method of adapting an atlas of the human basal ganglia to the brains of individual patients. Magnetic resonance images of the brain specimen were obtained before extraction from the skull and histological processing. Adaptation of the atlas to individual patient anatomy was performed by reshaping the atlas MR images to the images obtained in the individual patient using a hierarchical registration applied to a region of interest centered on the basal ganglia, and then applying the reshaping matrix to the atlas surfaces. Results were evaluated by direct visual inspection of the structures visible on MR images and atlas anatomy, by comparison with electrophysiological intraoperative data, and with previous atlas studies in patients with Parkinson disease. The method was both robust and accurate, never failing to provide an anatomically reliable atlas to patient registration. The registration obtained did not exceed a 1-mm mismatch with the electrophysiological signatures in the region of the subthalamic nucleus. This registration method applied to the basal ganglia atlas forms a powerful and reliable method for determining deep brain stimulation targets within the basal ganglia of individual patients.

Anatomical brain images alone can accurately diagnose chronic neuropsychiatric illnesses.

PubMed

Bansal, Ravi; Staib, Lawrence H; Laine, Andrew F; Hao, Xuejun; Xu, Dongrong; Liu, Jun; Weissman, Myrna; Peterson, Bradley S

2012-01-01

Diagnoses using imaging-based measures alone offer the hope of improving the accuracy of clinical diagnosis, thereby reducing the costs associated with incorrect treatments. Previous attempts to use brain imaging for diagnosis, however, have had only limited success in diagnosing patients who are independent of the samples used to derive the diagnostic algorithms. We aimed to develop a classification algorithm that can accurately diagnose chronic, well-characterized neuropsychiatric illness in single individuals, given the availability of sufficiently precise delineations of brain regions across several neural systems in anatomical MR images of the brain. We have developed an automated method to diagnose individuals as having one of various neuropsychiatric illnesses using only anatomical MRI scans. The method employs a semi-supervised learning algorithm that discovers natural groupings of brains based on the spatial patterns of variation in the morphology of the cerebral cortex and other brain regions. We used split-half and leave-one-out cross-validation analyses in large MRI datasets to assess the reproducibility and diagnostic accuracy of those groupings. In MRI datasets from persons with Attention-Deficit/Hyperactivity Disorder, Schizophrenia, Tourette Syndrome, Bipolar Disorder, or persons at high or low familial risk for Major Depressive Disorder, our method discriminated with high specificity and nearly perfect sensitivity the brains of persons who had one specific neuropsychiatric disorder from the brains of healthy participants and the brains of persons who had a different neuropsychiatric disorder. Although the classification algorithm presupposes the availability of precisely delineated brain regions, our findings suggest that patterns of morphological variation across brain surfaces, extracted from MRI scans alone, can successfully diagnose the presence of chronic neuropsychiatric disorders. Extensions of these methods are likely to provide biomarkers that will aid in identifying biological subtypes of those disorders, predicting disease course, and individualizing treatments for a wide range of neuropsychiatric illnesses.

Quantitative determination and pharmacokinetic study of the novel anti-Parkinson's disease candidate drug FLZ in rat brain by high performance liquid chromatography-tandem mass spectrometry.

PubMed

Hou, Jinfeng; Qu, Feng; Wu, Caisheng; Ren, Qiang; Zhang, Jinlan

2012-07-01

FLZ (N-[2-(4-hydroxy-phenyl)-ethyl]-2-(2,5-dimethoxy-phenyl)-3-(3-methoxy-4-hydroxyphenyl)-acrylamide) is a novel anti-Parkinson's disease candidate drug. A sensitive and specific high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method was developed and validated for the quantification of FLZ in rat brain. Carbamazepine was selected as the internal standard. Sample preparation involved double liquid-liquid extraction by n-hexane and ethyl acetate with high extraction efficiency. The chromatographic separation was achieved on a Zorbax SB-C(18) column (100 mm × 2.1 mm, 3.5 μm) with an isocratic elution system comprised of acetonitrile and 0.3% aqueous acetic acid at a flow rate of 0.3 ml/min. The elutes were detected under positive electrospray ionization (ESI) and the target analytes were quantified by multiple reaction monitoring (MRM) mode. The method was sensitive with the lowest limit of quantification (LLOQ) at 1.0 ng/g brain tissue. Good linearity (r>0.99) was obtained over the range of 1.0-400 ng/g. The intra- and inter-day precision ranged from 0.68% to 12%, while the accuracy between 92.7% and 111%. In addition, the stability, recovery and matrix effect involved in this method were also validated. The method was used to investigate the pharmacokinetics of FLZ in rat brain successfully after intravenous administration. The brain distribution studies showed that the brain distribution of FLZ was limited with the penetration ratio less than 0.1 in rats, with no target effect in the seven collected regions. Inhibition of P-glycoprotein (P-gp) by zosuquidar·3HCl ((2R)-1-{4-[(1aR,10bS)-1,1-difluoro-1,1a,6,10b-tetrahydrodibenzo[a,e]cyclopropa[c][7]annulen-6-yl]-1-piperazinyl}-3-(5-quinolinyloxy)-2-propanol trihydrochloride) resulted in a significant increase in brain-to-plasma ratio, while no significant increase by inhibition of breast cancer resistance protein (BCRP) by ko143 (2-methyl-2-propanyl 3-[(3S,6S,12aS)-6-isobutyl-9-methoxy-1,4-dioxo-1,2,3,4,6,7,12,12a-octahydropyrazino[1',2':1,6]pyrido[3,4-b]indol-3-yl]propanoate). The results indicated that FLZ had poor penetration to the brain due to the P-gp transport system. Copyright © 2012 Elsevier B.V. All rights reserved.

Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels.

PubMed

Soltaninejad, Mohammadreza; Yang, Guang; Lambrou, Tryphon; Allinson, Nigel; Jones, Timothy L; Barrick, Thomas R; Howe, Franklyn A; Ye, Xujiong

2018-04-01

Accurate segmentation of brain tumour in magnetic resonance images (MRI) is a difficult task due to various tumour types. Using information and features from multimodal MRI including structural MRI and isotropic (p) and anisotropic (q) components derived from the diffusion tensor imaging (DTI) may result in a more accurate analysis of brain images. We propose a novel 3D supervoxel based learning method for segmentation of tumour in multimodal MRI brain images (conventional MRI and DTI). Supervoxels are generated using the information across the multimodal MRI dataset. For each supervoxel, a variety of features including histograms of texton descriptor, calculated using a set of Gabor filters with different sizes and orientations, and first order intensity statistical features are extracted. Those features are fed into a random forests (RF) classifier to classify each supervoxel into tumour core, oedema or healthy brain tissue. The method is evaluated on two datasets: 1) Our clinical dataset: 11 multimodal images of patients and 2) BRATS 2013 clinical dataset: 30 multimodal images. For our clinical dataset, the average detection sensitivity of tumour (including tumour core and oedema) using multimodal MRI is 86% with balanced error rate (BER) 7%; while the Dice score for automatic tumour segmentation against ground truth is 0.84. The corresponding results of the BRATS 2013 dataset are 96%, 2% and 0.89, respectively. The method demonstrates promising results in the segmentation of brain tumour. Adding features from multimodal MRI images can largely increase the segmentation accuracy. The method provides a close match to expert delineation across all tumour grades, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management. Copyright © 2018 Elsevier B.V. All rights reserved.

Extraction, Analytical and Advanced Methods for Detection of Allura Red AC (E129) in Food and Beverages Products.

PubMed

Rovina, Kobun; Siddiquee, Shafiquzzaman; Shaarani, Sharifudin M

2016-01-01

Allura Red AC (E129) is an azo dye that widely used in drinks, juices, bakery, meat, and sweets products. High consumption of Allura Red has claimed an adverse effects of human health including allergies, food intolerance, cancer, multiple sclerosis, attention deficit hyperactivity disorder, brain damage, nausea, cardiac disease and asthma due to the reaction of aromatic azo compounds (R = R' = aromatic). Several countries have banned and strictly controlled the uses of Allura Red in food and beverage products. This review paper is critically summarized on the available analytical and advanced methods for determination of Allura Red and also concisely discussed on the acceptable daily intake, toxicology and extraction methods.

Extraction, Analytical and Advanced Methods for Detection of Allura Red AC (E129) in Food and Beverages Products

PubMed Central

Rovina, Kobun; Siddiquee, Shafiquzzaman; Shaarani, Sharifudin M.

2016-01-01

Allura Red AC (E129) is an azo dye that widely used in drinks, juices, bakery, meat, and sweets products. High consumption of Allura Red has claimed an adverse effects of human health including allergies, food intolerance, cancer, multiple sclerosis, attention deficit hyperactivity disorder, brain damage, nausea, cardiac disease and asthma due to the reaction of aromatic azo compounds (R = R′ = aromatic). Several countries have banned and strictly controlled the uses of Allura Red in food and beverage products. This review paper is critically summarized on the available analytical and advanced methods for determination of Allura Red and also concisely discussed on the acceptable daily intake, toxicology and extraction methods. PMID:27303385

Gray-level co-occurrence matrix analysis of several cell types in mouse brain using resolution-enhanced photothermal microscopy

NASA Astrophysics Data System (ADS)

Kobayashi, Takayoshi; Sundaram, Durga; Nakata, Kazuaki; Tsurui, Hiromichi

2017-03-01

Qualifications of intracellular structure were performed for the first time using the gray-level co-occurrence matrix (GLCM) method for images of cells obtained by resolution-enhanced photothermal imaging. The GLCM method has been used to extract five parameters of texture features for five different types of cells in mouse brain; pyramidal neurons and glial cells in the basal nucleus (BGl), dentate gyrus granule cells, cerebellar Purkinje cells, and cerebellar granule cells. The parameters are correlation, contrast, angular second moment (ASM), inverse difference moment (IDM), and entropy for the images of cells of interest in a mouse brain. The parameters vary depending on the pixel distance taken in the analysis method. Based on the obtained results, we identified that the most suitable GLCM parameter is IDM for pyramidal neurons and BGI, granule cells in the dentate gyrus, Purkinje cells and granule cells in the cerebellum. It was also found that the ASM is the most appropriate for neurons in the basal nucleus.

Nootropic activity of Crataeva nurvala Buch-Ham against scopolamine induced cognitive impairment

PubMed Central

Bhattacharjee, Atanu; Shashidhara, Shastry Chakrakodi; Saha, Santanu

2015-01-01

Loss of cognition is one of the age related mental problems and a characteristic symptom of neurodegenerative disorders like Alzheimer’s. Crataeva nurvala Buch-Ham, a well explored traditional Indian medicinal plant of Westernghats, is routinely used as folkloric medicine to treat various ailments in particular urolithiasis and neurological disorders associated with cognitive dysfunction. The objective of the study was to evaluate the nootropic activity of Crataeva nurvala Buch-Ham stem bark in different learning and memory paradigm viz. Elevated plus maze and Y-maze against scopolamine induced cognitive impairment. Moreover, to elucidate possible mechanism, we studied the influence of Crataeva nurvala ethanolic extract on central cholinergic activity via estimating the whole brain acetyl cholinesterase enzyme. Ethanolic extracts of Crataeva nurvala (100, 200 and 400 mg/kg body weight) were administered to adult Wistar rats for successive seven days and the acquisition, retention and retrieval of spatial recognition memory was determined against scopolamine (1 mg/kg, i.p.) induced amnesia through exteroceptive behavioral models viz. Elevated plus maze and Y-maze models. Further, whole brain acetyl cholinesterase enzyme was estimated through Ellman’s method. Pretreatment with Crataeva nurvala ethanolic extract significantly improved spatial learning and memory against scopolamine induced amnesia. Moreover, Crataeva nurvala extract decreased rat brain acetyl cholinesterase activity in a dose dependent manner and comparable to the standard drug Piracetam. The results indicate that ethanolic extract of Crataeva nurvala might be a useful as nootropic agent to delay the onset and reduce the severity of symptoms associated with dementia and Alzheimer’s disease. The underlying mechanism of action of its nootropic potentiality might be attributed to its anticholinesterase property. PMID:27065767

Resolving anatomical and functional structure in human brain organization: identifying mesoscale organization in weighted network representations.

PubMed

Lohse, Christian; Bassett, Danielle S; Lim, Kelvin O; Carlson, Jean M

2014-10-01

Human brain anatomy and function display a combination of modular and hierarchical organization, suggesting the importance of both cohesive structures and variable resolutions in the facilitation of healthy cognitive processes. However, tools to simultaneously probe these features of brain architecture require further development. We propose and apply a set of methods to extract cohesive structures in network representations of brain connectivity using multi-resolution techniques. We employ a combination of soft thresholding, windowed thresholding, and resolution in community detection, that enable us to identify and isolate structures associated with different weights. One such mesoscale structure is bipartivity, which quantifies the extent to which the brain is divided into two partitions with high connectivity between partitions and low connectivity within partitions. A second, complementary mesoscale structure is modularity, which quantifies the extent to which the brain is divided into multiple communities with strong connectivity within each community and weak connectivity between communities. Our methods lead to multi-resolution curves of these network diagnostics over a range of spatial, geometric, and structural scales. For statistical comparison, we contrast our results with those obtained for several benchmark null models. Our work demonstrates that multi-resolution diagnostic curves capture complex organizational profiles in weighted graphs. We apply these methods to the identification of resolution-specific characteristics of healthy weighted graph architecture and altered connectivity profiles in psychiatric disease.

Non-invasive imaging of oxygen extraction fraction in adults with sickle cell anaemia.

PubMed

Jordan, Lori C; Gindville, Melissa C; Scott, Allison O; Juttukonda, Meher R; Strother, Megan K; Kassim, Adetola A; Chen, Sheau-Chiann; Lu, Hanzhang; Pruthi, Sumit; Shyr, Yu; Donahue, Manus J

2016-03-01

Sickle cell anaemia is a monogenetic disorder with a high incidence of stroke. While stroke screening procedures exist for children with sickle cell anaemia, no accepted screening procedures exist for assessing stroke risk in adults. The purpose of this study is to use novel magnetic resonance imaging methods to evaluate physiological relationships between oxygen extraction fraction, cerebral blood flow, and clinical markers of cerebrovascular impairment in adults with sickle cell anaemia. The specific goal is to determine to what extent elevated oxygen extraction fraction may be uniquely present in patients with higher levels of clinical impairment and therefore may represent a candidate biomarker of stroke risk. Neurological evaluation, structural imaging, and the non-invasive T2-relaxation-under-spin-tagging magnetic resonance imaging method were applied in sickle cell anaemia (n = 34) and healthy race-matched control (n = 11) volunteers without sickle cell trait to assess whole-brain oxygen extraction fraction, cerebral blood flow, degree of vasculopathy, severity of anaemia, and presence of prior infarct; findings were interpreted in the context of physiological models. Cerebral blood flow and oxygen extraction fraction were elevated (P < 0.05) in participants with sickle cell anaemia (n = 27) not receiving monthly blood transfusions (interquartile range cerebral blood flow = 46.2-56.8 ml/100 g/min; oxygen extraction fraction = 0.39-0.50) relative to controls (interquartile range cerebral blood flow = 40.8-46.3 ml/100 g/min; oxygen extraction fraction = 0.33-0.38). Oxygen extraction fraction (P < 0.0001) but not cerebral blood flow was increased in participants with higher levels of clinical impairment. These data provide support for T2-relaxation-under-spin-tagging being able to quickly and non-invasively detect elevated oxygen extraction fraction in individuals with sickle cell anaemia with higher levels of clinical impairment. Our results support the premise that magnetic resonance imaging-based assessment of elevated oxygen extraction fraction might be a viable screening tool for evaluating stroke risk in adults with sickle cell anaemia. © The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Reference-based source separation method for identification of brain regions involved in a reference state from intracerebral EEG

PubMed Central

Samadi, Samareh; Amini, Ladan; Cosandier-Rimélé, Delphine; Soltanian-Zadeh, Hamid; Jutten, Christian

2013-01-01

In this paper, we present a fast method to extract the sources related to interictal epileptiform state. The method is based on general eigenvalue decomposition using two correlation matrices during: 1) periods including interictal epileptiform discharges (IED) as a reference activation model and 2) periods excluding IEDs or abnormal physiological signals as background activity. After extracting the most similar sources to the reference or IED state, IED regions are estimated by using multiobjective optimization. The method is evaluated using both realistic simulated data and actual intracerebral electroencephalography recordings of patients suffering from focal epilepsy. These patients are seizure-free after the resective surgery. Quantitative comparisons of the proposed IED regions with the visually inspected ictal onset zones by the epileptologist and another method of identification of IED regions reveal good performance. PMID:23428609

Face-elicited ERPs and affective attitude: brain electric microstate and tomography analyses.

PubMed

Pizzagalli, D; Lehmann, D; Koenig, T; Regard, M; Pascual-Marqui, R D

2000-03-01

Although behavioral studies have demonstrated that normative affective traits modulate the processing of facial and emotionally charged stimuli, direct electrophysiological evidence for this modulation is still lacking. Event-related potential (ERP) data associated with personal, traitlike approach- or withdrawal-related attitude (assessed post-recording and 14 months later) were investigated in 18 subjects during task-free (i.e. unrequested, spontaneous) emotional evaluation of faces. Temporal and spatial aspects of 27 channel ERP were analyzed with microstate analysis and low resolution electromagnetic tomography (LORETA), a new method to compute 3 dimensional cortical current density implemented in the Talairach brain atlas. Microstate analysis showed group differences 132-196 and 196-272 ms poststimulus, with right-shifted electric gravity centers for subjects with negative affective attitude. During these (over subjects reliably identifiable) personality-modulated, face-elicited microstates, LORETA revealed activation of bilateral occipito-temporal regions, reportedly associated with facial configuration extraction processes. Negative compared to positive affective attitude showed higher activity right temporal; positive compared to negative attitude showed higher activity left temporo-parieto-occipital. These temporal and spatial aspects suggest that the subject groups differed in brain activity at early, automatic, stimulus-related face processing steps when structural face encoding (configuration extraction) occurs. In sum, the brain functional microstates associated with affect-related personality features modulate brain mechanisms during face processing already at early information processing stages.

Kernel-Based Relevance Analysis with Enhanced Interpretability for Detection of Brain Activity Patterns

PubMed Central

Alvarez-Meza, Andres M.; Orozco-Gutierrez, Alvaro; Castellanos-Dominguez, German

2017-01-01

We introduce Enhanced Kernel-based Relevance Analysis (EKRA) that aims to support the automatic identification of brain activity patterns using electroencephalographic recordings. EKRA is a data-driven strategy that incorporates two kernel functions to take advantage of the available joint information, associating neural responses to a given stimulus condition. Regarding this, a Centered Kernel Alignment functional is adjusted to learning the linear projection that best discriminates the input feature set, optimizing the required free parameters automatically. Our approach is carried out in two scenarios: (i) feature selection by computing a relevance vector from extracted neural features to facilitating the physiological interpretation of a given brain activity task, and (ii) enhanced feature selection to perform an additional transformation of relevant features aiming to improve the overall identification accuracy. Accordingly, we provide an alternative feature relevance analysis strategy that allows improving the system performance while favoring the data interpretability. For the validation purpose, EKRA is tested in two well-known tasks of brain activity: motor imagery discrimination and epileptic seizure detection. The obtained results show that the EKRA approach estimates a relevant representation space extracted from the provided supervised information, emphasizing the salient input features. As a result, our proposal outperforms the state-of-the-art methods regarding brain activity discrimination accuracy with the benefit of enhanced physiological interpretation about the task at hand. PMID:29056897

Estimation of arterial input by a noninvasive image derived method in brain H2 15O PET study: confirmation of arterial location using MR angiography

NASA Astrophysics Data System (ADS)

Muinul Islam, Muhammad; Tsujikawa, Tetsuya; Mori, Tetsuya; Kiyono, Yasushi; Okazawa, Hidehiko

2017-06-01

A noninvasive method to estimate input function directly from H2 15O brain PET data for measurement of cerebral blood flow (CBF) was proposed in this study. The image derived input function (IDIF) method extracted the time-activity curves (TAC) of the major cerebral arteries at the skull base from the dynamic PET data. The extracted primordial IDIF showed almost the same radioactivity as the arterial input function (AIF) from sampled blood at the plateau part in the later phase, but significantly lower radioactivity in the initial arterial phase compared with that of AIF-TAC. To correct the initial part of the IDIF, a dispersion function was applied and two constants for the correction were determined by fitting with the individual AIF in 15 patients with unilateral arterial stenoocclusive lesions. The area under the curves (AUC) from the two input functions showed good agreement with the mean AUCIDIF/AUCAIF ratio of 0.92  ±  0.09. The final products of CBF and arterial-to-capillary vascular volume (V 0) obtained from the IDIF and AIF showed no difference, and had with high correlation coefficients.

Effect of pomegranate extracts on brain antioxidant markers and cholinesterase activity in high fat-high fructose diet induced obesity in rat model.

PubMed

Amri, Zahra; Ghorbel, Asma; Turki, Mouna; Akrout, Férièle Messadi; Ayadi, Fatma; Elfeki, Abdelfateh; Hammami, Mohamed

2017-06-27

To investigate beneficial effects of Pomegranate seeds oil (PSO), leaves (PL), juice (PJ) and (PP) on brain cholinesterase activity, brain oxidative stress and lipid profile in high-fat-high fructose diet (HFD) induced-obese rat. In vitro and in vivo cholinesterase activity, brain oxidative status, body and brain weight and plasma lipid profile were measured in control rats, HFD-fed rats and HFD-fed rats treated by PSO, PL, PJ and PP. In vitro study showed that PSO, PL, PP, PJ inhibited cholinesterase activity in dose dependant manner. PL extract displayed the highest inhibitory activity by IC50 of 151.85 mg/ml. For in vivo study, HFD regime induced a significant increase of cholinesterase activity in brain by 17.4% as compared to normal rats. However, the administration of PSO, PL, PJ and PP to HDF-rats decreased cholinesterase activity in brain respectively by 15.48%, 6.4%, 20% and 18.7% as compared to untreated HFD-rats. Moreover, HFD regime caused significant increase in brain stress, brain and body weight, and lipid profile disorders in blood. Furthermore, PSO, PL, PJ and PP modulated lipid profile in blood and prevented accumulation of lipid in brain and body evidenced by the decrease of their weights as compared to untreated HFD-rats. In addition administration of these extract protected brain from stress oxidant, evidenced by the decrease of malondialdehyde (MDA) and Protein carbonylation (PC) levels and the increase in superoxide dismutase (SOD) and glutathione peroxidase (GPx) levels. These findings highlight the neuroprotective effects of pomegranate extracts and one of mechanisms is the inhibition of cholinesterase and the stimulation of antioxidant capacity.

Brain-shift compensation using intraoperative ultrasound and constraint-based biomechanical simulation.

PubMed

Morin, Fanny; Courtecuisse, Hadrien; Reinertsen, Ingerid; Le Lann, Florian; Palombi, Olivier; Payan, Yohan; Chabanas, Matthieu

2017-08-01

During brain tumor surgery, planning and guidance are based on preoperative images which do not account for brain-shift. However, this deformation is a major source of error in image-guided neurosurgery and affects the accuracy of the procedure. In this paper, we present a constraint-based biomechanical simulation method to compensate for craniotomy-induced brain-shift that integrates the deformations of the blood vessels and cortical surface, using a single intraoperative ultrasound acquisition. Prior to surgery, a patient-specific biomechanical model is built from preoperative images, accounting for the vascular tree in the tumor region and brain soft tissues. Intraoperatively, a navigated ultrasound acquisition is performed directly in contact with the organ. Doppler and B-mode images are recorded simultaneously, enabling the extraction of the blood vessels and probe footprint, respectively. A constraint-based simulation is then executed to register the pre- and intraoperative vascular trees as well as the cortical surface with the probe footprint. Finally, preoperative images are updated to provide the surgeon with images corresponding to the current brain shape for navigation. The robustness of our method is first assessed using sparse and noisy synthetic data. In addition, quantitative results for five clinical cases are provided, first using landmarks set on blood vessels, then based on anatomical structures delineated in medical images. The average distances between paired vessels landmarks ranged from 3.51 to 7.32 (in mm) before compensation. With our method, on average 67% of the brain-shift is corrected (range [1.26; 2.33]) against 57% using one of the closest existing works (range [1.71; 2.84]). Finally, our method is proven to be fully compatible with a surgical workflow in terms of execution times and user interactions. In this paper, a new constraint-based biomechanical simulation method is proposed to compensate for craniotomy-induced brain-shift. While being efficient to correct this deformation, the method is fully integrable in a clinical process. Copyright © 2017 Elsevier B.V. All rights reserved.

Non-invasive MRI measurements of venous oxygenation, oxygen extraction fraction and oxygen consumption in neonates.

PubMed

De Vis, J B; Petersen, E T; Alderliesten, T; Groenendaal, F; de Vries, L S; van Bel, F; Benders, M J N L; Hendrikse, J

2014-07-15

Brain oxygen consumption reflects neuronal activity and can therefore be used to investigate brain development or neuronal injury in neonates. In this paper we present the first results of a non-invasive MRI method to evaluate whole brain oxygen consumption in neonates. For this study 51 neonates were included. The T1 and T2 of blood in the sagittal sinus were fitted using the 'T2 prepared tissue relaxation inversion recovery' pulse sequence (T2-TRIR). From the T1 and the T2 of blood, the venous oxygenation and the oxygen extraction fraction (OEF) were calculated. The cerebral metabolic rate of oxygen (CMRO2) was the resultant of the venous oxygenation and arterial spin labeling whole brain cerebral blood flow (CBF) measurements. Venous oxygenation was 59±14% (mean±sd), OEF was 40±14%, CBF was 14±5ml/100g/min and CMRO2 was 30±12μmol/100g/min. The OEF in preterms at term-equivalent age was higher than in the preterms and in the infants with hypoxic-ischemic encephalopathy (p<0.01). The OEF, CBF and CMRO2 increased (p<0.01, <0.05 and <0.01, respectively) with postnatal age. We presented an MRI technique to evaluate whole-brain oxygen consumption in neonates non-invasively. The measured values are in line with reference values found by invasive measurement techniques. Preterms and infants with HIE demonstrated significant lower oxygen extraction fraction than the preterms at term-equivalent age. This could be due to decreased neuronal activity as a reflection of brain development or as a result of tissue damage, increased cerebral blood flow due to immature or impaired autoregulation, or could be caused by differences in postnatal age. Copyright © 2014 Elsevier Inc. All rights reserved.

Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification.

PubMed

Chen, Xiaobo; Zhang, Han; Zhang, Lichi; Shen, Celina; Lee, Seong-Whan; Shen, Dinggang

2017-10-01

Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for diagnosing various neurodegenerative diseases, including Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Current studies mainly construct the FC networks between grey matter (GM) regions of the brain based on temporal co-variations of the blood oxygenation level-dependent (BOLD) signals, which reflects the synchronized neural activities. However, it was rarely investigated whether the FC detected within the white matter (WM) could provide useful information for diagnosis. Motivated by the recently proposed functional correlation tensors (FCT) computed from RS-fMRI and used to characterize the structured pattern of local FC in the WM, we propose in this article a novel MCI classification method based on the information conveyed by both the FC between the GM regions and that within the WM regions. Specifically, in the WM, the tensor-based metrics (e.g., fractional anisotropy [FA], similar to the metric calculated based on diffusion tensor imaging [DTI]) are first calculated based on the FCT and then summarized along each of the major WM fiber tracts connecting each pair of the brain GM regions. This could capture the functional information in the WM, in a similar network structure as the FC network constructed for the GM, based only on the same RS-fMRI data. Moreover, a sliding window approach is further used to partition the voxel-wise BOLD signal into multiple short overlapping segments. Then, both the FC and FCT between each pair of the brain regions can be calculated based on the BOLD signal segments in the GM and WM, respectively. In such a way, our method can generate dynamic FC and dynamic FCT to better capture functional information in both GM and WM and further integrate them together by using our developed feature extraction, selection, and ensemble learning algorithms. The experimental results verify that the dynamic FCT can provide valuable functional information in the WM; by combining it with the dynamic FC in the GM, the diagnosis accuracy for MCI subjects can be significantly improved even using RS-fMRI data alone. Hum Brain Mapp 38:5019-5034, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

A framework for correcting brain retraction based on an eXtended Finite Element Method using a laser range scanner.

PubMed

Li, Ping; Wang, Weiwei; Song, Zhijian; An, Yong; Zhang, Chenxi

2014-07-01

Brain retraction causes great distortion that limits the accuracy of an image-guided neurosurgery system that uses preoperative images. Therefore, brain retraction correction is an important intraoperative clinical application. We used a linear elastic biomechanical model, which deforms based on the eXtended Finite Element Method (XFEM) within a framework for brain retraction correction. In particular, a laser range scanner was introduced to obtain a surface point cloud of the exposed surgical field including retractors inserted into the brain. A brain retraction surface tracking algorithm converted these point clouds into boundary conditions applied to XFEM modeling that drive brain deformation. To test the framework, we performed a brain phantom experiment involving the retraction of tissue. Pairs of the modified Hausdorff distance between Canny edges extracted from model-updated images, pre-retraction, and post-retraction CT images were compared to evaluate the morphological alignment of our framework. Furthermore, the measured displacements of beads embedded in the brain phantom and the predicted ones were compared to evaluate numerical performance. The modified Hausdorff distance of 19 pairs of images decreased from 1.10 to 0.76 mm. The forecast error of 23 stainless steel beads in the phantom was between 0 and 1.73 mm (mean 1.19 mm). The correction accuracy varied between 52.8 and 100 % (mean 81.4 %). The results demonstrate that the brain retraction compensation can be incorporated intraoperatively into the model-updating process in image-guided neurosurgery systems.

The Aggregation Paths and Products of Aβ42 Dimers Are Distinct from Those of the Aβ42 Monomer.

PubMed

O'Malley, Tiernan T; Witbold, William M; Linse, Sara; Walsh, Dominic M

2016-11-08

Extracts of Alzheimer's disease (AD) brain that contain what appear to be sodium dodecyl sulfate-stable amyloid β-protein (Aβ) dimers potently block LTP and impair memory consolidation. Brain-derived dimers can be physically separated the Aβ monomer, consist primarily of Aβ42, and resist denaturation by chaotropic agents. In nature, covalently cross-linked Aβ dimers could be generated in two ways: by the formation of a dityrosine (DiY) or an isopeptide ε-(γ-glutamyl)-lysine (Q-K) bond. We enzymatically cross-linked recombinant Aβ42 monomer to produce DiY and Q-K dimers and then used a range of biophysical methods to study their aggregation. Both Q-K and DiY dimers aggregate to form soluble assemblies distinct from the fibrillar aggregates formed by the Aβ monomer. The results suggest that the cross-links disfavor fibril formation from Aβ dimers, thereby enhancing the concentration of soluble aggregates akin to those in aqueous extracts of AD brain. Thus, it seems that Aβ dimers may play an important role in determining the formation of soluble rather than insoluble aggregates.

The aggregation paths and products of Aβ42 dimers are distinct from Aβ42 monomer

PubMed Central

O'Malley, Tiernan T.; Witbold, William M.; Linse, Sara; Walsh, Dominic M.

2017-01-01

Extracts of Alzheimer's disease (AD) brain that contain what appear to be SDS-stable amyloid β-protein (Aβ) dimers potently block LTP and impair memory consolidation. Brain-derived dimers can be physically separated from Aβ monomer, consist primarily of Aβ42 and resist denaturation by powerful chaotropic agents. In nature, covalently cross-linked Aβ dimers could be generated in only one of two different ways - either by the formation of a dityrosine (DiY) or an isopeptide ε-(γ-glutamyl)-lysine (Q-K) bond. We enzymatically cross-linked recombinant Aβ42 monomer to produce DiY and Q-K dimers and then applied a range of biophysical methods to study their aggregation. Both Q-K and DiY dimers aggregate to form soluble assemblies distinct from the fibrillar aggregates formed by Aβ monomer. These results suggest that Aβ dimers allow the formation of soluble aggregates akin to those in aqueous extracts of AD brain. Thus it seems that Aβ dimers may play an important role in determining the formation of soluble rather than insoluble aggregates. PMID:27750419

Fast and robust segmentation of the striatum using deep convolutional neural networks.

PubMed

Choi, Hongyoon; Jin, Kyong Hwan

2016-12-01

Automated segmentation of brain structures is an important task in structural and functional image analysis. We developed a fast and accurate method for the striatum segmentation using deep convolutional neural networks (CNN). T1 magnetic resonance (MR) images were used for our CNN-based segmentation, which require neither image feature extraction nor nonlinear transformation. We employed two serial CNN, Global and Local CNN: The Global CNN determined approximate locations of the striatum. It performed a regression of input MR images fitted to smoothed segmentation maps of the striatum. From the output volume of Global CNN, cropped MR volumes which included the striatum were extracted. The cropped MR volumes and the output volumes of Global CNN were used for inputs of Local CNN. Local CNN predicted the accurate label of all voxels. Segmentation results were compared with a widely used segmentation method, FreeSurfer. Our method showed higher Dice Similarity Coefficient (DSC) (0.893±0.017 vs. 0.786±0.015) and precision score (0.905±0.018 vs. 0.690±0.022) than FreeSurfer-based striatum segmentation (p=0.06). Our approach was also tested using another independent dataset, which showed high DSC (0.826±0.038) comparable with that of FreeSurfer. Comparison with existing method Segmentation performance of our proposed method was comparable with that of FreeSurfer. The running time of our approach was approximately three seconds. We suggested a fast and accurate deep CNN-based segmentation for small brain structures which can be widely applied to brain image analysis. Copyright © 2016 Elsevier B.V. All rights reserved.

Feature selection and classification of multiparametric medical images using bagging and SVM

NASA Astrophysics Data System (ADS)

Fan, Yong; Resnick, Susan M.; Davatzikos, Christos

2008-03-01

This paper presents a framework for brain classification based on multi-parametric medical images. This method takes advantage of multi-parametric imaging to provide a set of discriminative features for classifier construction by using a regional feature extraction method which takes into account joint correlations among different image parameters; in the experiments herein, MRI and PET images of the brain are used. Support vector machine classifiers are then trained based on the most discriminative features selected from the feature set. To facilitate robust classification and optimal selection of parameters involved in classification, in view of the well-known "curse of dimensionality", base classifiers are constructed in a bagging (bootstrap aggregating) framework for building an ensemble classifier and the classification parameters of these base classifiers are optimized by means of maximizing the area under the ROC (receiver operating characteristic) curve estimated from their prediction performance on left-out samples of bootstrap sampling. This classification system is tested on a sex classification problem, where it yields over 90% classification rates for unseen subjects. The proposed classification method is also compared with other commonly used classification algorithms, with favorable results. These results illustrate that the methods built upon information jointly extracted from multi-parametric images have the potential to perform individual classification with high sensitivity and specificity.

Protective effects of Petroselinum crispum (Mill) Nyman ex A. W. Hill leaf extract on D-galactose-induced oxidative stress in mouse brain.

PubMed

Vora, Shreya R; Patil, Rahul B; Pillai, Meena M

2009-05-01

With an aim to examine the effect of ethanolic extract of P. crispum (Parsley) leaves on the D-galactose-induced oxidative stress in the brain of mouse, the activities of antioxidant enzymes (superoxide dismutase, catalase and glutathione peroxidase) involved in oxygen radical (OR)-detoxification and antiperoxidative defense were measured in conjunction with an index of lipid peroxidation in mitochondrial fraction of various regions of the mouse brain. A significant decrease in superoxide dismutase and glutathione peroxidase activity was observed in D-galactose-stressed mice, while catalase activity was increased. Treatment of D-galactose-stressed mice with the ethanolic extract of P. crispum showed protection against the induced oxidative stress in brain regions. Concentration of thiobarbituric acid-reactive product was greatly elevated in D-galactose stress-induced mice and was significantly reduced in the brain regions of these mice upon treatment with P. crispum. It is postulated that parsley shows a protective effect against mitochondrial oxidative damage in the mouse brain.

In situ characterization of the brain-microdevice interface using Device Capture Histology

PubMed Central

Woolley, Andrew J.; Desai, Himanshi A.; Steckbeck, Mitchell A.; Patel, Neil K.; Otto, Kevin J.

2011-01-01

Accurate assessment of brain-implantable microdevice bio-integration remains a formidable challenge. Prevailing histological methods require device extraction prior to tissue processing, often disrupting and removing the tissue of interest which had been surrounding the device. The Device-Capture Histology method, presented here, overcomes many limitations of the conventional Device-Explant Histology method, by collecting the device and surrounding tissue intact for subsequent labeling. With the implant remaining in situ, accurate and precise imaging of the morphologically preserved tissue at the brain/microdevice interface can then be collected and quantified. First, this article presents the Device-Capture Histology method for obtaining and processing the intact, undisturbed microdevice-tissue interface, and images using fluorescent labeling and confocal microscopy. Second, this article gives examples of how to quantify features found in the captured peridevice tissue. We also share histological data capturing 1) the impact of microdevice implantation on tissue, 2) the effects of an experimental anti-inflammatory coating, 3) a dense grouping of cell nuclei encapsulating a long-term implant, and 4) atypical oligodendrocyte organization neighboring a longterm implant. Data sets collected using the Device-Capture Histology method are presented to demonstrate the significant advantages of processing the intact microdevice-tissue interface, and to underscore the utility of the method in understanding the effects of the brain-implantable microdevices on nearby tissue. PMID:21802446

Assessment of Mexican Arnica (Heterotheca inuloides Cass) and Rosemary (Rosmarinus officinalis) Extracts on Dopamine and Selected Biomarkers of Oxidative Stress in Stomach and Brain of Salmonella typhimurium Infected rats.

PubMed

Guzmàn, David Calderón; Herrera, Maribel Ortiz; Brizuela, Norma Osnaya; Mejía, Gerardo Barragàn; García, Ernestina Hernàndez; Olguín, Hugo Juàrez; Peraza, Armando Valenzuela; Ruíz, Norma Labra; Del Angel, Daniel Santamaría

2017-01-01

The effects of some natural products on dopamine (DA) and 5-hydroxyindole acetic acid (5-HIAA) in brain of infected models are still unclear. The purpose of this study was to measure the effect of Mexican arnica/rosemary (MAR) water extract and oseltamivir on both biogenic amines and some oxidative biomarkers in the brain and stomach of young rats under infection condition. Female Wistar rats (weight 80 g) in the presence of MAR or absence (no-MAR) were treated as follows: group 1, buffer solution (controls); oseltamivir (100 mg/kg), group 2; culture of Salmonella typhimurium ( S.Typh ) (1 × 10 6 colony-forming units/rat) group 3; oseltamivir (100 mg/kg) + S.Typh (same dose) group 4. Drug and extracts were administered intraperitoneally every 24 h for 5 days, and S.Typh was given orally on days 1 and 3. On the fifth day, blood was collected to measure glucose and hemoglobin. The brains and stomachs were obtained to measure levels of DA, 5-HIAA, glutathione (GSH), TBARS, H 2 O 2 , and total ATPase activity using validated methods. DA levels increased in MAR group treated with oseltamivir alone but decreased in no-MAR group treated with oseltamivir plus S.Typh . 5-HIAA, GSH, and H 2 O 2 decreased in this last group, and ATPase activity increased in MAR group treated with oseltamivir plus S.Typh . TBARS (lipid peroxidation) increased in MAR group that received oseltamivir alone. Most of the biomarkers were not altered significantly in the stomach. MAR extract alters DA and metabolism of 5-HIAA in the brain of young animals infected. Antioxidant capacity may be involved in these effects. The purpose of this study was to measure the effect of Mexican arnica/rosemary water extract and oseltamivir on both biogenic amines and some oxidative biomarkers in the brain and stomach of young rats under infection condition. Results: Mexican arnica and rosemary extract alter dopamine and metabolism of 5-HIAA in the brain of young animals infected. Antioxidant capacity may be involved in these effects. Abbreviations used: AS: Automated system, ATP: Adenosine triphosphate, CNS: Central nervous system, CFU: Colony-forming unit, DA: Dopamine EDTA: Ethylenediaminetetraacetic acid, 5-HIAA: Äcido 5-hidroxindolacético (serotonina), GABA: γ-aminobutyric acid, GSH: Glutathione, H2O2: Hidrogen peroxide, HCLO4: Perchloric acid, iNOS: Inducible nitric oxide synthase, LPS: Lipopolysaccharides, MAR: Arnica/Rosemary, NaCl: Sodium Chloride, NOGSH: nitrosoglutathione, NOS: Nitric oxide, OPT: Ortho-phtaldialdehyde, Pbs: Phosphate buffered saline, pH: potential of Hydrogen, Pi: Inorganic phosphate, ROS: Reactive oxygen species, RNSs: Reactive nitrogen species Tba: Thiobarbaturic acid, TBARS: Thiobarbituric aid reactive, Tca: Trichloroacetic, Tris-HCL: Tris hydrochloride, TSA: Trypticasein Soya Agar.

3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set.

PubMed

Popuri, Karteek; Cobzas, Dana; Murtha, Albert; Jägersand, Martin

2012-07-01

Brain tumor segmentation is a required step before any radiation treatment or surgery. When performed manually, segmentation is time consuming and prone to human errors. Therefore, there have been significant efforts to automate the process. But, automatic tumor segmentation from MRI data is a particularly challenging task. Tumors have a large diversity in shape and appearance with intensities overlapping the normal brain tissues. In addition, an expanding tumor can also deflect and deform nearby tissue. In our work, we propose an automatic brain tumor segmentation method that addresses these last two difficult problems. We use the available MRI modalities (T1, T1c, T2) and their texture characteristics to construct a multidimensional feature set. Then, we extract clusters which provide a compact representation of the essential information in these features. The main idea in this work is to incorporate these clustered features into the 3D variational segmentation framework. In contrast to previous variational approaches, we propose a segmentation method that evolves the contour in a supervised fashion. The segmentation boundary is driven by the learned region statistics in the cluster space. We incorporate prior knowledge about the normal brain tissue appearance during the estimation of these region statistics. In particular, we use a Dirichlet prior that discourages the clusters from the normal brain region to be in the tumor region. This leads to a better disambiguation of the tumor from brain tissue. We evaluated the performance of our automatic segmentation method on 15 real MRI scans of brain tumor patients, with tumors that are inhomogeneous in appearance, small in size and in proximity to the major structures in the brain. Validation with the expert segmentation labels yielded encouraging results: Jaccard (58%), Precision (81%), Recall (67%), Hausdorff distance (24 mm). Using priors on the brain/tumor appearance, our proposed automatic 3D variational segmentation method was able to better disambiguate the tumor from the surrounding tissue.

Brain uptake of a non-radioactive pseudo-carrier and its effect on the biodistribution of [(18)F]AV-133 in mouse brain.

PubMed

Wu, Xianying; Zhou, Xue; Zhang, Shuxian; Zhang, Yan; Deng, Aifang; Han, Jie; Zhu, Lin; Kung, Hank F; Qiao, Jinping

2015-07-01

9-[(18)F]Fluoropropyl-(+)-dihydrotetrabenazine ([(18)F]AV-133) is a new PET imaging agent targeting vesicular monoamine transporter type II (VMAT2). To shorten the preparation of [(18)F]AV-133 and to make it more widely available, a simple and rapid purification method using solid-phase extraction (SPE) instead of high-pressure liquid chromatography (HPLC) was developed. The SPE method produced doses containing the non-radioactive pseudo-carrier 9-hydroxypropyl-(+)-dihydrotetrabenazine (AV-149). The objectives of this study were to evaluate the brain uptake of AV-149 by UPLC-MS/MS and its effect on the biodistribution of [(18)F]AV-133 in the brains of mice. The mice were injected with a bolus including [(18)F]AV-133 and different doses of AV-149. Brain tissue and blood samples were harvested. The effect of different amounts of AV-149 on [(18)F]AV-133 was evaluated by quantifying the brain distribution of radiolabelled tracer [(18)F]AV-133. The concentrations of AV-149 in the brain and plasma were analyzed using a UPLC-MS/MS method. The concentrations of AV-149 in the brain and plasma exhibited a good linear relationship with the doses. The receptor occupancy curve was fit, and the calculated ED50 value was 8.165mg/kg. The brain biodistribution and regional selectivity of [(18)F]AV-133 had no obvious differences at AV-149 doses lower than 0.1mg/kg. With increasing doses of AV-149, the brain biodistribution of [(18)F]AV-133 changed significantly. The results are important to further support that the improved radiolabelling procedure of [(18)F]AV-133 using an SPE method may be suitable for routine clinical application. Copyright © 2015 Elsevier Inc. All rights reserved.

The Effect of Flax Seed (Linum Usitatissimum) Hydroalcoholic Extract on Brain, Weight and Plasma Sexual Hormone Levels in Aged and Young Mice.

PubMed

Bahmanpour, Soghra; Kamali, Mahsa

2016-05-01

Flax is a food and fiber crop that is grown in some regions of the world. Its value will account for its great popularity as a food, medical and cosmetic applications. Flax fibers are taken from the stem of the plant and are two to three times as strong as cotton. In this study, we compared brain weight and plasma sex hormone levels in young and aged mice after the administration of Linum usitatissimum (flax seed) hydro alcoholic extract. In this study, 32 aged and 32 young mice were divided into 4 groups. Controls remained untreated and experimental groups were fed with flax seed hydroalcoholic extract by oral gavages during 3 weeks. After 3 weeks, the brain was removed and blood samples were collected to measure sex hormone levels by ELISA. Data analysis was done by statistical ANOVA test using SPSS version 18 (P<0.05). The results of this study shows that the brain weight of mice did not change significantly, but the sex hormone levels in the experimental groups in comparison with the control groups increased significantly (P<0.05). The hydroalcoholic extract of flax seed had no effect on the brain weight, but this extract improved the sexual hormone levels.

In vivo Magnetic Resonance Spectroscopy of cerebral glycogen metabolism in animals and humans.

PubMed

Khowaja, Ameer; Choi, In-Young; Seaquist, Elizabeth R; Öz, Gülin

2015-02-01

Glycogen serves as an important energy reservoir in the human body. Despite the abundance of glycogen in the liver and skeletal muscles, its concentration in the brain is relatively low, hence its significance has been questioned. A major challenge in studying brain glycogen metabolism has been the lack of availability of non-invasive techniques for quantification of brain glycogen in vivo. Invasive methods for brain glycogen quantification such as post mortem extraction following high energy microwave irradiation are not applicable in the human brain. With the advent of (13)C Magnetic Resonance Spectroscopy (MRS), it has been possible to measure brain glycogen concentrations and turnover in physiological conditions, as well as under the influence of stressors such as hypoglycemia and visual stimulation. This review presents an overview of the principles of the (13)C MRS methodology and its applications in both animals and humans to further our understanding of glycogen metabolism under normal physiological and pathophysiological conditions such as hypoglycemia unawareness.

Determination of the neuropharmacological drug nodakenin in rat plasma and brain tissues by liquid chromatography tandem mass spectrometry: Application to pharmacokinetic studies.

PubMed

Song, Yingshi; Yan, Huiyu; Xu, Jingbo; Ma, Hongxi

2017-09-01

A rapid and sensitive liquid chromatography tandem mass spectrometry detection using selected reaction monitoring in positive ionization mode was developed and validated for the quantification of nodakenin in rat plasma and brain. Pareruptorin A was used as internal standard. A single step liquid-liquid extraction was used for plasma and brain sample preparation. The method was validated with respect to selectivity, precision, accuracy, linearity, limit of quantification, recovery, matrix effect and stability. Lower limit of quantification of nodakenin was 2.0 ng/mL in plasma and brain tissue homogenates. Linear calibration curves were obtained over concentration ranges of 2.0-1000 ng/mL in plasma and brain tissue homogenates for nodakenin. Intra-day and inter-day precisions (relative standard deviation, RSD) were <15% in both biological media. This assay was successfully applied to plasma and brain pharmacokinetic studies of nodakenin in rats after intravenous administration. Copyright © 2017 John Wiley & Sons, Ltd.

In vivo Magnetic Resonance Spectroscopy of cerebral glycogen metabolism in animals and humans

PubMed Central

Khowaja, Ameer; Choi, In-Young; Seaquist, Elizabeth R.; Öz, Gülin

2015-01-01

Glycogen serves as an important energy reservoir in the human body. Despite the abundance of glycogen in the liver and skeletal muscles, its concentration in the brain is relatively low, hence its significance has been questioned. A major challenge in studying brain glycogen metabolism has been the lack of availability of non-invasive techniques for quantification of brain glycogen in vivo. Invasive methods for brain glycogen quantification such as post mortem extraction following high energy microwave irradiation are not applicable in the human brain. With the advent of 13C Magnetic Resonance Spectroscopy (MRS), it has been possible to measure brain glycogen concentrations and turnover in physiological conditions, as well as under the influence of stressors such as hypoglycemia and visual stimulation. This review presents an overview of the principles of the 13C MRS methodology and its applications in both animals and humans to further our understanding of glycogen metabolism under normal physiological and pathophysiological conditions such as hypoglycemia unawareness. PMID:24676563

Bottlenose dolphins perceive object features through echolocation.

PubMed

Harley, Heidi E; Putman, Erika A; Roitblat, Herbert L

2003-08-07

How organisms (including people) recognize distant objects is a fundamental question. The correspondence between object characteristics (distal stimuli), like visual shape, and sensory characteristics (proximal stimuli), like retinal projection, is ambiguous. The view that sensory systems are 'designed' to 'pick up' ecologically useful information is vague about how such mechanisms might work. In echolocating dolphins, which are studied as models for object recognition sonar systems, the correspondence between echo characteristics and object characteristics is less clear. Many cognitive scientists assume that object characteristics are extracted from proximal stimuli, but evidence for this remains ambiguous. For example, a dolphin may store 'sound templates' in its brain and identify whole objects by listening for a particular sound. Alternatively, a dolphin's brain may contain algorithms, derived through natural endowments or experience or both, which allow it to identify object characteristics based on sounds. The standard method used to address this question in many species is indirect and has led to equivocal results with dolphins. Here we outline an appropriate method and test it to show that dolphins extract object characteristics directly from echoes.

An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification.

PubMed

Siddiqui, Muhammad Faisal; Reza, Ahmed Wasif; Kanesan, Jeevan

2015-01-01

A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares support vector machine (LS-SVM) are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients' benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities from the individual subjects, therefore, it can be used as a significant tool in clinical practice.

Quantification of brain lipids by FTIR spectroscopy and partial least squares regression

NASA Astrophysics Data System (ADS)

Dreissig, Isabell; Machill, Susanne; Salzer, Reiner; Krafft, Christoph

2009-01-01

Brain tissue is characterized by high lipid content. Its content decreases and the lipid composition changes during transformation from normal brain tissue to tumors. Therefore, the analysis of brain lipids might complement the existing diagnostic tools to determine the tumor type and tumor grade. Objective of this work is to extract lipids from gray matter and white matter of porcine brain tissue, record infrared (IR) spectra of these extracts and develop a quantification model for the main lipids based on partial least squares (PLS) regression. IR spectra of the pure lipids cholesterol, cholesterol ester, phosphatidic acid, phosphatidylcholine, phosphatidylethanolamine, phosphatidylserine, phosphatidylinositol, sphingomyelin, galactocerebroside and sulfatide were used as references. Two lipid mixtures were prepared for training and validation of the quantification model. The composition of lipid extracts that were predicted by the PLS regression of IR spectra was compared with lipid quantification by thin layer chromatography.

The ratio of acetate-to-glucose oxidation in astrocytes from a single 13C NMR spectrum of cerebral cortex.

