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.

Biomarkers for Musculoskeletal Pain Conditions: Use of Brain Imaging and Machine Learning.

PubMed

Boissoneault, Jeff; Sevel, Landrew; Letzen, Janelle; Robinson, Michael; Staud, Roland

2017-01-01

Chronic musculoskeletal pain condition often shows poor correlations between tissue abnormalities and clinical pain. Therefore, classification of pain conditions like chronic low back pain, osteoarthritis, and fibromyalgia depends mostly on self report and less on objective findings like X-ray or magnetic resonance imaging (MRI) changes. However, recent advances in structural and functional brain imaging have identified brain abnormalities in chronic pain conditions that can be used for illness classification. Because the analysis of complex and multivariate brain imaging data is challenging, machine learning techniques have been increasingly utilized for this purpose. The goal of machine learning is to train specific classifiers to best identify variables of interest on brain MRIs (i.e., biomarkers). This report describes classification techniques capable of separating MRI-based brain biomarkers of chronic pain patients from healthy controls with high accuracy (70-92%) using machine learning, as well as critical scientific, practical, and ethical considerations related to their potential clinical application. Although self-report remains the gold standard for pain assessment, machine learning may aid in the classification of chronic pain disorders like chronic back pain and fibromyalgia as well as provide mechanistic information regarding their neural correlates.

Manifold Embedding and Semantic Segmentation for Intraoperative Guidance With Hyperspectral Brain Imaging.

PubMed

Ravi, Daniele; Fabelo, Himar; Callic, Gustavo Marrero; Yang, Guang-Zhong

2017-09-01

Recent advances in hyperspectral imaging have made it a promising solution for intra-operative tissue characterization, with the advantages of being non-contact, non-ionizing, and non-invasive. Working with hyperspectral images in vivo, however, is not straightforward as the high dimensionality of the data makes real-time processing challenging. In this paper, a novel dimensionality reduction scheme and a new processing pipeline are introduced to obtain a detailed tumor classification map for intra-operative margin definition during brain surgery. However, existing approaches to dimensionality reduction based on manifold embedding can be time consuming and may not guarantee a consistent result, thus hindering final tissue classification. The proposed framework aims to overcome these problems through a process divided into two steps: dimensionality reduction based on an extension of the T-distributed stochastic neighbor approach is first performed and then a semantic segmentation technique is applied to the embedded results by using a Semantic Texton Forest for tissue classification. Detailed in vivo validation of the proposed method has been performed to demonstrate the potential clinical value of the system.

Characterization of a Raman spectroscopy probe system for intraoperative brain tissue classification

PubMed Central

Desroches, Joannie; Jermyn, Michael; Mok, Kelvin; Lemieux-Leduc, Cédric; Mercier, Jeanne; St-Arnaud, Karl; Urmey, Kirk; Guiot, Marie-Christine; Marple, Eric; Petrecca, Kevin; Leblond, Frédéric

2015-01-01

A detailed characterization study is presented of a Raman spectroscopy system designed to maximize the volume of resected cancer tissue in glioma surgery based on in vivo molecular tissue characterization. It consists of a hand-held probe system measuring spectrally resolved inelastically scattered light interacting with tissue, designed and optimized for in vivo measurements. Factors such as linearity of the signal with integration time and laser power, and their impact on signal to noise ratio, are studied leading to optimal data acquisition parameters. The impact of ambient light sources in the operating room is assessed and recommendations made for optimal operating conditions. In vivo Raman spectra of normal brain, cancer and necrotic tissue were measured in 10 patients, demonstrating that real-time inelastic scattering measurements can distinguish necrosis from vital tissue (including tumor and normal brain tissue) with an accuracy of 87%, a sensitivity of 84% and a specificity of 89%. PMID:26203368

Partial volume correction of magnetic resonance spectroscopic imaging

NASA Astrophysics Data System (ADS)

Lu, Yao; Wu, Dee; Magnotta, Vincent A.

2007-03-01

The ability to study the biochemical composition of the brain is becoming important to better understand neurodegenerative and neurodevelopmental disorders. Magnetic Resonance Spectroscopy (MRS) can non-invasively provide quantification of brain metabolites in localized regions. The reliability of MRS is limited in part due to partial volume artifacts. This results from the relatively large voxels that are required to acquire sufficient signal-to-noise ratios for the studies. Partial volume artifacts result when a MRS voxel contains a mixture of tissue types. Concentrations of metabolites vary from tissue to tissue. When a voxel contains a heterogeneous tissue composition, the spectroscopic signal acquired from this voxel will consist of the signal from different tissues making reliable measurements difficult. We have developed a novel tool for the estimation of partial volume tissue composition within MRS voxels thus allowing for the correction of partial volume artifacts. In addition, the tool can localize MR spectra to anatomical regions of interest. The tool uses tissue classification information acquired as part of a structural MR scan for the same subject. The tissue classification information is co-registered with the spectroscopic data. The user can quantify the partial volume composition of each voxel and use this information as covariates for metabolite concentrations.

Automated pixel-wise brain tissue segmentation of diffusion-weighted images via machine learning.

PubMed

Ciritsis, Alexander; Boss, Andreas; Rossi, Cristina

2018-04-26

The diffusion-weighted (DW) MR signal sampled over a wide range of b-values potentially allows for tissue differentiation in terms of cellularity, microstructure, perfusion, and T 2 relaxivity. This study aimed to implement a machine learning algorithm for automatic brain tissue segmentation from DW-MRI datasets, and to determine the optimal sub-set of features for accurate segmentation. DWI was performed at 3 T in eight healthy volunteers using 15 b-values and 20 diffusion-encoding directions. The pixel-wise signal attenuation, as well as the trace and fractional anisotropy (FA) of the diffusion tensor, were used as features to train a support vector machine classifier for gray matter, white matter, and cerebrospinal fluid classes. The datasets of two volunteers were used for validation. For each subject, tissue classification was also performed on 3D T 1 -weighted data sets with a probabilistic framework. Confusion matrices were generated for quantitative assessment of image classification accuracy in comparison with the reference method. DWI-based tissue segmentation resulted in an accuracy of 82.1% on the validation dataset and of 82.2% on the training dataset, excluding relevant model over-fitting. A mean Dice coefficient (DSC) of 0.79 ± 0.08 was found. About 50% of the classification performance was attributable to five features (i.e. signal measured at b-values of 5/10/500/1200 s/mm 2 and the FA). This reduced set of features led to almost identical performances for the validation (82.2%) and the training (81.4%) datasets (DSC = 0.79 ± 0.08). Machine learning techniques applied to DWI data allow for accurate brain tissue segmentation based on both morphological and functional information. Copyright © 2018 John Wiley & Sons, Ltd.

An Intraoperative Visualization System Using Hyperspectral Imaging to Aid in Brain Tumor Delineation

PubMed Central

Ortega, Samuel; M. Callicó, Gustavo; Juárez, Eduardo; Bulters, Diederik; Szolna, Adam; Piñeiro, Juan F.; Sosa, Coralia; J. O’Shanahan, Aruma; Bisshopp, Sara; Hernández, María; Morera, Jesús; Ravi, Daniele; Kiran, B. Ravi; Vega, Aurelio; Báez-Quevedo, Abelardo; Yang, Guang-Zhong; Stanciulescu, Bogdan; Sarmiento, Roberto

2018-01-01

Hyperspectral imaging (HSI) allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range) with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substance is located in each pixel. The work presented in this paper aims to exploit the characteristics of HSI to develop a demonstrator capable of delineating tumor tissue from brain tissue during neurosurgical operations. Improved delineation of tumor boundaries is expected to improve the results of surgery. The developed demonstrator is composed of two hyperspectral cameras covering a spectral range of 400–1700 nm. Furthermore, a hardware accelerator connected to a control unit is used to speed up the hyperspectral brain cancer detection algorithm to achieve processing during the time of surgery. A labeled dataset comprised of more than 300,000 spectral signatures is used as the training dataset for the supervised stage of the classification algorithm. In this preliminary study, thematic maps obtained from a validation database of seven hyperspectral images of in vivo brain tissue captured and processed during neurosurgical operations demonstrate that the system is able to discriminate between normal and tumor tissue in the brain. The results can be provided during the surgical procedure (~1 min), making it a practical system for neurosurgeons to use in the near future to improve excision and potentially improve patient outcomes. PMID:29389893

An Intraoperative Visualization System Using Hyperspectral Imaging to Aid in Brain Tumor Delineation.

PubMed

Fabelo, Himar; Ortega, Samuel; Lazcano, Raquel; Madroñal, Daniel; M Callicó, Gustavo; Juárez, Eduardo; Salvador, Rubén; Bulters, Diederik; Bulstrode, Harry; Szolna, Adam; Piñeiro, Juan F; Sosa, Coralia; J O'Shanahan, Aruma; Bisshopp, Sara; Hernández, María; Morera, Jesús; Ravi, Daniele; Kiran, B Ravi; Vega, Aurelio; Báez-Quevedo, Abelardo; Yang, Guang-Zhong; Stanciulescu, Bogdan; Sarmiento, Roberto

2018-02-01

Hyperspectral imaging (HSI) allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range) with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substance is located in each pixel. The work presented in this paper aims to exploit the characteristics of HSI to develop a demonstrator capable of delineating tumor tissue from brain tissue during neurosurgical operations. Improved delineation of tumor boundaries is expected to improve the results of surgery. The developed demonstrator is composed of two hyperspectral cameras covering a spectral range of 400-1700 nm. Furthermore, a hardware accelerator connected to a control unit is used to speed up the hyperspectral brain cancer detection algorithm to achieve processing during the time of surgery. A labeled dataset comprised of more than 300,000 spectral signatures is used as the training dataset for the supervised stage of the classification algorithm. In this preliminary study, thematic maps obtained from a validation database of seven hyperspectral images of in vivo brain tissue captured and processed during neurosurgical operations demonstrate that the system is able to discriminate between normal and tumor tissue in the brain. The results can be provided during the surgical procedure (~1 min), making it a practical system for neurosurgeons to use in the near future to improve excision and potentially improve patient outcomes.

Unsupervised segmentation of brain regions with similar microstructural properties: application to alcoholism.

PubMed

Cosa, Alejandro; Canals, Santiago; Valles-Lluch, Ana; Moratal, David

2013-01-01

In this work, a novel brain MRI segmentation approach evaluates microstructural differences between groups. Going further from the traditional segmentation of brain tissues (white matter -WM-, gray matter -GM- and cerebrospinal fluid -CSF- or a mixture of them), a new way to classify brain areas is proposed using their microstructural MR properties. Eight rats were studied using the proposed methodology identifying regions which present microstructural differences as a consequence on one month of hard alcohol consumption. Differences in relaxation times of the tissues have been found in different brain regions (p<0.05). Furthermore, these changes allowed the automatic classification of the animals based on their drinking history (hit rate of 93.75 % of the cases).

Multi-channel MRI segmentation with graph cuts using spectral gradient and multidimensional Gaussian mixture model

NASA Astrophysics Data System (ADS)

Lecoeur, Jérémy; Ferré, Jean-Christophe; Collins, D. Louis; Morrisey, Sean P.; Barillot, Christian

2009-02-01

A new segmentation framework is presented taking advantage of multimodal image signature of the different brain tissues (healthy and/or pathological). This is achieved by merging three different modalities of gray-level MRI sequences into a single RGB-like MRI, hence creating a unique 3-dimensional signature for each tissue by utilising the complementary information of each MRI sequence. Using the scale-space spectral gradient operator, we can obtain a spatial gradient robust to intensity inhomogeneity. Even though it is based on psycho-visual color theory, it can be very efficiently applied to the RGB colored images. More over, it is not influenced by the channel assigment of each MRI. Its optimisation by the graph cuts paradigm provides a powerful and accurate tool to segment either healthy or pathological tissues in a short time (average time about ninety seconds for a brain-tissues classification). As it is a semi-automatic method, we run experiments to quantify the amount of seeds needed to perform a correct segmentation (dice similarity score above 0.85). Depending on the different sets of MRI sequences used, this amount of seeds (expressed as a relative number in pourcentage of the number of voxels of the ground truth) is between 6 to 16%. We tested this algorithm on brainweb for validation purpose (healthy tissue classification and MS lesions segmentation) and also on clinical data for tumours and MS lesions dectection and tissues classification.

Quantification and Segmentation of Brain Tissues from MR Images: A Probabilistic Neural Network Approach

PubMed Central

Wang, Yue; Adalý, Tülay; Kung, Sun-Yuan; Szabo, Zsolt

2007-01-01

This paper presents a probabilistic neural network based technique for unsupervised quantification and segmentation of brain tissues from magnetic resonance images. It is shown that this problem can be solved by distribution learning and relaxation labeling, resulting in an efficient method that may be particularly useful in quantifying and segmenting abnormal brain tissues where the number of tissue types is unknown and the distributions of tissue types heavily overlap. The new technique uses suitable statistical models for both the pixel and context images and formulates the problem in terms of model-histogram fitting and global consistency labeling. The quantification is achieved by probabilistic self-organizing mixtures and the segmentation by a probabilistic constraint relaxation network. The experimental results show the efficient and robust performance of the new algorithm and that it outperforms the conventional classification based approaches. PMID:18172510

Rotationally invariant clustering of diffusion MRI data using spherical harmonics

NASA Astrophysics Data System (ADS)

Liptrot, Matthew; Lauze, François

2016-03-01

We present a simple approach to the voxelwise classification of brain tissue acquired with diffusion weighted MRI (DWI). The approach leverages the power of spherical harmonics to summarise the diffusion information, sampled at many points over a sphere, using only a handful of coefficients. We use simple features that are invariant to the rotation of the highly orientational diffusion data. This provides a way to directly classify voxels whose diffusion characteristics are similar yet whose primary diffusion orientations differ. Subsequent application of machine-learning to the spherical harmonic coefficients therefore may permit classification of DWI voxels according to their inferred underlying fibre properties, whilst ignoring the specifics of orientation. After smoothing apparent diffusion coefficients volumes, we apply a spherical harmonic transform, which models the multi-directional diffusion data as a collection of spherical basis functions. We use the derived coefficients as voxelwise feature vectors for classification. Using a simple Gaussian mixture model, we examined the classification performance for a range of sub-classes (3-20). The results were compared against existing alternatives for tissue classification e.g. fractional anisotropy (FA) or the standard model used by Camino.1 The approach was implemented on both two publicly-available datasets: an ex-vivo pig brain and in-vivo human brain from the Human Connectome Project (HCP). We have demonstrated how a robust classification of DWI data can be performed without the need for a model reconstruction step. This avoids the potential confounds and uncertainty that such models may impose, and has the benefit of being computable directly from the DWI volumes. As such, the method could prove useful in subsequent pre-processing stages, such as model fitting, where it could inform about individual voxel complexities and improve model parameter choice.

Comparative Approach of MRI-Based Brain Tumor Segmentation and Classification Using Genetic Algorithm.

PubMed

Bahadure, Nilesh Bhaskarrao; Ray, Arun Kumar; Thethi, Har Pal

2018-01-17

The detection of a brain tumor and its classification from modern imaging modalities is a primary concern, but a time-consuming and tedious work was performed by radiologists or clinical supervisors. The accuracy of detection and classification of tumor stages performed by radiologists is depended on their experience only, so the computer-aided technology is very important to aid with the diagnosis accuracy. In this study, to improve the performance of tumor detection, we investigated comparative approach of different segmentation techniques and selected the best one by comparing their segmentation score. Further, to improve the classification accuracy, the genetic algorithm is employed for the automatic classification of tumor stage. The decision of classification stage is supported by extracting relevant features and area calculation. The experimental results of proposed technique are evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on segmentation score, accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 92.03% accuracy, 91.42% specificity, 92.36% sensitivity, and an average segmentation score between 0.82 and 0.93 demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 93.79% dice similarity index coefficient, which indicates better overlap between the automated extracted tumor regions with manually extracted tumor region by radiologists.

Temporally consistent probabilistic detection of new multiple sclerosis lesions in brain MRI.

PubMed

Elliott, Colm; Arnold, Douglas L; Collins, D Louis; Arbel, Tal

2013-08-01

Detection of new Multiple Sclerosis (MS) lesions on magnetic resonance imaging (MRI) is important as a marker of disease activity and as a potential surrogate for relapses. We propose an approach where sequential scans are jointly segmented, to provide a temporally consistent tissue segmentation while remaining sensitive to newly appearing lesions. The method uses a two-stage classification process: 1) a Bayesian classifier provides a probabilistic brain tissue classification at each voxel of reference and follow-up scans, and 2) a random-forest based lesion-level classification provides a final identification of new lesions. Generative models are learned based on 364 scans from 95 subjects from a multi-center clinical trial. The method is evaluated on sequential brain MRI of 160 subjects from a separate multi-center clinical trial, and is compared to 1) semi-automatically generated ground truth segmentations and 2) fully manual identification of new lesions generated independently by nine expert raters on a subset of 60 subjects. For new lesions greater than 0.15 cc in size, the classifier has near perfect performance (99% sensitivity, 2% false detection rate), as compared to ground truth. The proposed method was also shown to exceed the performance of any one of the nine expert manual identifications.

Automatic recognition and analysis of synapses. [in brain tissue

NASA Technical Reports Server (NTRS)

Ungerleider, J. A.; Ledley, R. S.; Bloom, F. E.

1976-01-01

An automatic system for recognizing synaptic junctions would allow analysis of large samples of tissue for the possible classification of specific well-defined sets of synapses based upon structural morphometric indices. In this paper the three steps of our system are described: (1) cytochemical tissue preparation to allow easy recognition of the synaptic junctions; (2) transmitting the tissue information to a computer; and (3) analyzing each field to recognize the synapses and make measurements on them.

Supervised novelty detection in brain tissue classification with an application to white matter hyperintensities

NASA Astrophysics Data System (ADS)

Kuijf, Hugo J.; Moeskops, Pim; de Vos, Bob D.; Bouvy, Willem H.; de Bresser, Jeroen; Biessels, Geert Jan; Viergever, Max A.; Vincken, Koen L.

2016-03-01

Novelty detection is concerned with identifying test data that differs from the training data of a classifier. In the case of brain MR images, pathology or imaging artefacts are examples of untrained data. In this proof-of-principle study, we measure the behaviour of a classifier during the classification of trained labels (i.e. normal brain tissue). Next, we devise a measure that distinguishes normal classifier behaviour from abnormal behavior that occurs in the case of a novelty. This will be evaluated by training a kNN classifier on normal brain tissue, applying it to images with an untrained pathology (white matter hyperintensities (WMH)), and determine if our measure is able to identify abnormal classifier behaviour at WMH locations. For our kNN classifier, behaviour is modelled as the mean, median, or q1 distance to the k nearest points. Healthy tissue was trained on 15 images; classifier behaviour was trained/tested on 5 images with leave-one-out cross-validation. For each trained class, we measure the distribution of mean/median/q1 distances to the k nearest point. Next, for each test voxel, we compute its Z-score with respect to the measured distribution of its predicted label. We consider a Z-score >=4 abnormal behaviour of the classifier, having a probability due to chance of 0.000032. Our measure identified >90% of WMH volume and also highlighted other non-trained findings. The latter being predominantly vessels, cerebral falx, brain mask errors, choroid plexus. This measure is generalizable to other classifiers and might help in detecting unexpected findings or novelties by measuring classifier behaviour.

Differentiating pediatric epileptic brain tissue from normal brain tissue by using time-dependent diffuse reflectance spectroscopy in vivo: comprehensive data analysis method in the time domain

NASA Astrophysics Data System (ADS)

Oh, Sanghoon; Fernald, Bradley; Bhatia, Sanjiv; Ragheb, John; Sandberg, David; Johnson, Mahlon; Lin, Wei-Chiang

2009-05-01

This research investigated the feasibility of using time-dependent diffuse reflectance spectroscopy to differentiate pediatric epileptic brain tissue from normal brain tissue. The optical spectroscopic technique monitored the dynamic optical properties of the cerebral cortex that are associated with its physiological, morphological, and compositional characteristics. Due to the transient irregular epileptic discharge activity within the epileptic brain tissue it was hypothesized that the lesion would express abnormal dynamic optical behavior that would alter normal dynamic behavior. Thirteen pediatric epilepsy patients and seven pediatric brain tumor patients (normal controls) were recruited for this clinical study. Dynamic optical properties were obtained from the cortical surface intraoperatively using a timedependent diffuse reflectance spectroscopy system. This system consisted of a fiber-optic probe, a tungsten-halogen light source, and a spectrophotometer. It acquired diffuse reflectance spectra with a spectral range of 204 nm to 932 nm at a rate of 33 spectra per second for approximately 12 seconds. Biopsy samples were taken from electrophysiologically abnormal cortex and evaluated by a neuropathologist, which served as a gold standard for lesion classification. For data analysis, spectral intensity changes of diffuse reflectance in the time domain at two different wavelengths from each investigated site were compared. Negative correlation segment, defined by the periods where the intensity changes at the two wavelengths were opposite in their slope polarity, were extracted. The total duration of negative correlation, referred to as the "negative correlation time index", was calculated by integrating the negative correlation segments. The negative correlation time indices from all investigated sites were sub-grouped according to the corresponding histological classifications. The difference between the mean indices of two subgroups was evaluated by standard t-test. These comparison and calculation procedures were carried out for all possible wavelength combinations between 400 nm and 800 nm with 2 nm increments. The positive group consisted of seven pathologically abnormal test sites, and the negative group consisted of 13 normal test sites from non-epileptic tumor patients. A standard t-test showed significant difference between negative correlation time indices from the two groups at the wavelength combinations of 700-760 nm versus 550-580 nm. An empirical discrimination algorithm based on the negative correlation time indices in this range produced 100% sensitivity and 85% specificity. Based on these results time-dependent diffuse reflectance spectroscopy with optimized data analysis methods differentiates epileptic brain tissue from normal brain tissue adequately, therefore can be utilized for surgical guidance, and may enhance the surgical outcome of pediatric epilepsy surgery.

Predicting Intracranial Pressure and Brain Tissue Oxygen Crises in Patients With Severe Traumatic Brain Injury.

PubMed

Myers, Risa B; Lazaridis, Christos; Jermaine, Christopher M; Robertson, Claudia S; Rusin, Craig G

2016-09-01

To develop computer algorithms that can recognize physiologic patterns in traumatic brain injury patients that occur in advance of intracranial pressure and partial brain tissue oxygenation crises. The automated early detection of crisis precursors can provide clinicians with time to intervene in order to prevent or mitigate secondary brain injury. A retrospective study was conducted from prospectively collected physiologic data. intracranial pressure, and partial brain tissue oxygenation crisis events were defined as intracranial pressure of greater than or equal to 20 mm Hg lasting at least 15 minutes and partial brain tissue oxygenation value of less than 10 mm Hg for at least 10 minutes, respectively. The physiologic data preceding each crisis event were used to identify precursors associated with crisis onset. Multivariate classification models were applied to recorded data in 30-minute epochs of time to predict crises between 15 and 360 minutes in the future. The neurosurgical unit of Ben Taub Hospital (Houston, TX). Our cohort consisted of 817 subjects with severe traumatic brain injury. Our algorithm can predict the onset of intracranial pressure crises with 30-minute advance warning with an area under the receiver operating characteristic curve of 0.86 using only intracranial pressure measurements and time since last crisis. An analogous algorithm can predict the start of partial brain tissue oxygenation crises with 30-minute advanced warning with an area under the receiver operating characteristic curve of 0.91. Our algorithms provide accurate and timely predictions of intracranial hypertension and tissue hypoxia crises in patients with severe traumatic brain injury. Almost all of the information needed to predict the onset of these events is contained within the signal of interest and the time since last crisis.

Detecting brain tumor in pathological slides using hyperspectral imaging

PubMed Central

Ortega, Samuel; Fabelo, Himar; Camacho, Rafael; de la Luz Plaza, María; Callicó, Gustavo M.; Sarmiento, Roberto

2018-01-01

Hyperspectral imaging (HSI) is an emerging technology for medical diagnosis. This research work presents a proof-of-concept on the use of HSI data to automatically detect human brain tumor tissue in pathological slides. The samples, consisting of hyperspectral cubes collected from 400 nm to 1000 nm, were acquired from ten different patients diagnosed with high-grade glioma. Based on the diagnosis provided by pathologists, a spectral library of normal and tumor tissues was created and processed using three different supervised classification algorithms. Results prove that HSI is a suitable technique to automatically detect high-grade tumors from pathological slides. PMID:29552415

Detecting brain tumor in pathological slides using hyperspectral imaging.

PubMed

Ortega, Samuel; Fabelo, Himar; Camacho, Rafael; de la Luz Plaza, María; Callicó, Gustavo M; Sarmiento, Roberto

2018-02-01

Hyperspectral imaging (HSI) is an emerging technology for medical diagnosis. This research work presents a proof-of-concept on the use of HSI data to automatically detect human brain tumor tissue in pathological slides. The samples, consisting of hyperspectral cubes collected from 400 nm to 1000 nm, were acquired from ten different patients diagnosed with high-grade glioma. Based on the diagnosis provided by pathologists, a spectral library of normal and tumor tissues was created and processed using three different supervised classification algorithms. Results prove that HSI is a suitable technique to automatically detect high-grade tumors from pathological slides.

Intraoperative optical biopsy for brain tumors using spectro-lifetime properties of intrinsic fluorophores

NASA Astrophysics Data System (ADS)

Vasefi, Fartash; Kittle, David S.; Nie, Zhaojun; Falcone, Christina; Patil, Chirag G.; Chu, Ray M.; Mamelak, Adam N.; Black, Keith L.; Butte, Pramod V.

2016-04-01

We have developed and tested a system for real-time intra-operative optical identification and classification of brain tissues using time-resolved fluorescence spectroscopy (TRFS). A supervised learning algorithm using linear discriminant analysis (LDA) employing selected intrinsic fluorescence decay temporal points in 6 spectral bands was employed to maximize statistical significance difference between training groups. The linear discriminant analysis on in vivo human tissues obtained by TRFS measurements (N = 35) were validated by histopathologic analysis and neuronavigation correlation to pre-operative MRI images. These results demonstrate that TRFS can differentiate between normal cortex, white matter and glioma.

International recommendation for a comprehensive neuropathologic workup of epilepsy surgery brain tissue: A consensus Task Force report from the ILAE Commission on Diagnostic Methods.

PubMed

Blümcke, Ingmar; Aronica, Eleonora; Miyata, Hajime; Sarnat, Harvey B; Thom, Maria; Roessler, Karl; Rydenhag, Bertil; Jehi, Lara; Krsek, Pavel; Wiebe, Samuel; Spreafico, Roberto

2016-03-01

Epilepsy surgery is an effective treatment in many patients with drug-resistant focal epilepsies. An early decision for surgical therapy is facilitated by a magnetic resonance imaging (MRI)-visible brain lesion congruent with the electrophysiologically abnormal brain region. Recent advances in the pathologic diagnosis and classification of epileptogenic brain lesions are helpful for clinical correlation, outcome stratification, and patient management. However, application of international consensus classification systems to common epileptic pathologies (e.g., focal cortical dysplasia [FCD] and hippocampal sclerosis [HS]) necessitates standardized protocols for neuropathologic workup of epilepsy surgery specimens. To this end, the Task Force of Neuropathology from the International League Against Epilepsy (ILAE) Commission on Diagnostic Methods developed a consensus standard operational procedure for tissue inspection, distribution, and processing. The aims are to provide a systematic framework for histopathologic workup, meeting minimal standards and maximizing current and future opportunities for morphofunctional correlations and molecular studies for both clinical care and research. Whenever feasible, anatomically intact surgical specimens are desirable to enable systematic analysis in selective hippocampectomies, temporal lobe resections, and lesional or nonlesional neocortical samples. Correct orientation of sample and the sample's relation to neurophysiologically aberrant sites requires good communication between pathology and neurosurgical teams. Systematic tissue sampling of 5-mm slabs along a defined anatomic axis and application of a limited immunohistochemical panel will ensure a reliable differential diagnosis of main pathologies encountered in epilepsy surgery. Wiley Periodicals, Inc. © 2016 International League Against Epilepsy.

Individually adapted imagery improves brain-computer interface performance in end-users with disability.

PubMed

Scherer, Reinhold; Faller, Josef; Friedrich, Elisabeth V C; Opisso, Eloy; Costa, Ursula; Kübler, Andrea; Müller-Putz, Gernot R

2015-01-01

Brain-computer interfaces (BCIs) translate oscillatory electroencephalogram (EEG) patterns into action. Different mental activities modulate spontaneous EEG rhythms in various ways. Non-stationarity and inherent variability of EEG signals, however, make reliable recognition of modulated EEG patterns challenging. Able-bodied individuals who use a BCI for the first time achieve - on average - binary classification performance of about 75%. Performance in users with central nervous system (CNS) tissue damage is typically lower. User training generally enhances reliability of EEG pattern generation and thus also robustness of pattern recognition. In this study, we investigated the impact of mental tasks on binary classification performance in BCI users with central nervous system (CNS) tissue damage such as persons with stroke or spinal cord injury (SCI). Motor imagery (MI), that is the kinesthetic imagination of movement (e.g. squeezing a rubber ball with the right hand), is the "gold standard" and mainly used to modulate EEG patterns. Based on our recent results in able-bodied users, we hypothesized that pair-wise combination of "brain-teaser" (e.g. mental subtraction and mental word association) and "dynamic imagery" (e.g. hand and feet MI) tasks significantly increases classification performance of induced EEG patterns in the selected end-user group. Within-day (How stable is the classification within a day?) and between-day (How well does a model trained on day one perform on unseen data of day two?) analysis of variability of mental task pair classification in nine individuals confirmed the hypothesis. We found that the use of the classical MI task pair hand vs. feed leads to significantly lower classification accuracy - in average up to 15% less - in most users with stroke or SCI. User-specific selection of task pairs was again essential to enhance performance. We expect that the gained evidence will significantly contribute to make imagery-based BCI technology become accessible to a larger population of users including individuals with special needs due to CNS damage.

Individually Adapted Imagery Improves Brain-Computer Interface Performance in End-Users with Disability

PubMed Central

Scherer, Reinhold; Faller, Josef; Friedrich, Elisabeth V. C.; Opisso, Eloy; Costa, Ursula; Kübler, Andrea; Müller-Putz, Gernot R.

2015-01-01

Brain-computer interfaces (BCIs) translate oscillatory electroencephalogram (EEG) patterns into action. Different mental activities modulate spontaneous EEG rhythms in various ways. Non-stationarity and inherent variability of EEG signals, however, make reliable recognition of modulated EEG patterns challenging. Able-bodied individuals who use a BCI for the first time achieve - on average - binary classification performance of about 75%. Performance in users with central nervous system (CNS) tissue damage is typically lower. User training generally enhances reliability of EEG pattern generation and thus also robustness of pattern recognition. In this study, we investigated the impact of mental tasks on binary classification performance in BCI users with central nervous system (CNS) tissue damage such as persons with stroke or spinal cord injury (SCI). Motor imagery (MI), that is the kinesthetic imagination of movement (e.g. squeezing a rubber ball with the right hand), is the "gold standard" and mainly used to modulate EEG patterns. Based on our recent results in able-bodied users, we hypothesized that pair-wise combination of "brain-teaser" (e.g. mental subtraction and mental word association) and "dynamic imagery" (e.g. hand and feet MI) tasks significantly increases classification performance of induced EEG patterns in the selected end-user group. Within-day (How stable is the classification within a day?) and between-day (How well does a model trained on day one perform on unseen data of day two?) analysis of variability of mental task pair classification in nine individuals confirmed the hypothesis. We found that the use of the classical MI task pair hand vs. feed leads to significantly lower classification accuracy - in average up to 15% less - in most users with stroke or SCI. User-specific selection of task pairs was again essential to enhance performance. We expect that the gained evidence will significantly contribute to make imagery-based BCI technology become accessible to a larger population of users including individuals with special needs due to CNS damage. PMID:25992718

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.

Multiclass feature selection for improved pediatric brain tumor segmentation

NASA Astrophysics Data System (ADS)

Ahmed, Shaheen; Iftekharuddin, Khan M.

2012-03-01

In our previous work, we showed that fractal-based texture features are effective in detection, segmentation and classification of posterior-fossa (PF) pediatric brain tumor in multimodality MRI. We exploited an information theoretic approach such as Kullback-Leibler Divergence (KLD) for feature selection and ranking different texture features. We further incorporated the feature selection technique with segmentation method such as Expectation Maximization (EM) for segmentation of tumor T and non tumor (NT) tissues. In this work, we extend the two class KLD technique to multiclass for effectively selecting the best features for brain tumor (T), cyst (C) and non tumor (NT). We further obtain segmentation robustness for each tissue types by computing Bay's posterior probabilities and corresponding number of pixels for each tissue segments in MRI patient images. We evaluate improved tumor segmentation robustness using different similarity metric for 5 patients in T1, T2 and FLAIR modalities.

Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering.

PubMed

Yang, Guang; Raschke, Felix; Barrick, Thomas R; Howe, Franklyn A

2015-09-01

To investigate whether nonlinear dimensionality reduction improves unsupervised classification of (1) H MRS brain tumor data compared with a linear method. In vivo single-voxel (1) H magnetic resonance spectroscopy (55 patients) and (1) H magnetic resonance spectroscopy imaging (MRSI) (29 patients) data were acquired from histopathologically diagnosed gliomas. Data reduction using Laplacian eigenmaps (LE) or independent component analysis (ICA) was followed by k-means clustering or agglomerative hierarchical clustering (AHC) for unsupervised learning to assess tumor grade and for tissue type segmentation of MRSI data. An accuracy of 93% in classification of glioma grade II and grade IV, with 100% accuracy in distinguishing tumor and normal spectra, was obtained by LE with unsupervised clustering, but not with the combination of k-means and ICA. With (1) H MRSI data, LE provided a more linear distribution of data for cluster analysis and better cluster stability than ICA. LE combined with k-means or AHC provided 91% accuracy for classifying tumor grade and 100% accuracy for identifying normal tissue voxels. Color-coded visualization of normal brain, tumor core, and infiltration regions was achieved with LE combined with AHC. The LE method is promising for unsupervised clustering to separate brain and tumor tissue with automated color-coding for visualization of (1) H MRSI data after cluster analysis. © 2014 Wiley Periodicals, Inc.

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

MR/PET quantification tools: Registration, segmentation, classification, and MR-based attenuation correction

PubMed Central

Fei, Baowei; Yang, Xiaofeng; Nye, Jonathon A.; Aarsvold, John N.; Raghunath, Nivedita; Cervo, Morgan; Stark, Rebecca; Meltzer, Carolyn C.; Votaw, John R.

2012-01-01

Purpose: Combined MR/PET is a relatively new, hybrid imaging modality. A human MR/PET prototype system consisting of a Siemens 3T Trio MR and brain PET insert was installed and tested at our institution. Its present design does not offer measured attenuation correction (AC) using traditional transmission imaging. This study is the development of quantification tools including MR-based AC for quantification in combined MR/PET for brain imaging. Methods: The developed quantification tools include image registration, segmentation, classification, and MR-based AC. These components were integrated into a single scheme for processing MR/PET data. The segmentation method is multiscale and based on the Radon transform of brain MR images. It was developed to segment the skull on T1-weighted MR images. A modified fuzzy C-means classification scheme was developed to classify brain tissue into gray matter, white matter, and cerebrospinal fluid. Classified tissue is assigned an attenuation coefficient so that AC factors can be generated. PET emission data are then reconstructed using a three-dimensional ordered sets expectation maximization method with the MR-based AC map. Ten subjects had separate MR and PET scans. The PET with [11C]PIB was acquired using a high-resolution research tomography (HRRT) PET. MR-based AC was compared with transmission (TX)-based AC on the HRRT. Seventeen volumes of interest were drawn manually on each subject image to compare the PET activities between the MR-based and TX-based AC methods. Results: For skull segmentation, the overlap ratio between our segmented results and the ground truth is 85.2 ± 2.6%. Attenuation correction results from the ten subjects show that the difference between the MR and TX-based methods was <6.5%. Conclusions: MR-based AC compared favorably with conventional transmission-based AC. Quantitative tools including registration, segmentation, classification, and MR-based AC have been developed for use in combined MR/PET. PMID:23039679

Colorization and Automated Segmentation of Human T2 MR Brain Images for Characterization of Soft Tissues

PubMed Central

Attique, Muhammad; Gilanie, Ghulam; Hafeez-Ullah; Mehmood, Malik S.; Naweed, Muhammad S.; Ikram, Masroor; Kamran, Javed A.; Vitkin, Alex

2012-01-01

Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described. PMID:22479421

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations.

PubMed

Fabelo, Himar; Ortega, Samuel; Ravi, Daniele; Kiran, B Ravi; Sosa, Coralia; Bulters, Diederik; Callicó, Gustavo M; Bulstrode, Harry; Szolna, Adam; Piñeiro, Juan F; Kabwama, Silvester; Madroñal, Daniel; Lazcano, Raquel; J-O'Shanahan, Aruma; Bisshopp, Sara; Hernández, María; Báez, Abelardo; Yang, Guang-Zhong; Stanciulescu, Bogdan; Salvador, Rubén; Juárez, Eduardo; Sarmiento, Roberto

2018-01-01

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area.

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations

PubMed Central

Kabwama, Silvester; Madroñal, Daniel; Lazcano, Raquel; J-O’Shanahan, Aruma; Bisshopp, Sara; Hernández, María; Báez, Abelardo; Yang, Guang-Zhong; Stanciulescu, Bogdan; Salvador, Rubén; Juárez, Eduardo; Sarmiento, Roberto

2018-01-01

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area. PMID:29554126

Regional brain volumetry and brain function in severely brain-injured patients.

PubMed

Annen, Jitka; Frasso, Gianluca; Crone, Julia Sophia; Heine, Lizette; Di Perri, Carol; Martial, Charlotte; Cassol, Helena; Demertzi, Athena; Naccache, Lionel; Laureys, Steven

2018-04-01

The relationship between residual brain tissue in patients with disorders of consciousness (DOC) and the clinical condition is unclear. This observational study aimed to quantify gray (GM) and white matter (WM) atrophy in states of (altered) consciousness. Structural T1-weighted magnetic resonance images were processed for 102 severely brain-injured and 52 healthy subjects. Regional brain volume was quantified for 158 (sub)cortical regions using Freesurfer. The relationship between regional brain volume and clinical characteristics of patients with DOC and conscious brain-injured patients was assessed using a linear mixed-effects model. Classification of patients with unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) using regional volumetric information was performed and compared to classification using cerebral glucose uptake from fluorodeoxyglucose positron emission tomography. For validation, the T1-based classifier was tested on independent datasets. Patients were characterized by smaller regional brain volumes than healthy subjects. Atrophy occurred faster in UWS compared to MCS (GM) and conscious (GM and WM) patients. Classification was successful (misclassification with leave-one-out cross-validation between 2% and 13%) and generalized to the independent data set with an area under the receiver operator curve of 79% (95% confidence interval [CI; 67-91.5]) for GM and 70% (95% CI [55.6-85.4]) for WM. Brain volumetry at the single-subject level reveals that regions in the default mode network and subcortical gray matter regions, as well as white matter regions involved in long range connectivity, are most important to distinguish levels of consciousness. Our findings suggest that changes of brain structure provide information in addition to the assessment of functional neuroimaging and thus should be evaluated as well. Ann Neurol 2018;83:842-853. © 2018 American Neurological Association.

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

Detecting stripe artifacts in ultrasound images.

PubMed

Maciak, Adam; Kier, Christian; Seidel, Günter; Meyer-Wiethe, Karsten; Hofmann, Ulrich G

2009-10-01

Brain perfusion diseases such as acute ischemic stroke are detectable through computed tomography (CT)-/magnetic resonance imaging (MRI)-based methods. An alternative approach makes use of ultrasound imaging. In this low-cost bedside method, noise and artifacts degrade the imaging process. Especially stripe artifacts show a similar signal behavior compared to acute stroke or brain perfusion diseases. This document describes how stripe artifacts can be detected and eliminated in ultrasound images obtained through harmonic imaging (HI). On the basis of this new method, both proper identification of areas with critically reduced brain tissue perfusion and classification between brain perfusion defects and ultrasound stripe artifacts are made possible.

Probabilistic brain tissue segmentation in neonatal magnetic resonance imaging.

PubMed

Anbeek, Petronella; Vincken, Koen L; Groenendaal, Floris; Koeman, Annemieke; van Osch, Matthias J P; van der Grond, Jeroen

2008-02-01

A fully automated method has been developed for segmentation of four different structures in the neonatal brain: white matter (WM), central gray matter (CEGM), cortical gray matter (COGM), and cerebrospinal fluid (CSF). The segmentation algorithm is based on information from T2-weighted (T2-w) and inversion recovery (IR) scans. The method uses a K nearest neighbor (KNN) classification technique with features derived from spatial information and voxel intensities. Probabilistic segmentations of each tissue type were generated. By applying thresholds on these probability maps, binary segmentations were obtained. These final segmentations were evaluated by comparison with a gold standard. The sensitivity, specificity, and Dice similarity index (SI) were calculated for quantitative validation of the results. High sensitivity and specificity with respect to the gold standard were reached: sensitivity >0.82 and specificity >0.9 for all tissue types. Tissue volumes were calculated from the binary and probabilistic segmentations. The probabilistic segmentation volumes of all tissue types accurately estimated the gold standard volumes. The KNN approach offers valuable ways for neonatal brain segmentation. The probabilistic outcomes provide a useful tool for accurate volume measurements. The described method is based on routine diagnostic magnetic resonance imaging (MRI) and is suitable for large population studies.

Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification.

PubMed

Juan-Albarracín, Javier; Fuster-Garcia, Elies; Manjón, José V; Robles, Montserrat; Aparici, F; Martí-Bonmatí, L; García-Gómez, Juan M

2015-01-01

Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation.

Tissue Tracking: Applications for Brain MRI Classification

DTIC Science & Technology

2007-01-01

General Hospital, Center for Morphometric Analysis.10,11 The IBSR data-sets are T1-weighted, 3D coronal brain scans after having been positionally...learned priors,” Image Processing, IEEE Transactions on 9(2), pp. 299–301, 2000. 5. P. Olver, G. Sapiro, and A. Tannenbaum, “Invariant Geometric Evolutions...MRI,” NeuroImage 22(3), pp. 1060–1075, 2004. 16. A. Zijdenbos, B. Dawant, R. Margolin, and A. Palmer, “ Morphometric analysis of white matter lesions in

Automatic CT Brain Image Segmentation Using Two Level Multiresolution Mixture Model of EM

NASA Astrophysics Data System (ADS)

Jiji, G. Wiselin; Dehmeshki, Jamshid

2014-04-01

Tissue classification in computed tomography (CT) brain images is an important issue in the analysis of several brain dementias. A combination of different approaches for the segmentation of brain images is presented in this paper. A multi resolution algorithm is proposed along with scaled versions using Gaussian filter and wavelet analysis that extends expectation maximization (EM) algorithm. It is found that it is less sensitive to noise and got more accurate image segmentation than traditional EM. Moreover the algorithm has been applied on 20 sets of CT of the human brain and compared with other works. The segmentation results show the advantages of the proposed work have achieved more promising results and the results have been tested with Doctors.

Brain tissue segmentation in 4D CT using voxel classification

NASA Astrophysics Data System (ADS)

van den Boom, R.; Oei, M. T. H.; Lafebre, S.; Oostveen, L. J.; Meijer, F. J. A.; Steens, S. C. A.; Prokop, M.; van Ginneken, B.; Manniesing, R.

2012-02-01

A method is proposed to segment anatomical regions of the brain from 4D computer tomography (CT) patient data. The method consists of a three step voxel classification scheme, each step focusing on structures that are increasingly difficult to segment. The first step classifies air and bone, the second step classifies vessels and the third step classifies white matter, gray matter and cerebrospinal fluid. As features the time averaged intensity value and the temporal intensity change value were used. In each step, a k-Nearest-Neighbor classifier was used to classify the voxels. Training data was obtained by placing regions of interest in reconstructed 3D image data. The method has been applied to ten 4D CT cerebral patient data. A leave-one-out experiment showed consistent and accurate segmentation results.

HELICoiD project: a new use of hyperspectral imaging for brain cancer detection in real-time during neurosurgical operations

NASA Astrophysics Data System (ADS)

Fabelo, Himar; Ortega, Samuel; Kabwama, Silvester; Callico, Gustavo M.; Bulters, Diederik; Szolna, Adam; Pineiro, Juan F.; Sarmiento, Roberto

2016-05-01

Hyperspectral images allow obtaining large amounts of information about the surface of the scene that is captured by the sensor. Using this information and a set of complex classification algorithms is possible to determine which material or substance is located in each pixel. The HELICoiD (HypErspectraL Imaging Cancer Detection) project is a European FET project that has the goal to develop a demonstrator capable to discriminate, with high precision, between normal and tumour tissues, operating in real-time, during neurosurgical operations. This demonstrator could help the neurosurgeons in the process of brain tumour resection, avoiding the excessive extraction of normal tissue and unintentionally leaving small remnants of tumour. Such precise delimitation of the tumour boundaries will improve the results of the surgery. The HELICoiD demonstrator is composed of two hyperspectral cameras obtained from Headwall. The first one in the spectral range from 400 to 1000 nm (visible and near infrared) and the second one in the spectral range from 900 to 1700 nm (near infrared). The demonstrator also includes an illumination system that covers the spectral range from 400 nm to 2200 nm. A data processing unit is in charge of managing all the parts of the demonstrator, and a high performance platform aims to accelerate the hyperspectral image classification process. Each one of these elements is installed in a customized structure specially designed for surgical environments. Preliminary results of the classification algorithms offer high accuracy (over 95%) in the discrimination between normal and tumour tissues.

BRAIN TUMOR SEGMENTATION WITH SYMMETRIC TEXTURE AND SYMMETRIC INTENSITY-BASED DECISION FORESTS.

PubMed

Bianchi, Anthony; Miller, James V; Tan, Ek Tsoon; Montillo, Albert

2013-04-01

Accurate automated segmentation of brain tumors in MR images is challenging due to overlapping tissue intensity distributions and amorphous tumor shape. However, a clinically viable solution providing precise quantification of tumor and edema volume would enable better pre-operative planning, treatment monitoring and drug development. Our contributions are threefold. First, we design efficient gradient and LBPTOP based texture features which improve classification accuracy over standard intensity features. Second, we extend our texture and intensity features to symmetric texture and symmetric intensity which further improve the accuracy for all tissue classes. Third, we demonstrate further accuracy enhancement by extending our long range features from 100mm to a full 200mm. We assess our brain segmentation technique on 20 patients in the BraTS 2012 dataset. Impact from each contribution is measured and the combination of all the features is shown to yield state-of-the-art accuracy and speed.

A stereotaxic, population-averaged T1w ovine brain atlas including cerebral morphology and tissue volumes

PubMed Central

Nitzsche, Björn; Frey, Stephen; Collins, Louis D.; Seeger, Johannes; Lobsien, Donald; Dreyer, Antje; Kirsten, Holger; Stoffel, Michael H.; Fonov, Vladimir S.; Boltze, Johannes

2015-01-01

Standard stereotaxic reference systems play a key role in human brain studies. Stereotaxic coordinate systems have also been developed for experimental animals including non-human primates, dogs, and rodents. However, they are lacking for other species being relevant in experimental neuroscience including sheep. Here, we present a spatial, unbiased ovine brain template with tissue probability maps (TPM) that offer a detailed stereotaxic reference frame for anatomical features and localization of brain areas, thereby enabling inter-individual and cross-study comparability. Three-dimensional data sets from healthy adult Merino sheep (Ovis orientalis aries, 12 ewes and 26 neutered rams) were acquired on a 1.5 T Philips MRI using a T1w sequence. Data were averaged by linear and non-linear registration algorithms. Moreover, animals were subjected to detailed brain volume analysis including examinations with respect to body weight (BW), age, and sex. The created T1w brain template provides an appropriate population-averaged ovine brain anatomy in a spatial standard coordinate system. Additionally, TPM for gray (GM) and white (WM) matter as well as cerebrospinal fluid (CSF) classification enabled automatic prior-based tissue segmentation using statistical parametric mapping (SPM). Overall, a positive correlation of GM volume and BW explained about 15% of the variance of GM while a positive correlation between WM and age was found. Absolute tissue volume differences were not detected, indeed ewes showed significantly more GM per bodyweight as compared to neutered rams. The created framework including spatial brain template and TPM represent a useful tool for unbiased automatic image preprocessing and morphological characterization in sheep. Therefore, the reported results may serve as a starting point for further experimental and/or translational research aiming at in vivo analysis in this species. PMID:26089780

Detection of white matter lesion regions in MRI using SLIC0 and convolutional neural network.

PubMed

Diniz, Pedro Henrique Bandeira; Valente, Thales Levi Azevedo; Diniz, João Otávio Bandeira; Silva, Aristófanes Corrêa; Gattass, Marcelo; Ventura, Nina; Muniz, Bernardo Carvalho; Gasparetto, Emerson Leandro

2018-04-19

White matter lesions are non-static brain lesions that have a prevalence rate up to 98% in the elderly population. Because they may be associated with several brain diseases, it is important that they are detected as soon as possible. Magnetic Resonance Imaging (MRI) provides three-dimensional data with the possibility to detect and emphasize contrast differences in soft tissues, providing rich information about the human soft tissue anatomy. However, the amount of data provided for these images is far too much for manual analysis/interpretation, representing a difficult and time-consuming task for specialists. This work presents a computational methodology capable of detecting regions of white matter lesions of the brain in MRI of FLAIR modality. The techniques highlighted in this methodology are SLIC0 clustering for candidate segmentation and convolutional neural networks for candidate classification. The methodology proposed here consists of four steps: (1) images acquisition, (2) images preprocessing, (3) candidates segmentation and (4) candidates classification. The methodology was applied on 91 magnetic resonance images provided by DASA, and achieved an accuracy of 98.73%, specificity of 98.77% and sensitivity of 78.79% with 0.005 of false positives, without any false positives reduction technique, in detection of white matter lesion regions. It is demonstrated the feasibility of the analysis of brain MRI using SLIC0 and convolutional neural network techniques to achieve success in detection of white matter lesions regions. Copyright © 2018. Published by Elsevier B.V.

Prediction of brain-computer interface aptitude from individual brain structure.

PubMed

Halder, S; Varkuti, B; Bogdan, M; Kübler, A; Rosenstiel, W; Sitaram, R; Birbaumer, N

2013-01-01

Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary. We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance. Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error). Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance. This confirms that structural brain traits contribute to individual performance in BCI use.

Prediction of brain-computer interface aptitude from individual brain structure

PubMed Central

Halder, S.; Varkuti, B.; Bogdan, M.; Kübler, A.; Rosenstiel, W.; Sitaram, R.; Birbaumer, N.

2013-01-01

Objective: Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary. Methods: We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance. Results: Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error). Conclusions: Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance. Significance: This confirms that structural brain traits contribute to individual performance in BCI use. PMID:23565083

Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification

PubMed Central

Juan-Albarracín, Javier; Fuster-Garcia, Elies; Manjón, José V.; Robles, Montserrat; Aparici, F.; Martí-Bonmatí, L.; García-Gómez, Juan M.

2015-01-01

Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation. PMID:25978453

Probabilistic multiple sclerosis lesion classification based on modeling regional intensity variability and local neighborhood information.

PubMed

Harmouche, Rola; Subbanna, Nagesh K; Collins, D Louis; Arnold, Douglas L; Arbel, Tal

2015-05-01

In this paper, a fully automatic probabilistic method for multiple sclerosis (MS) lesion classification is presented, whereby the posterior probability density function over healthy tissues and two types of lesions (T1-hypointense and T2-hyperintense) is generated at every voxel. During training, the system explicitly models the spatial variability of the intensity distributions throughout the brain by first segmenting it into distinct anatomical regions and then building regional likelihood distributions for each tissue class based on multimodal magnetic resonance image (MRI) intensities. Local class smoothness is ensured by incorporating neighboring voxel information in the prior probability through Markov random fields. The system is tested on two datasets from real multisite clinical trials consisting of multimodal MRIs from a total of 100 patients with MS. Lesion classification results based on the framework are compared with and without the regional information, as well as with other state-of-the-art methods against the labels from expert manual raters. The metrics for comparison include Dice overlap, sensitivity, and positive predictive rates for both voxel and lesion classifications. Statistically significant improvements in Dice values ( ), for voxel-based and lesion-based sensitivity values ( ), and positive predictive rates ( and respectively) are shown when the proposed method is compared to the method without regional information, and to a widely used method [1]. This holds particularly true in the posterior fossa, an area where classification is very challenging. The proposed method allows us to provide clinicians with accurate tissue labels for T1-hypointense and T2-hyperintense lesions, two types of lesions that differ in appearance and clinical ramifications, and with a confidence level in the classification, which helps clinicians assess the classification results.

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).

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.

Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI.