PubMed

Marin-Valencia, Isaac; Hooshyar, M Ali; Pichumani, Kumar; Sherry, A Dean; Malloy, Craig R

2015-01-01

The (13) C-labeling patterns in glutamate and glutamine from brain tissue are quite different after infusion of a mixture of (13) C-enriched glucose and acetate. Two processes contribute to this observation, oxidation of acetate by astrocytes but not neurons, and preferential incorporation of α-ketoglutarate into glutamate in neurons, and incorporation of α-ketoglutarate into glutamine in astrocytes. The acetate:glucose ratio, introduced previously for analysis of a single (13) C NMR spectrum, provides a useful index of acetate and glucose oxidation in the brain tissue. However, quantitation of relative substrate oxidation at the cell compartment level has not been reported. A simple mathematical method is presented to quantify the ratio of acetate-to-glucose oxidation in astrocytes, based on the standard assumption that neurons do not oxidize acetate. Mice were infused with [1,2-(13) C]acetate and [1,6-(13) C]glucose, and proton decoupled (13) C NMR spectra of cortex extracts were acquired. A fit of those spectra to the model indicated that (13) C-labeled acetate and glucose contributed approximately equally to acetyl-CoA (0.96) in astrocytes. As this method relies on a single (13) C NMR spectrum, it can be readily applied to multiple physiologic and pathologic conditions. Differences in (13) C labeling of brain glutamate and glutamine have been attributed to metabolic compartmentation. The acetate:glucose ratio, introduced for description of a (13) C NMR (nuclear magnetic resonance) spectrum, is an index of glucose and acetate oxidation in brain tissue. A simple mathematical method is presented to quantify the ratio of acetate-to-glucose oxidation in astrocytes from a single NMR spectrum. As kinetic analysis is not required, the method is readily applicable to analysis of tissue extracts. α-KG = alpha-ketoglutarate; CAC = citric acid cycle; GLN = glutamine; GLU = glutamate. © 2014 International Society for Neurochemistry.

Development of a novel niosomal system for oral delivery of Ginkgo biloba extract

PubMed Central

Jin, Ye; Wen, Jingyuan; Garg, Sanjay; Liu, Da; Zhou, Yulin; Teng, Lirong; Zhang, Weiyu

2013-01-01

Background The aim of this study was to develop an optimal niosomal system to deliver Ginkgo biloba extract (GbE) with improved oral bioavailability and to replace the conventional GbE tablets. Methods In this study, the film dispersion-homogenization method was used to prepare GbE niosomes. The resulting GbE niosome suspension was freeze-dried or spray-dried to improve the stability of the niosomes. GbE-loaded niosomes were formulated and characterized in terms of their morphology, particle size, zeta potential, entrapment efficiency, and angle of repose, and differential scanning calorimetry analysis was performed. In vitro release and in vivo distribution studies were also carried out. Results The particle size of the optimal delivery system prepared with Tween 80, Span 80, and cholesterol was about 141 nm. There was a significant difference (P < 0.05) in drug entrapment efficiency between the spray-drying method (about 77.5%) and the freeze-drying method (about 50.1%). The stability study revealed no significant change in drug entrapment efficiency for the GbE niosomes at 4°C and 25°C after 3 months. The in vitro release study suggested that GbE niosomes can prolong the release of flavonoid glycosides in phosphate-buffered solution (pH 6.8) for up to 48 hours. The in vivo distribution study showed that the flavonoid glycoside content in the heart, lung, kidney, brain, and blood of rats treated with the GbE niosome carrier system was greater than in the rats treated with the oral GbE tablet (P < 0.01). No flavonoid glycosides were detected in the brain tissue of rats given the oral GbE tablets, but they were detected in the brain tissue of rats given the GbE niosomes. Conclusion Niosomes are a promising oral system for delivery of GbE to the brain. PMID:23378764

Extraction of motor activity from the cervical spinal cord of behaving rats

NASA Astrophysics Data System (ADS)

Prasad, Abhishek; Sahin, Mesut

2006-12-01

Injury at the cervical region of the spinal cord results in the loss of the skeletal muscle control from below the shoulders and hence causes quadriplegia. The brain-computer interface technique is one way of generating a substitute for the lost command signals in these severely paralyzed individuals using the neural signals from the brain. In this study, we are investigating the feasibility of an alternative method where the volitional signals are extracted from the cervical spinal cord above the point of injury. A microelectrode array assembly was implanted chronically at the C5-C6 level of the spinal cord in rats. Neural recordings were made during the face cleaning behavior with forelimbs as this task involves cyclic forelimb movements and does not require any training. The correlation between the volitional motor signals and the elbow movements was studied. Linear regression technique was used to reconstruct the arm movement from the rectified-integrated version of the principal neural components. The results of this study demonstrate the feasibility of extracting the motor signals from the cervical spinal cord and using them for reconstruction of the elbow movements.

Landmark-based deep multi-instance learning for brain disease diagnosis.

PubMed

Liu, Mingxia; Zhang, Jun; Adeli, Ehsan; Shen, Dinggang

2018-01-01

In conventional Magnetic Resonance (MR) image based methods, two stages are often involved to capture brain structural information for disease diagnosis, i.e., 1) manually partitioning each MR image into a number of regions-of-interest (ROIs), and 2) extracting pre-defined features from each ROI for diagnosis with a certain classifier. However, these pre-defined features often limit the performance of the diagnosis, due to challenges in 1) defining the ROIs and 2) extracting effective disease-related features. In this paper, we propose a landmark-based deep multi-instance learning (LDMIL) framework for brain disease diagnosis. Specifically, we first adopt a data-driven learning approach to discover disease-related anatomical landmarks in the brain MR images, along with their nearby image patches. Then, our LDMIL framework learns an end-to-end MR image classifier for capturing both the local structural information conveyed by image patches located by landmarks and the global structural information derived from all detected landmarks. We have evaluated our proposed framework on 1526 subjects from three public datasets (i.e., ADNI-1, ADNI-2, and MIRIAD), and the experimental results show that our framework can achieve superior performance over state-of-the-art approaches. Copyright © 2017 Elsevier B.V. All rights reserved.

Symmetrical Location Characteristics of Corticospinal Tract Associated With Hand Movement in the Human Brain: A Probabilistic Diffusion Tensor Tractography.

PubMed

Lee, Dong-Hoon; Lee, Do-Wan; Han, Bong-Soo

2016-04-01

The purpose of this study is to elucidate the symmetrical characteristics of corticospinal tract (CST) related with hand movement in bilateral hemispheres using probabilistic fiber tracking method. Seventeen subjects were participated in this study. Fiber tracking was performed with 2 regions of interest, hand activated functional magnetic resonance imaging (fMRI) results and pontomedullary junction in each cerebral hemisphere. Each subject's extracted fiber tract was normalized with a brain template. To measure the symmetrical distributions of the CST related with hand movement, the laterality and anteriority indices were defined in upper corona radiata (CR), lower CR, and posterior limb of internal capsule. The measured laterality and anteriority indices between the hemispheres in each different brain location showed no significant differences with P < 0.05. There were significant differences in the measured indices among 3 different brain locations in each cerebral hemisphere with P < 0.001. Our results clearly showed that the hand CST had symmetric structures in bilateral hemispheres. The probabilistic fiber tracking with fMRI approach demonstrated that the hand CST can be successfully extracted regardless of crossing fiber problem. Our analytical approaches and results seem to be helpful for providing the database of CST somatotopy to neurologists and clinical researches.

Automatic epileptic seizure detection in EEGs using MF-DFA, SVM based on cloud computing.

PubMed

Zhang, Zhongnan; Wen, Tingxi; Huang, Wei; Wang, Meihong; Li, Chunfeng

2017-01-01

Epilepsy is a chronic disease with transient brain dysfunction that results from the sudden abnormal discharge of neurons in the brain. Since electroencephalogram (EEG) is a harmless and noninvasive detection method, it plays an important role in the detection of neurological diseases. However, the process of analyzing EEG to detect neurological diseases is often difficult because the brain electrical signals are random, non-stationary and nonlinear. In order to overcome such difficulty, this study aims to develop a new computer-aided scheme for automatic epileptic seizure detection in EEGs based on multi-fractal detrended fluctuation analysis (MF-DFA) and support vector machine (SVM). New scheme first extracts features from EEG by MF-DFA during the first stage. Then, the scheme applies a genetic algorithm (GA) to calculate parameters used in SVM and classify the training data according to the selected features using SVM. Finally, the trained SVM classifier is exploited to detect neurological diseases. The algorithm utilizes MLlib from library of SPARK and runs on cloud platform. Applying to a public dataset for experiment, the study results show that the new feature extraction method and scheme can detect signals with less features and the accuracy of the classification reached up to 99%. MF-DFA is a promising approach to extract features for analyzing EEG, because of its simple algorithm procedure and less parameters. The features obtained by MF-DFA can represent samples as well as traditional wavelet transform and Lyapunov exponents. GA can always find useful parameters for SVM with enough execution time. The results illustrate that the classification model can achieve comparable accuracy, which means that it is effective in epileptic seizure detection.

Inverse scattering approach to improving pattern recognition

NASA Astrophysics Data System (ADS)

Chapline, George; Fu, Chi-Yung

2005-05-01

The Helmholtz machine provides what may be the best existing model for how the mammalian brain recognizes patterns. Based on the observation that the "wake-sleep" algorithm for training a Helmholtz machine is similar to the problem of finding the potential for a multi-channel Schrodinger equation, we propose that the construction of a Schrodinger potential using inverse scattering methods can serve as a model for how the mammalian brain learns to extract essential information from sensory data. In particular, inverse scattering theory provides a conceptual framework for imagining how one might use EEG and MEG observations of brain-waves together with sensory feedback to improve human learning and pattern recognition. Longer term, implementation of inverse scattering algorithms on a digital or optical computer could be a step towards mimicking the seamless information fusion of the mammalian brain.

Inverse Scattering Approach to Improving Pattern Recognition

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chapline, G; Fu, C

2005-02-15

The Helmholtz machine provides what may be the best existing model for how the mammalian brain recognizes patterns. Based on the observation that the ''wake-sleep'' algorithm for training a Helmholtz machine is similar to the problem of finding the potential for a multi-channel Schrodinger equation, we propose that the construction of a Schrodinger potential using inverse scattering methods can serve as a model for how the mammalian brain learns to extract essential information from sensory data. In particular, inverse scattering theory provides a conceptual framework for imagining how one might use EEG and MEG observations of brain-waves together with sensorymore » feedback to improve human learning and pattern recognition. Longer term, implementation of inverse scattering algorithms on a digital or optical computer could be a step towards mimicking the seamless information fusion of the mammalian brain.« less

A hybrid brain-computer interface-based mail client.

PubMed

Yu, Tianyou; Li, Yuanqing; Long, Jinyi; Li, Feng

2013-01-01

Brain-computer interface-based communication plays an important role in brain-computer interface (BCI) applications; electronic mail is one of the most common communication tools. In this study, we propose a hybrid BCI-based mail client that implements electronic mail communication by means of real-time classification of multimodal features extracted from scalp electroencephalography (EEG). With this BCI mail client, users can receive, read, write, and attach files to their mail. Using a BCI mouse that utilizes hybrid brain signals, that is, motor imagery and P300 potential, the user can select and activate the function keys and links on the mail client graphical user interface (GUI). An adaptive P300 speller is employed for text input. The system has been tested with 6 subjects, and the experimental results validate the efficacy of the proposed method.

A Hybrid Brain-Computer Interface-Based Mail Client

PubMed Central

Yu, Tianyou; Li, Yuanqing; Long, Jinyi; Li, Feng

2013-01-01

Brain-computer interface-based communication plays an important role in brain-computer interface (BCI) applications; electronic mail is one of the most common communication tools. In this study, we propose a hybrid BCI-based mail client that implements electronic mail communication by means of real-time classification of multimodal features extracted from scalp electroencephalography (EEG). With this BCI mail client, users can receive, read, write, and attach files to their mail. Using a BCI mouse that utilizes hybrid brain signals, that is, motor imagery and P300 potential, the user can select and activate the function keys and links on the mail client graphical user interface (GUI). An adaptive P300 speller is employed for text input. The system has been tested with 6 subjects, and the experimental results validate the efficacy of the proposed method. PMID:23690880

Histological evaluation of brain damage caused by crude quinolizidine alkaloid extracts from lupines.

PubMed

Bañuelos Pineda, J; Nolasco Rodríguez, G; Monteon, J A; García López, P M; Ruiz Lopez, M A; García Estrada, J

2005-10-01

The effects of the intracerebroventricular (ICV) administration of crude extracts of lupin quinolizidine alkaloids (LQAs) were studied in adult rat brain tissue. Mature L. exaltatus and L. montanus seeds were collected in western Mexico, and the LQAs from these seeds were extracted and analyzed by capillary gas chromatography. This LQA extract was administered to the right lateral ventricle of adult rats through a stainless steel cannula on five consecutive days. While control animals received 10 microl of sesame oil daily (vehicle), the experimental rats (10 per group) received 20 ng of LQA from either L. exaltatus or from L. montanus. All the animals were sacrificed 40 h after receiving the last dose of alkaloids, and their brains were removed, fixed and coronal paraffin sections were stained with haematoxylin and eosin. Immediately after the administration of LQA the animals began grooming and suffered tachycardia, tachypnea, piloerection, tail erection, muscular contractions, loss of equilibrium, excitation, and unsteady walk. In the brains of the animals treated with LQA damaged neurons were identified. The most frequent abnormalities observed in this brain tissue were "red neurons" with shrunken eosinophilic cytoplasm, strongly stained pyknotic nuclei, neuronal swelling, spongiform neuropil, "ghost cells" (hypochromasia), and abundant neuronophagic figures in numerous brain areas. While some alterations in neurons were observed in control tissues, unlike those found in the animals treated with LQA these were not significant. Thus, the histopathological changes observed can be principally attributed to the administration of sparteine and lupanine present in the alkaloid extracts.

Global differential expression of genes located in the Down Syndrome Critical Region in normal human brain

PubMed Central

Montoya, Julio Cesar; Fajardo, Dianora; Peña, Angela; Sánchez, Adalberto; Domínguez, Martha C; Satizábal, José María

2014-01-01

Background: The information of gene expression obtained from databases, have made possible the extraction and analysis of data related with several molecular processes involving not only in brain homeostasis but its disruption in some neuropathologies; principally in Down syndrome and the Alzheimer disease. Objective: To correlate the levels of transcription of 19 genes located in the Down Syndrome Critical Region (DSCR) with their expression in several substructures of normal human brain. Methods: There were obtained expression profiles of 19 DSCR genes in 42 brain substructures, from gene expression values available at the database of the human brain of the Brain Atlas of the Allen Institute for Brain Sciences", (http://human.brain-map.org/). The co-expression patterns of DSCR genes in brain were calculated by using multivariate statistical methods. Results: Highest levels of gene expression were registered at caudate nucleus, nucleus accumbens and putamen among central areas of cerebral cortex. Increased expression levels of RCAN1 that encode by a protein involved in signal transduction process of the CNS were recorded for PCP4 that participates in the binding to calmodulin and TTC3; a protein that is associated with differentiation of neurons. That previously identified brain structures play a crucial role in the learning process, in different class of memory and in motor skills. Conclusion: The precise regulation of DSCR gene expression is crucial to maintain the brain homeostasis, especially in those areas with high levels of gene expression associated with a remarkable process of learning and cognition. PMID:25767303

Implantable brain computer interface: challenges to neurotechnology translation.

PubMed

Konrad, Peter; Shanks, Todd

2010-06-01

This article reviews three concepts related to implantable brain computer interface (BCI) devices being designed for human use: neural signal extraction primarily for motor commands, signal insertion to restore sensation, and technological challenges that remain. A significant body of literature has occurred over the past four decades regarding motor cortex signal extraction for upper extremity movement or computer interface. However, little is discussed regarding postural or ambulation command signaling. Auditory prosthesis research continues to represent the majority of literature on BCI signal insertion. Significant hurdles continue in the technological translation of BCI implants. These include developing a stable neural interface, significantly increasing signal processing capabilities, and methods of data transfer throughout the human body. The past few years, however, have provided extraordinary human examples of BCI implant potential. Despite technological hurdles, proof-of-concept animal and human studies provide significant encouragement that BCI implants may well find their way into mainstream medical practice in the foreseeable future.

Locality preserving non-negative basis learning with graph embedding.

PubMed

Ghanbari, Yasser; Herrington, John; Gur, Ruben C; Schultz, Robert T; Verma, Ragini

2013-01-01

The high dimensionality of connectivity networks necessitates the development of methods identifying the connectivity building blocks that not only characterize the patterns of brain pathology but also reveal representative population patterns. In this paper, we present a non-negative component analysis framework for learning localized and sparse sub-network patterns of connectivity matrices by decomposing them into two sets of discriminative and reconstructive bases. In order to obtain components that are designed towards extracting population differences, we exploit the geometry of the population by using a graphtheoretical scheme that imposes locality-preserving properties as well as maintaining the underlying distance between distant nodes in the original and the projected space. The effectiveness of the proposed framework is demonstrated by applying it to two clinical studies using connectivity matrices derived from DTI to study a population of subjects with ASD, as well as a developmental study of structural brain connectivity that extracts gender differences.

Application of quantum-behaved particle swarm optimization to motor imagery EEG classification.

PubMed

Hsu, Wei-Yen

2013-12-01

In this study, we propose a recognition system for single-trial analysis of motor imagery (MI) electroencephalogram (EEG) data. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the system chiefly consists of automatic artifact elimination, feature extraction, feature selection and classification. In addition to the use of independent component analysis, a similarity measure is proposed to further remove the electrooculographic (EOG) artifacts automatically. Several potential features, such as wavelet-fractal features, are then extracted for subsequent classification. Next, quantum-behaved particle swarm optimization (QPSO) is used to select features from the feature combination. Finally, selected sub-features are classified by support vector machine (SVM). Compared with without artifact elimination, feature selection using a genetic algorithm (GA) and feature classification with Fisher's linear discriminant (FLD) on MI data from two data sets for eight subjects, the results indicate that the proposed method is promising in brain-computer interface (BCI) applications.

Seizure classification in EEG signals utilizing Hilbert-Huang transform

PubMed Central

2011-01-01

Background Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals. Method Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering. Results The t-test results in a P-value < 0.02 and the clustering leads to accurate (94%) and specific (96%) results. The proposed method is also contrasted against the Multivariate Empirical Mode Decomposition that reaches 80% accuracy. Comparison results strengthen the contribution of this paper not only from the accuracy point of view but also with respect to its fast response and ease to use. Conclusion An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing can be achieved using the extracted instantaneous information of every IMF, but it would be most likely a hard task if only the average value is used. Extra benefits of this proposed system include low cost, and ease of interface. All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool. PMID:21609459

Gas chromatography-mass spectrometry of biofluids and extracts.

PubMed

Emwas, Abdul-Hamid M; Al-Talla, Zeyad A; Yang, Yang; Kharbatia, Najeh M

2015-01-01

Gas chromatography-mass spectrometry (GC-MS) has been widely used in metabonomics analyses of biofluid samples. Biofluids provide a wealth of information about the metabolism of the whole body and from multiple regions of the body that can be used to study general health status and organ function. Blood serum and blood plasma, for example, can provide a comprehensive picture of the whole body, while urine can be used to monitor the function of the kidneys, and cerebrospinal fluid (CSF) will provide information about the status of the brain and central nervous system (CNS). Different methods have been developed for the extraction of metabolites from biofluids, these ranging from solvent extracts, acids, heat denaturation, and filtration. These methods vary widely in terms of efficiency of protein removal and in the number of metabolites extracted. Consequently, for all biofluid-based metabonomics studies, it is vital to optimize and standardize all steps of sample preparation, including initial extraction of metabolites. In this chapter, recommendations are made of the optimum experimental conditions for biofluid samples for GC-MS, with a particular focus on blood serum and plasma samples.

Effects of the continuous administration of an Agaricus blazei extract to rats on oxidative parameters of the brain and liver during aging.

PubMed

de Sá-Nakanishi, Anacharis B; Soares, Andréia A; Natali, Maria R M; Comar, Jurandir Fernando; Peralta, Rosane M; Bracht, Adelar

2014-11-13

An investigation of the effects of an aqueous extract of Agaricus blazei, a medicinal mushroom, on the oxidative state of the brain and liver of rats during aging (7 to 23 months) was conducted. The treatment consisted in the daily intragastric administration of 50 mg/kg of the extract. The A. blazei treatment tended to maintain the ROS contents of the brain and liver at lower levels, but a significant difference was found only at the age of 23 months and in the brain. The TBARS levels in the brain were maintained at lower levels by the A. blazei treatment during the whole aging process with a specially pronounced difference at the age of 12 months. The total antioxidant capacity in the brain was higher in treated rats only at the age of 12 months. Compared with previous studies in which old rats (21 months) were treated during a short period of 21 days with 200 mg/kg, the effects of the A. blazei extract in the present study tended to be less pronounced. The results also indicate that the long and constant treatment presented a tendency of becoming less effective at ages above 12 months.

Apparent isotropic electrical property for electrical brain stimulation (EBS) using magnetic resonance diffusion weighted imaging (MR-DWI)

NASA Astrophysics Data System (ADS)

Lee, Mun Bae; Kwon, Oh-In

2018-04-01

Electrical brain stimulation (EBS) is an invasive electrotherapy and technique used in brain neurological disorders through direct or indirect stimulation using a small electric current. EBS has relied on computational modeling to achieve optimal stimulation effects and investigate the internal activations. Magnetic resonance diffusion weighted imaging (DWI) is commonly useful for diagnosis and investigation of tissue functions in various organs. The apparent diffusion coefficient (ADC) measures the intensity of water diffusion within biological tissues using DWI. By measuring trace ADC and magnetic flux density induced by the EBS, we propose a method to extract electrical properties including the effective extracellular ion-concentration (EEIC) and the apparent isotropic conductivity without any auxiliary additional current injection. First, the internal current density due to EBS is recovered using the measured one component of magnetic flux density. We update the EEIC by introducing a repetitive scheme called the diffusion weighting J-substitution algorithm using the recovered current density and the trace ADC. To verify the proposed method, we study an anesthetized canine brain to visualize electrical properties including electrical current density, effective extracellular ion-concentration, and effective isotropic conductivity by applying electrical stimulation of the brain.

Effect of Calea serrata Less. n-hexane extract on acetylcholinesterase of larvae ticks and brain Wistar rats.

PubMed

Ribeiro, Vera Lucia Sardá; Vanzella, Cláudia; Moysés, Felipe dos Santos; Santos, Jaqueline Campiol Dos; Martins, João Ricardo Souza; von Poser, Gilsane Lino; Siqueira, Ionara Rodrigues

2012-10-26

Acetylcholinesterase (AChE), an enzyme that hydrolyses acetylcholine (ACh) at cholinergic synapses, is a target for pesticides and its inhibition by organophosphates leads to paralysis and death of arthropods. It has been demonstrated that the n-hexane extract of Calea serrata had acaricidal activity against larvae of Rhipicephalus (Boophilus) microplus and Rhipicephalus sanguineus. The aim of the present study was to understand the mechanism of the acaricidal action of C. serrata n-hexane extract are specifically to investigate the in vitro anticholinesterase activity on larvae of R. microplus and in brain structures of male Wistar rats. The n-hexane extract significantly inhibited in vitro acetylcholinesterase activity in R. microplus larvae and rat brain structures. The results confirm that inhibition of acetylcholinesterase is a possible mechanism of action of hexane extract at C. serrata. Copyright © 2012 Elsevier B.V. All rights reserved.

Recovering fNIRS brain signals: physiological interference suppression with independent component analysis

NASA Astrophysics Data System (ADS)

Zhang, Y.; Shi, M.; Sun, J.; Yang, C.; Zhang, Yajuan; Scopesi, F.; Makobore, P.; Chin, C.; Serra, G.; Wickramasinghe, Y. A. B. D.; Rolfe, P.

2015-02-01

Brain activity can be monitored non-invasively by functional near-infrared spectroscopy (fNIRS), which has several advantages in comparison with other methods, such as flexibility, portability, low cost and fewer physical restrictions. However, in practice fNIRS measurements are often contaminated by physiological interference arising from cardiac contraction, breathing and blood pressure fluctuations, thereby severely limiting the utility of the method. Hence, further improvement is necessary to reduce or eliminate such interference in order that the evoked brain activity information can be extracted reliably from fNIRS data. In the present paper, the multi-distance fNIRS probe configuration has been adopted. The short-distance fNIRS measurement is treated as the virtual channel and the long-distance fNIRS measurement is treated as the measurement channel. Independent component analysis (ICA) is employed for the fNIRS recordings to separate the brain signals and the interference. Least-absolute deviation (LAD) estimator is employed to recover the brain activity signals. We also utilized Monte Carlo simulations based on a five-layer model of the adult human head to evaluate our methodology. The results demonstrate that the ICA algorithm has the potential to separate physiological interference in fNIRS data and the LAD estimator could be a useful criterion to recover the brain activity signals.

Dispositional study of opioids in mice pretreated with sympathomimetic agents.

PubMed

Dambisya, Y M; Chan, K; Wong, C L

1992-08-01

Brain and plasma levels of morphine and codeine were determined by an assay method involving solid-phase extraction and ion-pair reversed phase HPLC. Detection was by a variable wavelength UV-detector (for codeine) and an amperometric electro-chemical detector (for morphine) coupled in series. Ephedrine or phenylpropanolamine pretreatment did not interfere with the plasma disposition of morphine, evidenced by overlapping plasma concentration-time profiles. Brain opioid levels were equally unaffected by sympathomimetic pretreatment. The relative ratios of brain to plasma concentrations at the time corresponding to the respective peak anti-nociceptive activity for morphine and codeine revealed no significant differences. It is concluded that single doses of ephedrine and phenylpropanolamine do not affect the disposition of morphine and codeine in mice.

Identifying differences in brain activities and an accurate detection of autism spectrum disorder using resting state functional-magnetic resonance imaging : A spatial filtering approach.

PubMed

Subbaraju, Vigneshwaran; Suresh, Mahanand Belathur; Sundaram, Suresh; Narasimhan, Sundararajan

2017-01-01

This paper presents a new approach for detecting major differences in brain activities between Autism Spectrum Disorder (ASD) patients and neurotypical subjects using the resting state fMRI. Further the method also extracts discriminative features for an accurate diagnosis of ASD. The proposed approach determines a spatial filter that projects the covariance matrices of the Blood Oxygen Level Dependent (BOLD) time-series signals from both the ASD patients and neurotypical subjects in orthogonal directions such that they are highly separable. The inverse of this filter also provides a spatial pattern map within the brain that highlights those regions responsible for the distinguishable activities between the ASD patients and neurotypical subjects. For a better classification, highly discriminative log-variance features providing the maximum separation between the two classes are extracted from the projected BOLD time-series data. A detailed study has been carried out using the publicly available data from the Autism Brain Imaging Data Exchange (ABIDE) consortium for the different gender and age-groups. The study results indicate that for all the above categories, the regional differences in resting state activities are more commonly found in the right hemisphere compared to the left hemisphere of the brain. Among males, a clear shift in activities to the prefrontal cortex is observed for ASD patients while other parts of the brain show diminished activities compared to neurotypical subjects. Among females, such a clear shift is not evident; however, several regions, especially in the posterior and medial portions of the brain show diminished activities due to ASD. Finally, the classification performance obtained using the log-variance features is found to be better when compared to earlier studies in the literature. Copyright © 2016 Elsevier B.V. All rights reserved.

Automatic segmentation of cortical vessels in pre- and post-tumor resection laser range scan images

NASA Astrophysics Data System (ADS)

Ding, Siyi; Miga, Michael I.; Thompson, Reid C.; Garg, Ishita; Dawant, Benoit M.

2009-02-01

Measurement of intra-operative cortical brain movement is necessary to drive mechanical models developed to predict sub-cortical shift. At our institution, this is done with a tracked laser range scanner. This device acquires both 3D range data and 2D photographic images. 3D cortical brain movement can be estimated if 2D photographic images acquired over time can be registered. Previously, we have developed a method, which permits this registration using vessels visible in the images. But, vessel segmentation required the localization of starting and ending points for each vessel segment. Here, we propose a method, which automates the segmentation process further. This method involves several steps: (1) correction of lighting artifacts, (2) vessel enhancement, and (3) vessels' centerline extraction. Result obtained on 5 images obtained in the operating room suggests that our method is robust and is able to segment vessels reliably.

The neuroprotective effects of an ethanolic turmeric (Curcuma longa L.) extract against trimethyltin-induced oxidative stress in rats.

PubMed

Yuliani, Sapto; Mustofa; Partadiredja, Ginus

2018-03-07

Oxidative stress is known to contribute to the pathogenesis of neurodegenerative disorders. An ethanolic turmeric (Curcuma longa L.) extract containing curcumin has been reported to produce antioxidant effects. The present study aims to investigate the possible neuroprotective effects of the ethanolic turmeric extract against trimethyltin (TMT)-induced oxidative stress in Sprague Dawley rats. The ethanolic turmeric extract and citicoline were administered to the TMT exposed rats from day 1 to day 28 of the experiment. The TMT injection was administered on day 8 of the experiment. The plasma and brain malondialdehyde (MDA) and reduced glutathione (GSH) levels, and the activities of the superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GPx) enzymes in the brain were examined at the end of the experiment. The administration of 200 mg/kg bw of the ethanolic turmeric extract prevented oxidative stress by decreasing the plasma and brain MDA levels and increasing the SOD, CAT, and GPx enzyme activities and GSH levels in the brain. These effects seem to be comparable to those of citicoline. The ethanolic turmeric extract at a dose of 200 mg/kg bw may exert neuroprotective effects on TMT-exposed Sprague Dawley rats by preventing them from oxidative stress.

THD-Module Extractor: An Application for CEN Module Extraction and Interesting Gene Identification for Alzheimer's Disease.

PubMed

Kakati, Tulika; Kashyap, Hirak; Bhattacharyya, Dhruba K

2016-11-30

There exist many tools and methods for construction of co-expression network from gene expression data and for extraction of densely connected gene modules. In this paper, a method is introduced to construct co-expression network and to extract co-expressed modules having high biological significance. The proposed method has been validated on several well known microarray datasets extracted from a diverse set of species, using statistical measures, such as p and q values. The modules obtained in these studies are found to be biologically significant based on Gene Ontology enrichment analysis, pathway analysis, and KEGG enrichment analysis. Further, the method was applied on an Alzheimer's disease dataset and some interesting genes are found, which have high semantic similarity among them, but are not significantly correlated in terms of expression similarity. Some of these interesting genes, such as MAPT, CASP2, and PSEN2, are linked with important aspects of Alzheimer's disease, such as dementia, increase cell death, and deposition of amyloid-beta proteins in Alzheimer's disease brains. The biological pathways associated with Alzheimer's disease, such as, Wnt signaling, Apoptosis, p53 signaling, and Notch signaling, incorporate these interesting genes. The proposed method is evaluated in regard to existing literature.

THD-Module Extractor: An Application for CEN Module Extraction and Interesting Gene Identification for Alzheimer’s Disease

PubMed Central

Kakati, Tulika; Kashyap, Hirak; Bhattacharyya, Dhruba K.

2016-01-01

There exist many tools and methods for construction of co-expression network from gene expression data and for extraction of densely connected gene modules. In this paper, a method is introduced to construct co-expression network and to extract co-expressed modules having high biological significance. The proposed method has been validated on several well known microarray datasets extracted from a diverse set of species, using statistical measures, such as p and q values. The modules obtained in these studies are found to be biologically significant based on Gene Ontology enrichment analysis, pathway analysis, and KEGG enrichment analysis. Further, the method was applied on an Alzheimer’s disease dataset and some interesting genes are found, which have high semantic similarity among them, but are not significantly correlated in terms of expression similarity. Some of these interesting genes, such as MAPT, CASP2, and PSEN2, are linked with important aspects of Alzheimer’s disease, such as dementia, increase cell death, and deposition of amyloid-beta proteins in Alzheimer’s disease brains. The biological pathways associated with Alzheimer’s disease, such as, Wnt signaling, Apoptosis, p53 signaling, and Notch signaling, incorporate these interesting genes. The proposed method is evaluated in regard to existing literature. PMID:27901073

Filter Bank Regularized Common Spatial Pattern Ensemble for Small Sample Motor Imagery Classification.

PubMed

Park, Sang-Hoon; Lee, David; Lee, Sang-Goog

2018-02-01

For the last few years, many feature extraction methods have been proposed based on biological signals. Among these, the brain signals have the advantage that they can be obtained, even by people with peripheral nervous system damage. Motor imagery electroencephalograms (EEG) are inexpensive to measure, offer a high temporal resolution, and are intuitive. Therefore, these have received a significant amount of attention in various fields, including signal processing, cognitive science, and medicine. The common spatial pattern (CSP) algorithm is a useful method for feature extraction from motor imagery EEG. However, performance degradation occurs in a small-sample setting (SSS), because the CSP depends on sample-based covariance. Since the active frequency range is different for each subject, it is also inconvenient to set the frequency range to be different every time. In this paper, we propose the feature extraction method based on a filter bank to solve these problems. The proposed method consists of five steps. First, motor imagery EEG is divided by a using filter bank. Second, the regularized CSP (R-CSP) is applied to the divided EEG. Third, we select the features according to mutual information based on the individual feature algorithm. Fourth, parameter sets are selected for the ensemble. Finally, we classify using ensemble based on features. The brain-computer interface competition III data set IVa is used to evaluate the performance of the proposed method. The proposed method improves the mean classification accuracy by 12.34%, 11.57%, 9%, 4.95%, and 4.47% compared with CSP, SR-CSP, R-CSP, filter bank CSP (FBCSP), and SR-FBCSP. Compared with the filter bank R-CSP ( , ), which is a parameter selection version of the proposed method, the classification accuracy is improved by 3.49%. In particular, the proposed method shows a large improvement in performance in the SSS.

ICA model order selection of task co-activation networks.

PubMed

Ray, Kimberly L; McKay, D Reese; Fox, Peter M; Riedel, Michael C; Uecker, Angela M; Beckmann, Christian F; Smith, Stephen M; Fox, Peter T; Laird, Angela R

2013-01-01

Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders.

ICA model order selection of task co-activation networks

PubMed Central

Ray, Kimberly L.; McKay, D. Reese; Fox, Peter M.; Riedel, Michael C.; Uecker, Angela M.; Beckmann, Christian F.; Smith, Stephen M.; Fox, Peter T.; Laird, Angela R.

2013-01-01

Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders. PMID:24339802

Hemorrhage detection in MRI brain images using images features

NASA Astrophysics Data System (ADS)

Moraru, Luminita; Moldovanu, Simona; Bibicu, Dorin; Stratulat (Visan), Mirela

2013-11-01

The abnormalities appear frequently on Magnetic Resonance Images (MRI) of brain in elderly patients presenting either stroke or cognitive impairment. Detection of brain hemorrhage lesions in MRI is an important but very time-consuming task. This research aims to develop a method to extract brain tissue features from T2-weighted MR images of the brain using a selection of the most valuable texture features in order to discriminate between normal and affected areas of the brain. Due to textural similarity between normal and affected areas in brain MR images these operation are very challenging. A trauma may cause microstructural changes, which are not necessarily perceptible by visual inspection, but they could be detected by using a texture analysis. The proposed analysis is developed in five steps: i) in the pre-processing step: the de-noising operation is performed using the Daubechies wavelets; ii) the original images were transformed in image features using the first order descriptors; iii) the regions of interest (ROIs) were cropped from images feature following up the axial symmetry properties with respect to the mid - sagittal plan; iv) the variation in the measurement of features was quantified using the two descriptors of the co-occurrence matrix, namely energy and homogeneity; v) finally, the meaningful of the image features is analyzed by using the t-test method. P-value has been applied to the pair of features in order to measure they efficacy.

The Potential of Using Brain Images for Authentication

PubMed Central

Zhou, Zongtan; Shen, Hui; Hu, Dewen

2014-01-01

Biometric recognition (also known as biometrics) refers to the automated recognition of individuals based on their biological or behavioral traits. Examples of biometric traits include fingerprint, palmprint, iris, and face. The brain is the most important and complex organ in the human body. Can it be used as a biometric trait? In this study, we analyze the uniqueness of the brain and try to use the brain for identity authentication. The proposed brain-based verification system operates in two stages: gray matter extraction and gray matter matching. A modified brain segmentation algorithm is implemented for extracting gray matter from an input brain image. Then, an alignment-based matching algorithm is developed for brain matching. Experimental results on two data sets show that the proposed brain recognition system meets the high accuracy requirement of identity authentication. Though currently the acquisition of the brain is still time consuming and expensive, brain images are highly unique and have the potential possibility for authentication in view of pattern recognition. PMID:25126604

The potential of using brain images for authentication.

PubMed

Chen, Fanglin; Zhou, Zongtan; Shen, Hui; Hu, Dewen

2014-01-01

Biometric recognition (also known as biometrics) refers to the automated recognition of individuals based on their biological or behavioral traits. Examples of biometric traits include fingerprint, palmprint, iris, and face. The brain is the most important and complex organ in the human body. Can it be used as a biometric trait? In this study, we analyze the uniqueness of the brain and try to use the brain for identity authentication. The proposed brain-based verification system operates in two stages: gray matter extraction and gray matter matching. A modified brain segmentation algorithm is implemented for extracting gray matter from an input brain image. Then, an alignment-based matching algorithm is developed for brain matching. Experimental results on two data sets show that the proposed brain recognition system meets the high accuracy requirement of identity authentication. Though currently the acquisition of the brain is still time consuming and expensive, brain images are highly unique and have the potential possibility for authentication in view of pattern recognition.

Unsupervised Pathological Area Extraction using 3D T2 and FLAIR MR Images

NASA Astrophysics Data System (ADS)

Dvořák, Pavel; Bartušek, Karel; Smékal, Zdeněk

2014-12-01

This work discusses fully automated extraction of brain tumor and edema in 3D MR volumes. The goal of this work is the extraction of the whole pathological area using such an algorithm that does not require a human intervention. For the good visibility of these kinds of tissues both T2-weighted and FLAIR images were used. The proposed method was tested on 80 MR volumes of publicly available BRATS database, which contains high and low grade gliomas, both real and simulated. The performance was evaluated by the Dice coefficient, where the results were differentiated between high and low grade and real and simulated gliomas. The method reached promising results for all of the combinations of images: real high grade (0.73 ± 0.20), real low grade (0.81 ± 0.06), simulated high grade (0.81 ± 0.14), and simulated low grade (0.81 ± 0.04).

Protective Effects of Flax Seed (Linum Usitatissimum) Hydroalcoholic Extract on Fetus Brain in Aged and Young Mice.

PubMed

Kamali, Mahsa; Bahmanpour, Soghra

2016-05-01

One of the major problems of the aged women or older than 35 is getting pregnant in the late fertility life. Fertility rates begin to decline gradually at the age of 30, more so at 35, and markedly at 40. Even with fertility treatments such as in vitro fertilization, women have more difficulty in getting pregnant or may deliver abnormal fetus. The purpose of this study was to assess the effects of flax seed hydroalcoholic extract on the fetal brain of aged mice and its comparison with young mice. In this experimental study, 32 aged and 32 young mice were divided into 4 groups. Controls received no special treatment. The experimental mice groups, 3 weeks before mating, were fed with flax seed hydroalcoholic extract by oral gavages. After giving birth, the brains of the fetus were removed. Data analysis was performed by statistical test ANOVA using SPSS version 18 (P<0.05). The mean fetus brain weight of aged mother groups compared to the control group was increased significantly (P<0.05). This study showed that flax seed hydroalcoholic extract could improve fetal brain weights in the aged groups.

Mapping whole-brain activity with cellular resolution by light-sheet microscopy and high-throughput image analysis (Conference Presentation)

NASA Astrophysics Data System (ADS)

Silvestri, Ludovico; Rudinskiy, Nikita; Paciscopi, Marco; Müllenbroich, Marie Caroline; Costantini, Irene; Sacconi, Leonardo; Frasconi, Paolo; Hyman, Bradley T.; Pavone, Francesco S.

2016-03-01

Mapping neuronal activity patterns across the whole brain with cellular resolution is a challenging task for state-of-the-art imaging methods. Indeed, despite a number of technological efforts, quantitative cellular-resolution activation maps of the whole brain have not yet been obtained. Many techniques are limited by coarse resolution or by a narrow field of view. High-throughput imaging methods, such as light sheet microscopy, can be used to image large specimens with high resolution and in reasonable times. However, the bottleneck is then moved from image acquisition to image analysis, since many TeraBytes of data have to be processed to extract meaningful information. Here, we present a full experimental pipeline to quantify neuronal activity in the entire mouse brain with cellular resolution, based on a combination of genetics, optics and computer science. We used a transgenic mouse strain (Arc-dVenus mouse) in which neurons which have been active in the last hours before brain fixation are fluorescently labelled. Samples were cleared with CLARITY and imaged with a custom-made confocal light sheet microscope. To perform an automatic localization of fluorescent cells on the large images produced, we used a novel computational approach called semantic deconvolution. The combined approach presented here allows quantifying the amount of Arc-expressing neurons throughout the whole mouse brain. When applied to cohorts of mice subject to different stimuli and/or environmental conditions, this method helps finding correlations in activity between different neuronal populations, opening the possibility to infer a sort of brain-wide 'functional connectivity' with cellular resolution.

Cinnamon polyphenol extract exerts neuroprotective activity in traumatic brain injury through modulation of Nfr2 and cytokine expression.

PubMed

Yulug, Burak; Kilic, Ertugrul; Altunay, Serdar; Ersavas, Cenk; Orhan, Cemal; Dalay, Arman; Sahin, Nurhan; Tuzcu, Mehmet; Juturu, Vijaya; Sahin, Kazim

2018-04-30

Cinnamon cinnamon polyphenol extract is a traditional spice commonly used in different areas of the world for treatment of different disease conditions which are associated with inflammation and oxidative stress. Despite many preclinical studies showing the anti-oxidative, anti-inflammatory effects of CN, the underlying mechanisms in signaling pathways via which cinnamon protects the brain after brain trauma remained largely unknown. However, there is still no preclinical study delineating the possible molecular mechanism of neuroprotective effects cinnamon polyphenol extractin TBI.The primary aim of the current study was to test the hypothesis that cinnamon polyphenol extract administration would improve the histopathological outcomes and exert neuroprotective activity through its antioxidative and anti-inflammatory properties following TBI. To investigate the effects of cinnamon, we induced brain injury using a cold trauma model in mice that were treated with cinnamon polyphenol extract (10 mg/kg BW) or vehicle via intraperitoneal administration just after TBI. Mice were divided into two groups: TBI+vehicle group and TBI + cinnamon polyphenol extract group. Brain samples were collected 24 h later for analysis. We have shown that cinnamon polyphenol extract effectively reduced infarct and edema formation which were associated with significant alterations in inflammatory and oxidative parameters, including NF-κB, IL-1, IL-6, GFAP, NCAM and Nfr2 expressions. Our results identify an important neuroprotective role of cinnamon polyphenol extract in TBI which is mediated by its capability to suppress the inflammation and oxidative injury. Further, specially designed experimental studies to understand the molecular cross-talk between signaling pathways would provide valuable evidence for the therapeutic role of cinnamon in TBI and other TBI related conditions. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Recommendations for Development of New Standardized Forms of Cocoa Breeds and Cocoa Extract Processing for the Prevention of Alzheimer's Disease: Role of Cocoa in Promotion of Cognitive Resilience and Healthy Brain Aging.

PubMed

Dubner, Lauren; Wang, Jun; Ho, Lap; Ward, Libby; Pasinetti, Giulio M

2015-01-01

It is currently thought that the lackluster performance of translational paradigms in the prevention of age-related cognitive deteriorative disorders, such as Alzheimer's disease (AD), may be due to the inadequacy of the prevailing approach of targeting only a single mechanism. Age-related cognitive deterioration and certain neurodegenerative disorders, including AD, are characterized by complex relationships between interrelated biological phenotypes. Thus, alternative strategies that simultaneously target multiple underlying mechanisms may represent a more effective approach to prevention, which is a strategic priority of the National Alzheimer's Project Act and the National Institute on Aging. In this review article, we discuss recent strategies designed to clarify the mechanisms by which certain brain-bioavailable, bioactive polyphenols, in particular, flavan-3-ols also known as flavanols, which are highly represented in cocoa extracts, may beneficially influence cognitive deterioration, such as in AD, while promoting healthy brain aging. However, we note that key issues to improve consistency and reproducibility in the development of cocoa extracts as a potential future therapeutic agent requires a better understanding of the cocoa extract sources, their processing, and more standardized testing including brain bioavailability of bioactive metabolites and brain target engagement studies. The ultimate goal of this review is to provide recommendations for future developments of cocoa extracts as a therapeutic agent in AD.

Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease

PubMed Central

Plant, Claudia; Teipel, Stefan J.; Oswald, Annahita; Böhm, Christian; Meindl, Thomas; Mourao-Miranda, Janaina; Bokde, Arun W.; Hampel, Harald; Ewers, Michael

2010-01-01

Subjects with mild cognitive impairment (MCI) have an increased risk to develop Alzheimer's disease (AD). Voxel-based MRI studies have demonstrated that widely distributed cortical and subcortical brain areas show atrophic changes in MCI, preceding the onset of AD-type dementia. Here we developed a novel data mining framework in combination with three different classifiers including support vector machine (SVM), Bayes statistics, and voting feature intervals (VFI) to derive a quantitative index of pattern matching for the prediction of the conversion from MCI to AD. MRI was collected in 32 AD patients, 24 MCI subjects and 18 healthy controls (HC). Nine out of 24 MCI subjects converted to AD after an average follow-up interval of 2.5 years. Using feature selection algorithms, brain regions showing the highest accuracy for the discrimination between AD and HC were identified, reaching a classification accuracy of up to 92%. The extracted AD clusters were used as a search region to extract those brain areas that are predictive of conversion to AD within MCI subjects. The most predictive brain areas included the anterior cingulate gyrus and orbitofrontal cortex. The best prediction accuracy, which was cross-validated via train-and-test, was 75% for the prediction of the conversion from MCI to AD. The present results suggest that novel multivariate methods of pattern matching reach a clinically relevant accuracy for the a priori prediction of the progression from MCI to AD. PMID:19961938

Lactoferrin-modified rotigotine nanoparticles for enhanced nose-to-brain delivery: LESA-MS/MS-based drug biodistribution, pharmacodynamics, and neuroprotective effects

PubMed Central

Bi, Chenchen; Duan, Dongyu; Chu, Liuxiang; Yu, Xin; Wu, Zimei; Wang, Aiping; Sun, Kaoxiang

2018-01-01

Introduction Efficient delivery of rotigotine into the brain is crucial for obtaining maximum therapeutic efficacy for Parkinson’s disease (PD). Therefore, in the present study, we prepared lactoferrin-modified rotigotine nanoparticles (Lf-R-NPs) and studied their biodistribution, pharmacodynamics, and neuroprotective effects following nose-to-brain delivery in the rat 6-hydroxydopamine model of PD. Materials and methods The biodistribution of rotigotine nanoparticles (R-NPs) and Lf-R-NPs after intranasal administration was assessed by liquid extraction surface analysis coupled with tandem mass spectrometry. Contralateral rotations were quantified to evaluate pharmacodynamics. Tyrosine hydroxylase and dopamine transporter immunohistochemistry were performed to compare the neuroprotective effects of levodopa, R-NPs, and Lf-R-NPs. Results Liquid extraction surface analysis coupled with tandem mass spectrometry analysis, used to examine rotigotine biodistribution, showed that Lf-R-NPs more efficiently supplied rotigotine to the brain (with a greater sustained amount of the drug delivered to this organ, and with more effective targeting to the striatum) than R-NPs. The pharmacodynamic study revealed a significant difference (P<0.05) in contralateral rotations between rats treated with Lf-R-NPs and those treated with R-NPs. Furthermore, Lf-R-NPs significantly alleviated nigrostriatal dopaminergic neurodegeneration in the rat model of 6-hydroxydopamine-induced PD. Conclusion Our findings show that Lf-R-NPs deliver rotigotine more efficiently to the brain, thereby enhancing efficacy. Therefore, Lf-R-NPs might have therapeutic potential for the treatment of PD. PMID:29391788

Whole head quantitative susceptibility mapping using a least-norm direct dipole inversion method.

PubMed

Sun, Hongfu; Ma, Yuhan; MacDonald, M Ethan; Pike, G Bruce

2018-06-15

A new dipole field inversion method for whole head quantitative susceptibility mapping (QSM) is proposed. Instead of performing background field removal and local field inversion sequentially, the proposed method performs dipole field inversion directly on the total field map in a single step. To aid this under-determined and ill-posed inversion process and obtain robust QSM images, Tikhonov regularization is implemented to seek the local susceptibility solution with the least-norm (LN) using the L-curve criterion. The proposed LN-QSM does not require brain edge erosion, thereby preserving the cerebral cortex in the final images. This should improve its applicability for QSM-based cortical grey matter measurement, functional imaging and venography of full brain. Furthermore, LN-QSM also enables susceptibility mapping of the entire head without the need for brain extraction, which makes QSM reconstruction more automated and less dependent on intermediate pre-processing methods and their associated parameters. It is shown that the proposed LN-QSM method reduced errors in a numerical phantom simulation, improved accuracy in a gadolinium phantom experiment, and suppressed artefacts in nine subjects, as compared to two-step and other single-step QSM methods. Measurements of deep grey matter and skull susceptibilities from LN-QSM are consistent with established reconstruction methods. Copyright © 2018 Elsevier Inc. All rights reserved.

Ameliorative effect of Noni fruit extract on streptozotocin-induced memory impairment in mice.

PubMed

Pachauri, Shakti D; Verma, Priya Ranjan P; Dwivedi, Anil K; Tota, Santoshkumar; Khandelwal, Kiran; Saxena, Jitendra K; Nath, Chandishwar

2013-08-01

This study evaluated the effects of a standardized ethyl acetate extract of Morinda citrifolia L. (Noni) fruit on impairment of memory, brain energy metabolism, and cholinergic function in intracerebral streptozotocin (STZ)-treated mice. STZ (0.5 mg/kg) was administered twice at an interval of 48 h. Noni (50 and 100 mg/kg, postoperatively) was administered for 21 days following STZ administration. Memory function was evaluated using Morris Water Maze and passive avoidance tests, and brain levels of cholinergic function, oxidative stress, energy metabolism, and brain-derived neurotrophic factor (BDNF) were estimated. STZ caused memory impairment in Morris Water Maze and passive avoidance tests along with reduced brain levels of ATP, BDNF, and acetylcholine and increased acetylcholinesterase activity and oxidative stress. Treatment with Noni extract (100 mg/kg) prevented the STZ-induced memory impairment in both behavioral tests along with reduced oxidative stress and acetylcholinesterase activity, and increased brain levels of BDNF, acetylcholine, and ATP level. The study shows the beneficial effects of Noni fruit against STZ-induced memory impairment, which may be attributed to improved brain energy metabolism, cholinergic neurotransmission, BDNF, and antioxidative action.

Regulation of glutamate level in rat brain through activation of glutamate dehydrogenase by Corydalis ternata.

PubMed

Lee, Kwan Ho; Huh, Jae-Wan; Choi, Myung-Min; Yoon, Seung Yong; Yang, Seung-Ju; Hong, Hea Nam; Cho, Sung-Woo

2005-08-31

When treated with protopine and alkalized extracts of the tuber of Corydalis ternata for one year, significant decrease in glutamate level and increase in glutamate dehydrogenase (GDH) activity was observed in rat brains. The expression of GDH between the two groups remained unchanged as determined by Western and Northern blot analysis, suggesting a post-translational regulation of GDH activity in alkalized extracts treated rat brains. The stimulatory effects of alkalized extracts and protopine on the GDH activity was further examined in vitro with two types of human GDH isozymes, hGDH1 (house-keeping GDH) and hGDH2 (nerve-specific GDH). Alkalized extracts and protopine activated the human GDH isozymes up to 4.8-fold. hGDH2 (nerve- specific GDH) was more sensitively affected by 1 mM ADP than hGDH1 (house-keeping GDH) on the activation by alkalized extracts. Studies with cassette mutagenesis at ADP-binding site showed that hGDH2 was more sensitively regulated by ADP than hGDH1 on the activation by Corydalis ternata. Our results suggest that prolonged exposure to Corydalis ternata may be one of the ways to regulate glutamate concentration in brain through the activation of GDH.

An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information.

PubMed

Kumar, Shiu; Sharma, Alok; Tsunoda, Tatsuhiko

2017-12-28

Common spatial pattern (CSP) has been an effective technique for feature extraction in electroencephalography (EEG) based brain computer interfaces (BCIs). However, motor imagery EEG signal feature extraction using CSP generally depends on the selection of the frequency bands to a great extent. In this study, we propose a mutual information based frequency band selection approach. The idea of the proposed method is to utilize the information from all the available channels for effectively selecting the most discriminative filter banks. CSP features are extracted from multiple overlapping sub-bands. An additional sub-band has been introduced that cover the wide frequency band (7-30 Hz) and two different types of features are extracted using CSP and common spatio-spectral pattern techniques, respectively. Mutual information is then computed from the extracted features of each of these bands and the top filter banks are selected for further processing. Linear discriminant analysis is applied to the features extracted from each of the filter banks. The scores are fused together, and classification is done using support vector machine. The proposed method is evaluated using BCI Competition III dataset IVa, BCI Competition IV dataset I and BCI Competition IV dataset IIb, and it outperformed all other competing methods achieving the lowest misclassification rate and the highest kappa coefficient on all three datasets. Introducing a wide sub-band and using mutual information for selecting the most discriminative sub-bands, the proposed method shows improvement in motor imagery EEG signal classification.

Non-negative matrix factorisation methods for the spectral decomposition of MRS data from human brain tumours

PubMed Central

2012-01-01

Background In-vivo single voxel proton magnetic resonance spectroscopy (SV 1H-MRS), coupled with supervised pattern recognition (PR) methods, has been widely used in clinical studies of discrimination of brain tumour types and follow-up of patients bearing abnormal brain masses. SV 1H-MRS provides useful biochemical information about the metabolic state of tumours and can be performed at short (< 45 ms) or long (> 45 ms) echo time (TE), each with particular advantages. Short-TE spectra are more adequate for detecting lipids, while the long-TE provides a much flatter signal baseline in between peaks but also negative signals for metabolites such as lactate. Both, lipids and lactate, are respectively indicative of specific metabolic processes taking place. Ideally, the information provided by both TE should be of use for clinical purposes. In this study, we characterise the performance of a range of Non-negative Matrix Factorisation (NMF) methods in two respects: first, to derive sources correlated with the mean spectra of known tissue types (tumours and normal tissue); second, taking the best performing NMF method for source separation, we compare its accuracy for class assignment when using the mixing matrix directly as a basis for classification, as against using the method for dimensionality reduction (DR). For this, we used SV 1H-MRS data with positive and negative peaks, from a widely tested SV 1H-MRS human brain tumour database. Results The results reported in this paper reveal the advantage of using a recently described variant of NMF, namely Convex-NMF, as an unsupervised method of source extraction from SV1H-MRS. Most of the sources extracted in our experiments closely correspond to the mean spectra of some of the analysed tumour types. This similarity allows accurate diagnostic predictions to be made both in fully unsupervised mode and using Convex-NMF as a DR step previous to standard supervised classification. The obtained results are comparable to, or more accurate than those obtained with supervised techniques. Conclusions The unsupervised properties of Convex-NMF place this approach one step ahead of classical label-requiring supervised methods for the discrimination of brain tumour types, as it accounts for their increasingly recognised molecular subtype heterogeneity. The application of Convex-NMF in computer assisted decision support systems is expected to facilitate further improvements in the uptake of MRS-derived information by clinicians. PMID:22401579

Seizure classification in EEG signals utilizing Hilbert-Huang transform.

PubMed

Oweis, Rami J; Abdulhay, Enas W

2011-05-24

Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals. Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering. The t-test results in a P-value < 0.02 and the clustering leads to accurate (94%) and specific (96%) results. The proposed method is also contrasted against the Multivariate Empirical Mode Decomposition that reaches 80% accuracy. Comparison results strengthen the contribution of this paper not only from the accuracy point of view but also with respect to its fast response and ease to use. An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing can be achieved using the extracted instantaneous information of every IMF, but it would be most likely a hard task if only the average value is used. Extra benefits of this proposed system include low cost, and ease of interface. All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool.

On the interpretation of weight vectors of linear models in multivariate neuroimaging.

PubMed

Haufe, Stefan; Meinecke, Frank; Görgen, Kai; Dähne, Sven; Haynes, John-Dylan; Blankertz, Benjamin; Bießmann, Felix

2014-02-15

The increase in spatiotemporal resolution of neuroimaging devices is accompanied by a trend towards more powerful multivariate analysis methods. Often it is desired to interpret the outcome of these methods with respect to the cognitive processes under study. Here we discuss which methods allow for such interpretations, and provide guidelines for choosing an appropriate analysis for a given experimental goal: For a surgeon who needs to decide where to remove brain tissue it is most important to determine the origin of cognitive functions and associated neural processes. In contrast, when communicating with paralyzed or comatose patients via brain-computer interfaces, it is most important to accurately extract the neural processes specific to a certain mental state. These equally important but complementary objectives require different analysis methods. Determining the origin of neural processes in time or space from the parameters of a data-driven model requires what we call a forward model of the data; such a model explains how the measured data was generated from the neural sources. Examples are general linear models (GLMs). Methods for the extraction of neural information from data can be considered as backward models, as they attempt to reverse the data generating process. Examples are multivariate classifiers. Here we demonstrate that the parameters of forward models are neurophysiologically interpretable in the sense that significant nonzero weights are only observed at channels the activity of which is related to the brain process under study. In contrast, the interpretation of backward model parameters can lead to wrong conclusions regarding the spatial or temporal origin of the neural signals of interest, since significant nonzero weights may also be observed at channels the activity of which is statistically independent of the brain process under study. As a remedy for the linear case, we propose a procedure for transforming backward models into forward models. This procedure enables the neurophysiological interpretation of the parameters of linear backward models. We hope that this work raises awareness for an often encountered problem and provides a theoretical basis for conducting better interpretable multivariate neuroimaging analyses. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

Anticholinesterase activities of cold and hot aqueous extracts of F. racemosa stem bark.

PubMed

Ahmed, Faiyaz; Urooj, Asna

2010-04-01

The present study evaluated the anticholinesterase activity of cold and hot aqueous extracts of Ficus racemosa stem bark against rat brain acetylcholinesterase in vitro. Both the cold aqueous extract (FRC) and the hot aqueous extract (FRH) exhibited a dose dependent inhibition of rat brain acetylcholinesterase. FRH showed significantly higher (P

Alzheimer brain-derived tau oligomers propagate pathology from endogenous tau.

PubMed

Lasagna-Reeves, Cristian A; Castillo-Carranza, Diana L; Sengupta, Urmi; Guerrero-Munoz, Marcos J; Kiritoshi, Takaki; Neugebauer, Volker; Jackson, George R; Kayed, Rakez

2012-01-01

Intracerebral injection of brain extracts containing amyloid or tau aggregates in transgenic animals can induce cerebral amyloidosis and tau pathology. We extracted pure populations of tau oligomers directly from the cerebral cortex of Alzheimer disease (AD) brain. These oligomers are potent inhibitors of long term potentiation (LTP) in hippocampal brain slices and disrupt memory in wild type mice. We observed for the first time that these authentic brain-derived tau oligomers propagate abnormal tau conformation of endogenous murine tau after prolonged incubation. The conformation and hydrophobicity of tau oligomers play a critical role in the initiation and spread of tau pathology in the naïve host in a manner reminiscent of sporadic AD.

Decoding magnetoencephalographic rhythmic activity using spectrospatial information.

PubMed

Kauppi, Jukka-Pekka; Parkkonen, Lauri; Hari, Riitta; Hyvärinen, Aapo

2013-12-01

We propose a new data-driven decoding method called Spectral Linear Discriminant Analysis (Spectral LDA) for the analysis of magnetoencephalography (MEG). The method allows investigation of changes in rhythmic neural activity as a result of different stimuli and tasks. The introduced classification model only assumes that each "brain state" can be characterized as a combination of neural sources, each of which shows rhythmic activity at one or several frequency bands. Furthermore, the model allows the oscillation frequencies to be different for each such state. We present decoding results from 9 subjects in a four-category classification problem defined by an experiment involving randomly alternating epochs of auditory, visual and tactile stimuli interspersed with rest periods. The performance of Spectral LDA was very competitive compared with four alternative classifiers based on different assumptions concerning the organization of rhythmic brain activity. In addition, the spectral and spatial patterns extracted automatically on the basis of trained classifiers showed that Spectral LDA offers a novel and interesting way of analyzing spectrospatial oscillatory neural activity across the brain. All the presented classification methods and visualization tools are freely available as a Matlab toolbox. © 2013.

A novel Bayesian framework for discriminative feature extraction in Brain-Computer Interfaces.

PubMed

Suk, Heung-Il; Lee, Seong-Whan

2013-02-01

As there has been a paradigm shift in the learning load from a human subject to a computer, machine learning has been considered as a useful tool for Brain-Computer Interfaces (BCIs). In this paper, we propose a novel Bayesian framework for discriminative feature extraction for motor imagery classification in an EEG-based BCI in which the class-discriminative frequency bands and the corresponding spatial filters are optimized by means of the probabilistic and information-theoretic approaches. In our framework, the problem of simultaneous spatiospectral filter optimization is formulated as the estimation of an unknown posterior probability density function (pdf) that represents the probability that a single-trial EEG of predefined mental tasks can be discriminated in a state. In order to estimate the posterior pdf, we propose a particle-based approximation method by extending a factored-sampling technique with a diffusion process. An information-theoretic observation model is also devised to measure discriminative power of features between classes. From the viewpoint of classifier design, the proposed method naturally allows us to construct a spectrally weighted label decision rule by linearly combining the outputs from multiple classifiers. We demonstrate the feasibility and effectiveness of the proposed method by analyzing the results and its success on three public databases.

From Brain Maps to Cognitive Ontologies: Informatics and the Search for Mental Structure.

PubMed

Poldrack, Russell A; Yarkoni, Tal

2016-01-01

A major goal of cognitive neuroscience is to delineate how brain systems give rise to mental function. Here we review the increasingly large role informatics-driven approaches are playing in such efforts. We begin by reviewing a number of challenges conventional neuroimaging approaches face in trying to delineate brain-cognition mappings--for example, the difficulty in establishing the specificity of postulated associations. Next, we demonstrate how these limitations can potentially be overcome using complementary approaches that emphasize large-scale analysis--including meta-analytic methods that synthesize hundreds or thousands of studies at a time; latent-variable approaches that seek to extract structure from data in a bottom-up manner; and predictive modeling approaches capable of quantitatively inferring mental states from patterns of brain activity. We highlight the underappreciated but critical role for formal cognitive ontologies in helping to clarify, refine, and test theories of brain and cognitive function. Finally, we conclude with a speculative discussion of what future informatics developments may hold for cognitive neuroscience.

From brain maps to cognitive ontologies: informatics and the search for mental structure

PubMed Central

Poldrack, Russell A.; Yarkoni, Tal

2015-01-01

A major goal of cognitive neuroscience is to delineate how brain systems give rise to mental function. Here we review the increasingly large role informatics-driven approaches are playing in such efforts. We begin by reviewing a number of challenges conventional neuroimaging approaches face in trying to delineate brain-cognition mappings—for example, the difficulty in establishing the specificity of postulated associations. Next, we demonstrate how these limitations can potentially be overcome using complementary approaches that emphasize large-scale analysis—including meta-analytic methods that synthesize hundreds or thousands of studies at a time; latent-variable approaches that seek to extract structure from data in a bottom-up manner; and predictive modeling approaches capable of quantitatively inferring mental states from patterns of brain activity. We highlight the underappreciated but critical role for formal cognitive ontologies in helping to clarify, refine, and test theories of brain and cognitive function. Finally, we conclude with a speculative discussion of what future informatics developments may hold for cognitive neuroscience. PMID:26393866

Validation of oxygen extraction fraction measurement by qBOLD technique.

PubMed

He, Xiang; Zhu, Mingming; Yablonskiy, Dmitriy A

2008-10-01

Measurement of brain tissue oxygen extraction fraction (OEF) in both baseline and functionally activated states can provide important information on brain functioning in health and disease. The recently proposed quantitative BOLD (qBOLD) technique is MRI-based and provides a regional in vivo OEF measurement (He and Yablonskiy, MRM 2007, 57:115-126). It is based on a previously developed analytical BOLD model and incorporates prior knowledge about the brain tissue composition including the contributions from grey matter, white matter, cerebrospinal fluid, interstitial fluid and intravascular blood. The qBOLD model also allows for the separation of contributions to the BOLD signal from OEF and the deoxyhemoglobin containing blood volume (DBV). The objective of this study is to validate OEF measurements provided by the qBOLD approach. To this end we use a rat model and compare qBOLD OEF measurements against direct measurements of the blood oxygenation level obtained from venous blood drawn directly from the superior sagittal sinus. The cerebral venous oxygenation level of the rat was manipulated by utilizing different anestheisa methods. The study demonstrates a very good agreement between qBOLD approach and direct measurements. (c) 2008 Wiley-Liss, Inc.

Nanomedicine in Central Nervous System (CNS) Disorders: A Present and Future Prospective

PubMed Central

Soni, Shringika; Ruhela, Rakesh Kumar; Medhi, Bikash

2016-01-01

Purpose: For the past few decades central nervous system disorders were considered as a major strike on human health and social system of developing countries. The natural therapeutic methods for CNS disorders limited for many patients. Moreover, nanotechnology-based drug delivery to the brain may an exciting and promising platform to overcome the problem of BBB crossing. In this review, first we focused on the role of the blood-brain barrier in drug delivery; and second, we summarized synthesis methods of nanomedicine and their role in different CNS disorder. Method: We reviewed the PubMed databases and extracted several kinds of literature on neuro nanomedicines using keywords, CNS disorders, nanomedicine, and nanotechnology. The inclusion criteria included chemical and green synthesis methods for synthesis of nanoparticles encapsulated drugs and, their in-vivo and in-vitro studies. We excluded nanomedicine gene therapy and nanomaterial in brain imaging. Results: In this review, we tried to identify a highly efficient method for nanomedicine synthesis and their efficacy in neuronal disorders. SLN and PNP encapsulated drugs reported highly efficient by easily crossing BBB. Although, these neuro-nanomedicine play significant role in therapeutics but some metallic nanoparticles reported the adverse effect on developing the brain. Conclusion: Although impressive advancement has made via innovative potential drug development, but their efficacy is still moderate due to limited brain permeability. To overcome this constraint,powerful tool in CNS therapeutic intervention provided by nanotechnology-based drug delivery methods. Due to its small and biofunctionalization characteristics, nanomedicine can easily penetrate and facilitate the drug through the barrier. But still, understanding of their toxicity level, optimization and standardization are a long way to go. PMID:27766216

A PCA aided cross-covariance scheme for discriminative feature extraction from EEG signals.

PubMed

Zarei, Roozbeh; He, Jing; Siuly, Siuly; Zhang, Yanchun

2017-07-01

Feature extraction of EEG signals plays a significant role in Brain-computer interface (BCI) as it can significantly affect the performance and the computational time of the system. The main aim of the current work is to introduce an innovative algorithm for acquiring reliable discriminating features from EEG signals to improve classification performances and to reduce the time complexity. This study develops a robust feature extraction method combining the principal component analysis (PCA) and the cross-covariance technique (CCOV) for the extraction of discriminatory information from the mental states based on EEG signals in BCI applications. We apply the correlation based variable selection method with the best first search on the extracted features to identify the best feature set for characterizing the distribution of mental state signals. To verify the robustness of the proposed feature extraction method, three machine learning techniques: multilayer perceptron neural networks (MLP), least square support vector machine (LS-SVM), and logistic regression (LR) are employed on the obtained features. The proposed methods are evaluated on two publicly available datasets. Furthermore, we evaluate the performance of the proposed methods by comparing it with some recently reported algorithms. The experimental results show that all three classifiers achieve high performance (above 99% overall classification accuracy) for the proposed feature set. Among these classifiers, the MLP and LS-SVM methods yield the best performance for the obtained feature. The average sensitivity, specificity and classification accuracy for these two classifiers are same, which are 99.32%, 100%, and 99.66%, respectively for the BCI competition dataset IVa and 100%, 100%, and 100%, for the BCI competition dataset IVb. The results also indicate the proposed methods outperform the most recently reported methods by at least 0.25% average accuracy improvement in dataset IVa. The execution time results show that the proposed method has less time complexity after feature selection. The proposed feature extraction method is very effective for getting representatives information from mental states EEG signals in BCI applications and reducing the computational complexity of classifiers by reducing the number of extracted features. Copyright © 2017 Elsevier B.V. All rights reserved.

Alzheimer's Disease Early Diagnosis Using Manifold-Based Semi-Supervised Learning.

PubMed

Khajehnejad, Moein; Saatlou, Forough Habibollahi; Mohammadzade, Hoda

2017-08-20

Alzheimer's disease (AD) is currently ranked as the sixth leading cause of death in the United States and recent estimates indicate that the disorder may rank third, just behind heart disease and cancer, as a cause of death for older people. Clearly, predicting this disease in the early stages and preventing it from progressing is of great importance. The diagnosis of Alzheimer's disease (AD) requires a variety of medical tests, which leads to huge amounts of multivariate heterogeneous data. It can be difficult and exhausting to manually compare, visualize, and analyze this data due to the heterogeneous nature of medical tests; therefore, an efficient approach for accurate prediction of the condition of the brain through the classification of magnetic resonance imaging (MRI) images is greatly beneficial and yet very challenging. In this paper, a novel approach is proposed for the diagnosis of very early stages of AD through an efficient classification of brain MRI images, which uses label propagation in a manifold-based semi-supervised learning framework. We first apply voxel morphometry analysis to extract some of the most critical AD-related features of brain images from the original MRI volumes and also gray matter (GM) segmentation volumes. The features must capture the most discriminative properties that vary between a healthy and Alzheimer-affected brain. Next, we perform a principal component analysis (PCA)-based dimension reduction on the extracted features for faster yet sufficiently accurate analysis. To make the best use of the captured features, we present a hybrid manifold learning framework which embeds the feature vectors in a subspace. Next, using a small set of labeled training data, we apply a label propagation method in the created manifold space to predict the labels of the remaining images and classify them in the two groups of mild Alzheimer's and normal condition (MCI/NC). The accuracy of the classification using the proposed method is 93.86% for the Open Access Series of Imaging Studies (OASIS) database of MRI brain images, providing, compared to the best existing methods, a 3% lower error rate.

Protective effect of extract of Cordyceps sinensis in middle cerebral artery occlusion-induced focal cerebral ischemia in rats.

PubMed

Liu, Zhenquan; Li, Pengtao; Zhao, Dan; Tang, Huiling; Guo, Jianyou

2010-10-19

Ischemic hypoxic brain injury often causes irreversible brain damage. The lack of effective and widely applicable pharmacological treatments for ischemic stroke patients may explain a growing interest in traditional medicines. From the point of view of "self-medication" or "preventive medicine," Cordyceps sinensis was used in the prevention of cerebral ischemia in this paper. The right middle cerebral artery occlusion model was used in the study. The effects of Cordyceps sinensis (Caterpillar fungus) extract on mortality rate, neurobehavior, grip strength, lactate dehydrogenase, glutathione content, Lipid Peroxidation, glutathione peroxidase activity, glutathione reductase activity, catalase activity, Na+K+ATPase activity and glutathione S transferase activity in a rat model were studied respectively. Cordyceps sinensis extract significantly improved the outcome in rats after cerebral ischemia and reperfusion in terms of neurobehavioral function. At the same time, supplementation of Cordyceps sinensis extract significantly boosted the defense mechanism against cerebral ischemia by increasing antioxidants activity related to lesion pathogenesis. Restoration of the antioxidant homeostasis in the brain after reperfusion may have helped the brain recover from ischemic injury. These experimental results suggest that complement Cordyceps sinensis extract is protective after cerebral ischemia in specific way. The administration of Cordyceps sinensis extract significantly reduced focal cerebral ischemic/reperfusion injury. The defense mechanism against cerebral ischemia was by increasing antioxidants activity related to lesion pathogenesis.

Soluble Amyloid-beta Aggregates from Human Alzheimer’s Disease Brains

PubMed Central

Esparza, Thomas J.; Wildburger, Norelle C.; Jiang, Hao; Gangolli, Mihika; Cairns, Nigel J.; Bateman, Randall J.; Brody, David L.

2016-01-01

Soluble amyloid-beta (Aβ) aggregates likely contribute substantially to the dementia that characterizes Alzheimer’s disease. However, despite intensive study of in vitro preparations and animal models, little is known about the characteristics of soluble Aβ aggregates in the human Alzheimer’s disease brain. Here we present a new method for extracting soluble Aβ aggregates from human brains, separating them from insoluble aggregates and Aβ monomers using differential ultracentrifugation, and purifying them >6000 fold by dual antibody immunoprecipitation. The method resulted in <40% loss of starting material, no detectible ex vivo aggregation of monomeric Aβ, and no apparent ex vivo alterations in soluble aggregate sizes. By immunoelectron microscopy, soluble Aβ aggregates typically appear as clusters of 10–20 nanometer diameter ovoid structures with 2-3 amino-terminal Aβ antibody binding sites, distinct from previously characterized structures. This approach may facilitate investigation into the characteristics of native soluble Aβ aggregates, and deepen our understanding of Alzheimer’s dementia. PMID:27917876

A model for brain life history evolution.

PubMed

González-Forero, Mauricio; Faulwasser, Timm; Lehmann, Laurent

2017-03-01

Complex cognition and relatively large brains are distributed across various taxa, and many primarily verbal hypotheses exist to explain such diversity. Yet, mathematical approaches formalizing verbal hypotheses would help deepen the understanding of brain and cognition evolution. With this aim, we combine elements of life history and metabolic theories to formulate a metabolically explicit mathematical model for brain life history evolution. We assume that some of the brain's energetic expense is due to production (learning) and maintenance (memory) of energy-extraction skills (or cognitive abilities, knowledge, information, etc.). We also assume that individuals use such skills to extract energy from the environment, and can allocate this energy to grow and maintain the body, including brain and reproductive tissues. The model can be used to ask what fraction of growth energy should be allocated at each age, given natural selection, to growing brain and other tissues under various biological settings. We apply the model to find uninvadable allocation strategies under a baseline setting ("me vs nature"), namely when energy-extraction challenges are environmentally determined and are overcome individually but possibly with maternal help, and use modern-human data to estimate model's parameter values. The resulting uninvadable strategies yield predictions for brain and body mass throughout ontogeny and for the ages at maturity, adulthood, and brain growth arrest. We find that: (1) a me-vs-nature setting is enough to generate adult brain and body mass of ancient human scale and a sequence of childhood, adolescence, and adulthood stages; (2) large brains are favored by intermediately challenging environments, moderately effective skills, and metabolically expensive memory; and (3) adult skill is proportional to brain mass when metabolic costs of memory saturate the brain metabolic rate allocated to skills.

Microstates in resting-state EEG: current status and future directions.

PubMed

Khanna, Arjun; Pascual-Leone, Alvaro; Michel, Christoph M; Farzan, Faranak

2015-02-01

Electroencephalography (EEG) is a powerful method of studying the electrophysiology of the brain with high temporal resolution. Several analytical approaches to extract information from the EEG signal have been proposed. One method, termed microstate analysis, considers the multichannel EEG recording as a series of quasi-stable "microstates" that are each characterized by a unique topography of electric potentials over the entire channel array. Because this technique simultaneously considers signals recorded from all areas of the cortex, it is capable of assessing the function of large-scale brain networks whose disruption is associated with several neuropsychiatric disorders. In this review, we first introduce the method of EEG microstate analysis. We then review studies that have discovered significant changes in the resting-state microstate series in a variety of neuropsychiatric disorders and behavioral states. We discuss the potential utility of this method in detecting neurophysiological impairments in disease and monitoring neurophysiological changes in response to an intervention. Finally, we discuss how the resting-state microstate series may reflect rapid switching among neural networks while the brain is at rest, which could represent activity of resting-state networks described by other neuroimaging modalities. We conclude by commenting on the current and future status of microstate analysis, and suggest that EEG microstates represent a promising neurophysiological tool for understanding and assessing brain network dynamics on a millisecond timescale in health and disease. Copyright © 2014 Elsevier Ltd. All rights reserved.

Microstates in Resting-State EEG: Current Status and Future Directions

PubMed Central

Khanna, Arjun; Pascual-Leone, Alvaro; Michel, Christoph M.; Farzan, Faranak

2015-01-01

Electroencephalography (EEG) is a powerful method of studying the electrophysiology of the brain with high temporal resolution. Several analytical approaches to extract information from the EEG signal have been proposed. One method, termed microstate analysis, considers the multichannel EEG recording as a series of quasi-stable “microstates” that are each characterized by a unique topography of electric potentials over the entire channel array. Because this technique simultaneously considers signals recorded from all areas of the cortex, it is capable of assessing the function of large-scale brain networks whose disruption is associated with several neuropsychiatric disorders. In this review, we first introduce the method of EEG microstate analysis. We then review studies that have discovered significant changes in the resting-state microstate series in a variety of neuropsychiatric disorders and behavioral states. We discuss the potential utility of this method in detecting neurophysiological impairments in disease and monitoring neurophysiological changes in response to an intervention. Finally, we discuss how the resting-state microstate series may reflect rapid switching among neural networks while the brain is at rest, which could represent activity of resting-state networks described by other neuroimaging modalities. We conclude by commenting on the current and future status of microstate analysis, and suggest that EEG microstates represent a promising neurophysiological tool for understanding and assessing brain network dynamics on a millisecond timescale in health and disease. PMID:25526823

A Feature-Free 30-Disease Pathological Brain Detection System by Linear Regression Classifier.

PubMed

Chen, Yi; Shao, Ying; Yan, Jie; Yuan, Ti-Fei; Qu, Yanwen; Lee, Elizabeth; Wang, Shuihua

2017-01-01

Alzheimer's disease patients are increasing rapidly every year. Scholars tend to use computer vision methods to develop automatic diagnosis system. (Background) In 2015, Gorji et al. proposed a novel method using pseudo Zernike moment. They tested four classifiers: learning vector quantization neural network, pattern recognition neural network trained by Levenberg-Marquardt, by resilient backpropagation, and by scaled conjugate gradient. This study presents an improved method by introducing a relatively new classifier-linear regression classification. Our method selects one axial slice from 3D brain image, and employed pseudo Zernike moment with maximum order of 15 to extract 256 features from each image. Finally, linear regression classification was harnessed as the classifier. The proposed approach obtains an accuracy of 97.51%, a sensitivity of 96.71%, and a specificity of 97.73%. Our method performs better than Gorji's approach and five other state-of-the-art approaches. Therefore, it can be used to detect Alzheimer's disease. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Development of a brain MRI-based hidden Markov model for dementia recognition

PubMed Central

2013-01-01

Background Dementia is an age-related cognitive decline which is indicated by an early degeneration of cortical and sub-cortical structures. Characterizing those morphological changes can help to understand the disease development and contribute to disease early prediction and prevention. But modeling that can best capture brain structural variability and can be valid in both disease classification and interpretation is extremely challenging. The current study aimed to establish a computational approach for modeling the magnetic resonance imaging (MRI)-based structural complexity of the brain using the framework of hidden Markov models (HMMs) for dementia recognition. Methods Regularity dimension and semi-variogram were used to extract structural features of the brains, and vector quantization method was applied to convert extracted feature vectors to prototype vectors. The output VQ indices were then utilized to estimate parameters for HMMs. To validate its accuracy and robustness, experiments were carried out on individuals who were characterized as non-demented and mild Alzheimer's diseased. Four HMMs were constructed based on the cohort of non-demented young, middle-aged, elder and demented elder subjects separately. Classification was carried out using a data set including both non-demented and demented individuals with a wide age range. Results The proposed HMMs have succeeded in recognition of individual who has mild Alzheimer's disease and achieved a better classification accuracy compared to other related works using different classifiers. Results have shown the ability of the proposed modeling for recognition of early dementia. Conclusion The findings from this research will allow individual classification to support the early diagnosis and prediction of dementia. By using the brain MRI-based HMMs developed in our proposed research, it will be more efficient, robust and can be easily used by clinicians as a computer-aid tool for validating imaging bio-markers for early prediction of dementia. PMID:24564961

Quantitative Evaluation of Automated Skull-Stripping Methods Applied to Contemporary and Legacy Images: Effects of Diagnosis, Bias Correction, and Slice Location

PubMed Central

Fennema-Notestine, Christine; Ozyurt, I. Burak; Clark, Camellia P.; Morris, Shaunna; Bischoff-Grethe, Amanda; Bondi, Mark W.; Jernigan, Terry L.; Fischl, Bruce; Segonne, Florent; Shattuck, David W.; Leahy, Richard M.; Rex, David E.; Toga, Arthur W.; Zou, Kelly H.; BIRN, Morphometry; Brown, Gregory G.

2008-01-01

Performance of automated methods to isolate brain from nonbrain tissues in magnetic resonance (MR) structural images may be influenced by MR signal inhomogeneities, type of MR image set, regional anatomy, and age and diagnosis of subjects studied. The present study compared the performance of four methods: Brain Extraction Tool (BET; Smith [2002]: Hum Brain Mapp 17:143–155); 3dIntracranial (Ward [1999] Milwaukee: Biophysics Research Institute, Medical College of Wisconsin; in AFNI); a Hybrid Watershed algorithm (HWA, Segonne et al. [2004] Neuroimage 22:1060–1075; in FreeSurfer); and Brain Surface Extractor (BSE, Sandor and Leahy [1997] IEEE Trans Med Imag 16:41–54; Shattuck et al. [2001] Neuroimage 13:856 – 876) to manually stripped images. The methods were applied to uncorrected and bias-corrected datasets; Legacy and Contemporary T1-weighted image sets; and four diagnostic groups (depressed, Alzheimer’s, young and elderly control). To provide a criterion for outcome assessment, two experts manually stripped six sagittal sections for each dataset in locations where brain and nonbrain tissue are difficult to distinguish. Methods were compared on Jaccard similarity coefficients, Hausdorff distances, and an Expectation-Maximization algorithm. Methods tended to perform better on contemporary datasets; bias correction did not significantly improve method performance. Mesial sections were most difficult for all methods. Although AD image sets were most difficult to strip, HWA and BSE were more robust across diagnostic groups compared with 3dIntracranial and BET. With respect to specificity, BSE tended to perform best across all groups, whereas HWA was more sensitive than other methods. The results of this study may direct users towards a method appropriate to their T1-weighted datasets and improve the efficiency of processing for large, multisite neuroimaging studies. PMID:15986433

Heart rate calculation from ensemble brain wave using wavelet and Teager-Kaiser energy operator.

PubMed

Srinivasan, Jayaraman; Adithya, V

2015-01-01

Electroencephalogram (EEG) signal artifacts are caused by various factors, such as, Electro-oculogram (EOG), Electromyogram (EMG), Electrocardiogram (ECG), movement artifact and line interference. The relatively high electrical energy cardiac activity causes EEG artifacts. In EEG signal processing the general approach is to remove the ECG signal. In this paper, we introduce an automated method to extract the ECG signal from EEG using wavelet and Teager-Kaiser energy operator for R-peak enhancement and detection. From the detected R-peaks the heart rate (HR) is calculated for clinical diagnosis. To check the efficiency of our method, we compare the HR calculated from ECG signal recorded in synchronous with EEG. The proposed method yields a mean error of 1.4% for the heart rate and 1.7% for mean R-R interval. The result illustrates that, proposed method can be used for ECG extraction from single channel EEG and used in clinical diagnosis like estimation for stress analysis, fatigue, and sleep stages classification studies as a multi-model system. In addition, this method eliminates the dependence of additional synchronous ECG in extraction of ECG from EEG signal process.

A novel method and software for automatically classifying Alzheimer's disease patients by magnetic resonance imaging analysis.

PubMed

Previtali, F; Bertolazzi, P; Felici, G; Weitschek, E

2017-05-01

The cause of the Alzheimer's disease is poorly understood and to date no treatment to stop or reverse its progression has been discovered. In developed countries, the Alzheimer's disease is one of the most financially costly diseases due to the requirement of continuous treatments as well as the need of assistance or supervision with the most cognitively demanding activities as time goes by. The objective of this work is to present an automated approach for classifying the Alzheimer's disease from magnetic resonance imaging (MRI) patient brain scans. The method is fast and reliable for a suitable and straightforward deploy in clinical applications for helping diagnosing and improving the efficacy of medical treatments by recognising the disease state of the patient. Many features can be extracted from magnetic resonance images, but most are not suitable for the classification task. Therefore, we propose a new feature extraction technique from patients' MRI brain scans that is based on a recent computer vision method, called Oriented FAST and Rotated BRIEF. The extracted features are processed with the definition and the combination of two new metrics, i.e., their spatial position and their distribution around the patient's brain, and given as input to a function-based classifier (i.e., Support Vector Machines). We report the comparison with recent state-of-the-art approaches on two established medical data sets (ADNI and OASIS). In the case of binary classification (case vs control), our proposed approach outperforms most state-of-the-art techniques, while having comparable results with the others. Specifically, we obtain 100% (97%) of accuracy, 100% (97%) sensitivity and 99% (93%) specificity for the ADNI (OASIS) data set. When dealing with three or four classes (i.e., classification of all subjects) our method is the only one that reaches remarkable performance in terms of classification accuracy, sensitivity and specificity, outperforming the state-of-the-art approaches. In particular, in the ADNI data set we obtain a classification accuracy, sensitivity and specificity of 99% while in the OASIS data set a classification accuracy and sensitivity of 77% and specificity of 79% when dealing with four classes. By providing a quantitative comparison on the two established data sets with many state-of-the-art techniques, we demonstrated the effectiveness of our proposed approach in classifying the Alzheimer's disease from MRI patient brain scans. Copyright © 2017 Elsevier B.V. All rights reserved.

Robust space-time extraction of ventricular surface evolution using multiphase level sets

NASA Astrophysics Data System (ADS)

Drapaca, Corina S.; Cardenas, Valerie; Studholme, Colin

2004-05-01

This paper focuses on the problem of accurately extracting the CSF-tissue boundary, particularly around the ventricular surface, from serial structural MRI of the brain acquired in imaging studies of aging and dementia. This is a challenging problem because of the common occurrence of peri-ventricular lesions which locally alter the appearance of white matter. We examine a level set approach which evolves a four dimensional description of the ventricular surface over time. This has the advantage of allowing constraints on the contour in the temporal dimension, improving the consistency of the extracted object over time. We follow the approach proposed by Chan and Vese which is based on the Mumford and Shah model and implemented using the Osher and Sethian level set method. We have extended this to the 4 dimensional case to propagate a 4D contour toward the tissue boundaries through the evolution of a 5D implicit function. For convergence we use region-based information provided by the image rather than the gradient of the image. This is adapted to allow intensity contrast changes between time frames in the MRI sequence. Results on time sequences of 3D brain MR images are presented and discussed.

The potential role of Morus alba leaves extract on the brain of mice infected with Schistosoma mansoni.

PubMed

Bauomy, Amira A

2014-01-01

Schistosomiasis is a neglected tropical disease which is associated with neuropsychiatric and neuropathological disorders. Herein, the main goal of the presented work is to investigate the effect of Morus alba leaves extract in mice brain infected with Schistosoma mansoni. Since, the resistance of Schistosomes to antischistosomal drug (praziquantel) has been examined, schistosomiasis induced brain oxidative stress as evidenced by the decrease of glutathione level, total antioxidant capacity and the activity of catalase significantly, while a significant elevation in the levels of nitrite/nitrate and malondialdhyde. In addition, the infection resulted in neurochemical disturbances, the main inhibitory amino acid, γ- aminobutyric acid level was decreased. In contrast, the level of chloride ions and acetylcholine esterase activity were significantly increased. Moreover, the histopathological section showed some impairments in the brain. The treatment with Morus alba leaves extract ameliorated the induced disturbances in schistosome-infected mice where the levels of non-enzymatic and enzymatic antioxidants were elevated. On the other hand, the levels of nitrite/nitrate and malondialdhyde were significantly reduced. Likewise, treatment of mice with Morus alba leaves extract improved the altered levels of γ- aminobutyric acid level and chloride ion. Also, it improved the recorded impairments of the histopathological section in the brain of schistosome infected mice.

Development of solid-phase microextraction coupled with liquid chromatography for analysis of tramadol in brain tissue using its molecularly imprinted polymer.

PubMed

Habibi-Khorasani, Monireh; Mohammadpour, Amir Hooshang; Mohajeri, Seyed Ahmad

2017-02-01

In this work, performance of a molecularly imprinted polymer (MIP) as a selective solid-phase microextraction sorbent for the extraction and enrichment of tramadol in aqueous solution and rabbit brain tissue, is described. Binding properties of MIPs were studied in comparison with their nonimprinted polymer (NIP). Ten milligrams of the optimized MIP was then evaluated as a sorbent, for preconcentration, in molecularly imprinted solid-phase microextraction (MISPME) of tramadol from aqueous solution and rabbit brain tissue. The analytical method was calibrated in the range of 0.004 ppm (4 ng mL -1 ) and 10 ppm (10 μg mL -1 ) in aqueous media and in the ranges of 0.01 and 10 ppm in rabbit brain tissue, respectively. The results indicated significantly higher binding affinity of MIPs to tramadol, in comparison with NIP. The MISPME procedure was developed and optimized with a recovery of 81.12-107.54% in aqueous solution and 76.16-91.20% in rabbit brain tissue. The inter- and intra-day variation values were <8.24 and 5.06%, respectively. Finally the calibrated method was applied for determination of tramadol in real rabbit brain tissue samples after administration of a lethal dose. Our data demonstrated the potential of MISPME for rapid, sensitive and cost-effective sample analysis. Copyright © 2016 John Wiley & Sons, Ltd.

Effective Diagnosis of Alzheimer's Disease by Means of Association Rules

NASA Astrophysics Data System (ADS)

Chaves, R.; Ramírez, J.; Górriz, J. M.; López, M.; Salas-Gonzalez, D.; Illán, I.; Segovia, F.; Padilla, P.

In this paper we present a novel classification method of SPECT images for the early diagnosis of the Alzheimer's disease (AD). The proposed method is based on Association Rules (ARs) aiming to discover interesting associations between attributes contained in the database. The system uses firstly voxel-as-features (VAF) and Activation Estimation (AE) to find tridimensional activated brain regions of interest (ROIs) for each patient. These ROIs act as inputs to secondly mining ARs between activated blocks for controls, with a specified minimum support and minimum confidence. ARs are mined in supervised mode, using information previously extracted from the most discriminant rules for centering interest in the relevant brain areas, reducing the computational requirement of the system. Finally classification process is performed depending on the number of previously mined rules verified by each subject, yielding an up to 95.87% classification accuracy, thus outperforming recent developed methods for AD diagnosis.

Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis

PubMed Central

Xu, Rui; Zhen, Zonglei; Liu, Jia

2010-01-01

Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies. PMID:21152081

A Computer-Aided Analysis Method of SPECT Brain Images for Quantitative Treatment Monitoring: Performance Evaluations and Clinical Applications.

PubMed

Zheng, Xiujuan; Wei, Wentao; Huang, Qiu; Song, Shaoli; Wan, Jieqing; Huang, Gang

2017-01-01

The objective and quantitative analysis of longitudinal single photon emission computed tomography (SPECT) images are significant for the treatment monitoring of brain disorders. Therefore, a computer aided analysis (CAA) method is introduced to extract a change-rate map (CRM) as a parametric image for quantifying the changes of regional cerebral blood flow (rCBF) in longitudinal SPECT brain images. The performances of the CAA-CRM approach in treatment monitoring are evaluated by the computer simulations and clinical applications. The results of computer simulations show that the derived CRMs have high similarities with their ground truths when the lesion size is larger than system spatial resolution and the change rate is higher than 20%. In clinical applications, the CAA-CRM approach is used to assess the treatment of 50 patients with brain ischemia. The results demonstrate that CAA-CRM approach has a 93.4% accuracy of recovered region's localization. Moreover, the quantitative indexes of recovered regions derived from CRM are all significantly different among the groups and highly correlated with the experienced clinical diagnosis. In conclusion, the proposed CAA-CRM approach provides a convenient solution to generate a parametric image and derive the quantitative indexes from the longitudinal SPECT brain images for treatment monitoring.

A model for brain life history evolution

PubMed Central

Lehmann, Laurent

2017-01-01

Complex cognition and relatively large brains are distributed across various taxa, and many primarily verbal hypotheses exist to explain such diversity. Yet, mathematical approaches formalizing verbal hypotheses would help deepen the understanding of brain and cognition evolution. With this aim, we combine elements of life history and metabolic theories to formulate a metabolically explicit mathematical model for brain life history evolution. We assume that some of the brain’s energetic expense is due to production (learning) and maintenance (memory) of energy-extraction skills (or cognitive abilities, knowledge, information, etc.). We also assume that individuals use such skills to extract energy from the environment, and can allocate this energy to grow and maintain the body, including brain and reproductive tissues. The model can be used to ask what fraction of growth energy should be allocated at each age, given natural selection, to growing brain and other tissues under various biological settings. We apply the model to find uninvadable allocation strategies under a baseline setting (“me vs nature”), namely when energy-extraction challenges are environmentally determined and are overcome individually but possibly with maternal help, and use modern-human data to estimate model’s parameter values. The resulting uninvadable strategies yield predictions for brain and body mass throughout ontogeny and for the ages at maturity, adulthood, and brain growth arrest. We find that: (1) a me-vs-nature setting is enough to generate adult brain and body mass of ancient human scale and a sequence of childhood, adolescence, and adulthood stages; (2) large brains are favored by intermediately challenging environments, moderately effective skills, and metabolically expensive memory; and (3) adult skill is proportional to brain mass when metabolic costs of memory saturate the brain metabolic rate allocated to skills. PMID:28278153

Comparison of the antibacterial efficiency of neem leaf extracts, grape seed extracts and 3% sodium hypochlorite against E. feacalis – An in vitro study

PubMed Central

Ghonmode, Wasudeo Namdeo; Balsaraf, Omkar D; Tambe, Varsha H; Saujanya, K P; Patil, Ashishkumar K; Kakde, Deepak D

2013-01-01

Background: E. faecalis is the predominant micro-organism recovered from root canal of the teeth where previous endodontic treatment has failed. Thorough debridement and complete elimination of micro-organisms are objectives of an effective endodontic treatment. For many years, intracanal irrigants have been used as an adjunct to enhance antimicrobial effect of cleaning and shaping in endodontics. The constant increase in antibiotic-resistant strains and side-effects of synthetic drugs has promoted researchers to look for herbal alternatives. For thousands of years humans have sought to fortify their health and cure various illnesses with herbal remedies, but only few have been tried and tested to withstand modern scientific scrutiny. The present study was aimed to evaluate alternative, inexpensive simple and effective means of sanitization of the root canal systems. The antimicrobial efficacy of herbal alternatives as endodontic irrigants is evaluated and compared with the standard irrigant sodium hypochlorite. Materials & Methods: Neem leaf extracts, grape seed extracts, 3% Sodium hypochlorite, absolute ethanol, Enterococcus faecalis (ATCC 29212) cultures, Brain heart infusion media. The agar diffusion test was performed in brain heart infusion media and broth. The agar diffusion test was used to measure the zone of inhibition. Results: Neem leaf extracts and grape seed extracts showed zones of inhibition suggesting that they had anti-microbial properties. Neem leaf extracts showed significantly greater zones of inhibition than 3% sodium hypochlorite. Also interestingly grape seed extracts showed zones of inhibition but were not as significant as of neem extracts. Conclusion: Under the limitations of this study, it was concluded that neem leaf extract has a significant antimicrobial effect against E. faecalis. Microbial inhibition potential of neem leaf extract observed in this study opens perspectives for its use as an intracanal medication. How to cite this article: Ghonmode WN, Balsaraf OD, Tambe VH, Saujanya KP, Patil AK, Kakde DD. Comparison of the antibacterial efficiency of neem leaf extracts, grape seed extracts and 3% sodium hypochlorite against E. feacalis – An in vitro study. J Int Oral Health 2013; 5(6):61-6 . PMID:24453446

Freeze-Drying as Sample Preparation for Micellar Electrokinetic Capillary Chromatography – Electrochemical Separations of Neurochemicals in Drosophila Brains

PubMed Central

Berglund, E. Carina; Kuklinski, Nicholas J.; Karagündüz, Ekin; Ucar, Kubra; Hanrieder, Jörg; Ewing, Andrew G.

2013-01-01

Micellar electrokinetic capillary chromatography with electrochemical detection has been used to quantify biogenic amines in freeze-dried Drosophila melanogaster brains. Freeze drying samples offers a way to preserve the biological sample while making dissection of these tiny samples easier and faster. Fly samples were extracted in cold acetone and dried in a rotary evaporator. Extraction and drying times were optimized in order to avoid contamination by red-pigment from the fly eyes and still have intact brain structures. Single freeze-dried fly-brain samples were found to produce representative electropherograms as a single hand-dissected brain sample. Utilizing the faster dissection time that freeze drying affords, the number of brains in a fixed homogenate volume can be increased to concentrate the sample. Thus, concentrated brain samples containing five or fifteen preserved brains were analyzed for their neurotransmitter content, and five analytes; dopamine N-acetyloctopamine, Nacetylserotonin, N-acetyltyramine, N-acetyldopamine were found to correspond well with previously reported values. PMID:23387977

The preprocessed connectomes project repository of manually corrected skull-stripped T1-weighted anatomical MRI data.

PubMed

Puccio, Benjamin; Pooley, James P; Pellman, John S; Taverna, Elise C; Craddock, R Cameron

2016-10-25

Skull-stripping is the procedure of removing non-brain tissue from anatomical MRI data. This procedure can be useful for calculating brain volume and for improving the quality of other image processing steps. Developing new skull-stripping algorithms and evaluating their performance requires gold standard data from a variety of different scanners and acquisition methods. We complement existing repositories with manually corrected brain masks for 125 T1-weighted anatomical scans from the Nathan Kline Institute Enhanced Rockland Sample Neurofeedback Study. Skull-stripped images were obtained using a semi-automated procedure that involved skull-stripping the data using the brain extraction based on nonlocal segmentation technique (BEaST) software, and manually correcting the worst results. Corrected brain masks were added into the BEaST library and the procedure was repeated until acceptable brain masks were available for all images. In total, 85 of the skull-stripped images were hand-edited and 40 were deemed to not need editing. The results are brain masks for the 125 images along with a BEaST library for automatically skull-stripping other data. Skull-stripped anatomical images from the Neurofeedback sample are available for download from the Preprocessed Connectomes Project. The resulting brain masks can be used by researchers to improve preprocessing of the Neurofeedback data, as training and testing data for developing new skull-stripping algorithms, and for evaluating the impact on other aspects of MRI preprocessing. We have illustrated the utility of these data as a reference for comparing various automatic methods and evaluated the performance of the newly created library on independent data.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ogden, K; O’Dwyer, R; Bradford, T

Purpose: To reduce differences in features calculated from MRI brain scans acquired at different field strengths with or without Gadolinium contrast. Methods: Brain scans were processed for 111 epilepsy patients to extract hippocampus and thalamus features. Scans were acquired on 1.5 T scanners with Gadolinium contrast (group A), 1.5T scanners without Gd (group B), and 3.0 T scanners without Gd (group C). A total of 72 features were extracted. Features were extracted from original scans and from scans where the image pixel values were rescaled to the mean of the hippocampi and thalami values. For each data set, cluster analysismore » was performed on the raw feature set and for feature sets with normalization (conversion to Z scores). Two methods of normalization were used: The first was over all values of a given feature, and the second by normalizing within the patient group membership. The clustering software was configured to produce 3 clusters. Group fractions in each cluster were calculated. Results: For features calculated from both the non-rescaled and rescaled data, cluster membership was identical for both the non-normalized and normalized data sets. Cluster 1 was comprised entirely of Group A data, Cluster 2 contained data from all three groups, and Cluster 3 contained data from only groups 1 and 2. For the categorically normalized data sets there was a more uniform distribution of group data in the three Clusters. A less pronounced effect was seen in the rescaled image data features. Conclusion: Image Rescaling and feature renormalization can have a significant effect on the results of clustering analysis. These effects are also likely to influence the results of supervised machine learning algorithms. It may be possible to partly remove the influence of scanner field strength and the presence of Gadolinium based contrast in feature extraction for radiomics applications.« less

The muscle protein dysferlin accumulates in the Alzheimer brain

PubMed Central

Palamand, Divya; Strider, Jeff; Milone, Margherita; Pestronk, Alan

2006-01-01

Dysferlin is a transmembrane protein that is highly expressed in muscle. Dysferlin mutations cause limb-girdle dystrophy type 2B, Miyoshi myopathy and distal anterior compartment myopathy. Dysferlin has also been described in neural tissue. We studied dysferlin distribution in the brains of patients with Alzheimer disease (AD) and controls. Twelve brains, staged using the Clinical Dementia Rating were examined: 9 AD cases (mean age: 85.9 years and mean disease duration: 8.9 years), and 3 age-matched controls (mean age: 87.5 years). Dysferlin is a cytoplasmic protein in the pyramidal neurons of normal and AD brains. In addition, there were dysferlin-positive dystrophic neurites within Aβ plaques in the AD brain, distinct from tau-positive neurites. Western blots of total brain protein (RIPA) and sequential extraction buffers (high salt, high salt/Triton X-100, SDS and formic acid) of increasing protein extraction strength were performed to examine solubility state. In RIPA fractions, dysferlin was seen as 230–272 kDa bands in normal and AD brains. In serial extractions, there was a shift of dysferlin from soluble phase in high salt/Triton X-100 to the more insoluble SDS fraction in AD. Dysferlin is a new protein described in the AD brain that accumulates in association with neuritic plaques. In muscle, dysferlin plays a role in the repair of muscle membrane damage. The accumulation of dysferlin in the AD brain may be related to the inability of neurons to repair damage due to Aβ deposits accumulating in the AD brain. PMID:17024495

Periodic component analysis as a spatial filter for SSVEP-based brain-computer interface.