PubMed

Sauwen, N; Acou, M; Van Cauter, S; Sima, D M; Veraart, J; Maes, F; Himmelreich, U; Achten, E; Van Huffel, S

2016-01-01

Tumor segmentation is a particularly challenging task in high-grade gliomas (HGGs), as they are among the most heterogeneous tumors in oncology. An accurate delineation of the lesion and its main subcomponents contributes to optimal treatment planning, prognosis and follow-up. Conventional MRI (cMRI) is the imaging modality of choice for manual segmentation, and is also considered in the vast majority of automated segmentation studies. Advanced MRI modalities such as perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and magnetic resonance spectroscopic imaging (MRSI) have already shown their added value in tumor tissue characterization, hence there have been recent suggestions of combining different MRI modalities into a multi-parametric MRI (MP-MRI) approach for brain tumor segmentation. In this paper, we compare the performance of several unsupervised classification methods for HGG segmentation based on MP-MRI data including cMRI, DWI, MRSI and PWI. Two independent MP-MRI datasets with a different acquisition protocol were available from different hospitals. We demonstrate that a hierarchical non-negative matrix factorization variant which was previously introduced for MP-MRI tumor segmentation gives the best performance in terms of mean Dice-scores for the pathologic tissue classes on both datasets.

Mindcontrol: A web application for brain segmentation quality control.

PubMed

Keshavan, Anisha; Datta, Esha; M McDonough, Ian; Madan, Christopher R; Jordan, Kesshi; Henry, Roland G

2018-04-15

Tissue classification plays a crucial role in the investigation of normal neural development, brain-behavior relationships, and the disease mechanisms of many psychiatric and neurological illnesses. Ensuring the accuracy of tissue classification is important for quality research and, in particular, the translation of imaging biomarkers to clinical practice. Assessment with the human eye is vital to correct various errors inherent to all currently available segmentation algorithms. Manual quality assurance becomes methodologically difficult at a large scale - a problem of increasing importance as the number of data sets is on the rise. To make this process more efficient, we have developed Mindcontrol, an open-source web application for the collaborative quality control of neuroimaging processing outputs. The Mindcontrol platform consists of a dashboard to organize data, descriptive visualizations to explore the data, an imaging viewer, and an in-browser annotation and editing toolbox for data curation and quality control. Mindcontrol is flexible and can be configured for the outputs of any software package in any data organization structure. Example configurations for three large, open-source datasets are presented: the 1000 Functional Connectomes Project (FCP), the Consortium for Reliability and Reproducibility (CoRR), and the Autism Brain Imaging Data Exchange (ABIDE) Collection. These demo applications link descriptive quality control metrics, regional brain volumes, and thickness scalars to a 3D imaging viewer and editing module, resulting in an easy-to-implement quality control protocol that can be scaled for any size and complexity of study. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Automating cell detection and classification in human brain fluorescent microscopy images using dictionary learning and sparse coding.

PubMed

Alegro, Maryana; Theofilas, Panagiotis; Nguy, Austin; Castruita, Patricia A; Seeley, William; Heinsen, Helmut; Ushizima, Daniela M; Grinberg, Lea T

2017-04-15

Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility. Dictionary learning and sparse coding allow for constructing improved cell representations using IF images. These models are input for detection and segmentation methods. Classification occurs by means of color distances between cells and a learned set. Our method successfully detected and classified cells in 49 human brain images. We evaluated our results regarding true positive, false positive, false negative, precision, recall, false positive rate and F1 score metrics. We also measured user-experience and time saved compared to manual countings. We compared our results to four open-access IF-based cell-counting tools available in the literature. Our method showed improved accuracy for all data samples. The proposed method satisfactorily detects and classifies cells from human postmortem brain IF images, with potential to be generalized for applications in other counting tasks. Copyright © 2017 Elsevier B.V. All rights reserved.

Change detection and classification in brain MR images using change vector analysis.

PubMed

Simões, Rita; Slump, Cornelis

2011-01-01

The automatic detection of longitudinal changes in brain images is valuable in the assessment of disease evolution and treatment efficacy. Most existing change detection methods that are currently used in clinical research to monitor patients suffering from neurodegenerative diseases--such as Alzheimer's--focus on large-scale brain deformations. However, such patients often have other brain impairments, such as infarcts, white matter lesions and hemorrhages, which are typically overlooked by the deformation-based methods. Other unsupervised change detection algorithms have been proposed to detect tissue intensity changes. The outcome of these methods is typically a binary change map, which identifies changed brain regions. However, understanding what types of changes these regions underwent is likely to provide equally important information about lesion evolution. In this paper, we present an unsupervised 3D change detection method based on Change Vector Analysis. We compute and automatically threshold the Generalized Likelihood Ratio map to obtain a binary change map. Subsequently, we perform histogram-based clustering to classify the change vectors. We obtain a Kappa Index of 0.82 using various types of simulated lesions. The classification error is 2%. Finally, we are able to detect and discriminate both small changes and ventricle expansions in datasets from Mild Cognitive Impairment patients.

Unsupervised MRI segmentation of brain tissues using a local linear model and level set.

PubMed

Rivest-Hénault, David; Cheriet, Mohamed

2011-02-01

Real-world magnetic resonance imaging of the brain is affected by intensity nonuniformity (INU) phenomena which makes it difficult to fully automate the segmentation process. This difficult task is accomplished in this work by using a new method with two original features: (1) each brain tissue class is locally modeled using a local linear region representative, which allows us to account for the INU in an implicit way and to more accurately position the region's boundaries; and (2) the region models are embedded in the level set framework, so that the spatial coherence of the segmentation can be controlled in a natural way. Our new method has been tested on the ground-truthed Internet Brain Segmentation Repository (IBSR) database and gave promising results, with Tanimoto indexes ranging from 0.61 to 0.79 for the classification of the white matter and from 0.72 to 0.84 for the gray matter. To our knowledge, this is the first time a region-based level set model has been used to perform the segmentation of real-world MRI brain scans with convincing results. Copyright © 2011 Elsevier Inc. All rights reserved.

Efficient brain lesion segmentation using multi-modality tissue-based feature selection and support vector machines.

PubMed

Fiot, Jean-Baptiste; Cohen, Laurent D; Raniga, Parnesh; Fripp, Jurgen

2013-09-01

Support vector machines (SVM) are machine learning techniques that have been used for segmentation and classification of medical images, including segmentation of white matter hyper-intensities (WMH). Current approaches using SVM for WMH segmentation extract features from the brain and classify these followed by complex post-processing steps to remove false positives. The method presented in this paper combines advanced pre-processing, tissue-based feature selection and SVM classification to obtain efficient and accurate WMH segmentation. Features from 125 patients, generated from up to four MR modalities [T1-w, T2-w, proton-density and fluid attenuated inversion recovery(FLAIR)], differing neighbourhood sizes and the use of multi-scale features were compared. We found that although using all four modalities gave the best overall classification (average Dice scores of 0.54  ±  0.12, 0.72  ±  0.06 and 0.82  ±  0.06 respectively for small, moderate and severe lesion loads); this was not significantly different (p = 0.50) from using just T1-w and FLAIR sequences (Dice scores of 0.52  ±  0.13, 0.71  ±  0.08 and 0.81  ±  0.07). Furthermore, there was a negligible difference between using 5 × 5 × 5 and 3 × 3 × 3 features (p = 0.93). Finally, we show that careful consideration of features and pre-processing techniques not only saves storage space and computation time but also leads to more efficient classification, which outperforms the one based on all features with post-processing. Copyright © 2013 John Wiley & Sons, Ltd.

Solvent effects on differentiation of mouse brain tissue using laser microdissection ‘cut and drop’ sampling with direct mass spectral analysis

DOE PAGES

Cahill, John F.; Kertesz, Vilmos; Porta, Tiffany; ...

2018-02-08

Rationale: Laser microdissection-liquid vortex capture/electrospray ionization mass spectrometry (LMD-LVC/ESI-MS) has potential for on-line classification of tissue but an investigation into what analytical conditions provide best spectral differentiation has not been conducted. The effects of solvent, ionization polarity, and spectral acquisition parameters on differentiation of mouse brain tissue regions are described.Methods: Individual 40 × 40 μm microdissections from cortex, white, grey, granular, and nucleus regions of mouse brain tissue were analyzed using different capture/ESI solvents, in positive and negative ion mode ESI, using time-of-flight (TOF)-MS and sequential window acquisitions of all theoretical spectra (SWATH)-MS (a permutation of tandem-MS), and combinations thereof.more » Principal component analysis-linear discriminant analysis (PCA-LDA), applied to each mass spectral dataset, was used to determine the accuracy of differentiation of mouse brain tissue regions. Results: Mass spectral differences associated with capture/ESI solvent composition manifested as altered relative distributions of ions rather than the presence or absence of unique ions. In negative ion mode ESI, 80/20 (v/v) methanol/water yielded spectra with low signal/noise ratios relative to other solvents. PCA-LDA models acquired using 90/10 (v/v) methanol/chloroform differentiated tissue regions with 100% accuracy while data collected using methanol misclassified some samples. The combination of SWATH-MS and TOF-MS data improved differentiation accuracy.Conclusions: Combined TOF-MS and SWATH-MS data differentiated white, grey, granular, and nucleus mouse tissue regions with greater accuracy than when solely using TOF-MS data. Using 90/10 (v/v) methanol/chloroform, tissue regions were perfectly differentiated. Lastly, these results will guide future studies looking to utilize the potential of LMD-LVC/ESI-MS for tissue and disease differentiation.« less

Solvent effects on differentiation of mouse brain tissue using laser microdissection ‘cut and drop’ sampling with direct mass spectral analysis

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

Cahill, John F.; Kertesz, Vilmos; Porta, Tiffany

Rationale: Laser microdissection-liquid vortex capture/electrospray ionization mass spectrometry (LMD-LVC/ESI-MS) has potential for on-line classification of tissue but an investigation into what analytical conditions provide best spectral differentiation has not been conducted. The effects of solvent, ionization polarity, and spectral acquisition parameters on differentiation of mouse brain tissue regions are described.Methods: Individual 40 × 40 μm microdissections from cortex, white, grey, granular, and nucleus regions of mouse brain tissue were analyzed using different capture/ESI solvents, in positive and negative ion mode ESI, using time-of-flight (TOF)-MS and sequential window acquisitions of all theoretical spectra (SWATH)-MS (a permutation of tandem-MS), and combinations thereof.more » Principal component analysis-linear discriminant analysis (PCA-LDA), applied to each mass spectral dataset, was used to determine the accuracy of differentiation of mouse brain tissue regions. Results: Mass spectral differences associated with capture/ESI solvent composition manifested as altered relative distributions of ions rather than the presence or absence of unique ions. In negative ion mode ESI, 80/20 (v/v) methanol/water yielded spectra with low signal/noise ratios relative to other solvents. PCA-LDA models acquired using 90/10 (v/v) methanol/chloroform differentiated tissue regions with 100% accuracy while data collected using methanol misclassified some samples. The combination of SWATH-MS and TOF-MS data improved differentiation accuracy.Conclusions: Combined TOF-MS and SWATH-MS data differentiated white, grey, granular, and nucleus mouse tissue regions with greater accuracy than when solely using TOF-MS data. Using 90/10 (v/v) methanol/chloroform, tissue regions were perfectly differentiated. Lastly, these results will guide future studies looking to utilize the potential of LMD-LVC/ESI-MS for tissue and disease differentiation.« less

Developing 3D microscopy with CLARITY on human brain tissue: Towards a tool for informing and validating MRI-based histology.

PubMed

Morawski, Markus; Kirilina, Evgeniya; Scherf, Nico; Jäger, Carsten; Reimann, Katja; Trampel, Robert; Gavriilidis, Filippos; Geyer, Stefan; Biedermann, Bernd; Arendt, Thomas; Weiskopf, Nikolaus

2017-11-28

Recent breakthroughs in magnetic resonance imaging (MRI) enabled quantitative relaxometry and diffusion-weighted imaging with sub-millimeter resolution. Combined with biophysical models of MR contrast the emerging methods promise in vivo mapping of cyto- and myelo-architectonics, i.e., in vivo histology using MRI (hMRI) in humans. The hMRI methods require histological reference data for model building and validation. This is currently provided by MRI on post mortem human brain tissue in combination with classical histology on sections. However, this well established approach is limited to qualitative 2D information, while a systematic validation of hMRI requires quantitative 3D information on macroscopic voxels. We present a promising histological method based on optical 3D imaging combined with a tissue clearing method, Clear Lipid-exchanged Acrylamide-hybridized Rigid Imaging compatible Tissue hYdrogel (CLARITY), adapted for hMRI validation. Adapting CLARITY to the needs of hMRI is challenging due to poor antibody penetration into large sample volumes and high opacity of aged post mortem human brain tissue. In a pilot experiment we achieved transparency of up to 8 mm-thick and immunohistochemical staining of up to 5 mm-thick post mortem brain tissue by a combination of active and passive clearing, prolonged clearing and staining times. We combined 3D optical imaging of the cleared samples with tailored image processing methods. We demonstrated the feasibility for quantification of neuron density, fiber orientation distribution and cell type classification within a volume with size similar to a typical MRI voxel. The presented combination of MRI, 3D optical microscopy and image processing is a promising tool for validation of MRI-based microstructure estimates. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Fluorescence lifetime spectroscopy for guided therapy of brain tumors.

PubMed

Butte, Pramod V; Mamelak, Adam N; Nuno, Miriam; Bannykh, Serguei I; Black, Keith L; Marcu, Laura

2011-01-01

This study evaluates the potential of time-resolved laser induced fluorescence spectroscopy (TR-LIFS) as intra-operative tool for the delineation of brain tumor from normal brain. Forty two patients undergoing glioma (WHO grade I-IV) surgery were enrolled in this study. A TR-LIFS prototype apparatus (gated detection, fast digitizer) was used to induce in-vivo fluorescence using a pulsed N2 laser (337 nm excitation, 0.7 ns pulse width) and to record the time-resolved spectrum (360-550 nm range, 10 nm interval). The sites of TR-LIFS measurement were validated by conventional histopathology (H&E staining). Parameters derived from the TR-LIFS data including intensity values and time-resolved intensity decay features (average fluorescence lifetime and Laguerre coefficients values) were used for tissue characterization and classification. 71 areas of tumor and normal brain were analyzed. Several parameters allowed for the differentiation of distinct tissue types. For example, normal cortex (N=35) and normal white matter (N=12) exhibit a longer-lasting fluorescence emission at 390 nm (τ390=2.12±0.10 ns) when compared with 460 nm (τ460=1.16±0.08 ns). High grade glioma (grades III and IV) samples (N=17) demonstrate emission peaks at 460 nm, with large variation at 390 nm while low grade glioma (I and II) samples (N=7) demonstrated a peak fluorescence emission at 460 nm. A linear discriminant algorithm allowed for the classification of low-grade gliomas with 100% sensitivity and 98% specificity. High-grade glioma demonstrated a high degree of heterogeneity thus reducing the discrimination accuracy of these tumors to 47% sensitivity and 94% specificity. Current findings demonstrate that TR-LIFS holds the potential to diagnose brain tumors intra-operatively and to provide a valuable tool for aiding the neurosurgeon-neuropathologist team in to rapidly distinguish between tumor and normal brain during surgery. Copyright © 2010 Elsevier Inc. All rights reserved.

Diagnosis of meningioma by time-resolved fluorescence spectroscopy.

PubMed

Butte, Pramod V; Pikul, Brian K; Hever, Aviv; Yong, William H; Black, Keith L; Marcu, Laura

2005-01-01

We investigate the use of time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) as an adjunctive tool for the intraoperative rapid evaluation of tumor specimens and delineation of tumor from surrounding normal tissue. Tissue autofluorescence is induced with a pulsed nitrogen laser (337 nm, 1.2 ns) and the intensity decay profiles are recorded in the 370 to 500 nm spectral range with a fast digitizer (0.2 ns resolution). Experiments are conducted on excised specimens (meningioma, dura mater, cerebral cortex) from 26 patients (97 sites). Spectral intensities and time-dependent parameters derived from the time-resolved spectra of each site are used for tissue characterization. A linear discriminant analysis algorithm is used for tissue classification. Our results reveal that meningioma is characterized by unique fluorescence characteristics that enable discrimination of tumor from normal tissue with high sensitivity (>89%) and specificity (100%). The accuracy of classification is found to increase (92.8% cases in the training set and 91.8% in the cross-validated set correctly classified) when parameters from both the spectral and the time domain are used for discrimination. Our findings establish the feasibility of using TR-LIFS as a tool for the identification of meningiomas and enables further development of real-time diagnostic tools for analyzing surgical tissue specimens of meningioma or other brain tumors.

Diagnosis of meningioma by time-resolved fluorescence spectroscopy

PubMed Central

Butte, Pramod V.; Pikul, Brian K.; Hever, Aviv; Yong, William H.; Black, Keith L.; Marcu, Laura

2010-01-01

We investigate the use of time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) as an adjunctive tool for the intraoperative rapid evaluation of tumor specimens and delineation of tumor from surrounding normal tissue. Tissue autofluorescence is induced with a pulsed nitrogen laser (337 nm, 1.2 ns) and the intensity decay profiles are recorded in the 370 to 500 nm spectral range with a fast digitizer (0.2 ns resolution). Experiments are conducted on excised specimens (meningioma, dura mater, cerebral cortex) from 26 patients (97 sites). Spectral intensities and time-dependent parameters derived from the time-resolved spectra of each site are used for tissue characterization. A linear discriminant analysis algorithm is used for tissue classification. Our results reveal that meningioma is characterized by unique fluorescence characteristics that enable discrimination of tumor from normal tissue with high sensitivity (>89%) and specificity (100%). The accuracy of classification is found to increase (92.8% cases in the training set and 91.8% in the cross-validated set correctly classified) when parameters from both the spectral and the time domain are used for discrimination. Our findings establish the feasibility of using TR-LIFS as a tool for the identification of meningiomas and enables further development of real-time diagnostic tools for analyzing surgical tissue specimens of meningioma or other brain tumors. PMID:16409091

A modified method for MRF segmentation and bias correction of MR image with intensity inhomogeneity.

PubMed

Xie, Mei; Gao, Jingjing; Zhu, Chongjin; Zhou, Yan

2015-01-01

Markov random field (MRF) model is an effective method for brain tissue classification, which has been applied in MR image segmentation for decades. However, it falls short of the expected classification in MR images with intensity inhomogeneity for the bias field is not considered in the formulation. In this paper, we propose an interleaved method joining a modified MRF classification and bias field estimation in an energy minimization framework, whose initial estimation is based on k-means algorithm in view of prior information on MRI. The proposed method has a salient advantage of overcoming the misclassifications from the non-interleaved MRF classification for the MR image with intensity inhomogeneity. In contrast to other baseline methods, experimental results also have demonstrated the effectiveness and advantages of our algorithm via its applications in the real and the synthetic MR images.

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.

Multi-fractal texture features for brain tumor and edema segmentation

NASA Astrophysics Data System (ADS)

Reza, S.; Iftekharuddin, K. M.

2014-03-01

In this work, we propose a fully automatic brain tumor and edema segmentation technique in brain magnetic resonance (MR) images. Different brain tissues are characterized using the novel texture features such as piece-wise triangular prism surface area (PTPSA), multi-fractional Brownian motion (mBm) and Gabor-like textons, along with regular intensity and intensity difference features. Classical Random Forest (RF) classifier is used to formulate the segmentation task as classification of these features in multi-modal MRIs. The segmentation performance is compared with other state-of-art works using a publicly available dataset known as Brain Tumor Segmentation (BRATS) 2012 [1]. Quantitative evaluation is done using the online evaluation tool from Kitware/MIDAS website [2]. The results show that our segmentation performance is more consistent and, on the average, outperforms other state-of-the art works in both training and challenge cases in the BRATS competition.

Automating Cell Detection and Classification in Human Brain Fluorescent Microscopy Images Using Dictionary Learning and Sparse Coding

PubMed Central

Alegro, Maryana; Theofilas, Panagiotis; Nguy, Austin; Castruita, Patricia A.; Seeley, William; Heinsen, Helmut; Ushizima, Daniela M.

2017-01-01

Background Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility. New method Dictionary learning and sparse coding allow for constructing improved cell representations using IF images. These models are input for detection and segmentation methods. Classification occurs by means of color distances between cells and a learned set. Results Our method successfully detected and classified cells in 49 human brain images. We evaluated our results regarding true positive, false positive, false negative, precision, recall, false positive rate and F1 score metrics. We also measured user-experience and time saved compared to manual countings. Comparison with existing methods We compared our results to four open-access IF-based cell-counting tools available in the literature. Our method showed improved accuracy for all data samples. Conclusion The proposed method satisfactorily detects and classifies cells from human postmortem brain IF images, with potential to be generalized for applications in other counting tasks. PMID:28267565

A computational framework for the detection of subcortical brain dysmaturation in neonatal MRI using 3D Convolutional Neural Networks.

PubMed

Ceschin, Rafael; Zahner, Alexandria; Reynolds, William; Gaesser, Jenna; Zuccoli, Giulio; Lo, Cecilia W; Gopalakrishnan, Vanathi; Panigrahy, Ashok

2018-05-21

Deep neural networks are increasingly being used in both supervised learning for classification tasks and unsupervised learning to derive complex patterns from the input data. However, the successful implementation of deep neural networks using neuroimaging datasets requires adequate sample size for training and well-defined signal intensity based structural differentiation. There is a lack of effective automated diagnostic tools for the reliable detection of brain dysmaturation in the neonatal period, related to small sample size and complex undifferentiated brain structures, despite both translational research and clinical importance. Volumetric information alone is insufficient for diagnosis. In this study, we developed a computational framework for the automated classification of brain dysmaturation from neonatal MRI, by combining a specific deep neural network implementation with neonatal structural brain segmentation as a method for both clinical pattern recognition and data-driven inference into the underlying structural morphology. We implemented three-dimensional convolution neural networks (3D-CNNs) to specifically classify dysplastic cerebelli, a subset of surface-based subcortical brain dysmaturation, in term infants born with congenital heart disease. We obtained a 0.985 ± 0. 0241-classification accuracy of subtle cerebellar dysplasia in CHD using 10-fold cross-validation. Furthermore, the hidden layer activations and class activation maps depicted regional vulnerability of the superior surface of the cerebellum, (composed of mostly the posterior lobe and the midline vermis), in regards to differentiating the dysplastic process from normal tissue. The posterior lobe and the midline vermis provide regional differentiation that is relevant to not only to the clinical diagnosis of cerebellar dysplasia, but also genetic mechanisms and neurodevelopmental outcome correlates. These findings not only contribute to the detection and classification of a subset of neonatal brain dysmaturation, but also provide insight to the pathogenesis of cerebellar dysplasia in CHD. In addition, this is one of the first examples of the application of deep learning to a neuroimaging dataset, in which the hidden layer activation revealed diagnostically and biologically relevant features about the clinical pathogenesis. The code developed for this project is open source, published under the BSD License, and designed to be generalizable to applications both within and beyond neonatal brain imaging. Copyright © 2018 Elsevier Inc. All rights reserved.

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

Behavioral state classification in epileptic brain using intracranial electrophysiology

NASA Astrophysics Data System (ADS)

Kremen, Vaclav; Duque, Juliano J.; Brinkmann, Benjamin H.; Berry, Brent M.; Kucewicz, Michal T.; Khadjevand, Fatemeh; Van Gompel, Jamie; Stead, Matt; St. Louis, Erik K.; Worrell, Gregory A.

2017-04-01

Objective. Automated behavioral state classification can benefit next generation implantable epilepsy devices. In this study we explored the feasibility of automated awake (AW) and slow wave sleep (SWS) classification using wide bandwidth intracranial EEG (iEEG) in patients undergoing evaluation for epilepsy surgery. Approach. Data from seven patients (age 34+/- 12 , 4 women) who underwent intracranial depth electrode implantation for iEEG monitoring were included. Spectral power features (0.1-600 Hz) spanning several frequency bands from a single electrode were used to train and test a support vector machine classifier. Main results. Classification accuracy of 97.8  ±  0.3% (normal tissue) and 89.4  ±  0.8% (epileptic tissue) across seven subjects using multiple spectral power features from a single electrode was achieved. Spectral power features from electrodes placed in normal temporal neocortex were found to be more useful (accuracy 90.8  ±  0.8%) for sleep-wake state classification than electrodes located in normal hippocampus (87.1  ±  1.6%). Spectral power in high frequency band features (Ripple (80-250 Hz), Fast Ripple (250-600 Hz)) showed comparable performance for AW and SWS classification as the best performing Berger bands (Alpha, Beta, low Gamma) with accuracy  ⩾90% using a single electrode contact and single spectral feature. Significance. Automated classification of wake and SWS should prove useful for future implantable epilepsy devices with limited computational power, memory, and number of electrodes. Applications include quantifying patient sleep patterns and behavioral state dependent detection, prediction, and electrical stimulation therapies.

Altered Gastrointestinal Function in the Neuroligin-3 Mouse Model of Autism

DTIC Science & Technology

2013-10-01

GABA neurotransmission in the brain. This work aims to examine the spatiotemporal distribution patterns of NL3 and related proteins and mRNA in gut ...implicated in ASD are upregulated during gut development presynaptic localization of the neuroligin-3 protein 16. SECURITY CLASSIFICATION OF: U...related proteins and mRNA in gut tissue from these mice. This project aims to determine biological mechanisms contributing to gastrointestinal dysfunction

Effect of slice thickness on brain magnetic resonance image texture analysis

PubMed Central

2010-01-01

Background The accuracy of texture analysis in clinical evaluation of magnetic resonance images depends considerably on imaging arrangements and various image quality parameters. In this paper, we study the effect of slice thickness on brain tissue texture analysis using a statistical approach and classification of T1-weighted images of clinically confirmed multiple sclerosis patients. Methods We averaged the intensities of three consecutive 1-mm slices to simulate 3-mm slices. Two hundred sixty-four texture parameters were calculated for both the original and the averaged slices. Wilcoxon's signed ranks test was used to find differences between the regions of interest representing white matter and multiple sclerosis plaques. Linear and nonlinear discriminant analyses were applied with several separate training and test sets to determine the actual classification accuracy. Results Only moderate differences in distributions of the texture parameter value for 1-mm and simulated 3-mm-thick slices were found. Our study also showed that white matter areas are well separable from multiple sclerosis plaques even if the slice thickness differs between training and test sets. Conclusions Three-millimeter-thick magnetic resonance image slices acquired with a 1.5 T clinical magnetic resonance scanner seem to be sufficient for texture analysis of multiple sclerosis plaques and white matter tissue. PMID:20955567

Classifying magnetic resonance image modalities with convolutional neural networks

NASA Astrophysics Data System (ADS)

Remedios, Samuel; Pham, Dzung L.; Butman, John A.; Roy, Snehashis

2018-02-01

Magnetic Resonance (MR) imaging allows the acquisition of images with different contrast properties depending on the acquisition protocol and the magnetic properties of tissues. Many MR brain image processing techniques, such as tissue segmentation, require multiple MR contrasts as inputs, and each contrast is treated differently. Thus it is advantageous to automate the identification of image contrasts for various purposes, such as facilitating image processing pipelines, and managing and maintaining large databases via content-based image retrieval (CBIR). Most automated CBIR techniques focus on a two-step process: extracting features from data and classifying the image based on these features. We present a novel 3D deep convolutional neural network (CNN)- based method for MR image contrast classification. The proposed CNN automatically identifies the MR contrast of an input brain image volume. Specifically, we explored three classification problems: (1) identify T1-weighted (T1-w), T2-weighted (T2-w), and fluid-attenuated inversion recovery (FLAIR) contrasts, (2) identify pre vs postcontrast T1, (3) identify pre vs post-contrast FLAIR. A total of 3418 image volumes acquired from multiple sites and multiple scanners were used. To evaluate each task, the proposed model was trained on 2137 images and tested on the remaining 1281 images. Results showed that image volumes were correctly classified with 97.57% accuracy.

Distinction of brain tissue, low grade and high grade glioma with time-resolved fluorescence spectroscopy

PubMed Central

Yong, William H.; Butte, Pramod V.; Pikul, Brian K.; Jo, Javier A.; Fang, Qiyin; Papaioannou, Thanassis; Black, Keith L.; Marcu, Laura

2010-01-01

Neuropathology frozen section diagnoses are difficult in part because of the small tissue samples and the paucity of adjunctive rapid intraoperative stains. This study aims to explore the use of time-resolved laser-induced fluorescence spectroscopy as a rapid adjunctive tool for the diagnosis of glioma specimens and for distinction of glioma from normal tissues intraoperatively. Ten low grade gliomas, 15 high grade gliomas without necrosis, 6 high grade gliomas with necrosis and/or radiation effect, and 14 histologically uninvolved “normal” brain specimens are spectroscopicaly analyzed and contrasted. Tissue autofluorescence was induced with a pulsed Nitrogen laser (337 nm, 1.2 ns) and the transient intensity decay profiles were recorded in the 370-500 nm spectral range with a fast digitized (0.2 ns time resolution). Spectral intensities and time-dependent parameters derived from the time-resolved spectra of each site were used for tissue characterization. A linear discriminant analysis diagnostic algorithm was used for tissue classification. Both low and high grade gliomas can be distinguished from histologically uninvolved cerebral cortex and white matter with high accuracy (above 90%). In addition, the presence or absence of treatment effect and/or necrosis can be identified in high grade gliomas. Taking advantage of tissue autofluorescence, this technique facilitates a direct and rapid investigation of surgically obtained tissue. PMID:16368511

Fiber-probe optical spectroscopy discriminates normal brain from focal cortical dysplasia in pediatric subjects

NASA Astrophysics Data System (ADS)

Anand, Suresh; Cicchi, Riccardo; Giordano, Flavio; Conti, Valerio; Buccoliero, Anna Maria; Guerrini, Renzo; Pavone, Francesco S.

2017-02-01

Focal cortical dysplasia (FCD) is an abnormality in the cerebral cortex that is caused by malformations during cortical development. Currently, magnetic resonance imaging (MRI) and electro-corticography (ECoG) are used for detecting FCD. On the downside, MRI is very much insensitive to small malformations in the brain, while ECoG is an invasive and time consuming procedure. Recently, optical techniques were widely exploited as a minimally invasive and quantitative approaches for disease diagnosis. These techniques include fluorescence and Raman spectroscopy. The aim of this investigation is to study the diagnostic performances of optical spectroscopy incorporating fluorescence (at 378 nm and 445 nm excitation wavelengths) and Raman spectroscopy (at 785 nm excitation) for the discrimination of FCD from normal brain in pediatric subjects. The study included 10 normal and 17 FCD tissue sites from 3 normal and 7 FCD samples. The emission spectra of FCD at 378 nm excitation wavelength presented a blue-shifted peak with respect to normal tissue. Prominent spectral differences between normal and FCD tissue were observed at 1298 cm-1, 1302 cm-1, 1445 cm-1 and 1660 cm-1 using Raman spectroscopy. Tissue classification models were developed using a multivariate statistical method, principal component analysis. This study demonstrates that a combined spectroscopic approach can provide a better diagnostic capability for classifying normal and FCD tissues. Further, the implementation of the technology within a fiber probe could open the way for in vivo diagnostics and intra-operative surgical guidance.

Age-specific MRI templates for pediatric neuroimaging

PubMed Central

Sanchez, Carmen E.; Richards, John E.; Almli, C. Robert

2012-01-01

This study created a database of pediatric age-specific MRI brain templates for normalization and segmentation. Participants included children from 4.5 through 19.5 years, totaling 823 scans from 494 subjects. Open-source processing programs (FSL, SPM, ANTS) constructed head, brain and segmentation templates in 6 month intervals. The tissue classification (WM, GM, CSF) showed changes over age similar to previous reports. A volumetric analysis of age-related changes in WM and GM based on these templates showed expected increase/decrease pattern in GM and an increase in WM over the sampled ages. This database is available for use for neuroimaging studies (blindedforreview). PMID:22799759

Sci-Thur PM - Colourful Interactions: Highlights 04: A Fast Quantitative MRI Acquisition and Processing Pipeline for Radiation Treatment Planning and Simulation

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

Jutras, Jean-David

MRI-only Radiation Treatment Planning (RTP) is becoming increasingly popular because of a simplified work-flow, and less inconvenience to the patient who avoids multiple scans. The advantages of MRI-based RTP over traditional CT-based RTP lie in its superior soft-tissue contrast, and absence of ionizing radiation dose. The lack of electron-density information in MRI can be addressed by automatic tissue classification. To distinguish bone from air, which both appear dark in MRI, an ultra-short echo time (UTE) pulse sequence may be used. Quantitative MRI parametric maps can provide improved tissue segmentation/classification and better sensitivity in monitoring disease progression and treatment outcome thanmore » standard weighted images. Superior tumor contrast can be achieved on pure T{sub 1} images compared to conventional T{sub 1}-weighted images acquired in the same scan duration and voxel resolution. In this study, we have developed a robust and fast quantitative MRI acquisition and post-processing work-flow that integrates these latest advances into the MRI-based RTP of brain lesions. Using 3D multi-echo FLASH images at two different optimized flip angles (both acquired in under 9 min, and 1mm isotropic resolution), parametric maps of T{sub 1}, proton-density (M{sub 0}), and T{sub 2}{sup *} are obtained with high contrast-to-noise ratio, and negligible geometrical distortions, water-fat shifts and susceptibility effects. An additional 3D UTE MRI dataset is acquired (in under 4 min) and post-processed to classify tissues for dose simulation. The pipeline was tested on four healthy volunteers and a clinical trial on brain cancer patients is underway.« less

Oligoastrocytoma

MedlinePlus

... Pineal Tumor Pituitary Tumor PNET Schwannoma 2016 WHO Classification Risk Factors Brain Tumor Facts Brain Tumor Dictionary ... Pineal Tumor Pituitary Tumor PNET Schwannoma 2016 WHO Classification Risk Factors Brain Tumor Facts Brain Tumor Dictionary ...

Oligodendroglioma

MedlinePlus

... Pineal Tumor Pituitary Tumor PNET Schwannoma 2016 WHO Classification Risk Factors Brain Tumor Facts Brain Tumor Dictionary ... Pineal Tumor Pituitary Tumor PNET Schwannoma 2016 WHO Classification Risk Factors Brain Tumor Facts Brain Tumor Dictionary ...

Preprocessing and meta-classification for brain-computer interfaces.

PubMed

Hammon, Paul S; de Sa, Virginia R

2007-03-01

A brain-computer interface (BCI) is a system which allows direct translation of brain states into actions, bypassing the usual muscular pathways. A BCI system works by extracting user brain signals, applying machine learning algorithms to classify the user's brain state, and performing a computer-controlled action. Our goal is to improve brain state classification. Perhaps the most obvious way to improve classification performance is the selection of an advanced learning algorithm. However, it is now well known in the BCI community that careful selection of preprocessing steps is crucial to the success of any classification scheme. Furthermore, recent work indicates that combining the output of multiple classifiers (meta-classification) leads to improved classification rates relative to single classifiers (Dornhege et al., 2004). In this paper, we develop an automated approach which systematically analyzes the relative contributions of different preprocessing and meta-classification approaches. We apply this procedure to three data sets drawn from BCI Competition 2003 (Blankertz et al., 2004) and BCI Competition III (Blankertz et al., 2006), each of which exhibit very different characteristics. Our final classification results compare favorably with those from past BCI competitions. Additionally, we analyze the relative contributions of individual preprocessing and meta-classification choices and discuss which types of BCI data benefit most from specific algorithms.

Multiresolution texture models for brain tumor segmentation in MRI.

PubMed

Iftekharuddin, Khan M; Ahmed, Shaheen; Hossen, Jakir

2011-01-01

In this study we discuss different types of texture features such as Fractal Dimension (FD) and Multifractional Brownian Motion (mBm) for estimating random structures and varying appearance of brain tissues and tumors in magnetic resonance images (MRI). We use different selection techniques including KullBack - Leibler Divergence (KLD) for ranking different texture and intensity features. We then exploit graph cut, self organizing maps (SOM) and expectation maximization (EM) techniques to fuse selected features for brain tumors segmentation in multimodality T1, T2, and FLAIR MRI. We use different similarity metrics to evaluate quality and robustness of these selected features for tumor segmentation in MRI for real pediatric patients. We also demonstrate a non-patient-specific automated tumor prediction scheme by using improved AdaBoost classification based on these image features.

Age-specific MRI brain and head templates for healthy adults from 20 through 89 years of age

PubMed Central

Fillmore, Paul T.; Phillips-Meek, Michelle C.; Richards, John E.

2015-01-01

This study created and tested a database of adult, age-specific MRI brain and head templates. The participants included healthy adults from 20 through 89 years of age. The templates were done in five-year, 10-year, and multi-year intervals from 20 through 89 years, and consist of average T1W for the head and brain, and segmenting priors for gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). It was found that age-appropriate templates provided less biased tissue classification estimates than age-inappropriate reference data and reference data based on young adult templates. This database is available for use by other investigators and clinicians for their MRI studies, as well as other types of neuroimaging and electrophysiological research.1 PMID:25904864

Accurate classification of brain gliomas by discriminate dictionary learning based on projective dictionary pair learning of proton magnetic resonance spectra.

PubMed

Adebileje, Sikiru Afolabi; Ghasemi, Keyvan; Aiyelabegan, Hammed Tanimowo; Saligheh Rad, Hamidreza

2017-04-01

Proton magnetic resonance spectroscopy is a powerful noninvasive technique that complements the structural images of cMRI, which aids biomedical and clinical researches, by identifying and visualizing the compositions of various metabolites within the tissues of interest. However, accurate classification of proton magnetic resonance spectroscopy is still a challenging issue in clinics due to low signal-to-noise ratio, overlapping peaks of metabolites, and the presence of background macromolecules. This paper evaluates the performance of a discriminate dictionary learning classifiers based on projective dictionary pair learning method for brain gliomas proton magnetic resonance spectroscopy spectra classification task, and the result were compared with the sub-dictionary learning methods. The proton magnetic resonance spectroscopy data contain a total of 150 spectra (74 healthy, 23 grade II, 23 grade III, and 30 grade IV) from two databases. The datasets from both databases were first coupled together, followed by column normalization. The Kennard-Stone algorithm was used to split the datasets into its training and test sets. Performance comparison based on the overall accuracy, sensitivity, specificity, and precision was conducted. Based on the overall accuracy of our classification scheme, the dictionary pair learning method was found to outperform the sub-dictionary learning methods 97.78% compared with 68.89%, respectively. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

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.

Grade classification of neuroepithelial tumors using high-resolution magic-angle spinning proton nuclear magnetic resonance spectroscopy and pattern recognition.

PubMed

Chen, WenXue; Lou, HaiYan; Zhang, HongPing; Nie, Xiu; Lan, WenXian; Yang, YongXia; Xiang, Yun; Qi, JianPin; Lei, Hao; Tang, HuiRu; Chen, FenEr; Deng, Feng

2011-07-01

Clinical data have shown that survival rates vary considerably among brain tumor patients, according to the type and grade of the tumor. Metabolite profiles of intact tumor tissues measured with high-resolution magic-angle spinning proton nuclear magnetic resonance spectroscopy (HRMAS (1)H NMRS) can provide important information on tumor biology and metabolism. These metabolic fingerprints can then be used for tumor classification and grading, with great potential value for tumor diagnosis. We studied the metabolic characteristics of 30 neuroepithelial tumor biopsies, including two astrocytomas (grade I), 12 astrocytomas (grade II), eight anaplastic astrocytomas (grade III), three glioblastomas (grade IV) and five medulloblastomas (grade IV) from 30 patients using HRMAS (1)H NMRS. The results were correlated with pathological features using multivariate data analysis, including principal component analysis (PCA). There were significant differences in the levels of N-acetyl-aspartate (NAA), creatine, myo-inositol, glycine and lactate between tumors of different grades (P<0.05). There were also significant differences in the ratios of NAA/creatine, lactate/creatine, myo-inositol/creatine, glycine/creatine, scyllo-inositol/creatine and alanine/creatine (P<0.05). A soft independent modeling of class analogy model produced a predictive accuracy of 87% for high-grade (grade III-IV) brain tumors with a sensitivity of 87% and a specificity of 93%. HRMAS (1)H NMR spectroscopy in conjunction with pattern recognition thus provides a potentially useful tool for the rapid and accurate classification of human brain tumor grades.

A discriminative model-constrained EM approach to 3D MRI brain tissue classification and intensity non-uniformity correction

NASA Astrophysics Data System (ADS)

Wels, Michael; Zheng, Yefeng; Huber, Martin; Hornegger, Joachim; Comaniciu, Dorin

2011-06-01

We describe a fully automated method for tissue classification, which is the segmentation into cerebral gray matter (GM), cerebral white matter (WM), and cerebral spinal fluid (CSF), and intensity non-uniformity (INU) correction in brain magnetic resonance imaging (MRI) volumes. It combines supervised MRI modality-specific discriminative modeling and unsupervised statistical expectation maximization (EM) segmentation into an integrated Bayesian framework. While both the parametric observation models and the non-parametrically modeled INUs are estimated via EM during segmentation itself, a Markov random field (MRF) prior model regularizes segmentation and parameter estimation. Firstly, the regularization takes into account knowledge about spatial and appearance-related homogeneity of segments in terms of pairwise clique potentials of adjacent voxels. Secondly and more importantly, patient-specific knowledge about the global spatial distribution of brain tissue is incorporated into the segmentation process via unary clique potentials. They are based on a strong discriminative model provided by a probabilistic boosting tree (PBT) for classifying image voxels. It relies on the surrounding context and alignment-based features derived from a probabilistic anatomical atlas. The context considered is encoded by 3D Haar-like features of reduced INU sensitivity. Alignment is carried out fully automatically by means of an affine registration algorithm minimizing cross-correlation. Both types of features do not immediately use the observed intensities provided by the MRI modality but instead rely on specifically transformed features, which are less sensitive to MRI artifacts. Detailed quantitative evaluations on standard phantom scans and standard real-world data show the accuracy and robustness of the proposed method. They also demonstrate relative superiority in comparison to other state-of-the-art approaches to this kind of computational task: our method achieves average Dice coefficients of 0.93 ± 0.03 (WM) and 0.90 ± 0.05 (GM) on simulated mono-spectral and 0.94 ± 0.02 (WM) and 0.92 ± 0.04 (GM) on simulated multi-spectral data from the BrainWeb repository. The scores are 0.81 ± 0.09 (WM) and 0.82 ± 0.06 (GM) and 0.87 ± 0.05 (WM) and 0.83 ± 0.12 (GM) for the two collections of real-world data sets—consisting of 20 and 18 volumes, respectively—provided by the Internet Brain Segmentation Repository.

A discriminative model-constrained EM approach to 3D MRI brain tissue classification and intensity non-uniformity correction.

PubMed

Wels, Michael; Zheng, Yefeng; Huber, Martin; Hornegger, Joachim; Comaniciu, Dorin

2011-06-07

We describe a fully automated method for tissue classification, which is the segmentation into cerebral gray matter (GM), cerebral white matter (WM), and cerebral spinal fluid (CSF), and intensity non-uniformity (INU) correction in brain magnetic resonance imaging (MRI) volumes. It combines supervised MRI modality-specific discriminative modeling and unsupervised statistical expectation maximization (EM) segmentation into an integrated Bayesian framework. While both the parametric observation models and the non-parametrically modeled INUs are estimated via EM during segmentation itself, a Markov random field (MRF) prior model regularizes segmentation and parameter estimation. Firstly, the regularization takes into account knowledge about spatial and appearance-related homogeneity of segments in terms of pairwise clique potentials of adjacent voxels. Secondly and more importantly, patient-specific knowledge about the global spatial distribution of brain tissue is incorporated into the segmentation process via unary clique potentials. They are based on a strong discriminative model provided by a probabilistic boosting tree (PBT) for classifying image voxels. It relies on the surrounding context and alignment-based features derived from a probabilistic anatomical atlas. The context considered is encoded by 3D Haar-like features of reduced INU sensitivity. Alignment is carried out fully automatically by means of an affine registration algorithm minimizing cross-correlation. Both types of features do not immediately use the observed intensities provided by the MRI modality but instead rely on specifically transformed features, which are less sensitive to MRI artifacts. Detailed quantitative evaluations on standard phantom scans and standard real-world data show the accuracy and robustness of the proposed method. They also demonstrate relative superiority in comparison to other state-of-the-art approaches to this kind of computational task: our method achieves average Dice coefficients of 0.93 ± 0.03 (WM) and 0.90 ± 0.05 (GM) on simulated mono-spectral and 0.94 ± 0.02 (WM) and 0.92 ± 0.04 (GM) on simulated multi-spectral data from the BrainWeb repository. The scores are 0.81 ± 0.09 (WM) and 0.82 ± 0.06 (GM) and 0.87 ± 0.05 (WM) and 0.83 ± 0.12 (GM) for the two collections of real-world data sets-consisting of 20 and 18 volumes, respectively-provided by the Internet Brain Segmentation Repository.

Spectroscopic optical coherence tomography for ex vivo brain tumor analysis

NASA Astrophysics Data System (ADS)

Lenz, Marcel; Krug, Robin; Dillmann, Christopher; Gerling, Alexandra; Gerhardt, Nils C.; Welp, Hubert; Schmieder, Kirsten; Hofmann, Martin R.

2017-02-01

For neurosurgeries precise tumor resection is essential for the subsequent recovery of the patients since nearby healthy tissue that may be harmed has a huge impact on the life quality after the surgery. However, so far no satisfying methodology has been established to assist the surgeon during surgery to distinguish between healthy and tumor tissue. Optical Coherence Tomography (OCT) potentially enables non-contact in vivo image acquisition at penetration depths of 1-2 mm with a resolution of approximately 1-15 μm. To analyze the potential of OCT for distinction between brain tumors and healthy tissue, we used a commercially available Thorlabs Callisto system to measure healthy tissue and meningioma samples ex vivo. All samples were measured with the OCT system and three dimensional datasets were generated. Afterwards they were sent to the pathology for staining with hematoxylin and eosin and then investigated with a bright field microscope to verify the tissue type. This is the actual gold standard for ex vivo analysis. The images taken by the OCT system exhibit variations in the structure for different tissue types, but these variations may not be objectively evaluated from raw OCT images. Since an automated distinction between tumor and healthy tissue would be highly desirable to guide the surgeon, we applied Spectroscopic Optical Coherence Tomography to further enhance the differences between the tissue types. Pattern recognition and machine learning algorithms were applied to classify the derived spectroscopic information. Finally, the classification results are analyzed in comparison to the histological analysis of the samples.

An algorithm for automatic parameter adjustment for brain extraction in BrainSuite

NASA Astrophysics Data System (ADS)

Rajagopal, Gautham; Joshi, Anand A.; Leahy, Richard M.

2017-02-01

Brain Extraction (classification of brain and non-brain tissue) of MRI brain images is a crucial pre-processing step necessary for imaging-based anatomical studies of the human brain. Several automated methods and software tools are available for performing this task, but differences in MR image parameters (pulse sequence, resolution) and instrumentand subject-dependent noise and artefacts affect the performance of these automated methods. We describe and evaluate a method that automatically adapts the default parameters of the Brain Surface Extraction (BSE) algorithm to optimize a cost function chosen to reflect accurate brain extraction. BSE uses a combination of anisotropic filtering, Marr-Hildreth edge detection, and binary morphology for brain extraction. Our algorithm automatically adapts four parameters associated with these steps to maximize the brain surface area to volume ratio. We evaluate the method on a total of 109 brain volumes with ground truth brain masks generated by an expert user. A quantitative evaluation of the performance of the proposed algorithm showed an improvement in the mean (s.d.) Dice coefficient from 0.8969 (0.0376) for default parameters to 0.9509 (0.0504) for the optimized case. These results indicate that automatic parameter optimization can result in significant improvements in definition of the brain mask.

Evaluation of endogenous species involved in brain tumors using multiphoton photoacoustic spectroscopy

NASA Astrophysics Data System (ADS)

Dahal, Sudhir; Cullum, Brian M.

2013-05-01

It has been shown that using non-resonant multiphoton photoacoustic spectroscopy (NMPPAS), excised brain tumor (grade III astrocytoma) and healthy tissue can be differentiated from each other, even in neighboring biopsy samples[1, 2]. Because of this, this powerful technique offers a great deal of potential for use as a surgical guidance technique for tumor margining with up to cellular level spatial resolution[3]. NMPPAS spectra are obtained by monitoring the non-radiative relaxation pathways via ultrasonic detection, following two-photon excitation with light in the optical diagnostic window (740nm-1100nm). Based upon significant differences in the ratiometric absorption of the tissues following 970nm and 1100nm excitation, a clear classification of the tissue can be made. These differences are the result of variations in composition and oxidation state of certain endogenous biochemical species between healthy and malignant tissues. In this work, NADH, NAD+ and ATP were measured using NMPPAS in model gelatin tissue phantoms to begin to understand which species might be responsible for the observed spectral differences in the tissue. Each species was placed in specific pH environments to provide control over the ratio of oxidized to reduced forms of the species. Ratiometric analyses were then conducted to account for variability caused due to instrumental parameters. This paper will discuss the potential roles of each of the species for tumor determination and their contribution to the spectral signature.

Concussion classification via deep learning using whole-brain white matter fiber strains

PubMed Central

Cai, Yunliang; Wu, Shaoju; Zhao, Wei; Li, Zhigang; Wu, Zheyang

2018-01-01

Developing an accurate and reliable injury predictor is central to the biomechanical studies of traumatic brain injury. State-of-the-art efforts continue to rely on empirical, scalar metrics based on kinematics or model-estimated tissue responses explicitly pre-defined in a specific brain region of interest. They could suffer from loss of information. A single training dataset has also been used to evaluate performance but without cross-validation. In this study, we developed a deep learning approach for concussion classification using implicit features of the entire voxel-wise white matter fiber strains. Using reconstructed American National Football League (NFL) injury cases, leave-one-out cross-validation was employed to objectively compare injury prediction performances against two baseline machine learning classifiers (support vector machine (SVM) and random forest (RF)) and four scalar metrics via univariate logistic regression (Brain Injury Criterion (BrIC), cumulative strain damage measure of the whole brain (CSDM-WB) and the corpus callosum (CSDM-CC), and peak fiber strain in the CC). Feature-based machine learning classifiers including deep learning, SVM, and RF consistently outperformed all scalar injury metrics across all performance categories (e.g., leave-one-out accuracy of 0.828–0.862 vs. 0.690–0.776, and .632+ error of 0.148–0.176 vs. 0.207–0.292). Further, deep learning achieved the best cross-validation accuracy, sensitivity, AUC, and .632+ error. These findings demonstrate the superior performances of deep learning in concussion prediction and suggest its promise for future applications in biomechanical investigations of traumatic brain injury. PMID:29795640

Concussion classification via deep learning using whole-brain white matter fiber strains.

PubMed

Cai, Yunliang; Wu, Shaoju; Zhao, Wei; Li, Zhigang; Wu, Zheyang; Ji, Songbai

2018-01-01

Developing an accurate and reliable injury predictor is central to the biomechanical studies of traumatic brain injury. State-of-the-art efforts continue to rely on empirical, scalar metrics based on kinematics or model-estimated tissue responses explicitly pre-defined in a specific brain region of interest. They could suffer from loss of information. A single training dataset has also been used to evaluate performance but without cross-validation. In this study, we developed a deep learning approach for concussion classification using implicit features of the entire voxel-wise white matter fiber strains. Using reconstructed American National Football League (NFL) injury cases, leave-one-out cross-validation was employed to objectively compare injury prediction performances against two baseline machine learning classifiers (support vector machine (SVM) and random forest (RF)) and four scalar metrics via univariate logistic regression (Brain Injury Criterion (BrIC), cumulative strain damage measure of the whole brain (CSDM-WB) and the corpus callosum (CSDM-CC), and peak fiber strain in the CC). Feature-based machine learning classifiers including deep learning, SVM, and RF consistently outperformed all scalar injury metrics across all performance categories (e.g., leave-one-out accuracy of 0.828-0.862 vs. 0.690-0.776, and .632+ error of 0.148-0.176 vs. 0.207-0.292). Further, deep learning achieved the best cross-validation accuracy, sensitivity, AUC, and .632+ error. These findings demonstrate the superior performances of deep learning in concussion prediction and suggest its promise for future applications in biomechanical investigations of traumatic brain injury.

Automatic segmentation of MR brain images of preterm infants using supervised classification.

PubMed

Moeskops, Pim; Benders, Manon J N L; Chiţ, Sabina M; Kersbergen, Karina J; Groenendaal, Floris; de Vries, Linda S; Viergever, Max A; Išgum, Ivana

2015-09-01

Preterm birth is often associated with impaired brain development. The state and expected progression of preterm brain development can be evaluated using quantitative assessment of MR images. Such measurements require accurate segmentation of different tissue types in those images. This paper presents an algorithm for the automatic segmentation of unmyelinated white matter (WM), cortical grey matter (GM), and cerebrospinal fluid in the extracerebral space (CSF). The algorithm uses supervised voxel classification in three subsequent stages. In the first stage, voxels that can easily be assigned to one of the three tissue types are labelled. In the second stage, dedicated analysis of the remaining voxels is performed. The first and the second stages both use two-class classification for each tissue type separately. Possible inconsistencies that could result from these tissue-specific segmentation stages are resolved in the third stage, which performs multi-class classification. A set of T1- and T2-weighted images was analysed, but the optimised system performs automatic segmentation using a T2-weighted image only. We have investigated the performance of the algorithm when using training data randomly selected from completely annotated images as well as when using training data from only partially annotated images. The method was evaluated on images of preterm infants acquired at 30 and 40weeks postmenstrual age (PMA). When the method was trained using random selection from the completely annotated images, the average Dice coefficients were 0.95 for WM, 0.81 for GM, and 0.89 for CSF on an independent set of images acquired at 30weeks PMA. When the method was trained using only the partially annotated images, the average Dice coefficients were 0.95 for WM, 0.78 for GM and 0.87 for CSF for the images acquired at 30weeks PMA, and 0.92 for WM, 0.80 for GM and 0.85 for CSF for the images acquired at 40weeks PMA. Even though the segmentations obtained using training data from the partially annotated images resulted in slightly lower Dice coefficients, the performance in all experiments was close to that of a second human expert (0.93 for WM, 0.79 for GM and 0.86 for CSF for the images acquired at 30weeks, and 0.94 for WM, 0.76 for GM and 0.87 for CSF for the images acquired at 40weeks). These results show that the presented method is robust to age and acquisition protocol and that it performs accurate segmentation of WM, GM, and CSF when the training data is extracted from complete annotations as well as when the training data is extracted from partial annotations only. This extends the applicability of the method by reducing the time and effort necessary to create training data in a population with different characteristics. Copyright © 2015 Elsevier Inc. All rights reserved.