PubMed

Kiran Kumar, G R; Reddy, M Ramasubba

2018-06-08

Traditional Spatial filters used for steady-state visual evoked potential (SSVEP) extraction such as minimum energy combination (MEC) require the estimation of the background electroencephalogram (EEG) noise components. Even though this leads to improved performance in low signal to noise ratio (SNR) conditions, it makes such algorithms slow compared to the standard detection methods like canonical correlation analysis (CCA) due to the additional computational cost. In this paper, Periodic component analysis (πCA) is presented as an alternative spatial filtering approach to extract the SSVEP component effectively without involving extensive modelling of the noise. The πCA can separate out components corresponding to a given frequency of interest from the background electroencephalogram (EEG) by capturing the temporal information and does not generalize SSVEP based on rigid templates. Data from ten test subjects were used to evaluate the proposed method and the results demonstrate that the periodic component analysis acts as a reliable spatial filter for SSVEP extraction. Statistical tests were performed to validate the results. The experimental results show that πCA provides significant improvement in accuracy compared to standard CCA and MEC in low SNR conditions. The results demonstrate that πCA provides better detection accuracy compared to CCA and on par with that of MEC at a lower computational cost. Hence πCA is a reliable and efficient alternative detection algorithm for SSVEP based brain-computer interface (BCI). Copyright © 2018. Published by Elsevier B.V.

Depth discrimination in acousto-optic cerebral blood flow measurement simulation

NASA Astrophysics Data System (ADS)

Tsalach, A.; Schiffer, Z.; Ratner, E.; Breskin, I.; Zeitak, R.; Shechter, R.; Balberg, M.

2016-03-01

Monitoring cerebral blood flow (CBF) is crucial, as inadequate perfusion, even for relatively short periods of time, may lead to brain damage or even death. Thus, significant research efforts are directed at developing reliable monitoring tools that will enable continuous, bed side, simple and cost-effective monitoring of CBF. All existing non invasive bed side monitoring methods, which are mostly NIRS based, such as Laser Doppler or DCS, tend to underestimate CBF in adults, due to the indefinite effect of extra-cerebral tissues on the obtained signal. If those are to find place in day to day clinical practice, the contribution of extra-cerebral tissues must be eliminated and data from the depth (brain) should be extracted and discriminated. Recently, a novel technique, based on ultrasound modulation of light was developed for non-invasive, continuous CBF monitoring (termed ultrasound-tagged light (UTL or UT-NIRS)), and shown to correlate with readings of 133Xe SPECT and laser Doppler. We have assembled a comprehensive computerized simulation, modeling this acousto-optic technique in a highly scattering media. Using the combination of light and ultrasound, we show how depth information may be extracted, thus distinguishing between flow patterns taking place at different depths. Our algorithm, based on the analysis of light modulated by ultrasound, is presented and examined in a computerized simulation. Distinct depth discrimination ability is presented, suggesting that using such method one can effectively nullify the extra-cerebral tissues influence on the obtained signals, and specifically extract cerebral flow data.

Blood-brain barrier specific permeability assay reveals N-methylated tyramine derivatives in standardised leaf extracts and herbal products of Ginkgo biloba.

PubMed

Könczöl, Árpád; Rendes, Kata; Dékány, Miklós; Müller, Judit; Riethmüller, Eszter; Balogh, György Tibor

2016-11-30

The linkage between the central nervous system availability and neuropharmacological activity of the constituents of Ginkgo biloba L. extracts (GBE) is still incomplete. In this study, the in vitro blood-brain barrier (BBB) permeability profile of the standardised GBE was investigated by the parallel artificial membrane permeability assay (PAMPA). Biomarkers, such as terpene trilactones, flavonoid aglycones and ginkgotoxin exerted moderate or good BBB-permeability potential (BBB+), while glycosides and biflavones were predicted as unable to pass the BBB. N-methyltyramine (NMT) and N,N-dimethyltyramine or hordenine (Hor) were identified among BBB+ compounds, while subsequent direct HRMS analysis revealed tyramine (Tyr) and N,N,N-trimethyltyramine or candicine (Can) in GBE as trace constituents. Distribution of Tyr, NMT, Hor and Can was determined by a validated ion-exchange mechanism-based liquid chromatography-electrospray ionisation-mass spectrometry (LC-ESI-MS) method in G. biloba samples, such as herbal drugs and dietary supplements. The total content of the four tyramine derivatives in various GBEs ranged from 7.3 up to 6357μg/g dry extract with NMT and Hor as most abundant ones. Considering the pharmacological activities and the revealed fluctuation in the concentration of the analysed adrenergic protoalkaloids, the presented rapid LC-ESI-MS method is proposed for monitoring of the levels of Tyr, NMT, Hor and Can in G. biloba products. Copyright © 2016 Elsevier B.V. All rights reserved.

Vessel segmentation in 4D arterial spin labeling magnetic resonance angiography images of the brain

NASA Astrophysics Data System (ADS)

Phellan, Renzo; Lindner, Thomas; Falcão, Alexandre X.; Forkert, Nils D.

2017-03-01

4D arterial spin labeling magnetic resonance angiography (4D ASL MRA) is a non-invasive and safe modality for cerebrovascular imaging procedures. It uses the patient's magnetically labeled blood as intrinsic contrast agent, so that no external contrast media is required. It provides important 3D structure and blood flow information but a sufficient cerebrovascular segmentation is important since it can help clinicians to analyze and diagnose vascular diseases faster, and with higher confidence as compared to simple visual rating of raw ASL MRA images. This work presents a new method for automatic cerebrovascular segmentation in 4D ASL MRA images of the brain. In this process images are denoised, corresponding image label/control image pairs of the 4D ASL MRA sequences are subtracted, and temporal intensity averaging is used to generate a static representation of the vascular system. After that, sets of vessel and background seeds are extracted and provided as input for the image foresting transform algorithm to segment the vascular system. Four 4D ASL MRA datasets of the brain arteries of healthy subjects and corresponding time-of-flight (TOF) MRA images were available for this preliminary study. For evaluation of the segmentation results of the proposed method, the cerebrovascular system was automatically segmented in the high-resolution TOF MRA images using a validated algorithm and the segmentation results were registered to the 4D ASL datasets. Corresponding segmentation pairs were compared using the Dice similarity coefficient (DSC). On average, a DSC of 0.9025 was achieved, indicating that vessels can be extracted successfully from 4D ASL MRA datasets by the proposed segmentation method.

3D variational brain tumor segmentation on a clustered feature set

NASA Astrophysics Data System (ADS)

Popuri, Karteek; Cobzas, Dana; Jagersand, Martin; Shah, Sirish L.; Murtha, Albert

2009-02-01

Tumor segmentation from MRI data is a particularly challenging and time consuming task. Tumors have a large diversity in shape and appearance with intensities overlapping the normal brain tissues. In addition, an expanding tumor can also deflect and deform nearby tissue. Our work addresses these last two difficult problems. We use the available MRI modalities (T1, T1c, T2) and their texture characteristics to construct a multi-dimensional feature set. Further, we extract clusters which provide a compact representation of the essential information in these features. The main idea in this paper is to incorporate these clustered features into the 3D variational segmentation framework. In contrast to the previous variational approaches, we propose a segmentation method that evolves the contour in a supervised fashion. The segmentation boundary is driven by the learned inside and outside region voxel probabilities in the cluster space. We incorporate prior knowledge about the normal brain tissue appearance, during the estimation of these region statistics. In particular, we use a Dirichlet prior that discourages the clusters in the ventricles to be in the tumor and hence better disambiguate the tumor from brain tissue. We show the performance of our method on real MRI scans. The experimental dataset includes MRI scans, from patients with difficult instances, with tumors that are inhomogeneous in appearance, small in size and in proximity to the major structures in the brain. Our method shows good results on these test cases.

Achieving Accurate Automatic Sleep Staging on Manually Pre-processed EEG Data Through Synchronization Feature Extraction and Graph Metrics.

PubMed

Chriskos, Panteleimon; Frantzidis, Christos A; Gkivogkli, Polyxeni T; Bamidis, Panagiotis D; Kourtidou-Papadeli, Chrysoula

2018-01-01

Sleep staging, the process of assigning labels to epochs of sleep, depending on the stage of sleep they belong, is an arduous, time consuming and error prone process as the initial recordings are quite often polluted by noise from different sources. To properly analyze such data and extract clinical knowledge, noise components must be removed or alleviated. In this paper a pre-processing and subsequent sleep staging pipeline for the sleep analysis of electroencephalographic signals is described. Two novel methods of functional connectivity estimation (Synchronization Likelihood/SL and Relative Wavelet Entropy/RWE) are comparatively investigated for automatic sleep staging through manually pre-processed electroencephalographic recordings. A multi-step process that renders signals suitable for further analysis is initially described. Then, two methods that rely on extracting synchronization features from electroencephalographic recordings to achieve computerized sleep staging are proposed, based on bivariate features which provide a functional overview of the brain network, contrary to most proposed methods that rely on extracting univariate time and frequency features. Annotation of sleep epochs is achieved through the presented feature extraction methods by training classifiers, which are in turn able to accurately classify new epochs. Analysis of data from sleep experiments on a randomized, controlled bed-rest study, which was organized by the European Space Agency and was conducted in the "ENVIHAB" facility of the Institute of Aerospace Medicine at the German Aerospace Center (DLR) in Cologne, Germany attains high accuracy rates, over 90% based on ground truth that resulted from manual sleep staging by two experienced sleep experts. Therefore, it can be concluded that the above feature extraction methods are suitable for semi-automatic sleep staging.

Achieving Accurate Automatic Sleep Staging on Manually Pre-processed EEG Data Through Synchronization Feature Extraction and Graph Metrics

PubMed Central

Chriskos, Panteleimon; Frantzidis, Christos A.; Gkivogkli, Polyxeni T.; Bamidis, Panagiotis D.; Kourtidou-Papadeli, Chrysoula

2018-01-01

Sleep staging, the process of assigning labels to epochs of sleep, depending on the stage of sleep they belong, is an arduous, time consuming and error prone process as the initial recordings are quite often polluted by noise from different sources. To properly analyze such data and extract clinical knowledge, noise components must be removed or alleviated. In this paper a pre-processing and subsequent sleep staging pipeline for the sleep analysis of electroencephalographic signals is described. Two novel methods of functional connectivity estimation (Synchronization Likelihood/SL and Relative Wavelet Entropy/RWE) are comparatively investigated for automatic sleep staging through manually pre-processed electroencephalographic recordings. A multi-step process that renders signals suitable for further analysis is initially described. Then, two methods that rely on extracting synchronization features from electroencephalographic recordings to achieve computerized sleep staging are proposed, based on bivariate features which provide a functional overview of the brain network, contrary to most proposed methods that rely on extracting univariate time and frequency features. Annotation of sleep epochs is achieved through the presented feature extraction methods by training classifiers, which are in turn able to accurately classify new epochs. Analysis of data from sleep experiments on a randomized, controlled bed-rest study, which was organized by the European Space Agency and was conducted in the “ENVIHAB” facility of the Institute of Aerospace Medicine at the German Aerospace Center (DLR) in Cologne, Germany attains high accuracy rates, over 90% based on ground truth that resulted from manual sleep staging by two experienced sleep experts. Therefore, it can be concluded that the above feature extraction methods are suitable for semi-automatic sleep staging. PMID:29628883

Protective role of Scoparia dulcis plant extract on brain antioxidant status and lipidperoxidation in STZ diabetic male Wistar rats.

PubMed

Pari, Leelavinothan; Latha, Muniappan

2004-11-02

The aim of the study was to investigate the effect of aqueous extract of Scoparia dulcis on the occurrence of oxidative stress in the brain of rats during diabetes by measuring the extent of oxidative damage as well as the status of the antioxidant defense system. Aqueous extract of Scoparia dulcis plant was administered orally (200 mg/kg body weight) and the effect of extract on blood glucose, plasma insulin and the levels of thiobarbituric acid reactive substances (TBARS), hydroperoxides, superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx), glutathione-S-transferase (GST) and reduced glutathione (GSH) were estimated in streptozotocin (STZ) induced diabetic rats. Glibenclamide was used as standard reference drug. A significant increase in the activities of plasma insulin, superoxide dismutase, catalase, glutathione peroxidase, glutathione-S-transferase and reduced glutathione was observed in brain on treatment with 200 mg/kg body weight of Scoparia dulcis plant extract (SPEt) and glibenclamide for 6 weeks. Both the treated groups showed significant decrease in TBARS and hydroperoxides formation in brain, suggesting its role in protection against lipidperoxidation induced membrane damage. Since the study of induction of the antioxidant enzymes is considered to be a reliable marker for evaluating the antiperoxidative efficacy of the medicinal plant, these findings suggest a possible antiperoxidative role for Scoparia dulcis plant extract. Hence, in addition to antidiabetic effect, Scoparia dulcis possess antioxidant potential that may be used for therapeutic purposes.

Rapid and reliable quantitation of amino acids and myo-inositol in mouse brain by high performance liquid chromatography and tandem mass spectrometry.

PubMed

Bathena, Sai P; Huang, Jiangeng; Epstein, Adrian A; Gendelman, Howard E; Boska, Michael D; Alnouti, Yazen

2012-04-15

Amino acids and myo-inositol have long been proposed as putative biomarkers for neurodegenerative diseases. Accurate measures and stability have precluded their selective use. To this end, a sensitive liquid chromatography tandem mass spectrometry (LC-MS/MS) method based on multiple reaction monitoring was developed to simultaneously quantify glutamine, glutamate, γ-aminobutyric acid (GABA), aspartic acid, N-acetyl aspartic acid, taurine, choline, creatine, phosphocholine and myo-inositol in mouse brain by methanol extractions. Chromatography was performed using a hydrophilic interaction chromatography silica column within in a total run time of 15 min. The validated method is selective, sensitive, accurate, and precise. The method has a limit of quantification ranging from 2.5 to 20 ng/ml for a range of analytes and a dynamic range from 2.5-20 to 500-4000 ng/ml. This LC-MS/MS method was validated for biomarker discovery in models of human neurological disorders. Copyright © 2012 Elsevier B.V. All rights reserved.

Anti-amnesic effects of Ganoderma species: A possible cholinergic and antioxidant mechanism.

PubMed

Kaur, Ravneet; Singh, Varinder; Shri, Richa

2017-08-01

Mushrooms are valued for their nutritional as well as medicinal properties. Ganoderma species are used traditionally to treat neurological disorders but scientific evidence for this is insufficient. The present study was designed to systematically evaluate the anti-amnesic effect of selected Ganoderma species i.e. G. mediosinense and G. ramosissimum. Extracts of selected mushroom species were evaluated for their antioxidant activity and acetylcholinesterase (AChE) inhibition using in-vitro assays (DPPH and Ellman tests respectively). The anti-amnesic potential of the most active extract (i.e. 70% methanol extract of G. mediosinense) was confirmed using mouse model of scopolamine-induced amnesia. Mice were treated with bioactive extract and donepezil once orally before the induction of amnesia. Cognitive functions were evaluated using passive shock avoidance (PSA) and novel object recognition (NOR) tests. The effect on brain AChE activity, brain oxidative stress (TBARS level) and neuronal damage (H & E staining) were also assessed. In-vitro results showed strong antioxidant and AChE inhibitory activities by G. mediosinense extract (GME). Therefore, it was selected for in-vivo studies. GME pre-treatment (800mg/kg, p.o.) reversed the effect of scopolamine in mice, evident by significant decrease (p <0.05) in the transfer latency time and increase in object recognition index in PSA and NOR, respectively. GME significantly reduced the brain AChE activity and oxidative stress. Histopathological examination of brain tissues showed decrease in vacuolated cytoplasm and increase in pyramidal cells in brain hippocampal and cortical regions. GME exerts anti-amnesic effect through AChE inhibition and antioxidant mechanisms. Copyright © 2017. Published by Elsevier Masson SAS.

Distribution Assessments of Coumarins from Angelicae Pubescentis Radix in Rat Cerebrospinal Fluid and Brain by Liquid Chromatography Tandem Mass Spectrometry Analysis.

PubMed

Yang, Yan-Fang; Zhang, Lei; Yang, Xiu-Wei

2018-01-20

Angelicae Pubescentis Radix (APR) is a widely-used traditional Chinese medicine. Pharmacological studies have begun to probe its biological activities on neurological disorders recently. To assess the brain penetration and distribution of APR, a validated ultra-performance liquid chromatography tandem mass spectrometry method was applied to the simultaneous determinations of the main coumarins from APR in the rat cerebrospinal fluid (CSF) and brain after oral administration of APR extract, including psoralen, xanthotoxin, bergapten, isoimperatorin, columbianetin, columbianetin acetate, columbianadin, oxypeucedanin hydrate, angelol B, osthole, meranzin hydrate and nodakenetin. Most of the tested coumarins entered the rat CSF and brain quickly, and double-peak phenomena in concentration-time curves were similar to those of their plasma pharmacokinetics. Columbianetin had the highest concentration in the CSF and brain, while psoralen and columbianetin acetate had the largest percent of CSF/plasma and brain/plasma, indicating that these three coumarins may be worthy of further research on the possible nervous effects. Correlations between the in vivo brain distributions and plasma pharmacokinetics of these coumarins were well verified. These results provided valuable information for the overall in vivo brain distribution characteristics of APR and also for its further studies on the active substances for the central nervous system.

Epileptic Seizures Prediction Using Machine Learning Methods

PubMed Central

Usman, Syed Muhammad

2017-01-01

Epileptic seizures occur due to disorder in brain functionality which can affect patient's health. Prediction of epileptic seizures before the beginning of the onset is quite useful for preventing the seizure by medication. Machine learning techniques and computational methods are used for predicting epileptic seizures from Electroencephalograms (EEG) signals. However, preprocessing of EEG signals for noise removal and features extraction are two major issues that have an adverse effect on both anticipation time and true positive prediction rate. Therefore, we propose a model that provides reliable methods of both preprocessing and feature extraction. Our model predicts epileptic seizures' sufficient time before the onset of seizure starts and provides a better true positive rate. We have applied empirical mode decomposition (EMD) for preprocessing and have extracted time and frequency domain features for training a prediction model. The proposed model detects the start of the preictal state, which is the state that starts few minutes before the onset of the seizure, with a higher true positive rate compared to traditional methods, 92.23%, and maximum anticipation time of 33 minutes and average prediction time of 23.6 minutes on scalp EEG CHB-MIT dataset of 22 subjects. PMID:29410700

Joint source based analysis of multiple brain structures in studying major depressive disorder

NASA Astrophysics Data System (ADS)

Ramezani, Mahdi; Rasoulian, Abtin; Hollenstein, Tom; Harkness, Kate; Johnsrude, Ingrid; Abolmaesumi, Purang

2014-03-01

We propose a joint Source-Based Analysis (jSBA) framework to identify brain structural variations in patients with Major Depressive Disorder (MDD). In this framework, features representing position, orientation and size (i.e. pose), shape, and local tissue composition are extracted. Subsequently, simultaneous analysis of these features within a joint analysis method is performed to generate the basis sources that show signi cant di erences between subjects with MDD and those in healthy control. Moreover, in a cross-validation leave- one-out experiment, we use a Fisher Linear Discriminant (FLD) classi er to identify individuals within the MDD group. Results show that we can classify the MDD subjects with an accuracy of 76% solely based on the information gathered from the joint analysis of pose, shape, and tissue composition in multiple brain structures.

A novel framework for the local extraction of extra-axial cerebrospinal fluid from MR brain images

NASA Astrophysics Data System (ADS)

Mostapha, Mahmoud; Shen, Mark D.; Kim, SunHyung; Swanson, Meghan; Collins, D. Louis; Fonov, Vladimir; Gerig, Guido; Piven, Joseph; Styner, Martin A.

2018-03-01

The quantification of cerebrospinal fluid (CSF) in the human brain has shown to play an important role in early postnatal brain developmental. Extr a-axial fluid (EA-CSF), which is characterized by the CSF in the subarachnoid space, is promising in the early detection of children at risk for neurodevelopmental disorders. Currently, though, there is no tool to extract local EA-CSF measurements in a way that is suitable for localized analysis. In this paper, we propose a novel framework for the localized, cortical surface based analysis of EA-CSF. In our proposed processing, we combine probabilistic brain tissue segmentation, cortical surface reconstruction as well as streamline based local EA-CSF quantification. For streamline computation, we employ the vector field generated by solving a Laplacian partial differential equation (PDE) between the cortical surface and the outer CSF hull. To achieve sub-voxel accuracy while minimizing numerical errors, fourth-order Runge-Kutta (RK4) integration was used to generate the streamlines. Finally, the local EA-CSF is computed by integrating the CSF probability along the generated streamlines. The proposed local EA-CSF extraction tool was used to study the early postnatal brain development in typically developing infants. The results show that the proposed localized EA-CSF extraction pipeline can produce statistically significant regions that are not observed in previous global approach.

[Research on the methods for multi-class kernel CSP-based feature extraction].

PubMed

Wang, Jinjia; Zhang, Lingzhi; Hu, Bei

2012-04-01

To relax the presumption of strictly linear patterns in the common spatial patterns (CSP), we studied the kernel CSP (KCSP). A new multi-class KCSP (MKCSP) approach was proposed in this paper, which combines the kernel approach with multi-class CSP technique. In this approach, we used kernel spatial patterns for each class against all others, and extracted signal components specific to one condition from EEG data sets of multiple conditions. Then we performed classification using the Logistic linear classifier. Brain computer interface (BCI) competition III_3a was used in the experiment. Through the experiment, it can be proved that this approach could decompose the raw EEG singles into spatial patterns extracted from multi-class of single trial EEG, and could obtain good classification results.

The Ayurvedic plant Bacopa Monnieri inhibits inflammatory pathways in the brain

PubMed Central

Nemetchek, Michelle D.; Stierle, Andrea A.; Stierle, Donald B.; Lurie, Diana I.

2016-01-01

Ethnopharmacological Relevance Bacopa monnieri (L) Wettst (common name, bacopa) is a medicinal plant used in Ayurveda, the traditional system of medicine of India, as a nootropic. It is considered to be a “medhya rasayana”, an herb that sharpens the mind and the intellect. Bacopa is an important ingredient in many Ayurvedic herbal formulations designed to treat conditions such as memory loss, anxiety, poor cognition and loss of concentration. It has also been used in Ayurveda to treat inflammatory conditions such as arthritis. In modern biomedical studies, bacopa has been shown in animal models to inhibit the release of the pro-inflammatory cytokines TNF-α and IL-6. However, less is known regarding the anti-inflammatory activity of Bacopa in the brain. Aim Of The Study The current study examines the ability of Bacopa to inhibit the release of pro-inflammatory cytokines from microglial cells, the immune cells of the brain that participate in inflammation in the CNS. The effect of Bacopa on signaling enzymes associated with CNS inflammatory pathways was also studied. Materials And Methods Various extracts of Bacopa were prepared and examined in the N9 microglial cell line in order to determine if they inhibited the release of the proinflammatory cytokines TNF-α and IL-6. Extracts were also tested in cell free assays as inhibitors of caspase-1 and matrix metalloproteinase-3 (enzymes associated with inflammation) and caspase-3, which has been shown to cleave protein Tau, an early event in the development of Alzheimer's disease. Results The tea, infusion, and alkaloid extracts of bacopa, as well as Bacoside A significantly inhibited the release of TNF-α and IL-6 from activated N9 microglial cells in vitro. In addition, the tea, infusion, and alkaloid extracts of Bacopa effectively inhibited caspase 1 and 3, and matrix metalloproteinase-3 in the cell free assay. Conclusions Bacopa inhibits the release of inflammatory cytokines from microglial cells and inhibits enzymes associated with inflammation in the brain. Thus, Bacopa can limit inflammation in the CNS, and offers a promising source of novel therapeutics for the treatment of many CNS disorders. PMID:27473605

Zingiber officinale Mitigates Brain Damage and Improves Memory Impairment in Focal Cerebral Ischemic Rat

PubMed Central

Wattanathorn, Jintanaporn; Jittiwat, Jinatta; Tongun, Terdthai; Muchimapura, Supaporn; Ingkaninan, Kornkanok

2011-01-01

Cerebral ischemia is known to produce brain damage and related behavioral deficits including memory. Recently, accumulating lines of evidence showed that dietary enrichment with nutritional antioxidants could reduce brain damage and improve cognitive function. In this study, possible protective effect of Zingiber officinale, a medicinal plant reputed for neuroprotective effect against oxidative stress-related brain damage, on brain damage and memory deficit induced by focal cerebral ischemia was elucidated. Male adult Wistar rats were administrated an alcoholic extract of ginger rhizome orally 14 days before and 21 days after the permanent occlusion of right middle cerebral artery (MCAO). Cognitive function assessment was performed at 7, 14, and 21 days after MCAO using the Morris water maze test. The brain infarct volume and density of neurons in hippocampus were also determined. Furthermore, the level of malondialdehyde (MDA), superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-Px) in cerebral cortex, striatum, and hippocampus was also quantified at the end of experiment. The results showed that cognitive function and neurons density in hippocampus of rats receiving ginger rhizome extract were improved while the brain infarct volume was decreased. The cognitive enhancing effect and neuroprotective effect occurred partly via the antioxidant activity of the extract. In conclusion, our study demonstrated the beneficial effect of ginger rhizome to protect against focal cerebral ischemia. PMID:21197427

Alkaloid extracts from Jimson weed (Datura stramonium L.) modulate purinergic enzymes in rat brain.

PubMed

Ademiluyi, Adedayo O; Ogunsuyi, Opeyemi B; Oboh, Ganiyu

2016-09-01

Although some findings have reported the medicinal properties of Jimson weed (Datura stramonium L.), there exist some serious neurological effects such as hallucination, loss of memory and anxiety, which has been reported in folklore. Consequently, the modulatory effect of alkaloid extracts from leaf and fruit of Jimson weed on critical enzymes of the purinergic [ecto-nucleoside triphosphate diphosphohydrolase (E-NTPDase), ecto-5'-nucleotidase (E-NTDase), alkaline phosphatase (ALP) and Na + /K + ATPase] system of neurotransmission was the focus of this study. Alkaloid extracts were prepared by solvent extraction method and their interaction with the activities of these enzymes were assessed (in vitro) in rat brain tissue homogenate and in vivo in rats administered 100 and 200mg/kg body weight (p.o) of the extracts for thirty days, while administration of single dose (1mg/kg body weight; i.p.) of scopolamine served as the positive control. The extracts were also investigated for their Fe 2+ and Cu 2+ chelating abilities and GC-MS characterization of the extracts was also carried out. The results revealed that the extracts inhibited activates of E-NTPDase, E-NTDase and ALP in a concentration dependent manner, while stimulating the activity of Na + /K + ATPase (in vitro). Both extracts also exhibited Fe 2+ and Cu 2+ chelating abilities. Considering the EC 50 values, the fruit extract had significantly higher (P<0.05) modulatory effect on the enzymes' activity as well as metal chelating abilities, compared to the leaf extract; however, there was no significant difference (P>0.05) in both extracts' inhibitory effects on E-NTDase. The in vivo study revealed reduction in the activities of ENTPDase, E-NTDase, and Na + /K + ATPase in the extract-administered rat groups compared to the control group, while an elevation in ALP activity was observed in the extract-administered rat groups compared to the control group. GC-MS characterization revealed the presence of atropine, scopolamine, amphetamine, 3-methyoxyamphetamine, 3-ethoxyamhetamine cathine, spermine, phenlyephirine and 3-piperidinemethanol, among others in the extracts. Hence, alterations of activities of critical enzymes of purinergic signaling (in vitro and in vivo) by alkaloid extracts from leaf and fruit of Jimson weed suggest one of the mechanisms behind its neurological effects as reported in folklore. Copyright © 2016 Elsevier B.V. All rights reserved.

Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network.

PubMed

Zafar, Raheel; Kamel, Nidal; Naufal, Mohamad; Malik, Aamir Saeed; Dass, Sarat C; Ahmad, Rana Fayyaz; Abdullah, Jafri M; Reza, Faruque

2017-01-01

Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).

Wavelet-based localization of oscillatory sources from magnetoencephalography data.

PubMed

Lina, J M; Chowdhury, R; Lemay, E; Kobayashi, E; Grova, C

2014-08-01

Transient brain oscillatory activities recorded with Eelectroencephalography (EEG) or magnetoencephalography (MEG) are characteristic features in physiological and pathological processes. This study is aimed at describing, evaluating, and illustrating with clinical data a new method for localizing the sources of oscillatory cortical activity recorded by MEG. The method combines time-frequency representation and an entropic regularization technique in a common framework, assuming that brain activity is sparse in time and space. Spatial sparsity relies on the assumption that brain activity is organized among cortical parcels. Sparsity in time is achieved by transposing the inverse problem in the wavelet representation, for both data and sources. We propose an estimator of the wavelet coefficients of the sources based on the maximum entropy on the mean (MEM) principle. The full dynamics of the sources is obtained from the inverse wavelet transform, and principal component analysis of the reconstructed time courses is applied to extract oscillatory components. This methodology is evaluated using realistic simulations of single-trial signals, combining fast and sudden discharges (spike) along with bursts of oscillating activity. The method is finally illustrated with a clinical application using MEG data acquired on a patient with a right orbitofrontal epilepsy.

Automatic segmentation of multimodal brain tumor images based on classification of super-voxels.

PubMed

Kadkhodaei, M; Samavi, S; Karimi, N; Mohaghegh, H; Soroushmehr, S M R; Ward, K; All, A; Najarian, K

2016-08-01

Despite the rapid growth in brain tumor segmentation approaches, there are still many challenges in this field. Automatic segmentation of brain images has a critical role in decreasing the burden of manual labeling and increasing robustness of brain tumor diagnosis. We consider segmentation of glioma tumors, which have a wide variation in size, shape and appearance properties. In this paper images are enhanced and normalized to same scale in a preprocessing step. The enhanced images are then segmented based on their intensities using 3D super-voxels. Usually in images a tumor region can be regarded as a salient object. Inspired by this observation, we propose a new feature which uses a saliency detection algorithm. An edge-aware filtering technique is employed to align edges of the original image to the saliency map which enhances the boundaries of the tumor. Then, for classification of tumors in brain images, a set of robust texture features are extracted from super-voxels. Experimental results indicate that our proposed method outperforms a comparable state-of-the-art algorithm in term of dice score.

Co-activation patterns in resting-state fMRI signals.

PubMed

Liu, Xiao; Zhang, Nanyin; Chang, Catie; Duyn, Jeff H

2018-02-08

The brain is a complex system that integrates and processes information across multiple time scales by dynamically coordinating activities over brain regions and circuits. Correlations in resting-state functional magnetic resonance imaging (rsfMRI) signals have been widely used to infer functional connectivity of the brain, providing a metric of functional associations that reflects a temporal average over an entire scan (typically several minutes or longer). Not until recently was the study of dynamic brain interactions at much shorter time scales (seconds to minutes) considered for inference of functional connectivity. One method proposed for this objective seeks to identify and extract recurring co-activation patterns (CAPs) that represent instantaneous brain configurations at single time points. Here, we review the development and recent advancement of CAP methodology and other closely related approaches, as well as their applications and associated findings. We also discuss the potential neural origins and behavioral relevance of CAPs, along with methodological issues and future research directions in the analysis of fMRI co-activation patterns. Copyright © 2018 Elsevier Inc. All rights reserved.

Mapping population-based structural connectomes.

PubMed

Zhang, Zhengwu; Descoteaux, Maxime; Zhang, Jingwen; Girard, Gabriel; Chamberland, Maxime; Dunson, David; Srivastava, Anuj; Zhu, Hongtu

2018-05-15

Advances in understanding the structural connectomes of human brain require improved approaches for the construction, comparison and integration of high-dimensional whole-brain tractography data from a large number of individuals. This article develops a population-based structural connectome (PSC) mapping framework to address these challenges. PSC simultaneously characterizes a large number of white matter bundles within and across different subjects by registering different subjects' brains based on coarse cortical parcellations, compressing the bundles of each connection, and extracting novel connection weights. A robust tractography algorithm and streamline post-processing techniques, including dilation of gray matter regions, streamline cutting, and outlier streamline removal are applied to improve the robustness of the extracted structural connectomes. The developed PSC framework can be used to reproducibly extract binary networks, weighted networks and streamline-based brain connectomes. We apply the PSC to Human Connectome Project data to illustrate its application in characterizing normal variations and heritability of structural connectomes in healthy subjects. Copyright © 2018 Elsevier Inc. All rights reserved.

Effect of Punica granatum L. Flower Water Extract on Five Common Oral Bacteria and Bacterial Biofilm Formation on Orthodontic Wire

PubMed Central

VAHID DASTJERDI, Elahe; ABDOLAZIMI, Zahra; GHAZANFARIAN, Marzieh; AMDJADI, Parisa; KAMALINEJAD, Mohammad; MAHBOUBI, Arash

2014-01-01

Background: Use of herbal extracts and essences as natural antibacterial compounds has become increasingly popular for the control of oral infectious diseases. Therefore, finding natural antimicrobial products with the lowest side effects seems necessary. The present study sought to assess the effect of Punica granatum L. water extract on five oral bacteria and bacterial biofilm formation on orthodontic wire. Methods: Antibacterial property of P. granatum L. water extract was primarily evaluated in brain heart infusion agar medium using well-plate method. The minimum inhibitory concentration and minimum bactericidal concentration were determined by macro-dilution method. The inhibitory effect on orthodontic wire bacterial biofilm formation was evaluated using viable cell count in biofilm medium. At the final phase, samples were fixed and analyzed by Scanning Electron Microscopy. Results: The growth inhibition zone diameter was proportional to the extract concentration. The water extract demonstrated the maximum antibacterial effect on Streptococcus sanguinis ATCC 10556 with a minimum inhibitory concentration of 6.25 mg/ml and maximum bactericidal effect on S. sanguinis ATCC 10556 and S. sobrinus ATCC 27607 with minimum bactericidal concentration of 25 mg/ml. The water extract decreased bacterial biofilm formation by S. sanguinis, S. sobrinus, S. salivarius, S. mutans ATCC 35608 and E. faecalis CIP 55142 by 93.7–100%, 40.6–99.9%, 85.2–86.5%, 66.4–84.4% and 35.5–56.3% respectively. Conclusion: Punica granatum L. water extract had significant antibacterial properties against 5 oral bacteria and prevented orthodontic wire bacterial biofilm formation. However, further investigations are required to generalize these results to the clinical setting. PMID:26171362

An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement.

PubMed

Souza, Roberto; Lucena, Oeslle; Garrafa, Julia; Gobbi, David; Saluzzi, Marina; Appenzeller, Simone; Rittner, Letícia; Frayne, Richard; Lotufo, Roberto

2018-04-15

This paper presents an open, multi-vendor, multi-field strength magnetic resonance (MR) T1-weighted volumetric brain imaging dataset, named Calgary-Campinas-359 (CC-359). The dataset is composed of images of older healthy adults (29-80 years) acquired on scanners from three vendors (Siemens, Philips and General Electric) at both 1.5 T and 3 T. CC-359 is comprised of 359 datasets, approximately 60 subjects per vendor and magnetic field strength. The dataset is approximately age and gender balanced, subject to the constraints of the available images. It provides consensus brain extraction masks for all volumes generated using supervised classification. Manual segmentation results for twelve randomly selected subjects performed by an expert are also provided. The CC-359 dataset allows investigation of 1) the influences of both vendor and magnetic field strength on quantitative analysis of brain MR; 2) parameter optimization for automatic segmentation methods; and potentially 3) machine learning classifiers with big data, specifically those based on deep learning methods, as these approaches require a large amount of data. To illustrate the utility of this dataset, we compared to the results of a supervised classifier, the results of eight publicly available skull stripping methods and one publicly available consensus algorithm. A linear mixed effects model analysis indicated that vendor (p-value<0.001) and magnetic field strength (p-value<0.001) have statistically significant impacts on skull stripping results. Copyright © 2017 Elsevier Inc. All rights reserved.

Development of a High-Performance Liquid Chromatography–Tandem Mass Spectrometry Method for the Identification and Quantification of CP-47,497, CP-47,497-C8 and JWH-250 in Mouse Brain

PubMed Central

Samano, Kimberly L.; Poklis, Justin L.; Lichtman, Aron H.; Poklis, Alphonse

2014-01-01

While Marijuana continues to be the most widely used illicit drug, abuse of synthetic cannabinoid (SCB) compounds in ‘Spice’ or ‘K2’ herbal incense products has emerged as a significant public health concern in many European countries and in the USA. Several of these SCBs have been declared Schedule I controlled substances but detection and quantification in biological samples remain a challenge. Therefore, we present a liquid chromatography–tandem mass spectrometry method after liquid–liquid extraction for the quantitation of CP-47,497, CP-47,497-C8 and JWH-250 in mouse brain. We report data for linearity, limit of quantification, accuracy/bias, precision, recovery, selectivity, carryover, matrix effects and stability experiments which were developed and fully validated based on Scientific Working Group for Forensic Toxicology guidelines for forensic toxicology method validation. Acceptable coefficients of variation for accuracy/bias, within- and between-run precision and selectivity were determined, with all values within ±15% of the target concentration. Validation experiments revealed degradation of CP-47, 497 and CP-47,497-C8 at different temperatures, and significant ion suppression was produced in brain for all compounds tested. The method was successfully applied to detect and quantify CP-47,497 in brains from mice demonstrating significant cannabimimetic behavioral effects as assessed by the classical tetrad paradigm. PMID:24816398

An investigation on the mechanism of sublimed DHB matrix on molecular ion yields in SIMS imaging of brain tissue.

PubMed

Dowlatshahi Pour, Masoumeh; Malmberg, Per; Ewing, Andrew

2016-05-01

We have characterized the use of sublimation to deposit matrix-assisted laser desorption/ionization (MALDI) matrices in secondary ion mass spectrometry (SIMS) analysis, i.e. matrix-enhanced SIMS (ME-SIMS), a common surface modification method to enhance sensitivity for larger molecules and to increase the production of intact molecular ions. We use sublimation to apply a thin layer of a conventional MALDI matrix, 2,5-dihydroxybenzoic acid (DHB), onto rat brain cerebellum tissue to show how this technique can be used to enhance molecular yields in SIMS while still retaining a lateral resolution around 2 μm and also to investigate the mechanism of this enhancement. The results here illustrate that cholesterol, which is a dominant lipid species in the brain, is decreased on the tissue surface after deposition of matrix, particularly in white matter. The decrease of cholesterol is followed by an increased ion yield of several other lipid species. Depth profiling of the sublimed rat brain reveals that the lipid species are de facto extracted by the DHB matrix and concentrated in the top most layers of the sublimed matrix. This extraction/concentration of lipids directly leads to an increase of higher mass lipid ion yield. It is also possible that the decrease of cholesterol decreases the potential suppression of ion yield caused by cholesterol migration to the tissue surface. This result provides us with significant insights into the possible mechanisms involved when using sublimation to deposit this matrix in ME-SIMS.

The direct analysis of drug distribution of rotigotine-loaded microspheres from tissue sections by LESA coupled with tandem mass spectrometry.

PubMed

Xu, Li-Xiao; Wang, Tian-Tian; Geng, Yin-Yin; Wang, Wen-Yan; Li, Yin; Duan, Xiao-Kun; Xu, Bin; Liu, Charles C; Liu, Wan-Hui

2017-09-01

The direct analysis of drug distribution of rotigotine-loaded microspheres (RoMS) from tissue sections by liquid extraction surface analysis (LESA) coupled with tandem mass spectrometry (MS/MS) was demonstrated. The RoMS distribution in rat tissues assessed by the ambient LESA-MS/MS approach without extensive or tedious sample pretreatment was compared with that obtained by a conventional liquid chromatography tandem mass spectrometry (LC-MS/MS) method in which organ excision and subsequent solvent extraction were commonly employed before analysis. Results obtained from the two were well correlated for a majority of the organs, such as muscle, liver, stomach, and hippocampus. The distribution of RoMS in the brain, however, was found to be mainly focused in the hippocampus and striatum regions as shown by the LESA-imaged profiles. The LESA approach we developed is sensitive enough, with an estimated LLOQ at 0.05 ng/mL of rotigotine in brain tissue, and information-rich with minimal sample preparation, suitable, and promising in assisting the development of new drug delivery systems for controlled drug release and protection. Graphical abstract Workflow for the LESA-MS/MS imaging of brain tissue section after intramuscular RoMS administration.

Histological studies of neuroprotective effects of Curcuma longa Linn. on neuronal loss induced by dexamethasone treatment in the rat hippocampus.

PubMed

Issuriya, Acharaporn; Kumarnsit, Ekkasit; Wattanapiromsakul, Chatchai; Vongvatcharanon, Uraporn

2014-10-01

Long term exposure to dexamethasone (Dx) is associated with brain damage especially in the hippocampus via the oxidative stress pathway. Previously, an ethanolic extract from Curcuma longa Linn. (CL) containing the curcumin constituent has been reported to produce antioxidant effects. However, its neuroprotective property on brain histology has remained unexplored. This study has examined the effects of a CL extract on the densities of cresyl violet positive neurons and glial fibrillary acidic protein immunoreactive (GFAP-ir) astrocytes in the hippocampus of Dx treated male rats. It showed that 21 days of Dx treatment (0.5mg/kg, i.p. once daily) significantly reduced the densities of cresyl violet positive neurons in the sub-areas CA1, CA3 and the dentate gyrus, but not in the CA2 area. However, CL pretreatment (100mg/kg, p.o.) was found to significantly restore neuronal densities in the CA1 and dentate gyrus. In addition, Dx treatment also significantly decreased the densities of the GFAP-ir astrocytes in the sub-areas CA1, CA3 and the dentate gyrus. However, CL pretreatment (100mg/kg, p.o.) failed to protect the loss of astrocytes in these sub-areas. These findings confirm the neuroprotective effects of the CL extract and indicate that the cause of astrocyte loss might be partially reduced by a non-oxidative mechanism. Moreover, the detection of neuronal and glial densities was suitable method to study brain damage and the effects of treatment. Copyright © 2014 Elsevier GmbH. All rights reserved.

Demonstration of elevation and localization of Rho-kinase activity in the brain of a rat model of cerebral infarction.

PubMed

Yano, Kazuo; Kawasaki, Koh; Hattori, Tsuyoshi; Tawara, Shunsuke; Toshima, Yoshinori; Ikegaki, Ichiro; Sasaki, Yasuo; Satoh, Shin-ichi; Asano, Toshio; Seto, Minoru

2008-10-10

Evidence that Rho-kinase is involved in cerebral infarction has accumulated. However, it is uncertain whether Rho-kinase is activated in the brain parenchyma in cerebral infarction. To answer this question, we measured Rho-kinase activity in the brain in a rat cerebral infarction model. Sodium laurate was injected into the left internal carotid artery, inducing cerebral infarction in the ipsilateral hemisphere. At 6 h after injection, increase of activating transcription factor 3 (ATF3) and c-Fos was found in the ipsilateral hemisphere, suggesting that neuronal damage occurs. At 0.5, 3, and 6 h after injection of laurate, Rho-kinase activity in extracts of the cerebral hemispheres was measured by an ELISA method. Rho-kinase activity in extracts of the ipsilateral hemisphere was significantly increased compared with that in extracts of the contralateral hemisphere at 3 and 6 h but not 0.5 h after injection of laurate. Next, localization of Rho-kinase activity was evaluated by immunohistochemical analysis in sections of cortex and hippocampus including infarct area 6 h after injection of laurate. Staining for phosphorylation of myosin-binding subunit (phospho-MBS) and myosin light chain (phospho-MLC), substrates of Rho-kinase, was elevated in neuron and blood vessel, respectively, in ipsilateral cerebral sections, compared with those in contralateral cerebral sections. These findings indicate that Rho-kinase is activated in neuronal and vascular cells in a rat cerebral infarction model, and suggest that Rho-kinase could be an important target in the treatment of cerebral infarction.

Learning Effective Connectivity Network Structure from fMRI Data Based on Artificial Immune Algorithm

PubMed Central

Ji, Junzhong; Liu, Jinduo; Liang, Peipeng; Zhang, Aidong

2016-01-01

Many approaches have been designed to extract brain effective connectivity from functional magnetic resonance imaging (fMRI) data. However, few of them can effectively identify the connectivity network structure due to different defects. In this paper, a new algorithm is developed to infer the effective connectivity between different brain regions by combining artificial immune algorithm (AIA) with the Bayes net method, named as AIAEC. In the proposed algorithm, a brain effective connectivity network is mapped onto an antibody, and four immune operators are employed to perform the optimization process of antibodies, including clonal selection operator, crossover operator, mutation operator and suppression operator, and finally gets an antibody with the highest K2 score as the solution. AIAEC is then tested on Smith’s simulated datasets, and the effect of the different factors on AIAEC is evaluated, including the node number, session length, as well as the other potential confounding factors of the blood oxygen level dependent (BOLD) signal. It was revealed that, as contrast to other existing methods, AIAEC got the best performance on the majority of the datasets. It was also found that AIAEC could attain a relative better solution under the influence of many factors, although AIAEC was differently affected by the aforementioned factors. AIAEC is thus demonstrated to be an effective method for detecting the brain effective connectivity. PMID:27045295

Computerized Cognitive Rehabilitation of Attention and Executive Function in Acquired Brain Injury: A Systematic Review.

PubMed

Bogdanova, Yelena; Yee, Megan K; Ho, Vivian T; Cicerone, Keith D

Comprehensive review of the use of computerized treatment as a rehabilitation tool for attention and executive function in adults (aged 18 years or older) who suffered an acquired brain injury. Systematic review of empirical research. Two reviewers independently assessed articles using the methodological quality criteria of Cicerone et al. Data extracted included sample size, diagnosis, intervention information, treatment schedule, assessment methods, and outcome measures. A literature review (PubMed, EMBASE, Ovid, Cochrane, PsychINFO, CINAHL) generated a total of 4931 publications. Twenty-eight studies using computerized cognitive interventions targeting attention and executive functions were included in this review. In 23 studies, significant improvements in attention and executive function subsequent to training were reported; in the remaining 5, promising trends were observed. Preliminary evidence suggests improvements in cognitive function following computerized rehabilitation for acquired brain injury populations including traumatic brain injury and stroke. Further studies are needed to address methodological issues (eg, small sample size, inadequate control groups) and to inform development of guidelines and standardized protocols.

A Non-Parametric Approach for the Activation Detection of Block Design fMRI Simulated Data Using Self-Organizing Maps and Support Vector Machine.

PubMed

Bahrami, Sheyda; Shamsi, Mousa

2017-01-01

Functional magnetic resonance imaging (fMRI) is a popular method to probe the functional organization of the brain using hemodynamic responses. In this method, volume images of the entire brain are obtained with a very good spatial resolution and low temporal resolution. However, they always suffer from high dimensionality in the face of classification algorithms. In this work, we combine a support vector machine (SVM) with a self-organizing map (SOM) for having a feature-based classification by using SVM. Then, a linear kernel SVM is used for detecting the active areas. Here, we use SOM for feature extracting and labeling the datasets. SOM has two major advances: (i) it reduces dimension of data sets for having less computational complexity and (ii) it is useful for identifying brain regions with small onset differences in hemodynamic responses. Our non-parametric model is compared with parametric and non-parametric methods. We use simulated fMRI data sets and block design inputs in this paper and consider the contrast to noise ratio (CNR) value equal to 0.6 for simulated datasets. fMRI simulated dataset has contrast 1-4% in active areas. The accuracy of our proposed method is 93.63% and the error rate is 6.37%.

Feature Extraction from Subband Brain Signals and Its Classification

NASA Astrophysics Data System (ADS)

Mukul, Manoj Kumar; Matsuno, Fumitoshi

This paper considers both the non-stationarity as well as independence/uncorrelated criteria along with the asymmetry ratio over the electroencephalogram (EEG) signals and proposes a hybrid approach of the signal preprocessing methods before the feature extraction. A filter bank approach of the discrete wavelet transform (DWT) is used to exploit the non-stationary characteristics of the EEG signals and it decomposes the raw EEG signals into the subbands of different center frequencies called as rhythm. A post processing of the selected subband by the AMUSE algorithm (a second order statistics based ICA/BSS algorithm) provides the separating matrix for each class of the movement imagery. In the subband domain the orthogonality as well as orthonormality criteria over the whitening matrix and separating matrix do not come respectively. The human brain has an asymmetrical structure. It has been observed that the ratio between the norms of the left and right class separating matrices should be different for better discrimination between these two classes. The alpha/beta band asymmetry ratio between the separating matrices of the left and right classes will provide the condition to select an appropriate multiplier. So we modify the estimated separating matrix by an appropriate multiplier in order to get the required asymmetry and extend the AMUSE algorithm in the subband domain. The desired subband is further subjected to the updated separating matrix to extract subband sub-components from each class. The extracted subband sub-components sources are further subjected to the feature extraction (power spectral density) step followed by the linear discriminant analysis (LDA).

Finding Imaging Patterns of Structural Covariance via Non-Negative Matrix Factorization

PubMed Central

Sotiras, Aristeidis; Resnick, Susan M.; Davatzikos, Christos

2015-01-01

In this paper, we investigate the use of Non-Negative Matrix Factorization (NNMF) for the analysis of structural neuroimaging data. The goal is to identify the brain regions that co-vary across individuals in a consistent way, hence potentially being part of underlying brain networks or otherwise influenced by underlying common mechanisms such as genetics and pathologies. NNMF offers a directly data-driven way of extracting relatively localized co-varying structural regions, thereby transcending limitations of Principal Component Analysis (PCA), Independent Component Analysis (ICA) and other related methods that tend to produce dispersed components of positive and negative loadings. In particular, leveraging upon the well known ability of NNMF to produce parts-based representations of image data, we derive decompositions that partition the brain into regions that vary in consistent ways across individuals. Importantly, these decompositions achieve dimensionality reduction via highly interpretable ways and generalize well to new data as shown via split-sample experiments. We empirically validate NNMF in two data sets: i) a Diffusion Tensor (DT) mouse brain development study, and ii) a structural Magnetic Resonance (sMR) study of human brain aging. We demonstrate the ability of NNMF to produce sparse parts-based representations of the data at various resolutions. These representations seem to follow what we know about the underlying functional organization of the brain and also capture some pathological processes. Moreover, we show that these low dimensional representations favorably compare to descriptions obtained with more commonly used matrix factorization methods like PCA and ICA. PMID:25497684

Neural network-based brain tissue segmentation in MR images using extracted features from intraframe coding in H.264

NASA Astrophysics Data System (ADS)

Jafari, Mehdi; Kasaei, Shohreh

2012-01-01

Automatic brain tissue segmentation is a crucial task in diagnosis and treatment of medical images. This paper presents a new algorithm to segment different brain tissues, such as white matter (WM), gray matter (GM), cerebral spinal fluid (CSF), background (BKG), and tumor tissues. The proposed technique uses the modified intraframe coding yielded from H.264/(AVC), for feature extraction. Extracted features are then imposed to an artificial back propagation neural network (BPN) classifier to assign each block to its appropriate class. Since the newest coding standard, H.264/AVC, has the highest compression ratio, it decreases the dimension of extracted features and thus yields to a more accurate classifier with low computational complexity. The performance of the BPN classifier is evaluated using the classification accuracy and computational complexity terms. The results show that the proposed technique is more robust and effective with low computational complexity compared to other recent works.

Neural network-based brain tissue segmentation in MR images using extracted features from intraframe coding in H.264

NASA Astrophysics Data System (ADS)

Jafari, Mehdi; Kasaei, Shohreh

2011-12-01

Automatic brain tissue segmentation is a crucial task in diagnosis and treatment of medical images. This paper presents a new algorithm to segment different brain tissues, such as white matter (WM), gray matter (GM), cerebral spinal fluid (CSF), background (BKG), and tumor tissues. The proposed technique uses the modified intraframe coding yielded from H.264/(AVC), for feature extraction. Extracted features are then imposed to an artificial back propagation neural network (BPN) classifier to assign each block to its appropriate class. Since the newest coding standard, H.264/AVC, has the highest compression ratio, it decreases the dimension of extracted features and thus yields to a more accurate classifier with low computational complexity. The performance of the BPN classifier is evaluated using the classification accuracy and computational complexity terms. The results show that the proposed technique is more robust and effective with low computational complexity compared to other recent works.

Using UMLS to construct a generalized hierarchical concept-based dictionary of brain functions for information extraction from the fMRI literature.

PubMed

Hsiao, Mei-Yu; Chen, Chien-Chung; Chen, Jyh-Horng

2009-10-01

With a rapid progress in the field, a great many fMRI studies are published every year, to the extent that it is now becoming difficult for researchers to keep up with the literature, since reading papers is extremely time-consuming and labor-intensive. Thus, automatic information extraction has become an important issue. In this study, we used the Unified Medical Language System (UMLS) to construct a hierarchical concept-based dictionary of brain functions. To the best of our knowledge, this is the first generalized dictionary of this kind. We also developed an information extraction system for recognizing, mapping and classifying terms relevant to human brain study. The precision and recall of our system was on a par with that of human experts in term recognition, term mapping and term classification. Our approach presented in this paper presents an alternative to the more laborious, manual entry approach to information extraction.

Functional Brain Connectivity as a New Feature for P300 Speller.

PubMed

Kabbara, Aya; Khalil, Mohamad; El-Falou, Wassim; Eid, Hassan; Hassan, Mahmoud

2016-01-01

The brain is a large-scale complex network often referred to as the "connectome". Cognitive functions and information processing are mainly based on the interactions between distant brain regions. However, most of the 'feature extraction' methods used in the context of Brain Computer Interface (BCI) ignored the possible functional relationships between different signals recorded from distinct brain areas. In this paper, the functional connectivity quantified by the phase locking value (PLV) was introduced to characterize the evoked responses (ERPs) obtained in the case of target and non-targets visual stimuli. We also tested the possibility of using the functional connectivity in the context of 'P300 speller'. The proposed approach was compared to the well-known methods proposed in the state of the art of "P300 Speller", mainly the peak picking, the area, time/frequency based features, the xDAWN spatial filtering and the stepwise linear discriminant analysis (SWLDA). The electroencephalographic (EEG) signals recorded from ten subjects were analyzed offline. The results indicated that phase synchrony offers relevant information for the classification in a P300 speller. High synchronization between the brain regions was clearly observed during target trials, although no significant synchronization was detected for a non-target trial. The results showed also that phase synchrony provides higher performance than some existing methods for letter classification in a P300 speller principally when large number of trials is available. Finally, we tested the possible combination of both approaches (classical features and phase synchrony). Our findings showed an overall improvement of the performance of the P300-speller when using Peak picking, the area and frequency based features. Similar performances were obtained compared to xDAWN and SWLDA when using large number of trials.

Effects of Low Doses of Bisphenol A on the Metabolome of Perinatally Exposed CD-1 Mice

PubMed Central

Cabaton, Nicolas J.; Canlet, Cécile; Wadia, Perinaaz R.; Tremblay-Franco, Marie; Gautier, Roselyne; Molina, Jérôme; Sonnenschein, Carlos; Cravedi, Jean-Pierre; Rubin, Beverly S.; Soto, Ana M.

2013-01-01

Background: Bisphenol A (BPA) is a well-known endocrine disruptor used to manufacture polycarbonate plastics and epoxy resins. Exposure of pregnant rodents to low doses of BPA results in pleiotropic effects in their offspring. Objective: We used metabolomics—a method for determining metabolic changes in response to nutritional, pharmacological, or toxic stimuli—to examine metabolic shifts induced in vivo by perinatal exposure to low doses of BPA in CD-1 mice. Methods: Male offspring born to pregnant CD-1 mice that were exposed to vehicle or to 0.025, 0.25, or 25 µg BPA/kg body weight/day, from gestation day 8 through day 16 of lactation, were examined on postnatal day (PND) 2 or PND21. Aqueous extracts of newborns (PND2, whole animal) and of livers, brains, and serum samples from PND21 pups were submitted to 1H nuclear magnetic resonance spectroscopy. Data were analyzed using partial least squares discriminant analysis. Results: Examination of endogenous metabolic fingerprints revealed remarkable discrimination in whole extracts of the four PND2 newborn treatment groups, strongly suggesting changes in the global metabolism. Furthermore, statistical analyses of liver, serum, and brain samples collected on PND21 successfully discriminated among treatment groups. Variations in glucose, pyruvate, some amino acids, and neurotransmitters (γ-aminobutyric acid and glutamate) were identified. Conclusions: Low doses of BPA disrupt global metabolism, including energy metabolism and brain function, in perinatally exposed CD-1 mouse pups. Metabolomics can be used to highlight the effects of low doses of endocrine disruptors by linking perinatal exposure to changes in global metabolism. PMID:23425943

Automatic removal of eye-movement and blink artifacts from EEG signals.

PubMed

Gao, Jun Feng; Yang, Yong; Lin, Pan; Wang, Pei; Zheng, Chong Xun

2010-03-01

Frequent occurrence of electrooculography (EOG) artifacts leads to serious problems in interpreting and analyzing the electroencephalogram (EEG). In this paper, a robust method is presented to automatically eliminate eye-movement and eye-blink artifacts from EEG signals. Independent Component Analysis (ICA) is used to decompose EEG signals into independent components. Moreover, the features of topographies and power spectral densities of those components are extracted to identify eye-movement artifact components, and a support vector machine (SVM) classifier is adopted because it has higher performance than several other classifiers. The classification results show that feature-extraction methods are unsuitable for identifying eye-blink artifact components, and then a novel peak detection algorithm of independent component (PDAIC) is proposed to identify eye-blink artifact components. Finally, the artifact removal method proposed here is evaluated by the comparisons of EEG data before and after artifact removal. The results indicate that the method proposed could remove EOG artifacts effectively from EEG signals with little distortion of the underlying brain signals.

TRAFIC: fiber tract classification using deep learning

NASA Astrophysics Data System (ADS)

Ngattai Lam, Prince D.; Belhomme, Gaetan; Ferrall, Jessica; Patterson, Billie; Styner, Martin; Prieto, Juan C.

2018-03-01

We present TRAFIC, a fully automated tool for the labeling and classification of brain fiber tracts. TRAFIC classifies new fibers using a neural network trained using shape features computed from previously traced and manually corrected fiber tracts. It is independent from a DTI Atlas as it is applied to already traced fibers. This work is motivated by medical applications where the process of extracting fibers from a DTI atlas, or classifying fibers manually is time consuming and requires knowledge about brain anatomy. With this new approach we were able to classify traced fiber tracts obtaining encouraging results. In this report we will present in detail the methods used and the results achieved with our approach.

The effects of dexamethasone on rat brain cortical nuclear factor kappa B (NF-{kappa}B) in endotoxic shock

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang Zhi; Kang Jinsong; Li Yang

2006-08-01

To explore the molecular mechanism of brain tissue injury induced by lipopolysaccharide (LPS), we studied the effects of endotoxic shock on rat brain cortex NF-{kappa}B and the effects of dexamethasone on these changes. Rats were randomly divided into LPS, LPS + dexamethasone, and control groups. The DNA-binding activity of NF-{kappa}B was observed using electrophoretic mobility shift assay (EMSA). Protein expression in nuclear extracts was studied using Western blots, and nuclear translocation was observed using immunohistochemistry. These indices were assayed at 1 h and 4 h after intravenous injection of LPS (4 mg.kg{sup -1}). EMSA showed significantly increased NF-{kappa}B DNA-binding activitymore » in nuclear extracts from the LPS group at both 1 h and 4 h after LPS injection, compared with the control group (P < 0.01). For the LPS group, the NF-{kappa}B DNA-binding activity was greater at 1 h than at 4 h (P < 0.05). The expression of p65 and p50 protein in the nuclear extracts was also increased, as compared with the control group. However, the expression of p65 and p50 protein from cytosolic extracts did not show any significant change. Dexamethasone down-regulated not only NF-{kappa}B DNA-binding activity but also the expression of p65 protein in the nuclear extracts. From these data, we have concluded that NF-{kappa}B activation and nuclear translocation of NF-{kappa}B play a key role in the molecular mechanism of brain tissue injury in endotoxic shock. Dexamethasone may alleviate brain injury by inhibiting NF-{kappa}B activation.« less

Minocycline Reduces Spontaneous Hemorrhage in Mouse Models of Cerebral Amyloid Angiopathy

PubMed Central

Liao, Fan; Xiao, Qingli; Kraft, Andrew; Gonzales, Ernie; Perez, Ron; Greenberg, Steven M.; Holtzman, David; Lee, Jin-Moo

2015-01-01

Background and Purpose Cerebral Amyloid Angiopathy (CAA) is a common cause of recurrent intracerebral hemorrhage (ICH) in the elderly. Previous studies have shown that CAA induces inflammation and expression of matrix metalloproteinase-2 and -9 (gelatinases) in amyloid-laden vessels. Here, we inhibited both using minocycline in CAA mouse models to determine if spontaneous ICH could be reduced. Methods Tg2576 (n=16) and 5×FAD/ApoE4 knock-in mice (n=16), aged to 17 and 12 months, respectively, were treated with minocycline (50 mg/kg, i.p.) or saline every other day for two months. Brains were extracted and stained with X-34 (to quantify amyloid), Perl’s blue (to quantify hemorrhage), and immunostained to examined Aβ load, gliosis (GFAP, Iba-1), and vascular markers of blood-brain-barrier integrity (ZO-1 and collagen IV). Brain extracts were used to quantify mRNA for a variety of inflammatory genes. Results Minocycline treatment significantly reduced hemorrhage frequency in the brains of Tg2576 and 5×FAD/ApoE4 mice relative to the saline-treated mice, without affecting CAA load. Gliosis (GFAP and Iba-1 immunostaining), gelatinase activity, and expression of a variety of inflammatory genes (MMP-9, Nox4, CD45, S-100b, Iba-1) were also significantly reduced. Higher levels of microvascular tight junction and basal lamina proteins were found in the brains of minocycline-treated Tg2576 mice relative to saline-treated controls. Conclusions Minocycline reduced gliosis, inflammatory gene expression, gelatinase activity, and spontaneous hemorrhage in two different mouse models of CAA, supporting the importance of MMP-related and inflammatory pathways in ICH pathogenesis. As an FDA-approved drug, minocycline might be considered for clinical trials to test efficacy in preventing CAA-related ICH. PMID:25944329

Phenytoin versus Leviteracetam for Seizure Prophylaxis after brain injury – a meta analysis

PubMed Central

2012-01-01

Background Current standard therapy for seizure prophylaxis in Neuro-surgical patients involves the use of Phenytoin (PHY). However, a new drug Levetiracetam (LEV) is emerging as an alternate treatment choice. We aimed to conduct a meta-analysis to compare these two drugs in patients with brain injury. Methods An electronic search was performed in using Pubmed, Embase, and CENTRAL. We included studies that compared the use of LEV vs. PHY for seizure prophylaxis for brain injured patients (Traumatic brain injury, intracranial hemorrhage, intracranial neoplasms, and craniotomy). Data of all eligible studies was extracted on to a standardized abstraction sheet. Data about baseline population characteristics, type of intervention, study design and outcome was extracted. Our primary outcome was seizures. Results The literature search identified 2489 unduplicated papers. Of these 2456 papers were excluded by reading the abstracts and titles. Another 25 papers were excluded after reading their complete text. We selected 8 papers which comprised of 2 RCTs and 6 observational studies. The pooled estimate’s Odds Ratio 1.12 (95% CI = 0.34, 3.64) demonstrated no superiority of either drug at preventing the occurrence of early seizures. In a subset analysis of studies in which follow up for seizures lasted either 3 or 7 days, the effect estimate remained insignificant with an odds ratio of 0.96 (95% CI = 0.34, 2.76). Similarly, 2 trials reporting seizure incidence at 6 months also had insignificant pooled results while comparing drug efficacy. The pooled odds ratio was 0.96 (95% CI = 0.24, 3.79). Conclusions Levetiracetam and Phenytoin demonstrate equal efficacy in seizure prevention after brain injury. However, very few randomized controlled trials (RCTs) on the subject were found. Further evidence through a high quality RCT is highly recommended. PMID:22642837

Development of a brain MRI-based hidden Markov model for dementia recognition.

PubMed

Chen, Ying; Pham, Tuan D

2013-01-01

Dementia is an age-related cognitive decline which is indicated by an early degeneration of cortical and sub-cortical structures. Characterizing those morphological changes can help to understand the disease development and contribute to disease early prediction and prevention. But modeling that can best capture brain structural variability and can be valid in both disease classification and interpretation is extremely challenging. The current study aimed to establish a computational approach for modeling the magnetic resonance imaging (MRI)-based structural complexity of the brain using the framework of hidden Markov models (HMMs) for dementia recognition. Regularity dimension and semi-variogram were used to extract structural features of the brains, and vector quantization method was applied to convert extracted feature vectors to prototype vectors. The output VQ indices were then utilized to estimate parameters for HMMs. To validate its accuracy and robustness, experiments were carried out on individuals who were characterized as non-demented and mild Alzheimer's diseased. Four HMMs were constructed based on the cohort of non-demented young, middle-aged, elder and demented elder subjects separately. Classification was carried out using a data set including both non-demented and demented individuals with a wide age range. The proposed HMMs have succeeded in recognition of individual who has mild Alzheimer's disease and achieved a better classification accuracy compared to other related works using different classifiers. Results have shown the ability of the proposed modeling for recognition of early dementia. The findings from this research will allow individual classification to support the early diagnosis and prediction of dementia. By using the brain MRI-based HMMs developed in our proposed research, it will be more efficient, robust and can be easily used by clinicians as a computer-aid tool for validating imaging bio-markers for early prediction of dementia.

The successive projection algorithm as an initialization method for brain tumor segmentation using non-negative matrix factorization.

PubMed

Sauwen, Nicolas; Acou, Marjan; Bharath, Halandur N; Sima, Diana M; Veraart, Jelle; Maes, Frederik; Himmelreich, Uwe; Achten, Eric; Van Huffel, Sabine

2017-01-01

Non-negative matrix factorization (NMF) has become a widely used tool for additive parts-based analysis in a wide range of applications. As NMF is a non-convex problem, the quality of the solution will depend on the initialization of the factor matrices. In this study, the successive projection algorithm (SPA) is proposed as an initialization method for NMF. SPA builds on convex geometry and allocates endmembers based on successive orthogonal subspace projections of the input data. SPA is a fast and reproducible method, and it aligns well with the assumptions made in near-separable NMF analyses. SPA was applied to multi-parametric magnetic resonance imaging (MRI) datasets for brain tumor segmentation using different NMF algorithms. Comparison with common initialization methods shows that SPA achieves similar segmentation quality and it is competitive in terms of convergence rate. Whereas SPA was previously applied as a direct endmember extraction tool, we have shown improved segmentation results when using SPA as an initialization method, as it allows further enhancement of the sources during the NMF iterative procedure.

Novel Noninvasive Brain Disease Detection System Using a Facial Image Sensor

PubMed Central

Shu, Ting; Zhang, Bob; Tang, Yuan Yan

2017-01-01

Brain disease including any conditions or disabilities that affect the brain is fast becoming a leading cause of death. The traditional diagnostic methods of brain disease are time-consuming, inconvenient and non-patient friendly. As more and more individuals undergo examinations to determine if they suffer from any form of brain disease, developing noninvasive, efficient, and patient friendly detection systems will be beneficial. Therefore, in this paper, we propose a novel noninvasive brain disease detection system based on the analysis of facial colors. The system consists of four components. A facial image is first captured through a specialized sensor, where four facial key blocks are next located automatically from the various facial regions. Color features are extracted from each block to form a feature vector for classification via the Probabilistic Collaborative based Classifier. To thoroughly test the system and its performance, seven facial key block combinations were experimented. The best result was achieved using the second facial key block, where it showed that the Probabilistic Collaborative based Classifier is the most suitable. The overall performance of the proposed system achieves an accuracy −95%, a sensitivity −94.33%, a specificity −95.67%, and an average processing time (for one sample) of <1 min at brain disease detection. PMID:29292716

Ethanol extract of Oenanthe javanica increases cell proliferation and neuroblast differentiation in the adolescent rat dentate gyrus

PubMed Central

Chen, Bai Hui; Park, Joon Ha; Cho, Jeong Hwi; Kim, In Hye; Shin, Bich Na; Ahn, Ji Hyeon; Hwang, Seok Joon; Yan, Bing Chun; Tae, Hyun Jin; Lee, Jae Chul; Bae, Eun Joo; Lee, Yun Lyul; Kim, Jong Dai; Won, Moo-Ho; Kang, Il Jun

2015-01-01

Oenanthe javanica is an aquatic perennial herb that belongs to the Oenanthe genus in Apiaceae family, and it displays well-known medicinal properties such as protective effects against glutamate-induced neurotoxicity. However, few studies regarding effects of Oenanthe javanica on neurogenesis in the brain have been reported. In this study, we examined the effects of a normal diet and a diet containing ethanol extract of Oenanthe javanica on cell proliferation and neuroblast differentiation in the subgranular zone of the hippocampal dentate gyrus of adolescent rats using Ki-67 (an endogenous marker for cell proliferation) and doublecortin (a marker for neuroblast). Our results showed that Oenanthe javanica extract significantly increased the number of Ki-67-immunoreactive cells and doublecortin-immunoreactive neuroblasts in the subgranular zone of the dentate gyrus in the adolescent rats. In addition, the immunoreactivity of brain-derived neurotrophic factor was significantly increased in the dentate gyrus of the Oenanthe javanica extract-treated group compared with the control group. However, we did not find that vascular endothelial growth factor expression was increased in the Oenanthe javanica extract-treated group compared with the control group. These results indicate that Oenanthe javanica extract improves cell proliferation and neuroblast differentiation by increasing brain-derived neurotrophic factor immunoreactivity in the rat dentate gyrus. PMID:25883627

Changes in Brain Metallome/Metabolome Pattern due to a Single i.v. Injection of Manganese in Rats

PubMed Central

Neth, Katharina; Lucio, Marianna; Walker, Alesia; Zorn, Julia; Schmitt-Kopplin, Philippe; Michalke, Bernhard

2015-01-01

Exposure to high concentrations of Manganese (Mn) is known to potentially induce an accumulation in the brain, leading to a Parkinson related disease, called manganism. Versatile mechanisms of Mn-induced brain injury are discussed, with inactivation of mitochondrial defense against oxidative stress being a major one. So far, studies indicate that the main Mn-species entering the brain are low molecular mass (LMM) compounds such as Mn-citrate. Applying a single low dose MnCl2 injection in rats, we observed alterations in Mn-species pattern within the brain by analysis of aqueous brain extracts by size-exclusion chromatography—inductively coupled plasma mass spectrometry (SEC-ICP-MS). Additionally, electrospray ionization—ion cyclotron resonance-Fourier transform-mass spectrometry (ESI-ICR/FT-MS) measurement of methanolic brain extracts revealed a comprehensive analysis of changes in brain metabolisms after the single MnCl2 injection. Major alterations were observed for amino acid, fatty acid, glutathione, glucose and purine/pyrimidine metabolism. The power of this metabolomic approach is the broad and detailed overview of affected brain metabolisms. We also correlated results from the metallomic investigations (Mn concentrations and Mn-species in brain) with the findings from metabolomics. This strategy might help to unravel the role of different Mn-species during Mn-induced alterations in brain metabolism. PMID:26383269

Segmentation of neuroanatomy in magnetic resonance images

NASA Astrophysics Data System (ADS)

Simmons, Andrew; Arridge, Simon R.; Barker, G. J.; Tofts, Paul S.

1992-06-01

Segmentation in neurological magnetic resonance imaging (MRI) is necessary for feature extraction, volume measurement and for the three-dimensional display of neuroanatomy. Automated and semi-automated methods offer considerable advantages over manual methods because of their lack of subjectivity, their data reduction capabilities, and the time savings they give. We have used dual echo multi-slice spin-echo data sets which take advantage of the intrinsically multispectral nature of MRI. As a pre-processing step, a rf non-uniformity correction is applied and if the data is noisy the images are smoothed using a non-isotropic blurring method. Edge-based processing is used to identify the skin (the major outer contour) and the eyes. Edge-focusing has been used to significantly simplify edge images and thus allow simple postprocessing to pick out the brain contour in each slice of the data set. Edge- focusing is a technique which locates significant edges using a high degree of smoothing at a coarse level and tracks these edges to a fine level where the edges can be determined with high positional accuracy. Both 2-D and 3-D edge-detection methods have been compared. Once isolated, the brain is further processed to identify CSF, and, depending upon the MR pulse sequence used, the brain itself may be sub-divided into gray matter and white matter using semi-automatic contrast enhancement and clustering methods.

A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data

PubMed Central

Ortega-Martorell, Sandra; Ruiz, Héctor; Vellido, Alfredo; Olier, Iván; Romero, Enrique; Julià-Sapé, Margarida; Martín, José D.; Jarman, Ian H.; Arús, Carles; Lisboa, Paulo J. G.

2013-01-01

Background The clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic Resonance Spectroscopy can complement imaging by supplying a metabolic fingerprint of the tissue. This study analyzes single-voxel magnetic resonance spectra, which represent signal information in the frequency domain. Given that a single voxel may contain a heterogeneous mix of tissues, signal source identification is a relevant challenge for the problem of tumor type classification from the spectroscopic signal. Methodology/Principal Findings Non-negative matrix factorization techniques have recently shown their potential for the identification of meaningful sources from brain tissue spectroscopy data. In this study, we use a convex variant of these methods that is capable of handling negatively-valued data and generating sources that can be interpreted as tumor class prototypes. A novel approach to convex non-negative matrix factorization is proposed, in which prior knowledge about class information is utilized in model optimization. Class-specific information is integrated into this semi-supervised process by setting the metric of a latent variable space where the matrix factorization is carried out. The reported experimental study comprises 196 cases from different tumor types drawn from two international, multi-center databases. The results indicate that the proposed approach outperforms a purely unsupervised process by achieving near perfect correlation of the extracted sources with the mean spectra of the tumor types. It also improves tissue type classification. Conclusions/Significance We show that source extraction by unsupervised matrix factorization benefits from the integration of the available class information, so operating in a semi-supervised learning manner, for discriminative source identification and brain tumor labeling from single-voxel spectroscopy data. We are confident that the proposed methodology has wider applicability for biomedical signal processing. PMID:24376744

Alzheimer Classification Using a Minimum Spanning Tree of High-Order Functional Network on fMRI Dataset

PubMed Central

Guo, Hao; Liu, Lei; Chen, Junjie; Xu, Yong; Jie, Xiang

2017-01-01

Functional magnetic resonance imaging (fMRI) is one of the most useful methods to generate functional connectivity networks of the brain. However, conventional network generation methods ignore dynamic changes of functional connectivity between brain regions. Previous studies proposed constructing high-order functional connectivity networks that consider the time-varying characteristics of functional connectivity, and a clustering method was performed to decrease computational cost. However, random selection of the initial clustering centers and the number of clusters negatively affected classification accuracy, and the network lost neurological interpretability. Here we propose a novel method that introduces the minimum spanning tree method to high-order functional connectivity networks. As an unbiased method, the minimum spanning tree simplifies high-order network structure while preserving its core framework. The dynamic characteristics of time series are not lost with this approach, and the neurological interpretation of the network is guaranteed. Simultaneously, we propose a multi-parameter optimization framework that involves extracting discriminative features from the minimum spanning tree high-order functional connectivity networks. Compared with the conventional methods, our resting-state fMRI classification method based on minimum spanning tree high-order functional connectivity networks greatly improved the diagnostic accuracy for Alzheimer's disease. PMID:29249926

Support vector machine learning-based fMRI data group analysis.

PubMed

Wang, Ze; Childress, Anna R; Wang, Jiongjiong; Detre, John A

2007-07-15

To explore the multivariate nature of fMRI data and to consider the inter-subject brain response discrepancies, a multivariate and brain response model-free method is fundamentally required. Two such methods are presented in this paper by integrating a machine learning algorithm, the support vector machine (SVM), and the random effect model. Without any brain response modeling, SVM was used to extract a whole brain spatial discriminance map (SDM), representing the brain response difference between the contrasted experimental conditions. Population inference was then obtained through the random effect analysis (RFX) or permutation testing (PMU) on the individual subjects' SDMs. Applied to arterial spin labeling (ASL) perfusion fMRI data, SDM RFX yielded lower false-positive rates in the null hypothesis test and higher detection sensitivity for synthetic activations with varying cluster size and activation strengths, compared to the univariate general linear model (GLM)-based RFX. For a sensory-motor ASL fMRI study, both SDM RFX and SDM PMU yielded similar activation patterns to GLM RFX and GLM PMU, respectively, but with higher t values and cluster extensions at the same significance level. Capitalizing on the absence of temporal noise correlation in ASL data, this study also incorporated PMU in the individual-level GLM and SVM analyses accompanied by group-level analysis through RFX or group-level PMU. Providing inferences on the probability of being activated or deactivated at each voxel, these individual-level PMU-based group analysis methods can be used to threshold the analysis results of GLM RFX, SDM RFX or SDM PMU.

Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method.

PubMed

Guo, Xinyu; Dominick, Kelli C; Minai, Ali A; Li, Hailong; Erickson, Craig A; Lu, Long J

2017-01-01

The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD) by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN) with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS) is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD) controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS) is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes). Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150). Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t -test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross different pre-defined brain networks including the default-mode, cingulo-opercular, frontal-parietal, and cerebellum. Thirteen of them are statically significant between ASD and TD groups (two sample t -test p < 0.05) while 19 of them are not. The relationship between the statically significant FCs and the corresponding ASD behavior symptoms is discussed based on the literature and clinician's expert knowledge. Meanwhile, the potential reason of obtaining 19 FCs which are not statistically significant is also provided.

Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features.

PubMed

Wu, Wei; Chen, Albert Y C; Zhao, Liang; Corso, Jason J

2014-03-01

Detection and segmentation of a brain tumor such as glioblastoma multiforme (GBM) in magnetic resonance (MR) images are often challenging due to its intrinsically heterogeneous signal characteristics. A robust segmentation method for brain tumor MRI scans was developed and tested. Simple thresholds and statistical methods are unable to adequately segment the various elements of the GBM, such as local contrast enhancement, necrosis, and edema. Most voxel-based methods cannot achieve satisfactory results in larger data sets, and the methods based on generative or discriminative models have intrinsic limitations during application, such as small sample set learning and transfer. A new method was developed to overcome these challenges. Multimodal MR images are segmented into superpixels using algorithms to alleviate the sampling issue and to improve the sample representativeness. Next, features were extracted from the superpixels using multi-level Gabor wavelet filters. Based on the features, a support vector machine (SVM) model and an affinity metric model for tumors were trained to overcome the limitations of previous generative models. Based on the output of the SVM and spatial affinity models, conditional random fields theory was applied to segment the tumor in a maximum a posteriori fashion given the smoothness prior defined by our affinity model. Finally, labeling noise was removed using "structural knowledge" such as the symmetrical and continuous characteristics of the tumor in spatial domain. The system was evaluated with 20 GBM cases and the BraTS challenge data set. Dice coefficients were computed, and the results were highly consistent with those reported by Zikic et al. (MICCAI 2012, Lecture notes in computer science. vol 7512, pp 369-376, 2012). A brain tumor segmentation method using model-aware affinity demonstrates comparable performance with other state-of-the art algorithms.

Morus alba leaf extract mediates neuroprotection against glyphosate-induced toxicity and biochemical alterations in the brain.

PubMed

Rebai, Olfa; Belkhir, Manel; Boujelben, Adnen; Fattouch, Sami; Amri, Mohamed

2017-04-01

Recent studies demonstrate that glyphosate exposure is associated with oxidative stress and some neurological disorders such as Parkinson's pathology. Therefore, phytochemicals, in particular phenolic compounds, have attracted increasing attention as potential agents for neuroprotection. In the present study, we investigate the impact of glyphosate on the rat brain following i.p. injection and the possible molecular target of neuroprotective activity of the phenolic fraction from Morus alba leaf extract (MALE) and its ability to reduce oxidative damage in the brain. Wistar rats from 180 to 240 g were i.p. treated with a single dose of glyphosate (100 mg kg -1 b.w.) or MALE (100 μg mL -1  kg -1 b.w.) for 2 weeks. Brain homogenates were used to evaluate neurotoxicity induced by the pesticide. For this, biochemical parameters were measured. Data shows that MALE regulated oxidative stress and counteracted glyphosate-induced deleterious effects and oxidative damage in the brain, as it abrogated LDH, protein carbonyls, and malonyldialdehyde. MALE also appears to be able to scavenge H 2 O 2 levels, maintain iron and Ca 2+ homeostasis, and increase SOD activity. Thus, in vivo results showed that mulberry leaf extract is a potent protector against glyphosate-induced toxicity, and its protective effect could result from synergism or antagonism between the various bioactive phenolic compounds in the acetonic fraction from M. alba leaf extract.

Mental Task Classification Scheme Utilizing Correlation Coefficient Extracted from Interchannel Intrinsic Mode Function.

PubMed

Rahman, Md Mostafizur; Fattah, Shaikh Anowarul

2017-01-01

In view of recent increase of brain computer interface (BCI) based applications, the importance of efficient classification of various mental tasks has increased prodigiously nowadays. In order to obtain effective classification, efficient feature extraction scheme is necessary, for which, in the proposed method, the interchannel relationship among electroencephalogram (EEG) data is utilized. It is expected that the correlation obtained from different combination of channels will be different for different mental tasks, which can be exploited to extract distinctive feature. The empirical mode decomposition (EMD) technique is employed on a test EEG signal obtained from a channel, which provides a number of intrinsic mode functions (IMFs), and correlation coefficient is extracted from interchannel IMF data. Simultaneously, different statistical features are also obtained from each IMF. Finally, the feature matrix is formed utilizing interchannel correlation features and intrachannel statistical features of the selected IMFs of EEG signal. Different kernels of the support vector machine (SVM) classifier are used to carry out the classification task. An EEG dataset containing ten different combinations of five different mental tasks is utilized to demonstrate the classification performance and a very high level of accuracy is achieved by the proposed scheme compared to existing methods.

Antioxidant and inhibitory effect of red ginger (Zingiber officinale var. Rubra) and white ginger (Zingiber officinale Roscoe) on Fe(2+) induced lipid peroxidation in rat brain in vitro.

PubMed

Oboh, Ganiyu; Akinyemi, Ayodele J; Ademiluyi, Adedayo O

2012-01-01

Neurodegerative diseases have been linked to oxidative stress arising from peroxidation of membrane biomolecules and high levels of Fe have been reported to play an important role in neurodegenerative diseases and other brain disorder. Malondialdehyde (MDA) is the end-product of lipid peroxidation and the production of this aldehyde is used as a biomarker to measure the level of oxidative stress in an organism. The present study compares the protective properties of two varieties of ginger [red ginger (Zingiber officinale var. Rubra) and white ginger (Zingiber officinale Roscoe)] on Fe(2+) induced lipid peroxidation in rat brain in vitro. Incubation of the brain tissue homogenate in the presence of Fe caused a significant increase in the malondialdehyde (MDA) contents of the brain. However, the aqueous extract from both varieties of ginger caused a significant decrease in the MDA contents of the brain in a dose-dependent manner. However, the aqueous extract of red ginger had a significantly higher inhibitory effect on both Fe(2+)-induced lipid peroxidation in the rat brain homogenates than that of white ginger. This higher inhibitory effect of red ginger could be attributed to its significantly higher phytochemical content, Fe(2+) chelating ability, OH scavenging ability and reducing power. However, part of the mechanisms through which the extractable phytochemicals in ginger (red and white) protect the brain may be through their antioxidant activity, Fe(2+) chelating and OH scavenging ability. Therefore, oxidative stress in the brain could be potentially managed/prevented by dietary intake of ginger varieties (red ginger and white ginger rhizomes). Copyright © 2010 Elsevier GmbH. All rights reserved.

High-frequency combination coding-based steady-state visual evoked potential for brain computer interface

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Feng; Zhang, Xin; Xie, Jun

2015-03-10

This study presents a new steady-state visual evoked potential (SSVEP) paradigm for brain computer interface (BCI) systems. The goal of this study is to increase the number of targets using fewer stimulation high frequencies, with diminishing subject’s fatigue and reducing the risk of photosensitive epileptic seizures. The new paradigm is High-Frequency Combination Coding-Based High-Frequency Steady-State Visual Evoked Potential (HFCC-SSVEP).Firstly, we studied SSVEP high frequency(beyond 25 Hz)response of SSVEP, whose paradigm is presented on the LED. The SNR (Signal to Noise Ratio) of high frequency(beyond 40 Hz) response is very low, which is been unable to be distinguished through the traditional analysis method;more » Secondly we investigated the HFCC-SSVEP response (beyond 25 Hz) for 3 frequencies (25Hz, 33.33Hz, and 40Hz), HFCC-SSVEP produces n{sup n} with n high stimulation frequencies through Frequence Combination Code. Further, Animproved Hilbert-huang transform (IHHT)-based variable frequency EEG feature extraction method and a local spectrum extreme target identification algorithmare adopted to extract time-frequency feature of the proposed HFCC-SSVEP response.Linear predictions and fixed sifting (iterating) 10 time is used to overcome the shortage of end effect and stopping criterion,generalized zero-crossing (GZC) is used to compute the instantaneous frequency of the proposed SSVEP respondent signals, the improved HHT-based feature extraction method for the proposed SSVEP paradigm in this study increases recognition efficiency, so as to improve ITR and to increase the stability of the BCI system. what is more, SSVEPs evoked by high-frequency stimuli (beyond 25Hz) minimally diminish subject’s fatigue and prevent safety hazards linked to photo-induced epileptic seizures, So as to ensure the system efficiency and undamaging.This study tests three subjects in order to verify the feasibility of the proposed method.« less

Brain aluminium accumulation and oxidative stress in the presence of calcium silicate dental cements.

PubMed

Demirkaya, K; Demirdöğen, B Can; Torun, Z Öncel; Erdem, O; Çırak, E; Tunca, Y M

2017-10-01

Mineral trioxide aggregate (MTA) is a calcium silicate dental cement used for various applications in dentistry. This study was undertaken to test whether the presence of three commercial brands of calcium silicate dental cements in the dental extraction socket of rats would affect the brain aluminium (Al) levels and oxidative stress parameters. Right upper incisor was extracted and polyethylene tubes filled with MTA Angelus, MTA Fillapex or Theracal LC, or left empty for the control group, were inserted into the extraction socket. Rats were killed 7, 30 or 60 days after operation. Brain tissues were obtained before killing. Al levels were measured by atomic absorption spectrometry. Thiobarbituric acid reactive substances (TBARS) levels, catalase (CAT), superoxide dismutase (SOD) and glutathione peroxidase (GPx) activities were determined using spectrophotometry. A transient peak was observed in brain Al level of MTA Angelus group on day 7, while MTA Fillapex and Theracal LC groups reached highest brain Al level on day 60. Brain TBARS level, CAT, SOD and GPx activities transiently increased on day 7 and then returned to almost normal levels. This in vivo study for the first time indicated that initial washout may have occurred in MTA Angelus, while element leaching after the setting is complete may have taken place for MTA Fillapex and Theracal LC. Moreover, oxidative stress was induced and antioxidant enzymes were transiently upregulated. Further studies to search for oxidative neuronal damage should be done to completely understand the possible toxic effects of calcium silicate cements on the brain.

Orbital penetration associated with tooth extraction.

PubMed

Smith, Mark M; Smith, Eric M; La Croix, Noelle; Mould, John

2003-03-01

Three cats and 2 dogs were evaluated for ophthalmologic complications associated with tooth extraction procedures. Orbital penetration leading to ocular and, in one case, brain trauma was secondary to iatrogenic injury from a dental elevator. Outcomes included enucleation of the affected eye in 3 cases, and death from brain abscessation in 1 case. Early treatment or, preferably, referral to a veterinary ophthalmology specialist may prevent such outcomes. Awareness of the anatomical proximity of caudal maxillary tooth roots and the orbit, appropriate interpretation of diagnostic intraoral dental radiographs, and technical proficiency in tooth extraction techniques will minimize these complications in veterinary dental practice.

Cholecystokinin-converting enzymes in brain.

PubMed Central

Malesci, A; Straus, E; Yalow, R S

1980-01-01

Crude extracts of porcine cerebral cortical tissue convert cholecystokinin (CCK) to its COOH-terminal fragments, the dodecapeptide (CCK-12) and the octapeptide (CCK-8). The Sephadex G-75 void volume eluate of the crude extract cleaves the arginine-isoleucine bond and effects conversion only to CCK-12; the Sephadex G-50 void volume eluate of the same extract cleaves the arginine-aspartate bond as well, so that both CCK-12 and CCK-8 are end products. Thus, there are at least two enzymes; the one involved in the conversion to CCK-12 is of larger molecular radius than the other. The Km for the cleavage of CCK at the arginine-isoleucine bond by the Sephadex G-75 void volume eluate enzyme is 1.1 X 10(-6) M; the Km for trypsin cleavage of the same bond is 4.7 x 10(-6) M. The lower Vmax for the brain enzyme (1.5 x 10(-11) mol/min per g of extract) compared with trypsin (66 x 10(-11) mol/min per g of trypsin) simply reflects the lesser degree of purify of the brain extract than of the highly purified trypsin. Images PMID:6987659

Matrix-Assisted Laser Desorption/Ionisation - High-Energy Collision-Induced Dissociation of Steroids: Analysis of Oxysterols in Rat Brain

PubMed Central

Wang, Yuqin; Hornshaw, Martin; Alvelius, Gunvor; Bodin, Karl; Liu, Suya; Sjövall, Jan; Griffiths, William J.

2008-01-01

Neutral steroids have traditionally been analysed by gas chromatography – mass spectrometry (GC-MS) after necessary derivatisation reactions. However, GC-MS is unsuitable for the analysis of many conjugated steroids and those with unsuspected functional groups. Here we describe an alternative analytical method specifically designed for the analysis of oxosteroids and those with a 3β-hydroxy-Δ5 or 5α-hydrogen-3β-hydroxy structure. Steroids were derivatised with Girard P (GP) hydrazine to give GP hydrazones which are charged species and readily analysed by matrix-assisted laser desorption/ionization mass spectrometry. The resulting [M]+ ions were then subjected to high-energy collision-induced dissociation on a tandem time-of-flight instrument. The product-ion spectra give structurally informative fragment-ion patterns. The sensitivity of the analytical method is such that steroids structures can be determined from low pg (low fmole) amounts of sample. The utility of the method has been demonstrated by the analysis of oxysterols extracted from rat brain. PMID:16383324

Machine Learning Classification of Cirrhotic Patients with and without Minimal Hepatic Encephalopathy Based on Regional Homogeneity of Intrinsic Brain Activity.

PubMed

Chen, Qiu-Feng; Chen, Hua-Jun; Liu, Jun; Sun, Tao; Shen, Qun-Tai

2016-01-01

Machine learning-based approaches play an important role in examining functional magnetic resonance imaging (fMRI) data in a multivariate manner and extracting features predictive of group membership. This study was performed to assess the potential for measuring brain intrinsic activity to identify minimal hepatic encephalopathy (MHE) in cirrhotic patients, using the support vector machine (SVM) method. Resting-state fMRI data were acquired in 16 cirrhotic patients with MHE and 19 cirrhotic patients without MHE. The regional homogeneity (ReHo) method was used to investigate the local synchrony of intrinsic brain activity. Psychometric Hepatic Encephalopathy Score (PHES) was used to define MHE condition. SVM-classifier was then applied using leave-one-out cross-validation, to determine the discriminative ReHo-map for MHE. The discrimination map highlights a set of regions, including the prefrontal cortex, anterior cingulate cortex, anterior insular cortex, inferior parietal lobule, precentral and postcentral gyri, superior and medial temporal cortices, and middle and inferior occipital gyri. The optimized discriminative model showed total accuracy of 82.9% and sensitivity of 81.3%. Our results suggested that a combination of the SVM approach and brain intrinsic activity measurement could be helpful for detection of MHE in cirrhotic patients.

Standardized Bacopa monnieri extract ameliorates acute paraquat-induced oxidative stress, and neurotoxicity in prepubertal mice brain.

PubMed

Hosamani, Ravikumar; Krishna, Gokul; Muralidhara

2016-12-01

Bacopa monnieri (BM), an ayurvedic medicinal plant, has attracted considerable interest owing to its diverse neuropharmacological properties. Epidemiological studies have shown significant correlation between paraquat (PQ) exposure and increased risk for Parkinson's disease in humans. In this study, we examined the propensity of standardized extract of BM to attenuate acute PQ-induced oxidative stress, mitochondrial dysfunctions, and neurotoxicity in the different brain regions of prepubertal mice. To test this hypothesis, prepubertal mice provided orally with standardized BM extract (200 mg/kg body weight/day for 4 weeks) were challenged with an acute dose (15 mg/kg body weight, intraperitoneally) of PQ after 3 hours of last dose of extract. Mice were sacrificed after 48 hours of PQ injection, and different brain regions were isolated and subjected to biochemical determinations/quantification of central monoamine (dopamine, DA) levels (by high-performance liquid chromatography). Oral supplementation of BM for 4 weeks resulted in significant reduction in the basal levels of oxidative markers such as reactive oxygen species (ROS), malondialdehyde (MDA), and hydroperoxides (HP) in various brain regions. PQ at the administered dose elicited marked oxidative stress within 48 hours in various brain regions of mice. However, BM prophylaxis significantly improved oxidative homeostasis by restoring PQ-induced ROS, MDA, and HP levels and also by attenuating mitochondrial dysfunction. Interestingly, BM supplementation restored the activities of cholinergic enzymes along with the restoration of striatal DA levels among the PQ-treated mice. Based on these findings, we infer that BM prophylaxis renders the brain resistant to PQ-mediated oxidative perturbations and thus may be better exploited as a preventive approach to protect against oxidative-mediated neuronal dysfunctions.

BCI Competition IV – Data Set I: Learning Discriminative Patterns for Self-Paced EEG-Based Motor Imagery Detection

PubMed Central

Zhang, Haihong; Guan, Cuntai; Ang, Kai Keng; Wang, Chuanchu

2012-01-01

Detecting motor imagery activities versus non-control in brain signals is the basis of self-paced brain-computer interfaces (BCIs), but also poses a considerable challenge to signal processing due to the complex and non-stationary characteristics of motor imagery as well as non-control. This paper presents a self-paced BCI based on a robust learning mechanism that extracts and selects spatio-spectral features for differentiating multiple EEG classes. It also employs a non-linear regression and post-processing technique for predicting the time-series of class labels from the spatio-spectral features. The method was validated in the BCI Competition IV on Dataset I where it produced the lowest prediction error of class labels continuously. This report also presents and discusses analysis of the method using the competition data set. PMID:22347153

Comparison of methods of preserving tissues for pesticide analysis

USGS Publications Warehouse

Stickel, W.H.; Stickel, L.F.; Dyrland, R.A.; Hughes, D.L.

1984-01-01

Formalin preservation, freezing, spoiling followed by freezing, and phenoxyethanol were compared in terms of concentrations of DDT, DDD, DDE, endrin, and hepatachlor epoxide measured in brain, liver and carcass of birds fed dietary dosages of pesticides and in spiked egg homogenate. Phenoxyethanol proved to be an unsatisfactory preservative; the amount of 'extractable lipid' was excessive, and measurements of concentrations in replicates were erratic. Concentrations of residues in formalin-preserved and frozen samples did not differ significantly in any tissue. Percentage lipid in brains and eggs, however, were significantly lower in formalin-preserved samples. Samples of muscle and liver that had been spoiled before freezing yielded less DDD, and muscle samples yielded more DDT than formalin-preserved samples. The authors conclude that formalin preservation is a satisfactory method for preservation of field samples and that the warming and spoiling of samples that may occur unavoidably in the field will not result in misleading analytical results.

Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder.

PubMed

Zhao, Yu; Dong, Qinglin; Chen, Hanbo; Iraji, Armin; Li, Yujie; Makkie, Milad; Kou, Zhifeng; Liu, Tianming

2017-12-01

State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human brains. However, due to the variability of individual brain networks and the large scale of such networks needed for statistically meaningful group-level analysis, it is still a challenging and open problem to derive group-wise common networks as network atlases. Inspired by the superior spatial pattern description ability of the deep convolutional neural networks (CNNs), a novel deep 3D convolutional autoencoder (CAE) network is designed here to extract spatial brain network features effectively, based on which an Apache Spark enabled computational framework is developed for fast clustering of larger number of network maps into fine-granularity atlases. To evaluate this framework, 10 resting state networks (RSNs) were manually labeled from the sparsely decomposed networks of Human Connectome Project (HCP) fMRI data and 5275 network training samples were obtained, in total. Then the deep CAE models are trained by these functional networks' spatial maps, and the learned features are used to refine the original 10 RSNs into 17 network atlases that possess fine-granularity functional network patterns. Interestingly, it turned out that some manually mislabeled outliers in training networks can be corrected by the deep CAE derived features. More importantly, fine granularities of networks can be identified and they reveal unique network patterns specific to different brain task states. By further applying this method to a dataset of mild traumatic brain injury study, it shows that the technique can effectively identify abnormal small networks in brain injury patients in comparison with controls. In general, our work presents a promising deep learning and big data analysis solution for modeling functional connectomes, with fine granularities, based on fMRI data. Copyright © 2017 Elsevier B.V. All rights reserved.

CD38-dependent ADP-ribosyl cyclase activity in developing and adult mouse brain.

PubMed Central

Ceni, Claire; Pochon, Nathalie; Brun, Virginie; Muller-Steffner, Hélène; Andrieux, Annie; Grunwald, Didier; Schuber, Francis; De Waard, Michel; Lund, Frances; Villaz, Michel; Moutin, Marie-Jo

2003-01-01

CD38 is a transmembrane glycoprotein that is expressed in many tissues throughout the body. In addition to its major NAD+-glycohydrolase activity, CD38 is also able to synthesize cyclic ADP-ribose, an endogenous calcium-regulating molecule, from NAD+. In the present study, we have compared ADP-ribosyl cyclase and NAD+-glycohydrolase activities in protein extracts of brains from developing and adult wild-type and Cd38 -/- mice. In extracts from wild-type brain, cyclase activity was detected spectrofluorimetrically, using nicotinamide-guanine dinucleotide as a substrate (GDP-ribosyl cyclase activity), as early as embryonic day 15. The level of cyclase activity was similar in the neonate brain (postnatal day 1) and then increased greatly in the adult brain. Using [14C]NAD+ as a substrate and HPLC analysis, we found that ADP-ribose is the major product formed in the brain at all developmental stages. Under the same experimental conditions, neither NAD+-glycohydrolase nor GDP-ribosyl cyclase activity could be detected in extracts of brains from developing or adult Cd38 -/- mice, demonstrating that CD38 is the predominant constitutive enzyme endowed with these activities in brain at all developmental stages. The activity measurements correlated with the level of CD38 transcripts present in the brains of developing and adult wild-type mice. Using confocal microscopy we showed, in primary cultures of hippocampal cells, that CD38 is expressed by both neurons and glial cells, and is enriched in neuronal perikarya. Intracellular NAD+-glycohydrolase activity was measured in hippocampal cell cultures, and CD38-dependent cyclase activity was higher in brain fractions enriched in intracellular membranes. Taken together, these results lead us to speculate that CD38 might have an intracellular location in neural cells in addition to its plasma membrane location, and may play an important role in intracellular cyclic ADP-ribose-mediated calcium signalling in brain tissue. PMID:12403647

Influence of the segmentation on the characterization of cerebral networks of structural damage for patients with disorders of consciousness

NASA Astrophysics Data System (ADS)

Martínez, Darwin; Mahalingam, Jamuna J.; Soddu, Andrea; Franco, Hugo; Lepore, Natasha; Laureys, Steven; Gómez, Francisco

2015-01-01

Disorders of consciousness (DOC) are a consequence of a variety of severe brain injuries. DOC commonly results in anatomical brain modifications, which can affect cortical and sub-cortical brain structures. Postmortem studies suggest that severity of brain damage correlates with level of impairment in DOC. In-vivo studies in neuroimaging mainly focus in alterations on single structures. Recent evidence suggests that rather than one, multiple brain regions can be simultaneously affected by this condition. In other words, DOC may be linked to an underlying cerebral network of structural damage. Recently, geometrical spatial relationships among key sub-cortical brain regions, such as left and right thalamus and brain stem, have been used for the characterization of this network. This approach is strongly supported on automatic segmentation processes, which aim to extract regions of interests without human intervention. Nevertheless, patients with DOC usually present massive structural brain changes. Therefore, segmentation methods may highly influence the characterization of the underlying cerebral network structure. In this work, we evaluate the level of characterization obtained by using the spatial relationships as descriptor of a sub-cortical cerebral network (left and right thalamus) in patients with DOC, when different segmentation approaches are used (FSL, Free-surfer and manual segmentation). Our results suggest that segmentation process may play a critical role for the construction of robust and reliable structural characterization of DOC conditions.

Automatic, accurate, and reproducible segmentation of the brain and cerebro-spinal fluid in T1-weighted volume MRI scans and its application to serial cerebral and intracranial volumetry

NASA Astrophysics Data System (ADS)

Lemieux, Louis

2001-07-01

A new fully automatic algorithm for the segmentation of the brain and cerebro-spinal fluid (CSF) from T1-weighted volume MRI scans of the head was specifically developed in the context of serial intra-cranial volumetry. The method is an extension of a previously published brain extraction algorithm. The brain mask is used as a basis for CSF segmentation based on morphological operations, automatic histogram analysis and thresholding. Brain segmentation is then obtained by iterative tracking of the brain-CSF interface. Grey matter (GM), white matter (WM) and CSF volumes are calculated based on a model of intensity probability distribution that includes partial volume effects. Accuracy was assessed using a digital phantom scan. Reproducibility was assessed by segmenting pairs of scans from 20 normal subjects scanned 8 months apart and 11 patients with epilepsy scanned 3.5 years apart. Segmentation accuracy as measured by overlap was 98% for the brain and 96% for the intra-cranial tissues. The volume errors were: total brain (TBV): -1.0%, intra-cranial (ICV):0.1%, CSF: +4.8%. For repeated scans, matching resulted in improved reproducibility. In the controls, the coefficient of reliability (CR) was 1.5% for the TVB and 1.0% for the ICV. In the patients, the Cr for the ICV was 1.2%.