Epigenetic Patterns of PTSD: DNA Methylation In Serum of OIF/OEF Servicemembers

DTIC Science & Technology

2011-01-01

ascertained via query of the International Classification of Diseases , 9th Revision (ICD-9) codes 290-320. To attempt to control for confounding by other...other CNS tissues is not clear. Although relevant to a different class of disease , many of the aberrations that have been detected in the DNA of...valuable diagnostic tool in various diseases . (50-53) Compared with cultured cells, clinical specimens, such as whole blood, serum, and even brain

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%.

Analysis of dual tree M-band wavelet transform based features for brain image classification.

PubMed

Ayalapogu, Ratna Raju; Pabboju, Suresh; Ramisetty, Rajeswara Rao

2018-04-29

The most complex organ in the human body is the brain. The unrestrained growth of cells in the brain is called a brain tumor. The cause of a brain tumor is still unknown and the survival rate is lower than other types of cancers. Hence, early detection is very important for proper treatment. In this study, an efficient computer-aided diagnosis (CAD) system is presented for brain image classification by analyzing MRI of the brain. At first, the MRI brain images of normal and abnormal categories are modeled by using the statistical features of dual tree m-band wavelet transform (DTMBWT). A maximum margin classifier, support vector machine (SVM) is then used for the classification and validated with k-fold approach. Results show that the system provides promising results on a repository of molecular brain neoplasia data (REMBRANDT) with 97.5% accuracy using 4 th level statistical features of DTMBWT. Viewing the experimental results, we conclude that the system gives a satisfactory performance for the brain image classification. © 2018 International Society for Magnetic Resonance in Medicine.

Applications of magnetic resonance image segmentation in neurology

NASA Astrophysics Data System (ADS)

Heinonen, Tomi; Lahtinen, Antti J.; Dastidar, Prasun; Ryymin, Pertti; Laarne, Paeivi; Malmivuo, Jaakko; Laasonen, Erkki; Frey, Harry; Eskola, Hannu

1999-05-01

After the introduction of digital imagin devices in medicine computerized tissue recognition and classification have become important in research and clinical applications. Segmented data can be applied among numerous research fields including volumetric analysis of particular tissues and structures, construction of anatomical modes, 3D visualization, and multimodal visualization, hence making segmentation essential in modern image analysis. In this research project several PC based software were developed in order to segment medical images, to visualize raw and segmented images in 3D, and to produce EEG brain maps in which MR images and EEG signals were integrated. The software package was tested and validated in numerous clinical research projects in hospital environment.

Ensemble Semi-supervised Frame-work for Brain Magnetic Resonance Imaging Tissue Segmentation.

PubMed

Azmi, Reza; Pishgoo, Boshra; Norozi, Narges; Yeganeh, Samira

2013-04-01

Brain magnetic resonance images (MRIs) tissue segmentation is one of the most important parts of the clinical diagnostic tools. Pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. Supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to obtain. Moreover, they cannot use unlabeled data to train classifiers. On the other hand, unsupervised segmentation methods have no prior knowledge and lead to low level of performance. However, semi-supervised learning which uses a few labeled data together with a large amount of unlabeled data causes higher accuracy with less trouble. In this paper, we propose an ensemble semi-supervised frame-work for segmenting of brain magnetic resonance imaging (MRI) tissues that it has been used results of several semi-supervised classifiers simultaneously. Selecting appropriate classifiers has a significant role in the performance of this frame-work. Hence, in this paper, we present two semi-supervised algorithms expectation filtering maximization and MCo_Training that are improved versions of semi-supervised methods expectation maximization and Co_Training and increase segmentation accuracy. Afterward, we use these improved classifiers together with graph-based semi-supervised classifier as components of the ensemble frame-work. Experimental results show that performance of segmentation in this approach is higher than both supervised methods and the individual semi-supervised classifiers.

Analysis of Zolpidem in Postmortem Fluids and Tissues Using Ultra-Performance Liquid Chromatography-Mass Spectrometry

DTIC Science & Technology

2014-02-01

16. Abstract Zolpidem is a nonbenzodiazepine sedative hypnotic drug used for the short-term treatment of insomnia. Its use is common and wide-spread...Classif. (of this page) 21. No. of Pages 22. Price Unclassified Unclassified 15 Form DOT F 1700.7 (8-72) Reproduction of completed page authorized iii...INTRODUCTION Zolpidem is a nonbenzodiazepine sedative hypnotic drug used to treat insomnia by slowing the activity in the brain. It is prescribed as a short

Tumor segmentation of multi-echo MR T2-weighted images with morphological operators

NASA Astrophysics Data System (ADS)

Torres, W.; Martín-Landrove, M.; Paluszny, M.; Figueroa, G.; Padilla, G.

2009-02-01

In the present work an automatic brain tumor segmentation procedure based on mathematical morphology is proposed. The approach considers sequences of eight multi-echo MR T2-weighted images. The relaxation time T2 characterizes the relaxation of water protons in the brain tissue: white matter, gray matter, cerebrospinal fluid (CSF) or pathological tissue. Image data is initially regularized by the application of a log-convex filter in order to adjust its geometrical properties to those of noiseless data, which exhibits monotonously decreasing convex behavior. Finally the regularized data is analyzed by means of an 8-dimensional morphological eccentricity filter. In a first stage, the filter was used for the spatial homogenization of the tissues in the image, replacing each pixel by the most representative pixel within its structuring element, i.e. the one which exhibits the minimum total distance to all members in the structuring element. On the filtered images, the relaxation time T2 is estimated by means of least square regression algorithm and the histogram of T2 is determined. The T2 histogram was partitioned using the watershed morphological operator; relaxation time classes were established and used for tissue classification and segmentation of the image. The method was validated on 15 sets of MRI data with excellent results.

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

MO-F-CAMPUS-J-05: Toward MRI-Only Radiotherapy: Novel Tissue Segmentation and Pseudo-CT Generation Techniques Based On T1 MRI Sequences

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

Aouadi, S; McGarry, M; Hammoud, R

Purpose: To develop and validate a 4 class tissue segmentation approach (air cavities, background, bone and soft-tissue) on T1 -weighted brain MRI and to create a pseudo-CT for MRI-only radiation therapy verification. Methods: Contrast-enhanced T1-weighted fast-spin-echo sequences (TR = 756ms, TE= 7.152ms), acquired on a 1.5T GE MRI-Simulator, are used.MRIs are firstly pre-processed to correct for non uniformity using the non parametric, non uniformity intensity normalization algorithm. Subsequently, a logarithmic inverse scaling log(1/image) is applied, prior to segmentation, to better differentiate bone and air from soft-tissues. Finally, the following method is enrolled to classify intensities into air cavities, background, bonemore » and soft-tissue:Thresholded region growing with seed points in image corners is applied to get a mask of Air+Bone+Background. The background is, afterward, separated by the scan-line filling algorithm. The air mask is extracted by morphological opening followed by a post-processing based on knowledge about air regions geometry. The remaining rough bone pre-segmentation is refined by applying 3D geodesic active contours; bone segmentation evolves by the sum of internal forces from contour geometry and external force derived from image gradient magnitude.Pseudo-CT is obtained by assigning −1000HU to air and background voxels, performing linear mapping of soft-tissue MR intensities in [-400HU, 200HU] and inverse linear mapping of bone MR intensities in [200HU, 1000HU]. Results: Three brain patients having registered MRI and CT are used for validation. CT intensities classification into 4 classes is performed by thresholding. Dice and misclassification errors are quantified. Correct classifications for soft-tissue, bone, and air are respectively 89.67%, 77.8%, and 64.5%. Dice indices are acceptable for bone (0.74) and soft-tissue (0.91) but low for air regions (0.48). Pseudo-CT produces DRRs with acceptable clinical visual agreement to CT-based DRR. Conclusion: The proposed approach makes it possible to use T1-weighted MRI to generate accurate pseudo-CT from 4-class segmentation.« less

Classification of multiple sclerosis lesions using adaptive dictionary learning.

PubMed

Deshpande, Hrishikesh; Maurel, Pierre; Barillot, Christian

2015-12-01

This paper presents a sparse representation and an adaptive dictionary learning based method for automated classification of multiple sclerosis (MS) lesions in magnetic resonance (MR) images. Manual delineation of MS lesions is a time-consuming task, requiring neuroradiology experts to analyze huge volume of MR data. This, in addition to the high intra- and inter-observer variability necessitates the requirement of automated MS lesion classification methods. Among many image representation models and classification methods that can be used for such purpose, we investigate the use of sparse modeling. In the recent years, sparse representation has evolved as a tool in modeling data using a few basis elements of an over-complete dictionary and has found applications in many image processing tasks including classification. We propose a supervised classification approach by learning dictionaries specific to the lesions and individual healthy brain tissues, which include white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The size of the dictionaries learned for each class plays a major role in data representation but it is an even more crucial element in the case of competitive classification. Our approach adapts the size of the dictionary for each class, depending on the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients. The results demonstrate the effectiveness of our approach in MS lesion classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

Brain MRI analysis for Alzheimer's disease diagnosis using an ensemble system of deep convolutional neural networks.

PubMed

Islam, Jyoti; Zhang, Yanqing

2018-05-31

Alzheimer's disease is an incurable, progressive neurological brain disorder. Earlier detection of Alzheimer's disease can help with proper treatment and prevent brain tissue damage. Several statistical and machine learning models have been exploited by researchers for Alzheimer's disease diagnosis. Analyzing magnetic resonance imaging (MRI) is a common practice for Alzheimer's disease diagnosis in clinical research. Detection of Alzheimer's disease is exacting due to the similarity in Alzheimer's disease MRI data and standard healthy MRI data of older people. Recently, advanced deep learning techniques have successfully demonstrated human-level performance in numerous fields including medical image analysis. We propose a deep convolutional neural network for Alzheimer's disease diagnosis using brain MRI data analysis. While most of the existing approaches perform binary classification, our model can identify different stages of Alzheimer's disease and obtains superior performance for early-stage diagnosis. We conducted ample experiments to demonstrate that our proposed model outperformed comparative baselines on the Open Access Series of Imaging Studies dataset.

Visual brain activity patterns classification with simultaneous EEG-fMRI: A multimodal approach.

PubMed

Ahmad, Rana Fayyaz; Malik, Aamir Saeed; Kamel, Nidal; Reza, Faruque; Amin, Hafeez Ullah; Hussain, Muhammad

2017-01-01

Classification of the visual information from the brain activity data is a challenging task. Many studies reported in the literature are based on the brain activity patterns using either fMRI or EEG/MEG only. EEG and fMRI considered as two complementary neuroimaging modalities in terms of their temporal and spatial resolution to map the brain activity. For getting a high spatial and temporal resolution of the brain at the same time, simultaneous EEG-fMRI seems to be fruitful. In this article, we propose a new method based on simultaneous EEG-fMRI data and machine learning approach to classify the visual brain activity patterns. We acquired EEG-fMRI data simultaneously on the ten healthy human participants by showing them visual stimuli. Data fusion approach is used to merge EEG and fMRI data. Machine learning classifier is used for the classification purposes. Results showed that superior classification performance has been achieved with simultaneous EEG-fMRI data as compared to the EEG and fMRI data standalone. This shows that multimodal approach improved the classification accuracy results as compared with other approaches reported in the literature. The proposed simultaneous EEG-fMRI approach for classifying the brain activity patterns can be helpful to predict or fully decode the brain activity patterns.

Evaluation of an automatic brain segmentation method developed for neonates on adult MR brain images

NASA Astrophysics Data System (ADS)

Moeskops, Pim; Viergever, Max A.; Benders, Manon J. N. L.; Išgum, Ivana

2015-03-01

Automatic brain tissue segmentation is of clinical relevance in images acquired at all ages. The literature presents a clear distinction between methods developed for MR images of infants, and methods developed for images of adults. The aim of this work is to evaluate a method developed for neonatal images in the segmentation of adult images. The evaluated method employs supervised voxel classification in subsequent stages, exploiting spatial and intensity information. Evaluation was performed using images available within the MRBrainS13 challenge. The obtained average Dice coefficients were 85.77% for grey matter, 88.66% for white matter, 81.08% for cerebrospinal fluid, 95.65% for cerebrum, and 96.92% for intracranial cavity, currently resulting in the best overall ranking. The possibility of applying the same method to neonatal as well as adult images can be of great value in cross-sectional studies that include a wide age range.

Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks.

PubMed

Ertosun, Mehmet Günhan; Rubin, Daniel L

2015-01-01

Brain glioma is the most common primary malignant brain tumors in adults with different pathologic subtypes: Lower Grade Glioma (LGG) Grade II, Lower Grade Glioma (LGG) Grade III, and Glioblastoma Multiforme (GBM) Grade IV. The survival and treatment options are highly dependent of this glioma grade. We propose a deep learning-based, modular classification pipeline for automated grading of gliomas using digital pathology images. Whole tissue digitized images of pathology slides obtained from The Cancer Genome Atlas (TCGA) were used to train our deep learning modules. Our modular pipeline provides diagnostic quality statistics, such as precision, sensitivity and specificity, of the individual deep learning modules, and (1) facilitates training given the limited data in this domain, (2) enables exploration of different deep learning structures for each module, (3) leads to developing less complex modules that are simpler to analyze, and (4) provides flexibility, permitting use of single modules within the framework or use of other modeling or machine learning applications, such as probabilistic graphical models or support vector machines. Our modular approach helps us meet the requirements of minimum accuracy levels that are demanded by the context of different decision points within a multi-class classification scheme. Convolutional Neural Networks are trained for each module for each sub-task with more than 90% classification accuracies on validation data set, and achieved classification accuracy of 96% for the task of GBM vs LGG classification, 71% for further identifying the grade of LGG into Grade II or Grade III on independent data set coming from new patients from the multi-institutional repository.

Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks

PubMed Central

Ertosun, Mehmet Günhan; Rubin, Daniel L.

2015-01-01

Brain glioma is the most common primary malignant brain tumors in adults with different pathologic subtypes: Lower Grade Glioma (LGG) Grade II, Lower Grade Glioma (LGG) Grade III, and Glioblastoma Multiforme (GBM) Grade IV. The survival and treatment options are highly dependent of this glioma grade. We propose a deep learning-based, modular classification pipeline for automated grading of gliomas using digital pathology images. Whole tissue digitized images of pathology slides obtained from The Cancer Genome Atlas (TCGA) were used to train our deep learning modules. Our modular pipeline provides diagnostic quality statistics, such as precision, sensitivity and specificity, of the individual deep learning modules, and (1) facilitates training given the limited data in this domain, (2) enables exploration of different deep learning structures for each module, (3) leads to developing less complex modules that are simpler to analyze, and (4) provides flexibility, permitting use of single modules within the framework or use of other modeling or machine learning applications, such as probabilistic graphical models or support vector machines. Our modular approach helps us meet the requirements of minimum accuracy levels that are demanded by the context of different decision points within a multi-class classification scheme. Convolutional Neural Networks are trained for each module for each sub-task with more than 90% classification accuracies on validation data set, and achieved classification accuracy of 96% for the task of GBM vs LGG classification, 71% for further identifying the grade of LGG into Grade II or Grade III on independent data set coming from new patients from the multi-institutional repository. PMID:26958289

A subject-independent pattern-based Brain-Computer Interface

PubMed Central

Ray, Andreas M.; Sitaram, Ranganatha; Rana, Mohit; Pasqualotto, Emanuele; Buyukturkoglu, Korhan; Guan, Cuntai; Ang, Kai-Keng; Tejos, Cristián; Zamorano, Francisco; Aboitiz, Francisco; Birbaumer, Niels; Ruiz, Sergio

2015-01-01

While earlier Brain-Computer Interface (BCI) studies have mostly focused on modulating specific brain regions or signals, new developments in pattern classification of brain states are enabling real-time decoding and modulation of an entire functional network. The present study proposes a new method for real-time pattern classification and neurofeedback of brain states from electroencephalographic (EEG) signals. It involves the creation of a fused classification model based on the method of Common Spatial Patterns (CSPs) from data of several healthy individuals. The subject-independent model is then used to classify EEG data in real-time and provide feedback to new individuals. In a series of offline experiments involving training and testing of the classifier with individual data from 27 healthy subjects, a mean classification accuracy of 75.30% was achieved, demonstrating that the classification system at hand can reliably decode two types of imagery used in our experiments, i.e., happy emotional imagery and motor imagery. In a subsequent experiment it is shown that the classifier can be used to provide neurofeedback to new subjects, and that these subjects learn to “match” their brain pattern to that of the fused classification model in a few days of neurofeedback training. This finding can have important implications for future studies on neurofeedback and its clinical applications on neuropsychiatric disorders. PMID:26539089

Adipose Tissue Quantification by Imaging Methods: A Proposed Classification

PubMed Central

Shen, Wei; Wang, ZiMian; Punyanita, Mark; Lei, Jianbo; Sinav, Ahmet; Kral, John G.; Imielinska, Celina; Ross, Robert; Heymsfield, Steven B.

2007-01-01

Recent advances in imaging techniques and understanding of differences in the molecular biology of adipose tissue has rendered classical anatomy obsolete, requiring a new classification of the topography of adipose tissue. Adipose tissue is one of the largest body compartments, yet a classification that defines specific adipose tissue depots based on their anatomic location and related functions is lacking. The absence of an accepted taxonomy poses problems for investigators studying adipose tissue topography and its functional correlates. The aim of this review was to critically examine the literature on imaging of whole body and regional adipose tissue and to create the first systematic classification of adipose tissue topography. Adipose tissue terminology was examined in over 100 original publications. Our analysis revealed inconsistencies in the use of specific definitions, especially for the compartment termed “visceral” adipose tissue. This analysis leads us to propose an updated classification of total body and regional adipose tissue, providing a well-defined basis for correlating imaging studies of specific adipose tissue depots with molecular processes. PMID:12529479

Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI.

PubMed

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

2017-02-01

We propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI). The method is based on superpixel technique and classification of each superpixel. A number of novel image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomized trees (ERT) classifier is compared with support vector machine (SVM) to classify each superpixel into tumour and non-tumour. The proposed method is evaluated on two datasets: (1) Our own clinical dataset: 19 MRI FLAIR images of patients with gliomas of grade II to IV, and (2) BRATS 2012 dataset: 30 FLAIR images with 10 low-grade and 20 high-grade gliomas. The experimental results demonstrate the high detection and segmentation performance of the proposed method using ERT classifier. For our own cohort, the average detection sensitivity, balanced error rate and the Dice overlap measure for the segmented tumour against the ground truth are 89.48 %, 6 % and 0.91, respectively, while, for the BRATS dataset, the corresponding evaluation results are 88.09 %, 6 % and 0.88, respectively. This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.

Brain Decoding-Classification of Hand Written Digits from fMRI Data Employing Bayesian Networks

PubMed Central

Yargholi, Elahe'; Hossein-Zadeh, Gholam-Ali

2016-01-01

We are frequently exposed to hand written digits 0–9 in today's modern life. Success in decoding-classification of hand written digits helps us understand the corresponding brain mechanisms and processes and assists seriously in designing more efficient brain–computer interfaces. However, all digits belong to the same semantic category and similarity in appearance of hand written digits makes this decoding-classification a challenging problem. In present study, for the first time, augmented naïve Bayes classifier is used for classification of functional Magnetic Resonance Imaging (fMRI) measurements to decode the hand written digits which took advantage of brain connectivity information in decoding-classification. fMRI was recorded from three healthy participants, with an age range of 25–30. Results in different brain lobes (frontal, occipital, parietal, and temporal) show that utilizing connectivity information significantly improves decoding-classification and capability of different brain lobes in decoding-classification of hand written digits were compared to each other. In addition, in each lobe the most contributing areas and brain connectivities were determined and connectivities with short distances between their endpoints were recognized to be more efficient. Moreover, data driven method was applied to investigate the similarity of brain areas in responding to stimuli and this revealed both similarly active areas and active mechanisms during this experiment. Interesting finding was that during the experiment of watching hand written digits, there were some active networks (visual, working memory, motor, and language processing), but the most relevant one to the task was language processing network according to the voxel selection. PMID:27468261

Deep learning and texture-based semantic label fusion for brain tumor segmentation

NASA Astrophysics Data System (ADS)

Vidyaratne, L.; Alam, M.; Shboul, Z.; Iftekharuddin, K. M.

2018-02-01

Brain tumor segmentation is a fundamental step in surgical treatment and therapy. Many hand-crafted and learning based methods have been proposed for automatic brain tumor segmentation from MRI. Studies have shown that these approaches have their inherent advantages and limitations. This work proposes a semantic label fusion algorithm by combining two representative state-of-the-art segmentation algorithms: texture based hand-crafted, and deep learning based methods to obtain robust tumor segmentation. We evaluate the proposed method using publicly available BRATS 2017 brain tumor segmentation challenge dataset. The results show that the proposed method offers improved segmentation by alleviating inherent weaknesses: extensive false positives in texture based method, and the false tumor tissue classification problem in deep learning method, respectively. Furthermore, we investigate the effect of patient's gender on the segmentation performance using a subset of validation dataset. Note the substantial improvement in brain tumor segmentation performance proposed in this work has recently enabled us to secure the first place by our group in overall patient survival prediction task at the BRATS 2017 challenge.

Deep Learning and Texture-Based Semantic Label Fusion for Brain Tumor Segmentation.

PubMed

Vidyaratne, L; Alam, M; Shboul, Z; Iftekharuddin, K M

2018-01-01

Brain tumor segmentation is a fundamental step in surgical treatment and therapy. Many hand-crafted and learning based methods have been proposed for automatic brain tumor segmentation from MRI. Studies have shown that these approaches have their inherent advantages and limitations. This work proposes a semantic label fusion algorithm by combining two representative state-of-the-art segmentation algorithms: texture based hand-crafted, and deep learning based methods to obtain robust tumor segmentation. We evaluate the proposed method using publicly available BRATS 2017 brain tumor segmentation challenge dataset. The results show that the proposed method offers improved segmentation by alleviating inherent weaknesses: extensive false positives in texture based method, and the false tumor tissue classification problem in deep learning method, respectively. Furthermore, we investigate the effect of patient's gender on the segmentation performance using a subset of validation dataset. Note the substantial improvement in brain tumor segmentation performance proposed in this work has recently enabled us to secure the first place by our group in overall patient survival prediction task at the BRATS 2017 challenge.

The in Vitro Actions of Loxapine on Dopaminergic and Serotonergic Receptors. Time to Consider Atypical Classification of This Antipsychotic Drug?

PubMed Central

Ferreri, Florian; Drapier, Dominique; Baloche, Emmanuelle; Ouzid, Mehemed; Zimmer, Luc; Llorca, Pierre-Michel

2018-01-01

Abstract Background The denomination of typical antipsychotic for loxapine has poor relation to current knowledge of the molecule’s relevant modes of action. Materials and Methods Competition binding experiments were performed on expressed human recombinant receptors in CHO cells and HEK-293 cells for D1 to D5, 5-HT1A, 5-HT2A, 5-HT2C, 5-HT4, 5-HT6, and 5-HT7. In vitro autoradiographies using [11C]-Raclopride [18F]-Altanserin [18F]-MPPF [11C]-SB207145, and [18F]-2FNQ1P were measured in brain tissue of a male primate followed by addition of increasing doses of loxapine succinate. Results In cell cultures, the measured Kb confirmed high affinity of loxapine for the D2; intermediate affinity for the D1, D4, D5, 5-HT2C receptorsl and a lack of affinity toward D3, 5-HT1A, 5-HT4, 5-HT6, and 5-HT7 receptors. In brain tissue, PET autoradiographies showed a radiopharmaceutical displacement at low concentrations of loxapine on D2 and 5-HT2A receptors. Conclusion This preclinical study reveals that loxapine receptorial spectrum is close to an “atypical” profile (D2/5HT2A ratio, 1.14). Loxapine is rightly classified as a DS-RAn agent in the Neuroscience Based Nomenclature classification. PMID:29106549

Tissue classification for laparoscopic image understanding based on multispectral texture analysis

NASA Astrophysics Data System (ADS)

Zhang, Yan; Wirkert, Sebastian J.; Iszatt, Justin; Kenngott, Hannes; Wagner, Martin; Mayer, Benjamin; Stock, Christian; Clancy, Neil T.; Elson, Daniel S.; Maier-Hein, Lena

2016-03-01

Intra-operative tissue classification is one of the prerequisites for providing context-aware visualization in computer-assisted minimally invasive surgeries. As many anatomical structures are difficult to differentiate in conventional RGB medical images, we propose a classification method based on multispectral image patches. In a comprehensive ex vivo study we show (1) that multispectral imaging data is superior to RGB data for organ tissue classification when used in conjunction with widely applied feature descriptors and (2) that combining the tissue texture with the reflectance spectrum improves the classification performance. Multispectral tissue analysis could thus evolve as a key enabling technique in computer-assisted laparoscopy.

Brain tumor classification and segmentation using sparse coding and dictionary learning.

PubMed

Salman Al-Shaikhli, Saif Dawood; Yang, Michael Ying; Rosenhahn, Bodo

2016-08-01

This paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary consists of global image features (topological and texture features). The coupled dictionaries consist of coupled information: gray scale voxel values of the training image data and their associated label voxel values of the ground truth segmentation of the training data. For quantitative evaluation, the proposed framework is evaluated using different metrics. The segmentation results of the brain tumor segmentation (MICCAI-BraTS-2013) database are evaluated using five different metric scores, which are computed using the online evaluation tool provided by the BraTS-2013 challenge organizers. Experimental results demonstrate that the proposed approach achieves an accurate brain tumor classification and segmentation and outperforms the state-of-the-art methods.

Measuring the volume of brain tumour and determining its location in T2-weighted MRI images using hidden Markov random field: expectation maximization algorithm

NASA Astrophysics Data System (ADS)

Mat Jafri, Mohd. Zubir; Abdulbaqi, Hayder Saad; Mutter, Kussay N.; Mustapha, Iskandar Shahrim; Omar, Ahmad Fairuz

2017-06-01

A brain tumour is an abnormal growth of tissue in the brain. Most tumour volume measurement processes are carried out manually by the radiographer and radiologist without relying on any auto program. This manual method is a timeconsuming task and may give inaccurate results. Treatment, diagnosis, signs and symptoms of the brain tumours mainly depend on the tumour volume and its location. In this paper, an approach is proposed to improve volume measurement of brain tumors as well as using a new method to determine the brain tumour location. The current study presents a hybrid method that includes two methods. One method is hidden Markov random field - expectation maximization (HMRFEM), which employs a positive initial classification of the image. The other method employs the threshold, which enables the final segmentation. In this method, the tumour volume is calculated using voxel dimension measurements. The brain tumour location was determined accurately in T2- weighted MRI image using a new algorithm. According to the results, this process was proven to be more useful compared to the manual method. Thus, it provides the possibility of calculating the volume and determining location of a brain tumour.

Ensemble Semi-supervised Frame-work for Brain Magnetic Resonance Imaging Tissue Segmentation

PubMed Central

Azmi, Reza; Pishgoo, Boshra; Norozi, Narges; Yeganeh, Samira

2013-01-01

Brain magnetic resonance images (MRIs) tissue segmentation is one of the most important parts of the clinical diagnostic tools. Pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. Supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to obtain. Moreover, they cannot use unlabeled data to train classifiers. On the other hand, unsupervised segmentation methods have no prior knowledge and lead to low level of performance. However, semi-supervised learning which uses a few labeled data together with a large amount of unlabeled data causes higher accuracy with less trouble. In this paper, we propose an ensemble semi-supervised frame-work for segmenting of brain magnetic resonance imaging (MRI) tissues that it has been used results of several semi-supervised classifiers simultaneously. Selecting appropriate classifiers has a significant role in the performance of this frame-work. Hence, in this paper, we present two semi-supervised algorithms expectation filtering maximization and MCo_Training that are improved versions of semi-supervised methods expectation maximization and Co_Training and increase segmentation accuracy. Afterward, we use these improved classifiers together with graph-based semi-supervised classifier as components of the ensemble frame-work. Experimental results show that performance of segmentation in this approach is higher than both supervised methods and the individual semi-supervised classifiers. PMID:24098863

On the integrity of functional brain networks in schizophrenia, Parkinson's disease, and advanced age: Evidence from connectivity-based single-subject classification.

PubMed

Pläschke, Rachel N; Cieslik, Edna C; Müller, Veronika I; Hoffstaedter, Felix; Plachti, Anna; Varikuti, Deepthi P; Goosses, Mareike; Latz, Anne; Caspers, Svenja; Jockwitz, Christiane; Moebus, Susanne; Gruber, Oliver; Eickhoff, Claudia R; Reetz, Kathrin; Heller, Julia; Südmeyer, Martin; Mathys, Christian; Caspers, Julian; Grefkes, Christian; Kalenscher, Tobias; Langner, Robert; Eickhoff, Simon B

2017-12-01

Previous whole-brain functional connectivity studies achieved successful classifications of patients and healthy controls but only offered limited specificity as to affected brain systems. Here, we examined whether the connectivity patterns of functional systems affected in schizophrenia (SCZ), Parkinson's disease (PD), or normal aging equally translate into high classification accuracies for these conditions. We compared classification performance between pre-defined networks for each group and, for any given network, between groups. Separate support vector machine classifications of 86 SCZ patients, 80 PD patients, and 95 older adults relative to their matched healthy/young controls, respectively, were performed on functional connectivity in 12 task-based, meta-analytically defined networks using 25 replications of a nested 10-fold cross-validation scheme. Classification performance of the various networks clearly differed between conditions, as those networks that best classified one disease were usually non-informative for the other. For SCZ, but not PD, emotion-processing, empathy, and cognitive action control networks distinguished patients most accurately from controls. For PD, but not SCZ, networks subserving autobiographical or semantic memory, motor execution, and theory-of-mind cognition yielded the best classifications. In contrast, young-old classification was excellent based on all networks and outperformed both clinical classifications. Our pattern-classification approach captured associations between clinical and developmental conditions and functional network integrity with a higher level of specificity than did previous whole-brain analyses. Taken together, our results support resting-state connectivity as a marker of functional dysregulation in specific networks known to be affected by SCZ and PD, while suggesting that aging affects network integrity in a more global way. Hum Brain Mapp 38:5845-5858, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Magnetic resonance imaging-based cerebral tissue classification reveals distinct spatiotemporal patterns of changes after stroke in non-human primates.

PubMed

Bouts, Mark J R J; Westmoreland, Susan V; de Crespigny, Alex J; Liu, Yutong; Vangel, Mark; Dijkhuizen, Rick M; Wu, Ona; D'Arceuil, Helen E

2015-12-15

Spatial and temporal changes in brain tissue after acute ischemic stroke are still poorly understood. Aims of this study were three-fold: (1) to determine unique temporal magnetic resonance imaging (MRI) patterns at the acute, subacute and chronic stages after stroke in macaques by combining quantitative T2 and diffusion MRI indices into MRI 'tissue signatures', (2) to evaluate temporal differences in these signatures between transient (n = 2) and permanent (n = 2) middle cerebral artery occlusion, and (3) to correlate histopathology findings in the chronic stroke period to the acute and subacute MRI derived tissue signatures. An improved iterative self-organizing data analysis algorithm was used to combine T2, apparent diffusion coefficient (ADC), and fractional anisotropy (FA) maps across seven successive timepoints (1, 2, 3, 24, 72, 144, 240 h) which revealed five temporal MRI signatures, that were different from the normal tissue pattern (P < 0.001). The distribution of signatures between brains with permanent and transient occlusions varied significantly between groups (P < 0.001). Qualitative comparisons with histopathology revealed that these signatures represented regions with different histopathology. Two signatures identified areas of progressive injury marked by severe necrosis and the presence of gitter cells. Another signature identified less severe but pronounced neuronal and axonal degeneration, while the other signatures depicted tissue remodeling with vascular proliferation and astrogliosis. These exploratory results demonstrate the potential of temporally and spatially combined voxel-based methods to generate tissue signatures that may correlate with distinct histopathological features. The identification of distinct ischemic MRI signatures associated with specific tissue fates may further aid in assessing and monitoring the efficacy of novel pharmaceutical treatments for stroke in a pre-clinical and clinical setting.

A Ternary Hybrid EEG-NIRS Brain-Computer Interface for the Classification of Brain Activation Patterns during Mental Arithmetic, Motor Imagery, and Idle State.

PubMed

Shin, Jaeyoung; Kwon, Jinuk; Im, Chang-Hwan

2018-01-01

The performance of a brain-computer interface (BCI) can be enhanced by simultaneously using two or more modalities to record brain activity, which is generally referred to as a hybrid BCI. To date, many BCI researchers have tried to implement a hybrid BCI system by combining electroencephalography (EEG) and functional near-infrared spectroscopy (NIRS) to improve the overall accuracy of binary classification. However, since hybrid EEG-NIRS BCI, which will be denoted by hBCI in this paper, has not been applied to ternary classification problems, paradigms and classification strategies appropriate for ternary classification using hBCI are not well investigated. Here we propose the use of an hBCI for the classification of three brain activation patterns elicited by mental arithmetic, motor imagery, and idle state, with the aim to elevate the information transfer rate (ITR) of hBCI by increasing the number of classes while minimizing the loss of accuracy. EEG electrodes were placed over the prefrontal cortex and the central cortex, and NIRS optodes were placed only on the forehead. The ternary classification problem was decomposed into three binary classification problems using the "one-versus-one" (OVO) classification strategy to apply the filter-bank common spatial patterns filter to EEG data. A 10 × 10-fold cross validation was performed using shrinkage linear discriminant analysis (sLDA) to evaluate the average classification accuracies for EEG-BCI, NIRS-BCI, and hBCI when the meta-classification method was adopted to enhance classification accuracy. The ternary classification accuracies for EEG-BCI, NIRS-BCI, and hBCI were 76.1 ± 12.8, 64.1 ± 9.7, and 82.2 ± 10.2%, respectively. The classification accuracy of the proposed hBCI was thus significantly higher than those of the other BCIs ( p < 0.005). The average ITR for the proposed hBCI was calculated to be 4.70 ± 1.92 bits/minute, which was 34.3% higher than that reported for a previous binary hBCI study.

Cupping artifact correction and automated classification for high-resolution dedicated breast CT images.

PubMed

Yang, Xiaofeng; Wu, Shengyong; Sechopoulos, Ioannis; Fei, Baowei

2012-10-01

To develop and test an automated algorithm to classify the different tissues present in dedicated breast CT images. The original CT images are first corrected to overcome cupping artifacts, and then a multiscale bilateral filter is used to reduce noise while keeping edge information on the images. As skin and glandular tissues have similar CT values on breast CT images, morphologic processing is used to identify the skin mask based on its position information. A modified fuzzy C-means (FCM) classification method is then used to classify breast tissue as fat and glandular tissue. By combining the results of the skin mask with the FCM, the breast tissue is classified as skin, fat, and glandular tissue. To evaluate the authors' classification method, the authors use Dice overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on eight patient images. The correction method was able to correct the cupping artifacts and improve the quality of the breast CT images. For glandular tissue, the overlap ratios between the authors' automatic classification and manual segmentation were 91.6% ± 2.0%. A cupping artifact correction method and an automatic classification method were applied and evaluated for high-resolution dedicated breast CT images. Breast tissue classification can provide quantitative measurements regarding breast composition, density, and tissue distribution.

Cupping artifact correction and automated classification for high-resolution dedicated breast CT images

PubMed Central

Yang, Xiaofeng; Wu, Shengyong; Sechopoulos, Ioannis; Fei, Baowei

2012-01-01

Purpose: To develop and test an automated algorithm to classify the different tissues present in dedicated breast CT images. Methods: The original CT images are first corrected to overcome cupping artifacts, and then a multiscale bilateral filter is used to reduce noise while keeping edge information on the images. As skin and glandular tissues have similar CT values on breast CT images, morphologic processing is used to identify the skin mask based on its position information. A modified fuzzy C-means (FCM) classification method is then used to classify breast tissue as fat and glandular tissue. By combining the results of the skin mask with the FCM, the breast tissue is classified as skin, fat, and glandular tissue. To evaluate the authors’ classification method, the authors use Dice overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on eight patient images. Results: The correction method was able to correct the cupping artifacts and improve the quality of the breast CT images. For glandular tissue, the overlap ratios between the authors’ automatic classification and manual segmentation were 91.6% ± 2.0%. Conclusions: A cupping artifact correction method and an automatic classification method were applied and evaluated for high-resolution dedicated breast CT images. Breast tissue classification can provide quantitative measurements regarding breast composition, density, and tissue distribution. PMID:23039675

Enhancing the discrimination accuracy between metastases, gliomas and meningiomas on brain MRI by volumetric textural features and ensemble pattern recognition methods.

PubMed

Georgiadis, Pantelis; Cavouras, Dionisis; Kalatzis, Ioannis; Glotsos, Dimitris; Athanasiadis, Emmanouil; Kostopoulos, Spiros; Sifaki, Koralia; Malamas, Menelaos; Nikiforidis, George; Solomou, Ekaterini

2009-01-01

Three-dimensional (3D) texture analysis of volumetric brain magnetic resonance (MR) images has been identified as an important indicator for discriminating among different brain pathologies. The purpose of this study was to evaluate the efficiency of 3D textural features using a pattern recognition system in the task of discriminating benign, malignant and metastatic brain tissues on T1 postcontrast MR imaging (MRI) series. The dataset consisted of 67 brain MRI series obtained from patients with verified and untreated intracranial tumors. The pattern recognition system was designed as an ensemble classification scheme employing a support vector machine classifier, specially modified in order to integrate the least squares features transformation logic in its kernel function. The latter, in conjunction with using 3D textural features, enabled boosting up the performance of the system in discriminating metastatic, malignant and benign brain tumors with 77.14%, 89.19% and 93.33% accuracy, respectively. The method was evaluated using an external cross-validation process; thus, results might be considered indicative of the generalization performance of the system to "unseen" cases. The proposed system might be used as an assisting tool for brain tumor characterization on volumetric MRI series.

The new WHO 2016 classification of brain tumors-what neurosurgeons need to know.

PubMed

Banan, Rouzbeh; Hartmann, Christian

2017-03-01

The understanding of molecular alterations of tumors has severely changed the concept of classification in all fields of pathology. The availability of high-throughput technologies such as next-generation sequencing allows for a much more precise definition of tumor entities. Also in the field of brain tumors a dramatic increase of knowledge has occurred over the last years partially calling into question the purely morphologically based concepts that were used as exclusive defining criteria in the WHO 2007 classification. Review of the WHO 2016 classification of brain tumors as well as a search and review of publications in the literature relevant for brain tumor classification from 2007 up to now. The idea of incorporating the molecular features in classifying tumors of the central nervous system led the authors of the new WHO 2016 classification to encounter inevitable conceptual problems, particularly with respect to linking morphology to molecular alterations. As a solution they introduced the concept of a "layered diagnosis" to the classification of brain tumors that still allows at a lower level a purely morphologically based diagnosis while partially forcing the incorporation of molecular characteristics for an "integrated diagnosis" at the highest diagnostic level. In this context the broad availability of molecular assays was debated. On the one hand molecular antibodies specifically targeting mutated proteins should be available in nearly all neuropathological laboratories. On the other hand, different high-throughput assays are accessible only in few first-world neuropathological institutions. As examples oligodendrogliomas are now primarily defined by molecular characteristics since the required assays are generally established, whereas molecular grouping of ependymomas, found to clearly outperform morphologically based tumor interpretation, was rejected from inclusion in the WHO 2016 classification because the required assays are currently only established in a small number of institutions. In summary, while neuropathologists have now encountered various challenges in the transitional phase from the previous WHO 2007 version to the new WHO 2016 classification of brain tumors, clinical neurooncologists now face many new diagnoses allowing a clearly improved understanding that could offer them more effective therapeutic opportunities in neurooncological treatment. The new WHO 2016 classification presumably presents the highest number of modifications since the initial WHO classification of 1979 and thereby forces all professionals in the field of neurooncology to intensively understand the new concepts. This review article aims to present the basic concepts of the new WHO 2016 brain tumor classification for neurosurgeons with a focus on neurooncology.

A Unique Four-Hub Protein Cluster Associates to Glioblastoma Progression

PubMed Central

Simeone, Pasquale; Trerotola, Marco; Urbanella, Andrea; Lattanzio, Rossano; Ciavardelli, Domenico; Di Giuseppe, Fabrizio; Eleuterio, Enrica; Sulpizio, Marilisa; Eusebi, Vincenzo; Pession, Annalisa; Piantelli, Mauro; Alberti, Saverio

2014-01-01

Gliomas are the most frequent brain tumors. Among them, glioblastomas are malignant and largely resistant to available treatments. Histopathology is the gold standard for classification and grading of brain tumors. However, brain tumor heterogeneity is remarkable and histopathology procedures for glioma classification remain unsatisfactory for predicting disease course as well as response to treatment. Proteins that tightly associate with cancer differentiation and progression, can bear important prognostic information. Here, we describe the identification of protein clusters differentially expressed in high-grade versus low-grade gliomas. Tissue samples from 25 high-grade tumors, 10 low-grade tumors and 5 normal brain cortices were analyzed by 2D-PAGE and proteomic profiling by mass spectrometry. This led to identify 48 differentially expressed protein markers between tumors and normal samples. Protein clustering by multivariate analyses (PCA and PLS-DA) provided discrimination between pathological samples to an unprecedented extent, and revealed a unique network of deranged proteins. We discovered a novel glioblastoma control module centered on four major network hubs: Huntingtin, HNF4α, c-Myc and 14-3-3ζ. Immunohistochemistry, western blotting and unbiased proteome-wide meta-analysis revealed altered expression of this glioblastoma control module in human glioma samples as compared with normal controls. Moreover, the four-hub network was found to cross-talk with both p53 and EGFR pathways. In summary, the findings of this study indicate the existence of a unifying signaling module controlling glioblastoma pathogenesis and malignant progression, and suggest novel targets for development of diagnostic and therapeutic procedures. PMID:25050814

A review of classification algorithms for EEG-based brain-computer interfaces.

PubMed

Lotte, F; Congedo, M; Lécuyer, A; Lamarche, F; Arnaldi, B

2007-06-01

In this paper we review classification algorithms used to design brain-computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.

Application of machine learning methods to describe the effects of conjugated equine estrogens therapy on region-specific brain volumes.

PubMed

Casanova, Ramon; Espeland, Mark A; Goveas, Joseph S; Davatzikos, Christos; Gaussoin, Sarah A; Maldjian, Joseph A; Brunner, Robert L; Kuller, Lewis H; Johnson, Karen C; Mysiw, W Jerry; Wagner, Benjamin; Resnick, Susan M

2011-05-01

Use of conjugated equine estrogens (CEE) has been linked to smaller regional brain volumes in women aged ≥65 years; however, it is unknown whether this results in a broad-based characteristic pattern of effects. Structural magnetic resonance imaging was used to assess regional volumes of normal tissue and ischemic lesions among 513 women who had been enrolled in a randomized clinical trial of CEE therapy for an average of 6.6 years, beginning at ages 65-80 years. A multivariate pattern analysis, based on a machine learning technique that combined Random Forest and logistic regression with L(1) penalty, was applied to identify patterns among regional volumes associated with therapy and whether patterns discriminate between treatment groups. The multivariate pattern analysis detected smaller regional volumes of normal tissue within the limbic and temporal lobes among women that had been assigned to CEE therapy. Mean decrements ranged as high as 7% in the left entorhinal cortex and 5% in the left perirhinal cortex, which exceeded the effect sizes reported previously in frontal lobe and hippocampus. Overall accuracy of classification based on these patterns, however, was projected to be only 54.5%. Prescription of CEE therapy for an average of 6.6 years is associated with lower regional brain volumes, but it does not induce a characteristic spatial pattern of changes in brain volumes of sufficient magnitude to discriminate users and nonusers. Copyright © 2011 Elsevier Inc. All rights reserved.

Application of machine learning methods to describe the effects of conjugated equine estrogens therapy on region-specific brain volumes

PubMed Central

Casanova, Ramon; Espeland, Mark A.; Goveas, Joseph S.; Davatzikos, Christos; Gaussoin, Sarah A.; Maldjian, Joseph A.; Brunner, Robert L.; Kuller, Lewis H.; Johnson, Karen C.; Mysiw, W. Jerry; Wagner, Benjamin; Resnick, Susan M.

2011-01-01

Use of conjugated equine estrogens (CEE) has been linked to smaller regional brain volumes in women aged ≥65 years, however it is unknown whether this results in a broad-based characteristic pattern of effects. Structural MRI was used to assess regional volumes of normal tissue and ischemic lesions among 513 women who had been enrolled in a randomized clinical trial of CEE therapy for an average of 6.6 years, beginning at ages 65-80 years. A multivariate pattern analysis, based on a machine learning technique that combined Random Forest and logistic regression with L1 penalty, was applied to identify patterns among regional volumes associated with therapy and whether patterns discriminate between treatment groups. The multivariate pattern analysis detected smaller regional volumes of normal tissue within the limbic and temporal lobes among women that had been assigned to CEE therapy. Mean decrements ranged as high as 7% in the left entorhinal cortex and 5% in the left perirhinal cortex, which exceeded the effect sizes reported previously in frontal lobe and hippocampus. Overall accuracy of classification based on these patterns, however, was projected to be only 54.5%. Prescription of CEE therapy for an average of 6.6 years is associated with lower regional brain volumes, but it does not induce a characteristic spatial pattern of changes in brain volumes of sufficient magnitude to discriminate users and non-users. PMID:21292420

Neurodegenerative dementias: From MR Physics lab to assessment room

NASA Astrophysics Data System (ADS)

Bruno, S. D.; Cercignani, M.; Wheeler-Kingshott, C. A. M.

2012-11-01

Theimpact of neuroimaging on the study and understanding of dementing illnesses has been enormous. Here we review the main MR structural technical developments applied to Alzheimer's disease and fronto-temporal dementia, two forms of neurodegenerative disorders that have a number of similarities but also several differences. The possibility of detecting increasingly subtle brain changes, together with the need of handling larger and larger data sets, keeping up with the ever expanding aging population, are perhaps the main driving forces behind recent MR technique developments in the field of dementia. The measurement of atrophy is now integrated by more advanced approaches, investigating the alterations of the architecture of brain tissues beyond pure volumetric loss. Brain connectivity is now studied in vivo with techniques such as diffusion tensor imaging and tractography. Also, automated methods of subject classification open up new possibilities of rapid and cost-effective diagnosis. The inter-disciplinary efforts are changing the clinical scenario of dementia care from one of helpless defeat to one of promising innovation.

Subnetwork mining on functional connectivity network for classification of minimal hepatic encephalopathy.

PubMed

Zhang, Daoqiang; Tu, Liyang; Zhang, Long-Jiang; Jie, Biao; Lu, Guang-Ming

2018-06-01

Hepatic encephalopathy (HE), as a complication of cirrhosis, is a serious brain disease, which may lead to death. Accurate diagnosis of HE and its intermediate stage, i.e., minimal HE (MHE), is very important for possibly early diagnosis and treatment. Brain connectivity network, as a simple representation of brain interaction, has been widely used for the brain disease (e.g., HE and MHE) analysis. However, those studies mainly focus on finding disease-related abnormal connectivity between brain regions, although a large number of studies have indicated that some brain diseases are usually related to local structure of brain connectivity network (i.e., subnetwork), rather than solely on some single brain regions or connectivities. Also, mining such disease-related subnetwork is a challenging task because of the complexity of brain network. To address this problem, we proposed a novel frequent-subnetwork-based method to mine disease-related subnetworks for MHE classification. Specifically, we first mine frequent subnetworks from both groups, i.e., MHE patients and non-HE (NHE) patients, respectively. Then we used the graph-kernel based method to select the most discriminative subnetworks for subsequent classification. We evaluate our proposed method on a MHE dataset with 77 cirrhosis patients, including 38 MHE patients and 39 NHE patients. The results demonstrate that our proposed method can not only obtain the improved classification performance in comparison with state-of-the-art network-based methods, but also identify disease-related subnetworks which can help us better understand the pathology of the brain diseases.

New approaches for measuring changes in the cortical surface using an automatic reconstruction algorithm

NASA Astrophysics Data System (ADS)

Pham, Dzung L.; Han, Xiao; Rettmann, Maryam E.; Xu, Chenyang; Tosun, Duygu; Resnick, Susan; Prince, Jerry L.

2002-05-01

In previous work, the authors presented a multi-stage procedure for the semi-automatic reconstruction of the cerebral cortex from magnetic resonance images. This method suffered from several disadvantages. First, the tissue classification algorithm used can be sensitive to noise within the image. Second, manual interaction was required for masking out undesired regions of the brain image, such as the ventricles and putamen. Third, iterated median filters were used to perform a topology correction on the initial cortical surface, resulting in an overly smoothed initial surface. Finally, the deformable surface used to converge to the cortex had difficulty capturing narrow gyri. In this work, all four disadvantages of the procedure have been addressed. A more robust tissue classification algorithm is employed and the manual masking step is replaced by an automatic method involving level set deformable models. Instead of iterated median filters, an algorithm developed specifically for topology correction is used. The last disadvantage is addressed using an algorithm that artificially separates adjacent sulcal banks. The new procedure is more automated but also more accurate than the previous one. Its utility is demonstrated by performing a preliminary study on data from the Baltimore Longitudinal Study of Aging.

Subtle volume differences in brain parenchyma of children surviving medulloblastoma

NASA Astrophysics Data System (ADS)

Reddick, Wilburn E.; Mulhern, Raymond K.; Elkin, T. David; Glass, John O.; Langston, James W.

1998-07-01

The overriding incentive for accurate quantification of the functional status of children treated for brain tumors emerges from the clinician's desire to balance the efficacy and chronic toxicity of therapies used for the developing child. A hybrid combination of the Kohonen self-organizing map (SOM) for segmentation and a multilayer backpropagation (MLBP) neural network for classification removes observer variances to yield a reproducible and accurate identification of tissues. A group of 17 volunteers and 77 patients from a larger ongoing study of pediatric patients with brain tumors were used to investigate the sensitivity of segmented volumes to determine atrophy as measured by two radiologists. The atrophy study revealed a significant relationship for brain parenchyma, CSF and white matter volumes with atrophy while gray matter had no significant relationship. Brain parenchyma and subsequently white matter were found to be inversely proportional to increasing grades of atrophy. An additional study compared fifteen age-matched patients treated with irradiation and surgery with patients treated with surgery alone. The age-matched study of patients demonstrated that brain volumes in the irradiated patients were significantly decreased compared to those treated with surgery alone. Further investigation of this difference revealed that white matter was significantly reduced while gray matter was relatively unchanged.

Schwannoma

MedlinePlus

... Classification Risk Factors Brain Tumor Facts Brain Tumor Dictionary Webinars Anytime Learning About Us Our Founders Board ... Factors Brain Tumor Statistics ABTA Publications Brain Tumor Dictionary Upcoming Webinars Anytime Learning Brain Tumor Educational Presentations ...

Optimal design of a bank of spatio-temporal filters for EEG signal classification.

PubMed

Higashi, Hiroshi; Tanaka, Toshihisa

2011-01-01

The spatial weights for electrodes called common spatial pattern (CSP) are known to be effective in EEG signal classification for motor imagery based brain computer interfaces (MI-BCI). To achieve accurate classification in CSP, the frequency filter should be properly designed. To this end, several methods for designing the filter have been proposed. However, the existing methods cannot consider plural brain activities described with different frequency bands and different spatial patterns such as activities of mu and beta rhythms. In order to efficiently extract these brain activities, we propose a method to design plural filters and spatial weights which extract desired brain activity. The proposed method designs finite impulse response (FIR) filters and the associated spatial weights by optimization of an objective function which is a natural extension of CSP. Moreover, we show by a classification experiment that the bank of FIR filters which are designed by introducing an orthogonality into the objective function can extract good discriminative features. Moreover, the experiment result suggests that the proposed method can automatically detect and extract brain activities related to motor imagery.

Banking brain tissue for research.

PubMed

Klioueva, Natasja; Bovenberg, Jasper; Huitinga, Inge

2017-01-01

Well-characterized human brain tissue is crucial for scientific breakthroughs in research of the human brain and brain diseases. However, the collection, characterization, management, and accessibility of brain human tissue are rather complex. Well-characterized human brain tissue is often provided from private, sometimes small, brain tissue collections by (neuro)pathologic experts. However, to meet the increasing demand for human brain tissue from the scientific community, many professional brain-banking activities aiming at both neurologic and psychiatric diseases as well as healthy controls are currently being initiated worldwide. Professional biobanks are open-access and in many cases run donor programs. They are therefore costly and need effective business plans to guarantee long-term sustainability. Here we discuss the ethical, legal, managerial, and financial aspects of professional brain banks. Copyright © 2017 Elsevier B.V. All rights reserved.

Tissue classification using depth-dependent ultrasound time series analysis: in-vitro animal study

NASA Astrophysics Data System (ADS)

Imani, Farhad; Daoud, Mohammad; Moradi, Mehdi; Abolmaesumi, Purang; Mousavi, Parvin

2011-03-01

Time series analysis of ultrasound radio-frequency (RF) signals has been shown to be an effective tissue classification method. Previous studies of this method for tissue differentiation at high and clinical-frequencies have been reported. In this paper, analysis of RF time series is extended to improve tissue classification at the clinical frequencies by including novel features extracted from the time series spectrum. The primary feature examined is the Mean Central Frequency (MCF) computed for regions of interest (ROIs) in the tissue extending along the axial axis of the transducer. In addition, the intercept and slope of a line fitted to the MCF-values of the RF time series as a function of depth have been included. To evaluate the accuracy of the new features, an in vitro animal study is performed using three tissue types: bovine muscle, bovine liver, and chicken breast, where perfect two-way classification is achieved. The results show statistically significant improvements over the classification accuracies with previously reported features.

Choroid Plexus

MedlinePlus

... Classification Risk Factors Brain Tumor Facts Brain Tumor Dictionary Webinars Anytime Learning About Us Our Founders Board ... Factors Brain Tumor Statistics ABTA Publications Brain Tumor Dictionary Upcoming Webinars Anytime Learning Brain Tumor Educational Presentations ...

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.

Brain tumor segmentation based on local independent projection-based classification.

PubMed

Huang, Meiyan; Yang, Wei; Wu, Yao; Jiang, Jun; Chen, Wufan; Feng, Qianjin

2014-10-01

Brain tumor segmentation is an important procedure for early tumor diagnosis and radiotherapy planning. Although numerous brain tumor segmentation methods have been presented, enhancing tumor segmentation methods is still challenging because brain tumor MRI images exhibit complex characteristics, such as high diversity in tumor appearance and ambiguous tumor boundaries. To address this problem, we propose a novel automatic tumor segmentation method for MRI images. This method treats tumor segmentation as a classification problem. Additionally, the local independent projection-based classification (LIPC) method is used to classify each voxel into different classes. A novel classification framework is derived by introducing the local independent projection into the classical classification model. Locality is important in the calculation of local independent projections for LIPC. Locality is also considered in determining whether local anchor embedding is more applicable in solving linear projection weights compared with other coding methods. Moreover, LIPC considers the data distribution of different classes by learning a softmax regression model, which can further improve classification performance. In this study, 80 brain tumor MRI images with ground truth data are used as training data and 40 images without ground truth data are used as testing data. The segmentation results of testing data are evaluated by an online evaluation tool. The average dice similarities of the proposed method for segmenting complete tumor, tumor core, and contrast-enhancing tumor on real patient data are 0.84, 0.685, and 0.585, respectively. These results are comparable to other state-of-the-art methods.

Characterization of groups using composite kernels and multi-source fMRI analysis data: application to schizophrenia

PubMed Central

Castro, Eduardo; Martínez-Ramón, Manel; Pearlson, Godfrey; Sui, Jing; Calhoun, Vince D.

2011-01-01

Pattern classification of brain imaging data can enable the automatic detection of differences in cognitive processes of specific groups of interest. Furthermore, it can also give neuroanatomical information related to the regions of the brain that are most relevant to detect these differences by means of feature selection procedures, which are also well-suited to deal with the high dimensionality of brain imaging data. This work proposes the application of recursive feature elimination using a machine learning algorithm based on composite kernels to the classification of healthy controls and patients with schizophrenia. This framework, which evaluates nonlinear relationships between voxels, analyzes whole-brain fMRI data from an auditory task experiment that is segmented into anatomical regions and recursively eliminates the uninformative ones based on their relevance estimates, thus yielding the set of most discriminative brain areas for group classification. The collected data was processed using two analysis methods: the general linear model (GLM) and independent component analysis (ICA). GLM spatial maps as well as ICA temporal lobe and default mode component maps were then input to the classifier. A mean classification accuracy of up to 95% estimated with a leave-two-out cross-validation procedure was achieved by doing multi-source data classification. In addition, it is shown that the classification accuracy rate obtained by using multi-source data surpasses that reached by using single-source data, hence showing that this algorithm takes advantage of the complimentary nature of GLM and ICA. PMID:21723948

Classifying High-noise EEG in Complex Environments for Brain-computer Interaction Technologies

DTIC Science & Technology

2012-02-01

differentiation in the brain signal that our classification approach seeks to identify despite the noise in the recorded EEG signal and the complexity of...performed two offline classifications , one using BCILab (1), the other using LibSVM (2). Distinct classifiers were trained for each individual in...order to improve individual classifier performance (3). The highest classification performance results were obtained using individual frequency bands

Effect of vitro preservation on mechanical properties of brain tissue

NASA Astrophysics Data System (ADS)

Zhang, Wei; Liu, Yi-fan; Liu, Li-fu; Niu, Ying; Ma, Jian-li; Wu, Cheng-wei

2017-05-01

To develop the protective devices for preventing traumatic brain injuries, it requires the accurate characterization of the mechanical properties of brain tissue. For this, it necessary to elucidate the effect of vitro preservation on the mechanical performance of brain tissue as usually the measurements are carried out in vitro. In this paper, the thermal behavior of brain tissue preserved for various period of time was first investigated and the mechanical properties were also measured. Both reveals the deterioration with prolonged preservation duration. The observations of brain tissue slices indicates the brain tissue experiences karyorrhexis and karyorrhexis in sequence, which accounts for the deterioration phenomena.

Improving the Performance of the Prony Method Using a Wavelet Domain Filter for MRI Denoising

PubMed Central

Lentini, Marianela; Paluszny, Marco

2014-01-01

The Prony methods are used for exponential fitting. We use a variant of the Prony method for abnormal brain tissue detection in sequences of T 2 weighted magnetic resonance images. Here, MR images are considered to be affected only by Rician noise, and a new wavelet domain bilateral filtering process is implemented to reduce the noise in the images. This filter is a modification of Kazubek's algorithm and we use synthetic images to show the ability of the new procedure to suppress noise and compare its performance with respect to the original filter, using quantitative and qualitative criteria. The tissue classification process is illustrated using a real sequence of T 2 MR images, and the filter is applied to each image before using the variant of the Prony method. PMID:24834108

Improving the performance of the prony method using a wavelet domain filter for MRI denoising.

PubMed

Jaramillo, Rodney; Lentini, Marianela; Paluszny, Marco

2014-01-01

The Prony methods are used for exponential fitting. We use a variant of the Prony method for abnormal brain tissue detection in sequences of T 2 weighted magnetic resonance images. Here, MR images are considered to be affected only by Rician noise, and a new wavelet domain bilateral filtering process is implemented to reduce the noise in the images. This filter is a modification of Kazubek's algorithm and we use synthetic images to show the ability of the new procedure to suppress noise and compare its performance with respect to the original filter, using quantitative and qualitative criteria. The tissue classification process is illustrated using a real sequence of T 2 MR images, and the filter is applied to each image before using the variant of the Prony method.

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.

Cysts

MedlinePlus

... Pituitary Tumor PNET Schwannoma 2016 WHO Classification Risk Factors Brain Tumor Facts Brain Tumor Dictionary Webinars Anytime Learning About Us Our Founders Board of Directors Staff Leadership Strategic Plan Financials News Careers Brain Tumor Information Brain Anatomy Brain ...

Astrocytoma

MedlinePlus

... Pituitary Tumor PNET Schwannoma 2016 WHO Classification Risk Factors Brain Tumor Facts Brain Tumor Dictionary Webinars Anytime Learning About Us Our Founders Board of Directors Staff Leadership Strategic Plan Financials News Careers Brain Tumor Information Brain Anatomy Brain ...

Chondrosarcoma

MedlinePlus

... Pituitary Tumor PNET Schwannoma 2016 WHO Classification Risk Factors Brain Tumor Facts Brain Tumor Dictionary Webinars Anytime Learning About Us Our Founders Board of Directors Staff Leadership Strategic Plan Financials News Careers Brain Tumor Information Brain Anatomy Brain ...

Ependymoma

MedlinePlus

... Pituitary Tumor PNET Schwannoma 2016 WHO Classification Risk Factors Brain Tumor Facts Brain Tumor Dictionary Webinars Anytime Learning About Us Our Founders Board of Directors Staff Leadership Strategic Plan Financials News Careers Brain Tumor Information Brain Anatomy Brain ...

Iatrogenic Bone and Soft Tissue Trauma in Robotic-Arm Assisted Total Knee Arthroplasty Compared With Conventional Jig-Based Total Knee Arthroplasty: A Prospective Cohort Study and Validation of a New Classification System.

PubMed

Kayani, Babar; Konan, Sujith; Pietrzak, Jurek R T; Haddad, Fares S

2018-03-27

The objective of this study was to compare macroscopic bone and soft tissue injury between robotic-arm assisted total knee arthroplasty (RA-TKA) and conventional jig-based total knee arthroplasty (CJ-TKA) and create a validated classification system for reporting iatrogenic bone and periarticular soft tissue injury after TKA. This study included 30 consecutive CJ-TKAs followed by 30 consecutive RA-TKAs performed by a single surgeon. Intraoperative photographs of the femur, tibia, and periarticular soft tissues were taken before implantation of prostheses. Using these outcomes, the macroscopic soft tissue injury (MASTI) classification system was developed to grade iatrogenic bone and soft tissue injuries. Interobserver and Intraobserver validity of the proposed classification system was assessed. Patients undergoing RA-TKA had reduced medial soft tissue injury in both passively correctible (P < .05) and noncorrectible varus deformities (P < .05); more pristine femoral (P < .05) and tibial (P < .05) bone resection cuts; and improved MASTI scores compared to CJ-TKA (P < .05). There was high interobserver (intraclass correlation coefficient 0.92 [95% confidence interval: 0.88-0.96], P < .05) and intraobserver agreement (intraclass correlation coefficient 0.94 [95% confidence interval: 0.92-0.97], P < .05) of the proposed MASTI classification system. There is reduced bone and periarticular soft tissue injury in patients undergoing RA-TKA compared to CJ-TKA. The proposed MASTI classification system is a reproducible grading scheme for describing iatrogenic bone and soft tissue injury in TKA. RA-TKA is associated with reduced bone and soft tissue injury compared with conventional jig-based TKA. The proposed MASTI classification may facilitate further research correlating macroscopic soft tissue injury during TKA to long-term clinical and functional outcomes. Copyright © 2018 Elsevier Inc. All rights reserved.

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

EEG classification of emotions using emotion-specific brain functional network.

PubMed

Gonuguntla, V; Shafiq, G; Wang, Y; Veluvolu, K C

2015-08-01

The brain functional network perspective forms the basis to relate mechanisms of brain functions. This work analyzes the network mechanisms related to human emotion based on synchronization measure - phase-locking value in EEG to formulate the emotion specific brain functional network. Based on network dissimilarities between emotion and rest tasks, most reactive channel pairs and the reactive band corresponding to emotions are identified. With the identified most reactive pairs, the subject-specific functional network is formed. The identified subject-specific and emotion-specific dynamic network pattern show significant synchrony variation in line with the experiment protocol. The same network pattern are then employed for classification of emotions. With the study conducted on the 4 subjects, an average classification accuracy of 62 % was obtained with the proposed technique.

Effect of filtration of signals of brain activity on quality of recognition of brain activity patterns using artificial intelligence methods

NASA Astrophysics Data System (ADS)

Hramov, Alexander E.; Frolov, Nikita S.; Musatov, Vyachaslav Yu.

2018-02-01

In present work we studied features of the human brain states classification, corresponding to the real movements of hands and legs. For this purpose we used supervised learning algorithm based on feed-forward artificial neural networks (ANNs) with error back-propagation along with the support vector machine (SVM) method. We compared the quality of operator movements classification by means of EEG signals obtained experimentally in the absence of preliminary processing and after filtration in different ranges up to 25 Hz. It was shown that low-frequency filtering of multichannel EEG data significantly improved accuracy of operator movements classification.

Comparison of Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia.

PubMed

Tohka, Jussi; Moradi, Elaheh; Huttunen, Heikki

2016-07-01

We present a comparative split-half resampling analysis of various data driven feature selection and classification methods for the whole brain voxel-based classification analysis of anatomical magnetic resonance images. We compared support vector machines (SVMs), with or without filter based feature selection, several embedded feature selection methods and stability selection. While comparisons of the accuracy of various classification methods have been reported previously, the variability of the out-of-training sample classification accuracy and the set of selected features due to independent training and test sets have not been previously addressed in a brain imaging context. We studied two classification problems: 1) Alzheimer's disease (AD) vs. normal control (NC) and 2) mild cognitive impairment (MCI) vs. NC classification. In AD vs. NC classification, the variability in the test accuracy due to the subject sample did not vary between different methods and exceeded the variability due to different classifiers. In MCI vs. NC classification, particularly with a large training set, embedded feature selection methods outperformed SVM-based ones with the difference in the test accuracy exceeding the test accuracy variability due to the subject sample. The filter and embedded methods produced divergent feature patterns for MCI vs. NC classification that suggests the utility of the embedded feature selection for this problem when linked with the good generalization performance. The stability of the feature sets was strongly correlated with the number of features selected, weakly correlated with the stability of classification accuracy, and uncorrelated with the average classification accuracy.

Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline

PubMed Central

Fan, Yong; Batmanghelich, Nematollah; Clark, Chris M.; Davatzikos, Christos

2010-01-01

Spatial patterns of brain atrophy in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) were measured via methods of computational neuroanatomy. These patterns were spatially complex and involved many brain regions. In addition to the hippocampus and the medial temporal lobe gray matter, a number of other regions displayed significant atrophy, including orbitofrontal and medial-prefrontal grey matter, cingulate (mainly posterior), insula, uncus, and temporal lobe white matter. Approximately 2/3 of the MCI group presented patterns of atrophy that overlapped with AD, whereas the remaining 1/3 overlapped with cognitively normal individuals, thereby indicating that some, but not all, MCI patients have significant and extensive brain atrophy in this cohort of MCI patients. Importantly, the group with AD-like patterns presented much higher rate of MMSE decline in follow-up visits; conversely, pattern classification provided relatively high classification accuracy (87%) of the individuals that presented relatively higher MMSE decline within a year from baseline. High-dimensional pattern classification, a nonlinear multivariate analysis, provided measures of structural abnormality that can potentially be useful for individual patient classification, as well as for predicting progression and examining multivariate relationships in group analyses. PMID:18053747

Artificial neural network detects human uncertainty

NASA Astrophysics Data System (ADS)

Hramov, Alexander E.; Frolov, Nikita S.; Maksimenko, Vladimir A.; Makarov, Vladimir V.; Koronovskii, Alexey A.; Garcia-Prieto, Juan; Antón-Toro, Luis Fernando; Maestú, Fernando; Pisarchik, Alexander N.

2018-03-01

Artificial neural networks (ANNs) are known to be a powerful tool for data analysis. They are used in social science, robotics, and neurophysiology for solving tasks of classification, forecasting, pattern recognition, etc. In neuroscience, ANNs allow the recognition of specific forms of brain activity from multichannel EEG or MEG data. This makes the ANN an efficient computational core for brain-machine systems. However, despite significant achievements of artificial intelligence in recognition and classification of well-reproducible patterns of neural activity, the use of ANNs for recognition and classification of patterns in neural networks still requires additional attention, especially in ambiguous situations. According to this, in this research, we demonstrate the efficiency of application of the ANN for classification of human MEG trials corresponding to the perception of bistable visual stimuli with different degrees of ambiguity. We show that along with classification of brain states associated with multistable image interpretations, in the case of significant ambiguity, the ANN can detect an uncertain state when the observer doubts about the image interpretation. With the obtained results, we describe the possible application of ANNs for detection of bistable brain activity associated with difficulties in the decision-making process.

Window classification of brain CT images in biomedical articles.

PubMed

Xue, Zhiyun; Antani, Sameer; Long, L Rodney; Demner-Fushman, Dina; Thoma, George R

2012-01-01

Effective capability to search biomedical articles based on visual properties of article images may significantly augment information retrieval in the future. In this paper, we present a new method to classify the window setting types of brain CT images. Windowing is a technique frequently used in the evaluation of CT scans, and is used to enhance contrast for the particular tissue or abnormality type being evaluated. In particular, it provides radiologists with an enhanced view of certain types of cranial abnormalities, such as the skull lesions and bone dysplasia which are usually examined using the " bone window" setting and illustrated in biomedical articles using "bone window images". Due to the inherent large variations of images among articles, it is important that the proposed method is robust. Our algorithm attained 90% accuracy in classifying images as bone window or non-bone window in a 210 image data set.

GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation.

PubMed

Bakas, Spyridon; Zeng, Ke; Sotiras, Aristeidis; Rathore, Saima; Akbari, Hamed; Gaonkar, Bilwaj; Rozycki, Martin; Pati, Sarthak; Davatzikos, Christos

2016-01-01

We present an approach for segmenting low- and high-grade gliomas in multimodal magnetic resonance imaging volumes. The proposed approach is based on a hybrid generative-discriminative model. Firstly, a generative approach based on an Expectation-Maximization framework that incorporates a glioma growth model is used to segment the brain scans into tumor, as well as healthy tissue labels. Secondly, a gradient boosting multi-class classification scheme is used to refine tumor labels based on information from multiple patients. Lastly, a probabilistic Bayesian strategy is employed to further refine and finalize the tumor segmentation based on patient-specific intensity statistics from the multiple modalities. We evaluated our approach in 186 cases during the training phase of the BRAin Tumor Segmentation (BRATS) 2015 challenge and report promising results. During the testing phase, the algorithm was additionally evaluated in 53 unseen cases, achieving the best performance among the competing methods.

Structural brain changes versus self-report: machine-learning classification of chronic fatigue syndrome patients.

PubMed

Sevel, Landrew S; Boissoneault, Jeff; Letzen, Janelle E; Robinson, Michael E; Staud, Roland

2018-05-30

Chronic fatigue syndrome (CFS) is a disorder associated with fatigue, pain, and structural/functional abnormalities seen during magnetic resonance brain imaging (MRI). Therefore, we evaluated the performance of structural MRI (sMRI) abnormalities in the classification of CFS patients versus healthy controls and compared it to machine learning (ML) classification based upon self-report (SR). Participants included 18 CFS patients and 15 healthy controls (HC). All subjects underwent T1-weighted sMRI and provided visual analogue-scale ratings of fatigue, pain intensity, anxiety, depression, anger, and sleep quality. sMRI data were segmented using FreeSurfer and 61 regions based on functional and structural abnormalities previously reported in patients with CFS. Classification was performed in RapidMiner using a linear support vector machine and bootstrap optimism correction. We compared ML classifiers based on (1) 61 a priori sMRI regional estimates and (2) SR ratings. The sMRI model achieved 79.58% classification accuracy. The SR (accuracy = 95.95%) outperformed both sMRI models. Estimates from multiple brain areas related to cognition, emotion, and memory contributed strongly to group classification. This is the first ML-based group classification of CFS. Our findings suggest that sMRI abnormalities are useful for discriminating CFS patients from HC, but SR ratings remain most effective in classification tasks.

A New Antigen Retrieval Technique for Human Brain Tissue

PubMed Central

Byne, William; Haroutunian, Vahram; García-Villanueva, Mercedes; Rábano, Alberto; García-Amado, María; Prensa, Lucía; Giménez-Amaya, José Manuel

2008-01-01

Immunohistochemical staining of tissues is a powerful tool used to delineate the presence or absence of an antigen. During the last 30 years, antigen visualization in human brain tissue has been significantly limited by the masking effect of fixatives. In the present study, we have used a new method for antigen retrieval in formalin-fixed human brain tissue and examined the effectiveness of this protocol to reveal masked antigens in tissues with both short and long formalin fixation times. This new method, which is based on the use of citraconic acid, has not been previously utilized in brain tissue although it has been employed in various other tissues such as tonsil, ovary, skin, lymph node, stomach, breast, colon, lung and thymus. Thus, we reported here a novel method to carry out immunohistochemical studies in free-floating human brain sections. Since fixation of brain tissue specimens in formaldehyde is a commonly method used in brain banks, this new antigen retrieval method could facilitate immunohistochemical studies of brains with prolonged formalin fixation times. PMID:18852880

Quantum Cascade Laser-Based Infrared Microscopy for Label-Free and Automated Cancer Classification in Tissue Sections.

PubMed

Kuepper, Claus; Kallenbach-Thieltges, Angela; Juette, Hendrik; Tannapfel, Andrea; Großerueschkamp, Frederik; Gerwert, Klaus

2018-05-16

A feasibility study using a quantum cascade laser-based infrared microscope for the rapid and label-free classification of colorectal cancer tissues is presented. Infrared imaging is a reliable, robust, automated, and operator-independent tissue classification method that has been used for differential classification of tissue thin sections identifying tumorous regions. However, long acquisition time by the so far used FT-IR-based microscopes hampered the clinical translation of this technique. Here, the used quantum cascade laser-based microscope provides now infrared images for precise tissue classification within few minutes. We analyzed 110 patients with UICC-Stage II and III colorectal cancer, showing 96% sensitivity and 100% specificity of this label-free method as compared to histopathology, the gold standard in routine clinical diagnostics. The main hurdle for the clinical translation of IR-Imaging is overcome now by the short acquisition time for high quality diagnostic images, which is in the same time range as frozen sections by pathologists.

A minimum spanning forest based classification method for dedicated breast CT images

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

Pike, Robert; Sechopoulos, Ioannis; Fei, Baowei, E-mail: bfei@emory.edu

Purpose: To develop and test an automated algorithm to classify different types of tissue in dedicated breast CT images. Methods: Images of a single breast of five different patients were acquired with a dedicated breast CT clinical prototype. The breast CT images were processed by a multiscale bilateral filter to reduce noise while keeping edge information and were corrected to overcome cupping artifacts. As skin and glandular tissue have similar CT values on breast CT images, morphologic processing is used to identify the skin based on its position information. A support vector machine (SVM) is trained and the resulting modelmore » used to create a pixelwise classification map of fat and glandular tissue. By combining the results of the skin mask with the SVM results, the breast tissue is classified as skin, fat, and glandular tissue. This map is then used to identify markers for a minimum spanning forest that is grown to segment the image using spatial and intensity information. To evaluate the authors’ classification method, they use DICE overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on five patient images. Results: Comparison between the automatic and the manual segmentation shows that the minimum spanning forest based classification method was able to successfully classify dedicated breast CT image with average DICE ratios of 96.9%, 89.8%, and 89.5% for fat, glandular, and skin tissue, respectively. Conclusions: A 2D minimum spanning forest based classification method was proposed and evaluated for classifying the fat, skin, and glandular tissue in dedicated breast CT images. The classification method can be used for dense breast tissue quantification, radiation dose assessment, and other applications in breast imaging.« less

Brain Immune Interactions as the Basis of Gulf War Illness: Gulf War Illness Consortium (GWIC)

DTIC Science & Technology

2016-10-01

between the immune system and the brain. The GWIC includes both clinical ( human ) and preclinical (animal and cell) studies and researchers in the 10...47 pesticides , DFP, sarin 16. Price Code (Leave Bl k) 17. Security Classification of Report Unclassified 18. Security Classification of this...stronger and longer proinflammatory signaling effects between the immune system and the brain. The GWIC includes both clinical ( human ) and

Automated classification of optical coherence tomography images of human atrial tissue

NASA Astrophysics Data System (ADS)

Gan, Yu; Tsay, David; Amir, Syed B.; Marboe, Charles C.; Hendon, Christine P.

2016-10-01

Tissue composition of the atria plays a critical role in the pathology of cardiovascular disease, tissue remodeling, and arrhythmogenic substrates. Optical coherence tomography (OCT) has the ability to capture the tissue composition information of the human atria. In this study, we developed a region-based automated method to classify tissue compositions within human atria samples within OCT images. We segmented regional information without prior information about the tissue architecture and subsequently extracted features within each segmented region. A relevance vector machine model was used to perform automated classification. Segmentation of human atrial ex vivo datasets was correlated with trichrome histology and our classification algorithm had an average accuracy of 80.41% for identifying adipose, myocardium, fibrotic myocardium, and collagen tissue compositions.

Quantitative fluorescence in intracranial tumor: implications for ALA-induced PpIX as an intraoperative biomarker

PubMed Central

Valdés, Pablo A.; Leblond, Frederic; Kim, Anthony; Harris, Brent T.; Wilson, Brian C.; Fan, Xiaoyao; Tosteson, Tor D.; Hartov, Alex; Ji, Songbai; Erkmen, Kadir; Simmons, Nathan E.; Paulsen, Keith D.; Roberts, David W.

2011-01-01

Object Accurate discrimination between tumor and normal tissue is crucial for optimal tumor resection. Qualitative fluorescence of protoporphyrin IX (PpIX), synthesized endogenously following δ-aminolevulinic acid (ALA) administration, has been used for this purpose in high-grade glioma (HGG). The authors show that diagnostically significant but visually imperceptible concentrations of PpIX can be quantitatively measured in vivo and used to discriminate normal from neoplastic brain tissue across a range of tumor histologies. Methods The authors studied 14 patients with diagnoses of low-grade glioma (LGG), HGG, meningioma, and metastasis under an institutional review board–approved protocol for fluorescence-guided resection. The primary aim of the study was to compare the diagnostic capabilities of a highly sensitive, spectrally resolved quantitative fluorescence approach to conventional fluorescence imaging for detection of neoplastic tissue in vivo. Results A significant difference in the quantitative measurements of PpIX concentration occurred in all tumor groups compared with normal brain tissue. Receiver operating characteristic (ROC) curve analysis of PpIX concentration as a diagnostic variable for detection of neoplastic tissue yielded a classification efficiency of 87% (AUC = 0.95, specificity = 92%, sensitivity = 84%) compared with 66% (AUC = 0.73, specificity = 100%, sensitivity = 47%) for conventional fluorescence imaging (p < 0.0001). More than 81% (57 of 70) of the quantitative fluorescence measurements that were below the threshold of the surgeon's visual perception were classified correctly in an analysis of all tumors. Conclusions These findings are clinically profound because they demonstrate that ALA-induced PpIX is a targeting biomarker for a variety of intracranial tumors beyond HGGs. This study is the first to measure quantitative ALA-induced PpIX concentrations in vivo, and the results have broad implications for guidance during resection of intracranial tumors. PMID:21438658

Deep learning for hybrid EEG-fNIRS brain-computer interface: application to motor imagery classification.

PubMed

Chiarelli, Antonio Maria; Croce, Pierpaolo; Merla, Arcangelo; Zappasodi, Filippo

2018-06-01

Brain-computer interface (BCI) refers to procedures that link the central nervous system to a device. BCI was historically performed using electroencephalography (EEG). In the last years, encouraging results were obtained by combining EEG with other neuroimaging technologies, such as functional near infrared spectroscopy (fNIRS). A crucial step of BCI is brain state classification from recorded signal features. Deep artificial neural networks (DNNs) recently reached unprecedented complex classification outcomes. These performances were achieved through increased computational power, efficient learning algorithms, valuable activation functions, and restricted or back-fed neurons connections. By expecting significant overall BCI performances, we investigated the capabilities of combining EEG and fNIRS recordings with state-of-the-art deep learning procedures. We performed a guided left and right hand motor imagery task on 15 subjects with a fixed classification response time of 1 s and overall experiment length of 10 min. Left versus right classification accuracy of a DNN in the multi-modal recording modality was estimated and it was compared to standalone EEG and fNIRS and other classifiers. At a group level we obtained significant increase in performance when considering multi-modal recordings and DNN classifier with synergistic effect. BCI performances can be significantly improved by employing multi-modal recordings that provide electrical and hemodynamic brain activity information, in combination with advanced non-linear deep learning classification procedures.

Mannitol Improves Brain Tissue Oxygenation in a Model of Diffuse Traumatic Brain Injury.

PubMed

Schilte, Clotilde; Bouzat, Pierre; Millet, Anne; Boucheix, Perrine; Pernet-Gallay, Karin; Lemasson, Benjamin; Barbier, Emmanuel L; Payen, Jean-François

2015-10-01

Based on evidence supporting a potential relation between posttraumatic brain hypoxia and microcirculatory derangements with cell edema, we investigated the effects of the antiedematous agent mannitol on brain tissue oxygenation in a model of diffuse traumatic brain injury. Experimental study. Neurosciences and physiology laboratories. Adult male Wistar rats. Thirty minutes after diffuse traumatic brain injury (impact-acceleration model), rats were IV administered with either a saline solution (traumatic brain injury-saline group) or 20% mannitol (1 g/kg) (traumatic brain injury-mannitol group). Sham-saline and sham-mannitol groups received no insult. Two series of experiments were conducted 2 hours after traumatic brain injury (or equivalent) to investigate 1) the effect of mannitol on brain edema and oxygenation, using a multiparametric magnetic resonance-based approach (n = 10 rats per group) to measure the apparent diffusion coefficient, tissue oxygen saturation, mean transit time, and blood volume fraction in the cortex and caudoputamen; 2) the effect of mannitol on brain tissue PO2 and on venous oxygen saturation of the superior sagittal sinus (n = 5 rats per group); and 3) the cortical ultrastructural changes after treatment (n = 1 per group, taken from the first experiment). Compared with the sham-saline group, the traumatic brain injury-saline group had significantly lower tissue oxygen saturation, brain tissue PO2, and venous oxygen saturation of the superior sagittal sinus values concomitant with diffuse brain edema. These effects were associated with microcirculatory collapse due to astrocyte swelling. Treatment with mannitol after traumatic brain injury reversed all these effects. In the absence of traumatic brain injury, mannitol had no effect on brain oxygenation. Mean transit time and blood volume fraction were comparable between the four groups of rats. The development of posttraumatic brain edema can limit the oxygen utilization by brain tissue without evidence of brain ischemia. Our findings indicate that an antiedematous agent such as mannitol can improve brain tissue oxygenation, possibly by limiting astrocyte swelling and restoring capillary perfusion.

Towards automated spectroscopic tissue classification in thyroid and parathyroid surgery.

PubMed

Schols, Rutger M; Alic, Lejla; Wieringa, Fokko P; Bouvy, Nicole D; Stassen, Laurents P S

2017-03-01

In (para-)thyroid surgery iatrogenic parathyroid injury should be prevented. To aid the surgeons' eye, a camera system enabling parathyroid-specific image enhancement would be useful. Hyperspectral camera technology might work, provided that the spectral signature of parathyroid tissue offers enough specific features to be reliably and automatically distinguished from surrounding tissues. As a first step to investigate this, we examined the feasibility of wide band diffuse reflectance spectroscopy (DRS) for automated spectroscopic tissue classification, using silicon (Si) and indium-gallium-arsenide (InGaAs) sensors. DRS (350-1830 nm) was performed during (para-)thyroid resections. From the acquired spectra 36 features at predefined wavelengths were extracted. The best features for classification of parathyroid from adipose or thyroid were assessed by binary logistic regression for Si- and InGaAs-sensor ranges. Classification performance was evaluated by leave-one-out cross-validation. In 19 patients 299 spectra were recorded (62 tissue sites: thyroid = 23, parathyroid = 21, adipose = 18). Classification accuracy of parathyroid-adipose was, respectively, 79% (Si), 82% (InGaAs) and 97% (Si/InGaAs combined). Parathyroid-thyroid classification accuracies were 80% (Si), 75% (InGaAs), 82% (Si/InGaAs combined). Si and InGaAs sensors are fairly accurate for automated spectroscopic classification of parathyroid, adipose and thyroid tissues. Combination of both sensor technologies improves accuracy. Follow-up research, aimed towards hyperspectral imaging seems justified. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

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

Issues of diagnostic review in brain tumor studies: from the Brain Tumor Epidemiology Consortium.

PubMed

Davis, Faith G; Malmer, Beatrice S; Aldape, Ken; Barnholtz-Sloan, Jill S; Bondy, Melissa L; Brännström, Thomas; Bruner, Janet M; Burger, Peter C; Collins, V Peter; Inskip, Peter D; Kruchko, Carol; McCarthy, Bridget J; McLendon, Roger E; Sadetzki, Siegal; Tihan, Tarik; Wrensch, Margaret R; Buffler, Patricia A

2008-03-01

Epidemiologists routinely conduct centralized single pathology reviews to minimize interobserver diagnostic variability, but this practice does not facilitate the combination of studies across geographic regions and institutions where diagnostic practices differ. A meeting of neuropathologists and epidemiologists focused on brain tumor classification issues in the context of protocol needs for consortial studies (http://epi.grants.cancer.gov/btec/). It resulted in recommendations relevant to brain tumors and possibly other rare disease studies. Two categories of brain tumors have enough general agreement over time, across regions, and between individual pathologists that one can consider using existing diagnostic data without further review: glioblastomas and meningiomas (as long as uniform guidelines such as those provided by the WHO are used). Prospective studies of these tumors benefit from collection of pathology reports, at a minimum recording the pathology department and classification system used in the diagnosis. Other brain tumors, such as oligodendroglioma, are less distinct and require careful histopathologic review for consistent classification across study centers. Epidemiologic study protocols must consider the study specific aims, diagnostic changes that have taken place over time, and other issues unique to the type(s) of tumor being studied. As diagnostic changes are being made rapidly, there are no readily available answers on disease classification issues. It is essential that epidemiologists and neuropathologists collaborate to develop appropriate study designs and protocols for specific hypothesis and populations.

Psychiatric Brain Banking: Three Perspectives on Current Trends and Future Directions

PubMed Central

Deep-Soboslay, Amy; Benes, Francine M.; Haroutunian, Vahram; Ellis, Justin K.; Kleinman, Joel E.; Hyde, Thomas M.

2011-01-01

Introduction The study of postmortem human brain tissue is central to the advancement of the neurobiological studies of psychiatric illness, particularly for the study of brain-specific isoforms and molecules. Methods The state-of-the-art methods and recommendations for maintaining a successful brain bank for psychiatric disorders are discussed, using the convergence of viewpoints from three brain collections, the National Institute of Mental Health Brain Collection (NIMH), the Harvard Brain Tissue Resource Center (HBTRC), and the Mt. Sinai School of Medicine Brain Bank (MSSM-BB), with diverse research interests and divergent approaches to tissue acquisition. Results While the NIMH obtains donations from medical examiners for its collection, and places particular emphasis on clinical diagnosis, toxicology, and building lifespan control cohorts, the HBTRC is uniquely designed as a repository whose sole purpose is to collect large-volume, high quality brain tissue from community-based donors based on relationships across an expansive nationwide network, and places emphasis on the accessibility of its bank in disseminating tissue and related data to research groups worldwide. The MSSM-BB collection has shown that, with dedication, prospective recruitment is a successful approach to tissue donation, and places particular emphasis on rigorous clinical diagnosis through antemortem contact with donors. The MSSM-BB places great importance on stereological tissue sampling methods for neuroanatomical studies, and frozen tissue sampling approaches that enable multiple assessments (RNA, DNA, protein, enzyme activity, binding, etc.) of the same tissue block. Promising scientific approaches for elucidating the molecular and cellular pathways in brain that may contribute to schizophrenia and/or bipolar disorder, such as cell culture techniques and microarray-based gene expression and genotyping studies are briefly discussed. Conclusions Despite unique perspectives from three established brain collections, there is a consensus that (1) diverse strategies for tissue acquisition, (2) rigor in tissue and diagnostic characterization, (3) the importance of sample accessibility, and (4) continual application of innovative scientific approaches to the study of brain tissue are all integral to the success and future of psychiatric brain banking. The future of neuropsychiatric research depends upon in the availability of high quality brain specimens from large numbers of subjects, including non-psychiatric controls. PMID:20673875

Efficacy of hidden markov model over support vector machine on multiclass classification of healthy and cancerous cervical tissues

NASA Astrophysics Data System (ADS)

Mukhopadhyay, Sabyasachi; Kurmi, Indrajit; Pratiher, Sawon; Mukherjee, Sukanya; Barman, Ritwik; Ghosh, Nirmalya; Panigrahi, Prasanta K.

2018-02-01

In this paper, a comparative study between SVM and HMM has been carried out for multiclass classification of cervical healthy and cancerous tissues. In our study, the HMM methodology is more promising to produce higher accuracy in classification.

Multivariate classification of infrared spectra of cell and tissue samples

DOEpatents

Haaland, David M.; Jones, Howland D. T.; Thomas, Edward V.

1997-01-01

Multivariate classification techniques are applied to spectra from cell and tissue samples irradiated with infrared radiation to determine if the samples are normal or abnormal (cancerous). Mid and near infrared radiation can be used for in vivo and in vitro classifications using at least different wavelengths.

MicroRNA-211 expression is down-regulated and associated with poor prognosis in human glioma.

PubMed

Zhang, Jun; Lv, Jianguang; Zhang, Feng; Che, Hongmin; Liao, Qiwei; Huang, Wobin; Li, Shaopeng; Li, Yuqian

2017-07-01

Accumulating evidence has supported the role of microRNAs in the initiation and development of malignant tumors. MicroRNA-211 (miR-211), which was reported to involve in diverse physiological activities in several cancers, was investigated for its expression in human glioma and adjacent normal brain tissues, as well as its correlation with patient prognosis. Glioma tissues and adjacent normal brain tissues were obtained from 82 patients who underwent surgical resection, and quantitative real-time polymerase chain reaction was performed to assess the expression level of miR-211. Here, we found that miR-211 was significantly decreased in glioma tissues compared with adjacent normal brain tissues (glioma, 3.52 ± 0.14 vs. normal, 4.96 ± 0.17, p < 0.001), and inversely associated with ascending WHO classification (grade III-IV, 3.16 ± 0.21 vs. grade I-II, 4.22 ± 0.26, p < 0.001). Then, the correlation of miR-211 with clinicopathological factors was investigated by Pearson's Chi square test, indicating that miR-211 might be a potential biomarker to predict the malignant status of glioma. Further, Kaplan-Meier curves with log-rank analysis were carried out to determine the relationship between miR-211 expression level and the overall survival rate of glioma patients. Our data showed that there was a close correlation between down-regulated miR-211 and shorter survival time in 82 patients (p = 0.026). Finally, the multivariate Cox regression analysis indicated that WHO grade (HR = 2.437, 95% CI 1.251-4.966, p = 0.007), KPS (HR = 2.215, 95% CI 1.168-4.259, p = 0.016), and miR-211 expression level (HR = 3.614, 95% CI 2.152-6.748, p < 0.001) were considered as independent risk factors for glioma prognosis. These results suggested that lower miR-211 expression might be a marker for poor prognosis of glioma patients.

Boosting brain connectome classification accuracy in Alzheimer's disease using higher-order singular value decomposition

PubMed Central

Zhan, Liang; Liu, Yashu; Wang, Yalin; Zhou, Jiayu; Jahanshad, Neda; Ye, Jieping; Thompson, Paul M.

2015-01-01

Alzheimer's disease (AD) is a progressive brain disease. Accurate detection of AD and its prodromal stage, mild cognitive impairment (MCI), are crucial. There is also a growing interest in identifying brain imaging biomarkers that help to automatically differentiate stages of Alzheimer's disease. Here, we focused on brain structural networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer's Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying different stages of Alzheimer's disease. PMID:26257601

Defining traumatic brain injury in children and youth using international classification of diseases version 10 codes: a systematic review protocol.

PubMed

Chan, Vincy; Thurairajah, Pravheen; Colantonio, Angela

2013-11-13

Although healthcare administrative data are commonly used for traumatic brain injury research, there is currently no consensus or consistency on using the International Classification of Diseases version 10 codes to define traumatic brain injury among children and youth. This protocol is for a systematic review of the literature to explore the range of International Classification of Diseases version 10 codes that are used to define traumatic brain injury in this population. The databases MEDLINE, MEDLINE In-Process, Embase, PsychINFO, CINAHL, SPORTDiscus, and Cochrane Database of Systematic Reviews will be systematically searched. Grey literature will be searched using Grey Matters and Google. Reference lists of included articles will also be searched. Articles will be screened using predefined inclusion and exclusion criteria and all full-text articles that meet the predefined inclusion criteria will be included for analysis. The study selection process and reasons for exclusion at the full-text level will be presented using a PRISMA study flow diagram. Information on the data source of included studies, year and location of study, age of study population, range of incidence, and study purpose will be abstracted into a separate table and synthesized for analysis. All International Classification of Diseases version 10 codes will be listed in tables and the codes that are used to define concussion, acquired traumatic brain injury, head injury, or head trauma will be identified. The identification of the optimal International Classification of Diseases version 10 codes to define this population in administrative data is crucial, as it has implications for policy, resource allocation, planning of healthcare services, and prevention strategies. It also allows for comparisons across countries and studies. This protocol is for a review that identifies the range and most common diagnoses used to conduct surveillance for traumatic brain injury in children and youth. This is an important first step in reaching an appropriate definition using International Classification of Diseases version 10 codes and can inform future work on reaching consensus on the codes to define traumatic brain injury for this vulnerable population.

Brain tumor classification of microscopy images using deep residual learning

NASA Astrophysics Data System (ADS)

Ishikawa, Yota; Washiya, Kiyotada; Aoki, Kota; Nagahashi, Hiroshi

2016-12-01

The crisis rate of brain tumor is about one point four in ten thousands. In general, cytotechnologists take charge of cytologic diagnosis. However, the number of cytotechnologists who can diagnose brain tumors is not sufficient, because of the necessity of highly specialized skill. Computer-Aided Diagnosis by computational image analysis may dissolve the shortage of experts and support objective pathological examinations. Our purpose is to support a diagnosis from a microscopy image of brain cortex and to identify brain tumor by medical image processing. In this study, we analyze Astrocytes that is a type of glia cell of central nerve system. It is not easy for an expert to discriminate brain tumor correctly since the difference between astrocytes and low grade astrocytoma (tumors formed from Astrocyte) is very slight. In this study, we present a novel method to segment cell regions robustly using BING objectness estimation and to classify brain tumors using deep convolutional neural networks (CNNs) constructed by deep residual learning. BING is a fast object detection method and we use pretrained BING model to detect brain cells. After that, we apply a sequence of post-processing like Voronoi diagram, binarization, watershed transform to obtain fine segmentation. For classification using CNNs, a usual way of data argumentation is applied to brain cells database. Experimental results showed 98.5% accuracy of classification and 98.2% accuracy of segmentation.

Frequency of brain tissue donation for research after suicide.

PubMed

Longaray, Vanessa K; Padoan, Carolina S; Goi, Pedro D; da Fonseca, Rodrigo C; Vieira, Daniel C; Oliveira, Francine H de; Kapczinski, Flávio; Magalhães, Pedro V

2017-01-01

To describe the frequency of brain tissue donation for research purposes by families of individuals that committed suicide. All requests for brain tissue donation to a brain biorepository made to the families of individuals aged 18-60 years who had committed suicide between March 2014 and February 2016 were included. Cases presenting with brain damage due to acute trauma were excluded. Fifty-six cases of suicide were reported. Of these, 24 fulfilled the exclusion criteria, and 11 others were excluded because no next of kin was found to provide informed consent. Of the 21 remaining cases, brain tissue donation was authorized in nine (tissue fragments in seven and the entire organ in two). Donation of brain tissue from suicide cases for research purposes is feasible. The acceptance rate of 42.8% in our sample is in accordance with international data on such donations, and similar to rates reported for neurodegenerative diseases.

An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease

PubMed Central

Schmitter, Daniel; Roche, Alexis; Maréchal, Bénédicte; Ribes, Delphine; Abdulkadir, Ahmed; Bach-Cuadra, Meritxell; Daducci, Alessandro; Granziera, Cristina; Klöppel, Stefan; Maeder, Philippe; Meuli, Reto; Krueger, Gunnar

2014-01-01

Voxel-based morphometry from conventional T1-weighted images has proved effective to quantify Alzheimer's disease (AD) related brain atrophy and to enable fairly accurate automated classification of AD patients, mild cognitive impaired patients (MCI) and elderly controls. Little is known, however, about the classification power of volume-based morphometry, where features of interest consist of a few brain structure volumes (e.g. hippocampi, lobes, ventricles) as opposed to hundreds of thousands of voxel-wise gray matter concentrations. In this work, we experimentally evaluate two distinct volume-based morphometry algorithms (FreeSurfer and an in-house algorithm called MorphoBox) for automatic disease classification on a standardized data set from the Alzheimer's Disease Neuroimaging Initiative. Results indicate that both algorithms achieve classification accuracy comparable to the conventional whole-brain voxel-based morphometry pipeline using SPM for AD vs elderly controls and MCI vs controls, and higher accuracy for classification of AD vs MCI and early vs late AD converters, thereby demonstrating the potential of volume-based morphometry to assist diagnosis of mild cognitive impairment and Alzheimer's disease. PMID:25429357

Recursive partitioning analysis (RPA) classification predicts survival in patients with brain metastases from sarcoma.

PubMed

Grossman, Rachel; Ram, Zvi

2014-12-01

Sarcoma rarely metastasizes to the brain, and there are no specific treatment guidelines for these tumors. The recursive partitioning analysis (RPA) classification is a well-established prognostic scale used in many malignancies. In this study we assessed the clinical characteristics of metastatic sarcoma to the brain and the validity of the RPA classification system in a subset of 21 patients who underwent surgical resection of metastatic sarcoma to the brain We retrospectively analyzed the medical, radiological, surgical, pathological, and follow-up clinical records of 21 patients who were operated for metastatic sarcoma to the brain between 1996 and 2012. Gliosarcomas, sarcomas of the head and neck with local extension into the brain, and metastatic sarcomas to the spine were excluded from this reported series. The patients' mean age was 49.6 ± 14.2 years (range, 25-75 years) at the time of diagnosis. Sixteen patients had a known history of systemic sarcoma, mostly in the extremities, and had previously received systemic chemotherapy and radiation therapy for their primary tumor. The mean maximal tumor diameter in the brain was 4.9 ± 1.7 cm (range 1.7-7.2 cm). The group's median preoperative Karnofsky Performance Scale was 80, with 14 patients presenting with Karnofsky Performance Scale of 70 or greater. The median overall survival was 7 months (range 0.2-204 months). The median survival time stratified by the Radiation Therapy Oncology Group RPA classes were 31, 7, and 2 months for RPA class I, II, and III, respectively (P = 0.0001). This analysis is the first to support the prognostic utility of the Radiation Therapy Oncology Group RPA classification for sarcoma brain metastases and may be used as a treatment guideline tool in this rare disease. Copyright © 2014 Elsevier Inc. All rights reserved.

Monitoring of tissue ablation using time series of ultrasound RF data.

PubMed

Imani, Farhad; Wu, Mark Z; Lasso, Andras; Burdette, Everett C; Daoud, Mohammad; Fitchinger, Gabor; Abolmaesumi, Purang; Mousavi, Parvin

2011-01-01

This paper is the first report on the monitoring of tissue ablation using ultrasound RF echo time series. We calcuate frequency and time domain features of time series of RF echoes from stationary tissue and transducer, and correlate them with ablated and non-ablated tissue properties. We combine these features in a nonlinear classification framework and demonstrate up to 99% classification accuracy in distinguishing ablated and non-ablated regions of tissue, in areas as small as 12mm2 in size. We also demonstrate significant improvement of ablated tissue classification using RF time series compared to the conventional approach of using single RF scan lines. The results of this study suggest RF echo time series as a promising approach for monitoring ablation, and capturing the changes in the tissue microstructure as a result of heat-induced necrosis.

Metastasis Infiltration: An Investigation of the Postoperative Brain-Tumor Interface

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

Raore, Bethwel; Schniederjan, Matthew; Prabhu, Roshan

Purpose: This study aims to evaluate brain infiltration of metastatic tumor cells past the main tumor resection margin to assess the biological basis for the use of stereotactic radiosurgery treatment of the tumor resection cavity and visualized resection edge or clinical target volume. Methods and Materials: Resection margin tissue was obtained after gross total resection of a small group of metastatic lesions from a variety of primary sources. The tissue at the border of the tumor and brain tissue was carefully oriented and processed to evaluate the presence of tumor cells within brain tissue and their distance from the resectionmore » margin. Results: Microscopic assessment of the radially oriented tissue samples showed no tumor cells infiltrating the surrounding brain tissue. Among the positive findings were reactive astrocytosis observed on the brain tissue immediately adjacent to the tumor resection bed margin. Conclusions: The lack of evidence of metastatic tumor cell infiltration into surrounding brain suggests the need to target only a narrow depth of the resection cavity margin to minimize normal tissue injury and prevent treatment size-dependent stereotactic radiosurgery complications.« less

Hybrid Brain-Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review.

PubMed

Hong, Keum-Shik; Khan, Muhammad Jawad

2017-01-01

In this article, non-invasive hybrid brain-computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared spectroscopy (fNIRS), electromyography (EMG), electrooculography (EOG), and eye tracker. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy and reduce the signal detection time. Currently, such combinations of EEG + fNIRS and EEG + EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, and features) relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active brain-computer interface (BCI) accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP) and motor imagination with P300. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided.

Reliability of a novel, semi-quantitative scale for classification of structural brain magnetic resonance imaging in children with cerebral palsy.

PubMed

Fiori, Simona; Cioni, Giovanni; Klingels, Katrjin; Ortibus, Els; Van Gestel, Leen; Rose, Stephen; Boyd, Roslyn N; Feys, Hilde; Guzzetta, Andrea

2014-09-01

To describe the development of a novel rating scale for classification of brain structural magnetic resonance imaging (MRI) in children with cerebral palsy (CP) and to assess its interrater and intrarater reliability. The scale consists of three sections. Section 1 contains descriptive information about the patient and MRI. Section 2 contains the graphical template of brain hemispheres onto which the lesion is transposed. Section 3 contains the scoring system for the quantitative analysis of the lesion characteristics, grouped into different global scores and subscores that assess separately side, regions, and depth. A larger interrater and intrarater reliability study was performed in 34 children with CP (22 males, 12 females; mean age at scan of 9 y 5 mo [SD 3 y 3 mo], range 4 y-16 y 11 mo; Gross Motor Function Classification System level I, [n=22], II [n=10], and level III [n=2]). Very high interrater and intrarater reliability of the total score was found with indices above 0.87. Reliability coefficients of the lobar and hemispheric subscores ranged between 0.53 and 0.95. Global scores for hemispheres, basal ganglia, brain stem, and corpus callosum showed reliability coefficients above 0.65. This study presents the first visual, semi-quantitative scale for classification of brain structural MRI in children with CP. The high degree of reliability of the scale supports its potential application for investigating the relationship between brain structure and function and examining treatment response according to brain lesion severity in children with CP. © 2014 Mac Keith Press.

NMR imaging of cell phone radiation absorption in brain tissue

PubMed Central

Gultekin, David H.; Moeller, Lothar

2013-01-01

A method is described for measuring absorbed electromagnetic energy radiated from cell phone antennae into ex vivo brain tissue. NMR images the 3D thermal dynamics inside ex vivo bovine brain tissue and equivalent gel under exposure to power and irradiation time-varying radio frequency (RF) fields. The absorbed RF energy in brain tissue converts into Joule heat and affects the nuclear magnetic shielding and the Larmor precession. The resultant temperature increase is measured by the resonance frequency shift of hydrogen protons in brain tissue. This proposed application of NMR thermometry offers sufficient spatial and temporal resolution to characterize the hot spots from absorbed cell phone radiation in aqueous media and biological tissues. Specific absorption rate measurements averaged over 1 mg and 10 s in the brain tissue cover the total absorption volume. Reference measurements with fiber optic temperature sensors confirm the accuracy of the NMR thermometry. PMID:23248293

NMR imaging of cell phone radiation absorption in brain tissue.

PubMed

Gultekin, David H; Moeller, Lothar

2013-01-02

A method is described for measuring absorbed electromagnetic energy radiated from cell phone antennae into ex vivo brain tissue. NMR images the 3D thermal dynamics inside ex vivo bovine brain tissue and equivalent gel under exposure to power and irradiation time-varying radio frequency (RF) fields. The absorbed RF energy in brain tissue converts into Joule heat and affects the nuclear magnetic shielding and the Larmor precession. The resultant temperature increase is measured by the resonance frequency shift of hydrogen protons in brain tissue. This proposed application of NMR thermometry offers sufficient spatial and temporal resolution to characterize the hot spots from absorbed cell phone radiation in aqueous media and biological tissues. Specific absorption rate measurements averaged over 1 mg and 10 s in the brain tissue cover the total absorption volume. Reference measurements with fiber optic temperature sensors confirm the accuracy of the NMR thermometry.

Mechanical characterization of human brain tumors from patients and comparison to potential surgical phantoms.