Label-free imaging of brain and brain tumor specimens with combined two-photon excited fluorescence and second harmonic generation microscopy

NASA Astrophysics Data System (ADS)

Jiang, Liwei; Wang, Xingfu; Wu, Zanyi; Du, Huiping; Wang, Shu; Li, Lianhuang; Fang, Na; Lin, Peihua; Chen, Jianxin; Kang, Dezhi; Zhuo, Shuangmu

2017-10-01

Label-free imaging techniques are gaining acceptance within the medical imaging field, including brain imaging, because they have the potential to be applied to intraoperative in situ identifications of pathological conditions. In this paper, we describe the use of two-photon excited fluorescence (TPEF) and second harmonic generation (SHG) microscopy in combination for the label-free detection of brain and brain tumor specimens; gliomas. Two independently detecting channels were chosen to subsequently collect TPEF/SHG signals from the specimen to increase TPEF/SHG image contrasts. Our results indicate that the combined TPEF/SHG microscopic techniques can provide similar rat brain structural information and produce a similar resolution like conventional H&E staining in neuropathology; including meninges, cerebral cortex, white-matter structure corpus callosum, choroid plexus, hippocampus, striatum, and cerebellar cortex. It can simultaneously detect infiltrating human brain tumor cells, the extracellular matrix collagen fiber of connective stroma within brain vessels and collagen depostion in tumor microenvironments. The nuclear-to-cytoplasmic ratio and collagen content can be extracted as quantitative indicators for differentiating brain gliomas from healthy brain tissues. With the development of two-photon fiberscopes and microendoscope probes and their clinical applications, the combined TPEF and SHG microcopy may become an important multimodal, nonlinear optical imaging approach for real-time intraoperative histological diagnostics of residual brain tumors. These occur in various brain regions during ongoing surgeries through the method of simultaneously identifying tumor cells, and the change of tumor microenvironments, without the need for the removal biopsies and without the need for tissue labelling or fluorescent markers.

Imaging MALDI MS of Dosed Brain Tissues Utilizing an Alternative Analyte Pre-extraction Approach

NASA Astrophysics Data System (ADS)

Quiason, Cristine M.; Shahidi-Latham, Sheerin K.

2015-06-01

Matrix-assisted laser desorption ionization (MALDI) imaging mass spectrometry has been adopted in the pharmaceutical industry as a useful tool to detect xenobiotic distribution within tissues. A unique sample preparation approach for MALDI imaging has been described here for the extraction and detection of cobimetinib and clozapine, which were previously undetectable in mouse and rat brain using a single matrix application step. Employing a combination of a buffer wash and a cyclohexane pre-extraction step prior to standard matrix application, the xenobiotics were successfully extracted and detected with an 8 to 20-fold gain in sensitivity. This alternative approach for sample preparation could serve as an advantageous option when encountering difficult to detect analytes.

The influence of Brazilian plant extracts on Streptococcus mutans biofilm

PubMed Central

BARNABÉ, Michele; SARACENI, Cíntia Helena Coury; DUTRA-CORREA, Maristela; SUFFREDINI, Ivana Barbosa

2014-01-01

Nineteen plant extracts obtained from plants from the Brazilian Amazon showed activity against planktonic Streptococcus mutans, an important bacterium involved in the first steps of biofilm formation and the subsequent initiation of several oral diseases. Objective Our goal was to verify whether plant extracts that showed activity against planktonic S. mutans could prevent the organization of or even disrupt a single-species biofilm made by the same bacteria. Material and Methods Plant extracts were tested on a single-bacteria biofilm prepared using the Zürich method. Each plant extract was tested at a concentration 5 times higher than its minimum inhibitory concentration (MIC). Discs of hydroxyapatite were submersed overnight in brain-heart infusion broth enriched with saccharose 5%, which provided sufficient time for biofilm formation. The discs were then submersed in extract solutions for one minute, three times per day, for two subsequent days. The discs were then washed with saline three times, at ten seconds each, after each treatment. Supports were allowed to remain in the enriched medium for one additional night. At the end of the process, the bacteria were removed from the discs by vortexing and were counted. Results Only two of 19 plant extracts showed activity in the present assay: EB1779, obtained from Dioscorea altissima, and EB1673, obtained from Annona hypoglauca. Although the antibacterial activity of the plant extracts was first observed against planktonic S. mutans, influence over biofilm formation was not necessarily observed in the biofilm model. The present results motivate us to find new natural products to be used in dentistry. PMID:25466471

A Nth-order linear algorithm for extracting diffuse correlation spectroscopy blood flow indices in heterogeneous tissues.

PubMed

Shang, Yu; Yu, Guoqiang

2014-09-29

Conventional semi-infinite analytical solutions of correlation diffusion equation may lead to errors when calculating blood flow index (BFI) from diffuse correlation spectroscopy (DCS) measurements in tissues with irregular geometries. Very recently, we created an algorithm integrating a N th-order linear model of autocorrelation function with the Monte Carlo simulation of photon migrations in homogenous tissues with arbitrary geometries for extraction of BFI (i.e., αD B ). The purpose of this study is to extend the capability of the N th-order linear algorithm for extracting BFI in heterogeneous tissues with arbitrary geometries. The previous linear algorithm was modified to extract BFIs in different types of tissues simultaneously through utilizing DCS data at multiple source-detector separations. We compared the proposed linear algorithm with the semi-infinite homogenous solution in a computer model of adult head with heterogeneous tissue layers of scalp, skull, cerebrospinal fluid, and brain. To test the capability of the linear algorithm for extracting relative changes of cerebral blood flow (rCBF) in deep brain, we assigned ten levels of αD B in the brain layer with a step decrement of 10% while maintaining αD B values constant in other layers. Simulation results demonstrate the accuracy (errors < 3%) of high-order ( N  ≥ 5) linear algorithm in extracting BFIs in different tissue layers and rCBF in deep brain. By contrast, the semi-infinite homogenous solution resulted in substantial errors in rCBF (34.5% ≤ errors ≤ 60.2%) and BFIs in different layers. The N th-order linear model simplifies data analysis, thus allowing for online data processing and displaying. Future study will test this linear algorithm in heterogeneous tissues with different levels of blood flow variations and noises.

Anti-epileptogenic and antioxidant effect of Lavandula officinalis aerial part extract against pentylenetetrazol-induced kindling in male mice.

PubMed

Rahmati, Batool; Khalili, Mohsen; Roghani, Mehrdad; Ahghari, Parisa

2013-06-21

Repeated application of Lavandula officinalis (L. officinalis) has been recommended for a long time in Iranian traditional medicine for some of nervous disorders like epilepsy and dementia. However, there is no available report for the effect of chronic administration of Lavandula extract in development (acquisition) of epilepsy. Therefore, this study was designed to investigate the anti-epileptogenic and antioxidant activity of repeated administration of Lavandula officinalis extract on pentylenetetrazol (PTZ) kindling seizures in mice model. Lavandula officinalis was tested for its ability (i) to suppress the seizure intensity and lethal effects of PTZ in kindled mice (anti-epileptogenic effect), (ii) to attenuate the PTZ-induced oxidative injury in the brain tissue (antioxidant effect) when given as a pretreatment prior to each PTZ injection during kindling development. Valproate (Val), a major antiepileptic drug, was also tested for comparison. Val and Lavandula officinalis extract showed anti-epileptogenic properties as they reduced seizure score of kindled mice and PTZ-induced mortality. In this regard, Lavandula officinalis was more effective than Val. Both Lavandula officinalis and Val suppressed brain nitric oxide (NO) level of kindled mice in comparison with the control and PTZ group. Meanwhile, Lavandula officinalis suppressed NO level more than Val and Lavandula officinalis also decreased brain MDA level relative to PTZ group. This is the first report to demonstrate NO suppressing and anti-epileptogenic effect of chronic administration of Lavandula officinalis extract on acquisition of epilepsy in PTZ kindling mice model. In this regard, Lavandula officinalis extract was more effective than Val, possibly and in part via brain NO suppression. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Natural image classification driven by human brain activity

NASA Astrophysics Data System (ADS)

Zhang, Dai; Peng, Hanyang; Wang, Jinqiao; Tang, Ming; Xue, Rong; Zuo, Zhentao

2016-03-01

Natural image classification has been a hot topic in computer vision and pattern recognition research field. Since the performance of an image classification system can be improved by feature selection, many image feature selection methods have been developed. However, the existing supervised feature selection methods are typically driven by the class label information that are identical for different samples from the same class, ignoring with-in class image variability and therefore degrading the feature selection performance. In this study, we propose a novel feature selection method, driven by human brain activity signals collected using fMRI technique when human subjects were viewing natural images of different categories. The fMRI signals associated with subjects viewing different images encode the human perception of natural images, and therefore may capture image variability within- and cross- categories. We then select image features with the guidance of fMRI signals from brain regions with active response to image viewing. Particularly, bag of words features based on GIST descriptor are extracted from natural images for classification, and a sparse regression base feature selection method is adapted to select image features that can best predict fMRI signals. Finally, a classification model is built on the select image features to classify images without fMRI signals. The validation experiments for classifying images from 4 categories of two subjects have demonstrated that our method could achieve much better classification performance than the classifiers built on image feature selected by traditional feature selection methods.

Online Motor Imagery Training Effect for the Appearance of Event Related Desynchronization (ERD)

NASA Astrophysics Data System (ADS)

Takahashi, Mitsuru; Gouko, Manabu; Ito, Koji

Stroke patients have some motor deficits, but they can regain their motor abilities by rehabilitation. In the aspect of rehabilitation, voluntary movement is very important. We propose a system which can make a closed loop in brain for stroke patients like voluntary movement. Event Related Desynchronization (ERD) is used to extract patients' motor intention, and then Functional Electrical Stimulation (FES) stimuls their paralyzed muscles. In many Brain Computer Interface (BCI) researches, subjects are trained for several months or years to do the task, because of the difficulty to extract clear ERD without training. Thinking about applying for stroke patients, motor imagery training should be shorter, because of the brain plasticity. We did a pilot study about the effect of visual feedback training for three days with healthy subjects. The result indicated that ERD could be clearly extracted in three days, but the training effect differs in each subjects.

Detection of Movement Related Cortical Potentials from EEG Using Constrained ICA for Brain-Computer Interface Applications.

PubMed

Karimi, Fatemeh; Kofman, Jonathan; Mrachacz-Kersting, Natalie; Farina, Dario; Jiang, Ning

2017-01-01

The movement related cortical potential (MRCP), a slow cortical potential from the scalp electroencephalogram (EEG), has been used in real-time brain-computer-interface (BCI) systems designed for neurorehabilitation. Detecting MPCPs in real time with high accuracy and low latency is essential in these applications. In this study, we propose a new MRCP detection method based on constrained independent component analysis (cICA). The method was tested for MRCP detection during executed and imagined ankle dorsiflexion of 24 healthy participants, and compared with four commonly used spatial filters for MRCP detection in an offline experiment. The effect of cICA and the compared spatial filters on the morphology of the extracted MRCP was evaluated by two indices quantifying the signal-to-noise ratio and variability of the extracted MRCP. The performance of the filters for detection was then directly compared for accuracy and latency. The latency obtained with cICA (-34 ± 29 ms motor execution (ME) and 28 ± 16 ms for motor imagery (MI) dataset) was significantly smaller than with all other spatial filters. Moreover, cICA resulted in greater true positive rates (87.11 ± 11.73 for ME and 86.66 ± 6.96 for MI dataset) and lower false positive rates (20.69 ± 13.68 for ME and 19.31 ± 12.60 for MI dataset) compared to the other methods. These results confirm the superiority of cICA in MRCP detection with respect to previously proposed EEG filtering approaches.

Finding imaging patterns of structural covariance via Non-Negative Matrix Factorization.

PubMed

Sotiras, Aristeidis; Resnick, Susan M; Davatzikos, Christos

2015-03-01

In this paper, we investigate the use of Non-Negative Matrix Factorization (NNMF) for the analysis of structural neuroimaging data. The goal is to identify the brain regions that co-vary across individuals in a consistent way, hence potentially being part of underlying brain networks or otherwise influenced by underlying common mechanisms such as genetics and pathologies. NNMF offers a directly data-driven way of extracting relatively localized co-varying structural regions, thereby transcending limitations of Principal Component Analysis (PCA), Independent Component Analysis (ICA) and other related methods that tend to produce dispersed components of positive and negative loadings. In particular, leveraging upon the well known ability of NNMF to produce parts-based representations of image data, we derive decompositions that partition the brain into regions that vary in consistent ways across individuals. Importantly, these decompositions achieve dimensionality reduction via highly interpretable ways and generalize well to new data as shown via split-sample experiments. We empirically validate NNMF in two data sets: i) a Diffusion Tensor (DT) mouse brain development study, and ii) a structural Magnetic Resonance (sMR) study of human brain aging. We demonstrate the ability of NNMF to produce sparse parts-based representations of the data at various resolutions. These representations seem to follow what we know about the underlying functional organization of the brain and also capture some pathological processes. Moreover, we show that these low dimensional representations favorably compare to descriptions obtained with more commonly used matrix factorization methods like PCA and ICA. Copyright © 2014 Elsevier Inc. All rights reserved.

Identifying HIV associated neurocognitive disorder using large-scale Granger causality analysis on resting-state functional MRI

NASA Astrophysics Data System (ADS)

DSouza, Adora M.; Abidin, Anas Z.; Leistritz, Lutz; Wismüller, Axel

2017-02-01

We investigate the applicability of large-scale Granger Causality (lsGC) for extracting a measure of multivariate information flow between pairs of regional brain activities from resting-state functional MRI (fMRI) and test the effectiveness of these measures for predicting a disease state. Such pairwise multivariate measures of interaction provide high-dimensional representations of connectivity profiles for each subject and are used in a machine learning task to distinguish between healthy controls and individuals presenting with symptoms of HIV Associated Neurocognitive Disorder (HAND). Cognitive impairment in several domains can occur as a result of HIV infection of the central nervous system. The current paradigm for assessing such impairment is through neuropsychological testing. With fMRI data analysis, we aim at non-invasively capturing differences in brain connectivity patterns between healthy subjects and subjects presenting with symptoms of HAND. To classify the extracted interaction patterns among brain regions, we use a prototype-based learning algorithm called Generalized Matrix Learning Vector Quantization (GMLVQ). Our approach to characterize connectivity using lsGC followed by GMLVQ for subsequent classification yields good prediction results with an accuracy of 87% and an area under the ROC curve (AUC) of up to 0.90. We obtain a statistically significant improvement (p<0.01) over a conventional Granger causality approach (accuracy = 0.76, AUC = 0.74). High accuracy and AUC values using our multivariate method to connectivity analysis suggests that our approach is able to better capture changes in interaction patterns between different brain regions when compared to conventional Granger causality analysis known from the literature.

Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach.

PubMed

Hussain, Lal

2018-06-01

Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.

Antihypernociceptive and antioxidant effects of Petersianthus macrocarpus stem bark extracts in rats with complete Freund's adjuvant-induced persistent inflammatory pain.

PubMed

Bomba, Francis Desire Tatsinkou; Wandji, Bibiane Aimée; Fofié, Christian Kuete; Kamanyi, Albert; Nguelefack, Télesphore Benoit

2017-03-14

Background Petersianthus macrocarpus (P. Beauv.) Liben (Lecythidaceae) is a plant used in Cameroonian folk medicine to cure ailments such as inflammation and pain. Previous work showed that aqueous (AEPM) and methanol (MEPM) extracts from the stem bark of P. macrocarpus possess acute analgesic activities. The present study evaluates whether the same extracts could inhibit persistent hyperalgesia induced by complete Freund's adjuvant (CFA) in rats. Methods Inflammatory pain was induced by intraplantar injection of CFA into the left hind paw of Wistar rats. AEPM and MEPM were administered either acutely or chronically by the oral route at the doses of 100 and 200 mg/kg/day. The mechanical hyperalgesia was tested using an analgesimeter, while the locomotion activity at the end of experiment was evaluated with an open-field device. Nitric oxide (NO), malondialdehyde (MDA) and superoxide dismutase (SOD) contents were assayed in the brain and spinal cord of rats subjected to 14 days chronic treatment. Results AEPM and MEPM at both doses significantly (p<0.001) inhibited the acute and chronic mechanical hyperalgesia induced by CFA. Although not significant, both extracts increased the mobility of CFA-injected animals. AEPM significantly (p<0.01) reduced the level of nitrate at 100 mg/kg, MDA at 200 mg/kg and significantly (p<0.05) increased the SOD in the spinal cord. MEPM significantly increased the SOD content and reduced the MDA concentration in the brain but had no effect on the nitrate. Conclusions AEPM and MEPM exhibit acute and chronic antihyperalgesic activities. In addition, both extracts possess antioxidant properties that might strengthen their chronic antihyperalgesic effects.

Robust Cell Detection of Histopathological Brain Tumor Images Using Sparse Reconstruction and Adaptive Dictionary Selection

PubMed Central

Su, Hai; Xing, Fuyong; Yang, Lin

2016-01-01

Successful diagnostic and prognostic stratification, treatment outcome prediction, and therapy planning depend on reproducible and accurate pathology analysis. Computer aided diagnosis (CAD) is a useful tool to help doctors make better decisions in cancer diagnosis and treatment. Accurate cell detection is often an essential prerequisite for subsequent cellular analysis. The major challenge of robust brain tumor nuclei/cell detection is to handle significant variations in cell appearance and to split touching cells. In this paper, we present an automatic cell detection framework using sparse reconstruction and adaptive dictionary learning. The main contributions of our method are: 1) A sparse reconstruction based approach to split touching cells; 2) An adaptive dictionary learning method used to handle cell appearance variations. The proposed method has been extensively tested on a data set with more than 2000 cells extracted from 32 whole slide scanned images. The automatic cell detection results are compared with the manually annotated ground truth and other state-of-the-art cell detection algorithms. The proposed method achieves the best cell detection accuracy with a F1 score = 0.96. PMID:26812706

Reproducible Tissue Homogenization and Protein Extraction for Quantitative Proteomics Using MicroPestle-Assisted Pressure-Cycling Technology.

PubMed

Shao, Shiying; Guo, Tiannan; Gross, Vera; Lazarev, Alexander; Koh, Ching Chiek; Gillessen, Silke; Joerger, Markus; Jochum, Wolfram; Aebersold, Ruedi

2016-06-03

The reproducible and efficient extraction of proteins from biopsy samples for quantitative analysis is a critical step in biomarker and translational research. Recently, we described a method consisting of pressure-cycling technology (PCT) and sequential windowed acquisition of all theoretical fragment ions-mass spectrometry (SWATH-MS) for the rapid quantification of thousands of proteins from biopsy-size tissue samples. As an improvement of the method, we have incorporated the PCT-MicroPestle into the PCT-SWATH workflow. The PCT-MicroPestle is a novel, miniaturized, disposable mechanical tissue homogenizer that fits directly into the microTube sample container. We optimized the pressure-cycling conditions for tissue lysis with the PCT-MicroPestle and benchmarked the performance of the system against the conventional PCT-MicroCap method using mouse liver, heart, brain, and human kidney tissues as test samples. The data indicate that the digestion of the PCT-MicroPestle-extracted proteins yielded 20-40% more MS-ready peptide mass from all tissues tested with a comparable reproducibility when compared to the conventional PCT method. Subsequent SWATH-MS analysis identified a higher number of biologically informative proteins from a given sample. In conclusion, we have developed a new device that can be seamlessly integrated into the PCT-SWATH workflow, leading to increased sample throughput and improved reproducibility at both the protein extraction and proteomic analysis levels when applied to the quantitative proteomic analysis of biopsy-level samples.

Dysautonomia after pediatric brain injury

PubMed Central

KIRK, KATHERINE A; SHOYKHET, MICHAEL; JEONG, JONG H; TYLER-KABARA, ELIZABETH C; HENDERSON, MARYANNE J; BELL, MICHAEL J; FINK, ERICKA L

2012-01-01

AIM Dysautonomia after brain injury is a diagnosis based on fever, tachypnea, hypertension, tachycardia, diaphoresis, and/or dystonia. It occurs in 8 to 33% of brain-injured adults and is associated with poor outcome. We hypothesized that brain-injured children with dysautonomia have worse outcomes and prolonged rehabilitation, and sought to determine the prevalence of dysautonomia in children and to characterize its clinical features. METHOD We developed a database of children (n=249, 154 males, 95 females; mean (SD) age 11y 10mo [5y 7mo]) with traumatic brain injury, cardiac arrest, stroke, infection of the central nervous system, or brain neoplasm admitted to The Children’s Institute of Pittsburgh for rehabilitation between 2002 and 2009. Dysautonomia diagnosis, injury type, clinical signs, length of stay, and Functional Independence Measure for Children (WeeFIM) testing were extracted from medical records, and analysed for differences between groups with and without dysautonomia. RESULTS Dysautonomia occurred in 13% of children with brain injury (95% confidence interval 9.3–18.0%), occurring in 10% after traumatic brain injury and 31% after cardiac arrest. The combination of hypertension, diaphoresis, and dystonia best predicted a diagnosis of dysautonomia (area under the curve=0.92). Children with dysautonomia had longer stays, worse WeeFIM scores, and improved less on the score’s motor component (all p≤0.001). INTERPRETATION Dysautonomia is common in children with brain injury and is associated with prolonged rehabilitation. Prospective study and standardized diagnostic approaches are needed to maximize outcomes. PMID:22712762

Alzheimer's Disease Detection by Pseudo Zernike Moment and Linear Regression Classification.

PubMed

Wang, Shui-Hua; Du, Sidan; Zhang, Yin; Phillips, Preetha; Wu, Le-Nan; Chen, Xian-Qing; Zhang, Yu-Dong

2017-01-01

This study presents an improved method based on "Gorji et al. Neuroscience. 2015" by introducing a relatively new classifier-linear regression classification. Our method selects one axial slice from 3D brain image, and employed pseudo Zernike moment with maximum order of 15 to extract 256 features from each image. Finally, linear regression classification was harnessed as the classifier. The proposed approach obtains an accuracy of 97.51%, a sensitivity of 96.71%, and a specificity of 97.73%. Our method performs better than Gorji's approach and five other state-of-the-art approaches. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Identifying the arterial input function from dynamic contrast-enhanced magnetic resonance images using an apex-seeking technique

NASA Astrophysics Data System (ADS)

Martel, Anne L.

2004-04-01

In order to extract quantitative information from dynamic contrast-enhanced MR images (DCE-MRI) it is usually necessary to identify an arterial input function. This is not a trivial problem if there are no major vessels present in the field of view. Most existing techniques rely on operator intervention or use various curve parameters to identify suitable pixels but these are often specific to the anatomical region or the acquisition method used. They also require the signal from several pixels to be averaged in order to improve the signal to noise ratio, however this introduces errors due to partial volume effects. We have described previously how factor analysis can be used to automatically separate arterial and venous components from DCE-MRI studies of the brain but although that method works well for single slice images through the brain when the blood brain barrier technique is intact, it runs into problems for multi-slice images with more complex dynamics. This paper will describe a factor analysis method that is more robust in such situations and is relatively insensitive to the number of physiological components present in the data set. The technique is very similar to that used to identify spectral end-members from multispectral remote sensing images.

Encoding the local connectivity patterns of fMRI for cognitive task and state classification.

PubMed

Onal Ertugrul, Itir; Ozay, Mete; Yarman Vural, Fatos T

2018-06-15

In this work, we propose a novel framework to encode the local connectivity patterns of brain, using Fisher vectors (FV), vector of locally aggregated descriptors (VLAD) and bag-of-words (BoW) methods. We first obtain local descriptors, called mesh arc descriptors (MADs) from fMRI data, by forming local meshes around anatomical regions, and estimating their relationship within a neighborhood. Then, we extract a dictionary of relationships, called brain connectivity dictionary by fitting a generative Gaussian mixture model (GMM) to a set of MADs, and selecting codewords at the mean of each component of the mixture. Codewords represent connectivity patterns among anatomical regions. We also encode MADs by VLAD and BoW methods using k-Means clustering. We classify cognitive tasks using the Human Connectome Project (HCP) task fMRI dataset and cognitive states using the Emotional Memory Retrieval (EMR). We train support vector machines (SVMs) using the encoded MADs. Results demonstrate that, FV encoding of MADs can be successfully employed for classification of cognitive tasks, and outperform VLAD and BoW representations. Moreover, we identify the significant Gaussians in mixture models by computing energy of their corresponding FV parts, and analyze their effect on classification accuracy. Finally, we suggest a new method to visualize the codewords of the learned brain connectivity dictionary.

Classification of brain signals associated with imagination of hand grasping, opening and reaching by means of wavelet-based common spatial pattern and mutual information.

PubMed

Amanpour, Behzad; Erfanian, Abbas

2013-01-01

An important issue in designing a practical brain-computer interface (BCI) is the selection of mental tasks to be imagined. Different types of mental tasks have been used in BCI including left, right, foot, and tongue motor imageries. However, the mental tasks are different from the actions to be controlled by the BCI. It is desirable to select a mental task to be consistent with the desired action to be performed by BCI. In this paper, we investigated the detecting the imagination of the hand grasping, hand opening, and hand reaching in one hand using electroencephalographic (EEG) signals. The results show that the ERD/ERS patterns, associated with the imagination of hand grasping, opening, and reaching are different. For classification of brain signals associated with these mental tasks and feature extraction, a method based on wavelet packet, regularized common spatial pattern (CSP), and mutual information is proposed. The results of an offline analysis on five subjects show that the two-class mental tasks can be classified with an average accuracy of 77.6% using proposed method. In addition, we examine the proposed method on datasets IVa from BCI Competition III and IIa from BCI Competition IV.

Automatic identification of the reference system based on the fourth ventricular landmarks in T1-weighted MR images.

PubMed

Fu, Yili; Gao, Wenpeng; Chen, Xiaoguang; Zhu, Minwei; Shen, Weigao; Wang, Shuguo

2010-01-01

The reference system based on the fourth ventricular landmarks (including the fastigial point and ventricular floor plane) is used in medical image analysis of the brain stem. The objective of this study was to develop a rapid, robust, and accurate method for the automatic identification of this reference system on T1-weighted magnetic resonance images. The fully automated method developed in this study consisted of four stages: preprocessing of the data set, expectation-maximization algorithm-based extraction of the fourth ventricle in the region of interest, a coarse-to-fine strategy for identifying the fastigial point, and localization of the base point. The method was evaluated on 27 Brain Web data sets qualitatively and 18 Internet Brain Segmentation Repository data sets and 30 clinical scans quantitatively. The results of qualitative evaluation indicated that the method was robust to rotation, landmark variation, noise, and inhomogeneity. The results of quantitative evaluation indicated that the method was able to identify the reference system with an accuracy of 0.7 +/- 0.2 mm for the fastigial point and 1.1 +/- 0.3 mm for the base point. It took <6 seconds for the method to identify the related landmarks on a personal computer with an Intel Core 2 6300 processor and 2 GB of random-access memory. The proposed method for the automatic identification of the reference system based on the fourth ventricular landmarks was shown to be rapid, robust, and accurate. The method has potentially utility in image registration and computer-aided surgery.

Quantitation of repaglinide and metabolites in mouse whole-body thin tissue sections using droplet-based liquid microjunction surface sampling-high-performance liquid chromatography-electrospray ionization tandem mass spectrometry.

PubMed

Chen, Weiqi; Wang, Lifei; Van Berkel, Gary J; Kertesz, Vilmos; Gan, Jinping

2016-03-25

Herein, quantitation aspects of a fully automated autosampler/HPLC-MS/MS system applied for unattended droplet-based surface sampling of repaglinide dosed thin tissue sections with subsequent HPLC separation and mass spectrometric analysis of parent drug and various drug metabolites were studied. Major organs (brain, lung, liver, kidney and muscle) from whole-body thin tissue sections and corresponding organ homogenates prepared from repaglinide dosed mice were sampled by surface sampling and by bulk extraction, respectively, and analyzed by HPLC-MS/MS. A semi-quantitative agreement between data obtained by surface sampling and that by employing organ homogenate extraction was observed. Drug concentrations obtained by the two methods followed the same patterns for post-dose time points (0.25, 0.5, 1 and 2 h). Drug amounts determined in the specific tissues was typically higher when analyzing extracts from the organ homogenates. In addition, relative comparison of the levels of individual metabolites between the two analytical methods also revealed good semi-quantitative agreement. Copyright © 2015 Elsevier B.V. All rights reserved.

Cortical Signal Analysis and Advances in Functional Near-Infrared Spectroscopy Signal: A Review.

PubMed

Kamran, Muhammad A; Mannan, Malik M Naeem; Jeong, Myung Yung

2016-01-01

Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging modality that measures the concentration changes of oxy-hemoglobin (HbO) and de-oxy hemoglobin (HbR) at the same time. It is an emerging cortical imaging modality with a good temporal resolution that is acceptable for brain-computer interface applications. Researchers have developed several methods in last two decades to extract the neuronal activation related waveform from the observed fNIRS time series. But still there is no standard method for analysis of fNIRS data. This article presents a brief review of existing methodologies to model and analyze the activation signal. The purpose of this review article is to give a general overview of variety of existing methodologies to extract useful information from measured fNIRS data including pre-processing steps, effects of differential path length factor (DPF), variations and attributes of hemodynamic response function (HRF), extraction of evoked response, removal of physiological noises, instrumentation, and environmental noises and resting/activation state functional connectivity. Finally, the challenges in the analysis of fNIRS signal are summarized.

Cortical Signal Analysis and Advances in Functional Near-Infrared Spectroscopy Signal: A Review

PubMed Central

Kamran, Muhammad A.; Mannan, Malik M. Naeem; Jeong, Myung Yung

2016-01-01

Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging modality that measures the concentration changes of oxy-hemoglobin (HbO) and de-oxy hemoglobin (HbR) at the same time. It is an emerging cortical imaging modality with a good temporal resolution that is acceptable for brain-computer interface applications. Researchers have developed several methods in last two decades to extract the neuronal activation related waveform from the observed fNIRS time series. But still there is no standard method for analysis of fNIRS data. This article presents a brief review of existing methodologies to model and analyze the activation signal. The purpose of this review article is to give a general overview of variety of existing methodologies to extract useful information from measured fNIRS data including pre-processing steps, effects of differential path length factor (DPF), variations and attributes of hemodynamic response function (HRF), extraction of evoked response, removal of physiological noises, instrumentation, and environmental noises and resting/activation state functional connectivity. Finally, the challenges in the analysis of fNIRS signal are summarized. PMID:27375458

Quantitation of repaglinide and metabolites in mouse whole-body thin tissue sections using droplet-based liquid microjunction surface sampling-high-performance liquid chromatography-electrospray ionization tandem mass spectrometry

DOE PAGES

Chen, Weiqi; Wang, Lifei; Van Berkel, Gary J.; ...

2015-11-03

Herein, quantitation aspects of a fully automated autosampler/HPLC-MS/MS system applied for unattended droplet-based surface sampling of repaglinide dosed thin tissue sections with subsequent HPLC separation and mass spectrometric analysis of parent drug and various drug metabolites was studied. Major organs (brain, lung, liver, kidney, muscle) from whole-body thin tissue sections and corresponding organ homogenates prepared from repaglinide dosed mice were sampled by surface sampling and by bulk extraction, respectively, and analyzed by HPLC-MS/MS. A semi-quantitative agreement between data obtained by surface sampling and that by employing organ homogenate extraction was observed. Drug concentrations obtained by the two methods followed themore » same patterns for post-dose time points (0.25, 0.5, 1 and 2 h). Drug amounts determined in the specific tissues was typically higher when analyzing extracts from the organ homogenates. Furthermore, relative comparison of the levels of individual metabolites between the two analytical methods also revealed good semi-quantitative agreement.« less

Quantitation of repaglinide and metabolites in mouse whole-body thin tissue sections using droplet-based liquid microjunction surface sampling-high-performance liquid chromatography-electrospray ionization tandem mass spectrometry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Weiqi; Wang, Lifei; Van Berkel, Gary J.

Herein, quantitation aspects of a fully automated autosampler/HPLC-MS/MS system applied for unattended droplet-based surface sampling of repaglinide dosed thin tissue sections with subsequent HPLC separation and mass spectrometric analysis of parent drug and various drug metabolites was studied. Major organs (brain, lung, liver, kidney, muscle) from whole-body thin tissue sections and corresponding organ homogenates prepared from repaglinide dosed mice were sampled by surface sampling and by bulk extraction, respectively, and analyzed by HPLC-MS/MS. A semi-quantitative agreement between data obtained by surface sampling and that by employing organ homogenate extraction was observed. Drug concentrations obtained by the two methods followed themore » same patterns for post-dose time points (0.25, 0.5, 1 and 2 h). Drug amounts determined in the specific tissues was typically higher when analyzing extracts from the organ homogenates. Furthermore, relative comparison of the levels of individual metabolites between the two analytical methods also revealed good semi-quantitative agreement.« less

Morinda citrifolia L. Leaf Extract Protects against Cerebral Ischemia and Osteoporosis in an In Vivo Experimental Model of Menopause

PubMed Central

Thipkaew, Cholathip; Thukham-mee, Wipawee; Wannanon, Panakaporn

2018-01-01

We aimed to determine the protective effects against cerebral ischemia and osteoporosis of Morinda citrifolia extract in experimental menopause. The neuroprotective effect was assessed by giving M. citrifolia leaf extract at doses of 2, 10, and 50 mg/kg BW to the bilateral ovariectomized (OVX) rats for 7 days. Then, they were occluded in the right middle cerebral artery (MCAO) for 90 minutes. The neurological score, brain infarction volume, oxidative stress status, and ERK1/2 and eNOS activities were assessed 24 hours later. M. citrifolia improved neurological score, brain infarction, and brain oxidative stress status in the cortex of OVX rats plus the MCAO. No changes in ERK 1/2 signal pathway and NOS expression were observed in this area. Our data suggested that the neuroprotective effect of the extract might occur partly via the improvement of oxidative stress status in the cortex. The antiosteoporotic effect in OVX rats was also assessed after an 84-day intervention of M. citrifolia. The serum levels of calcium, osteocalcin, and alkaline phosphatase and osteoblast density in the tibia were increased, but the density of osteoclast was decreased in OVX rats which received the extract. Therefore, the current data suggested that the extract possessed antiosteoporotic effect by increasing bone formation but decreasing bone resorption. PMID:29765488

Morinda citrifolia L. Leaf Extract Protects against Cerebral Ischemia and Osteoporosis in an In Vivo Experimental Model of Menopause.

PubMed

Wattanathorn, Jintanaporn; Thipkaew, Cholathip; Thukham-Mee, Wipawee; Muchimapura, Supaporn; Wannanon, Panakaporn; Tong-Un, Terdthai

2018-01-01

We aimed to determine the protective effects against cerebral ischemia and osteoporosis of Morinda citrifolia extract in experimental menopause. The neuroprotective effect was assessed by giving M. citrifolia leaf extract at doses of 2, 10, and 50 mg/kg BW to the bilateral ovariectomized (OVX) rats for 7 days. Then, they were occluded in the right middle cerebral artery (MCAO) for 90 minutes. The neurological score, brain infarction volume, oxidative stress status, and ERK1/2 and eNOS activities were assessed 24 hours later. M. citrifolia improved neurological score, brain infarction, and brain oxidative stress status in the cortex of OVX rats plus the MCAO. No changes in ERK 1/2 signal pathway and NOS expression were observed in this area. Our data suggested that the neuroprotective effect of the extract might occur partly via the improvement of oxidative stress status in the cortex. The antiosteoporotic effect in OVX rats was also assessed after an 84-day intervention of M. citrifolia . The serum levels of calcium, osteocalcin, and alkaline phosphatase and osteoblast density in the tibia were increased, but the density of osteoclast was decreased in OVX rats which received the extract. Therefore, the current data suggested that the extract possessed antiosteoporotic effect by increasing bone formation but decreasing bone resorption.

SIGNALING PATHWAYS REGULATED BY BRASSICACEAE EXTRACT INHIBIT THE FORMATION OF ADVANCED GLYCATED END PRODUCTS IN RAT BRAIN.

PubMed

Al-Malki, Abdulrahman L; Barbour, Elie K; Ea, Huwait; Moselhy, Said S; ALZahrani, Anas Hassan Saeed; Kumosani, Taha A

2017-01-01

The goal of this study was identification signaling molecules mediated the formation of AGEs in brain of rats injected with CdCl2 and the role of camel whey proteins and Brassicaceae extract on formation of AGEs in brain. Ninety male rats were randomly grouped into five groups; Normal control (GpI) and the other rats (groups II-V) were received a single dose of cadmium chloride i.p (5 μg/kg/b.w) for induction of neurodegeneration. Rats in groups III-V were treated daily with whey protein (1g/kg b.w) or Brassicaceae extract (1mg/kg b.w) or combined respectively for 12 weeks. It was found that whey protein combined with Brassicaceae extract prevented the formation of AGEs and enhance the antioxidant activity compared with untreated group (p <0.001). Serum tumor necrosis factor (TNF-α) and interleukine (IL-6) levels were significantly decreased (p<0.01) in rats treated with whey protein and Brassicaceae extract formation compared with untreated. The combined treatment showed a better impact than individual ones (p<0.001). The level of cAMP but not cGMP were lowered in combined treatment than individual (p<0.01). It can be postulated that Whey protein + Brassicaceae extract formation could have potential benefits in the prevention of the onset and progression of neuropathy in patients.

Monoamine reuptake inhibition and mood-enhancing potential of a specified oregano extract.

PubMed

Mechan, Annis O; Fowler, Ann; Seifert, Nicole; Rieger, Henry; Wöhrle, Tina; Etheve, Stéphane; Wyss, Adrian; Schüler, Göde; Colletto, Biagio; Kilpert, Claus; Aston, James; Elliott, J Martin; Goralczyk, Regina; Mohajeri, M Hasan

2011-04-01

A healthy, balanced diet is essential for both physical and mental well-being. Such a diet must include an adequate intake of micronutrients, essential fatty acids, amino acids and antioxidants. The monoamine neurotransmitters, serotonin, dopamine and noradrenaline, are derived from dietary amino acids and are involved in the modulation of mood, anxiety, cognition, sleep regulation and appetite. The capacity of nutritional interventions to elevate brain monoamine concentrations and, as a consequence, with the potential for mood enhancement, has not been extensively evaluated. The present study investigated an extract from oregano leaves, with a specified range of active constituents, identified via an unbiased, high-throughput screening programme. The oregano extract was demonstrated to inhibit the reuptake and degradation of the monoamine neurotransmitters in a dose-dependent manner, and microdialysis experiments in rats revealed an elevation of extracellular serotonin levels in the brain. Furthermore, following administration of oregano extract, behavioural responses were observed in mice that parallel the beneficial effects exhibited by monoamine-enhancing compounds when used in human subjects. In conclusion, these data show that an extract prepared from leaves of oregano, a major constituent of the Mediterranean diet, is brain-active, with moderate triple reuptake inhibitory activity, and exhibits positive behavioural effects in animal models. We postulate that such an extract may be effective in enhancing mental well-being in humans.

Automated diagnosis of Alzheimer's disease with multi-atlas based whole brain segmentations

NASA Astrophysics Data System (ADS)

Luo, Yuan; Tang, Xiaoying

2017-03-01

Voxel-based analysis is widely used in quantitative analysis of structural brain magnetic resonance imaging (MRI) and automated disease detection, such as Alzheimer's disease (AD). However, noise at the voxel level may cause low sensitivity to AD-induced structural abnormalities. This can be addressed with the use of a whole brain structural segmentation approach which greatly reduces the dimension of features (the number of voxels). In this paper, we propose an automatic AD diagnosis system that combines such whole brain segmen- tations with advanced machine learning methods. We used a multi-atlas segmentation technique to parcellate T1-weighted images into 54 distinct brain regions and extract their structural volumes to serve as the features for principal-component-analysis-based dimension reduction and support-vector-machine-based classification. The relationship between the number of retained principal components (PCs) and the diagnosis accuracy was systematically evaluated, in a leave-one-out fashion, based on 28 AD subjects and 23 age-matched healthy subjects. Our approach yielded pretty good classification results with 96.08% overall accuracy being achieved using the three foremost PCs. In addition, our approach yielded 96.43% specificity, 100% sensitivity, and 0.9891 area under the receiver operating characteristic curve.

A brain-computer interface to support functional recovery.

PubMed

Kjaer, Troels W; Sørensen, Helge B

2013-01-01

Brain-computer interfaces (BCI) register changes in brain activity and utilize this to control computers. The most widely used method is based on registration of electrical signals from the cerebral cortex using extracranially placed electrodes also called electroencephalography (EEG). The features extracted from the EEG may, besides controlling the computer, also be fed back to the patient for instance as visual input. This facilitates a learning process. BCI allow us to utilize brain activity in the rehabilitation of patients after stroke. The activity of the cerebral cortex varies with the type of movement we imagine, and by letting the patient know the type of brain activity best associated with the intended movement the rehabilitation process may be faster and more efficient. The focus of BCI utilization in medicine has changed in recent years. While we previously focused on devices facilitating communication in the rather few patients with locked-in syndrome, much interest is now devoted to the therapeutic use of BCI in rehabilitation. For this latter group of patients, the device is not intended to be a lifelong assistive companion but rather a 'teacher' during the rehabilitation period. Copyright © 2013 S. Karger AG, Basel.

A diagnosis model for early Tourette syndrome children based on brain structural network characteristics

NASA Astrophysics Data System (ADS)

Wen, Hongwei; Liu, Yue; Wang, Jieqiong; Zhang, Jishui; Peng, Yun; He, Huiguang

2016-03-01

Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder characterized by the presence of multiple motor and vocal tics. Tic generation has been linked to disturbed networks of brain areas involved in planning, controlling and execution of action. The aim of our work is to select topological characteristics of structural network which were most efficient for estimating the classification models to identify early TS children. Here we employed the diffusion tensor imaging (DTI) and deterministic tractography to construct the structural networks of 44 TS children and 48 age and gender matched healthy children. We calculated four different connection matrices (fiber number, mean FA, averaged fiber length weighted and binary matrices) and then applied graph theoretical methods to extract the regional nodal characteristics of structural network. For each weighted or binary network, nodal degree, nodal efficiency and nodal betweenness were selected as features. Support Vector Machine Recursive Feature Extraction (SVM-RFE) algorithm was used to estimate the best feature subset for classification. The accuracy of 88.26% evaluated by a nested cross validation was achieved on combing best feature subset of each network characteristic. The identified discriminative brain nodes mostly located in the basal ganglia and frontal cortico-cortical networks involved in TS children which was associated with tic severity. Our study holds promise for early identification and predicting prognosis of TS children.

Robust Long-Range Coordination of Spontaneous Neural Activity in Waking, Sleep and Anesthesia.

PubMed

Liu, Xiao; Yanagawa, Toru; Leopold, David A; Fujii, Naotaka; Duyn, Jeff H

2015-09-01

Although the emerging field of functional connectomics relies increasingly on the analysis of spontaneous fMRI signal covariation to infer the spatial fingerprint of the brain's large-scale functional networks, the nature of the underlying neuro-electrical activity remains incompletely understood. In part, this lack in understanding owes to the invasiveness of electrophysiological acquisition, the difficulty in their simultaneous recording over large cortical areas, and the absence of fully established methods for unbiased extraction of network information from these data. Here, we demonstrate a novel, data-driven approach to analyze spontaneous signal variations in electrocorticographic (ECoG) recordings from nearly entire hemispheres of macaque monkeys. Based on both broadband analysis and analysis of specific frequency bands, the ECoG signals were found to co-vary in patterns that resembled the fMRI networks reported in previous studies. The extracted patterns were robust against changes in consciousness associated with sleep and anesthesia, despite profound changes in intrinsic characteristics of the raw signals, including their spectral signatures. These results suggest that the spatial organization of large-scale brain networks results from neural activity with a broadband spectral feature and is a core aspect of the brain's physiology that does not depend on the state of consciousness. Published by Oxford University Press 2014. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Potential antianxiety activity of Fumaria indica: A preclinical study

PubMed Central

Singh, Gireesh K.; Chauhan, Sudhir K.; Rai, Geeta; Chatterjee, Shyam S.; Kumar, Vikas

2013-01-01

Background: In the view of diverse CNS modulating properties of Fumaria indica, present study was planned to evaluate its putative anxiolytic activity in behavioural models of rats, followed by elucidation of mechanism of observed activity through biochemical estimations. Materials and Methods: Effects of seven daily 100, 200 and 400 mg/kg oral doses of a Fumaria indica extract (FI) was compared with those of an acute oral dose (5 mg/kg) of lorazepam in a battery of rat models consisting of open-field, elevated plus and zero maze, social interaction, and novelty induced feeding tests. Results: Dose dependant antianxiety effects of FI observed in all tests were qualitatively similar to those of the reference anxiolytic drug. Although FI treatments did not alter the concentrations of noradrenaline and serotonin in hippocampus and hypothalamus, concentrations of both these monoamines were dose dependently elevated in prefrontal cortex of FI treated animals. Flunitrazepam binding in brain frontal cortex was also elevated by the extract. Moreover, higher levels of brain expressions of the cytokines TNF-α, IL-1β, and IL-10 observed in animals with prior experience on elevated plus maze were almost completely reversed by the lowest dose of FI tested in the behavioral models. Conclusion: Taken together, these observations strongly suggest that FI is a functionally novel type of antianxiety agent, and that inhibition of cytokine expressions in the brain could be involved in its mode of action. PMID:23661988

Comparison of water-based foam and carbon dioxide gas emergency depopulation methods of turkeys.

PubMed

Rankin, M K; Alphin, R L; Benson, E R; Johnson, A L; Hougentogler, D P; Mohankumar, P

2013-12-01

Recommended response strategies for outbreaks of avian influenza and other highly contagious poultry diseases include surveillance, quarantine, depopulation, disposal, and decontamination. The best methods of emergency mass depopulation should maximize human health and safety while minimizing disease spread and animal welfare concerns. The goal of this project was to evaluate the effectiveness of 2 mass depopulation methods on adult tom turkeys. The methods tested were carbon dioxide gassing and water-based foam. The time to unconsciousness, motion cessation, brain death, and altered terminal cardiac activity were recorded for each bird through the use of an electroencephalogram, accelerometer, and electrocardiogram. Critical times for physiological events were extracted from sensor data and compiled in a spreadsheet for statistical analysis. A statistically significant difference was observed in time to brain death, with water-based foam resulting in faster brain death (µ = 190 s) than CO2 gas (µ = 242 s). Though not statistically significant, differences were found comparing the time to unconsciousness (foam: µ = 64 s; CO2 gas: µ = 90 s), motion cessation (foam: µ = 182 s; CO2 gas: µ = 153 s), and altered terminal cardiac activity (foam: µ = 208 s; CO2 gas µ = 242 s) between foam and CO2 depopulation treatments. The results of this study demonstrate that water-based foam can be used to effectively depopulate market size male turkeys.

Preattentive extraction of abstract feature conjunctions from auditory stimulation as reflected by the mismatch negativity (MMN).

PubMed

Paavilainen, P; Simola, J; Jaramillo, M; Näätänen, R; Winkler, I

2001-03-01

Brain mechanisms extracting invariant information from varying auditory inputs were studied using the mismatch-negativity (MMN) brain response. We wished to determine whether the preattentive sound-analysis mechanisms, reflected by MMN, are capable of extracting invariant relationships based on abstract conjunctions between two sound features. The standard stimuli varied over a large range in frequency and intensity dimensions following the rule that the higher the frequency, the louder the intensity. The occasional deviant stimuli violated this frequency-intensity relationship and elicited an MMN. The results demonstrate that preattentive processing of auditory stimuli extends to unexpectedly complex relationships between the stimulus features.

Unilateral Opening of Rat Blood-Brain Barrier Assisted by Diagnostic Ultrasound Targeted Microbubbles Destruction.

PubMed

Xu, Yali; Cui, Hai; Zhu, Qiong; Hua, Xing; Xia, Hongmei; Tan, Kaibin; Gao, Yunhua; Zhao, Jing; Liu, Zheng

2016-01-01

Objective. Blood-brain barrier (BBB) is a key obstacle that prevents the medication from blood to the brain. Microbubble-enhanced cavitation by focused ultrasound can open the BBB and proves to be valuable in the brain drug delivery. The study aimed to explore the feasibility, efficacy, and safety of unilateral opening of BBB using diagnostic ultrasound targeted microbubbles destruction in rats. Methods. A transtemporal bone irradiation of diagnostic ultrasound and intravenous injection of lipid-coated microbubbles were performed at unilateral hemisphere. Pathological changes were monitored. Evans Blue extravasation grades, extraction from brain tissue, and fluorescence optical density were quantified. Lanthanum nitrate was traced by transmission electron microscopy. Results. After diagnostic ultrasound mediated microbubbles destruction, Evans Blue extravasation and fluorescence integrated optical density were significantly higher in the irradiated hemisphere than the contralateral side (all p < 0.01). Erythrocytes extravasations were demonstrated in the ultrasound-exposed hemisphere (4 ± 1, grade 2) while being invisible in the control side. Lanthanum nitrate tracers leaked through interendothelial cleft and spread to the nerve fiber existed in the irradiation side. Conclusions. Transtemporal bone irradiation under DUS mediated microbubble destruction provides us with a more accessible, safer, and higher selective BBB opening approach in rats, which is advantageous in brain targeted drugs delivery.

Recovery correction technique for NMR spectroscopy of perchloric acid extracts using DL-valine-2,3-d2: validation and application to 5-fluorouracil-induced brain damage.

PubMed

Nakagami, Ryutaro; Yamaguchi, Masayuki; Ezawa, Kenji; Kimura, Sadaaki; Hamamichi, Shusei; Sekine, Norio; Furukawa, Akira; Niitsu, Mamoru; Fujii, Hirofumi

2014-01-01

We explored a recovery correction technique that can correct metabolite loss during perchloric acid (PCA) extraction and minimize inter-assay variance in quantitative (1)H nuclear magnetic resonance (NMR) spectroscopy of the brain and evaluated its efficacy in 5-fluorouracil (5-FU)- and saline-administered rats. We measured the recovery of creatine and dl-valine-2,3-d2 from PCA extract containing both compounds (0.5 to 8 mM). We intravenously administered either 5-FU for 4 days (total, 100 mg/kg body weight) or saline into 2 groups of 11 rats each. We subsequently performed PCA extraction of the whole brain on Day 9, externally adding 7 µmol of dl-valine-2,3-d2. We estimated metabolite concentrations using an NMR spectrometer with recovery correction, correcting metabolite concentrations based on the recovery factor of dl-valine-2,3-d2. For each metabolite concentration, we calculated the coefficient of variation (CEV) and compared differences between the 2 groups using unpaired t-test. Equivalent recoveries of dl-valine-2,3-d2 (89.4 ± 3.9%) and creatine (89.7 ± 3.9%) in the PCA extract of the mixed solution indicated the suitability of dl-valine-2,3-d2 as an internal reference. In the rat study, recovery of dl-valine-2,3-d2 was 90.6 ± 9.2%. Nine major metabolite concentrations adjusted by recovery of dl-valine-2,3-d2 in saline-administered rats were comparable to data in the literature. CEVs of these metabolites were reduced from 10 to 17% before to 7 to 16% after correction. The significance of differences in alanine and taurine between the 5-FU- and saline-administered groups was determined only after recovery correction (0.75 ± 0.12 versus 0.86 ± 0.07 for alanine; 5.17 ± 0.59 versus 5.66 ± 0.42 for taurine [µmol/g brain tissue]; P < 0.05). A new recovery correction technique corrected metabolite loss during PCA extraction, minimized inter-assay variance in quantitative (1)H NMR spectroscopy of brain tissue, and effectively detected inter-group differences in concentrations of brain metabolites between 5-FU- and saline-administered rats.

The Effect of Rosa Damascena Extract on Expression of Neurotrophic Factors in the CA1 Neurons of Adult Rat Hippocampus Following Ischemia.