PubMed

Stewart, Daniel C; Rubiano, Andrés; Dyson, Kyle; Simmons, Chelsey S

2017-01-01

While mechanical properties of the brain have been investigated thoroughly, the mechanical properties of human brain tumors rarely have been directly quantified due to the complexities of acquiring human tissue. Quantifying the mechanical properties of brain tumors is a necessary prerequisite, though, to identify appropriate materials for surgical tool testing and to define target parameters for cell biology and tissue engineering applications. Since characterization methods vary widely for soft biological and synthetic materials, here, we have developed a characterization method compatible with abnormally shaped human brain tumors, mouse tumors, animal tissue and common hydrogels, which enables direct comparison among samples. Samples were tested using a custom-built millimeter-scale indenter, and resulting force-displacement data is analyzed to quantify the steady-state modulus of each sample. We have directly quantified the quasi-static mechanical properties of human brain tumors with effective moduli ranging from 0.17-16.06 kPa for various pathologies. Of the readily available and inexpensive animal tissues tested, chicken liver (steady-state modulus 0.44 ± 0.13 kPa) has similar mechanical properties to normal human brain tissue while chicken crassus gizzard muscle (steady-state modulus 3.00 ± 0.65 kPa) has similar mechanical properties to human brain tumors. Other materials frequently used to mimic brain tissue in mechanical tests, like ballistic gel and chicken breast, were found to be significantly stiffer than both normal and diseased brain tissue. We have directly compared quasi-static properties of brain tissue, brain tumors, and common mechanical surrogates, though additional tests would be required to determine more complex constitutive models.

Polarimetry based partial least square classification of ex vivo healthy and basal cell carcinoma human skin tissues.

PubMed

Ahmad, Iftikhar; Ahmad, Manzoor; Khan, Karim; Ikram, Masroor

2016-06-01

Optical polarimetry was employed for assessment of ex vivo healthy and basal cell carcinoma (BCC) tissue samples from human skin. Polarimetric analyses revealed that depolarization and retardance for healthy tissue group were significantly higher (p<0.001) compared to BCC tissue group. Histopathology indicated that these differences partially arise from BCC-related characteristic changes in tissue morphology. Wilks lambda statistics demonstrated the potential of all investigated polarimetric properties for computer assisted classification of the two tissue groups. Based on differences in polarimetric properties, partial least square (PLS) regression classified the samples with 100% accuracy, sensitivity and specificity. These findings indicate that optical polarimetry together with PLS statistics hold promise for automated pathology classification. Copyright © 2016 Elsevier B.V. All rights reserved.

Connectivity Strength-Weighted Sparse Group Representation-Based Brain Network Construction for MCI Classification

PubMed Central

Yu, Renping; Zhang, Han; An, Le; Chen, Xiaobo; Wei, Zhihui; Shen, Dinggang

2017-01-01

Brain functional network analysis has shown great potential in understanding brain functions and also in identifying biomarkers for brain diseases, such as Alzheimer's disease (AD) and its early stage, mild cognitive impairment (MCI). In these applications, accurate construction of biologically meaningful brain network is critical. Sparse learning has been widely used for brain network construction; however, its l1-norm penalty simply penalizes each edge of a brain network equally, without considering the original connectivity strength which is one of the most important inherent linkwise characters. Besides, based on the similarity of the linkwise connectivity, brain network shows prominent group structure (i.e., a set of edges sharing similar attributes). In this article, we propose a novel brain functional network modeling framework with a “connectivity strength-weighted sparse group constraint.” In particular, the network modeling can be optimized by considering both raw connectivity strength and its group structure, without losing the merit of sparsity. Our proposed method is applied to MCI classification, a challenging task for early AD diagnosis. Experimental results based on the resting-state functional MRI, from 50 MCI patients and 49 healthy controls, show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 84.8%) than other competing methods (e.g., sparse representation, accuracy = 65.6%). Post hoc inspection of the informative features further shows more biologically meaningful brain functional connectivities obtained by our proposed method. PMID:28150897

On the use of interaction error potentials for adaptive brain computer interfaces.

PubMed

Llera, A; van Gerven, M A J; Gómez, V; Jensen, O; Kappen, H J

2011-12-01

We propose an adaptive classification method for the Brain Computer Interfaces (BCI) which uses Interaction Error Potentials (IErrPs) as a reinforcement signal and adapts the classifier parameters when an error is detected. We analyze the quality of the proposed approach in relation to the misclassification of the IErrPs. In addition we compare static versus adaptive classification performance using artificial and MEG data. We show that the proposed adaptive framework significantly improves the static classification methods. Copyright © 2011 Elsevier Ltd. All rights reserved.

Integrating molecular markers into the World Health Organization classification of CNS tumors: a survey of the neuro-oncology community.

PubMed

Aldape, Kenneth; Nejad, Romina; Louis, David N; Zadeh, Gelareh

2017-03-01

Molecular markers provide important biological and clinical information related to the classification of brain tumors, and the integration of relevant molecular parameters into brain tumor classification systems has been a widely discussed topic in neuro-oncology over the past decade. With recent advances in the development of clinically relevant molecular signatures and the 2016 World Health Organization (WHO) update, the views of the neuro-oncology community on such changes would be informative for implementing this process. A survey with 8 questions regarding molecular markers in tumor classification was sent to an email list of Society for Neuro-Oncology members and attendees of prior meetings (n=5065). There were 403 respondents. Analysis was performed using whole group response, based on self-reported subspecialty. The survey results show overall strong support for incorporating molecular knowledge into the classification and clinical management of brain tumors. Across all 7 subspecialty groups, ≥70% of respondents agreed to this integration. Interestingly, some variability is seen among subspecialties, notably with lowest support from neuropathologists, which may reflect their roles in implementing such diagnostic technologies. Based on a survey provided to the neuro-oncology community, we report strong support for the integration of molecular markers into the WHO classification of brain tumors, as well as for using an integrated "layered" diagnostic format. While membership from each specialty showed support, there was variation by specialty in enthusiasm regarding proposed changes. The initial results of this survey influenced the deliberations underlying the 2016 WHO classification of tumors of the central nervous system. © The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.

Mechanical properties of porcine brain tissue in vivo and ex vivo estimated by MR elastography.

PubMed

Guertler, Charlotte A; Okamoto, Ruth J; Schmidt, John L; Badachhape, Andrew A; Johnson, Curtis L; Bayly, Philip V

2018-03-01

The mechanical properties of brain tissue in vivo determine the response of the brain to rapid skull acceleration. These properties are thus of great interest to the developers of mathematical models of traumatic brain injury (TBI) or neurosurgical simulations. Animal models provide valuable insight that can improve TBI modeling. In this study we compare estimates of mechanical properties of the Yucatan mini-pig brain in vivo and ex vivo using magnetic resonance elastography (MRE) at multiple frequencies. MRE allows estimations of properties in soft tissue, either in vivo or ex vivo, by imaging harmonic shear wave propagation. Most direct measurements of brain mechanical properties have been performed using samples of brain tissue ex vivo. It has been observed that direct estimates of brain mechanical properties depend on the frequency and amplitude of loading, as well as the time post-mortem and condition of the sample. Using MRE in the same animals at overlapping frequencies, we observe that porcine brain tissue in vivo appears stiffer than porcine brain tissue samples ex vivo at frequencies of 100 Hz and 125 Hz, but measurements show closer agreement at lower frequencies. Copyright © 2018 Elsevier Ltd. All rights reserved.

In vitro 3D regeneration-like growth of human patient brain tissue.

PubMed

Tang-Schomer, M D; Wu, W B; Kaplan, D L; Bookland, M J

2018-05-01

In vitro culture of primary neurons is widely adapted with embryonic but not mature brain tissue. Here, we extended a previously developed bioengineered three-dimensional (3D) embryonic brain tissue model to resected normal patient brain tissue in an attempt to regenerate human neurons in vitro. Single cells and small sized (diameter < 100 μm) spheroids from dissociated brain tissue were seeded into 3D silk fibroin-based scaffolds, with or without collagen or Matrigel, and compared with two-dimensional cultures and scaffold-free suspension cultures. Changes of cell phenotypes (neuronal, astroglial, neural progenitor, and neuroepithelial) were quantified with flow cytometry and analyzed with a new method of statistical analysis specifically designed for percentage comparison. Compared with a complete lack of viable cells in conventional neuronal cell culture condition, supplements of vascular endothelial growth factor-containing pro-endothelial cell condition led to regenerative growth of neurons and astroglial cells from "normal" human brain tissue of epilepsy surgical patients. This process involved delayed expansion of Nestin+ neural progenitor cells, emergence of TUJ1+ immature neurons, and Vimentin+ neuroepithelium-like cell sheet formation in prolonged cultures (14 weeks). Micro-tissue spheroids, but not single cells, supported the brain tissue growth, suggesting importance of preserving native cell-cell interactions. The presence of 3D scaffold, but not hydrogel, allowed for Vimentin+ cell expansion, indicating a different growth mechanism than pluripotent cell-based brain organoid formation. The slow and delayed process implied an origin of quiescent neural precursors in the neocortex tissue. Further optimization of the 3D tissue model with primary human brain cells could provide personalized brain disease models. Copyright © 2018 John Wiley & Sons, Ltd.

Multi-class biological tissue classification based on a multi-classifier: Preliminary study of an automatic output power control for ultrasonic surgical units.

PubMed

Youn, Su Hyun; Sim, Taeyong; Choi, Ahnryul; Song, Jinsung; Shin, Ki Young; Lee, Il Kwon; Heo, Hyun Mu; Lee, Daeweon; Mun, Joung Hwan

2015-06-01

Ultrasonic surgical units (USUs) have the advantage of minimizing tissue damage during surgeries that require tissue dissection by reducing problems such as coagulation and unwanted carbonization, but the disadvantage of requiring manual adjustment of power output according to the target tissue. In order to overcome this limitation, it is necessary to determine the properties of in vivo tissues automatically. We propose a multi-classifier that can accurately classify tissues based on the unique impedance of each tissue. For this purpose, a multi-classifier was built based on single classifiers with high classification rates, and the classification accuracy of the proposed model was compared with that of single classifiers for various electrode types (Type-I: 6 mm invasive; Type-II: 3 mm invasive; Type-III: surface). The sensitivity and positive predictive value (PPV) of the multi-classifier by cross checks were determined. According to the 10-fold cross validation results, the classification accuracy of the proposed model was significantly higher (p<0.05 or <0.01) than that of existing single classifiers for all electrode types. In particular, the classification accuracy of the proposed model was highest when the 3mm invasive electrode (Type-II) was used (sensitivity=97.33-100.00%; PPV=96.71-100.00%). The results of this study are an important contribution to achieving automatic optimal output power adjustment of USUs according to the properties of individual tissues. Copyright © 2015 Elsevier Ltd. All rights reserved.

Audit of practice in sudden unexpected death in epilepsy (SUDEP) post mortems and neuropathological findings

PubMed Central

Michalak, Zuzanna; Wright, Gabriella; Dawson, Timothy; Hilton, David; Joshi, Abhijit; Diehl, Beate; Koepp, Matthias; Lhatoo, Samden; Sander, Josemir W.; Sisodiya, Sanjay M.

2015-01-01

Aims Sudden unexpected death in epilepsy (SUDEP) is one of the leading causes of death in people with epilepsy. For classification of definite SUDEP, a post mortem (PM), including anatomical and toxicological examination, is mandatory to exclude other causes of death. We audited PM practice as well as the value of brain examination in SUDEP. Methods We reviewed 145 PM reports in SUDEP cases from four UK neuropathology centres. Data were extracted for clinical epilepsy details, circumstances of death and neuropathological findings. Results Macroscopic brain abnormalities were identified in 52% of cases. Mild brain swelling was present in 28%, and microscopic pathologies relevant to cause or effect of seizures were seen in 89%. Examination based on whole fixed brains (76.6% of all PMs), and systematic regional sampling was associated with higher detection rates of underlying pathology (P < 0.01). Information was more frequently recorded regarding circumstances of death and body position/location than clinical epilepsy history and investigations. Conclusion Our findings support the contribution of examination of the whole fixed brain in SUDEP, with high rates of detection of relevant pathology. Availability of full clinical epilepsy‐related information at the time of PM could potentially further improve detection through targeted tissue sampling. Apart from confirmation of SUDEP, complete neuropathological examination contributes to evaluation of risk factors as well as helping to direct future research into underlying causes. PMID:26300477

Automated Tissue Classification Framework for Reproducible Chronic Wound Assessment

PubMed Central

Mukherjee, Rashmi; Manohar, Dhiraj Dhane; Das, Dev Kumar; Achar, Arun; Mitra, Analava; Chakraborty, Chandan

2014-01-01

The aim of this paper was to develop a computer assisted tissue classification (granulation, necrotic, and slough) scheme for chronic wound (CW) evaluation using medical image processing and statistical machine learning techniques. The red-green-blue (RGB) wound images grabbed by normal digital camera were first transformed into HSI (hue, saturation, and intensity) color space and subsequently the “S” component of HSI color channels was selected as it provided higher contrast. Wound areas from 6 different types of CW were segmented from whole images using fuzzy divergence based thresholding by minimizing edge ambiguity. A set of color and textural features describing granulation, necrotic, and slough tissues in the segmented wound area were extracted using various mathematical techniques. Finally, statistical learning algorithms, namely, Bayesian classification and support vector machine (SVM), were trained and tested for wound tissue classification in different CW images. The performance of the wound area segmentation protocol was further validated by ground truth images labeled by clinical experts. It was observed that SVM with 3rd order polynomial kernel provided the highest accuracies, that is, 86.94%, 90.47%, and 75.53%, for classifying granulation, slough, and necrotic tissues, respectively. The proposed automated tissue classification technique achieved the highest overall accuracy, that is, 87.61%, with highest kappa statistic value (0.793). PMID:25114925

21 CFR 882.1310 - Cortical electrode.

Code of Federal Regulations, 2010 CFR

2010-04-01

...) Identification. A cortical electrode is an electrode which is temporarily placed on the surface of the brain for stimulating the brain or recording the brain's electrical activity. (b) Classification. Class II (performance...

21 CFR 882.1310 - Cortical electrode.

Code of Federal Regulations, 2012 CFR

2012-04-01

...) Identification. A cortical electrode is an electrode which is temporarily placed on the surface of the brain for stimulating the brain or recording the brain's electrical activity. (b) Classification. Class II (performance...

21 CFR 882.1310 - Cortical electrode.

Code of Federal Regulations, 2011 CFR

2011-04-01

...) Identification. A cortical electrode is an electrode which is temporarily placed on the surface of the brain for stimulating the brain or recording the brain's electrical activity. (b) Classification. Class II (performance...

21 CFR 882.1310 - Cortical electrode.

Code of Federal Regulations, 2014 CFR

2014-04-01

...) Identification. A cortical electrode is an electrode which is temporarily placed on the surface of the brain for stimulating the brain or recording the brain's electrical activity. (b) Classification. Class II (performance...

21 CFR 882.1310 - Cortical electrode.

Code of Federal Regulations, 2013 CFR

2013-04-01

...) Identification. A cortical electrode is an electrode which is temporarily placed on the surface of the brain for stimulating the brain or recording the brain's electrical activity. (b) Classification. Class II (performance...

Development of acute hydrocephalus does not change brain tissue mechanical properties in adult rats, but in juvenile rats

PubMed Central

Pong, Alice C.; Jugé, Lauriane; Bilston, Lynne E.; Cheng, Shaokoon

2017-01-01

Introduction Regional changes in brain stiffness were previously demonstrated in an experimental obstructive hydrocephalus juvenile rat model. The open cranial sutures in the juvenile rats have influenced brain compression and mechanical properties during hydrocephalus development and the extent by which closed cranial sutures in adult hydrocephalic rat models affect brain stiffness in-vivo remains unclear. The aims of this study were to determine changes in brain tissue mechanical properties and brain structure size during hydrocephalus development in adult rat with fixed cranial volume and how these changes were related to brain tissue deformation. Methods Hydrocephalus was induced in 9 female ten weeks old Sprague-Dawley rats by injecting 60 μL of a kaolin suspension (25%) into the cisterna magna under anaesthesia. 6 sham-injected age-matched female SD rats were used as controls. MR imaging (9.4T, Bruker) was performed 1 day before and then at 3 days post injection. T2-weighted anatomical MR images were collected to quantify ventricle and brain tissue cross-sectional areas. MR elastography (800 Hz) was used to measure the brain stiffness (G*, shear modulus). Results Brain tissue in the adult hydrocephalic rats was more compressed than the juvenile hydrocephalic rats because the skulls of the adult hydrocephalic rats were unable to expand like the juvenile rats. In the adult hydrocephalic rats, the cortical gray matter thickness and the caudate-putamen cross-sectional area decreased (Spearman, P < 0.001 for both) but there were no significant changes in cranial cross-sectional area (Spearman, P = 0.35), cortical gray matter stiffness (Spearman, P = 0.24) and caudate-putamen (Spearman, P = 0.11) stiffness. No significant changes in the size of brain structures were observed in the controls. Conclusions This study showed that although brain tissue in the adult hydrocephalic rats was severely compressed, their brain tissue stiffness did not change significantly. These results are in contrast with our previous findings in juvenile hydrocephalic rats which had significantly less brain compression (as the brain circumference was able to stretch with the cranium due to the open skull sutures) and had a significant increase in caudate putamen stiffness. These results suggest that change in brain mechanical properties in hydrocephalus is complex and is not solely dependent on brain tissue deformation. Further studies on the interactions between brain tissue stiffness, deformation, tissue oedema and neural damage are necessary before MRE can be used as a tool to track changes in brain biomechanics in hydrocephalus. PMID:28837671

Development of acute hydrocephalus does not change brain tissue mechanical properties in adult rats, but in juvenile rats.

PubMed

Pong, Alice C; Jugé, Lauriane; Bilston, Lynne E; Cheng, Shaokoon

2017-01-01

Regional changes in brain stiffness were previously demonstrated in an experimental obstructive hydrocephalus juvenile rat model. The open cranial sutures in the juvenile rats have influenced brain compression and mechanical properties during hydrocephalus development and the extent by which closed cranial sutures in adult hydrocephalic rat models affect brain stiffness in-vivo remains unclear. The aims of this study were to determine changes in brain tissue mechanical properties and brain structure size during hydrocephalus development in adult rat with fixed cranial volume and how these changes were related to brain tissue deformation. Hydrocephalus was induced in 9 female ten weeks old Sprague-Dawley rats by injecting 60 μL of a kaolin suspension (25%) into the cisterna magna under anaesthesia. 6 sham-injected age-matched female SD rats were used as controls. MR imaging (9.4T, Bruker) was performed 1 day before and then at 3 days post injection. T2-weighted anatomical MR images were collected to quantify ventricle and brain tissue cross-sectional areas. MR elastography (800 Hz) was used to measure the brain stiffness (G*, shear modulus). Brain tissue in the adult hydrocephalic rats was more compressed than the juvenile hydrocephalic rats because the skulls of the adult hydrocephalic rats were unable to expand like the juvenile rats. In the adult hydrocephalic rats, the cortical gray matter thickness and the caudate-putamen cross-sectional area decreased (Spearman, P < 0.001 for both) but there were no significant changes in cranial cross-sectional area (Spearman, P = 0.35), cortical gray matter stiffness (Spearman, P = 0.24) and caudate-putamen (Spearman, P = 0.11) stiffness. No significant changes in the size of brain structures were observed in the controls. This study showed that although brain tissue in the adult hydrocephalic rats was severely compressed, their brain tissue stiffness did not change significantly. These results are in contrast with our previous findings in juvenile hydrocephalic rats which had significantly less brain compression (as the brain circumference was able to stretch with the cranium due to the open skull sutures) and had a significant increase in caudate putamen stiffness. These results suggest that change in brain mechanical properties in hydrocephalus is complex and is not solely dependent on brain tissue deformation. Further studies on the interactions between brain tissue stiffness, deformation, tissue oedema and neural damage are necessary before MRE can be used as a tool to track changes in brain biomechanics in hydrocephalus.

Fine-Granularity Functional Interaction Signatures for Characterization of Brain Conditions

PubMed Central

Hu, Xintao; Zhu, Dajiang; Lv, Peili; Li, Kaiming; Han, Junwei; Wang, Lihong; Shen, Dinggang; Guo, Lei; Liu, Tianming

2014-01-01

In the human brain, functional activity occurs at multiple spatial scales. Current studies on functional brain networks and their alterations in brain diseases via resting-state functional magnetic resonance imaging (rs-fMRI) are generally either at local scale (regionally confined analysis and inter-regional functional connectivity analysis) or at global scale (graph theoretic analysis). In contrast, inferring functional interaction at fine-granularity sub-network scale has not been adequately explored yet. Here our hypothesis is that functional interaction measured at fine-granularity subnetwork scale can provide new insight into the neural mechanisms of neurological and psychological conditions, thus offering complementary information for healthy and diseased population classification. In this paper, we derived fine-granularity functional interaction (FGFI) signatures in subjects with Mild Cognitive Impairment (MCI) and Schizophrenia by diffusion tensor imaging (DTI) and rsfMRI, and used patient-control classification experiments to evaluate the distinctiveness of the derived FGFI features. Our experimental results have shown that the FGFI features alone can achieve comparable classification performance compared with the commonly used inter-regional connectivity features. However, the classification performance can be substantially improved when FGFI features and inter-regional connectivity features are integrated, suggesting the complementary information achieved from the FGFI signatures. PMID:23319242

Optical pathology of human brain metastasis of lung cancer using combined resonance Raman and spatial frequency spectroscopies

NASA Astrophysics Data System (ADS)

Zhou, Yan; Liu, Cheng-hui; Pu, Yang; Cheng, Gangge; Zhou, Lixin; Chen, Jun; Zhu, Ke; Alfano, Robert R.

2016-03-01

Raman spectroscopy has become widely used for diagnostic purpose of breast, lung and brain cancers. This report introduced a new approach based on spatial frequency spectra analysis of the underlying tissue structure at different stages of brain tumor. Combined spatial frequency spectroscopy (SFS), Resonance Raman (RR) spectroscopic method is used to discriminate human brain metastasis of lung cancer from normal tissues for the first time. A total number of thirty-one label-free micrographic images of normal and metastatic brain cancer tissues obtained from a confocal micro- Raman spectroscopic system synchronously with examined RR spectra of the corresponding samples were collected from the identical site of tissue. The difference of the randomness of tissue structures between the micrograph images of metastatic brain tumor tissues and normal tissues can be recognized by analyzing spatial frequency. By fitting the distribution of the spatial frequency spectra of human brain tissues as a Gaussian function, the standard deviation, σ, can be obtained, which was used to generate a criterion to differentiate human brain cancerous tissues from the normal ones using Support Vector Machine (SVM) classifier. This SFS-SVM analysis on micrograph images presents good results with sensitivity (85%), specificity (75%) in comparison with gold standard reports of pathology and immunology. The dual-modal advantages of SFS combined with RR spectroscopy method may open a new way in the neuropathology applications.

Objective breast tissue image classification using Quantitative Transmission ultrasound tomography

NASA Astrophysics Data System (ADS)

Malik, Bilal; Klock, John; Wiskin, James; Lenox, Mark

2016-12-01

Quantitative Transmission Ultrasound (QT) is a powerful and emerging imaging paradigm which has the potential to perform true three-dimensional image reconstruction of biological tissue. Breast imaging is an important application of QT and allows non-invasive, non-ionizing imaging of whole breasts in vivo. Here, we report the first demonstration of breast tissue image classification in QT imaging. We systematically assess the ability of the QT images’ features to differentiate between normal breast tissue types. The three QT features were used in Support Vector Machines (SVM) classifiers, and classification of breast tissue as either skin, fat, glands, ducts or connective tissue was demonstrated with an overall accuracy of greater than 90%. Finally, the classifier was validated on whole breast image volumes to provide a color-coded breast tissue volume. This study serves as a first step towards a computer-aided detection/diagnosis platform for QT.

A discriminative model-constrained graph cuts approach to fully automated pediatric brain tumor segmentation in 3-D MRI.

PubMed

Wels, Michael; Carneiro, Gustavo; Aplas, Alexander; Huber, Martin; Hornegger, Joachim; Comaniciu, Dorin

2008-01-01

In this paper we present a fully automated approach to the segmentation of pediatric brain tumors in multi-spectral 3-D magnetic resonance images. It is a top-down segmentation approach based on a Markov random field (MRF) model that combines probabilistic boosting trees (PBT) and lower-level segmentation via graph cuts. The PBT algorithm provides a strong discriminative observation model that classifies tumor appearance while a spatial prior takes into account the pair-wise homogeneity in terms of classification labels and multi-spectral voxel intensities. The discriminative model relies not only on observed local intensities but also on surrounding context for detecting candidate regions for pathology. A mathematically sound formulation for integrating the two approaches into a unified statistical framework is given. The proposed method is applied to the challenging task of detection and delineation of pediatric brain tumors. This segmentation task is characterized by a high non-uniformity of both the pathology and the surrounding non-pathologic brain tissue. A quantitative evaluation illustrates the robustness of the proposed method. Despite dealing with more complicated cases of pediatric brain tumors the results obtained are mostly better than those reported for current state-of-the-art approaches to 3-D MR brain tumor segmentation in adult patients. The entire processing of one multi-spectral data set does not require any user interaction, and takes less time than previously proposed methods.

Impact of Neurodegenerative Diseases on Drug Binding to Brain Tissues: From Animal Models to Human Samples.

PubMed

Ugarte, Ana; Corbacho, David; Aymerich, María S; García-Osta, Ana; Cuadrado-Tejedor, Mar; Oyarzabal, Julen

2018-04-19

Drug efficacy in the central nervous system (CNS) requires an additional step after crossing the blood-brain barrier. Therapeutic agents must reach their targets in the brain to modulate them; thus, the free drug concentration hypothesis is a key parameter for in vivo pharmacology. Here, we report the impact of neurodegeneration (Alzheimer's disease (AD) and Parkinson's disease (PD) compared with healthy controls) on the binding of 10 known drugs to postmortem brain tissues from animal models and humans. Unbound drug fractions, for some drugs, are significantly different between healthy and injured brain tissues (AD or PD). In addition, drugs binding to brain tissues from AD and PD animal models do not always recapitulate their binding to the corresponding human injured brain tissues. These results reveal potentially relevant implications for CNS drug discovery.

Time resolved dosimetry of human brain exposed to low frequency pulsed magnetic fields.

PubMed

Paffi, Alessandra; Camera, Francesca; Lucano, Elena; Apollonio, Francesca; Liberti, Micaela

2016-06-21

An accurate dosimetry is a key issue to understanding brain stimulation and related interaction mechanisms with neuronal tissues at the basis of the increasing amount of literature revealing the effects on human brain induced by low-level, low frequency pulsed magnetic fields (PMFs). Most literature on brain dosimetry estimates the maximum E field value reached inside the tissue without considering its time pattern or tissue dispersivity. Nevertheless a time-resolved dosimetry, accounting for dispersive tissues behavior, becomes necessary considering that the threshold for an effect onset may vary depending on the pulse waveform and that tissues may filter the applied stimulatory fields altering the predicted stimulatory waveform's size and shape. In this paper a time-resolved dosimetry has been applied on a realistic brain model exposed to the signal presented in Capone et al (2009 J. Neural Transm. 116 257-65), accounting for the broadband dispersivity of brain tissues up to several kHz, to accurately reconstruct electric field and current density waveforms inside different brain tissues. The results obtained by exposing the Duke's brain model to this PMF signal show that the E peak in the brain is considerably underestimated if a simple monochromatic dosimetry is carried out at the pulse repetition frequency of 75 Hz.

Time resolved dosimetry of human brain exposed to low frequency pulsed magnetic fields

NASA Astrophysics Data System (ADS)

Paffi, Alessandra; Camera, Francesca; Lucano, Elena; Apollonio, Francesca; Liberti, Micaela

2016-06-01

An accurate dosimetry is a key issue to understanding brain stimulation and related interaction mechanisms with neuronal tissues at the basis of the increasing amount of literature revealing the effects on human brain induced by low-level, low frequency pulsed magnetic fields (PMFs). Most literature on brain dosimetry estimates the maximum E field value reached inside the tissue without considering its time pattern or tissue dispersivity. Nevertheless a time-resolved dosimetry, accounting for dispersive tissues behavior, becomes necessary considering that the threshold for an effect onset may vary depending on the pulse waveform and that tissues may filter the applied stimulatory fields altering the predicted stimulatory waveform’s size and shape. In this paper a time-resolved dosimetry has been applied on a realistic brain model exposed to the signal presented in Capone et al (2009 J. Neural Transm. 116 257-65), accounting for the broadband dispersivity of brain tissues up to several kHz, to accurately reconstruct electric field and current density waveforms inside different brain tissues. The results obtained by exposing the Duke’s brain model to this PMF signal show that the E peak in the brain is considerably underestimated if a simple monochromatic dosimetry is carried out at the pulse repetition frequency of 75 Hz.

Temperature-dependent elastic properties of brain tissues measured with the shear wave elastography method.

PubMed

Liu, Yan-Lin; Li, Guo-Yang; He, Ping; Mao, Ze-Qi; Cao, Yanping

2017-01-01

Determining the mechanical properties of brain tissues is essential in such cases as the surgery planning and surgical training using virtual reality based simulators, trauma research and the diagnosis of some diseases that alter the elastic properties of brain tissues. Here, we suggest a protocol to measure the temperature-dependent elastic properties of brain tissues in physiological saline using the shear wave elastography method. Experiments have been conducted on six porcine brains. Our results show that the shear moduli of brain tissues decrease approximately linearly with a slope of -0.041±0.006kPa/°C when the temperature T increases from room temperature (~23°C) to body temperature (~37°C). A case study has been further conducted which shows that the shear moduli are insensitive to the temperature variation when T is in the range of 37 to 43°C and will increase when T is higher than 43°C. With the present experimental setup, temperature-dependent elastic properties of brain tissues can be measured in a simulated physiological environment and a non-destructive manner. Thus the method suggested here offers a unique tool for the mechanical characterization of brain tissues with potential applications in brain biomechanics research. Copyright © 2016 Elsevier Ltd. All rights reserved.

Mechanical characterization of human brain tumors from patients and comparison to potential surgical phantoms

PubMed Central

Rubiano, Andrés; Dyson, Kyle; Simmons, Chelsey S.

2017-01-01

While mechanical properties of the brain have been investigated thoroughly, the mechanical properties of human brain tumors rarely have been directly quantified due to the complexities of acquiring human tissue. Quantifying the mechanical properties of brain tumors is a necessary prerequisite, though, to identify appropriate materials for surgical tool testing and to define target parameters for cell biology and tissue engineering applications. Since characterization methods vary widely for soft biological and synthetic materials, here, we have developed a characterization method compatible with abnormally shaped human brain tumors, mouse tumors, animal tissue and common hydrogels, which enables direct comparison among samples. Samples were tested using a custom-built millimeter-scale indenter, and resulting force-displacement data is analyzed to quantify the steady-state modulus of each sample. We have directly quantified the quasi-static mechanical properties of human brain tumors with effective moduli ranging from 0.17–16.06 kPa for various pathologies. Of the readily available and inexpensive animal tissues tested, chicken liver (steady-state modulus 0.44 ± 0.13 kPa) has similar mechanical properties to normal human brain tissue while chicken crassus gizzard muscle (steady-state modulus 3.00 ± 0.65 kPa) has similar mechanical properties to human brain tumors. Other materials frequently used to mimic brain tissue in mechanical tests, like ballistic gel and chicken breast, were found to be significantly stiffer than both normal and diseased brain tissue. We have directly compared quasi-static properties of brain tissue, brain tumors, and common mechanical surrogates, though additional tests would be required to determine more complex constitutive models. PMID:28582392

Connectivity strength-weighted sparse group representation-based brain network construction for MCI classification.

PubMed

Yu, Renping; Zhang, Han; An, Le; Chen, Xiaobo; Wei, Zhihui; Shen, Dinggang

2017-05-01

Brain functional network analysis has shown great potential in understanding brain functions and also in identifying biomarkers for brain diseases, such as Alzheimer's disease (AD) and its early stage, mild cognitive impairment (MCI). In these applications, accurate construction of biologically meaningful brain network is critical. Sparse learning has been widely used for brain network construction; however, its l 1 -norm penalty simply penalizes each edge of a brain network equally, without considering the original connectivity strength which is one of the most important inherent linkwise characters. Besides, based on the similarity of the linkwise connectivity, brain network shows prominent group structure (i.e., a set of edges sharing similar attributes). In this article, we propose a novel brain functional network modeling framework with a "connectivity strength-weighted sparse group constraint." In particular, the network modeling can be optimized by considering both raw connectivity strength and its group structure, without losing the merit of sparsity. Our proposed method is applied to MCI classification, a challenging task for early AD diagnosis. Experimental results based on the resting-state functional MRI, from 50 MCI patients and 49 healthy controls, show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 84.8%) than other competing methods (e.g., sparse representation, accuracy = 65.6%). Post hoc inspection of the informative features further shows more biologically meaningful brain functional connectivities obtained by our proposed method. Hum Brain Mapp 38:2370-2383, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Biased visualization of hypoperfused tissue by computed tomography due to short imaging duration: improved classification by image down-sampling and vascular models.

PubMed

Mikkelsen, Irene Klærke; Jones, P Simon; Ribe, Lars Riisgaard; Alawneh, Josef; Puig, Josep; Bekke, Susanne Lise; Tietze, Anna; Gillard, Jonathan H; Warburton, Elisabeth A; Pedraza, Salva; Baron, Jean-Claude; Østergaard, Leif; Mouridsen, Kim

2015-07-01

Lesion detection in acute stroke by computed-tomography perfusion (CTP) can be affected by incomplete bolus coverage in veins and hypoperfused tissue, so-called bolus truncation (BT), and low contrast-to-noise ratio (CNR). We examined the BT-frequency and hypothesized that image down-sampling and a vascular model (VM) for perfusion calculation would improve normo- and hypoperfused tissue classification. CTP datasets from 40 acute stroke patients were retrospectively analysed for BT. In 16 patients with hypoperfused tissue but no BT, repeated 2-by-2 image down-sampling and uniform filtering was performed, comparing CNR to perfusion-MRI levels and tissue classification to that of unprocessed data. By simulating reduced scan duration, the minimum scan-duration at which estimated lesion volumes came within 10% of their true volume was compared for VM and state-of-the-art algorithms. BT in veins and hypoperfused tissue was observed in 9/40 (22.5%) and 17/40 patients (42.5%), respectively. Down-sampling to 128 × 128 resolution yielded CNR comparable to MR data and improved tissue classification (p = 0.0069). VM reduced minimum scan duration, providing reliable maps of cerebral blood flow and mean transit time: 5 s (p = 0.03) and 7 s (p < 0.0001), respectively). BT is not uncommon in stroke CTP with 40-s scan duration. Applying image down-sampling and VM improve tissue classification. • Too-short imaging duration is common in clinical acute stroke CTP imaging. • The consequence is impaired identification of hypoperfused tissue in acute stroke patients. • The vascular model is less sensitive than current algorithms to imaging duration. • Noise reduction by image down-sampling improves identification of hypoperfused tissue by CTP.

Individual Human Brain Areas Can Be Identified from Their Characteristic Spectral Activation Fingerprints.

PubMed

Keitel, Anne; Gross, Joachim

2016-06-01

The human brain can be parcellated into diverse anatomical areas. We investigated whether rhythmic brain activity in these areas is characteristic and can be used for automatic classification. To this end, resting-state MEG data of 22 healthy adults was analysed. Power spectra of 1-s long data segments for atlas-defined brain areas were clustered into spectral profiles ("fingerprints"), using k-means and Gaussian mixture (GM) modelling. We demonstrate that individual areas can be identified from these spectral profiles with high accuracy. Our results suggest that each brain area engages in different spectral modes that are characteristic for individual areas. Clustering of brain areas according to similarity of spectral profiles reveals well-known brain networks. Furthermore, we demonstrate task-specific modulations of auditory spectral profiles during auditory processing. These findings have important implications for the classification of regional spectral activity and allow for novel approaches in neuroimaging and neurostimulation in health and disease.

Brief Report: The Role of National Brain and Tissue Banks in Research on Autism and Developmental Disorders.

ERIC Educational Resources Information Center

Zielke, H. Ronald; And Others

1996-01-01

This paper describes the establishment and work of two brain and tissue banks, which collect brain and other tissues from newly deceased individuals with autism and make these tissues available to researchers. Issues in tissue collection are identified, including the importance of advance planning, religious concerns of families, and the need for…

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.

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.

Classification of kidney and liver tissue using ultrasound backscatter data

NASA Astrophysics Data System (ADS)

Aalamifar, Fereshteh; Rivaz, Hassan; Cerrolaza, Juan J.; Jago, James; Safdar, Nabile; Boctor, Emad M.; Linguraru, Marius G.

2015-03-01

Ultrasound (US) tissue characterization provides valuable information for the initialization of automatic segmentation algorithms, and can further provide complementary information for diagnosis of pathologies. US tissue characterization is challenging due to the presence of various types of image artifacts and dependence on the sonographer's skills. One way of overcoming this challenge is by characterizing images based on the distribution of the backscatter data derived from the interaction between US waves and tissue. The goal of this work is to classify liver versus kidney tissue in 3D volumetric US data using the distribution of backscatter US data recovered from end-user displayed Bmode image available in clinical systems. To this end, we first propose the computation of a large set of features based on the homodyned-K distribution of the speckle as well as the correlation coefficients between small patches in 3D images. We then utilize the random forests framework to select the most important features for classification. Experiments on in-vivo 3D US data from nine pediatric patients with hydronephrosis showed an average accuracy of 94% for the classification of liver and kidney tissues showing a good potential of this work to assist in the classification and segmentation of abdominal soft tissue.

21 CFR 882.1935 - Near Infrared (NIR) Brain Hematoma Detector.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Near Infrared (NIR) Brain Hematoma Detector. 882... Infrared (NIR) Brain Hematoma Detector. (a) Identification. A Near Infrared (NIR) Brain Hematoma Detector... evaluate suspected brain hematomas. (b) Classification. Class II (special controls). The special controls...

21 CFR 882.1935 - Near Infrared (NIR) Brain Hematoma Detector.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Near Infrared (NIR) Brain Hematoma Detector. 882... Infrared (NIR) Brain Hematoma Detector. (a) Identification. A Near Infrared (NIR) Brain Hematoma Detector... evaluate suspected brain hematomas. (b) Classification. Class II (special controls). The special controls...

The brain MRI classification problem from wavelets perspective

NASA Astrophysics Data System (ADS)

Bendib, Mohamed M.; Merouani, Hayet F.; Diaba, Fatma

2015-02-01

Haar and Daubechies 4 (DB4) are the most used wavelets for brain MRI (Magnetic Resonance Imaging) classification. The former is simple and fast to compute while the latter is more complex and offers a better resolution. This paper explores the potential of both of them in performing Normal versus Pathological discrimination on the one hand, and Multiclassification on the other hand. The Whole Brain Atlas is used as a validation database, and the Random Forest (RF) algorithm is employed as a learning approach. The achieved results are discussed and statistically compared.

Migraine classification using magnetic resonance imaging resting-state functional connectivity data.

PubMed

Chong, Catherine D; Gaw, Nathan; Fu, Yinlin; Li, Jing; Wu, Teresa; Schwedt, Todd J

2017-08-01

Background This study used machine-learning techniques to develop discriminative brain-connectivity biomarkers from resting-state functional magnetic resonance neuroimaging ( rs-fMRI) data that distinguish between individual migraine patients and healthy controls. Methods This study included 58 migraine patients (mean age = 36.3 years; SD = 11.5) and 50 healthy controls (mean age = 35.9 years; SD = 11.0). The functional connections of 33 seeded pain-related regions were used as input for a brain classification algorithm that tested the accuracy of determining whether an individual brain MRI belongs to someone with migraine or to a healthy control. Results The best classification accuracy using a 10-fold cross-validation method was 86.1%. Resting functional connectivity of the right middle temporal, posterior insula, middle cingulate, left ventromedial prefrontal and bilateral amygdala regions best discriminated the migraine brain from that of a healthy control. Migraineurs with longer disease durations were classified more accurately (>14 years; 96.7% accuracy) compared to migraineurs with shorter disease durations (≤14 years; 82.1% accuracy). Conclusions Classification of migraine using rs-fMRI provides insights into pain circuits that are altered in migraine and could potentially contribute to the development of a new, noninvasive migraine biomarker. Migraineurs with longer disease burden were classified more accurately than migraineurs with shorter disease burden, potentially indicating that disease duration leads to reorganization of brain circuitry.

Prediction of brain deformations and risk of traumatic brain injury due to closed-head impact: quantitative analysis of the effects of boundary conditions and brain tissue constitutive model.

PubMed

Wang, Fang; Han, Yong; Wang, Bingyu; Peng, Qian; Huang, Xiaoqun; Miller, Karol; Wittek, Adam

2018-05-12

In this study, we investigate the effects of modelling choices for the brain-skull interface (layers of tissues between the brain and skull that determine boundary conditions for the brain) and the constitutive model of brain parenchyma on the brain responses under violent impact as predicted using computational biomechanics model. We used the head/brain model from Total HUman Model for Safety (THUMS)-extensively validated finite element model of the human body that has been applied in numerous injury biomechanics studies. The computations were conducted using a well-established nonlinear explicit dynamics finite element code LS-DYNA. We employed four approaches for modelling the brain-skull interface and four constitutive models for the brain tissue in the numerical simulations of the experiments on post-mortem human subjects exposed to violent impacts reported in the literature. The brain-skull interface models included direct representation of the brain meninges and cerebrospinal fluid, outer brain surface rigidly attached to the skull, frictionless sliding contact between the brain and skull, and a layer of spring-type cohesive elements between the brain and skull. We considered Ogden hyperviscoelastic, Mooney-Rivlin hyperviscoelastic, neo-Hookean hyperviscoelastic and linear viscoelastic constitutive models of the brain tissue. Our study indicates that the predicted deformations within the brain and related brain injury criteria are strongly affected by both the approach of modelling the brain-skull interface and the constitutive model of the brain parenchyma tissues. The results suggest that accurate prediction of deformations within the brain and risk of brain injury due to violent impact using computational biomechanics models may require representation of the meninges and subarachnoidal space with cerebrospinal fluid in the model and application of hyperviscoelastic (preferably Ogden-type) constitutive model for the brain tissue.

Expression of Bcl-2 and NF-κB in brain tissue after acute renal ischemia-reperfusion in rats.

PubMed

Zhang, Na; Cheng, Gen-Yang; Liu, Xian-Zhi; Zhang, Feng-Jiang

2014-05-01

To investigate the effect of acute renal ischemia reperfusion on brain tissue. Fourty eight rats were randomly divided into four groups (n=12): sham operation group, 30 min ischemia 60 min reperfusion group, 60 min ischemia 60 min reperfusion group, and 120 min ischemia 60 min reperfusion group. The brain tissues were taken after the experiment. TUNEL assay was used to detect the brain cell apoptosis, and western blot was used to detect the expression of apoptosis-related proteins and inflammatory factors. Renal ischemia-reperfusion induced apoptosis of brain tissues, and the apoptosis increased with prolongation of ischemia time. The detection at the molecular level showed decreased Bcl-2 expression, increased Bax expression, upregulated expression of NF-κB and its downstream factor COX-2/PGE2. Acute renal ischemia-reperfusion can cause brain tissue damage, manifested as induced brain tissues apoptosis and inflammation activation. Copyright © 2014 Hainan Medical College. Published by Elsevier B.V. All rights reserved.

HIV-1 Phylogenetic analysis shows HIV-1 transits through the meninges to brain and peripheral tissues

PubMed Central

Lamers, Susanna L.; Gray, Rebecca R.; Salemi, Marco; Huysentruyt, Leanne C.; McGrath, Michael

2010-01-01

Brain infection by the human immunodeficiency virus type 1 (HIV-1) has been investigated in many reports with a variety of conclusions concerning the time of entry and degree of viral compartmentalization. To address these diverse findings, we sequenced HIV-1 gp120 clones from a wide range of brain, peripheral and meningeal tissues from five patients who died from several HIV-1 associated disease pathologies. High-resolution phylogenetic analysis confirmed previous studies that showed a significant degree of compartmentalization in brain and peripheral tissue subpopulations. Some intermixing between the HIV-1 subpopulations was evident, especially in patients that died from pathologies other than HIV-associated dementia. Interestingly, the major tissue harboring virus from both the brain and peripheral tissues was the meninges. These results show that 1) HIV-1 is clearly capable of migrating out of the brain, 2) the meninges are the most likely primary transport tissues, and 3) infected brain macrophages comprise an important HIV reservoir during highly active antiretroviral therapy. PMID:21055482

Combined Bisulfite Restriction Analysis for brain tissue identification.

PubMed

Samsuwan, Jarunya; Muangsub, Tachapol; Yanatatsaneejit, Pattamawadee; Mutirangura, Apiwat; Kitkumthorn, Nakarin

2018-05-01

According to the tissue-specific methylation database (doi: 10.1016/j.gene.2014.09.060), methylation at CpG locus cg03096975 in EML2 has been preliminarily proven to be specific to brain tissue. In this study, we enlarged sample size and developed a technique for identifying brain tissue in aged samples. Combined Bisulfite Restriction Analysis-for EML2 (COBRA-EML2) technique was established and validated in various organ samples obtained from 108 autopsies. In addition, this technique was also tested for its reliability, minimal DNA concentration detected, and use in aged samples and in samples obtained from specific brain compartments and spinal cord. COBRA-EML2 displayed 100% sensitivity and specificity for distinguishing brain tissue from other tissues, showed high reliability, was capable of detecting minimal DNA concentration (0.015ng/μl), could be used for identifying brain tissue in aged samples. In summary, COBRA-EML2 is a technique to identify brain tissue. This analysis is useful in criminal cases since it can identify the vital organ tissues from small samples acquired from criminal scenes. The results from this analysis can be counted as a medical and forensic marker supporting criminal investigations, and as one of the evidences in court rulings. Copyright © 2018 Elsevier B.V. All rights reserved.

Macrophage Responses to Epithelial Dysfunction Promote Lung Fibrosis in Aging

DTIC Science & Technology

2017-10-01

alveolar macrophages based on single cell molecular classification in patients with pulmonary fibrosis. We have recruited a planned number of patients...biomarkers expressed by human tissue-resident and monocyte-derived alveolar macrophages based on single cell molecular classification in patients with...identify novel biomarkers expressed by human tissue-resident and monocyte- derived alveolar macrophages based on single cell molecular classification

Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases.

PubMed

Rondina, Jane Maryam; Ferreira, Luiz Kobuti; de Souza Duran, Fabio Luis; Kubo, Rodrigo; Ono, Carla Rachel; Leite, Claudia Costa; Smid, Jerusa; Nitrini, Ricardo; Buchpiguel, Carlos Alberto; Busatto, Geraldo F

2018-01-01

Machine learning techniques such as support vector machine (SVM) have been applied recently in order to accurately classify individuals with neuropsychiatric disorders such as Alzheimer's disease (AD) based on neuroimaging data. However, the multivariate nature of the SVM approach often precludes the identification of the brain regions that contribute most to classification accuracy. Multiple kernel learning (MKL) is a sparse machine learning method that allows the identification of the most relevant sources for the classification. By parcelating the brain into regions of interest (ROI) it is possible to use each ROI as a source to MKL (ROI-MKL). We applied MKL to multimodal neuroimaging data in order to: 1) compare the diagnostic performance of ROI-MKL and whole-brain SVM in discriminating patients with AD from demographically matched healthy controls and 2) identify the most relevant brain regions to the classification. We used two atlases (AAL and Brodmann's) to parcelate the brain into ROIs and applied ROI-MKL to structural (T1) MRI, 18 F-FDG-PET and regional cerebral blood flow SPECT (rCBF-SPECT) data acquired from the same subjects (20 patients with early AD and 18 controls). In ROI-MKL, each ROI received a weight (ROI-weight) that indicated the region's relevance to the classification. For each ROI, we also calculated whether there was a predominance of voxels indicating decreased or increased regional activity (for 18 F-FDG-PET and rCBF-SPECT) or volume (for T1-MRI) in AD patients. Compared to whole-brain SVM, the ROI-MKL approach resulted in better accuracies (with either atlas) for classification using 18 F-FDG-PET (92.5% accuracy for ROI-MKL versus 84% for whole-brain), but not when using rCBF-SPECT or T1-MRI. Although several cortical and subcortical regions contributed to discrimination, high ROI-weights and predominance of hypometabolism and atrophy were identified specially in medial parietal and temporo-limbic cortical regions. Also, the weight of discrimination due to a pattern of increased voxel-weight values in AD individuals was surprisingly high (ranging from approximately 20% to 40% depending on the imaging modality), located mainly in primary sensorimotor and visual cortices and subcortical nuclei. The MKL-ROI approach highlights the high discriminative weight of a subset of brain regions of known relevance to AD, the selection of which contributes to increased classification accuracy when applied to 18 F-FDG-PET data. Moreover, the MKL-ROI approach demonstrates that brain regions typically spared in mild stages of AD also contribute substantially in the individual discrimination of AD patients from controls.

Physically-based in silico light sheet microscopy for visualizing fluorescent brain models

PubMed Central

2015-01-01

Background We present a physically-based computational model of the light sheet fluorescence microscope (LSFM). Based on Monte Carlo ray tracing and geometric optics, our method simulates the operational aspects and image formation process of the LSFM. This simulated, in silico LSFM creates synthetic images of digital fluorescent specimens that can resemble those generated by a real LSFM, as opposed to established visualization methods producing visually-plausible images. We also propose an accurate fluorescence rendering model which takes into account the intrinsic characteristics of fluorescent dyes to simulate the light interaction with fluorescent biological specimen. Results We demonstrate first results of our visualization pipeline to a simplified brain tissue model reconstructed from the somatosensory cortex of a young rat. The modeling aspects of the LSFM units are qualitatively analysed, and the results of the fluorescence model were quantitatively validated against the fluorescence brightness equation and characteristic emission spectra of different fluorescent dyes. AMS subject classification Modelling and simulation PMID:26329404

Robotic multimodality stereotactic brain tissue identification: work in progress

NASA Technical Reports Server (NTRS)

Andrews, R.; Mah, R.; Galvagni, A.; Guerrero, M.; Papasin, R.; Wallace, M.; Winters, J.

1997-01-01

Real-time identification of tissue would improve procedures such as stereotactic brain biopsy (SBX), functional and implantation neurosurgery, and brain tumor excision. To standard SBX equipment has been added: (1) computer-controlled stepper motors to drive the biopsy needle/probe precisely; (2) multiple microprobes to track tissue density, detect blood vessels and changes in blood flow, and distinguish the various tissues being penetrated; (3) neural net learning programs to allow real-time comparisons of current data with a normative data bank; (4) three-dimensional graphic displays to follow the probe as it traverses brain tissue. The probe can differentiate substances such as pig brain, differing consistencies of the 'brain-like' foodstuff tofu, and gels made to simulate brain, as well as detect blood vessels imbedded in these substances. Multimodality probes should improve the safety, efficacy, and diagnostic accuracy of SBX and other neurosurgical procedures.

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.

A family of hyperelastic models for human brain tissue

NASA Astrophysics Data System (ADS)

Mihai, L. Angela; Budday, Silvia; Holzapfel, Gerhard A.; Kuhl, Ellen; Goriely, Alain

2017-09-01

Experiments on brain samples under multiaxial loading have shown that human brain tissue is both extremely soft when compared to other biological tissues and characterized by a peculiar elastic response under combined shear and compression/tension: there is a significant increase in shear stress with increasing axial compression compared to a moderate increase with increasing axial tension. Recent studies have revealed that many widely used constitutive models for soft biological tissues fail to capture this characteristic response. Here, guided by experiments of human brain tissue, we develop a family of modeling approaches that capture the elasticity of brain tissue under varying simple shear superposed on varying axial stretch by exploiting key observations about the behavior of the nonlinear shear modulus, which can be obtained directly from the experimental data.

Terahertz spectroscopy of brain tissue from a mouse model of Alzheimer's disease

NASA Astrophysics Data System (ADS)

Shi, Lingyan; Shumyatsky, Pavel; Rodríguez-Contreras, Adrián; Alfano, Robert

2016-01-01

The terahertz (THz) absorption and index of refraction of brain tissues from a mouse model of Alzheimer's disease (AD) and a control wild-type (normal) mouse were compared using THz time-domain spectroscopy (THz-TDS). Three dominating absorption peaks associated to torsional-vibrational modes were observed in AD tissue, at about 1.44, 1.8, and 2.114 THz, closer to the peaks of free tryptophan molecules than in normal tissue. A possible reason is that there is more free tryptophan in AD brain tissue, while in normal brain tissue more tryptophan is attached to other molecules. Our study suggests that THz-absorption modes may be used as an AD biomarker fingerprint in brain, and that THz-TDS is a promising technique for early diagnosis of AD.

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%.

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.

Effects of Antioxidant N-acetylcysteine Against Paraquat-Induced Oxidative Stress in Vital Tissues of Mice

PubMed Central

Ortiz, Maricelly Santiago; Forti, Kevin Muñoz; Suárez Martinez, Edu B.; Muñoz, Lenin Godoy; Husain, Kazim

2016-01-01

Paraquat (PQ) is a commonly used herbicide that induces oxidative stress via reactive oxygen species (ROS) generation. This study aimed to investigate the effects of the antioxidant N-acetylcysteine (NAC) against PQ-induced oxidative stress in mice. Male Balb/C mice (24) were randomly divided into 4 groups and treated for 3 weeks: 1) control (saline), 2) NAC (0.5% in diet), 3) PQ (20 mg/kg, IP) and 4) combination (PQ + NAC). Afterwards mice were sacrificed and oxidative stress markers were analyzed. Our data showed no significant change in serum antioxidant capacity. PQ enhanced lipid peroxidation (MDA) levels in liver tissue compared to control whereas NAC decreased MDA levels (p<0.05). NAC significantly increased MDA in brain tissue (p<0.05). PQ significantly depleted glutathione (GSH) levels in liver (p=0.001) and brain tissue (p<0.05) but non-significant GSH depletion in lung tissue. NAC counteracted PQ, showing a moderate increase GSH levels in liver and brain tissues. PQ significantly increased 8-oxodeoxyguanosine (8-OH-dG) levels (p<0.05) in liver tissue compared to control without a significant change in brain tissue. NAC treatment ameliorated PQ-induced oxidative DNA damage in the liver tissue. PQ significantly decreased the relative mtDNA amplification and increased the frequency of lesions in liver and brain tissue (p<0.0001), while NAC restored the DNA polymerase activity in liver tissue but not in brain tissue. In conclusion, PQ induced lipid peroxidation, oxidative nuclear DNA and mtDNA damage in liver tissues and depleted liver and brain GSH levels. NAC supplementation ameliorated the PQ-induced oxidative stress response in liver tissue of mice. PMID:27398384

The clinical outcomes of deep gray matter injury in children with cerebral palsy in relation with brain magnetic resonance imaging.

PubMed

Choi, Ja Young; Choi, Yoon Seong; Rha, Dong-Wook; Park, Eun Sook

2016-08-01

In the present study we investigated the nature and extent of clinical outcomes using various classifications and analyzed the relationship between brain magnetic resonance imaging (MRI) findings and the extent of clinical outcomes in children with cerebral palsy (CP) with deep gray matter injury. The deep gray matter injuries of 69 children were classified into hypoxic ischemic encephalopathy (HIE) and kernicterus patterns. HIE patterns were divided into four groups (I-IV) based on severity. Functional classification was investigated using the gross motor function classification system-expanded and revised, manual ability classification system, communication function classification system, and tests of cognitive function, and other associated problems. The severity of HIE pattern on brain MRI was strongly correlated with the severity of clinical outcomes in these various domains. Children with a kernicterus pattern showed a wide range of clinical outcomes in these areas. Children with severe HIE are at high risk of intellectual disability (ID) or epilepsy and children with a kernicterus pattern are at risk of hearing impairment and/or ID. Grading severity of HIE pattern on brain MRI is useful for predicting overall outcomes. The clinical outcomes of children with a kernicterus pattern range widely from mild to severe. Delineation of the clinical outcomes of children with deep gray matter injury, which are a common abnormal brain MRI finding in children with CP, is necessary. The present study provides clinical outcomes for various domains in children with deep gray matter injury on brain MRI. The deep gray matter injuries were divided into two major groups; HIE and kernicterus patterns. Our study showed that severity of HIE pattern on brain MRI was strongly associated with the severity of impairments in gross motor function, manual ability, communication function, and cognition. These findings suggest that severity of HIE pattern can be useful for predicting the severity of impairments. Conversely, children with a kernicterus pattern showed a wide range of clinical outcomes in various domains. Children with severe HIE pattern are at high risk of ID or epilepsy and children with kernicterus pattern are at risk of hearing impairment or ID. The strength of our study was the assessment of clinical outcomes after 3 years of age using standardized classification systems in various domains in children with deep gray matter injury. Copyright © 2016 Elsevier Ltd. All rights reserved.

BECon: a tool for interpreting DNA methylation findings from blood in the context of brain.

PubMed

Edgar, R D; Jones, M J; Meaney, M J; Turecki, G; Kobor, M S

2017-08-01

Tissue differences are one of the largest contributors to variability in the human DNA methylome. Despite the tissue-specific nature of DNA methylation, the inaccessibility of human brain samples necessitates the frequent use of surrogate tissues such as blood, in studies of associations between DNA methylation and brain function and health. Results from studies of surrogate tissues in humans are difficult to interpret in this context, as the connection between blood-brain DNA methylation is tenuous and not well-documented. Here, we aimed to provide a resource to the community to aid interpretation of blood-based DNA methylation results in the context of brain tissue. We used paired samples from 16 individuals from three brain regions and whole blood, run on the Illumina 450 K Human Methylation Array to quantify the concordance of DNA methylation between tissues. From these data, we have made available metrics on: the variability of cytosine-phosphate-guanine dinucleotides (CpGs) in our blood and brain samples, the concordance of CpGs between blood and brain, and estimations of how strongly a CpG is affected by cell composition in both blood and brain through the web application BECon (Blood-Brain Epigenetic Concordance; https://redgar598.shinyapps.io/BECon/). We anticipate that BECon will enable biological interpretation of blood-based human DNA methylation results, in the context of brain.

Two-tier tissue decomposition for histopathological image representation and classification.

PubMed

Gultekin, Tunc; Koyuncu, Can Fahrettin; Sokmensuer, Cenk; Gunduz-Demir, Cigdem

2015-01-01

In digital pathology, devising effective image representations is crucial to design robust automated diagnosis systems. To this end, many studies have proposed to develop object-based representations, instead of directly using image pixels, since a histopathological image may contain a considerable amount of noise typically at the pixel-level. These previous studies mostly employ color information to define their objects, which approximately represent histological tissue components in an image, and then use the spatial distribution of these objects for image representation and classification. Thus, object definition has a direct effect on the way of representing the image, which in turn affects classification accuracies. In this paper, our aim is to design a classification system for histopathological images. Towards this end, we present a new model for effective representation of these images that will be used by the classification system. The contributions of this model are twofold. First, it introduces a new two-tier tissue decomposition method for defining a set of multityped objects in an image. Different than the previous studies, these objects are defined combining texture, shape, and size information and they may correspond to individual histological tissue components as well as local tissue subregions of different characteristics. As its second contribution, it defines a new metric, which we call dominant blob scale, to characterize the shape and size of an object with a single scalar value. Our experiments on colon tissue images reveal that this new object definition and characterization provides distinguishing representation of normal and cancerous histopathological images, which is effective to obtain more accurate classification results compared to its counterparts.

Assistance to neurosurgical planning: using a fuzzy spatial graph model of the brain for locating anatomical targets in MRI

NASA Astrophysics Data System (ADS)

Villéger, Alice; Ouchchane, Lemlih; Lemaire, Jean-Jacques; Boire, Jean-Yves

2007-03-01

Symptoms of neurodegenerative pathologies such as Parkinson's disease can be relieved through Deep Brain Stimulation. This neurosurgical technique relies on high precision positioning of electrodes in specific areas of the basal ganglia and the thalamus. These subcortical anatomical targets must be located at pre-operative stage, from a set of MRI acquired under stereotactic conditions. In order to assist surgical planning, we designed a semi-automated image analysis process for extracting anatomical areas of interest. Complementary information, provided by both patient's data and expert knowledge, is represented as fuzzy membership maps, which are then fused by means of suitable possibilistic operators in order to achieve the segmentation of targets. More specifically, theoretical prior knowledge on brain anatomy is modelled within a 'virtual atlas' organised as a spatial graph: a list of vertices linked by edges, where each vertex represents an anatomical structure of interest and contains relevant information such as tissue composition, whereas each edge represents a spatial relationship between two structures, such as their relative directions. The model is built using heterogeneous sources of information such as qualitative descriptions from the expert, or quantitative information from prelabelled images. For each patient, tissue membership maps are extracted from MR data through a classification step. Prior model and patient's data are then matched by using a research algorithm (or 'strategy') which simultaneously computes an estimation of the location of every structures. The method was tested on 10 clinical images, with promising results. Location and segmentation results were statistically assessed, opening perspectives for enhancements.

Segmentation of bone and soft tissue regions in digital radiographic images of extremities

NASA Astrophysics Data System (ADS)

Pakin, S. Kubilay; Gaborski, Roger S.; Barski, Lori L.; Foos, David H.; Parker, Kevin J.

2001-07-01

This paper presents an algorithm for segmentation of computed radiography (CR) images of extremities into bone and soft tissue regions. The algorithm is a region-based one in which the regions are constructed using a growing procedure with two different statistical tests. Following the growing process, tissue classification procedure is employed. The purpose of the classification is to label each region as either bone or soft tissue. This binary classification goal is achieved by using a voting procedure that consists of clustering of regions in each neighborhood system into two classes. The voting procedure provides a crucial compromise between local and global analysis of the image, which is necessary due to strong exposure variations seen on the imaging plate. Also, the existence of regions whose size is large enough such that exposure variations can be observed through them makes it necessary to use overlapping blocks during the classification. After the classification step, resulting bone and soft tissue regions are refined by fitting a 2nd order surface to each tissue, and reevaluating the label of each region according to the distance between the region and surfaces. The performance of the algorithm is tested on a variety of extremity images using manually segmented images as gold standard. The experiments showed that our algorithm provided a bone boundary with an average area overlap of 90% compared to the gold standard.

Automated classification of multiphoton microscopy images of ovarian tissue using deep learning.

PubMed

Huttunen, Mikko J; Hassan, Abdurahman; McCloskey, Curtis W; Fasih, Sijyl; Upham, Jeremy; Vanderhyden, Barbara C; Boyd, Robert W; Murugkar, Sangeeta

2018-06-01

Histopathological image analysis of stained tissue slides is routinely used in tumor detection and classification. However, diagnosis requires a highly trained pathologist and can thus be time-consuming, labor-intensive, and potentially risk bias. Here, we demonstrate a potential complementary approach for diagnosis. We show that multiphoton microscopy images from unstained, reproductive tissues can be robustly classified using deep learning techniques. We fine-train four pretrained convolutional neural networks using over 200 murine tissue images based on combined second-harmonic generation and two-photon excitation fluorescence contrast, to classify the tissues either as healthy or associated with high-grade serous carcinoma with over 95% sensitivity and 97% specificity. Our approach shows promise for applications involving automated disease diagnosis. It could also be readily applied to other tissues, diseases, and related classification problems. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

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.

Decoding multiple sound categories in the human temporal cortex using high resolution fMRI.

PubMed

Zhang, Fengqing; Wang, Ji-Ping; Kim, Jieun; Parrish, Todd; Wong, Patrick C M

2015-01-01

Perception of sound categories is an important aspect of auditory perception. The extent to which the brain's representation of sound categories is encoded in specialized subregions or distributed across the auditory cortex remains unclear. Recent studies using multivariate pattern analysis (MVPA) of brain activations have provided important insights into how the brain decodes perceptual information. In the large existing literature on brain decoding using MVPA methods, relatively few studies have been conducted on multi-class categorization in the auditory domain. Here, we investigated the representation and processing of auditory categories within the human temporal cortex using high resolution fMRI and MVPA methods. More importantly, we considered decoding multiple sound categories simultaneously through multi-class support vector machine-recursive feature elimination (MSVM-RFE) as our MVPA tool. Results show that for all classifications the model MSVM-RFE was able to learn the functional relation between the multiple sound categories and the corresponding evoked spatial patterns and classify the unlabeled sound-evoked patterns significantly above chance. This indicates the feasibility of decoding multiple sound categories not only within but across subjects. However, the across-subject variation affects classification performance more than the within-subject variation, as the across-subject analysis has significantly lower classification accuracies. Sound category-selective brain maps were identified based on multi-class classification and revealed distributed patterns of brain activity in the superior temporal gyrus and the middle temporal gyrus. This is in accordance with previous studies, indicating that information in the spatially distributed patterns may reflect a more abstract perceptual level of representation of sound categories. Further, we show that the across-subject classification performance can be significantly improved by averaging the fMRI images over items, because the irrelevant variations between different items of the same sound category are reduced and in turn the proportion of signals relevant to sound categorization increases.

Dissociating mental states related to doing nothing by means of fMRI pattern classification.

PubMed

Kühn, Simone; Bodammer, Nils Christian; Brass, Marcel

2010-12-01

Most juridical systems recognize intentional non-actions - the failure to render assistance - as intentional acts by regarding them as in principle culpable. This raises the fundamental question whether intentional non-actions can be distinguished from simply not doing anything. Classical GLM analysis on functional magnetic resonance imaging (fMRI) data reveals that not doing anything is associated with resting state brain areas whereas intentionally non-acting is associated with brain activity in left inferior parietal lobe and left dorsal premotor cortex. By means of pattern classification we quantify the accuracy with which we can distinguish these two mental states on the basis of brain activity. In order to identify brain regions that harbour a distributed, overlapping representation of voluntary non-actions and the decision not to act we performed pattern classification on brain areas that did not appear in the GLM contrasts. The prediction rate is not reduced and we show that the prediction relies mostly on brain areas that have been associated with action production and motor imagery as supplementary motor area, right inferior frontal gyrus and right middle temporal area (V5/MT). Hence our data support the implicit assumption of legal practice that voluntary non-action shares important features with overt voluntary action. Copyright © 2010 Elsevier Inc. All rights reserved.

Classifying visuomotor workload in a driving simulator using subject specific spatial brain patterns

PubMed Central

Dijksterhuis, Chris; de Waard, Dick; Brookhuis, Karel A.; Mulder, Ben L. J. M.; de Jong, Ritske

2013-01-01

A passive Brain Computer Interface (BCI) is a system that responds to the spontaneously produced brain activity of its user and could be used to develop interactive task support. A human-machine system that could benefit from brain-based task support is the driver-car interaction system. To investigate the feasibility of such a system to detect changes in visuomotor workload, 34 drivers were exposed to several levels of driving demand in a driving simulator. Driving demand was manipulated by varying driving speed and by asking the drivers to comply to individually set lane keeping performance targets. Differences in the individual driver's workload levels were classified by applying the Common Spatial Pattern (CSP) and Fisher's linear discriminant analysis to frequency filtered electroencephalogram (EEG) data during an off line classification study. Several frequency ranges, EEG cap configurations, and condition pairs were explored. It was found that classifications were most accurate when based on high frequencies, larger electrode sets, and the frontal electrodes. Depending on these factors, classification accuracies across participants reached about 95% on average. The association between high accuracies and high frequencies suggests that part of the underlying information did not originate directly from neuronal activity. Nonetheless, average classification accuracies up to 75–80% were obtained from the lower EEG ranges that are likely to reflect neuronal activity. For a system designer, this implies that a passive BCI system may use several frequency ranges for workload classifications. PMID:23970851

Spatially aggregated multiclass pattern classification in functional MRI using optimally selected functional brain areas.

PubMed

Zheng, Weili; Ackley, Elena S; Martínez-Ramón, Manel; Posse, Stefan

2013-02-01

In previous works, boosting aggregation of classifier outputs from discrete brain areas has been demonstrated to reduce dimensionality and improve the robustness and accuracy of functional magnetic resonance imaging (fMRI) classification. However, dimensionality reduction and classification of mixed activation patterns of multiple classes remain challenging. In the present study, the goals were (a) to reduce dimensionality by combining feature reduction at the voxel level and backward elimination of optimally aggregated classifiers at the region level, (b) to compare region selection for spatially aggregated classification using boosting and partial least squares regression methods and (c) to resolve mixed activation patterns using probabilistic prediction of individual tasks. Brain activation maps from interleaved visual, motor, auditory and cognitive tasks were segmented into 144 functional regions. Feature selection reduced the number of feature voxels by more than 50%, leaving 95 regions. The two aggregation approaches further reduced the number of regions to 30, resulting in more than 75% reduction of classification time and misclassification rates of less than 3%. Boosting and partial least squares (PLS) were compared to select the most discriminative and the most task correlated regions, respectively. Successful task prediction in mixed activation patterns was feasible within the first block of task activation in real-time fMRI experiments. This methodology is suitable for sparsifying activation patterns in real-time fMRI and for neurofeedback from distributed networks of brain activation. Copyright © 2013 Elsevier Inc. All rights reserved.

Automatic brain tissue segmentation based on graph filter.

PubMed

Kong, Youyong; Chen, Xiaopeng; Wu, Jiasong; Zhang, Pinzheng; Chen, Yang; Shu, Huazhong

2018-05-09

Accurate segmentation of brain tissues from magnetic resonance imaging (MRI) is of significant importance in clinical applications and neuroscience research. Accurate segmentation is challenging due to the tissue heterogeneity, which is caused by noise, bias filed and partial volume effects. To overcome this limitation, this paper presents a novel algorithm for brain tissue segmentation based on supervoxel and graph filter. Firstly, an effective supervoxel method is employed to generate effective supervoxels for the 3D MRI image. Secondly, the supervoxels are classified into different types of tissues based on filtering of graph signals. The performance is evaluated on the BrainWeb 18 dataset and the Internet Brain Segmentation Repository (IBSR) 18 dataset. The proposed method achieves mean dice similarity coefficient (DSC) of 0.94, 0.92 and 0.90 for the segmentation of white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) for BrainWeb 18 dataset, and mean DSC of 0.85, 0.87 and 0.57 for the segmentation of WM, GM and CSF for IBSR18 dataset. The proposed approach can well discriminate different types of brain tissues from the brain MRI image, which has high potential to be applied for clinical applications.

Computational analysis of transcranial magnetic stimulation in the presence of deep brain stimulation probes

NASA Astrophysics Data System (ADS)

Syeda, F.; Holloway, K.; El-Gendy, A. A.; Hadimani, R. L.

2017-05-01

Transcranial Magnetic Stimulation is an emerging non-invasive treatment for depression, Parkinson's disease, and a variety of other neurological disorders. Many Parkinson's patients receive the treatment known as Deep Brain Stimulation, but often require additional therapy for speech and swallowing impairment. Transcranial Magnetic Stimulation has been explored as a possible treatment by stimulating the mouth motor area of the brain. We have calculated induced electric field, magnetic field, and temperature distributions in the brain using finite element analysis and anatomically realistic heterogeneous head models fitted with Deep Brain Stimulation leads. A Figure of 8 coil, current of 5000 A, and frequency of 2.5 kHz are used as simulation parameters. Results suggest that Deep Brain Stimulation leads cause surrounding tissues to experience slightly increased E-field (Δ Emax =30 V/m), but not exceeding the nominal values induced in brain tissue by Transcranial Magnetic Stimulation without leads (215 V/m). The maximum temperature in the brain tissues surrounding leads did not change significantly from the normal human body temperature of 37 °C. Therefore, we ascertain that Transcranial Magnetic Stimulation in the mouth motor area may stimulate brain tissue surrounding Deep Brain Stimulation leads, but will not cause tissue damage.

Long-Term Implanted cOFM Probe Causes Minimal Tissue Reaction in the Brain

PubMed Central

Hochmeister, Sonja; Asslaber, Martin; Kroath, Thomas; Pieber, Thomas R.; Sinner, Frank

2014-01-01

This study investigated the histological tissue reaction to long-term implanted cerebral open flow microperfusion (cOFM) probes in the frontal lobe of the rat brain. Most probe-based cerebral fluid sampling techniques are limited in application time due to the formation of a glial scar that hinders substance exchange between brain tissue and the probe. A glial scar not only functions as a diffusion barrier but also alters metabolism and signaling in extracellular brain fluid. cOFM is a recently developed probe-based technique to continuously sample extracellular brain fluid with an intact blood-brain barrier. After probe implantation, a 2 week healing period is needed for blood-brain barrier reestablishment. Therefore, cOFM probes need to stay in place and functional for at least 15 days after implantation to ensure functionality. Probe design and probe materials are optimized to evoke minimal tissue reaction even after a long implantation period. Qualitative and quantitative histological tissue analysis revealed no continuous glial scar formation around the cOFM probe 30 days after implantation and only a minor tissue reaction regardless of perfusion of the probe. PMID:24621608

HIV-1 phylogenetic analysis shows HIV-1 transits through the meninges to brain and peripheral tissues.

PubMed

Lamers, Susanna L; Gray, Rebecca R; Salemi, Marco; Huysentruyt, Leanne C; McGrath, Michael S

2011-01-01

Brain infection by the human immunodeficiency virus type 1 (HIV-1) has been investigated in many reports with a variety of conclusions concerning the time of entry and degree of viral compartmentalization. To address these diverse findings, we sequenced HIV-1 gp120 clones from a wide range of brain, peripheral and meningeal tissues from five patients who died from several HIV-1 associated disease pathologies. High-resolution phylogenetic analysis confirmed previous studies that showed a significant degree of compartmentalization in brain and peripheral tissue subpopulations. Some intermixing between the HIV-1 subpopulations was evident, especially in patients that died from pathologies other than HIV-associated dementia. Interestingly, the major tissue harboring virus from both the brain and peripheral tissues was the meninges. These results show that (1) HIV-1 is clearly capable of migrating out of the brain, (2) the meninges are the most likely primary transport tissues, and (3) infected brain macrophages comprise an important HIV reservoir during highly active antiretroviral therapy. Copyright © 2010 Elsevier B.V. All rights reserved.

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.

Unsupervised classification of operator workload from brain signals.

PubMed

Schultze-Kraft, Matthias; Dähne, Sven; Gugler, Manfred; Curio, Gabriel; Blankertz, Benjamin

2016-06-01

In this study we aimed for the classification of operator workload as it is expected in many real-life workplace environments. We explored brain-signal based workload predictors that differ with respect to the level of label information required for training, including entirely unsupervised approaches. Subjects executed a task on a touch screen that required continuous effort of visual and motor processing with alternating difficulty. We first employed classical approaches for workload state classification that operate on the sensor space of EEG and compared those to the performance of three state-of-the-art spatial filtering methods: common spatial patterns (CSPs) analysis, which requires binary label information; source power co-modulation (SPoC) analysis, which uses the subjects' error rate as a target function; and canonical SPoC (cSPoC) analysis, which solely makes use of cross-frequency power correlations induced by different states of workload and thus represents an unsupervised approach. Finally, we investigated the effects of fusing brain signals and peripheral physiological measures (PPMs) and examined the added value for improving classification performance. Mean classification accuracies of 94%, 92% and 82% were achieved with CSP, SPoC, cSPoC, respectively. These methods outperformed the approaches that did not use spatial filtering and they extracted physiologically plausible components. The performance of the unsupervised cSPoC is significantly increased by augmenting it with PPM features. Our analyses ensured that the signal sources used for classification were of cortical origin and not contaminated with artifacts. Our findings show that workload states can be successfully differentiated from brain signals, even when less and less information from the experimental paradigm is used, thus paving the way for real-world applications in which label information may be noisy or entirely unavailable.

Unsupervised classification of operator workload from brain signals

NASA Astrophysics Data System (ADS)

Schultze-Kraft, Matthias; Dähne, Sven; Gugler, Manfred; Curio, Gabriel; Blankertz, Benjamin

2016-06-01

Objective. In this study we aimed for the classification of operator workload as it is expected in many real-life workplace environments. We explored brain-signal based workload predictors that differ with respect to the level of label information required for training, including entirely unsupervised approaches. Approach. Subjects executed a task on a touch screen that required continuous effort of visual and motor processing with alternating difficulty. We first employed classical approaches for workload state classification that operate on the sensor space of EEG and compared those to the performance of three state-of-the-art spatial filtering methods: common spatial patterns (CSPs) analysis, which requires binary label information; source power co-modulation (SPoC) analysis, which uses the subjects’ error rate as a target function; and canonical SPoC (cSPoC) analysis, which solely makes use of cross-frequency power correlations induced by different states of workload and thus represents an unsupervised approach. Finally, we investigated the effects of fusing brain signals and peripheral physiological measures (PPMs) and examined the added value for improving classification performance. Main results. Mean classification accuracies of 94%, 92% and 82% were achieved with CSP, SPoC, cSPoC, respectively. These methods outperformed the approaches that did not use spatial filtering and they extracted physiologically plausible components. The performance of the unsupervised cSPoC is significantly increased by augmenting it with PPM features. Significance. Our analyses ensured that the signal sources used for classification were of cortical origin and not contaminated with artifacts. Our findings show that workload states can be successfully differentiated from brain signals, even when less and less information from the experimental paradigm is used, thus paving the way for real-world applications in which label information may be noisy or entirely unavailable.

Autopsy consent, brain collection, and standardized neuropathologic assessment of ADNI participants: The essential role of the Neuropathology Core

PubMed Central

Cairns, Nigel J.; Taylor-Reinwald, Lisa; Morris, John C.

2010-01-01

Background Our objectives are to facilitate autopsy consent, brain collection, and perform standardized neuropathologic assessments of all Alzheimer's Disease Neuroimaging Initiative (ADNI) participants who come to autopsy at the 58 ADNI sites in the USA and Canada. Methods Building on the expertise and resources of the existing Alzheimer's Disease Research Center (ADRC) at Washington University School of Medicine, St. Louis, MO, a Neuropathology Core (NPC) to serve ADNI was established with one new highly motivated research coordinator. The ADNI-NPC coordinator provides training materials and protocols to assist clinicians at ADNI sites in obtaining voluntary consent for brain autopsy in ADNI participants. Secondly, the ADNI-NPC maintains a central laboratory to provide uniform neuropathologic assessments using the operational criteria for the classification of AD and other pathologies defined by the National Alzheimer Coordinating Center (NACC). Thirdly, the ADNI-NPC maintains a state-of-the-art brain bank of ADNI-derived brain tissue to promote biomarker and multi-disciplinary clinicopathologic studies. Results During the initial year of funding of the ADNI Neuropathology Core, there was notable improvement in the autopsy rate to 44.4%. In the most recent year of funding (September 1st, 2008 to August 31st 2009), our autopsy rate improved to 71.5%. Although the overall numbers to date are small, these data demonstrate that the Neuropathology Core has established the administrative organization with the participating sites to harvest brains from ADNI participants who come to autopsy. Conclusions Within two years of operation, the Neuropathology Core has: (1) implemented a protocol to solicit permission for brain autopsy in ADNI participants at all 58 sites who die and (2) to send appropriate brain tissue from the decedents to the Neuropathology Core for a standardized, uniform, and state-of-the-art neuropathologic assessment. The benefit to ADNI of the implementation of the NPC is very clear. Prior to the establishment of the NPC in September 2007, there were 6 deaths but no autopsies in ADNI participants. Subsequent to the establishment of the Core there have been 17 deaths of ADNI participants and 10 autopsies. Hence, the autopsy rate has gone from 0% to 59%. The third major accomplishment is the detection of co-existent pathologies with AD in the autopsied cases. It is possible that these co-morbidities may contribute to any variance in ADNI data. PMID:20451876

Identification of the boundary between normal brain tissue and ischemia region using two-photon excitation fluorescence microscopy

NASA Astrophysics Data System (ADS)

Du, Huiping; Wang, Shu; Wang, Xingfu; Zhu, Xiaoqin; Zhuo, Shuangmu; Chen, Jianxin

2016-10-01

Ischemic stroke is one of the common neurological diseases, and it is becoming the leading causes of death and permanent disability around the world. Early and accurate identification of the potentially salvageable boundary region of ischemia brain tissues may enable selection of the most appropriate candidates for early stroke therapies. In this work, TPEF microscopy was used to image the microstructures of normal brain tissues, ischemia regions and the boundary region between normal and ischemia brain tissues. The ischemia brain tissues from Sprague-Dawley (SD) rats were subjected to 6 hours of middle cerebral artery occlusion (MCAO). Our study demonstrates that TPEF microscopy has the ability to not only reveal the morphological changes of the neurons but also identify the boundary between normal brain tissue and ischemia region, which correspond well to the hematoxylin and eosin (H and E) stained images. With the development of miniaturized TPEF microscope imaging devices, TPEF microscopy can be developed into an effectively diagnostic and monitoring tool for cerebral ischemia.

Simple adaptive sparse representation based classification schemes for EEG based brain-computer interface applications.

PubMed

Shin, Younghak; Lee, Seungchan; Ahn, Minkyu; Cho, Hohyun; Jun, Sung Chan; Lee, Heung-No

2015-11-01

One of the main problems related to electroencephalogram (EEG) based brain-computer interface (BCI) systems is the non-stationarity of the underlying EEG signals. This results in the deterioration of the classification performance during experimental sessions. Therefore, adaptive classification techniques are required for EEG based BCI applications. In this paper, we propose simple adaptive sparse representation based classification (SRC) schemes. Supervised and unsupervised dictionary update techniques for new test data and a dictionary modification method by using the incoherence measure of the training data are investigated. The proposed methods are very simple and additional computation for the re-training of the classifier is not needed. The proposed adaptive SRC schemes are evaluated using two BCI experimental datasets. The proposed methods are assessed by comparing classification results with the conventional SRC and other adaptive classification methods. On the basis of the results, we find that the proposed adaptive schemes show relatively improved classification accuracy as compared to conventional methods without requiring additional computation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Combined EEG-fNIRS decoding of motor attempt and imagery for brain switch control: an offline study in patients with tetraplegia.

PubMed

Blokland, Yvonne; Spyrou, Loukianos; Thijssen, Dick; Eijsvogels, Thijs; Colier, Willy; Floor-Westerdijk, Marianne; Vlek, Rutger; Bruhn, Jorgen; Farquhar, Jason

2014-03-01

Combining electrophysiological and hemodynamic features is a novel approach for improving current performance of brain switches based on sensorimotor rhythms (SMR). This study was conducted with a dual purpose: to test the feasibility of using a combined electroencephalogram/functional near-infrared spectroscopy (EEG-fNIRS) SMR-based brain switch in patients with tetraplegia, and to examine the performance difference between motor imagery and motor attempt for this user group. A general improvement was found when using both EEG and fNIRS features for classification as compared to using the single-modality EEG classifier, with average classification rates of 79% for attempted movement and 70% for imagined movement. For the control group, rates of 87% and 79% were obtained, respectively, where the "attempted movement" condition was replaced with "actual movement." A combined EEG-fNIRS system might be especially beneficial for users who lack sufficient control of current EEG-based brain switches. The average classification performance in the patient group for attempted movement was significantly higher than for imagined movement using the EEG-only as well as the combined classifier, arguing for the case of a paradigm shift in current brain switch research.

Taking label-free optical spectroscopy techniques into the operating theatre: biopsy needles and surgical guidance probes (Conference Presentation)

NASA Astrophysics Data System (ADS)

Leblond, Frédéric

2017-02-01

Recent advances will be described relating to the development and clinical translation of optical spectroscopy techniques designed to guide surgical interventions in brain and prostate oncology applications. The use of molecular imaging guidance systems can enable true intra-operative tissue identification, increasing the effectiveness of cancer surgery and potentially positively impacting patient survival. Surgical resection is a fundamental cancer treatment, but its effectiveness is reduced by the inability to rapidly and accurately identify cancer margins. We will introduce a portable intraoperative label-free multimodal optical spectroscopy system combining intrinsic fluorescence, diffuse reflectance, and Raman spectroscopy that can identify cancer in situ during surgery. We will show that this on-line guidance system can detect primary cancer such as glioma as well as metastatic melanoma and cancer of the lung and colon with an accuracy, sensitivity, and specificity of 97%, 100%, and 93% respectively. Moreover, a method will be presented, along with preliminary tissue classification results, based on the interrogation of whole human prostates from prostatectomies. The development and in vivo validation of an optical brain needle biopsy instrument will be presented demonstrating its ability to detect bulk tumor using Raman spectroscopy with the goal of reducing the number of non-diagnostic samples during a procedure. The extraction of tissue can cause life-threatening hemorrhage because of significant blood vessel injury during the procedure. We will demonstrate that a sub-diffuse optical tomography technique integrated with a commercial biopsy needle can detect the presence of blood vessels to limit the hemorrhage risk.

Postmortem changes in the neuroanatomical characteristics of the primate brain: the hippocampal formation

PubMed Central

Lavenex, Pierre; Lavenex, Pamela Banta; Bennett, Jeffrey L.; Amaral, David G.

2009-01-01

Comparative studies of the structural organization of the brain are fundamental to our understanding of human brain function. However, whereas brains of experimental animals are fixed by perfusion of a fixative through the vasculature, human or ape brains are fixed by immersion after varying postmortem intervals. Although differential treatments might affect the fundamental characteristics of the tissue, this question has not been evaluated empirically in primate brains. Monkey brains were either perfused, or acquired after varying postmortem intervals before immersion-fixation in 4% paraformaldehyde. We found that the fixation method affected the neuroanatomical characteristics of the monkey hippocampal formation. Soma size was smaller in Nissl-stained, immersion-fixed tissue, although overall brain volume was larger, as compared to perfusion-fixed tissue. Non-phosphorylated high-molecular-weight neurofilament immunoreactivity was lower in CA3 pyramidal neurons, dentate mossy cells and the entorhinal cortex, whereas it was higher in the mossy fiber pathway in immersion-fixed tissue. Serotonin-immunoreactive fibers were well-stained in perfused tissue but were undetectable in immersion-fixed tissue. Although regional immunoreactivity patterns for calcium-binding proteins were not affected, intracellular staining degraded with increasing postmortem intervals. Somatostatin-immunoreactive clusters of large axonal varicosities, previously reported only in humans, were observed in immersion-fixed monkey tissue. In addition, calretinin-immunoreactive multipolar neurons, previously observed only in rodents, were found in the rostral dentate gyrus in both perfused and immersion-fixed brains. In conclusion, comparative studies of the brain must evaluate the effects of fixation on the staining pattern of each marker in every structure of interest before drawing conclusions about species differences. PMID:18972553

Postmortem changes in the neuroanatomical characteristics of the primate brain: hippocampal formation.

PubMed

Lavenex, Pierre; Lavenex, Pamela Banta; Bennett, Jeffrey L; Amaral, David G

2009-01-01

Comparative studies of the structural organization of the brain are fundamental to our understanding of human brain function. However, whereas brains of experimental animals are fixed by perfusion of a fixative through the vasculature, human or ape brains are fixed by immersion after varying postmortem intervals. Although differential treatments might affect the fundamental characteristics of the tissue, this question has not been evaluated empirically in primate brains. Monkey brains were either perfused or acquired after varying postmortem intervals before immersion-fixation in 4% paraformaldehyde. We found that the fixation method affected the neuroanatomical characteristics of the monkey hippocampal formation. Soma size was smaller in Nissl-stained, immersion-fixed tissue, although overall brain volume was larger as compared to perfusion-fixed tissue. Nonphosphorylated high-molecular-weight neurofilament immunoreactivity was lower in CA3 pyramidal neurons, dentate mossy cells, and the entorhinal cortex, whereas it was higher in the mossy fiber pathway in immersion-fixed tissue. Serotonin-immunoreactive fibers were well stained in perfused tissue but were undetectable in immersion-fixed tissue. Although regional immunoreactivity patterns for calcium-binding proteins were not affected, intracellular staining degraded with increasing postmortem intervals. Somatostatin-immunoreactive clusters of large axonal varicosities, previously reported only in humans, were observed in immersion-fixed monkey tissue. In addition, calretinin-immunoreactive multipolar neurons, previously observed only in rodents, were found in the rostral dentate gyrus in both perfused and immersion-fixed brains. In conclusion, comparative studies of the brain must evaluate the effects of fixation on the staining pattern of each marker in every structure of interest before drawing conclusions about species differences.

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.

Effects of the Variation in Brain Tissue Mechanical Properties on the Intracranial Response of a 6-Year-Old Child.

PubMed

Cui, Shihai; Li, Haiyan; Li, Xiangnan; Ruan, Jesse

2015-01-01

Brain tissue mechanical properties are of importance to investigate child head injury using finite element (FE) method. However, these properties used in child head FE model normally vary in a large range in published literatures because of the insufficient child cadaver experiments. In this work, a head FE model with detailed anatomical structures is developed from the computed tomography (CT) data of a 6-year-old healthy child head. The effects of brain tissue mechanical properties on traumatic brain response are also analyzed by reconstruction of a head impact on engine hood according to Euro-NCAP testing regulation using FE method. The result showed that the variations of brain tissue mechanical parameters in linear viscoelastic constitutive model had different influences on the intracranial response. Furthermore, the opposite trend was obtained in the predicted shear stress and shear strain of brain tissues caused by the variations of mentioned parameters.

Individual Human Brain Areas Can Be Identified from Their Characteristic Spectral Activation Fingerprints

PubMed Central

Keitel, Anne; Gross, Joachim

2016-01-01

The human brain can be parcellated into diverse anatomical areas. We investigated whether rhythmic brain activity in these areas is characteristic and can be used for automatic classification. To this end, resting-state MEG data of 22 healthy adults was analysed. Power spectra of 1-s long data segments for atlas-defined brain areas were clustered into spectral profiles (“fingerprints”), using k-means and Gaussian mixture (GM) modelling. We demonstrate that individual areas can be identified from these spectral profiles with high accuracy. Our results suggest that each brain area engages in different spectral modes that are characteristic for individual areas. Clustering of brain areas according to similarity of spectral profiles reveals well-known brain networks. Furthermore, we demonstrate task-specific modulations of auditory spectral profiles during auditory processing. These findings have important implications for the classification of regional spectral activity and allow for novel approaches in neuroimaging and neurostimulation in health and disease. PMID:27355236

Three-dimensional textural features of conventional MRI improve diagnostic classification of childhood brain tumours.

PubMed

Fetit, Ahmed E; Novak, Jan; Peet, Andrew C; Arvanitits, Theodoros N

2015-09-01

The aim of this study was to assess the efficacy of three-dimensional texture analysis (3D TA) of conventional MR images for the classification of childhood brain tumours in a quantitative manner. The dataset comprised pre-contrast T1 - and T2-weighted MRI series obtained from 48 children diagnosed with brain tumours (medulloblastoma, pilocytic astrocytoma and ependymoma). 3D and 2D TA were carried out on the images using first-, second- and higher order statistical methods. Six supervised classification algorithms were trained with the most influential 3D and 2D textural features, and their performances in the classification of tumour types, using the two feature sets, were compared. Model validation was carried out using the leave-one-out cross-validation (LOOCV) approach, as well as stratified 10-fold cross-validation, in order to provide additional reassurance. McNemar's test was used to test the statistical significance of any improvements demonstrated by 3D-trained classifiers. Supervised learning models trained with 3D textural features showed improved classification performances to those trained with conventional 2D features. For instance, a neural network classifier showed 12% improvement in area under the receiver operator characteristics curve (AUC) and 19% in overall classification accuracy. These improvements were statistically significant for four of the tested classifiers, as per McNemar's tests. This study shows that 3D textural features extracted from conventional T1 - and T2-weighted images can improve the diagnostic classification of childhood brain tumours. Long-term benefits of accurate, yet non-invasive, diagnostic aids include a reduction in surgical procedures, improvement in surgical and therapy planning, and support of discussions with patients' families. It remains necessary, however, to extend the analysis to a multicentre cohort in order to assess the scalability of the techniques used. Copyright © 2015 John Wiley & Sons, Ltd.

Multimodality Instrument for Tissue Characterization

NASA Technical Reports Server (NTRS)

Mah, Robert W. (Inventor); Andrews, Russell J. (Inventor)

2000-01-01

A system with multimodality instrument for tissue identification includes a computer-controlled motor driven heuristic probe with a multisensory tip is discussed. For neurosurgical applications, the instrument is mounted on a stereotactic frame for the probe to penetrate the brain in a precisely controlled fashion. The resistance of the brain tissue being penetrated is continually monitored by a miniaturized strain gauge attached to the probe tip. Other modality sensors may be mounted near the probe tip to provide real-time tissue characterizations and the ability to detect the proximity of blood vessels, thus eliminating errors normally associated with registration of pre-operative scans, tissue swelling, elastic tissue deformation, human judgement, etc., and rendering surgical procedures safer, more accurate, and efficient. A neural network, program adaptively learns the information on resistance and other characteristic features of normal brain tissue during the surgery and provides near real-time modeling. A fuzzy logic interface to the neural network program incorporates expert medical knowledge in the learning process. Identification of abnormal brain tissue is determined by the detection of change and comparison with previously learned models of abnormal brain tissues. The operation of the instrument is controlled through a user friendly graphical interface. Patient data is presented in a 3D stereographics display. Acoustic feedback of selected information may optionally be provided. Upon detection of the close proximity to blood vessels or abnormal brain tissue, the computer-controlled motor immediately stops probe penetration.

Classification of mathematics deficiency using shape and scale analysis of 3D brain structures

NASA Astrophysics Data System (ADS)

Kurtek, Sebastian; Klassen, Eric; Gore, John C.; Ding, Zhaohua; Srivastava, Anuj

2011-03-01

We investigate the use of a recent technique for shape analysis of brain substructures in identifying learning disabilities in third-grade children. This Riemannian technique provides a quantification of differences in shapes of parameterized surfaces, using a distance that is invariant to rigid motions and re-parameterizations. Additionally, it provides an optimal registration across surfaces for improved matching and comparisons. We utilize an efficient gradient based method to obtain the optimal re-parameterizations of surfaces. In this study we consider 20 different substructures in the human brain and correlate the differences in their shapes with abnormalities manifested in deficiency of mathematical skills in 106 subjects. The selection of these structures is motivated in part by the past links between their shapes and cognitive skills, albeit in broader contexts. We have studied the use of both individual substructures and multiple structures jointly for disease classification. Using a leave-one-out nearest neighbor classifier, we obtained a 62.3% classification rate based on the shape of the left hippocampus. The use of multiple structures resulted in an improved classification rate of 71.4%.

Genetic Programming and Frequent Itemset Mining to Identify Feature Selection Patterns of iEEG and fMRI Epilepsy Data

PubMed Central

Smart, Otis; Burrell, Lauren

2014-01-01

Pattern classification for intracranial electroencephalogram (iEEG) and functional magnetic resonance imaging (fMRI) signals has furthered epilepsy research toward understanding the origin of epileptic seizures and localizing dysfunctional brain tissue for treatment. Prior research has demonstrated that implicitly selecting features with a genetic programming (GP) algorithm more effectively determined the proper features to discern biomarker and non-biomarker interictal iEEG and fMRI activity than conventional feature selection approaches. However for each the iEEG and fMRI modalities, it is still uncertain whether the stochastic properties of indirect feature selection with a GP yield (a) consistent results within a patient data set and (b) features that are specific or universal across multiple patient data sets. We examined the reproducibility of implicitly selecting features to classify interictal activity using a GP algorithm by performing several selection trials and subsequent frequent itemset mining (FIM) for separate iEEG and fMRI epilepsy patient data. We observed within-subject consistency and across-subject variability with some small similarity for selected features, indicating a clear need for patient-specific features and possible need for patient-specific feature selection or/and classification. For the fMRI, using nearest-neighbor classification and 30 GP generations, we obtained over 60% median sensitivity and over 60% median selectivity. For the iEEG, using nearest-neighbor classification and 30 GP generations, we obtained over 65% median sensitivity and over 65% median selectivity except one patient. PMID:25580059

Latent feature representation with stacked auto-encoder for AD/MCI diagnosis

PubMed Central

Lee, Seong-Whan

2014-01-01

Recently, there have been great interests for computer-aided diagnosis of Alzheimer’s disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Unlike the previous methods that considered simple low-level features such as gray matter tissue volumes from MRI, and mean signal intensities from PET, in this paper, we propose a deep learning-based latent feature representation with a stacked auto-encoder (SAE). We believe that there exist latent non-linear complicated patterns inherent in the low-level features such as relations among features. Combining the latent information with the original features helps build a robust model in AD/MCI classification, with high diagnostic accuracy. Furthermore, thanks to the unsupervised characteristic of the pre-training in deep learning, we can benefit from the target-unrelated samples to initialize parameters of SAE, thus finding optimal parameters in fine-tuning with the target-related samples, and further enhancing the classification performances across four binary classification problems: AD vs. healthy normal control (HC), MCI vs. HC, AD vs. MCI, and MCI converter (MCI-C) vs. MCI non-converter (MCI-NC). In our experiments on ADNI dataset, we validated the effectiveness of the proposed method, showing the accuracies of 98.8, 90.7, 83.7, and 83.3 % for AD/HC, MCI/HC, AD/MCI, and MCI-C/MCI-NC classification, respectively. We believe that deep learning can shed new light on the neuroimaging data analysis, and our work presented the applicability of this method to brain disease diagnosis. PMID:24363140

The Importance of Brain Banks for Molecular Neuropathological Research: The New South Wales Tissue Resource Centre Experience

PubMed Central

Dedova, Irina; Harding, Antony; Sheedy, Donna; Garrick, Therese; Sundqvist, Nina; Hunt, Clare; Gillies, Juliette; Harper, Clive G.

2009-01-01

New developments in molecular neuropathology have evoked increased demands for postmortem human brain tissue. The New South Wales Tissue Resource Centre (TRC) at The University of Sydney has grown from a small tissue collection into one of the leading international brain banking facilities, which operates with best practice and quality control protocols. The focus of this tissue collection is on schizophrenia and allied disorders, alcohol use disorders and controls. This review highlights changes in TRC operational procedures dictated by modern neuroscience, and provides examples of applications of modern molecular techniques to study the neuropathogenesis of many different brain disorders. PMID:19333451

Different modes of herpes simplex virus type 1 spread in brain and skin tissues.

PubMed

Tsalenchuck, Yael; Tzur, Tomer; Steiner, Israel; Panet, Amos

2014-02-01

Herpes simplex virus type 1 (HSV-1) initially infects the skin and subsequently spreads to the nervous system. To investigate and compare HSV-1 mode of propagation in the two clinically relevant tissues, we have established ex vivo infection models, using native tissues of mouse and human skin, as well as mouse brain, maintained in organ cultures. HSV-1, which is naturally restricted to the human, infects and spreads in the mouse and human skin tissues in a similar fashion, thus validating the mouse model. The spread of HSV-1 in the skin was concentric to form typical plaques of limited size, predominantly of cytopathic cells. By contrast, HSV-1 spread in the brain tissue was directed along specific neuronal networks with no apparent cytopathic effect. Two additional differences were noted following infection of the skin and brain tissues. First, only a negligible amount of extracellular progeny virus was produced of the infected brain tissues, while substantial quantity of infectious progeny virus was released to the media of the infected skin. Second, antibodies against HSV-1, added following the infection, effectively restricted viral spread in the skin but have no effect on viral spread in the brain tissue. Taken together, these results reveal that HSV-1 spread within the brain tissue mostly by direct transfer from cell to cell, while in the skin the progeny extracellular virus predominates, thus facilitating the infection to new individuals.

Audit of practice in sudden unexpected death in epilepsy (SUDEP) post mortems and neuropathological findings.

PubMed

Thom, Maria; Michalak, Zuzanna; Wright, Gabriella; Dawson, Timothy; Hilton, David; Joshi, Abhijit; Diehl, Beate; Koepp, Matthias; Lhatoo, Samden; Sander, Josemir W; Sisodiya, Sanjay M

2016-08-01

Sudden unexpected death in epilepsy (SUDEP) is one of the leading causes of death in people with epilepsy. For classification of definite SUDEP, a post mortem (PM), including anatomical and toxicological examination, is mandatory to exclude other causes of death. We audited PM practice as well as the value of brain examination in SUDEP. We reviewed 145 PM reports in SUDEP cases from four UK neuropathology centres. Data were extracted for clinical epilepsy details, circumstances of death and neuropathological findings. Macroscopic brain abnormalities were identified in 52% of cases. Mild brain swelling was present in 28%, and microscopic pathologies relevant to cause or effect of seizures were seen in 89%. Examination based on whole fixed brains (76.6% of all PMs), and systematic regional sampling was associated with higher detection rates of underlying pathology (P < 0.01). Information was more frequently recorded regarding circumstances of death and body position/location than clinical epilepsy history and investigations. Our findings support the contribution of examination of the whole fixed brain in SUDEP, with high rates of detection of relevant pathology. Availability of full clinical epilepsy-related information at the time of PM could potentially further improve detection through targeted tissue sampling. Apart from confirmation of SUDEP, complete neuropathological examination contributes to evaluation of risk factors as well as helping to direct future research into underlying causes. © 2015 The Authors. Neuropathology and Applied Neurobiology published by John Wiley & Sons Ltd on behalf of British Neuropathological Society.

Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components

NASA Astrophysics Data System (ADS)

Müller-Putz, Gernot R.; Scherer, Reinhold; Brauneis, Christian; Pfurtscheller, Gert

2005-12-01

Brain-computer interfaces (BCIs) can be realized on the basis of steady-state evoked potentials (SSEPs). These types of brain signals resulting from repetitive stimulation have the same fundamental frequency as the stimulation but also include higher harmonics. This study investigated how the classification accuracy of a 4-class BCI system can be improved by incorporating visually evoked harmonic oscillations. The current study revealed that the use of three SSVEP harmonics yielded a significantly higher classification accuracy than was the case for one or two harmonics. During feedback experiments, the five subjects investigated reached a classification accuracy between 42.5% and 94.4%.

Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components.

PubMed

Müller-Putz, Gernot R; Scherer, Reinhold; Brauneis, Christian; Pfurtscheller, Gert

2005-12-01

Brain-computer interfaces (BCIs) can be realized on the basis of steady-state evoked potentials (SSEPs). These types of brain signals resulting from repetitive stimulation have the same fundamental frequency as the stimulation but also include higher harmonics. This study investigated how the classification accuracy of a 4-class BCI system can be improved by incorporating visually evoked harmonic oscillations. The current study revealed that the use of three SSVEP harmonics yielded a significantly higher classification accuracy than was the case for one or two harmonics. During feedback experiments, the five subjects investigated reached a classification accuracy between 42.5% and 94.4%.

Hyperspectral imaging solutions for brain tissue metabolic and hemodynamic monitoring: past, current and future developments

NASA Astrophysics Data System (ADS)

Giannoni, Luca; Lange, Frédéric; Tachtsidis, Ilias

2018-04-01

Hyperspectral imaging (HSI) technologies have been used extensively in medical research, targeting various biological phenomena and multiple tissue types. Their high spectral resolution over a wide range of wavelengths enables acquisition of spatial information corresponding to different light-interacting biological compounds. This review focuses on the application of HSI to monitor brain tissue metabolism and hemodynamics in life sciences. Different approaches involving HSI have been investigated to assess and quantify cerebral activity, mainly focusing on: (1) mapping tissue oxygen delivery through measurement of changes in oxygenated (HbO2) and deoxygenated (HHb) hemoglobin; and (2) the assessment of the cerebral metabolic rate of oxygen (CMRO2) to estimate oxygen consumption by brain tissue. Finally, we introduce future perspectives of HSI of brain metabolism, including its potential use for imaging optical signals from molecules directly involved in cellular energy production. HSI solutions can provide remarkable insight in understanding cerebral tissue metabolism and oxygenation, aiding investigation on brain tissue physiological processes.

A study on the antioxidant effect of Coriolus versicolor polysaccharide in rat brain tissues.

PubMed

Chen, Jiayu; Jin, Xiaoyan; Zhang, Liting; Yang, Linjun

2013-01-01

The objective of the study was to investigate the antioxidant effect of Chinese medicine Coriolus versicolor polysaccharide on brain tissue and its mechanism in rats. SOD, MDA and GSH-Px levels in rat brain tissues were determined with SD rats as the animal model. The results showed that Coriolus versicolor polysaccharide can reduce the lipid peroxidation level in brain tissues during exhaustive exercise in rats, and can accelerate the removal of free radicals. The study concluded that its antioxidant effect is relatively apparent.

Classification and localization of early-stage Alzheimer's disease in magnetic resonance images using a patch-based classifier ensemble.

PubMed

Simões, Rita; van Cappellen van Walsum, Anne-Marie; Slump, Cornelis H

2014-09-01

Classification methods have been proposed to detect Alzheimer’s disease (AD) using magnetic resonance images. Most rely on features such as the shape/volume of brain structures that need to be defined a priori. In this work, we propose a method that does not require either the segmentation of specific brain regions or the nonlinear alignment to a template. Besides classification, we also analyze which brain regions are discriminative between a group of normal controls and a group of AD patients. We perform 3D texture analysis using Local Binary Patterns computed at local image patches in the whole brain, combined in a classifier ensemble.We evaluate our method in a publicly available database including very mild-to-mild AD subjects and healthy elderly controls. For the subject cohort including only mild AD subjects, the best results are obtained using a combination of large (30×30×30 and 40×40×40 voxels) patches. A spatial analysis on the best performing patches shows that these are located in the medial-temporal lobe and in the periventricular regions. When very mild AD subjects are included in the dataset, the small (10×10×10 voxels) patches perform best, with the most discriminative ones being located near the left hippocampus. We show that our method is able not only to perform accurate classification, but also to localize dis-criminative brain regions, which are in accordance with the medical literature. This is achieved without the need to segment-specific brain structures and without performing nonlinear registration to a template, indicating that the method may be suitable for a clinical implementation that can help to diagnose AD at an earlier stage.

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.

Discriminative analysis of early Alzheimer's disease based on two intrinsically anti-correlated networks with resting-state fMRI.

PubMed

Wang, Kun; Jiang, Tianzi; Liang, Meng; Wang, Liang; Tian, Lixia; Zhang, Xinqing; Li, Kuncheng; Liu, Zhening

2006-01-01

In this work, we proposed a discriminative model of Alzheimer's disease (AD) on the basis of multivariate pattern classification and functional magnetic resonance imaging (fMRI). This model used the correlation/anti-correlation coefficients of two intrinsically anti-correlated networks in resting brains, which have been suggested by two recent studies, as the feature of classification. Pseudo-Fisher Linear Discriminative Analysis (pFLDA) was then performed on the feature space and a linear classifier was generated. Using leave-one-out (LOO) cross validation, our results showed a correct classification rate of 83%. We also compared the proposed model with another one based on the whole brain functional connectivity. Our proposed model outperformed the other one significantly, and this implied that the two intrinsically anti-correlated networks may be a more susceptible part of the whole brain network in the early stage of AD.