PubMed

Moniri, Seyedeh Farzaneh; Hedayatpour, Azim; Hassanzadeh, Gholamreza; Vazirian, Mahdi; Karimian, Morteza; Belaran, Maryam; Ejtemaie Mehr, Shahram; Akbari, Mohamad

2017-12-01

Ischemic stroke is an important cause of death and disability in the world. Brain ischemia causes damage to brain cell, and among brain neurons, pyramidal neurons of the hippocampal CA1 region are more susceptive to ischemic injury. Recent findings suggest that neurotrophic factors protect against ischemic cell death. A dietary component of Rosa damascene extract possibly is associated with expression of neurotrophic factors mRNA following ischemia, so it can have therapeutic effect on cerebral ischemia. The present study attempts to evaluate the neuroprotective effect of Rosa damascene extract on adult rat hippocampal neurons following ischemic brain injury. Forty-eight adult male Wistar rats (weighing 250±20 gr and ages 10-12 weeks) used in this study, animals randomly were divided into 6 groups including Control, ischemia/ reperfusion (IR), vehicle and three treated groups (IR+0.5, 1, 2 mg/ml extract). Global ischemia was induced by bilateral common carotid arteries occlusion for 20 minutes. The treatment was done by different doses of Rosa damascena extract for 30 days. After 30 days cell death and gene expression in neurons of the CA1 region of the hippocampus were evaluated by Nissl staining and real time PCR assay. We found a significant decrease in NGF, BDNF and NT3 mRNA expression in neurons of CA1 region of the hippocampus in ischemia group compared to control group (P<0.0001). Our results also revealed that the number of dark neurons significantly increases in ischemia group compared to control group (P<0.0001). Following treatment with Rosa damascene extract reduced the number of dark neurons that was associated with NGF, NT3, and BDNF mRNA expression. All doses level had positive effects, but the most effective dose of Rosa damascena extract was 1 mg/ml. Our results suggest that neuroprotective activity of Rosa damascena can enhance hippocampal CA1 neuronal survival after global ischemia.

Environmental monitoring using acetylcholinesterase inhibition in vitro. A case study in two Mexican lagoons.

PubMed

Rodríguez-Fuentes, G; Gold-Bouchot, G

2000-01-01

Cholinesterase inhibition is considered a specific biomarker of exposure and effect for organophosphorous pesticides. Its use for monitoring has been hindered, particularly in tropical countries where organophosphates are widely used for malaria and dengue control, because of the frequent lack of suitable controls. An in vitro technique is proposed as a biochemical method for monitoring pollutant mixtures in sediment toxicity tests. Brain homogenate from the fish Oreochromis niloticus is used as the enzyme source. Optimum incubation time, extraction solvent and effect of crude oil on acetylcholinesterase (AChE) are reported. The method described was used in sediments from two Mexican lagoons, located in an oil extraction area where pesticides are used in agriculture and vector control campaigns. AChE inhibitions from 3 to 21% were found in these lagoons, even in the presence of high concentrations of petroleum.

Identification of arteries and veins in cerebral angiography fluoroscopic images

NASA Astrophysics Data System (ADS)

Andra Tache, Irina

2017-11-01

In the present study a new method for pixels tagging into arteries and veins classes from temporal cerebral angiography is presented. This need comes from the neurosurgeon who is evaluating the fluoroscopic angiography and the magnetic resonance images from the brain in order to locate the fistula of the patients who suffer from arterio-venous malformation. The method includes the elimination of the background pixels from a previous segmentation and the generation of the time intensity curves for each remaining pixel. The later undergo signal processing in order to extract the characteristic parameters needed for applying the k-means clustering algorithm. Some of the parameters are: the phase and the maximum amplitude extracted from the Fourier transform, the standard deviation and the mean value. The tagged classes are represented into images which then are re-classified by an expert into artery and vein pixels.

A generative probabilistic model and discriminative extensions for brain lesion segmentation – with application to tumor and stroke

PubMed Central

Menze, Bjoern H.; Van Leemput, Koen; Lashkari, Danial; Riklin-Raviv, Tammy; Geremia, Ezequiel; Alberts, Esther; Gruber, Philipp; Wegener, Susanne; Weber, Marc-André; Székely, Gabor; Ayache, Nicholas; Golland, Polina

2016-01-01

We introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM) to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as “tumor core” or “fluid-filled structure”, but without a one-to-one correspondence to the hypo-or hyper-intense lesion areas identified by the generative model. We test the approach in two image sets: the publicly available BRATS set of glioma patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the generative-discriminative model to be one of the top ranking methods in the BRATS evaluation. PMID:26599702

A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation--With Application to Tumor and Stroke.

PubMed

Menze, Bjoern H; Van Leemput, Koen; Lashkari, Danial; Riklin-Raviv, Tammy; Geremia, Ezequiel; Alberts, Esther; Gruber, Philipp; Wegener, Susanne; Weber, Marc-Andre; Szekely, Gabor; Ayache, Nicholas; Golland, Polina

2016-04-01

We introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM), to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as "tumor core" or "fluid-filled structure", but without a one-to-one correspondence to the hypo- or hyper-intense lesion areas identified by the generative model. We test the approach in two image sets: the publicly available BRATS set of glioma patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the extended discriminative -discriminative model to be one of the top ranking methods in the BRATS evaluation.

Non-rigid registration of 3D ultrasound for neurosurgery using automatic feature detection and matching.

PubMed

Machado, Inês; Toews, Matthew; Luo, Jie; Unadkat, Prashin; Essayed, Walid; George, Elizabeth; Teodoro, Pedro; Carvalho, Herculano; Martins, Jorge; Golland, Polina; Pieper, Steve; Frisken, Sarah; Golby, Alexandra; Wells, William

2018-06-04

The brain undergoes significant structural change over the course of neurosurgery, including highly nonlinear deformation and resection. It can be informative to recover the spatial mapping between structures identified in preoperative surgical planning and the intraoperative state of the brain. We present a novel feature-based method for achieving robust, fully automatic deformable registration of intraoperative neurosurgical ultrasound images. A sparse set of local image feature correspondences is first estimated between ultrasound image pairs, after which rigid, affine and thin-plate spline models are used to estimate dense mappings throughout the image. Correspondences are derived from 3D features, distinctive generic image patterns that are automatically extracted from 3D ultrasound images and characterized in terms of their geometry (i.e., location, scale, and orientation) and a descriptor of local image appearance. Feature correspondences between ultrasound images are achieved based on a nearest-neighbor descriptor matching and probabilistic voting model similar to the Hough transform. Experiments demonstrate our method on intraoperative ultrasound images acquired before and after opening of the dura mater, during resection and after resection in nine clinical cases. A total of 1620 automatically extracted 3D feature correspondences were manually validated by eleven experts and used to guide the registration. Then, using manually labeled corresponding landmarks in the pre- and post-resection ultrasound images, we show that our feature-based registration reduces the mean target registration error from an initial value of 3.3 to 1.5 mm. This result demonstrates that the 3D features promise to offer a robust and accurate solution for 3D ultrasound registration and to correct for brain shift in image-guided neurosurgery.

Interaction of Plant Extracts with Central Nervous System Receptors

PubMed Central

Lundstrom, Kenneth; Pham, Huyen Thanh; Dinh, Long Doan

2017-01-01

Background: Plant extracts have been used in traditional medicine for the treatment of various maladies including neurological diseases. Several central nervous system receptors have been demonstrated to interact with plant extracts and components affecting the pharmacology and thereby potentially playing a role in human disease and treatment. For instance, extracts from Hypericum perforatum (St. John’s wort) targeted several CNS receptors. Similarly, extracts from Piper nigrum, Stephania cambodica, and Styphnolobium japonicum exerted inhibition of agonist-induced activity of the human neurokinin-1 receptor. Methods: Different methods have been established for receptor binding and functional assays based on radioactive and fluorescence-labeled ligands in cell lines and primary cell cultures. Behavioral studies of the effect of plant extracts have been conducted in rodents. Plant extracts have further been subjected to mood and cognition studies in humans. Results: Mechanisms of action at molecular and cellular levels have been elucidated for medicinal plants in support of standardization of herbal products and identification of active extract compounds. In several studies, plant extracts demonstrated affinity to a number of CNS receptors in parallel indicating the complexity of this interaction. In vivo studies showed modifications of CNS receptor affinity and behavioral responses in animal models after treatment with medicinal herbs. Certain plant extracts demonstrated neuroprotection and enhanced cognitive performance, respectively, when evaluated in humans. Noteworthy, the penetration of plant extracts and their protective effect on the blood-brain-barrier are discussed. Conclusion: The affinity of plant extracts and their isolated compounds for CNS receptors indicates an important role for medicinal plants in the treatment of neurological disorders. Moreover, studies in animal and human models have confirmed a scientific basis for the application of medicinal herbs. However, additional investigations related to plant extracts and their isolated compounds, as well as their application in animal models and the conducting of clinical trials, are required. PMID:28930228

A Convenient Method for Extraction and Analysis with High-Pressure Liquid Chromatography of Catecholamine Neurotransmitters and Their Metabolites.

PubMed

Xie, Li; Chen, Liqin; Gu, Pan; Wei, Lanlan; Kang, Xuejun

2018-03-01

The extraction and analysis of catecholamine neurotransmitters in biological fluids is of great importance in assessing nervous system function and related diseases, but their precise measurement is still a challenge. Many protocols have been described for neurotransmitter measurement by a variety of instruments, including high-pressure liquid chromatography (HPLC). However, there are shortcomings, such as complicated operation or hard-to-detect multiple targets, which cannot be avoided, and presently, the dominant analysis technique is still HPLC coupled with sensitive electrochemical or fluorimetric detection, due to its high sensitivity and good selectivity. Here, a detailed protocol is described for the pretreatment and detection of catecholamines with high pressure liquid chromatography with electrochemical detection (HPLC-ECD) in real urine samples of infants, using electrospun composite nanofibers composed of polymeric crown ether with polystyrene as adsorbent, also known as the packed-fiber solid phase extraction (PFSPE) method. We show how urine samples can be easily precleaned by a nanofiber-packed solid phase column, and how the analytes in the sample can be rapidly enriched, desorbed, and detected on an ECD system. PFSPE greatly simplifies the pretreatment procedures for biological samples, allowing for decreased time, expense, and reduction of the loss of targets. Overall, this work illustrates a simple and convenient protocol for solid-phase extraction coupled to an HPLC-ECD system for simultaneous determination of three monoamine neurotransmitters (norepinephrine (NE), epinephrine (E), dopamine (DA)) and two of their metabolites (3-methoxy-4-hydroxyphenylglycol (MHPG) and 3,4-dihydroxy-phenylacetic acid (DOPAC)) in infants' urine. The established protocol was applied to assess the differences of urinary catecholamines and their metabolites between high-risk infants with perinatal brain damage and healthy controls. Comparative analysis revealed a significant difference in urinary MHPG between the two groups, indicating that the catecholamine metabolites may be an important candidate marker for early diagnosis of cases at risk for brain damage in infants.

Methanol extract of Nigella sativa seed induces changes in the levels of neurotransmitter amino acids in male rat brain regions.

PubMed

El-Naggar, Tarek; Carretero, María Emilia; Arce, Carmen; Gómez-Serranillos, María Pilar

2017-12-01

Nigella sativa L. (Ranunculaceae) (NS) has been used for medicinal and culinary purposes. Different parts of the plant are used to treat many disorders. This study investigates the effects of NS methanol extract on brain neurotransmitter amino acid levels. We measured the changes in aspartate, glutamate, glycine and γ-aminobutyric acid in five brain regions of male Wistar rats after methanol extract treatment. Animals were injected intraperitoneally with saline solution (controls) or NS methanol extract (equivalent of 2.5 g/kg body weight) and sacrificed 1 h later or after administering 1 daily dose for 8 days. The neurotransmitters were measured in the hypothalamus, cortex, striatum, hippocampus and thalamus by HPLC. Results showed significant changes in amino acids compared to basal values. Glutamate increased significantly (16-36%) in the regions analyzed except the striatum. Aspartate in the hypothalamus (50 and 76%) and glycine in hippocampus (32 and 25%), thalamus (66 and 29%) and striatum (75 and 48%) also increased with the two treatment intervals. γ-Aminobutyric acid significantly increased in the hippocampus (38 and 32%) and thalamus (22 and 40%) but decreased in the cortex and hypothalamus although in striatum only after eight days of treatment (24%). Our results suggest that injected methanol extract modifies amino acid levels in the rat brain regions. These results could be of interest since some neurodegenerative diseases are related to amino acid level imbalances in the central nervous system, suggesting the prospect for therapeutic use of NS against these disorders.

Mucuna pruriens seed extract reduces oxidative stress in nigrostriatal tissue and improves neurobehavioral activity in paraquat-induced Parkinsonian mouse model.

PubMed

Yadav, Satyndra Kumar; Prakash, Jay; Chouhan, Shikha; Singh, Surya Pratap

2013-06-01

Parkinson's disease (PD) is a neurodegenerative disease which causes rigidity, resting tremor and postural instability. Treatment for this disease is still under investigation. Mucuna pruriens (L.), is a traditional herbal medicine, used in India since 1500 B.C., as a neuroprotective agent. In this present study, we evaluated the therapeutic effects of aqueous extract of M. pruriens (Mp) seed in Parkinsonian mouse model developed by chronic exposure to paraquat (PQ). Results of our study revealed that the nigrostriatal portion of Parkinsonian mouse brain showed significantly increased levels of nitrite, malondialdehyde (MDA) and reduced levels of catalase compared to the control. In the Parkinsonian mice hanging time was decreased, whereas narrow beam walk time and foot printing errors were increased. Treatment with aqueous seed extract of Mp significantly increased the catalase activity and decreased the MDA and nitrite level, compared to untreated Parkinsonian mouse brain. Mp treatment also improved the behavioral abnormalities. It increased hanging time, whereas it decreased narrow beam walk time and foot printing error compared to untreated Parkinsonian mouse brain. Furthermore, we observed a significant reduction in tyrosine hydroxylase (TH) immunoreactivity in the substantia nigra (SN) and striatum region of the brain, after treatment with PQ which was considerably restored by the use of Mp seed extract. Our result suggested that Mp seed extract treatment significantly reduced the PQ induced neurotoxicity as evident by decrease in oxidative damage, physiological abnormalities and immunohistochemical changes in the Parkinsonian mouse. Copyright © 2013 Elsevier Ltd. All rights reserved.

Neuroprotection and enhanced neurogenesis by extract from the tropical plant Knema laurina after inflammatory damage in living brain tissue.

PubMed

Häke, Ines; Schönenberger, Silvia; Neumann, Jens; Franke, Katrin; Paulsen-Merker, Katrin; Reymann, Klaus; Ismail, Ghazally; Bin Din, Laily; Said, Ikram M; Latiff, A; Wessjohann, Ludger; Zipp, Frauke; Ullrich, Oliver

2009-01-03

Inflammatory reactions in the CNS, resulting from a loss of control and involving a network of non-neuronal and neuronal cells, are major contributors to the onset and progress of several major neurodegenerative diseases. Therapeutic strategies should therefore keep or restore the well-controlled and finely-tuned balance of immune reactions, and protect neurons from inflammatory damage. In our study, we selected plants of the Malaysian rain forest by an ethnobotanic survey, and investigated them in cell-based-assay-systems and in living brain tissue cultures in order to identify anti-inflammatory and neuroprotective effects. We found that alcoholic extracts from the tropical plant Knema laurina (Black wild nutmeg) exhibited highly anti-inflammatory and neuroprotective effects in cell culture experiments, reduced NO- and IL-6-release from activated microglia cells dose-dependently, and protected living brain tissue from microglia-mediated inflammatory damage at a concentration of 30 microg/ml. On the intracellular level, the extract inhibited ERK-1/2-phosphorylation, IkB-phosphorylation and subsequently NF-kB-translocation in microglia cells. K. laurina belongs to the family of Myristicaceae, which have been used for centuries for treatment of digestive and inflammatory diseases and is also a major food plant of the Giant Hornbill. Moreover, extract from K. laurina promotes also neurogenesis in living brain tissue after oxygen-glucose deprivation. In conclusion, extract from K. laurina not only controls and limits inflammatory reaction after primary neuronal damage, it promotes moreover neurogenesis if given hours until days after stroke-like injury.

Feature extraction inspired by V1 in visual cortex

NASA Astrophysics Data System (ADS)

Lv, Chao; Xu, Yuelei; Zhang, Xulei; Ma, Shiping; Li, Shuai; Xin, Peng; Zhu, Mingning; Ma, Hongqiang

2018-04-01

Target feature extraction plays an important role in pattern recognition. It is the most complicated activity in the brain mechanism of biological vision. Inspired by high properties of primary visual cortex (V1) in extracting dynamic and static features, a visual perception model was raised. Firstly, 28 spatial-temporal filters with different orientations, half-squaring operation and divisive normalization were adopted to obtain the responses of V1 simple cells; then, an adjustable parameter was added to the output weight so that the response of complex cells was got. Experimental results indicate that the proposed V1 model can perceive motion information well. Besides, it has a good edge detection capability. The model inspired by V1 has good performance in feature extraction and effectively combines brain-inspired intelligence with computer vision.

Brain segmentation and the generation of cortical surfaces

NASA Technical Reports Server (NTRS)

Joshi, M.; Cui, J.; Doolittle, K.; Joshi, S.; Van Essen, D.; Wang, L.; Miller, M. I.

1999-01-01

This paper describes methods for white matter segmentation in brain images and the generation of cortical surfaces from the segmentations. We have developed a system that allows a user to start with a brain volume, obtained by modalities such as MRI or cryosection, and constructs a complete digital representation of the cortical surface. The methodology consists of three basic components: local parametric modeling and Bayesian segmentation; surface generation and local quadratic coordinate fitting; and surface editing. Segmentations are computed by parametrically fitting known density functions to the histogram of the image using the expectation maximization algorithm [DLR77]. The parametric fits are obtained locally rather than globally over the whole volume to overcome local variations in gray levels. To represent the boundary of the gray and white matter we use triangulated meshes generated using isosurface generation algorithms [GH95]. A complete system of local parametric quadratic charts [JWM+95] is superimposed on the triangulated graph to facilitate smoothing and geodesic curve tracking. Algorithms for surface editing include extraction of the largest closed surface. Results for several macaque brains are presented comparing automated and hand surface generation. Copyright 1999 Academic Press.

Multiple "buy buttons" in the brain: Forecasting chocolate sales at point-of-sale based on functional brain activation using fMRI.

PubMed

Kühn, Simone; Strelow, Enrique; Gallinat, Jürgen

2016-08-01

We set out to forecast consumer behaviour in a supermarket based on functional magnetic resonance imaging (fMRI). Data was collected while participants viewed six chocolate bar communications and product pictures before and after each communication. Then self-reports liking judgement were collected. fMRI data was extracted from a priori selected brain regions: nucleus accumbens, medial orbitofrontal cortex, amygdala, hippocampus, inferior frontal gyrus, dorsomedial prefrontal cortex assumed to contribute positively and dorsolateral prefrontal cortex and insula were hypothesized to contribute negatively to sales. The resulting values were rank ordered. After our fMRI-based forecast an instore test was conducted in a supermarket on n=63.617 shoppers. Changes in sales were best forecasted by fMRI signal during communication viewing, second best by a comparison of brain signal during product viewing before and after communication and least by explicit liking judgements. The results demonstrate the feasibility of applying neuroimaging methods in a relatively small sample to correctly forecast sales changes at point-of-sale. Copyright © 2016. Published by Elsevier Inc.

Concurrent white matter bundles and grey matter networks using independent component analysis.

PubMed

O'Muircheartaigh, Jonathan; Jbabdi, Saad

2018-04-15

Developments in non-invasive diffusion MRI tractography techniques have permitted the investigation of both the anatomy of white matter pathways connecting grey matter regions and their structural integrity. In parallel, there has been an expansion in automated techniques aimed at parcellating grey matter into distinct regions based on functional imaging. Here we apply independent component analysis to whole-brain tractography data to automatically extract brain networks based on their associated white matter pathways. This method decomposes the tractography data into components that consist of paired grey matter 'nodes' and white matter 'edges', and automatically separates major white matter bundles, including known cortico-cortical and cortico-subcortical tracts. We show how this framework can be used to investigate individual variations in brain networks (in terms of both nodes and edges) as well as their associations with individual differences in behaviour and anatomy. Finally, we investigate correspondences between tractography-based brain components and several canonical resting-state networks derived from functional MRI. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Simultaneous determination of eight bioactive compounds by LC-MS/MS and its application to the pharmacokinetics, liver first-pass effect, liver and brain distribution of orally administrated Gouteng-Baitouweng (GB) in rats.

PubMed

Tian, Xiaoting; Xu, Zhou; Chen, Mingcang; Hu, Pei; Liu, Fang; Sun, Zhaolin; Liu, Huan; Guo, Xiaozheng; Li, Zhixiong; Huang, Chenggang

2018-05-01

Only focusing on the circulating levels is insufficient for the comprehensive understanding of the physiological disposition of herbal medicine in vivo. Therefore, we conducted the comprehensive investigation on the in vivo dynamic process of orally administrated Gouteng-Baitouweng (GB), a classical herb pair with anti-Parkinson potentials. Serving as the technical base, a sensitive and selective liquid chromatography-tandem mass spectrometry method was established and validated in the plasma, liver and brain, for simultaneous determination of five alkaloids (rhynchophylline, isorhynchophylline, corynoxeine, isocorynoxeine and geissoschizine methyl ether) and three saponins (anemoside B4, anemoside A3 and 23-hydroxybetulinic acid). Following liquid-liquid extraction, favorable chromatographic behaviors of eight analytes were obtained on Waters Xbrigde C18 column within 13 min. This method elicited good linearity for the analytes at the concentration range of 0.3-1000 or 1.8-6000 ng/mL with favorable precision, accuracy and stability. Following oral administration of GB (25 g/kg) in rats, this method was applied to the quantitative analysis in the portal vein plasma, liver, systemic plasma, and brain. Consequently, anemoside B4 was of the highest exposure, followed by 23-hydroxybetulinic acid, anemoside A3, rhynchophylline and isocorynoxeine in vivo. Notably, three saponins were all observed with certain exposure in the brain, along with rhynchophylline at low levels. Besides, five alkaloids and 23-hydroxybetulinic acid underwent serious liver first-pass effect. Hence, the pharmacokinetics, liver first-pass effect, liver and brain distribution of ingredients in GB were clarified, which laid a solid foundation for interpreting its efficacy and safety. Copyright © 2018. Published by Elsevier B.V.

Identifying Multimodal Intermediate Phenotypes between Genetic Risk Factors and Disease Status in Alzheimer’s Disease

PubMed Central

Hao, Xiaoke; Yao, Xiaohui; Yan, Jingwen; Risacher, Shannon L.; Saykin, Andrew J.; Zhang, Daoqiang; Shen, Li

2016-01-01

Neuroimaging genetics has attracted growing attention and interest, which is thought to be a powerful strategy to examine the influence of genetic variants (i.e., single nucleotide polymorphisms (SNPs)) on structures or functions of human brain. In recent studies, univariate or multivariate regression analysis methods are typically used to capture the effective associations between genetic variants and quantitative traits (QTs) such as brain imaging phenotypes. The identified imaging QTs, although associated with certain genetic markers, may not be all disease specific. A useful, but underexplored, scenario could be to discover only those QTs associated with both genetic markers and disease status for revealing the chain from genotype to phenotype to symptom. In addition, multimodal brain imaging phenotypes are extracted from different perspectives and imaging markers consistently showing up in multimodalities may provide more insights for mechanistic understanding of diseases (i.e., Alzheimer’s disease (AD)). In this work, we propose a general framework to exploit multi-modal brain imaging phenotypes as intermediate traits that bridge genetic risk factors and multi-class disease status. We applied our proposed method to explore the relation between the well-known AD risk SNP APOE rs429358 and three baseline brain imaging modalities (i.e., structural magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography (FDG-PET) and F-18 florbetapir PET scans amyloid imaging (AV45)) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The empirical results demonstrate that our proposed method not only helps improve the performances of imaging genetic associations, but also discovers robust and consistent regions of interests (ROIs) across multi-modalities to guide the disease-induced interpretation. PMID:27277494

Object Extraction in Cluttered Environments via a P300-Based IFCE

PubMed Central

He, Huidong; Xian, Bin; Zeng, Ming; Zhou, Huihui; Niu, Linwei; Chen, Genshe

2017-01-01

One of the fundamental issues for robot navigation is to extract an object of interest from an image. The biggest challenges for extracting objects of interest are how to use a machine to model the objects in which a human is interested and extract them quickly and reliably under varying illumination conditions. This article develops a novel method for segmenting an object of interest in a cluttered environment by combining a P300-based brain computer interface (BCI) and an improved fuzzy color extractor (IFCE). The induced P300 potential identifies the corresponding region of interest and obtains the target of interest for the IFCE. The classification results not only represent the human mind but also deliver the associated seed pixel and fuzzy parameters to extract the specific objects in which the human is interested. Then, the IFCE is used to extract the corresponding objects. The results show that the IFCE delivers better performance than the BP network or the traditional FCE. The use of a P300-based IFCE provides a reliable solution for assisting a computer in identifying an object of interest within images taken under varying illumination intensities. PMID:28740505

Microwave irradiation decreases ATP, increases free [Mg2+], and alters in vivo intracellular reactions in rat brain

PubMed Central

Srivastava, Shireesh; Kashiwaya, Yoshihiro; Chen, Xuesong; Geiger, Jonathan D.; Pawlosky, Robert; Veech, Richard L.

2012-01-01

Rapid inactivation of metabolism is essential for accurately determining the concentrations of metabolic intermediates in the in vivo state. We compared a broad spectrum of energetic intermediate metabolites and neurotransmitters in brains obtained by microwave irradiation to those obtained by freeze blowing, the most rapid method of extracting and freezing rat brain. The concentrations of many intermediates, cytosolic free NAD(P)+/NAD(P)H ratios, as well as neurotransmitters were not affected by the microwave procedure. However, the brain concentrations of ATP were about 30% lower, whereas those of ADP, AMP, and GDP were higher in the microwave-irradiated compared with the freeze-blown brains. In addition, the hydrolysis of approximately 1 μmol/g of ATP, a major in vivo Mg2+-binding site, was related to approximately five-fold increase in free [Mg2+] (0.53 ± 0.07 mM in freeze blown vs. 2.91 mM ± 0.48 mM in microwaved brains), as determined from the ratio [citrate]/[isocitrate]. Consequently, many intracellular properties, such as the phosphorylation potential and the ΔG’ of ATP hydrolysis were significantly altered in microwaved tissue. The determinations of some glycolytic and TCA cycle metabolites, the phosphorylation potential, and the ΔG’ of ATP hydrolysis do not represent the in vivo state when using microwave-fixed brain tissue. PMID:23013291

Effects of an Agaricus blazei aqueous extract pretreatment on paracetamol-induced brain and liver injury in rats.

PubMed

Soares, Andréia A; de Oliveira, Andrea L; Sá-Nakanishi, Anacharis B; Comar, Jurandir F; Rampazzo, Ana P S; Vicentini, Fernando A; Natali, Maria R M; Gomes da Costa, Sandra M; Bracht, Adelar; Peralta, Rosane M

2013-01-01

The action of an Agaricus blazei aqueous extract pretreatment on paracetamol injury in rats was examined not only in terms of the classical indicators (e.g., levels of hepatic enzymes in the plasma) but also in terms of functional and metabolic parameters (e.g., gluconeogenesis). Considering solely the classical indicators for tissue damage, the results can be regarded as an indication that the A. blazei extract is able to provide a reasonable degree of protection against the paracetamol injury in both the hepatic and brain tissues. The A. blazei pretreatment largely prevented the increased levels of hepatic enzymes in the plasma (ASP, ALT, LDH, and ALP) and practically normalized the TBARS levels in both liver and brain tissues. With respect to the functional and metabolic parameters of the liver, however, the extract provided little or no protection. This includes morphological signs of inflammation and the especially important functional parameter gluconeogenesis, which was impaired by paracetamol. Considering these results and the long list of extracts and substances that are said to have hepatoprotective effects, it would be useful to incorporate evaluations of functional parameters into the experimental protocols of studies aiming to attribute or refute effective hepatoprotective actions to natural products.

Effects of an Agaricus blazei Aqueous Extract Pretreatment on Paracetamol-Induced Brain and Liver Injury in Rats

PubMed Central

Soares, Andréia A.; de Oliveira, Andrea L.; Sá-Nakanishi, Anacharis B.; Comar, Jurandir F.; Rampazzo, Ana P. S.; Vicentini, Fernando A.; Natali, Maria R. M.; Gomes da Costa, Sandra M.; Peralta, Rosane M.

2013-01-01

The action of an Agaricus blazei aqueous extract pretreatment on paracetamol injury in rats was examined not only in terms of the classical indicators (e.g., levels of hepatic enzymes in the plasma) but also in terms of functional and metabolic parameters (e.g., gluconeogenesis). Considering solely the classical indicators for tissue damage, the results can be regarded as an indication that the A. blazei extract is able to provide a reasonable degree of protection against the paracetamol injury in both the hepatic and brain tissues. The A. blazei pretreatment largely prevented the increased levels of hepatic enzymes in the plasma (ASP, ALT, LDH, and ALP) and practically normalized the TBARS levels in both liver and brain tissues. With respect to the functional and metabolic parameters of the liver, however, the extract provided little or no protection. This includes morphological signs of inflammation and the especially important functional parameter gluconeogenesis, which was impaired by paracetamol. Considering these results and the long list of extracts and substances that are said to have hepatoprotective effects, it would be useful to incorporate evaluations of functional parameters into the experimental protocols of studies aiming to attribute or refute effective hepatoprotective actions to natural products. PMID:23984368

Diverse action of lipoteichoic acid and lipopolysaccharide on neuroinflammation, blood-brain barrier disruption, and anxiety in mice.

PubMed

Mayerhofer, Raphaela; Fröhlich, Esther E; Reichmann, Florian; Farzi, Aitak; Kogelnik, Nora; Fröhlich, Eleonore; Sattler, Wolfgang; Holzer, Peter

2017-02-01

Microbial metabolites are known to affect immune system, brain, and behavior via activation of pattern recognition receptors such as Toll-like receptor 4 (TLR4). Unlike the effect of the TLR4 agonist lipopolysaccharide (LPS), the role of other TLR agonists in immune-brain communication is insufficiently understood. We therefore hypothesized that the TLR2 agonist lipoteichoic acid (LTA) causes immune activation in the periphery and brain, stimulates the hypothalamic-pituitary-adrenal (HPA) axis and has an adverse effect on blood-brain barrier (BBB) and emotional behavior. Since LTA preparations may be contaminated by LPS, an extract of LTA (LTA extract ), purified LTA (LTA pure ), and pure LPS (LPS ultrapure ) were compared with each other in their effects on molecular and behavioral parameters 3h after intraperitoneal (i.p.) injection to male C57BL/6N mice. The LTA extract (20mg/kg) induced anxiety-related behavior in the open field test, enhanced the circulating levels of particular cytokines and the cerebral expression of cytokine mRNA, and blunted the cerebral expression of tight junction protein mRNA. A dose of LPS ultrapure matching the amount of endotoxin/LPS contaminating the LTA extract reproduced several of the molecular and behavioral effects of LTA extract . LTA pure (20mg/kg) increased plasma levels of tumor necrosis factor-α (TNF-α), interleukin-6 and interferon-γ, and enhanced the transcription of TNF-α, interleukin-1β and other cytokines in the amygdala and prefrontal cortex. These neuroinflammatory effects of LTA pure were associated with transcriptional down-regulation of tight junction-associated proteins (claudin 5, occludin) in the brain. LTA pure also enhanced circulating corticosterone, but failed to alter locomotor and anxiety-related behavior in the open field test. These data disclose that TLR2 agonism by LTA causes peripheral immune activation and initiates neuroinflammatory processes in the brain that are associated with down-regulation of BBB components and activation of the HPA axis, although emotional behavior (anxiety) is not affected. The results obtained with an LTA preparation contaminated with LPS hint at a facilitatory interaction between TLR2 and TLR4, the adverse impact of which on long-term neuroinflammation, disruption of the BBB and mental health warrants further analysis. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

Decoding Lifespan Changes of the Human Brain Using Resting-State Functional Connectivity MRI

PubMed Central

Wang, Lubin; Su, Longfei; Shen, Hui; Hu, Dewen

2012-01-01

The development of large-scale functional brain networks is a complex, lifelong process that can be investigated using resting-state functional connectivity MRI (rs-fcMRI). In this study, we aimed to decode the developmental dynamics of the whole-brain functional network in seven decades (8–79 years) of the human lifespan. We first used parametric curve fitting to examine linear and nonlinear age effect on the resting human brain, and then combined manifold learning and support vector machine methods to predict individuals' “brain ages” from rs-fcMRI data. We found that age-related changes in interregional functional connectivity exhibited spatially and temporally specific patterns. During brain development from childhood to senescence, functional connections tended to linearly increase in the emotion system and decrease in the sensorimotor system; while quadratic trajectories were observed in functional connections related to higher-order cognitive functions. The complex patterns of age effect on the whole-brain functional network could be effectively represented by a low-dimensional, nonlinear manifold embedded in the functional connectivity space, which uncovered the inherent structure of brain maturation and aging. Regression of manifold coordinates with age further showed that the manifold representation extracted sufficient information from rs-fcMRI data to make prediction about individual brains' functional development levels. Our study not only gives insights into the neural substrates that underlie behavioral and cognitive changes over age, but also provides a possible way to quantitatively describe the typical and atypical developmental progression of human brain function using rs-fcMRI. PMID:22952990

Decoding lifespan changes of the human brain using resting-state functional connectivity MRI.

PubMed

Wang, Lubin; Su, Longfei; Shen, Hui; Hu, Dewen

2012-01-01

The development of large-scale functional brain networks is a complex, lifelong process that can be investigated using resting-state functional connectivity MRI (rs-fcMRI). In this study, we aimed to decode the developmental dynamics of the whole-brain functional network in seven decades (8-79 years) of the human lifespan. We first used parametric curve fitting to examine linear and nonlinear age effect on the resting human brain, and then combined manifold learning and support vector machine methods to predict individuals' "brain ages" from rs-fcMRI data. We found that age-related changes in interregional functional connectivity exhibited spatially and temporally specific patterns. During brain development from childhood to senescence, functional connections tended to linearly increase in the emotion system and decrease in the sensorimotor system; while quadratic trajectories were observed in functional connections related to higher-order cognitive functions. The complex patterns of age effect on the whole-brain functional network could be effectively represented by a low-dimensional, nonlinear manifold embedded in the functional connectivity space, which uncovered the inherent structure of brain maturation and aging. Regression of manifold coordinates with age further showed that the manifold representation extracted sufficient information from rs-fcMRI data to make prediction about individual brains' functional development levels. Our study not only gives insights into the neural substrates that underlie behavioral and cognitive changes over age, but also provides a possible way to quantitatively describe the typical and atypical developmental progression of human brain function using rs-fcMRI.

EEG feature selection method based on decision tree.

PubMed

Duan, Lijuan; Ge, Hui; Ma, Wei; Miao, Jun

2015-01-01

This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.

Reproducibility and discriminability of brain patterns of semantic categories enhanced by congruent audiovisual stimuli.

PubMed

Li, Yuanqing; Wang, Guangyi; Long, Jinyi; Yu, Zhuliang; Huang, Biao; Li, Xiaojian; Yu, Tianyou; Liang, Changhong; Li, Zheng; Sun, Pei

2011-01-01

One of the central questions in cognitive neuroscience is the precise neural representation, or brain pattern, associated with a semantic category. In this study, we explored the influence of audiovisual stimuli on the brain patterns of concepts or semantic categories through a functional magnetic resonance imaging (fMRI) experiment. We used a pattern search method to extract brain patterns corresponding to two semantic categories: "old people" and "young people." These brain patterns were elicited by semantically congruent audiovisual, semantically incongruent audiovisual, unimodal visual, and unimodal auditory stimuli belonging to the two semantic categories. We calculated the reproducibility index, which measures the similarity of the patterns within the same category. We also decoded the semantic categories from these brain patterns. The decoding accuracy reflects the discriminability of the brain patterns between two categories. The results showed that both the reproducibility index of brain patterns and the decoding accuracy were significantly higher for semantically congruent audiovisual stimuli than for unimodal visual and unimodal auditory stimuli, while the semantically incongruent stimuli did not elicit brain patterns with significantly higher reproducibility index or decoding accuracy. Thus, the semantically congruent audiovisual stimuli enhanced the within-class reproducibility of brain patterns and the between-class discriminability of brain patterns, and facilitate neural representations of semantic categories or concepts. Furthermore, we analyzed the brain activity in superior temporal sulcus and middle temporal gyrus (STS/MTG). The strength of the fMRI signal and the reproducibility index were enhanced by the semantically congruent audiovisual stimuli. Our results support the use of the reproducibility index as a potential tool to supplement the fMRI signal amplitude for evaluating multimodal integration.

Brain bank of the Brazilian aging brain study group - a milestone reached and more than 1,600 collected brains.

PubMed

Grinberg, Lea Tenenholz; Ferretti, Renata Eloah de Lucena; Farfel, José Marcelo; Leite, Renata; Pasqualucci, Carlos Augusto; Rosemberg, Sérgio; Nitrini, Ricardo; Saldiva, Paulo Hilário Nascimento; Filho, Wilson Jacob

2007-01-01

Brain banking remains a necessity for the study of aging brain processes and related neurodegenerative diseases. In the present paper, we report the methods applied at and the first results of the Brain Bank of the Brazilian Aging Brain Study Group (BBBABSG) which has two main aims: (1) To collect a large number of brains of elderly comprising non-demented subjects and a large spectrum of pathologies related to aging brain processes, (2) To provide quality material to a multidisciplinar research network unraveling multiple aspects of aging brain processes and related neurodegenerative diseases. The subjects are selected from the Sao Paulo Autopsy Service. Brain parts are frozen and fixated. CSF, carotids, kidney, heart and blood are also collected and DNA is extracted. The neuropathological examinations are carried out based on accepted criteria, using immunohistochemistry. Functional status are assessed through a collateral source based on a clinical protocol. Protocols are approved by the local ethics committee and a written informed consent form is obtained. During the first 21 months, 1,602 samples were collected and were classified by Clinical Dementia Rating as CDR0: 65.7%; CDR0.5:12.6%, CDR1:8.2%, CDR2:5.4%, and CDR3:8.1%. On average, the cost for the processing each case stood at 400 US dollars. To date, 14 laboratories have been benefited by the BBBABSG. The high percentage of non- demented subjects and the ethnic diversity of this series may be significantly contributive toward aging brain processes and related neurodegenerative diseases understanding since BBBABSG outcomes may provide investigators the answers to some additional questions.

Reproducibility and Discriminability of Brain Patterns of Semantic Categories Enhanced by Congruent Audiovisual Stimuli

PubMed Central

Long, Jinyi; Yu, Zhuliang; Huang, Biao; Li, Xiaojian; Yu, Tianyou; Liang, Changhong; Li, Zheng; Sun, Pei

2011-01-01

One of the central questions in cognitive neuroscience is the precise neural representation, or brain pattern, associated with a semantic category. In this study, we explored the influence of audiovisual stimuli on the brain patterns of concepts or semantic categories through a functional magnetic resonance imaging (fMRI) experiment. We used a pattern search method to extract brain patterns corresponding to two semantic categories: “old people” and “young people.” These brain patterns were elicited by semantically congruent audiovisual, semantically incongruent audiovisual, unimodal visual, and unimodal auditory stimuli belonging to the two semantic categories. We calculated the reproducibility index, which measures the similarity of the patterns within the same category. We also decoded the semantic categories from these brain patterns. The decoding accuracy reflects the discriminability of the brain patterns between two categories. The results showed that both the reproducibility index of brain patterns and the decoding accuracy were significantly higher for semantically congruent audiovisual stimuli than for unimodal visual and unimodal auditory stimuli, while the semantically incongruent stimuli did not elicit brain patterns with significantly higher reproducibility index or decoding accuracy. Thus, the semantically congruent audiovisual stimuli enhanced the within-class reproducibility of brain patterns and the between-class discriminability of brain patterns, and facilitate neural representations of semantic categories or concepts. Furthermore, we analyzed the brain activity in superior temporal sulcus and middle temporal gyrus (STS/MTG). The strength of the fMRI signal and the reproducibility index were enhanced by the semantically congruent audiovisual stimuli. Our results support the use of the reproducibility index as a potential tool to supplement the fMRI signal amplitude for evaluating multimodal integration. PMID:21750692

Alterations in Normal Aging Revealed by Cortical Brain Network Constructed Using IBASPM.

PubMed

Li, Wan; Yang, Chunlan; Shi, Feng; Wang, Qun; Wu, Shuicai; Lu, Wangsheng; Li, Shaowu; Nie, Yingnan; Zhang, Xin

2018-04-16

Normal aging has been linked with the decline of cognitive functions, such as memory and executive skills. One of the prominent approaches to investigate the age-related alterations in the brain is by examining the cortical brain connectome. IBASPM is a toolkit to realize individual atlas-based volume measurement. Hence, this study seeks to determine what further alterations can be revealed by cortical brain networks formed by IBASPM-extracted regional gray matter volumes. We found the reduced strength of connections between the superior temporal pole and middle temporal pole in the right hemisphere, global hubs as the left fusiform gyrus and right Rolandic operculum in the young and aging groups, respectively, and significantly reduced inter-module connection of one module in the aging group. These new findings are consistent with the phenomenon of normal aging mentioned in previous studies and suggest that brain network built with the IBASPM could provide supplementary information to some extent. The individualization of morphometric features extraction deserved to be given more attention in future cortical brain network research.

Evaluation of MLACF based calculated attenuation brain PET imaging for FDG patient studies

NASA Astrophysics Data System (ADS)

Bal, Harshali; Panin, Vladimir Y.; Platsch, Guenther; Defrise, Michel; Hayden, Charles; Hutton, Chloe; Serrano, Benjamin; Paulmier, Benoit; Casey, Michael E.

2017-04-01

Calculating attenuation correction for brain PET imaging rather than using CT presents opportunities for low radiation dose applications such as pediatric imaging and serial scans to monitor disease progression. Our goal is to evaluate the iterative time-of-flight based maximum-likelihood activity and attenuation correction factors estimation (MLACF) method for clinical FDG brain PET imaging. FDG PET/CT brain studies were performed in 57 patients using the Biograph mCT (Siemens) four-ring scanner. The time-of-flight PET sinograms were acquired using the standard clinical protocol consisting of a CT scan followed by 10 min of single-bed PET acquisition. Images were reconstructed using CT-based attenuation correction (CTAC) and used as a gold standard for comparison. Two methods were compared with respect to CTAC: a calculated brain attenuation correction (CBAC) and MLACF based PET reconstruction. Plane-by-plane scaling was performed for MLACF images in order to fix the variable axial scaling observed. The noise structure of the MLACF images was different compared to those obtained using CTAC and the reconstruction required a higher number of iterations to obtain comparable image quality. To analyze the pooled data, each dataset was registered to a standard template and standard regions of interest were extracted. An SUVr analysis of the brain regions of interest showed that CBAC and MLACF were each well correlated with CTAC SUVrs. A plane-by-plane error analysis indicated that there were local differences for both CBAC and MLACF images with respect to CTAC. Mean relative error in the standard regions of interest was less than 5% for both methods and the mean absolute relative errors for both methods were similar (3.4%  ±  3.1% for CBAC and 3.5%  ±  3.1% for MLACF). However, the MLACF method recovered activity adjoining the frontal sinus regions more accurately than CBAC method. The use of plane-by-plane scaling of MLACF images was found to be a crucial step in order to obtain improved activity estimates. Presence of local errors in both MLACF and CBAC based reconstructions would require the use of a normal database for clinical assessment. However, further work is required in order to assess the clinical advantage of MLACF over CBAC based method.

Robust Averaging of Covariances for EEG Recordings Classification in Motor Imagery Brain-Computer Interfaces.

PubMed

Uehara, Takashi; Sartori, Matteo; Tanaka, Toshihisa; Fiori, Simone

2017-06-01

The estimation of covariance matrices is of prime importance to analyze the distribution of multivariate signals. In motor imagery-based brain-computer interfaces (MI-BCI), covariance matrices play a central role in the extraction of features from recorded electroencephalograms (EEGs); therefore, correctly estimating covariance is crucial for EEG classification. This letter discusses algorithms to average sample covariance matrices (SCMs) for the selection of the reference matrix in tangent space mapping (TSM)-based MI-BCI. Tangent space mapping is a powerful method of feature extraction and strongly depends on the selection of a reference covariance matrix. In general, the observed signals may include outliers; therefore, taking the geometric mean of SCMs as the reference matrix may not be the best choice. In order to deal with the effects of outliers, robust estimators have to be used. In particular, we discuss and test the use of geometric medians and trimmed averages (defined on the basis of several metrics) as robust estimators. The main idea behind trimmed averages is to eliminate data that exhibit the largest distance from the average covariance calculated on the basis of all available data. The results of the experiments show that while the geometric medians show little differences from conventional methods in terms of classification accuracy in the classification of electroencephalographic recordings, the trimmed averages show significant improvement for all subjects.

Unwarping confocal microscopy images of bee brains by nonrigid registration to a magnetic resonance microscopy image.

PubMed

Rohlfing, Torsten; Schaupp, Frank; Haddad, Daniel; Brandt, Robert; Haase, Axel; Menzel, Randolf; Maurer, Calvin R

2005-01-01

Confocal microscopy (CM) is a powerful image acquisition technique that is well established in many biological applications. It provides 3-D acquisition with high spatial resolution and can acquire several different channels of complementary image information. Due to the specimen extraction and preparation process, however, the shapes of imaged objects may differ considerably from their in vivo appearance. Magnetic resonance microscopy (MRM) is an evolving variant of magnetic resonance imaging, which achieves microscopic resolutions using a high magnetic field and strong magnetic gradients. Compared to CM imaging, MRM allows for in situ imaging and is virtually free of geometrical distortions. We propose to combine the advantages of both methods by unwarping CM images using a MRM reference image. Our method incorporates a sequence of image processing operators applied to the MRM image, followed by a two-stage intensity-based registration to compute a nonrigid coordinate transformation between the CM images and the MRM image. We present results obtained using CM images from the brains of 20 honey bees and a MRM image of an in situ bee brain. Copyright 2005 Society of Photo-Optical Instrumentation Engineers.

Grape seed and skin extract prevents high-fat diet-induced brain lipotoxicity in rat.

PubMed

Charradi, Kamel; Elkahoui, Salem; Karkouch, Ines; Limam, Ferid; Hassine, Fethy Ben; Aouani, Ezzedine

2012-09-01

Obesity is related to an elevated risk of dementia and the physiologic mechanisms whereby fat adversely affects the brain are poorly understood. The present investigation analyzed the effect of a high fat diet (HFD) on brain steatosis and oxidative stress and the intracellular mediators involved in signal transduction, as well as the protection offered by grape seed and skin extract (GSSE). HFD induced ectopic deposition of cholesterol and phospholipid but not triglyceride. Moreover brain lipotoxicity is linked to an oxidative stress characterized by increased lipoperoxidation and carbonylation, inhibition of glutathione peroxidase and superoxide dismutase activities, depletion of manganese and a concomitant increase in ionizable calcium and acetylcholinesterase activity. Importantly GSSE alleviated all the deleterious effects of HFD treatment. Altogether our data indicated that HFD could find some potential application in the treatment of manganism and that GSSE should be used as a safe anti-lipotoxic agent in the prevention and treatment of fat-induced brain injury.

Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements

NASA Astrophysics Data System (ADS)

Taulu, S.; Simola, J.

2006-04-01

Limitations of traditional magnetoencephalography (MEG) exclude some important patient groups from MEG examinations, such as epilepsy patients with a vagus nerve stimulator, patients with magnetic particles on the head or having magnetic dental materials that cause severe movement-related artefact signals. Conventional interference rejection methods are not able to remove the artefacts originating this close to the MEG sensor array. For example, the reference array method is unable to suppress interference generated by sources closer to the sensors than the reference array, about 20-40 cm. The spatiotemporal signal space separation method proposed in this paper recognizes and removes both external interference and the artefacts produced by these nearby sources, even on the scalp. First, the basic separation into brain-related and external interference signals is accomplished with signal space separation based on sensor geometry and Maxwell's equations only. After this, the artefacts from nearby sources are extracted by a simple statistical analysis in the time domain, and projected out. Practical examples with artificial current dipoles and interference sources as well as data from real patients demonstrate that the method removes the artefacts without altering the field patterns of the brain signals.

Altered voxel-wise gray matter structural brain networks in schizophrenia: Association with brain genetic expression pattern.

PubMed

Liu, Feng; Tian, Hongjun; Li, Jie; Li, Shen; Zhuo, Chuanjun

2018-05-04

Previous seed- and atlas-based structural covariance/connectivity analyses have demonstrated that patients with schizophrenia is accompanied by aberrant structural connection and abnormal topological organization. However, it remains unclear whether this disruption is present in unbiased whole-brain voxel-wise structural covariance networks (SCNs) and whether brain genetic expression variations are linked with network alterations. In this study, ninety-five patients with schizophrenia and 95 matched healthy controls were recruited and gray matter volumes were extracted from high-resolution structural magnetic resonance imaging scans. Whole-brain voxel-wise gray matter SCNs were constructed at the group level and were further analyzed by using graph theory method. Nonparametric permutation tests were employed for group comparisons. In addition, regression modes along with random effect analysis were utilized to explore the associations between structural network changes and gene expression from the Allen Human Brain Atlas. Compared with healthy controls, the patients with schizophrenia showed significantly increased structural covariance strength (SCS) in the right orbital part of superior frontal gyrus and bilateral middle frontal gyrus, while decreased SCS in the bilateral superior temporal gyrus and precuneus. The altered SCS showed reproducible correlations with the expression profiles of the gene classes involved in therapeutic targets and neurodevelopment. Overall, our findings not only demonstrate that the topological architecture of whole-brain voxel-wise SCNs is impaired in schizophrenia, but also provide evidence for the possible role of therapeutic targets and neurodevelopment-related genes in gray matter structural brain networks in schizophrenia.

Alteration in Memory and Electroencephalogram Waves with Sub-acute Noise Stress in Albino Rats and Safeguarded by Scoparia dulcis

PubMed Central

Loganathan, Sundareswaran; Rathinasamy, Sheeladevi

2016-01-01

Background: Noise stress has different effects on memory and novelty and the link between them with an electroencephalogram (EEG) has not yet been reported. Objective: To find the effect of sub-acute noise stress on the memory and novelty along with EEG and neurotransmitter changes. Materials and Methods: Eight-arm maze (EAM) and Y-maze to analyze the memory and novelty by novel object test. Four groups of rats were used: Control, control treated with Scoparia dulcis extract, noise exposed, and noise exposed which received Scoparia extract. Results: The results showed no marked difference observed between control and control treated with Scoparia extract on EAM, Y-maze, novel object test, and EEG in both prefrontal and occipital region, however, noise stress exposed rats showed significant increase in the reference memory and working memory error in EAM and latency delay, triad errors in Y-maze, and prefrontal and occipital EEG frequency rate with the corresponding increase in plasma corticosterone and epinephrine, and significant reduction in the novelty test, and significant reduction in the novelty test, amplitude of prefrontal, occipital EEG, and acetylcholine. Conclusion: These noise stress induced changes in EAM, Y-maze, novel object test, and neurotransmitters were significantly prevented when treated with Scoparia extract and these changes may be due to the normalizing action of Scoparia extract on the brain, which altered due to noise stress. SUMMARY Noise stress exposure causes EEG, behavior, and neurotransmitter alteration in the frontoparietal and occipital regions mainly involved in planning and recognition memoryOnly the noise stress exposed animals showed the significant alteration in the EEG, behavior, and neurotransmittersHowever, these noise stress induced changes in EEG behavior and neurotransmitters were significantly prevented when treated with Scoparia extractThese changes may be due to the normalizing action of Scoparia dulcis (adoptogen) on the brain which altered by noise stress. Abbreviations used: EEG: Electroencephalogram, dB: Decibel, EPI: Epinephrine, ACH: Acetylcholine, EAM: Eight-arm maze PMID:27041862

Radiochemical micro assays for the determination of choline acetyltransferase and acetylcholinesterase activities

PubMed Central

Fonnum, F.