A Brainnetome Atlas Based Mild Cognitive Impairment Identification Using Hurst Exponent

PubMed Central

Long, Zhuqing; Jing, Bin; Guo, Ru; Li, Bo; Cui, Feiyi; Wang, Tingting; Chen, Hongwen

2018-01-01

Mild cognitive impairment (MCI), which generally represents the transition state between normal aging and the early changes related to Alzheimer’s disease (AD), has drawn increasing attention from neuroscientists due that efficient AD treatments need early initiation ahead of irreversible brain tissue damage. Thus effective MCI identification methods are desperately needed, which may be of great importance for the clinical intervention of AD. In this article, the range scaled analysis, which could effectively detect the temporal complexity of a time series, was utilized to calculate the Hurst exponent (HE) of functional magnetic resonance imaging (fMRI) data at a voxel level from 64 MCI patients and 60 healthy controls (HCs). Then the average HE values of each region of interest (ROI) in brainnetome atlas were extracted and compared between MCI and HC. At last, the abnormal average HE values were adopted as the classification features for a proposed support vector machine (SVM) based identification algorithm, and the classification performance was estimated with leave-one-out cross-validation (LOOCV). Our results indicated 83.1% accuracy, 82.8% sensitivity and 83.3% specificity, and an area under curve of 0.88, suggesting that the HE index could serve as an effective feature for the MCI identification. Furthermore, the abnormal HE brain regions in MCI were predominately involved in left middle frontal gyrus, right hippocampus, bilateral parahippocampal gyrus, bilateral amygdala, left cingulate gyrus, left insular gyrus, left fusiform gyrus, left superior parietal gyrus, left orbital gyrus and left basal ganglia. PMID:29692721

Differentiation of cancerous and normal brain tissue using label free fluorescence and Stokes shift spectroscopy

NASA Astrophysics Data System (ADS)

Zhou, Yan; Wang, Leana; Liu, Cheng-hui; He, Yong; Yu, Xinguang; Cheng, Gangge; Wang, Peng; Shu, Cheng; Alfano, Robert R.

2016-03-01

In this report, optical biopsy was applied to diagnose human brain cancer in vitro for the identification of brain cancer from normal tissues by native fluorescence and Stokes shift spectra (SSS). 77 brain specimens including three types of human brain tissues (normal, glioma and brain metastasis of lung cancers) were studied. In order to observe spectral changes of fluorophores via fluorescence, the selected excitation wavelength of UV at 300 and 340 nm for emission spectra and a different Stokes Shift spectra with intervals Δλ = 40 nm were measured. The fluorescence spectra and SSS from multiple key native molecular markers, such as tryptophan, collagen, NADH, alanine, ceroid and lipofuscin were observed in normal and diseased brain tissues. Two diagnostic criteria were established based on the ratios of the peak intensities and peak position in both fluorescence and SSS spectra. It was observed that the ratio of the spectral peak intensity of tryptophan (340 nm) to NADH (440 nm) increased in glioma, meningioma (benign), malignant meninges tumor, and brain metastasis of lung cancer tissues in comparison with normal tissues. The ratio of the SS spectral peak (Δλ = 40 nm) intensities from 292 nm to 366 nm had risen similarly in all grades of tumors.

Correlation between light scattering signal and tissue reversibility in rat brain exposed to hypoxia

NASA Astrophysics Data System (ADS)

Kawauchi, Satoko; Sato, Shunichi; Uozumi, Yoichi; Nawashiro, Hiroshi; Ishihara, Miya; Kikuchi, Makoto

2010-02-01

Light scattering signal is a potential indicator of tissue viability in brain because cellular and subcellular structural integrity should be associated with cell viability in brain tissue. We previously performed multiwavelength diffuse reflectance measurement for a rat global ischemic brain model and observed a unique triphasic change in light scattering at a certain time after oxygen and glucose deprivation. This triphasic scattering change (TSC) was shown to precede cerebral ATP exhaustion, suggesting that loss of brain tissue viability can be predicted by detecting scattering signal. In the present study, we examined correlation between light scattering signal and tissue reversibility in rat brain in vivo. We performed transcranial diffuse reflectance measurement for rat brain; under spontaneous respiration, hypoxia was induced for the rat by nitrogen gas inhalation and reoxygenation was started at various time points. We observed a TSC, which started at 140 +/- 15 s after starting nitrogen gas inhalation (mean +/- SD, n=8). When reoxygenation was started before the TSC, all rats survived (n=7), while no rats survived when reoxygenation was started after the TSC (n=8). When reoxygenation was started during the TSC, rats survived probabilistically (n=31). Disability of motor function was not observed for the survived rats. These results indicate that TSC can be used as an indicator of loss of tissue reversibility in brains, providing useful information on the critical time zone for treatment to rescue the brain.

The Relationship of Three-Dimensional Human Skull Motion to Brain Tissue Deformation in Magnetic Resonance Elastography Studies

PubMed Central

Badachhape, Andrew A.; Okamoto, Ruth J.; Durham, Ramona S.; Efron, Brent D.; Nadell, Sam J.; Johnson, Curtis L.; Bayly, Philip V.

2017-01-01

In traumatic brain injury (TBI), membranes such as the dura mater, arachnoid mater, and pia mater play a vital role in transmitting motion from the skull to brain tissue. Magnetic resonance elastography (MRE) is an imaging technique developed for noninvasive estimation of soft tissue material parameters. In MRE, dynamic deformation of brain tissue is induced by skull vibrations during magnetic resonance imaging (MRI); however, skull motion and its mode of transmission to the brain remain largely uncharacterized. In this study, displacements of points in the skull, reconstructed using data from an array of MRI-safe accelerometers, were compared to displacements of neighboring material points in brain tissue, estimated from MRE measurements. Comparison of the relative amplitudes, directions, and temporal phases of harmonic motion in the skulls and brains of six human subjects shows that the skull–brain interface significantly attenuates and delays transmission of motion from skull to brain. In contrast, in a cylindrical gelatin “phantom,” displacements of the rigid case (reconstructed from accelerometer data) were transmitted to the gelatin inside (estimated from MRE data) with little attenuation or phase lag. This quantitative characterization of the skull–brain interface will be valuable in the parameterization and validation of computer models of TBI. PMID:28267188

The Relationship of Three-Dimensional Human Skull Motion to Brain Tissue Deformation in Magnetic Resonance Elastography Studies.

PubMed

Badachhape, Andrew A; Okamoto, Ruth J; Durham, Ramona S; Efron, Brent D; Nadell, Sam J; Johnson, Curtis L; Bayly, Philip V

2017-05-01

In traumatic brain injury (TBI), membranes such as the dura mater, arachnoid mater, and pia mater play a vital role in transmitting motion from the skull to brain tissue. Magnetic resonance elastography (MRE) is an imaging technique developed for noninvasive estimation of soft tissue material parameters. In MRE, dynamic deformation of brain tissue is induced by skull vibrations during magnetic resonance imaging (MRI); however, skull motion and its mode of transmission to the brain remain largely uncharacterized. In this study, displacements of points in the skull, reconstructed using data from an array of MRI-safe accelerometers, were compared to displacements of neighboring material points in brain tissue, estimated from MRE measurements. Comparison of the relative amplitudes, directions, and temporal phases of harmonic motion in the skulls and brains of six human subjects shows that the skull-brain interface significantly attenuates and delays transmission of motion from skull to brain. In contrast, in a cylindrical gelatin "phantom," displacements of the rigid case (reconstructed from accelerometer data) were transmitted to the gelatin inside (estimated from MRE data) with little attenuation or phase lag. This quantitative characterization of the skull-brain interface will be valuable in the parameterization and validation of computer models of TBI.

Joint tumor segmentation and dense deformable registration of brain MR images.

PubMed

Parisot, Sarah; Duffau, Hugues; Chemouny, Stéphane; Paragios, Nikos

2012-01-01

In this paper we propose a novel graph-based concurrent registration and segmentation framework. Registration is modeled with a pairwise graphical model formulation that is modular with respect to the data and regularization term. Segmentation is addressed by adopting a similar graphical model, using image-based classification techniques while producing a smooth solution. The two problems are coupled via a relaxation of the registration criterion in the presence of tumors as well as a segmentation through a registration term aiming the separation between healthy and diseased tissues. Efficient linear programming is used to solve both problems simultaneously. State of the art results demonstrate the potential of our method on a large and challenging low-grade glioma data set.

Volumetric neuroimage analysis extensions for the MIPAV software package.

PubMed

Bazin, Pierre-Louis; Cuzzocreo, Jennifer L; Yassa, Michael A; Gandler, William; McAuliffe, Matthew J; Bassett, Susan S; Pham, Dzung L

2007-09-15

We describe a new collection of publicly available software tools for performing quantitative neuroimage analysis. The tools perform semi-automatic brain extraction, tissue classification, Talairach alignment, and atlas-based measurements within a user-friendly graphical environment. They are implemented as plug-ins for MIPAV, a freely available medical image processing software package from the National Institutes of Health. Because the plug-ins and MIPAV are implemented in Java, both can be utilized on nearly any operating system platform. In addition to the software plug-ins, we have also released a digital version of the Talairach atlas that can be used to perform regional volumetric analyses. Several studies are conducted applying the new tools to simulated and real neuroimaging data sets.

High-sensitivity terahertz imaging of traumatic brain injury in a rat model

NASA Astrophysics Data System (ADS)

Zhao, Hengli; Wang, Yuye; Chen, Linyu; Shi, Jia; Ma, Kang; Tang, Longhuang; Xu, Degang; Yao, Jianquan; Feng, Hua; Chen, Tunan

2018-03-01

We demonstrated that different degrees of experimental traumatic brain injury (TBI) can be differentiated clearly in fresh slices of rat brain tissues using transmission-type terahertz (THz) imaging system. The high absorption region in THz images corresponded well with the injured area in visible images and magnetic resonance imaging results. The THz image and absorption characteristics of dehydrated paraffin-embedded brain slices and the hematoxylin and eosin (H&E)-stained microscopic images were investigated to account for the intrinsic differences in the THz images for the brain tissues suffered from different degrees of TBI and normal tissue aside from water. The THz absorption coefficients of rat brain tissues showed an increase in the aggravation of brain damage, particularly in the high-frequency range, whereas the cell density decreased as the order of mild, moderate, and severe TBI tissues compared with the normal tissue. Our results indicated that the different degrees of TBI were distinguishable owing to the different water contents and probable hematoma components distribution rather than intrinsic cell intensity. These promising results suggest that THz imaging has great potential as an alternative method for the fast diagnosis of TBI.

Assessing paedophilia based on the haemodynamic brain response to face images.

PubMed

Ponseti, Jorge; Granert, Oliver; Van Eimeren, Thilo; Jansen, Olav; Wolff, Stephan; Beier, Klaus; Deuschl, Günther; Huchzermeier, Christian; Stirn, Aglaja; Bosinski, Hartmut; Roman Siebner, Hartwig

2016-01-01

Objective assessment of sexual preferences may be of relevance in the treatment and prognosis of child sexual offenders. Previous research has indicated that this can be achieved by pattern classification of brain responses to sexual child and adult images. Our recent research showed that human face processing is tuned to sexual age preferences. This observation prompted us to test whether paedophilia can be inferred based on the haemodynamic brain responses to adult and child faces. Twenty-four men sexually attracted to prepubescent boys or girls (paedophiles) and 32 men sexually attracted to men or women (teleiophiles) were exposed to images of child and adult, male and female faces during a functional magnetic resonance imaging (fMRI) session. A cross-validated, automatic pattern classification algorithm of brain responses to facial stimuli yielded four misclassified participants (three false positives), corresponding to a specificity of 91% and a sensitivity of 95%. These results indicate that the functional response to facial stimuli can be reliably used for fMRI-based classification of paedophilia, bypassing the problem of showing child sexual stimuli to paedophiles.

Electroconvulsive therapy-induced brain functional connectivity predicts therapeutic efficacy in patients with schizophrenia: a multivariate pattern recognition study.

PubMed

Li, Peng; Jing, Ri-Xing; Zhao, Rong-Jiang; Ding, Zeng-Bo; Shi, Le; Sun, Hong-Qiang; Lin, Xiao; Fan, Teng-Teng; Dong, Wen-Tian; Fan, Yong; Lu, Lin

2017-05-11

Previous studies suggested that electroconvulsive therapy can influence regional metabolism and dopamine signaling, thereby alleviating symptoms of schizophrenia. It remains unclear what patients may benefit more from the treatment. The present study sought to identify biomarkers that predict the electroconvulsive therapy response in individual patients. Thirty-four schizophrenia patients and 34 controls were included in this study. Patients were scanned prior to treatment and after 6 weeks of treatment with antipsychotics only (n = 16) or a combination of antipsychotics and electroconvulsive therapy (n = 13). Subject-specific intrinsic connectivity networks were computed for each subject using a group information-guided independent component analysis technique. Classifiers were built to distinguish patients from controls and quantify brain states based on intrinsic connectivity networks. A general linear model was built on the classification scores of first scan (referred to as baseline classification scores) to predict treatment response. Classifiers built on the default mode network, the temporal lobe network, the language network, the corticostriatal network, the frontal-parietal network, and the cerebellum achieved a cross-validated classification accuracy of 83.82%, with specificity of 91.18% and sensitivity of 76.47%. After the electroconvulsive therapy, psychosis symptoms of the patients were relieved and classification scores of the patients were decreased. Moreover, the baseline classification scores were predictive for the treatment outcome. Schizophrenia patients exhibited functional deviations in multiple intrinsic connectivity networks which were able to distinguish patients from healthy controls at an individual level. Patients with lower classification scores prior to treatment had better treatment outcome, indicating that the baseline classification scores before treatment is a good predictor for treatment outcome. CONNECTIVITY NETWORKS REVEAL GOOD CANDIDATES FOR BRAIN STIMULATION: Connectivity patterns in the brain may help identify patients with schizophrenia most likely to benefit from electroconvulsive therapy. A team led by Lin Lu from Peking University, China, and Yong Fan from the University of Pennsylvania, USA, took functional magnetic resonance imaging (MRI) scans of 34 people with schizophrenia and 34 control individuals without mental illness. Those with schizophrenia were scanned before and after treatment; some received antipsychotics alone, others received medication plus electroconvulsive therapy. The researchers created organizational brain maps known as "intrinsic connectivity networks" for each individual, and showed that the neuroimaging pattern could discriminate between people with and without schizophrenia. For the schizophrenia patients, the connectivity networks taken prior to treatment also helped predict who would benefit from the brain-stimulation procedure. Such a biomarker could prove a useful diagnostic tool for clinicians.

Alterations of functional connectivities from early to middle adulthood: Clues from multivariate pattern analysis of resting-state fMRI data.

PubMed

Tian, Lixia; Ma, Lin; Wang, Linlin

2016-04-01

In contrast to extended research interests in the maturation and aging of human brain, alterations of brain structure and function from early to middle adulthood have been much less studied. The aim of the present study was to investigate the extent and pattern of the alterations of functional interactions between brain regions from early to middle adulthood. We carried out the study by multivariate pattern analysis of resting-state fMRI (RS-fMRI) data of 63 adults aged 18 to 45 years. Specifically, using elastic net, we performed brain age estimation and age-group classification (young adults aged 18-28 years vs. middle-aged adults aged 35-45 years) based on the resting-state functional connectivities (RSFCs) between 160 regions of interest (ROIs) evaluated on the RS-fMRI data of each subject. The results indicate that the estimated brain ages were significantly correlated with the chronological age (R=0.78, MAE=4.81), and a classification rate of 94.44% and area under the receiver operating characteristic curve (AUC) of 0.99 were obtained when classifying the young and middle-aged adults. These results provide strong evidence that functional interactions between brain regions undergo notable alterations from early to middle adulthood. By analyzing the RSFCs that contribute to brain age estimation/age-group classification, we found that a majority of the RSFCs were inter-network, and we speculate that inter-network RSFCs might mature late but age early as compared to intra-network ones. In addition, the strengthening/weakening of the RSFCs associated with the left/right hemispheric ROIs, the weakening of cortico-cerebellar RSFCs and the strengthening of the RSFCs between the default mode network and other networks contributed much to both brain age estimation and age-group classification. All these alterations might reflect that aging of brain function is already in progress in middle adulthood. Overall, the present study indicated that the RSFCs undergo notable alterations from early to middle adulthood and highlighted the necessity of careful considerations of possible influences of these alterations in related studies. Copyright © 2016 Elsevier Inc. All rights reserved.

Effects of high-pressure oxygen therapy on brain tissue water content and AQP4 expression in rabbits with cerebral hemorrhage.

PubMed

Wu, Jing; Chen, Jiong; Guo, Hua; Peng, Fang

2014-12-01

To investigate the effects of different atmosphere absolutes (ATA) of high-pressure oxygen (HPO) on brain tissue water content and Aquaporin-4 (AQP4) expression in rabbits with cerebral hemorrhage. 180 New Zealand white rabbits were selected and randomly divided into normal group (n = 30), control group (n = 30) and cerebral hemorrhage group (n = 120), and cerebral hemorrhage group was divided into group A, B, C and D with 30 rabbits in each group. The groups received 1.0, 1.8, 2.0 and 2.2 ATA of HPO treatments, respectively. Ten rabbits in each group were killed at first, third and fifth day to detect the brain tissue water content and change of AQP4 expression. In cerebral hemorrhage group, brain tissue water content and AQP4 expression after model establishment were first increased, then decreased and reached the maximum on third day (p < 0.05). Brain tissue water content and AQP4 expression in control group and cerebral hemorrhage group were significantly higher than normal group at different time points (p < 0.05). In contrast, brain tissue water content and AQP4 expression in group C were significantly lower than in group A, group B, group D and control group (p < 0.05). In control group, AQP4-positive cells significantly increased after model establishment, which reached maximum on third day, and positive cells in group C were significantly less than in group A, group B and group D. We also found that AQP4 expression were positively correlated with brain tissue water content (r = 0.719, p < 0.05) demonstrated by significantly increased AQP4 expression along with increased brain tissue water content. In conclusion, HPO can decrease AQP4 expression in brain tissue of rabbits with cerebral hemorrhage to suppress the progression of brain edema and promote repairing of injured tissue. 2.0 ATA HPO exerts best effects, which provides an experimental basis for ATA selection of HPO in treating cerebral hemorrhage.

Identification of autism spectrum disorder using deep learning and the ABIDE dataset.

PubMed

Heinsfeld, Anibal Sólon; Franco, Alexandre Rosa; Craddock, R Cameron; Buchweitz, Augusto; Meneguzzi, Felipe

2018-01-01

The goal of the present study was to apply deep learning algorithms to identify autism spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the patients brain activation patterns. We investigated ASD patients brain imaging data from a world-wide multi-site database known as ABIDE (Autism Brain Imaging Data Exchange). ASD is a brain-based disorder characterized by social deficits and repetitive behaviors. According to recent Centers for Disease Control data, ASD affects one in 68 children in the United States. We investigated patterns of functional connectivity that objectively identify ASD participants from functional brain imaging data, and attempted to unveil the neural patterns that emerged from the classification. The results improved the state-of-the-art by achieving 70% accuracy in identification of ASD versus control patients in the dataset. The patterns that emerged from the classification show an anticorrelation of brain function between anterior and posterior areas of the brain; the anticorrelation corroborates current empirical evidence of anterior-posterior disruption in brain connectivity in ASD. We present the results and identify the areas of the brain that contributed most to differentiating ASD from typically developing controls as per our deep learning model.

Monitoring brain temperature by time-resolved near-infrared spectroscopy: pilot study

NASA Astrophysics Data System (ADS)

Bakhsheshi, Mohammad Fazel; Diop, Mamadou; St. Lawrence, Keith; Lee, Ting-Yim

2014-05-01

Mild hypothermia (HT) is an effective neuroprotective strategy for a variety of acute brain injuries. However, the wide clinical adaptation of HT has been hampered by the lack of a reliable noninvasive method for measuring brain temperature, since core measurements have been shown to not always reflect brain temperature. The goal of this work was to develop a noninvasive optical technique for measuring brain temperature that exploits both the temperature dependency of water absorption and the high concentration of water in brain (80%-90%). Specifically, we demonstrate the potential of time-resolved near-infrared spectroscopy (TR-NIRS) to measure temperature in tissue-mimicking phantoms (in vitro) and deep brain tissue (in vivo) during heating and cooling, respectively. For deep brain tissue temperature monitoring, experiments were conducted on newborn piglets wherein hypothermia was induced by gradual whole body cooling. Brain temperature was concomitantly measured by TR-NIRS and a thermocouple probe implanted in the brain. Our proposed TR-NIRS method was able to measure the temperature of tissue-mimicking phantoms and brain tissues with a correlation of 0.82 and 0.66 to temperature measured with a thermometer, respectively. The mean difference between the TR-NIRS and thermometer measurements was 0.15°C±1.1°C for the in vitro experiments and 0.5°C±1.6°C for the in vivo measurements.

Gadolinium-based Contrast Media, Cerebrospinal Fluid and the Glymphatic System: Possible Mechanisms for the Deposition of Gadolinium in the Brain.

PubMed

Taoka, Toshiaki; Naganawa, Shinji

2018-04-10

After Kanda's first report in 2014 on gadolinium (Gd) deposition in brain tissue, a considerable number of studies have investigated the explanation for the observation. Gd deposition in brain tissue after repeated administration of gadolinium-based contrast medium (GBCM) has been histologically proven, and chelate stability has been shown to affect the deposition. However, the mechanism for this deposition has not been fully elucidated. Recently, a hypothesis was introduced that involves the 'glymphatic system', which is a coined word that combines 'gl' for glia cell and 'lymphatic' system. According to this hypothesis, the perivascular space functions as a conduit for cerebrospinal fluid to flow into the brain parenchyma. The perivascular space around the arteries allows cerebrospinal fluid to enter the interstitial space of the brain tissue through water channels controlled by aquaporin 4. The cerebrospinal fluid entering the interstitial space clears waste proteins from the tissue. It then flows into the perivascular space around the vein and is discharged outside the brain. In addition to the hypothesis regarding the glymphatic system, some reports have described that after GBCM administration, some of the GBCM distributes through systemic blood circulation and remains in other compartments including the cerebrospinal fluid. It is thought that the GBCM distributed into the cerebrospinal fluid cavity via the glymphatic system may remain in brain tissue for a longer duration compared to the GBCM in systemic circulation. Glymphatic system may of course act as a clearance system for GBCM from brain tissue. Based on these findings, the mechanism for Gd deposition in the brain will be discussed in this review. The authors speculate that the glymphatic system may be the major contributory factor to the deposition and clearance of gadolinium in brain tissue.

Gadolinium-based Contrast Media, Cerebrospinal Fluid and the Glymphatic System: Possible Mechanisms for the Deposition of Gadolinium in the Brain

PubMed Central

Taoka, Toshiaki; Naganawa, Shinji

2018-01-01

After Kanda’s first report in 2014 on gadolinium (Gd) deposition in brain tissue, a considerable number of studies have investigated the explanation for the observation. Gd deposition in brain tissue after repeated administration of gadolinium-based contrast medium (GBCM) has been histologically proven, and chelate stability has been shown to affect the deposition. However, the mechanism for this deposition has not been fully elucidated. Recently, a hypothesis was introduced that involves the ‘glymphatic system’, which is a coined word that combines ‘gl’ for glia cell and ‘lymphatic’ system. According to this hypothesis, the perivascular space functions as a conduit for cerebrospinal fluid to flow into the brain parenchyma. The perivascular space around the arteries allows cerebrospinal fluid to enter the interstitial space of the brain tissue through water channels controlled by aquaporin 4. The cerebrospinal fluid entering the interstitial space clears waste proteins from the tissue. It then flows into the perivascular space around the vein and is discharged outside the brain. In addition to the hypothesis regarding the glymphatic system, some reports have described that after GBCM administration, some of the GBCM distributes through systemic blood circulation and remains in other compartments including the cerebrospinal fluid. It is thought that the GBCM distributed into the cerebrospinal fluid cavity via the glymphatic system may remain in brain tissue for a longer duration compared to the GBCM in systemic circulation. Glymphatic system may of course act as a clearance system for GBCM from brain tissue. Based on these findings, the mechanism for Gd deposition in the brain will be discussed in this review. The authors speculate that the glymphatic system may be the major contributory factor to the deposition and clearance of gadolinium in brain tissue. PMID:29367513

Individual classification of ADHD patients by integrating multiscale neuroimaging markers and advanced pattern recognition techniques

PubMed Central

Cheng, Wei; Ji, Xiaoxi; Zhang, Jie; Feng, Jianfeng

2012-01-01

Accurate classification or prediction of the brain state across individual subject, i.e., healthy, or with brain disorders, is generally a more difficult task than merely finding group differences. The former must be approached with highly informative and sensitive biomarkers as well as effective pattern classification/feature selection approaches. In this paper, we propose a systematic methodology to discriminate attention deficit hyperactivity disorder (ADHD) patients from healthy controls on the individual level. Multiple neuroimaging markers that are proved to be sensitive features are identified, which include multiscale characteristics extracted from blood oxygenation level dependent (BOLD) signals, such as regional homogeneity (ReHo) and amplitude of low-frequency fluctuations. Functional connectivity derived from Pearson, partial, and spatial correlation is also utilized to reflect the abnormal patterns of functional integration, or, dysconnectivity syndromes in the brain. These neuroimaging markers are calculated on either voxel or regional level. Advanced feature selection approach is then designed, including a brain-wise association study (BWAS). Using identified features and proper feature integration, a support vector machine (SVM) classifier can achieve a cross-validated classification accuracy of 76.15% across individuals from a large dataset consisting of 141 healthy controls and 98 ADHD patients, with the sensitivity being 63.27% and the specificity being 85.11%. Our results show that the most discriminative features for classification are primarily associated with the frontal and cerebellar regions. The proposed methodology is expected to improve clinical diagnosis and evaluation of treatment for ADHD patient, and to have wider applications in diagnosis of general neuropsychiatric disorders. PMID:22888314

ADHD classification using bag of words approach on network features

NASA Astrophysics Data System (ADS)

Solmaz, Berkan; Dey, Soumyabrata; Rao, A. Ravishankar; Shah, Mubarak

2012-02-01

Attention Deficit Hyperactivity Disorder (ADHD) is receiving lots of attention nowadays mainly because it is one of the common brain disorders among children and not much information is known about the cause of this disorder. In this study, we propose to use a novel approach for automatic classification of ADHD conditioned subjects and control subjects using functional Magnetic Resonance Imaging (fMRI) data of resting state brains. For this purpose, we compute the correlation between every possible voxel pairs within a subject and over the time frame of the experimental protocol. A network of voxels is constructed by representing a high correlation value between any two voxels as an edge. A Bag-of-Words (BoW) approach is used to represent each subject as a histogram of network features; such as the number of degrees per voxel. The classification is done using a Support Vector Machine (SVM). We also investigate the use of raw intensity values in the time series for each voxel. Here, every subject is represented as a combined histogram of network and raw intensity features. Experimental results verified that the classification accuracy improves when the combined histogram is used. We tested our approach on a highly challenging dataset released by NITRC for ADHD-200 competition and obtained promising results. The dataset not only has a large size but also includes subjects from different demography and edge groups. To the best of our knowledge, this is the first paper to propose BoW approach in any functional brain disorder classification and we believe that this approach will be useful in analysis of many brain related conditions.

A Raman spectroscopy bio-sensor for tissue discrimination in surgical robotics.

PubMed

Ashok, Praveen C; Giardini, Mario E; Dholakia, Kishan; Sibbett, Wilson

2014-01-01

We report the development of a fiber-based Raman sensor to be used in tumour margin identification during endoluminal robotic surgery. Although this is a generic platform, the sensor we describe was adapted for the ARAKNES (Array of Robots Augmenting the KiNematics of Endoluminal Surgery) robotic platform. On such a platform, the Raman sensor is intended to identify ambiguous tissue margins during robot-assisted surgeries. To maintain sterility of the probe during surgical intervention, a disposable sleeve was specially designed. A straightforward user-compatible interface was implemented where a supervised multivariate classification algorithm was used to classify different tissue types based on specific Raman fingerprints so that it could be used without prior knowledge of spectroscopic data analysis. The protocol avoids inter-patient variability in data and the sensor system is not restricted for use in the classification of a particular tissue type. Representative tissue classification assessments were performed using this system on excised tissue. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Interactive classification and content-based retrieval of tissue images

NASA Astrophysics Data System (ADS)

Aksoy, Selim; Marchisio, Giovanni B.; Tusk, Carsten; Koperski, Krzysztof

2002-11-01

We describe a system for interactive classification and retrieval of microscopic tissue images. Our system models tissues in pixel, region and image levels. Pixel level features are generated using unsupervised clustering of color and texture values. Region level features include shape information and statistics of pixel level feature values. Image level features include statistics and spatial relationships of regions. To reduce the gap between low-level features and high-level expert knowledge, we define the concept of prototype regions. The system learns the prototype regions in an image collection using model-based clustering and density estimation. Different tissue types are modeled using spatial relationships of these regions. Spatial relationships are represented by fuzzy membership functions. The system automatically selects significant relationships from training data and builds models which can also be updated using user relevance feedback. A Bayesian framework is used to classify tissues based on these models. Preliminary experiments show that the spatial relationship models we developed provide a flexible and powerful framework for classification and retrieval of tissue images.

Mathematical modelling of blood-brain barrier failure and edema

NASA Astrophysics Data System (ADS)

Waters, Sarah; Lang, Georgina; Vella, Dominic; Goriely, Alain

2015-11-01

Injuries such as traumatic brain injury and stroke can result in increased blood-brain barrier permeability. This increase may lead to water accumulation in the brain tissue resulting in vasogenic edema. Although the initial injury may be localised, the resulting edema causes mechanical damage and compression of the vasculature beyond the original injury site. We employ a biphasic mixture model to investigate the consequences of blood-brain barrier permeability changes within a region of brain tissue and the onset of vasogenic edema. We find that such localised changes can indeed result in brain tissue swelling and that the type of damage that results (stress damage or strain damage) depends on the ability of the brain to clear edema fluid.

Responses of the Human Brain to Mild Dehydration and Rehydration Explored In Vivo by 1H-MR Imaging and Spectroscopy.

PubMed

Biller, A; Reuter, M; Patenaude, B; Homola, G A; Breuer, F; Bendszus, M; Bartsch, A J

2015-12-01

As yet, there are no in vivo data on tissue water changes and associated morphometric changes involved in the osmo-adaptation of normal brains. Our aim was to evaluate osmoadaptive responses of the healthy human brain to osmotic challenges of de- and rehydration by serial measurements of brain volume, tissue fluid, and metabolites. Serial T1-weighted and (1)H-MR spectroscopy data were acquired in 15 healthy individuals at normohydration, on 12 hours of dehydration, and during 1 hour of oral rehydration. Osmotic challenges were monitored by serum measures, including osmolality and hematocrit. MR imaging data were analyzed by using FreeSurfer and LCModel. On dehydration, serum osmolality increased by 0.67% and brain tissue fluid decreased by 1.63%, on average. MR imaging morphometry demonstrated corresponding decreases of cortical thickness and volumes of the whole brain, cortex, white matter, and hypothalamus/thalamus. These changes reversed during rehydration. Continuous fluid ingestion of 1 L of water for 1 hour within the scanner lowered serum osmolality by 0.96% and increased brain tissue fluid by 0.43%, on average. Concomitantly, cortical thickness and volumes of the whole brain, cortex, white matter, and hypothalamus/thalamus increased. Changes in brain tissue fluid were related to volume changes of the whole brain, the white matter, and hypothalamus/thalamus. Only volume changes of the hypothalamus/thalamus significantly correlated with serum osmolality. This is the first study simultaneously evaluating changes in brain tissue fluid, metabolites, volume, and cortical thickness. Our results reflect cellular volume regulatory mechanisms at a macroscopic level and emphasize that it is essential to control for hydration levels in studies on brain morphometry and metabolism in order to avoid confounding the findings. © 2015 by American Journal of Neuroradiology.

Polythelia pilosa: a particular form of accessory mammary tissue.

PubMed

Camacho, F; González-Cámpora, R

1998-01-01

The old Kajawa classification which considered eight possible forms of aberrant mammary tissue has been recently modified into a simpler one that considers this condition only when there is glandular parenchyma or when the aberrant tissue is not a glandular tissue but a nipple, an areola or both. This new classification disregards 'polythelia pilosa' defined as an 'isolated patch of hairs only'. To demonstrate that polythelia pilosa is at least a marker of subjacent accessory mammary tissue and, consequently, that the term should be incorporated into the current classification. Among 72 cases of aberrant or accessory mammary tissue, we have studied 14 cases (7 men and 7 women) that were clinically diagnosed as 'visible isolated patches of hairs, apparently without pigmentation nor structures of areola or nipple'. We excised such isolated patches in 3 women. The histopathological examination showed an acanthotic and hyperpigmented epithelium with central depression closed by keratin plugs; in the dermis there were follicles with hairs surrounded by hypertrophic sebaceous glands. In the deepest portion, abundant secretory glomerules and excretory ducts of apocrine gland type could be observed. Since the biopsy of isolated patches of hairs demonstrated structures of either areolar or apocrine glandular tissue, we think that the term 'polythelia pilosa' should be reinstated into the classification as it is at least a marker of true aberrant mammary structures in men and hirsute women.

Tactile event-related potentials in amyotrophic lateral sclerosis (ALS): Implications for brain-computer interface.

PubMed

Silvoni, S; Konicar, L; Prats-Sedano, M A; Garcia-Cossio, E; Genna, C; Volpato, C; Cavinato, M; Paggiaro, A; Veser, S; De Massari, D; Birbaumer, N

2016-01-01

We investigated neurophysiological brain responses elicited by a tactile event-related potential paradigm in a sample of ALS patients. Underlying cognitive processes and neurophysiological signatures for brain-computer interface (BCI) are addressed. We stimulated the palm of the hand in a group of fourteen ALS patients and a control group of ten healthy participants and recorded electroencephalographic signals in eyes-closed condition. Target and non-target brain responses were analyzed and classified offline. Classification errors served as the basis for neurophysiological brain response sub-grouping. A combined behavioral and quantitative neurophysiological analysis of sub-grouped data showed neither significant between-group differences, nor significant correlations between classification performance and the ALS patients' clinical state. Taking sequential effects of stimuli presentation into account, analyses revealed mean classification errors of 19.4% and 24.3% in healthy participants and ALS patients respectively. Neurophysiological correlates of tactile stimuli presentation are not altered by ALS. Tactile event-related potentials can be used to monitor attention level and task performance in ALS and may constitute a viable basis for future BCIs. Implications for brain-computer interface implementation of the proposed method for patients in critical conditions, such as the late stage of ALS and the (completely) locked-in state, are discussed. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Histopathological classification criteria of rat model of chronic prostatitis/chronic pelvic pain syndrome.

PubMed

Wang, Xianjin; Zhong, Shan; Xu, Tianyuan; Xia, Leilei; Zhang, Xiaohua; Zhu, Zhaowei; Zhang, Minguang; Shen, Zhoujun

2015-02-01

A variety of murine models of experimental prostatitis that mimic the phenotype of human chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) have been developed. However, there is still a lack of explicit diagnosis criteria about those animal model. Our study is to establish histopathological classification criteria, which will be conducive to evaluate the animal models. We firstly established a rat model of experimental autoimmune prostatitis that is considered a valid model for CP/CPPS. For modelling, male Sprague-Dawley rats were immunized with autologous prostate tissue homogenate supernatant emulsified with complete Freund's adjuvant by subcutaneous injection into abdominal flank and simultaneously immunized with pertussis-diphtheria-tetanus vaccine by intraperitoneal injection. Three immunizations were administered semimonthly. At the 45th day, animals were killed, and prostate tissues were examined for morphology. Histologically, the prostate tissues were characterized by lymphoproliferation, atrophy of acini, and chronic inflammatory cells infiltration in the stromal connective tissue around the acini or ducts. Finally, we built histopathological classification criteria incorporating inflammation locations (mesenchyme, glands, periglandular tissues), ranges (focal, multifocal, diffuse), and grades (grade I-IV). To verify the effectiveness and practicability of the histopathological classification criteria, we conducted the treatment study with one of the alpha blockers, tamsulosin. The histopathological classification criteria of rat model of CP/CPPS will serve for further research of the pathogenesis and treatment strategies of the disease.

Classification of microscopic images of breast tissue

NASA Astrophysics Data System (ADS)

Ballerini, Lucia; Franzen, Lennart

2004-05-01

Breast cancer is the most common form of cancer among women. The diagnosis is usually performed by the pathologist, that subjectively evaluates tissue samples. The aim of our research is to develop techniques for the automatic classification of cancerous tissue, by analyzing histological samples of intact tissue taken with a biopsy. In our study, we considered 200 images presenting four different conditions: normal tissue, fibroadenosis, ductal cancer and lobular cancer. Methods to extract features have been investigated and described. One method is based on granulometries, which are size-shape descriptors widely used in mathematical morphology. Applications of granulometries lead to distribution functions whose moments are used as features. A second method is based on fractal geometry, that seems very suitable to quantify biological structures. The fractal dimension of binary images has been computed using the euclidean distance mapping. Image classification has then been performed using the extracted features as input of a back-propagation neural network. A new method that combines genetic algorithms and morphological filters has been also investigated. In this case, the classification is based on a correlation measure. Very encouraging results have been obtained with pilot experiments using a small subset of images as training set. Experimental results indicate the effectiveness of the proposed methods. Cancerous tissue was correctly classified in 92.5% of the cases.

Differential metabolism of 4-hydroxynonenal in liver, lung and brain of mice and rats

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

Zheng, Ruijin; Dragomir, Ana-Cristina; Mishin, Vladimir

2014-08-15

The lipid peroxidation end-product 4-hydroxynonenal (4-HNE) is generated in tissues during oxidative stress. As a reactive aldehyde, it forms Michael adducts with nucleophiles, a process that disrupts cellular functioning. Liver, lung and brain are highly sensitive to xenobiotic-induced oxidative stress and readily generate 4-HNE. In the present studies, we compared 4-HNE metabolism in these tissues, a process that protects against tissue injury. 4-HNE was degraded slowly in total homogenates and S9 fractions of mouse liver, lung and brain. In liver, but not lung or brain, NAD(P)+ and NAD(P)H markedly stimulated 4-HNE metabolism. Similar results were observed in rat S9 fractionsmore » from these tissues. In liver, lung and brain S9 fractions, 4-HNE formed protein adducts. When NADH was used to stimulate 4-HNE metabolism, the formation of protein adducts was suppressed in liver, but not lung or brain. In both mouse and rat tissues, 4-HNE was also metabolized by glutathione S-transferases. The greatest activity was noted in livers of mice and in lungs of rats; relatively low glutathione S-transferase activity was detected in brain. In mouse hepatocytes, 4-HNE was rapidly taken up and metabolized. Simultaneously, 4-HNE-protein adducts were formed, suggesting that 4-HNE metabolism in intact cells does not prevent protein modifications. These data demonstrate that, in contrast to liver, lung and brain have a limited capacity to metabolize 4-HNE. The persistence of 4-HNE in these tissues may increase the likelihood of tissue injury during oxidative stress. - Highlights: • Lipid peroxidation generates 4-hydroxynonenal, a highly reactive aldehyde. • Rodent liver, but not lung or brain, is efficient in degrading 4-hydroxynonenal. • 4-hydroxynonenal persists in tissues with low metabolism, causing tissue damage.« less

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

Honorio, J.; Goldstein, R.; Honorio, J.

We propose a simple, well grounded classification technique which is suited for group classification on brain fMRI data sets that have high dimensionality, small number of subjects, high noise level, high subject variability, imperfect registration and capture subtle cognitive effects. We propose threshold-split region as a new feature selection method and majority voteas the classification technique. Our method does not require a predefined set of regions of interest. We use average acros ssessions, only one feature perexperimental condition, feature independence assumption, and simple classifiers. The seeming counter-intuitive approach of using a simple design is supported by signal processing and statisticalmore » theory. Experimental results in two block design data sets that capture brain function under distinct monetary rewards for cocaine addicted and control subjects, show that our method exhibits increased generalization accuracy compared to commonly used feature selection and classification techniques.« less

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.

Effects of the Variation in Brain Tissue Mechanical Properties on the Intracranial Response of a 6-Year-Old Child

PubMed Central

Cui, Shihai; Li, Haiyan; Li, Xiangnan; Ruan, Jesse

2015-01-01

Brain tissue mechanical properties are of importance to investigate child head injury using finite element (FE) method. However, these properties used in child head FE model normally vary in a large range in published literatures because of the insufficient child cadaver experiments. In this work, a head FE model with detailed anatomical structures is developed from the computed tomography (CT) data of a 6-year-old healthy child head. The effects of brain tissue mechanical properties on traumatic brain response are also analyzed by reconstruction of a head impact on engine hood according to Euro-NCAP testing regulation using FE method. The result showed that the variations of brain tissue mechanical parameters in linear viscoelastic constitutive model had different influences on the intracranial response. Furthermore, the opposite trend was obtained in the predicted shear stress and shear strain of brain tissues caused by the variations of mentioned parameters. PMID:26495031

Boosting Classification Accuracy of Diffusion MRI Derived Brain Networks for the Subtypes of Mild Cognitive Impairment Using Higher Order Singular Value Decomposition

PubMed Central

Zhan, L.; Liu, Y.; Zhou, J.; Ye, J.; Thompson, P.M.

2015-01-01

Mild cognitive impairment (MCI) is an intermediate stage between normal aging and Alzheimer's disease (AD), and around 10-15% of people with MCI develop AD each year. More recently, MCI has been further subdivided into early and late stages, and there is interest in identifying sensitive brain imaging biomarkers that help to differentiate stages of MCI. Here, we focused on anatomical brain networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer's Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying early versus late MCI. PMID:26413202

New KF-PP-SVM classification method for EEG in brain-computer interfaces.

PubMed

Yang, Banghua; Han, Zhijun; Zan, Peng; Wang, Qian

2014-01-01

Classification methods are a crucial direction in the current study of brain-computer interfaces (BCIs). To improve the classification accuracy for electroencephalogram (EEG) signals, a novel KF-PP-SVM (kernel fisher, posterior probability, and support vector machine) classification method is developed. Its detailed process entails the use of common spatial patterns to obtain features, based on which the within-class scatter is calculated. Then the scatter is added into the kernel function of a radial basis function to construct a new kernel function. This new kernel is integrated into the SVM to obtain a new classification model. Finally, the output of SVM is calculated based on posterior probability and the final recognition result is obtained. To evaluate the effectiveness of the proposed KF-PP-SVM method, EEG data collected from laboratory are processed with four different classification schemes (KF-PP-SVM, KF-SVM, PP-SVM, and SVM). The results showed that the overall average improvements arising from the use of the KF-PP-SVM scheme as opposed to KF-SVM, PP-SVM and SVM schemes are 2.49%, 5.83 % and 6.49 % respectively.

Usefulness of Ilae 2010 classification in Mexican epilepsy patients.

PubMed

Leyva, Ildefonso Rodríguez; Gómez, Juan Francisco Hernández; Enríquez, Fernando Cortés; Sierra, Juan Francisco Hernández

2017-05-15

Advances in neuroimaging, genomics, and molecular biology have improved the understanding of the pathogenesis of epilepsy. That is why the International League Against Epilepsy (ILAE) has created a new classification system. The present study aims to evaluate the association between epilepsy cases classified by the ILAE 2010 classification proposal, electroencephalography (EEG), and magnetic resonance imaging brain findings (MRI). Prospective cross-sectional design of 277 cases of epilepsy seen at the Epilepsy Clinic, Hospital Central "Dr. Ignacio Morones Prieto", were compared with the ILAE classification based on the etiology and clinical manifestations and their MRI and EEG findings. Cochran, Mantell, Haenzel test with significance p<0.05. MRI findings were associated with the etiology of the ILAE classification. According to EEG findings, the structural-metabolic etiology patients had more dysfunctional reports than genetic or unknown etiology patients (p<0.05). The adoption of the ILAE classification is recommended, as it can provide useful guidance towards the etiology of cases of epilepsy even when brain MRIs and EEGs are not available. Copyright © 2017 Elsevier B.V. All rights reserved.

Visualization and tissue classification of human breast cancer images using ultrahigh-resolution OCT.

PubMed

Yao, Xinwen; Gan, Yu; Chang, Ernest; Hibshoosh, Hanina; Feldman, Sheldon; Hendon, Christine

2017-03-01

Breast cancer is one of the most common cancers, and recognized as the third leading cause of mortality in women. Optical coherence tomography (OCT) enables three dimensional visualization of biological tissue with micrometer level resolution at high speed, and can play an important role in early diagnosis and treatment guidance of breast cancer. In particular, ultra-high resolution (UHR) OCT provides images with better histological correlation. This paper compared UHR OCT performance with standard OCT in breast cancer imaging qualitatively and quantitatively. Automatic tissue classification algorithms were used to automatically detect invasive ductal carcinoma in ex vivo human breast tissue. Human breast tissues, including non-neoplastic/normal tissues from breast reduction and tumor samples from mastectomy specimens, were excised from patients at Columbia University Medical Center. The tissue specimens were imaged by two spectral domain OCT systems at different wavelengths: a home-built ultra-high resolution (UHR) OCT system at 800 nm (measured as 2.72 μm axial and 5.52 μm lateral) and a commercial OCT system at 1,300 nm with standard resolution (measured as 6.5 μm axial and 15 μm lateral), and their imaging performances were analyzed qualitatively. Using regional features derived from OCT images produced by the two systems, we developed an automated classification algorithm based on relevance vector machine (RVM) to differentiate hollow-structured adipose tissue against solid tissue. We further developed B-scan based features for RVM to classify invasive ductal carcinoma (IDC) against normal fibrous stroma tissue among OCT datasets produced by the two systems. For adipose classification, 32 UHR OCT B-scans from 9 normal specimens, and 28 standard OCT B-scans from 6 normal and 4 IDC specimens were employed. For IDC classification, 152 UHR OCT B-scans from 6 normal and 13 IDC specimens, and 104 standard OCT B-scans from 5 normal and 8 IDC specimens were employed. We have demonstrated that UHR OCT images can produce images with better feature delineation compared with images produced by 1,300 nm OCT system. UHR OCT images of a variety of tissue types found in human breast tissue were presented. With a limited number of datasets, we showed that both OCT systems can achieve a good accuracy in identifying adipose tissue. Classification in UHR OCT images achieved higher sensitivity (94%) and specificity (93%) of adipose tissue than the sensitivity (91%) and specificity (76%) in 1,300 nm OCT images. In IDC classification, similarly, we achieved better results with UHR OCT images, featured an overall accuracy of 84%, sensitivity of 89% and specificity of 71% in this preliminary study. In this study, we provided UHR OCT images of different normal and malignant breast tissue types, and qualitatively and quantitatively studied the texture and optical features from OCT images of human breast tissue at different resolutions. We developed an automated approach to differentiate adipose tissue, fibrous stroma, and IDC within human breast tissues. Our work may open the door toward automatic intraoperative OCT evaluation of early-stage breast cancer. Lasers Surg. Med. 49:258-269, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Backscatter and attenuation properties of mammalian brain tissues

NASA Astrophysics Data System (ADS)

Wijekularatne, Pushpani Vihara

Traumatic Brain Injury (TBI) is a common category of brain injuries, which contributes to a substantial number of deaths and permanent disability all over the world. Ultrasound technology plays a major role in tissue characterization due to its low cost and portability that could be used to bridge a wide gap in the TBI diagnostic process. This research addresses the ultrasonic properties of mammalian brain tissues focusing on backscatter and attenuation. Orientation dependence and spatial averaging of data were analyzed using the same method resulting from insertion of tissue sample between a transducer and a reference reflector. Apparent backscatter transfer function (ABTF) at 1 to 10 MHz, attenuation coefficient and backscatter coefficient (BSC) at 1 to 5 MHz frequency ranges were measured on ovine brain tissue samples. The resulting ABTF was a monotonically decreasing function of frequency and the attenuation coefficient and BSC generally were increasing functions of frequency, results consistent with other soft tissues such as liver, blood and heart.

Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline

PubMed Central

Wang, Jiahui; Vachet, Clement; Rumple, Ashley; Gouttard, Sylvain; Ouziel, Clémentine; Perrot, Emilie; Du, Guangwei; Huang, Xuemei; Gerig, Guido; Styner, Martin

2014-01-01

Automated segmenting and labeling of individual brain anatomical regions, in MRI are challenging, due to the issue of individual structural variability. Although atlas-based segmentation has shown its potential for both tissue and structure segmentation, due to the inherent natural variability as well as disease-related changes in MR appearance, a single atlas image is often inappropriate to represent the full population of datasets processed in a given neuroimaging study. As an alternative for the case of single atlas segmentation, the use of multiple atlases alongside label fusion techniques has been introduced using a set of individual “atlases” that encompasses the expected variability in the studied population. In our study, we proposed a multi-atlas segmentation scheme with a novel graph-based atlas selection technique. We first paired and co-registered all atlases and the subject MR scans. A directed graph with edge weights based on intensity and shape similarity between all MR scans is then computed. The set of neighboring templates is selected via clustering of the graph. Finally, weighted majority voting is employed to create the final segmentation over the selected atlases. This multi-atlas segmentation scheme is used to extend a single-atlas-based segmentation toolkit entitled AutoSeg, which is an open-source, extensible C++ based software pipeline employing BatchMake for its pipeline scripting, developed at the Neuro Image Research and Analysis Laboratories of the University of North Carolina at Chapel Hill. AutoSeg performs N4 intensity inhomogeneity correction, rigid registration to a common template space, automated brain tissue classification based skull-stripping, and the multi-atlas segmentation. The multi-atlas-based AutoSeg has been evaluated on subcortical structure segmentation with a testing dataset of 20 adult brain MRI scans and 15 atlas MRI scans. The AutoSeg achieved mean Dice coefficients of 81.73% for the subcortical structures. PMID:24567717

A Dense Poly(ethylene glycol) Coating Improves Penetration of Large Polymeric Nanoparticles within Brain Tissue

PubMed Central

Nance, Elizabeth A.; Woodworth, Graeme F.; Sailor, Kurt A.; Shih, Ting-Yu; Xu, Qingguo; Swaminathan, Ganesh; Xiang, Dennis; Eberhart, Charles; Hanes, Justin

2013-01-01

Prevailing opinion suggests that only substances up to 64 nm in diameter can move at appreciable rates through the brain extracellular space (ECS). This size range is large enough to allow diffusion of signaling molecules, nutrients, and metabolic waste products, but too small to allow efficient penetration of most particulate drug delivery systems and viruses carrying therapeutic genes, thereby limiting effectiveness of many potential therapies. We analyzed the movements of nanoparticles of various diameters and surface coatings within fresh human and rat brain tissue ex vivo and mouse brain in vivo. Nanoparticles as large as 114-nm in diameter diffused within the human and rat brain, but only if they were densely coated with poly(ethylene glycol) (PEG). Using these minimally adhesive PEG-coated particles, we estimated that human brain tissue ECS has some pores larger than 200 nm, and that more than one-quarter of all pores are ≥100 nm. These findings were confirmed in vivo in mice, where 40- and 100-nm, but not 200-nm, nanoparticles, spread rapidly within brain tissue, only if densely coated with PEG. Similar results were observed in rat brain tissue with paclitaxel-loaded biodegradable nanoparticles of similar size (85 nm) and surface properties. The ability to achieve brain penetration with larger nanoparticles is expected to allow more uniform, longer-lasting, and effective delivery of drugs within the brain, and may find use in the treatment of brain tumors, stroke, neuroinflammation, and other brain diseases where the blood-brain barrier is compromised or where local delivery strategies are feasible. PMID:22932224

Classifying EEG for Brain-Computer Interface: Learning Optimal Filters for Dynamical System Features

PubMed Central

Song, Le; Epps, Julien

2007-01-01

Classification of multichannel EEG recordings during motor imagination has been exploited successfully for brain-computer interfaces (BCI). In this paper, we consider EEG signals as the outputs of a networked dynamical system (the cortex), and exploit synchronization features from the dynamical system for classification. Herein, we also propose a new framework for learning optimal filters automatically from the data, by employing a Fisher ratio criterion. Experimental evaluations comparing the proposed dynamical system features with the CSP and the AR features reveal their competitive performance during classification. Results also show the benefits of employing the spatial and the temporal filters optimized using the proposed learning approach. PMID:18364986

Oscillatory neural network for pattern recognition: trajectory based classification and supervised learning.

PubMed

Miller, Vonda H; Jansen, Ben H

2008-12-01

Computer algorithms that match human performance in recognizing written text or spoken conversation remain elusive. The reasons why the human brain far exceeds any existing recognition scheme to date in the ability to generalize and to extract invariant characteristics relevant to category matching are not clear. However, it has been postulated that the dynamic distribution of brain activity (spatiotemporal activation patterns) is the mechanism by which stimuli are encoded and matched to categories. This research focuses on supervised learning using a trajectory based distance metric for category discrimination in an oscillatory neural network model. Classification is accomplished using a trajectory based distance metric. Since the distance metric is differentiable, a supervised learning algorithm based on gradient descent is demonstrated. Classification of spatiotemporal frequency transitions and their relation to a priori assessed categories is shown along with the improved classification results after supervised training. The results indicate that this spatiotemporal representation of stimuli and the associated distance metric is useful for simple pattern recognition tasks and that supervised learning improves classification results.