1969-01-01

1. The methods for the assay of choline acetyltransferase were based on the reaction between labelled acetyl-CoA and unlabelled choline to give labelled acetylcholine. 2. Both synthetic acetyl-CoA and acetyl-CoA formed from sodium [1-14C]acetate or sodium [3H]acetate by incubation with CoA, ATP, Mg2+ and extract from acetone-dried pigeon liver were used. 3. [1-14C]Acetylcholine was isolated by extraction with ketonic sodium tetraphenylboron. 4. [3H]Acetylcholine was precipitated with sodium tetraphenylboron to remove a ketone-soluble contaminant in sodium [3H]acetate and then extracted with ketonic sodium tetraphenylboron. 5. The values of choline acetyltransferase activity obtained in the presence of sodium cyanide or EDTA and synthetic acetyl-CoA were similar to those obtained with acetyl-CoA synthesized in situ. 6. The assay of acetylcholinesterase was based on the formation of labelled acetate from labelled acetylcholine. The labelled acetylcholine could be quantitatively removed from the acetate by extraction with ketonic sodium tetraphenylboron. 7. The methods were tested with samples from central and peripheral nervous tissues and purified enzymes. 8. The blank values for choline acetyltransferase and acetylcholinesterase corresponded to the activities in 20ng. and 5ng. of brain tissue respectively. PMID:4982085

Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method

PubMed Central

Guo, Xinyu; Dominick, Kelli C.; Minai, Ali A.; Li, Hailong; Erickson, Craig A.; Lu, Long J.

2017-01-01

The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD) by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN) with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS) is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD) controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS) is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes). Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150). Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t-test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross different pre-defined brain networks including the default-mode, cingulo-opercular, frontal-parietal, and cerebellum. Thirteen of them are statically significant between ASD and TD groups (two sample t-test p < 0.05) while 19 of them are not. The relationship between the statically significant FCs and the corresponding ASD behavior symptoms is discussed based on the literature and clinician's expert knowledge. Meanwhile, the potential reason of obtaining 19 FCs which are not statistically significant is also provided. PMID:28871217

ODF Maxima Extraction in Spherical Harmonic Representation via Analytical Search Space Reduction

PubMed Central

Aganj, Iman; Lenglet, Christophe; Sapiro, Guillermo

2015-01-01

By revealing complex fiber structure through the orientation distribution function (ODF), q-ball imaging has recently become a popular reconstruction technique in diffusion-weighted MRI. In this paper, we propose an analytical dimension reduction approach to ODF maxima extraction. We show that by expressing the ODF, or any antipodally symmetric spherical function, in the common fourth order real and symmetric spherical harmonic basis, the maxima of the two-dimensional ODF lie on an analytically derived one-dimensional space, from which we can detect the ODF maxima. This method reduces the computational complexity of the maxima detection, without compromising the accuracy. We demonstrate the performance of our technique on both artificial and human brain data. PMID:20879302

Associative image analysis: a method for automated quantification of 3D multi-parameter images of brain tissue

PubMed Central

Bjornsson, Christopher S; Lin, Gang; Al-Kofahi, Yousef; Narayanaswamy, Arunachalam; Smith, Karen L; Shain, William; Roysam, Badrinath

2009-01-01

Brain structural complexity has confounded prior efforts to extract quantitative image-based measurements. We present a systematic ‘divide and conquer’ methodology for analyzing three-dimensional (3D) multi-parameter images of brain tissue to delineate and classify key structures, and compute quantitative associations among them. To demonstrate the method, thick (~100 μm) slices of rat brain tissue were labeled using 3 – 5 fluorescent signals, and imaged using spectral confocal microscopy and unmixing algorithms. Automated 3D segmentation and tracing algorithms were used to delineate cell nuclei, vasculature, and cell processes. From these segmentations, a set of 23 intrinsic and 8 associative image-based measurements was computed for each cell. These features were used to classify astrocytes, microglia, neurons, and endothelial cells. Associations among cells and between cells and vasculature were computed and represented as graphical networks to enable further analysis. The automated results were validated using a graphical interface that permits investigator inspection and corrective editing of each cell in 3D. Nuclear counting accuracy was >89%, and cell classification accuracy ranged from 81–92% depending on cell type. We present a software system named FARSIGHT implementing our methodology. Its output is a detailed XML file containing measurements that may be used for diverse quantitative hypothesis-driven and exploratory studies of the central nervous system. PMID:18294697

Cinnamon extract inhibits tau aggregation associated with Alzheimer’s Disease in vitro

USDA-ARS?s Scientific Manuscript database

An aqueous extract of Ceylon cinnamon (C. zeylanicum) was found to inhibit tau aggregation and filament formation, hallmarks of Alzheimer’s disease (AD) in vitro using brain cells taken from patients who died with AD. The extract also promoted complete disassembly of recombinant tau filaments, and ...

The effects of Mucuna pruriens extract on histopathological and biochemical features in the rat model of ischemia.

PubMed

Nayak, Vanishri S; Kumar, Nitesh; D'Souza, Antony S; Nayak, Sunil S; Cheruku, Sri P; Pai, K Sreedhara Ranganath

2017-12-13

Stroke is considered to be one of the most important causes of death worldwide. Global ischemia causes widespread brain injury and infarctions in various regions of the brain. Oxidative stress can be considered an important factor in the development of tissue damage, which is caused because of arterial occlusion with subsequent reperfusion. Kapikacchu or Mucuna pruriens, commonly known as velvet bean, is well known for its aphrodisiac activities. It is also used in the treatment of snakebites, depressive neurosis, and Parkinson's disease. Although this plant has different pharmacological actions, its neuroprotective activity has received minimal attention. Thus, this study was carried out with the aim of evaluating the neuroprotective action of M. pruriens in bilateral carotid artery occlusion-induced global cerebral ischemia in Wistar rats. The carotid arteries of both sides were occluded for 30 min and reperfused to induce global cerebral ischemia. The methanolic plant extract was administered to the study animals for 10 days. The brains of the Wistar rats were isolated by decapitation and observed for histopathological and biochemical changes. Cerebral ischemia resulted in significant neurological damage in the brains of the rats that were not treated by M. pruriens. The group subjected to treatment by the M. pruriens extract showed significant protection against brain damage compared with the negative control group, which indicates the therapeutic potential of this plant in ischemia.

'Prion-like' propagation of the synucleinopathy of M83 transgenic mice depends on the mouse genotype and type of inoculum.

PubMed

Sargent, Dorian; Verchère, Jérémy; Lazizzera, Corinne; Gaillard, Damien; Lakhdar, Latifa; Streichenberger, Nathalie; Morignat, Eric; Bétemps, Dominique; Baron, Thierry

2017-10-01

The M83 transgenic mouse is a model of human synucleinopathies that develops severe motor impairment correlated with accumulation of the pathological Ser129-phosphorylated α-synuclein (α-syn P ) in the brain and spinal cord. M83 disease can be accelerated by intracerebral inoculation of brain extracts from sick M83 mice. This has also recently been described using peripheral routes, injecting recombinant preformed α-syn fibrils into the muscle or the peritoneum. Here, we inoculated homozygous and/or hemizygous M83 neonates via the intraperitoneal and/or intracerebral routes with two different brain extracts: one from sick M83 mice inoculated with brain extract from other sick M83 mice, and the other derived from a human multiple system atrophy source passaged in M83 mice. Detection of α-syn P using ELISA and western blot confirmed the disease in mice. The distribution of α-syn P in the central nervous system was similar, independently of the inoculum or inoculation route, consistent with previous studies describing M83 disease. ELISA tests revealed higher levels of α-syn P in homozygous than in hemizygous sick M83 mice, at least after IC inoculation. Interestingly, the immunoreactivity of α-syn P detected by ELISA was significantly lower in M83 mice inoculated with the multiple system atrophy inoculum than in M83 mice inoculated with the M83 inoculum, at the first two passages. 'Prion-like' propagation of the synucleinopathy up to the clinical disease was accelerated by both intracerebral and intraperitoneal inoculations of brain extracts from sick mice. This acceleration, however, depends on the levels of α-syn expression by the mouse and the type of inoculum. © 2017 International Society for Neurochemistry.

Evidence for a Phe-Gly-Leu-amide-like allatostatin in the beetle Tenebrio molitor.

PubMed

Elliott, Karen L; Chan, Kuen Kuen; Stay, Barbara

2010-03-01

The allatostatins (ASTs) with Phe-Gly-Leu-amide C-terminal sequence are multifunctional neuropeptides discovered as inhibitors of juvenile hormone (JH) synthesis by corpora allata (CA) of cockroaches. Although these ASTs inhibit JH synthesis only in cockroaches, crickets, termites and locusts, isolation of peptides or of cDNA/genomic DNA or analysis of genomes indicates their occurrence in many orders of insects with the exception of coleopterans. The gene for these ASTs has not been found in the genome of the red flour beetle Tribolium castaneum (Family Tenebrionidae). Yet, in view of widespread occurrence of these peptides in insects, crustaceans and nematodes, they would be expected to occur in beetles. This study provides evidence for the presence of FGLa-like ASTs in the tenebrionid beetle, Tenebrio molitor, and scarabid beetle, Popillia japonica. Extract of brain from both beetles inhibited JH synthesis by cockroach CA dose dependently and reversibly. 20 brain equivalents of T. molitor and P. japonica extracts inhibited JH synthesis 64+/-5 and 65+/-0.6% respectively. Antibody against cockroach allatostatin (Diploptera punctata AST-7) used in an enzyme-linked immunosorbent assay reacted with brain extract of these beetles. Antibody against D. punctata AST-5 localized FGLa-like ASTs in the brain and subesophageal ganglion of T. molitor and P. japonica. In addition, pretreatment of T. molitor brain extract with anti-D. punctata AST-5 reduced the inhibition of JH synthesis and pretreatment of anti-D. punctata AST-5 with D. punctata AST-5 diminished the immunoreactivity of the antibody. Thus we predict that FGLa-like allatostatins will be found in beetles. (c) 2009 Elsevier Inc. All rights reserved.

Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer's disease.

PubMed

Khazaee, Ali; Ebrahimzadeh, Ata; Babajani-Feremi, Abbas

2016-09-01

The study of brain networks by resting-state functional magnetic resonance imaging (rs-fMRI) is a promising method for identifying patients with dementia from healthy controls (HC). Using graph theory, different aspects of the brain network can be efficiently characterized by calculating measures of integration and segregation. In this study, we combined a graph theoretical approach with advanced machine learning methods to study the brain network in 89 patients with mild cognitive impairment (MCI), 34 patients with Alzheimer's disease (AD), and 45 age-matched HC. The rs-fMRI connectivity matrix was constructed using a brain parcellation based on a 264 putative functional areas. Using the optimal features extracted from the graph measures, we were able to accurately classify three groups (i.e., HC, MCI, and AD) with accuracy of 88.4 %. We also investigated performance of our proposed method for a binary classification of a group (e.g., MCI) from two other groups (e.g., HC and AD). The classification accuracies for identifying HC from AD and MCI, AD from HC and MCI, and MCI from HC and AD, were 87.3, 97.5, and 72.0 %, respectively. In addition, results based on the parcellation of 264 regions were compared to that of the automated anatomical labeling atlas (AAL), consisted of 90 regions. The accuracy of classification of three groups using AAL was degraded to 83.2 %. Our results show that combining the graph measures with the machine learning approach, on the basis of the rs-fMRI connectivity analysis, may assist in diagnosis of AD and MCI.

A Novel Multilayer Correlation Maximization Model for Improving CCA-Based Frequency Recognition in SSVEP Brain-Computer Interface.

PubMed

Jiao, Yong; Zhang, Yu; Wang, Yu; Wang, Bei; Jin, Jing; Wang, Xingyu

2018-05-01

Multiset canonical correlation analysis (MsetCCA) has been successfully applied to optimize the reference signals by extracting common features from multiple sets of electroencephalogram (EEG) for steady-state visual evoked potential (SSVEP) recognition in brain-computer interface application. To avoid extracting the possible noise components as common features, this study proposes a sophisticated extension of MsetCCA, called multilayer correlation maximization (MCM) model for further improving SSVEP recognition accuracy. MCM combines advantages of both CCA and MsetCCA by carrying out three layers of correlation maximization processes. The first layer is to extract the stimulus frequency-related information in using CCA between EEG samples and sine-cosine reference signals. The second layer is to learn reference signals by extracting the common features with MsetCCA. The third layer is to re-optimize the reference signals set in using CCA with sine-cosine reference signals again. Experimental study is implemented to validate effectiveness of the proposed MCM model in comparison with the standard CCA and MsetCCA algorithms. Superior performance of MCM demonstrates its promising potential for the development of an improved SSVEP-based brain-computer interface.

Identifying patients with Alzheimer's disease using resting-state fMRI and graph theory.

PubMed

Khazaee, Ali; Ebrahimzadeh, Ata; Babajani-Feremi, Abbas

2015-11-01

Study of brain network on the basis of resting-state functional magnetic resonance imaging (fMRI) has provided promising results to investigate changes in connectivity among different brain regions because of diseases. Graph theory can efficiently characterize different aspects of the brain network by calculating measures of integration and segregation. In this study, we combine graph theoretical approaches with advanced machine learning methods to study functional brain network alteration in patients with Alzheimer's disease (AD). Support vector machine (SVM) was used to explore the ability of graph measures in diagnosis of AD. We applied our method on the resting-state fMRI data of twenty patients with AD and twenty age and gender matched healthy subjects. The data were preprocessed and each subject's graph was constructed by parcellation of the whole brain into 90 distinct regions using the automated anatomical labeling (AAL) atlas. The graph measures were then calculated and used as the discriminating features. Extracted network-based features were fed to different feature selection algorithms to choose most significant features. In addition to the machine learning approach, statistical analysis was performed on connectivity matrices to find altered connectivity patterns in patients with AD. Using the selected features, we were able to accurately classify patients with AD from healthy subjects with accuracy of 100%. Results of this study show that pattern recognition and graph of brain network, on the basis of the resting state fMRI data, can efficiently assist in the diagnosis of AD. Classification based on the resting-state fMRI can be used as a non-invasive and automatic tool to diagnosis of Alzheimer's disease. Copyright © 2015 International Federation of Clinical Neurophysiology. All rights reserved.

Defining the Phosphodiesterase Superfamily Members in Rat Brain Microvessels

PubMed Central

2011-01-01

Eleven phosphodiesterase (PDE) families are known, each having several different isoforms and splice variants. Recent evidence indicates that expression of individual PDE family members is tissue-specific. Little is known concerning detailed PDE component expression in brain microvessels where the blood-brain-barrier and the local cerebral blood flow are thought to be regulated by PDEs. The present study attempted to identify PDE family members that are expressed in brain microvessels. Adult male F344 rats were sacrificed and blocks of the cerebral cortex and infratentorial areas were dissected. Microvessels were isolated using a filtration method, and total RNA was extracted. RNA quality and quantity were determined using an Agilent bioanalyzer. The isolated cortical and infratentorial microvessel total RNA amounts were 2720 ± 750 ng (n = 2) and 250 ± 40 ng (n = 2), respectively. Microarrays with 22 000 transcripts demonstrated that there were 16 PDE transcripts in the PDE superfamily, exhibiting quantifiable density in the microvessels. An additional immunofluorescent study verified that PDE4D (cAMP-specific) and PDE5A (cGMP-specific) were colocalized with RECA-1 (an endothelial marker) in the cerebral cortex using both F344 rats and Sprague–Dawley rats (n = 3–6/strain). In addition, PDE4D and PDE5A were found to be colocalized with alpha-smooth muscle actin which delineates cerebral arteries and arterioles as well as pericytes. In conclusion, a filtration method followed by microarray analyses allows PDE components to be identified in brain microvessels, and confirmed that PDE4D and PDE5A are the primary forms expressed in rat brain microvessels. PMID:22860158

Grape seed polyphenolic extract specifically decreases aβ*56 in the brains of Tg2576 mice.

PubMed

Liu, Peng; Kemper, Lisa J; Wang, Jun; Zahs, Kathleen R; Ashe, Karen H; Pasinetti, Giulio M

2011-01-01

Amyloid-β (Aβ) oligomers, found in the brains of Alzheimer's disease (AD) patients and transgenic mouse models of AD, cause synaptotoxicity and memory impairment. Grape seed polyphenolic extract (GSPE) inhibits Aβ oligomerization in vitro and attenuates cognitive impairment and AD-related neuropathology in the brains of transgenic mice. In the current study, GSPE was administered to Tg2576 mice for a period of five months. Treatment significantly decreased brain levels of Aβ*56, a 56-kDa Aβ oligomer previously shown to induce memory dysfunction in rodents, without changing the levels of transgenic amyloid-β protein precursor, monomeric Aβ, or other Aβ oligomers. These results thus provide the first demonstration that a safe and affordable intervention can lower the levels of a memory-impairing Aβ oligomer in vivo and strongly suggest that GSPE should be further tested as a potential prevention and/or therapy for AD.

Determination of nitrosourea compounds in brain tissue by gas chromatography and electron capture detection.

PubMed

Hassenbusch, S J; Colvin, O M; Anderson, J H

1995-07-01

A relatively simple, high-sensitivity gas chromatographic assay is described for nitrosourea compounds, such as BCNU [1,3-bis(2-chloroethyl)-1-nitrosourea] and MeCCNU [1-(2-chloroethyl)-3-(trans-4-methylcyclohexyl)-1-nitrosourea], in small biopsy samples of brain and other tissues. After extraction with ethyl acetate, secondary amines in BCNU and MeCCNU are derivatized with trifluoroacetic anhydride. Compounds are separated and quantitated by gas chromatography using a capillary column with temperature programming and an electron capture detector. Standard curves of BCNU indicate a coefficient of variance of 0.066 +/- 0.018, a correlation coefficient of 0.929, and an extraction efficiency from whole brain of 68% with a minimum detectable amount of 20 ng in 5-10 mg samples. The assay has been facile and sensitive in over 1000 brain biopsy specimens after intravenous and intraarterial infusions of BCNU.

Neuroimaging Feature Terminology: A Controlled Terminology for the Annotation of Brain Imaging Features.

PubMed

Iyappan, Anandhi; Younesi, Erfan; Redolfi, Alberto; Vrooman, Henri; Khanna, Shashank; Frisoni, Giovanni B; Hofmann-Apitius, Martin

2017-01-01

Ontologies and terminologies are used for interoperability of knowledge and data in a standard manner among interdisciplinary research groups. Existing imaging ontologies capture general aspects of the imaging domain as a whole such as methodological concepts or calibrations of imaging instruments. However, none of the existing ontologies covers the diagnostic features measured by imaging technologies in the context of neurodegenerative diseases. Therefore, the Neuro-Imaging Feature Terminology (NIFT) was developed to organize the knowledge domain of measured brain features in association with neurodegenerative diseases by imaging technologies. The purpose is to identify quantitative imaging biomarkers that can be extracted from multi-modal brain imaging data. This terminology attempts to cover measured features and parameters in brain scans relevant to disease progression. In this paper, we demonstrate the systematic retrieval of measured indices from literature and how the extracted knowledge can be further used for disease modeling that integrates neuroimaging features with molecular processes.

Protective effect of Uncaria tomentosa extract against oxidative stress and genotoxicity induced by glyphosate-Roundup® using zebrafish (Danio rerio) as a model.

PubMed

Santo, Glaucia Dal; Grotto, Alan; Boligon, Aline A; Da Costa, Bárbara; Rambo, Cassiano L; Fantini, Emily A; Sauer, Elisa; Lazzarotto, Luan M V; Bertoncello, Kanandra T; Júnior, Osmar Tomazelli; Garcia, Solange C; Siebel, Anna M; Rosemberg, Denis B; Magro, Jacir Dal; Conterato, Greicy M M; Zanatta, Leila

2018-04-01

Oxidative stress and DNA damage are involved in the glyphosate-based herbicide toxicity. Uncaria tomentosa (UT; Rubiaceae) is a plant species from South America containing bioactive compounds with known beneficial properties. The objective of this work was to evaluate the antioxidant and antigenotoxic potential of UT extract in a model of acute exposure to glyphosate-Roundup® (GR) in zebrafish (Danio rerio). We showed that UT (1.0 mg/mL) prevented the decrease of brain total thiols, the increase of lipid peroxidation in both brain and liver, and the decrease of liver GPx activity caused after 96 h of GR (5.0 mg/L) exposure. In addition, UT partially protected against the increase of micronucleus frequency induced by GR exposure in fish brain. Overall, our results indicate that UT protects against damage induced by a glyphosate-based herbicide by providing antioxidant and antigenotoxic effects, which may be related to the phenolic compounds identified in the extract.

Effect of Piper betle leaf extract on alcoholic toxicity in the rat brain.

PubMed

Saravanan, R; Rajendra Prasad, N; Pugalendi, K V

2003-01-01

The protective effect of Piper betle, a commonly used masticatory, has been examined in the brain of ethanol-administered Wistar rats. Brain of ethanol-treated rats exhibited increased levels of lipids, lipid peroxidation, and disturbances in antioxidant defense. Subsequent to the experimental induction of toxicity (i.e., the initial period of 30 days), aqueous P. betle extract was simultaneously administered in three different doses (100, 200, and 300 mg kg(-1)) for 30 days along with the daily dose of alcohol. P. betle coadministration resulted in significant reduction of lipid levels (free fatty acids, cholesterol, and phospholipids) and lipid peroxidation markers such as thiobarbituric acid reactive substances and hydroperoxides. Further, antioxidants, like reduced glutathione, vitamin C, vitamin E, superoxide dismutase, catalase, and glutathione peroxidase, were increased in P. betle-coadministered rats. The higher dose of extract (300 mg kg(-1)) was more effective, and these results indicate the neuroprotective effect of P. betle in ethanol-treated rats.

Antidepressant-like deliverables from the sea: evidence on the efficacy of three different brown seaweeds via involvement of monoaminergic system.

PubMed

Siddiqui, Pirzada Jamal Ahmed; Khan, Adnan; Uddin, Nizam; Khaliq, Saima; Rasheed, Munawwer; Nawaz, Shazia; Hanif, Muhammad; Dar, Ahsana

2017-07-01

Brown seaweeds exhibit several health benefits in treating and managing wide array of ailments. In this study, the antidepressant-like effect of methaolic extracts from Sargassum swartzii (SS), Stoechospermum marginatum (SM), and Nizamuddinia zanardinii (NZ) was examined in forced swimming test (FST), in rats. Oral administration of SS, SM, and NZ extract (30-60 mg/kg) exhibited antidepressant-like activity in FST by reducing immobility time as compared to control group, without inducing significant change in ambulatory behavior in open field test. In order to evaluate the involvement of monoaminergic system, rats were pretreated with the inhibitor of brain serotonin stores p-chlorophenylalanin (PCPA), dopamine (SCH23390 and sulpiride), and adrenoceptor (prazosin and propranolol) antagonists. Rats receiving treatment for 28 days were decapitated and brains were analyzed for monoamine levels. It may be concluded that the extracts of SS, SM, and NZ produces antidepressant-like activity via modulation of brain monoaminergic system in a rat model.

Prediction of standard-dose brain PET image by using MRI and low-dose brain [18F]FDG PET images

PubMed Central

Kang, Jiayin; Gao, Yaozong; Shi, Feng; Lalush, David S.; Lin, Weili; Shen, Dinggang

2015-01-01

Purpose: Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient’s exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [18F]FDG PET image by using a low-dose brain [18F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. Methods: The authors employ a regression forest for predicting the standard-dose brain [18F]FDG PET image by low-dose brain [18F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [18F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. Results: The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [18F]FDG PET image and substantially enhanced image quality of low-dose brain [18F]FDG PET image. Conclusions: In this paper, the authors propose a framework to generate standard-dose brain [18F]FDG PET image using low-dose brain [18F]FDG PET and MRI images. Both the visual and quantitative results indicate that the standard-dose brain [18F]FDG PET can be well-predicted using MRI and low-dose brain [18F]FDG PET. PMID:26328979

Prediction of standard-dose brain PET image by using MRI and low-dose brain [{sup 18}F]FDG PET images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kang, Jiayin; Gao, Yaozong; Shi, Feng

Purpose: Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient’s exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. Asmore » yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [{sup 18}F]FDG PET image by using a low-dose brain [{sup 18}F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. Methods: The authors employ a regression forest for predicting the standard-dose brain [{sup 18}F]FDG PET image by low-dose brain [{sup 18}F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [{sup 18}F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. Results: The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [{sup 18}F]FDG PET image and substantially enhanced image quality of low-dose brain [{sup 18}F]FDG PET image. Conclusions: In this paper, the authors propose a framework to generate standard-dose brain [{sup 18}F]FDG PET image using low-dose brain [{sup 18}F]FDG PET and MRI images. Both the visual and quantitative results indicate that the standard-dose brain [{sup 18}F]FDG PET can be well-predicted using MRI and low-dose brain [{sup 18}F]FDG PET.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shang, Yu; Yu, Guoqiang, E-mail: guoqiang.yu@uky.edu

Conventional semi-infinite analytical solutions of correlation diffusion equation may lead to errors when calculating blood flow index (BFI) from diffuse correlation spectroscopy (DCS) measurements in tissues with irregular geometries. Very recently, we created an algorithm integrating a Nth-order linear model of autocorrelation function with the Monte Carlo simulation of photon migrations in homogenous tissues with arbitrary geometries for extraction of BFI (i.e., αD{sub B}). The purpose of this study is to extend the capability of the Nth-order linear algorithm for extracting BFI in heterogeneous tissues with arbitrary geometries. The previous linear algorithm was modified to extract BFIs in different typesmore » of tissues simultaneously through utilizing DCS data at multiple source-detector separations. We compared the proposed linear algorithm with the semi-infinite homogenous solution in a computer model of adult head with heterogeneous tissue layers of scalp, skull, cerebrospinal fluid, and brain. To test the capability of the linear algorithm for extracting relative changes of cerebral blood flow (rCBF) in deep brain, we assigned ten levels of αD{sub B} in the brain layer with a step decrement of 10% while maintaining αD{sub B} values constant in other layers. Simulation results demonstrate the accuracy (errors < 3%) of high-order (N ≥ 5) linear algorithm in extracting BFIs in different tissue layers and rCBF in deep brain. By contrast, the semi-infinite homogenous solution resulted in substantial errors in rCBF (34.5% ≤ errors ≤ 60.2%) and BFIs in different layers. The Nth-order linear model simplifies data analysis, thus allowing for online data processing and displaying. Future study will test this linear algorithm in heterogeneous tissues with different levels of blood flow variations and noises.« less

Mentha piperita as a pivotal neuro-protective agent against gamma irradiation induced DNA fragmentation and apoptosis : Mentha extract as a neuroprotective against gamma irradiation.

PubMed

Hassan, Hanaa A; Hafez, Hani S; Goda, Mona S

2013-01-01

Ionizing radiation is classified as a potent carcinogen, and its injury to living cells, in particular to DNA, is due to oxidative stress enhancing apoptotic cell death. Our present study aimed to characterize and semi-quantify the radiation-induced apoptosis in CNS and the activity of Mentha extracts as neuron-protective agent. Our results through flow cytometry exhibited the significant disturbance and arrest in cell cycle in % of M1: SubG1 phase, M2: G0/1 phase of diploid cycle, M3: S phase and M4: G2/M phase of cell cycle in brain tissue (p < 0.05). Significant increase in % of apoptosis and P53 protein expression as apoptotic biomarkers were coincided with significant decrease in Bcl(2) as an anti-apoptotic marker. The biochemical analysis recorded a significant decrease in the levels of reduced glutathione, superoxide dismutase, deoxyribonucleic acid (DNA) and ribonucleic acid contents. Moreover, numerous histopathological alterations were detected in brain tissues of gamma irradiated mice such as signs of chromatolysis in pyramidal cells of cortex, nuclear vacuolation, numerous apoptotic cell, and neural degeneration. On the other hand, gamma irradiated mice pretreated with Mentha extract showed largely an improvement in all the above tested parameters through a homeostatic state for the content of brain apoptosis and stabilization of DNA cycle with a distinct improvement in cell cycle analysis and antioxidant defense system. Furthermore, the aforementioned effects of Mentha extracts through down-regulation of P53 expression and up-regulation of Bcl(2) domain protected brain structure from extensive damage. Therefore, Mentha extract seems to have a significant role to ameliorate the neuronal injury induced by gamma irradiation.

BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities.

PubMed

Griffanti, Ludovica; Zamboni, Giovanna; Khan, Aamira; Li, Linxin; Bonifacio, Guendalina; Sundaresan, Vaanathi; Schulz, Ursula G; Kuker, Wilhelm; Battaglini, Marco; Rothwell, Peter M; Jenkinson, Mark

2016-11-01

Reliable quantification of white matter hyperintensities of presumed vascular origin (WMHs) is increasingly needed, given the presence of these MRI findings in patients with several neurological and vascular disorders, as well as in elderly healthy subjects. We present BIANCA (Brain Intensity AbNormality Classification Algorithm), a fully automated, supervised method for WMH detection, based on the k-nearest neighbour (k-NN) algorithm. Relative to previous k-NN based segmentation methods, BIANCA offers different options for weighting the spatial information, local spatial intensity averaging, and different options for the choice of the number and location of the training points. BIANCA is multimodal and highly flexible so that the user can adapt the tool to their protocol and specific needs. We optimised and validated BIANCA on two datasets with different MRI protocols and patient populations (a "predominantly neurodegenerative" and a "predominantly vascular" cohort). BIANCA was first optimised on a subset of images for each dataset in terms of overlap and volumetric agreement with a manually segmented WMH mask. The correlation between the volumes extracted with BIANCA (using the optimised set of options), the volumes extracted from the manual masks and visual ratings showed that BIANCA is a valid alternative to manual segmentation. The optimised set of options was then applied to the whole cohorts and the resulting WMH volume estimates showed good correlations with visual ratings and with age. Finally, we performed a reproducibility test, to evaluate the robustness of BIANCA, and compared BIANCA performance against existing methods. Our findings suggest that BIANCA, which will be freely available as part of the FSL package, is a reliable method for automated WMH segmentation in large cross-sectional cohort studies. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Borate-aided anion exchange high-performance liquid chromatography of uridine diphosphate-sugars in brain, heart, adipose and liver tissues.

PubMed

Oikari, Sanna; Venäläinen, Tuula; Tammi, Markku

2014-01-03

In this paper we describe a method optimized for the purification of uridine diphosphate (UDP)-sugars from liver, adipose tissue, brain, and heart, with highly reproducible up to 85% recoveries. Rapid tissue homogenization in cold ethanol, lipid removal by butanol extraction, and purification with a graphitized carbon column resulted in isolation of picomolar quantities of the UDP-sugars from 10 to 30mg of tissue. The UDP-sugars were baseline separated from each other, and from all major nucleotides using a CarboPac PA1 anion exchange column eluted with a gradient of acetate and borate buffers. The extraction and purification protocol produced samples with few unidentified peaks. UDP-N-acetylglucosamine was a dominant UDP-sugar in all the rat tissues studied. However, brain and adipose tissue showed high UDP-glucose levels, equal to that of UDP-N-acetylglucosamine. The UDP-N-acetylglucosamine showed 2.3-2.7 times higher levels than UDP-N-acetylgalactosamine in all tissues, and about the same ratio was found between UDP-glucose and UDP-galactose in adipose tissue and brain (2.6 and 2.8, respectively). Interestingly, the UDP-glucose/UDP-galactose ratio was markedly lower in liver (1.1) and heart (1.7). The UDP-N-acetylglucosamine/UDP-glucuronic acid ratio was also constant, between 9.7 and 7.7, except in liver with the ratio as low as 1.8. The distinct UDP-glucose/galactose ratio, and the abundance of UDP-glucuronic acid may reflect the specific role of liver in glycogen synthesis, and metabolism of hormones and xenobiotics, respectively, using these UDP-sugars as substrates. Copyright © 2013 Elsevier B.V. All rights reserved.

Formal Models of the Network Co-occurrence Underlying Mental Operations.

PubMed

Bzdok, Danilo; Varoquaux, Gaël; Grisel, Olivier; Eickenberg, Michael; Poupon, Cyril; Thirion, Bertrand

2016-06-01

Systems neuroscience has identified a set of canonical large-scale networks in humans. These have predominantly been characterized by resting-state analyses of the task-unconstrained, mind-wandering brain. Their explicit relationship to defined task performance is largely unknown and remains challenging. The present work contributes a multivariate statistical learning approach that can extract the major brain networks and quantify their configuration during various psychological tasks. The method is validated in two extensive datasets (n = 500 and n = 81) by model-based generation of synthetic activity maps from recombination of shared network topographies. To study a use case, we formally revisited the poorly understood difference between neural activity underlying idling versus goal-directed behavior. We demonstrate that task-specific neural activity patterns can be explained by plausible combinations of resting-state networks. The possibility of decomposing a mental task into the relative contributions of major brain networks, the "network co-occurrence architecture" of a given task, opens an alternative access to the neural substrates of human cognition.

Formal Models of the Network Co-occurrence Underlying Mental Operations

PubMed Central

Bzdok, Danilo; Varoquaux, Gaël; Grisel, Olivier; Eickenberg, Michael; Poupon, Cyril; Thirion, Bertrand

2016-01-01

Systems neuroscience has identified a set of canonical large-scale networks in humans. These have predominantly been characterized by resting-state analyses of the task-unconstrained, mind-wandering brain. Their explicit relationship to defined task performance is largely unknown and remains challenging. The present work contributes a multivariate statistical learning approach that can extract the major brain networks and quantify their configuration during various psychological tasks. The method is validated in two extensive datasets (n = 500 and n = 81) by model-based generation of synthetic activity maps from recombination of shared network topographies. To study a use case, we formally revisited the poorly understood difference between neural activity underlying idling versus goal-directed behavior. We demonstrate that task-specific neural activity patterns can be explained by plausible combinations of resting-state networks. The possibility of decomposing a mental task into the relative contributions of major brain networks, the "network co-occurrence architecture" of a given task, opens an alternative access to the neural substrates of human cognition. PMID:27310288

Cortical network architecture for context processing in primate brain

PubMed Central

Chao, Zenas C; Nagasaka, Yasuo; Fujii, Naotaka

2015-01-01

Context is information linked to a situation that can guide behavior. In the brain, context is encoded by sensory processing and can later be retrieved from memory. How context is communicated within the cortical network in sensory and mnemonic forms is unknown due to the lack of methods for high-resolution, brain-wide neuronal recording and analysis. Here, we report the comprehensive architecture of a cortical network for context processing. Using hemisphere-wide, high-density electrocorticography, we measured large-scale neuronal activity from monkeys observing videos of agents interacting in situations with different contexts. We extracted five context-related network structures including a bottom-up network during encoding and, seconds later, cue-dependent retrieval of the same network with the opposite top-down connectivity. These findings show that context is represented in the cortical network as distributed communication structures with dynamic information flows. This study provides a general methodology for recording and analyzing cortical network neuronal communication during cognition. DOI: http://dx.doi.org/10.7554/eLife.06121.001 PMID:26416139

Assessing Environmental Exposure to β-N-Methylamino-L-Alanine (BMAA) in Complex Sample Matrices: a Comparison of the Three Most Popular LC-MS/MS Methods.

PubMed

Baker, Teesha C; Tymm, Fiona J M; Murch, Susan J

2018-01-01

β-N-Methylamino-L-alanine (BMAA) is a naturally occurring non-protein amino acid produced by cyanobacteria, accumulated through natural food webs, found in mammalian brain tissues. Recent evidence indicates an association between BMAA and neurological disease. The accurate detection and quantification of BMAA in food and environmental samples are critical to understanding BMAA metabolism and limiting human exposure. To date, there have been more than 78 reports on BMAA in cyanobacteria and human samples, but different methods give conflicting data and divergent interpretations in the literature. The current work was designed to determine whether orthogonal chromatography and mass spectrometry methods give consistent data interpretation from a single sample matrix using the three most common analytical methods. The methods were recreated as precisely as possible from the literature with optimization of the mass spectrometry parameters specific to the instrument. Four sample matrices, cyanobacteria, human brain, blue crab, and Spirulina, were analyzed as 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC) derivatives, propyl chloroformate (PCF) derivatives separated by reverse phase chromatography, or underivatized extracts separated by HILIC chromatography. The three methods agreed on positive detection of BMAA in cyanobacteria and no detected BMAA in the sample of human brain matrix. Interpretation was less clear for a sample of blue crab which was strongly positive for BMAA by AQC and PCF but negative by HILIC and for four spirulina raw materials that were negative by PCF but positive by AQC and HILIC. Together, these data demonstrate that the methods gave different results and that the choices in interpretation of the methods determined whether BMAA was detected. Failure to detect BMAA cannot be considered proof of absence.

Determination of catechins and catechin gallates in tissues by liquid chromatography with coulometric array detection and selective solid phase extraction.

PubMed

Chu, Kai On; Wang, Chi Chiu; Chu, Ching Yan; Rogers, Michael Scott; Choy, Kwong Wai; Pang, Chi Pui

2004-10-25

Catechins levels in organ tissues, particularly liver, determined by published methods are unexpectedly low, probably due to the release of oxidative enzymes, metal ions and reactive metabolites from tissue cells during homogenization and to the pro-oxidant effects of ascorbic acid during sample processing in the presence of metal ions. We describe a new method for simultaneous analysis of eight catechins in tissue: (+)-catechin (C), (-)-epicatechin (EC), (-)-gallocatechin (GC), (-)-epigallocatechin (EGC), (-)-catechin gallate (CG), (-)-epicatechin gallate (ECG), (-)-gallocatechin gallate (GCG) and (-)-epigallocatechin gallate (EGCG) (Fig. 1). The new extraction procedure utilized a methanol/ethylacetate/dithionite (2:1:3) mixture during homogenization for simultaneous enzyme precipitation and antioxidant protection. Selective solid phase extraction was used to remove most interfering bio-matrices. Reversed phase HPLC with CoulArray detection was used to determine the eight catechins simultaneously within 25 min. Good linearity (>0.9922) was obtained in the range 20-4000 ng/g. The coefficients of variance (CV) were less than 5%. Absolute recovery ranged from 62 to 96%, accuracy 92.5 +/- 4.5 to 104.9 +/- 6%. The detection limit was 5 ng/g. This method is capable for determining catechins in rat tissues of liver, brain, spleen, and kidney. The method is robust, reproducible, with high recovery, and has been validated for both in vitro and in vivo sample analysis.

A fresh look at functional link neural network for motor imagery-based brain-computer interface.

PubMed

Hettiarachchi, Imali T; Babaei, Toktam; Nguyen, Thanh; Lim, Chee P; Nahavandi, Saeid

2018-05-04

Artificial neural networks (ANNs) are one of the widely used classifiers in the brain-computer interface (BCI) systems-based on noninvasive electroencephalography (EEG) signals. Among the different ANN architectures, the most commonly applied for BCI classifiers is the multilayer perceptron (MLP). When appropriately designed with optimal number of neuron layers and number of neurons per layer, the ANN can act as a universal approximator. However, due to the low signal-to-noise ratio of EEG signal data, overtraining problem may become an inherent issue, causing these universal approximators to fail in real-time applications. In this study we introduce a higher order neural network, namely the functional link neural network (FLNN) as a classifier for motor imagery (MI)-based BCI systems, to remedy the drawbacks in MLP. We compare the proposed method with competing classifiers such as linear decomposition analysis, naïve Bayes, k-nearest neighbours, support vector machine and three MLP architectures. Two multi-class benchmark datasets from the BCI competitions are used. Common spatial pattern algorithm is utilized for feature extraction to build classification models. FLNN reports the highest average Kappa value over multiple subjects for both the BCI competition datasets, under similarly preprocessed data and extracted features. Further, statistical comparison results over multiple subjects show that the proposed FLNN classification method yields the best performance among the competing classifiers. Findings from this study imply that the proposed method, which has less computational complexity compared to the MLP, can be implemented effectively in practical MI-based BCI systems. Copyright © 2018 Elsevier B.V. All rights reserved.

Automatic classification of written descriptions by healthy adults: An overview of the application of natural language processing and machine learning techniques to clinical discourse analysis

PubMed Central

Toledo, Cíntia Matsuda; Cunha, Andre; Scarton, Carolina; Aluísio, Sandra

2014-01-01

Discourse production is an important aspect in the evaluation of brain-injured individuals. We believe that studies comparing the performance of brain-injured subjects with that of healthy controls must use groups with compatible education. A pioneering application of machine learning methods using Brazilian Portuguese for clinical purposes is described, highlighting education as an important variable in the Brazilian scenario. Objective The aims were to describe how to: (i) develop machine learning classifiers using features generated by natural language processing tools to distinguish descriptions produced by healthy individuals into classes based on their years of education; and (ii) automatically identify the features that best distinguish the groups. Methods The approach proposed here extracts linguistic features automatically from the written descriptions with the aid of two Natural Language Processing tools: Coh-Metrix-Port and AIC. It also includes nine task-specific features (three new ones, two extracted manually, besides description time; type of scene described – simple or complex; presentation order – which type of picture was described first; and age). In this study, the descriptions by 144 of the subjects studied in Toledo18 were used,which included 200 healthy Brazilians of both genders. Results and Conclusion A Support Vector Machine (SVM) with a radial basis function (RBF) kernel is the most recommended approach for the binary classification of our data, classifying three of the four initial classes. CfsSubsetEval (CFS) is a strong candidate to replace manual feature selection methods. PMID:29213908

Gross feature recognition of Anatomical Images based on Atlas grid (GAIA): Incorporating the local discrepancy between an atlas and a target image to capture the features of anatomic brain MRI.

PubMed

Qin, Yuan-Yuan; Hsu, Johnny T; Yoshida, Shoko; Faria, Andreia V; Oishi, Kumiko; Unschuld, Paul G; Redgrave, Graham W; Ying, Sarah H; Ross, Christopher A; van Zijl, Peter C M; Hillis, Argye E; Albert, Marilyn S; Lyketsos, Constantine G; Miller, Michael I; Mori, Susumu; Oishi, Kenichi

2013-01-01

We aimed to develop a new method to convert T1-weighted brain MRIs to feature vectors, which could be used for content-based image retrieval (CBIR). To overcome the wide range of anatomical variability in clinical cases and the inconsistency of imaging protocols, we introduced the Gross feature recognition of Anatomical Images based on Atlas grid (GAIA), in which the local intensity alteration, caused by pathological (e.g., ischemia) or physiological (development and aging) intensity changes, as well as by atlas-image misregistration, is used to capture the anatomical features of target images. As a proof-of-concept, the GAIA was applied for pattern recognition of the neuroanatomical features of multiple stages of Alzheimer's disease, Huntington's disease, spinocerebellar ataxia type 6, and four subtypes of primary progressive aphasia. For each of these diseases, feature vectors based on a training dataset were applied to a test dataset to evaluate the accuracy of pattern recognition. The feature vectors extracted from the training dataset agreed well with the known pathological hallmarks of the selected neurodegenerative diseases. Overall, discriminant scores of the test images accurately categorized these test images to the correct disease categories. Images without typical disease-related anatomical features were misclassified. The proposed method is a promising method for image feature extraction based on disease-related anatomical features, which should enable users to submit a patient image and search past clinical cases with similar anatomical phenotypes.

Classification of tumor based on magnetic resonance (MR) brain images using wavelet energy feature and neuro-fuzzy model

NASA Astrophysics Data System (ADS)

Damayanti, A.; Werdiningsih, I.

2018-03-01

The brain is the organ that coordinates all the activities that occur in our bodies. Small abnormalities in the brain will affect body activity. Tumor of the brain is a mass formed a result of cell growth not normal and unbridled in the brain. MRI is a non-invasive medical test that is useful for doctors in diagnosing and treating medical conditions. The process of classification of brain tumor can provide the right decision and correct treatment and right on the process of treatment of brain tumor. In this study, the classification process performed to determine the type of brain tumor disease, namely Alzheimer’s, Glioma, Carcinoma and normal, using energy coefficient and ANFIS. Process stages in the classification of images of MR brain are the extraction of a feature, reduction of a feature, and process of classification. The result of feature extraction is a vector approximation of each wavelet decomposition level. The feature reduction is a process of reducing the feature by using the energy coefficients of the vector approximation. The feature reduction result for energy coefficient of 100 per feature is 1 x 52 pixels. This vector will be the input on the classification using ANFIS with Fuzzy C-Means and FLVQ clustering process and LM back-propagation. Percentage of success rate of MR brain images recognition using ANFIS-FLVQ, ANFIS, and LM back-propagation was obtained at 100%.

Brain modulation of Dufour's gland ester biosynthesis in vitro in the honeybee ( Apis mellifera)

NASA Astrophysics Data System (ADS)

Katzav-Gozansky, Tamar; Hefetz, Abraham; Soroker, Victoria

2007-05-01

Caste-specific pheromone biosynthesis is a prerequisite for reproductive skew in the honeybee. Nonetheless, this process is not hardwired but plastic, in that egg-laying workers produce a queen-like pheromone. Studies with Dufour’s gland pheromone revealed that, in vivo, workers’ gland biosynthesis matches the social status of the worker, i.e., sterile workers showed a worker-like pattern whereas fertile workers showed a queen-like pattern (production of the queen-specific esters). However, when incubated in vitro, the gland spontaneously exhibits the queen-like pattern, irrespective of its original worker type, prompting the notion that ester production in workers is under inhibitory control that is queen-dependent. We tested this hypothesis by exposing queen or worker Dufour’s glands in vitro to brain extracts of queens, queenright (sterile) workers and males. Unexpectedly, worker brain extracts activated the queen-like esters biosynthesis in workers’ Dufour’s gland. This stimulation was gender-specific; queen or worker brains demonstrated a stimulatory activity, but male brains did not. Queen gland could not be further stimulated. Bioassays with heated and filtered extracts indicate that the stimulatory brain factor is below 3,000 Da. We suggest that pheromone production in Dufour’s gland is under dual, negative positive control. Under queenright conditions, the inhibitor is released and blocks ester biosynthesis, whereas under queenless conditions, the activator is released, activating ester biosynthesis in the gland. This is consistent with the hypothesis that queenright workers are unequivocally recognized as non-fertile, whereas queenless workers try to become “false queens” as part of the reproductive competition.

Effects of Grape Skin Extract on Age-Related Mitochondrial Dysfunction, Memory and Life Span in C57BL/6J Mice.

PubMed

Asseburg, Heike; Schäfer, Carmina; Müller, Madeleine; Hagl, Stephanie; Pohland, Maximilian; Berressem, Dirk; Borchiellini, Marta; Plank, Christina; Eckert, Gunter P

2016-09-01

Dementia contributes substantially to the burden of disability experienced at old age, and mitochondrial dysfunction (MD) was identified as common final pathway in brain aging and Alzheimer's disease. Due to its early appearance, MD is a promising target for nutritional prevention strategies and polyphenols as potential neurohormetic inducers may be strong neuroprotective candidates. This study aimed to investigate the effects of a polyphenol-rich grape skin extract (PGE) on age-related dysfunctions of brain mitochondria, memory, life span and potential hormetic pathways in C57BL/6J mice. PGE was administered at a dose of 200 mg/kg body weight/d in a 3-week short-term, 6-month long-term and life-long study. MD in the brains of aged mice (19-22 months old) compared to young mice (3 months old) was demonstrated by lower ATP levels and by impaired mitochondrial respiratory complex activity (except for mice treated with antioxidant-depleted food pellets). Long-term PGE feeding partly enhanced brain mitochondrial respiration with only minor beneficial effect on brain ATP levels and memory of aged mice. Life-long PGE feeding led to a transient but significant shift of survival curve toward higher survival rates but without effect on the overall survival. The moderate effects of PGE were associated with elevated SIRT1 but not SIRT3 mRNA expressions in brain and liver tissue. The beneficial effects of the grape extract may have been influenced by the profile of bioavailable polyphenols and the starting point of interventions.

Sulci segmentation using geometric active contours

NASA Astrophysics Data System (ADS)

Torkaman, Mahsa; Zhu, Liangjia; Karasev, Peter; Tannenbaum, Allen

2017-02-01

Sulci are groove-like regions lying in the depth of the cerebral cortex between gyri, which together, form a folded appearance in human and mammalian brains. Sulci play an important role in the structural analysis of the brain, morphometry (i.e., the measurement of brain structures), anatomical labeling and landmark-based registration.1 Moreover, sulcal morphological changes are related to cortical thickness, whose measurement may provide useful information for studying variety of psychiatric disorders. Manually extracting sulci requires complying with complex protocols, which make the procedure both tedious and error prone.2 In this paper, we describe an automatic procedure, employing geometric active contours, which extract the sulci. Sulcal boundaries are obtained by minimizing a certain energy functional whose minimum is attained at the boundary of the given sulci.

Atypical Balance between Occipital and Fronto-Parietal Activation for Visual Shape Extraction in Dyslexia

PubMed Central

Zhang, Ying; Whitfield-Gabrieli, Susan; Christodoulou, Joanna A.; Gabrieli, John D. E.

2013-01-01

Reading requires the extraction of letter shapes from a complex background of text, and an impairment in visual shape extraction would cause difficulty in reading. To investigate the neural mechanisms of visual shape extraction in dyslexia, we used functional magnetic resonance imaging (fMRI) to examine brain activation while adults with or without dyslexia responded to the change of an arrow’s direction in a complex, relative to a simple, visual background. In comparison to adults with typical reading ability, adults with dyslexia exhibited opposite patterns of atypical activation: decreased activation in occipital visual areas associated with visual perception, and increased activation in frontal and parietal regions associated with visual attention. These findings indicate that dyslexia involves atypical brain organization for fundamental processes of visual shape extraction even when reading is not involved. Overengagement in higher-order association cortices, required to compensate for underengagment in lower-order visual cortices, may result in competition for top-down attentional resources helpful for fluent reading. PMID:23825653

Video-based eye tracking for neuropsychiatric assessment.

PubMed

Adhikari, Sam; Stark, David E

2017-01-01

This paper presents a video-based eye-tracking method, ideally deployed via a mobile device or laptop-based webcam, as a tool for measuring brain function. Eye movements and pupillary motility are tightly regulated by brain circuits, are subtly perturbed by many disease states, and are measurable using video-based methods. Quantitative measurement of eye movement by readily available webcams may enable early detection and diagnosis, as well as remote/serial monitoring, of neurological and neuropsychiatric disorders. We successfully extracted computational and semantic features for 14 testing sessions, comprising 42 individual video blocks and approximately 17,000 image frames generated across several days of testing. Here, we demonstrate the feasibility of collecting video-based eye-tracking data from a standard webcam in order to assess psychomotor function. Furthermore, we were able to demonstrate through systematic analysis of this data set that eye-tracking features (in particular, radial and tangential variance on a circular visual-tracking paradigm) predict performance on well-validated psychomotor tests. © 2017 New York Academy of Sciences.