A hybrid method for classifying cognitive states from fMRI data.

PubMed

Parida, S; Dehuri, S; Cho, S-B; Cacha, L A; Poznanski, R R

2015-09-01

Functional magnetic resonance imaging (fMRI) makes it possible to detect brain activities in order to elucidate cognitive-states. The complex nature of fMRI data requires under-standing of the analyses applied to produce possible avenues for developing models of cognitive state classification and improving brain activity prediction. While many models of classification task of fMRI data analysis have been developed, in this paper, we present a novel hybrid technique through combining the best attributes of genetic algorithms (GAs) and ensemble decision tree technique that consistently outperforms all other methods which are being used for cognitive-state classification. Specifically, this paper illustrates the combined effort of decision-trees ensemble and GAs for feature selection through an extensive simulation study and discusses the classification performance with respect to fMRI data. We have shown that our proposed method exhibits significant reduction of the number of features with clear edge classification accuracy over ensemble of decision-trees.

Systemic Analysis of Brain Mechanisms Underlying Learning Disabilities: An Introduction to the Brain Behavior Relationship.

ERIC Educational Resources Information Center

Scaramella-Nowinski, Valerie L.

The paper presents a discussion of human mental processes as they relate to learning disabilities. Pathognomonic symptoms associated with disturbances to brain areas or functional systems are discussed, as well as treatment procedures. This brain behavior relationship is offered as a basis for a classification system that is seen to more clearly…

Multivariate neuroanatomical classification of cognitive subtypes in schizophrenia: A support vector machine learning approach

PubMed Central

Gould, Ian C.; Shepherd, Alana M.; Laurens, Kristin R.; Cairns, Murray J.; Carr, Vaughan J.; Green, Melissa J.

2014-01-01

Heterogeneity in the structural brain abnormalities associated with schizophrenia has made identification of reliable neuroanatomical markers of the disease difficult. The use of more homogenous clinical phenotypes may improve the accuracy of predicting psychotic disorder/s on the basis of observable brain disturbances. Here we investigate the utility of cognitive subtypes of schizophrenia – ‘cognitive deficit’ and ‘cognitively spared’ – in determining whether multivariate patterns of volumetric brain differences can accurately discriminate these clinical subtypes from healthy controls, and from each other. We applied support vector machine classification to grey- and white-matter volume data from 126 schizophrenia patients previously allocated to the cognitive spared subtype, 74 cognitive deficit schizophrenia patients, and 134 healthy controls. Using this method, cognitive subtypes were distinguished from healthy controls with up to 72% accuracy. Cross-validation analyses between subtypes achieved an accuracy of 71%, suggesting that some common neuroanatomical patterns distinguish both subtypes from healthy controls. Notably, cognitive subtypes were best distinguished from one another when the sample was stratified by sex prior to classification analysis: cognitive subtype classification accuracy was relatively low (<60%) without stratification, and increased to 83% for females with sex stratification. Distinct neuroanatomical patterns predicted cognitive subtype status in each sex: sex-specific multivariate patterns did not predict cognitive subtype status in the other sex above chance, and weight map analyses demonstrated negative correlations between the spatial patterns of weights underlying classification for each sex. These results suggest that in typical mixed-sex samples of schizophrenia patients, the volumetric brain differences between cognitive subtypes are relatively minor in contrast to the large common disease-associated changes. Volumetric differences that distinguish between cognitive subtypes on a case-by-case basis appear to occur in a sex-specific manner that is consistent with previous evidence of disrupted relationships between brain structure and cognition in male, but not female, schizophrenia patients. Consideration of sex-specific differences in brain organization is thus likely to assist future attempts to distinguish subgroups of schizophrenia patients on the basis of neuroanatomical features. PMID:25379435

Determination of friction coefficient in unconfined compression of brain tissue.

PubMed

Rashid, Badar; Destrade, Michel; Gilchrist, Michael D

2012-10-01

Unconfined compression tests are more convenient to perform on cylindrical samples of brain tissue than tensile tests in order to estimate mechanical properties of the brain tissue because they allow homogeneous deformations. The reliability of these tests depends significantly on the amount of friction generated at the specimen/platen interface. Thus, there is a crucial need to find an approximate value of the friction coefficient in order to predict a possible overestimation of stresses during unconfined compression tests. In this study, a combined experimental-computational approach was adopted to estimate the dynamic friction coefficient μ of porcine brain matter against metal platens in compressive tests. Cylindrical samples of porcine brain tissue were tested up to 30% strain at variable strain rates, both under bonded and lubricated conditions in the same controlled environment. It was established that μ was equal to 0.09±0.03, 0.18±0.04, 0.18±0.04 and 0.20±0.02 at strain rates of 1, 30, 60 and 90/s, respectively. Additional tests were also performed to analyze brain tissue under lubricated and bonded conditions, with and without initial contact of the top platen with the brain tissue, with different specimen aspect ratios and with different lubricants (Phosphate Buffer Saline (PBS), Polytetrafluoroethylene (PTFE) and Silicone). The test conditions (lubricant used, biological tissue, loading velocity) adopted in this study were similar to the studies conducted by other research groups. This study will help to understand the amount of friction generated during unconfined compression of brain tissue for strain rates of up to 90/s. Copyright © 2012 Elsevier Ltd. All rights reserved.

Fluorescent marker-based and marker-free discrimination between healthy and cancerous human tissues using hyper-spectral imaging

NASA Astrophysics Data System (ADS)

Arnold, Thomas; De Biasio, Martin; Leitner, Raimund

2015-06-01

Two problems are addressed in this paper (i) the fluorescent marker-based and the (ii) marker-free discrimination between healthy and cancerous human tissues. For both applications the performance of hyper-spectral methods are quantified. Fluorescent marker-based tissue classification uses a number of fluorescent markers to dye specific parts of a human cell. The challenge is that the emission spectra of the fluorescent dyes overlap considerably. They are, furthermore disturbed by the inherent auto-fluorescence of human tissue. This results in ambiguities and decreased image contrast causing difficulties for the treatment decision. The higher spectral resolution introduced by tunable-filter-based spectral imaging in combination with spectral unmixing techniques results in an improvement of the image contrast and therefore more reliable information for the physician to choose the treatment decision. Marker-free tissue classification is based solely on the subtle spectral features of human tissue without the use of artificial markers. The challenge in this case is that the spectral differences between healthy and cancerous tissues are subtle and embedded in intra- and inter-patient variations of these features. The contributions of this paper are (i) the evaluation of hyper-spectral imaging in combination with spectral unmixing techniques for fluorescence marker-based tissue classification, (ii) the evaluation of spectral imaging for marker-free intra surgery tissue classification. Within this paper, we consider real hyper-spectral fluorescence and endoscopy data sets to emphasize the practical capability of the proposed methods. It is shown that the combination of spectral imaging with multivariate statistical methods can improve the sensitivity and specificity of the detection and the staging of cancerous tissues compared to standard procedures.

Dielectric properties of dog brain tissue measured in vitro across the 0.3-3 GHz band.

PubMed

Mohammed, Beadaa; Bialkowski, Konstanty; Abbosh, Amin; Mills, Paul C; Bradley, Andrew P

2016-09-22

Dielectric properties of dead Greyhound female dogs' brain tissues at different ages were measured at room temperature across the frequency range of 0.3-3 GHz. Measurements were made on excised tissues, in vitro in the laboratory, to carry out dielectric tests on sample tissues. Each dataset for a brain tissue was parametrized using the Cole-Cole expression, and the relevant Cole-Cole parameters for four tissue types are provided. A comparison was made with the database available in literature for other animals and human brain tissue. Results of two types of tissues (white matter and skull) showed systematic variation in dielectric properties as a function of animal age, whereas no significant change related to age was noticed for other tissues. Results provide critical information regarding dielectric properties of animal tissues for a realistic animal head model that can be used to verify the validity and reliability of a microwave head scanner for animals prior to testing on live animals. Bioelectromagnetics. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

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.

Decoding Multiple Sound Categories in the Human Temporal Cortex Using High Resolution fMRI

PubMed Central

Zhang, Fengqing; Wang, Ji-Ping; Kim, Jieun; Parrish, Todd; Wong, Patrick C. M.

2015-01-01

Perception of sound categories is an important aspect of auditory perception. The extent to which the brain’s representation of sound categories is encoded in specialized subregions or distributed across the auditory cortex remains unclear. Recent studies using multivariate pattern analysis (MVPA) of brain activations have provided important insights into how the brain decodes perceptual information. In the large existing literature on brain decoding using MVPA methods, relatively few studies have been conducted on multi-class categorization in the auditory domain. Here, we investigated the representation and processing of auditory categories within the human temporal cortex using high resolution fMRI and MVPA methods. More importantly, we considered decoding multiple sound categories simultaneously through multi-class support vector machine-recursive feature elimination (MSVM-RFE) as our MVPA tool. Results show that for all classifications the model MSVM-RFE was able to learn the functional relation between the multiple sound categories and the corresponding evoked spatial patterns and classify the unlabeled sound-evoked patterns significantly above chance. This indicates the feasibility of decoding multiple sound categories not only within but across subjects. However, the across-subject variation affects classification performance more than the within-subject variation, as the across-subject analysis has significantly lower classification accuracies. Sound category-selective brain maps were identified based on multi-class classification and revealed distributed patterns of brain activity in the superior temporal gyrus and the middle temporal gyrus. This is in accordance with previous studies, indicating that information in the spatially distributed patterns may reflect a more abstract perceptual level of representation of sound categories. Further, we show that the across-subject classification performance can be significantly improved by averaging the fMRI images over items, because the irrelevant variations between different items of the same sound category are reduced and in turn the proportion of signals relevant to sound categorization increases. PMID:25692885

Decoding of Ankle Flexion and Extension from Cortical Current Sources Estimated from Non-invasive Brain Activity Recording Methods.

PubMed

Mejia Tobar, Alejandra; Hyoudou, Rikiya; Kita, Kahori; Nakamura, Tatsuhiro; Kambara, Hiroyuki; Ogata, Yousuke; Hanakawa, Takashi; Koike, Yasuharu; Yoshimura, Natsue

2017-01-01

The classification of ankle movements from non-invasive brain recordings can be applied to a brain-computer interface (BCI) to control exoskeletons, prosthesis, and functional electrical stimulators for the benefit of patients with walking impairments. In this research, ankle flexion and extension tasks at two force levels in both legs, were classified from cortical current sources estimated by a hierarchical variational Bayesian method, using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings. The hierarchical prior for the current source estimation from EEG was obtained from activated brain areas and their intensities from an fMRI group (second-level) analysis. The fMRI group analysis was performed on regions of interest defined over the primary motor cortex, the supplementary motor area, and the somatosensory area, which are well-known to contribute to movement control. A sparse logistic regression method was applied for a nine-class classification (eight active tasks and a resting control task) obtaining a mean accuracy of 65.64% for time series of current sources, estimated from the EEG and the fMRI signals using a variational Bayesian method, and a mean accuracy of 22.19% for the classification of the pre-processed of EEG sensor signals, with a chance level of 11.11%. The higher classification accuracy of current sources, when compared to EEG classification accuracy, was attributed to the high number of sources and the different signal patterns obtained in the same vertex for different motor tasks. Since the inverse filter estimation for current sources can be done offline with the present method, the present method is applicable to real-time BCIs. Finally, due to the highly enhanced spatial distribution of current sources over the brain cortex, this method has the potential to identify activation patterns to design BCIs for the control of an affected limb in patients with stroke, or BCIs from motor imagery in patients with spinal cord injury.

Brain tissue segmentation based on DTI data

PubMed Central

Liu, Tianming; Li, Hai; Wong, Kelvin; Tarokh, Ashley; Guo, Lei; Wong, Stephen T.C.

2008-01-01

We present a method for automated brain tissue segmentation based on the multi-channel fusion of diffusion tensor imaging (DTI) data. The method is motivated by the evidence that independent tissue segmentation based on DTI parametric images provides complementary information of tissue contrast to the tissue segmentation based on structural MRI data. This has important applications in defining accurate tissue maps when fusing structural data with diffusion data. In the absence of structural data, tissue segmentation based on DTI data provides an alternative means to obtain brain tissue segmentation. Our approach to the tissue segmentation based on DTI data is to classify the brain into two compartments by utilizing the tissue contrast existing in a single channel. Specifically, because the apparent diffusion coefficient (ADC) values in the cerebrospinal fluid (CSF) are more than twice that of gray matter (GM) and white matter (WM), we use ADC images to distinguish CSF and non-CSF tissues. Additionally, fractional anisotropy (FA) images are used to separate WM from non-WM tissues, as highly directional white matter structures have much larger fractional anisotropy values. Moreover, other channels to separate tissue are explored, such as eigenvalues of the tensor, relative anisotropy (RA), and volume ratio (VR). We developed an approach based on the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm that combines these two-class maps to obtain a complete tissue segmentation map of CSF, GM, and WM. Evaluations are provided to demonstrate the performance of our approach. Experimental results of applying this approach to brain tissue segmentation and deformable registration of DTI data and spoiled gradient-echo (SPGR) data are also provided. PMID:17804258

Accuracy of Raman spectroscopy in differentiating brain tumor from normal brain tissue.

PubMed

Zhang, Jing; Fan, Yimeng; He, Min; Ma, Xuelei; Song, Yanlin; Liu, Ming; Xu, Jianguo

2017-05-30

Raman spectroscopy could be applied to distinguish tumor from normal tissues. This meta-analysis was conducted to assess the accuracy of Raman spectroscopy in differentiating brain tumor from normal brain tissue. PubMed and Embase were searched to identify suitable studies prior to Jan 1st, 2016. We estimated the pooled sensitivity, specificity, positive and negative likelihood ratios (LR), diagnostic odds ratio (DOR), and constructed summary receiver operating characteristics (SROC) curves to identity the accuracy of Raman spectroscopy in differentiating brain tumor from normal brain tissue. A total of six studies with 1951 spectra were included. For glioma, the pooled sensitivity and specificity of Raman spectroscopy were 0.96 (95% CI 0.94-0.97) and 0.99 (95% CI 0.98-0.99), respectively. The area under the curve (AUC) was 0.9831. For meningioma, the pooled sensitivity and specificity were 0.98 (95% CI 0.94-1.00) and 1.00 (95% CI 0.98-1.00), respectively. The AUC was 0.9955. This meta-analysis suggested that Raman spectroscopy could be an effective and accurate tool for differentiating glioma and meningioma from normal brain tissue, which would help us both avoid removal of normal tissue and minimize the volume of residual tumor.

A Comprehensive Repository of Normal and Tumor Human Breast Tissues and Cells

DTIC Science & Technology

1999-07-01

mother was reported to have had cancer of the uterine cervix at the age of 22. Both maternal grandparents had died of colon cancer in their sixties...1 mutation). The repository also includes breast epithelial and stromal cell strains derived from non cancerous breast tissue as well as peripheral...tissue banks. 14. SUBJECT TERMS Breast Cancer 17. SECURITY CLASSIFICATION OF REPORT Unclassified 18. SECURITY CLASSIFICATION OF THIS PAGE

The Identification of Aluminum in Human Brain Tissue Using Lumogallion and Fluorescence Microscopy

PubMed Central

Mirza, Ambreen; King, Andrew; Troakes, Claire; Exley, Christopher

2016-01-01

Aluminum in human brain tissue is implicated in the etiologies of neurodegenerative diseases including Alzheimer’s disease. While methods for the accurate and precise measurement of aluminum in human brain tissue are widely acknowledged, the same cannot be said for the visualization of aluminum. Herein we have used transversely-heated graphite furnace atomic absorption spectrometry to measure aluminum in the brain of a donor with Alzheimer’s disease, and we have developed and validated fluorescence microscopy and the fluor lumogallion to show the presence of aluminum in the same tissue. Aluminum is observed as characteristic orange fluorescence that is neither reproduced by other metals nor explained by autofluorescence. This new and relatively simple method to visualize aluminum in human brain tissue should enable more rigorous testing of the aluminum hypothesis of Alzheimer’s disease (and other neurological conditions) in the future. PMID:27472886

Tissue classification and segmentation of pressure injuries using convolutional neural networks.

PubMed

Zahia, Sofia; Sierra-Sosa, Daniel; Garcia-Zapirain, Begonya; Elmaghraby, Adel

2018-06-01

This paper presents a new approach for automatic tissue classification in pressure injuries. These wounds are localized skin damages which need frequent diagnosis and treatment. Therefore, a reliable and accurate systems for segmentation and tissue type identification are needed in order to achieve better treatment results. Our proposed system is based on a Convolutional Neural Network (CNN) devoted to performing optimized segmentation of the different tissue types present in pressure injuries (granulation, slough, and necrotic tissues). A preprocessing step removes the flash light and creates a set of 5x5 sub-images which are used as input for the CNN network. The network output will classify every sub-image of the validation set into one of the three classes studied. The metrics used to evaluate our approach show an overall average classification accuracy of 92.01%, an average total weighted Dice Similarity Coefficient of 91.38%, and an average precision per class of 97.31% for granulation tissue, 96.59% for necrotic tissue, and 77.90% for slough tissue. Our system has been proven to make recognition of complicated structures in biomedical images feasible. Copyright © 2018 Elsevier B.V. All rights reserved.

Deep two-photon microscopic imaging through brain tissue using the second singlet state from fluorescent agent chlorophyll α in spinach leaf

NASA Astrophysics Data System (ADS)

Shi, Lingyan; Rodríguez-Contreras, Adrián; Budansky, Yury; Pu, Yang; An Nguyen, Thien; Alfano, Robert R.

2014-06-01

Two-photon (2P) excitation of the second singlet (S) state was studied to achieve deep optical microscopic imaging in brain tissue when both the excitation (800 nm) and emission (685 nm) wavelengths lie in the "tissue optical window" (650 to 950 nm). S2 state technique was used to investigate chlorophyll α (Chl α) fluorescence inside a spinach leaf under a thick layer of freshly sliced rat brain tissue in combination with 2P microscopic imaging. Strong emission at the peak wavelength of 685 nm under the 2P S state of Chl α enabled the imaging depth up to 450 μm through rat brain tissue.

Deep two-photon microscopic imaging through brain tissue using the second singlet state from fluorescent agent chlorophyll α in spinach leaf.

PubMed

Shi, Lingyan; Rodríguez-Contreras, Adrián; Budansky, Yury; Pu, Yang; Nguyen, Thien An; Alfano, Robert R

2014-06-01

Two-photon (2P) excitation of the second singlet (S₂) state was studied to achieve deep optical microscopic imaging in brain tissue when both the excitation (800 nm) and emission (685 nm) wavelengths lie in the "tissue optical window" (650 to 950 nm). S₂ state technique was used to investigate chlorophyll α (Chl α) fluorescence inside a spinach leaf under a thick layer of freshly sliced rat brain tissue in combination with 2P microscopic imaging. Strong emission at the peak wavelength of 685 nm under the 2P S₂ state of Chl α enabled the imaging depth up to 450 μm through rat brain tissue.

Conformable actively multiplexed high-density surface electrode array for brain interfacing

DOEpatents

Rogers, John; Kim, Dae-Hyeong; Litt, Brian; Viventi, Jonathan

2015-01-13

Provided are methods and devices for interfacing with brain tissue, specifically for monitoring and/or actuation of spatio-temporal electrical waveforms. The device is conformable having a high electrode density and high spatial and temporal resolution. A conformable substrate supports a conformable electronic circuit and a barrier layer. Electrodes are positioned to provide electrical contact with a brain tissue. A controller monitors or actuates the electrodes, thereby interfacing with the brain tissue. In an aspect, methods are provided to monitor or actuate spatio-temporal electrical waveform over large brain surface areas by any of the devices disclosed herein.

Deep learning for brain tumor classification

NASA Astrophysics Data System (ADS)

Paul, Justin S.; Plassard, Andrew J.; Landman, Bennett A.; Fabbri, Daniel

2017-03-01

Recent research has shown that deep learning methods have performed well on supervised machine learning, image classification tasks. The purpose of this study is to apply deep learning methods to classify brain images with different tumor types: meningioma, glioma, and pituitary. A dataset was publicly released containing 3,064 T1-weighted contrast enhanced MRI (CE-MRI) brain images from 233 patients with either meningioma, glioma, or pituitary tumors split across axial, coronal, or sagittal planes. This research focuses on the 989 axial images from 191 patients in order to avoid confusing the neural networks with three different planes containing the same diagnosis. Two types of neural networks were used in classification: fully connected and convolutional neural networks. Within these two categories, further tests were computed via the augmentation of the original 512×512 axial images. Training neural networks over the axial data has proven to be accurate in its classifications with an average five-fold cross validation of 91.43% on the best trained neural network. This result demonstrates that a more general method (i.e. deep learning) can outperform specialized methods that require image dilation and ring-forming subregions on tumors.

Pattern classification of brain activation during emotional processing in subclinical depression: psychosis proneness as potential confounding factor.

PubMed

Modinos, Gemma; Mechelli, Andrea; Pettersson-Yeo, William; Allen, Paul; McGuire, Philip; Aleman, Andre

2013-01-01

We used Support Vector Machine (SVM) to perform multivariate pattern classification based on brain activation during emotional processing in healthy participants with subclinical depressive symptoms. Six-hundred undergraduate students completed the Beck Depression Inventory II (BDI-II). Two groups were subsequently formed: (i) subclinical (mild) mood disturbance (n = 17) and (ii) no mood disturbance (n = 17). Participants also completed a self-report questionnaire on subclinical psychotic symptoms, the Community Assessment of Psychic Experiences Questionnaire (CAPE) positive subscale. The functional magnetic resonance imaging (fMRI) paradigm entailed passive viewing of negative emotional and neutral scenes. The pattern of brain activity during emotional processing allowed correct group classification with an overall accuracy of 77% (p = 0.002), within a network of regions including the amygdala, insula, anterior cingulate cortex and medial prefrontal cortex. However, further analysis suggested that the classification accuracy could also be explained by subclinical psychotic symptom scores (correlation with SVM weights r = 0.459, p = 0.006). Psychosis proneness may thus be a confounding factor for neuroimaging studies in subclinical depression.

Finite difference time domain (FDTD) modeling of implanted deep brain stimulation electrodes and brain tissue.

PubMed

Gabran, S R I; Saad, J H; Salama, M M A; Mansour, R R

2009-01-01

This paper demonstrates the electromagnetic modeling and simulation of an implanted Medtronic deep brain stimulation (DBS) electrode using finite difference time domain (FDTD). The model is developed using Empire XCcel and represents the electrode surrounded with brain tissue assuming homogenous and isotropic medium. The model is created to study the parameters influencing the electric field distribution within the tissue in order to provide reference and benchmarking data for DBS and intra-cortical electrode development.

Robust tissue classification for reproducible wound assessment in telemedicine environments

NASA Astrophysics Data System (ADS)

Wannous, Hazem; Treuillet, Sylvie; Lucas, Yves

2010-04-01

In telemedicine environments, a standardized and reproducible assessment of wounds, using a simple free-handled digital camera, is an essential requirement. However, to ensure robust tissue classification, particular attention must be paid to the complete design of the color processing chain. We introduce the key steps including color correction, merging of expert labeling, and segmentation-driven classification based on support vector machines. The tool thus developed ensures stability under lighting condition, viewpoint, and camera changes, to achieve accurate and robust classification of skin tissues. Clinical tests demonstrate that such an advanced tool, which forms part of a complete 3-D and color wound assessment system, significantly improves the monitoring of the healing process. It achieves an overlap score of 79.3 against 69.1% for a single expert, after mapping on the medical reference developed from the image labeling by a college of experts.

In vivo evaluation of needle force and friction stress during insertion at varying insertion speed into the brain.

PubMed

Casanova, Fernando; Carney, Paul R; Sarntinoranont, Malisa

2014-11-30

Convection enhanced delivery (CED) infuses drugs directly into brain tissue. Needle insertion is required and results in tissue damage which can promote flowback along the needle track and improper targeting. The goal of this study was to evaluate friction stress (calculated from needle insertion force) as a measure of tissue contact and damage during needle insertion for varying insertion speeds. Forces and surface dimpling during needle insertion were measured in rat brain in vivo. Needle retraction forces were used to calculate friction stresses. These measures were compared to track damage from a previous study. Differences between brain tissues and soft hydrogels were evaluated for varying insertion speeds: 0.2, 2, and 10mm/s. In brain tissue, average insertion force and surface dimpling increased with increasing insertion speed. Average friction stress along the needle-tissue interface decreased with insertion speed (from 0.58 ± 0.27 to 0.16 ± 0.08 kPa). Friction stress varied between brain regions: cortex (0.227 ± 0.27 kPa), external capsule (0.222 ± 0.19 kPa), and CPu (0.383 ± 0.30 kPa). Hydrogels exhibited opposite trends for dimpling and friction stress with insertion speed. Previously, increasing needle damage with insertion speed has been measured with histological methods. Friction stress appears to decrease with increasing tissue damage and decreasing tissue contact, providing the potential for in vivo and real time evaluation along the needle track. Force derived friction stress decreased with increasing insertion speed and was smaller within white matter regions. Hydrogels exhibited opposite trends to brain tissue. Copyright © 2014 Elsevier B.V. All rights reserved.

Non-invasive intraoperative optical coherence tomography of the resection cavity during surgery of intrinsic brain tumors

NASA Astrophysics Data System (ADS)

Giese, A.; Böhringer, H. J.; Leppert, J.; Kantelhardt, S. R.; Lankenau, E.; Koch, P.; Birngruber, R.; Hüttmann, G.

2006-02-01

Optical coherence tomography (OCT) is a non-invasive imaging technique with a micrometer resolution. It allows non-contact / non-invasive analysis of central nervous system tissues with a penetration depth of 1-3,5 mm reaching a spatial resolution of approximately 4-15 μm. We have adapted spectral-domain OCT (SD-OCT) and time-domain OCT (TD-OCT) for intraoperative detection of residual tumor during brain tumor surgery. Human brain tumor tissue and areas of the resection cavity were analyzed during the resection of gliomas using this new technology. The site of analysis was registered using a neuronavigation system and biopsies were taken and submitted to routine histology. We have used post image acquisition processing to compensate for movements of the brain and to realign A-scan images for calculation of a light attenuation factor. OCT imaging of normal cortex and white matter showed a typical light attenuation profile. Tumor tissue depending on the cellularity of the specimen showed a loss of the normal light attenuation profile resulting in altered light attenuation coefficients compared to normal brain. Based on this parameter and the microstructure of the tumor tissue, which was entirely absent in normal tissue, OCT analysis allowed the discrimination of normal brain tissue, invaded brain, solid tumor tissue, and necrosis. Following macroscopically complete resections OCT analysis of the resection cavity displayed the typical microstructure and light attenuation profile of tumor tissue in some specimens, which in routine histology contained microscopic residual tumor tissue. We have demonstrated that this technology may be applied to the intraoperative detection of residual tumor during resection of human gliomas.

Automated identification of brain tumors from single MR images based on segmentation with refined patient-specific priors

PubMed Central

Sanjuán, Ana; Price, Cathy J.; Mancini, Laura; Josse, Goulven; Grogan, Alice; Yamamoto, Adam K.; Geva, Sharon; Leff, Alex P.; Yousry, Tarek A.; Seghier, Mohamed L.

2013-01-01

Brain tumors can have different shapes or locations, making their identification very challenging. In functional MRI, it is not unusual that patients have only one anatomical image due to time and financial constraints. Here, we provide a modified automatic lesion identification (ALI) procedure which enables brain tumor identification from single MR images. Our method rests on (A) a modified segmentation-normalization procedure with an explicit “extra prior” for the tumor and (B) an outlier detection procedure for abnormal voxel (i.e., tumor) classification. To minimize tissue misclassification, the segmentation-normalization procedure requires prior information of the tumor location and extent. We therefore propose that ALI is run iteratively so that the output of Step B is used as a patient-specific prior in Step A. We test this procedure on real T1-weighted images from 18 patients, and the results were validated in comparison to two independent observers' manual tracings. The automated procedure identified the tumors successfully with an excellent agreement with the manual segmentation (area under the ROC curve = 0.97 ± 0.03). The proposed procedure increases the flexibility and robustness of the ALI tool and will be particularly useful for lesion-behavior mapping studies, or when lesion identification and/or spatial normalization are problematic. PMID:24381535

Expert identification of visual primitives used by CNNs during mammogram classification

NASA Astrophysics Data System (ADS)

Wu, Jimmy; Peck, Diondra; Hsieh, Scott; Dialani, Vandana; Lehman, Constance D.; Zhou, Bolei; Syrgkanis, Vasilis; Mackey, Lester; Patterson, Genevieve

2018-02-01

This work interprets the internal representations of deep neural networks trained for classification of diseased tissue in 2D mammograms. We propose an expert-in-the-loop inter- pretation method to label the behavior of internal units in convolutional neural networks (CNNs). Expert radiologists identify that the visual patterns detected by the units are correlated with meaningful medical phenomena such as mass tissue and calcificated vessels. We demonstrate that several trained CNN models are able to produce explanatory descriptions to support the final classification decisions. We view this as an important first step toward interpreting the internal representations of medical classification CNNs and explaining their predictions.

Enhancement of Sexual Behavior in Female Rats by Neonatal Transplantation of Brain Tissue from Males

NASA Astrophysics Data System (ADS)

Arendash, Gary W.; Gorski, Roger A.

1982-09-01

Transplantation of preoptic tissue from male rat neonates into the preoptic area of female littermates increased masculine and feminine sexual behavior in the recipients during adulthood. This suggests that functional connections develop between the transplanted neural tissue and the host brain. A new intraparenchymal brain transplantation technique was used to achieve these results.

Multimodal optical coherence tomography for in vivo imaging of brain tissue structure and microvascular network at glioblastoma

NASA Astrophysics Data System (ADS)

Yashin, Konstantin S.; Kiseleva, Elena B.; Gubarkova, Ekaterina V.; Matveev, Lev A.; Karabut, Maria M.; Elagin, Vadim V.; Sirotkina, Marina A.; Medyanik, Igor A.; Kravets, L. Y.; Gladkova, Natalia D.

2017-02-01

In the case of infiltrative brain tumors the surgeon faces difficulties in determining their boundaries to achieve total resection. The aim of the investigation was to evaluate the performance of multimodal OCT (MM OCT) for differential diagnostics of normal brain tissue and glioma using an experimental model of glioblastoma. The spectral domain OCT device that was used for the study provides simultaneously two modes: cross-polarization and microangiographic OCT. The comparative analysis of the both OCT modalities images from tumorous and normal brain tissue areas concurrently with histologic correlation shows certain difference between when accordingly to morphological and microvascular tissue features.

Influence of strain rate on indentation response of porcine brain.

PubMed

Qian, Long; Zhao, Hongwei; Guo, Yue; Li, Yuanshang; Zhou, Mingxing; Yang, Liguo; Wang, Zhiwei; Sun, Yifan

2018-06-01

Knowledge of brain tissue mechanical properties may be critical for formulating hypotheses about some specific diseases mechanisms and its accurate simulations such as traumatic brain injury (TBI) and tumor growth. Compared to traditional tests (e.g. tensile and compression), indentation shows superiority by virtue of its pinpoint and nondestructive/quasi-nondestructive. As a viscoelastic material, the properties of brain tissue depend on the strain rate by definition. However most efforts focus on the aspect of velocity in the field of brain indentation, rather than strain rate. The influence of strain rate on indentation response of brain tissue is taken little attention. Further, by comparing different results from literatures, it is also obvious that strain rate rather than velocity is more appropriate to characterize mechanical properties of brain. In this paper, to systematically characterize the influence of strain rate, a series of indentation-relaxation tests n = 210) are performed on the cortex of porcine brain using a custom-designed indentation device. The mechanical response that correlates with indenter diameters, depths of indentation and velocities, is revealed for the indentation portion, and elastic behavior of brain tissue is analyzed as the function of strain rate. Similarly, a linear viscoelastic model with a Prony series is employed for the indentation-relaxation portion, wherein the brain tissue shows more viscous and responds more quickly with increasing strain rate. Understanding the effect of strain rate on mechanical properties of brain indentation may be far-reaching for brain injury biomechanics and accurate simulations, but be important for bridging between indentation results of different literatures. Copyright © 2018 Elsevier Ltd. All rights reserved.

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

Alzheimer disease detection from structural MR images using FCM based weighted probabilistic neural network.

PubMed

Duraisamy, Baskar; Shanmugam, Jayanthi Venkatraman; Annamalai, Jayanthi

2018-02-19

An early intervention of Alzheimer's disease (AD) is highly essential due to the fact that this neuro degenerative disease generates major life-threatening issues, especially memory loss among patients in society. Moreover, categorizing NC (Normal Control), MCI (Mild Cognitive Impairment) and AD early in course allows the patients to experience benefits from new treatments. Therefore, it is important to construct a reliable classification technique to discriminate the patients with or without AD from the bio medical imaging modality. Hence, we developed a novel FCM based Weighted Probabilistic Neural Network (FWPNN) classification algorithm and analyzed the brain images related to structural MRI modality for better discrimination of class labels. Initially our proposed framework begins with brain image normalization stage. In this stage, ROI regions related to Hippo-Campus (HC) and Posterior Cingulate Cortex (PCC) from the brain images are extracted using Automated Anatomical Labeling (AAL) method. Subsequently, nineteen highly relevant AD related features are selected through Multiple-criterion feature selection method. At last, our novel FWPNN classification algorithm is imposed to remove suspicious samples from the training data with an end goal to enhance the classification performance. This newly developed classification algorithm combines both the goodness of supervised and unsupervised learning techniques. The experimental validation is carried out with the ADNI subset and then to the Bordex-3 city dataset. Our proposed classification approach achieves an accuracy of about 98.63%, 95.4%, 96.4% in terms of classification with AD vs NC, MCI vs NC and AD vs MCI. The experimental results suggest that the removal of noisy samples from the training data can enhance the decision generation process of the expert systems.

Near infrared Raman spectra of human brain lipids

NASA Astrophysics Data System (ADS)

Krafft, Christoph; Neudert, Lars; Simat, Thomas; Salzer, Reiner

2005-05-01

Human brain tissue, in particular white matter, contains high lipid content. These brain lipids can be divided into three principal classes: neutral lipids including the steroid cholesterol, phospholipids and sphingolipids. Major lipids in normal human brain tissue are phosphatidylcholine, phosphatidylethanolamine, phosphatidylserine, phosphatidylinositol, phosphatidic acid, sphingomyelin, galactocerebrosides, gangliosides, sulfatides and cholesterol. Minor lipids are cholesterolester and triacylglycerides. During transformation from normal brain tissue to tumors, composition and concentration of lipids change in a specific way. Therefore, analysis of lipids might be used as a diagnostic parameter to distinguish normal tissue from tumors and to determine the tumor type and tumor grade. Raman spectroscopy has been suggested as an analytical tool to detect these changes even under intra-operative conditions. We recorded Raman spectra of the 12 major and minor brain lipids with 785 nm excitation in order to identify their spectral fingerprints for qualitative and quantitative analyses.

VA's National PTSD Brain Bank: a National Resource for Research.

PubMed

Friedman, Matthew J; Huber, Bertrand R; Brady, Christopher B; Ursano, Robert J; Benedek, David M; Kowall, Neil W; McKee, Ann C

2017-08-25

The National PTSD Brain Bank (NPBB) is a brain tissue biorepository established to support research on the causes, progression, and treatment of PTSD. It is a six-part consortium led by VA's National Center for PTSD with participating sites at VA medical centers in Boston, MA; Durham, NC; Miami, FL; West Haven, CT; and White River Junction, VT along with the Uniformed Services University of Health Sciences. It is also well integrated with VA's Boston-based brain banks that focus on Alzheimer's disease, ALS, chronic traumatic encephalopathy, and other neurological disorders. This article describes the organization and operations of NPBB with specific attention to: tissue acquisition, tissue processing, diagnostic assessment, maintenance of a confidential data biorepository, adherence to ethical standards, governance, accomplishments to date, and future challenges. Established in 2014, NPBB has already acquired and distributed brain tissue to support research on how PTSD affects brain structure and function.

Adjustment of localized alveolar ridge defects by soft tissue transplantation to improve mucogingival esthetics: a proposal for clinical classification and an evaluation of procedures.

PubMed

Studer, S; Naef, R; Schärer, P

1997-12-01

Esthetically correct treatment of a localized alveolar ridge defect is a frequent prosthetic challenge. Such defects can be overcome not only by a variety of prosthetic means, but also by several periodontal surgical techniques, notably soft tissue augmentations. Preoperative classification of the localized alveolar ridge defect can be greatly useful in evaluating the prognosis and technical difficulties involved. A semiquantitative classification, dependent on the severity of vertical and horizontal dimensional loss, is proposed to supplement the recognized qualitative classification of a ridge defect. Various methods of soft tissue augmentation are evaluated, based on initial volumetric measurements. The roll flap technique is proposed when the problem is related to ridge quality (single-tooth defect with little horizontal and vertical loss). Larger defects in which a volumetric problem must be solved are corrected through the subepithelial connective tissue technique. Additional mucogingival problems (eg, insufficient gingival width, high frenum, gingival scarring, or tattoo) should not be corrected simultaneously with augmentation procedures. In these cases, the onlay transplant technique is favored.

Predicting primary progressive aphasias with support vector machine approaches in structural MRI data.

PubMed

Bisenius, Sandrine; Mueller, Karsten; Diehl-Schmid, Janine; Fassbender, Klaus; Grimmer, Timo; Jessen, Frank; Kassubek, Jan; Kornhuber, Johannes; Landwehrmeyer, Bernhard; Ludolph, Albert; Schneider, Anja; Anderl-Straub, Sarah; Stuke, Katharina; Danek, Adrian; Otto, Markus; Schroeter, Matthias L

2017-01-01

Primary progressive aphasia (PPA) encompasses the three subtypes nonfluent/agrammatic variant PPA, semantic variant PPA, and the logopenic variant PPA, which are characterized by distinct patterns of language difficulties and regional brain atrophy. To validate the potential of structural magnetic resonance imaging data for early individual diagnosis, we used support vector machine classification on grey matter density maps obtained by voxel-based morphometry analysis to discriminate PPA subtypes (44 patients: 16 nonfluent/agrammatic variant PPA, 17 semantic variant PPA, 11 logopenic variant PPA) from 20 healthy controls (matched for sample size, age, and gender) in the cohort of the multi-center study of the German consortium for frontotemporal lobar degeneration. Here, we compared a whole-brain with a meta-analysis-based disease-specific regions-of-interest approach for support vector machine classification. We also used support vector machine classification to discriminate the three PPA subtypes from each other. Whole brain support vector machine classification enabled a very high accuracy between 91 and 97% for identifying specific PPA subtypes vs. healthy controls, and 78/95% for the discrimination between semantic variant vs. nonfluent/agrammatic or logopenic PPA variants. Only for the discrimination between nonfluent/agrammatic and logopenic PPA variants accuracy was low with 55%. Interestingly, the regions that contributed the most to the support vector machine classification of patients corresponded largely to the regions that were atrophic in these patients as revealed by group comparisons. Although the whole brain approach took also into account regions that were not covered in the regions-of-interest approach, both approaches showed similar accuracies due to the disease-specificity of the selected networks. Conclusion, support vector machine classification of multi-center structural magnetic resonance imaging data enables prediction of PPA subtypes with a very high accuracy paving the road for its application in clinical settings.

Ensemble Classification of Alzheimer's Disease and Mild Cognitive Impairment Based on Complex Graph Measures from Diffusion Tensor Images

PubMed Central

Ebadi, Ashkan; Dalboni da Rocha, Josué L.; Nagaraju, Dushyanth B.; Tovar-Moll, Fernanda; Bramati, Ivanei; Coutinho, Gabriel; Sitaram, Ranganatha; Rashidi, Parisa

2017-01-01

The human brain is a complex network of interacting regions. The gray matter regions of brain are interconnected by white matter tracts, together forming one integrative complex network. In this article, we report our investigation about the potential of applying brain connectivity patterns as an aid in diagnosing Alzheimer's disease and Mild Cognitive Impairment (MCI). We performed pattern analysis of graph theoretical measures derived from Diffusion Tensor Imaging (DTI) data representing structural brain networks of 45 subjects, consisting of 15 patients of Alzheimer's disease (AD), 15 patients of MCI, and 15 healthy subjects (CT). We considered pair-wise class combinations of subjects, defining three separate classification tasks, i.e., AD-CT, AD-MCI, and CT-MCI, and used an ensemble classification module to perform the classification tasks. Our ensemble framework with feature selection shows a promising performance with classification accuracy of 83.3% for AD vs. MCI, 80% for AD vs. CT, and 70% for MCI vs. CT. Moreover, our findings suggest that AD can be related to graph measures abnormalities at Brodmann areas in the sensorimotor cortex and piriform cortex. In this way, node redundancy coefficient and load centrality in the primary motor cortex were recognized as good indicators of AD in contrast to MCI. In general, load centrality, betweenness centrality, and closeness centrality were found to be the most relevant network measures, as they were the top identified features at different nodes. The present study can be regarded as a “proof of concept” about a procedure for the classification of MRI markers between AD dementia, MCI, and normal old individuals, due to the small and not well-defined groups of AD and MCI patients. Future studies with larger samples of subjects and more sophisticated patient exclusion criteria are necessary toward the development of a more precise technique for clinical diagnosis. PMID:28293162

Predicting decisions in human social interactions using real-time fMRI and pattern classification.

PubMed

Hollmann, Maurice; Rieger, Jochem W; Baecke, Sebastian; Lützkendorf, Ralf; Müller, Charles; Adolf, Daniela; Bernarding, Johannes

2011-01-01

Negotiation and trade typically require a mutual interaction while simultaneously resting in uncertainty which decision the partner ultimately will make at the end of the process. Assessing already during the negotiation in which direction one's counterpart tends would provide a tremendous advantage. Recently, neuroimaging techniques combined with multivariate pattern classification of the acquired data have made it possible to discriminate subjective states of mind on the basis of their neuronal activation signature. However, to enable an online-assessment of the participant's mind state both approaches need to be extended to a real-time technique. By combining real-time functional magnetic resonance imaging (fMRI) and online pattern classification techniques, we show that it is possible to predict human behavior during social interaction before the interacting partner communicates a specific decision. Average accuracy reached approximately 70% when we predicted online the decisions of volunteers playing the ultimatum game, a well-known paradigm in economic game theory. Our results demonstrate the successful online analysis of complex emotional and cognitive states using real-time fMRI, which will enable a major breakthrough for social fMRI by providing information about mental states of partners already during the mutual interaction. Interestingly, an additional whole brain classification across subjects confirmed the online results: anterior insula, ventral striatum, and lateral orbitofrontal cortex, known to act in emotional self-regulation and reward processing for adjustment of behavior, appeared to be strong determinants of later overt behavior in the ultimatum game. Using whole brain classification we were also able to discriminate between brain processes related to subjective emotional and motivational states and brain processes related to the evaluation of objective financial incentives.

PREDICTING APHASIA TYPE FROM BRAIN DAMAGE MEASURED WITH STRUCTURAL MRI

PubMed Central

Yourganov, Grigori; Smith, Kimberly G.; Fridriksson, Julius; Rorden, Chris

2015-01-01

Chronic aphasia is a common consequence of a left-hemisphere stroke. Since the early insights by Broca and Wernicke, studying the relationship between the loci of cortical damage and patterns of language impairment has been one of the concerns of aphasiology. We utilized multivariate classification in a cross-validation framework to predict the type of chronic aphasia from the spatial pattern of brain damage. Our sample consisted of 98 patients with five types of aphasia (Broca’s, Wernicke’s, global, conduction, and anomic), classified based on scores on the Western Aphasia Battery. Binary lesion maps were obtained from structural MRI scans (obtained at least 6 months poststroke, and within 2 days of behavioural assessment); after spatial normalization, the lesions were parcellated into a disjoint set of brain areas. The proportion of damage to the brain areas was used to classify patients’ aphasia type. To create this parcellation, we relied on five brain atlases; our classifier (support vector machine) could differentiate between different kinds of aphasia using any of the five parcellations. In our sample, the best classification accuracy was obtained when using a novel parcellation that combined two previously published brain atlases, with the first atlas providing the segmentation of grey matter, and the second atlas used to segment the white matter. For each aphasia type, we computed the relative importance of different brain areas for distinguishing it from other aphasia types; our findings were consistent with previously published reports of lesion locations implicated in different types of aphasia. Overall, our results revealed that automated multivariate classification could distinguish between aphasia types based on damage to atlas-defined brain areas. PMID:26465238

Predicting aphasia type from brain damage measured with structural MRI.

PubMed

Yourganov, Grigori; Smith, Kimberly G; Fridriksson, Julius; Rorden, Chris

2015-12-01

Chronic aphasia is a common consequence of a left-hemisphere stroke. Since the early insights by Broca and Wernicke, studying the relationship between the loci of cortical damage and patterns of language impairment has been one of the concerns of aphasiology. We utilized multivariate classification in a cross-validation framework to predict the type of chronic aphasia from the spatial pattern of brain damage. Our sample consisted of 98 patients with five types of aphasia (Broca's, Wernicke's, global, conduction, and anomic), classified based on scores on the Western Aphasia Battery (WAB). Binary lesion maps were obtained from structural MRI scans (obtained at least 6 months poststroke, and within 2 days of behavioural assessment); after spatial normalization, the lesions were parcellated into a disjoint set of brain areas. The proportion of damage to the brain areas was used to classify patients' aphasia type. To create this parcellation, we relied on five brain atlases; our classifier (support vector machine - SVM) could differentiate between different kinds of aphasia using any of the five parcellations. In our sample, the best classification accuracy was obtained when using a novel parcellation that combined two previously published brain atlases, with the first atlas providing the segmentation of grey matter, and the second atlas used to segment the white matter. For each aphasia type, we computed the relative importance of different brain areas for distinguishing it from other aphasia types; our findings were consistent with previously published reports of lesion locations implicated in different types of aphasia. Overall, our results revealed that automated multivariate classification could distinguish between aphasia types based on damage to atlas-defined brain areas. Copyright © 2015 Elsevier Ltd. All rights reserved.

A simple quantitative diagnostic alternative for MGMT DNA-methylation testing on RCL2 fixed paraffin embedded tumors using restriction coupled qPCR.

PubMed

Pulverer, Walter; Hofner, Manuela; Preusser, Matthias; Dirnberger, Elisabeth; Hainfellner, Johannes A; Weinhaeusel, Andreas

2014-01-01

MGMT promoter methylation is associated with favorable prognosis and chemosensitivity in glioblastoma multiforme (GBM), especially in elderly patients. We aimed to develop a simple methylation-sensitive restriction enzyme (MSRE)-based quantitative PCR (qPCR) assay, allowing the quantification of MGMT promoter methylation. DNA was extracted from non-neoplastic brain (n = 24) and GBM samples (n = 20) upon 3 different sample conservation conditions (-80 °C, formalin-fixed and paraffin-embedded (FFPE); RCL2-fixed). We evaluated the suitability of each fixation method with respect to the MSRE-coupled qPCR methylation analyses. Methylation data were validated by MALDITOF. qPCR was used for evaluation of alternative tissue conservation procedures. DNA from FFPE tissue failed reliable testing; DNA from both RCL2-fixed and fresh frozen tissues performed equally well and was further used for validation of the quantitative MGMT methylation assay (limit of detection (LOD): 19.58 pg), using individual's undigested sample DNA for calibration. MGMT methylation analysis in non-neoplastic brain identified a background methylation of 0.10 ± 11% which we used for defining a cut-off of 0.32% for patient stratification. Of GBM patients 9 were MGMT methylationpositive (range: 0.56 - 91.95%), and 11 tested negative. MALDI-TOF measurements resulted in a concordant classification of 94% of GBM samples in comparison to qPCR. The presented methodology allows quantitative MGMT promoter methylation analyses. An amount of 200 ng DNA is sufficient for triplicate analyses including control reactions and individual calibration curves, thus excluding any DNA qualityderived bias. The combination of RCL2-fixation and quantitative methylation analyses improves pathological routine examination when histological and molecular analyses on limited amounts of tumor samples are necessary for patient stratification.

Fingolimod inhibits brain atrophy and promotes brain-derived neurotrophic factor in an animal model of multiple sclerosis.

PubMed

Smith, Paul A; Schmid, Cindy; Zurbruegg, Stefan; Jivkov, Magali; Doelemeyer, Arno; Theil, Diethilde; Dubost, Valérie; Beckmann, Nicolau

2018-05-15

Longitudinal brain atrophy quantification is a critical efficacy measurement in multiple sclerosis (MS) clinical trials and the determination of No Evidence of Disease Activity (NEDA). Utilising fingolimod as a clinically validated therapy we evaluated the use of repeated brain tissue volume measures during chronic experimental autoimmune encephalomyelitis (EAE) as a new preclinical efficacy measure. Brain volume changes were quantified using magnetic resonance imaging (MRI) at 7 Tesla and correlated to treatment-induced brain derived neurotrophic factor (BDNF) measured in blood, cerebrospinal fluid, spinal cord and brain. Serial brain MRI measurements revealed slow progressive brain volume loss in vehicle treated EAE mice despite a stable clinical score. Fingolimod (1 mg/kg) significantly ameliorated brain tissue atrophy in the cerebellum and striatum when administered from established EAE disease onwards. Fingolimod-dependent tissue preservation was associated with induction of BDNF specifically within the brain and co-localized with neuronal soma. In contrast, therapeutic teriflunomide (3 mg/kg) treatment failed to inhibit CNS autoimmune mediated brain degeneration. Finally, weekly anti-IL-17A antibody (15 mg/kg) treatment was highly efficacious and preserved whole brain, cerebellum and striatum volume. Fingolimod-mediated BDNF increases within the CNS may contribute to limiting progressive tissue loss during chronic neuroinflammation. Copyright © 2018 Elsevier B.V. All rights reserved.

Heterogeneous data fusion for brain tumor classification.

PubMed

Metsis, Vangelis; Huang, Heng; Andronesi, Ovidiu C; Makedon, Fillia; Tzika, Aria

2012-10-01

Current research in biomedical informatics involves analysis of multiple heterogeneous data sets. This includes patient demographics, clinical and pathology data, treatment history, patient outcomes as well as gene expression, DNA sequences and other information sources such as gene ontology. Analysis of these data sets could lead to better disease diagnosis, prognosis, treatment and drug discovery. In this report, we present a novel machine learning framework for brain tumor classification based on heterogeneous data fusion of metabolic and molecular datasets, including state-of-the-art high-resolution magic angle spinning (HRMAS) proton (1H) magnetic resonance spectroscopy and gene transcriptome profiling, obtained from intact brain tumor biopsies. Our experimental results show that our novel framework outperforms any analysis using individual dataset.

Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles.

PubMed

Barker, Jocelyn; Hoogi, Assaf; Depeursinge, Adrien; Rubin, Daniel L

2016-05-01

Computerized analysis of digital pathology images offers the potential of improving clinical care (e.g. automated diagnosis) and catalyzing research (e.g. discovering disease subtypes). There are two key challenges thwarting computerized analysis of digital pathology images: first, whole slide pathology images are massive, making computerized analysis inefficient, and second, diverse tissue regions in whole slide images that are not directly relevant to the disease may mislead computerized diagnosis algorithms. We propose a method to overcome both of these challenges that utilizes a coarse-to-fine analysis of the localized characteristics in pathology images. An initial surveying stage analyzes the diversity of coarse regions in the whole slide image. This includes extraction of spatially localized features of shape, color and texture from tiled regions covering the slide. Dimensionality reduction of the features assesses the image diversity in the tiled regions and clustering creates representative groups. A second stage provides a detailed analysis of a single representative tile from each group. An Elastic Net classifier produces a diagnostic decision value for each representative tile. A weighted voting scheme aggregates the decision values from these tiles to obtain a diagnosis at the whole slide level. We evaluated our method by automatically classifying 302 brain cancer cases into two possible diagnoses (glioblastoma multiforme (N = 182) versus lower grade glioma (N = 120)) with an accuracy of 93.1% (p < 0.001). We also evaluated our method in the dataset provided for the 2014 MICCAI Pathology Classification Challenge, in which our method, trained and tested using 5-fold cross validation, produced a classification accuracy of 100% (p < 0.001). Our method showed high stability and robustness to parameter variation, with accuracy varying between 95.5% and 100% when evaluated for a wide range of parameters. Our approach may be useful to automatically differentiate between the two cancer subtypes. Copyright © 2015 Elsevier B.V. All rights reserved.

Quantifying structural alterations in Alzheimer's disease brains using quantitative phase imaging (Conference Presentation)

NASA Astrophysics Data System (ADS)

Lee, Moosung; Lee, Eeksung; Jung, JaeHwang; Yu, Hyeonseung; Kim, Kyoohyun; Yoon, Jonghee; Lee, Shinhwa; Jeong, Yong; Park, YongKeun

2017-02-01

Imaging brain tissues is an essential part of neuroscience because understanding brain structure provides relevant information about brain functions and alterations associated with diseases. Magnetic resonance imaging and positron emission tomography exemplify conventional brain imaging tools, but these techniques suffer from low spatial resolution around 100 μm. As a complementary method, histopathology has been utilized with the development of optical microscopy. The traditional method provides the structural information about biological tissues to cellular scales, but relies on labor-intensive staining procedures. With the advances of illumination sources, label-free imaging techniques based on nonlinear interactions, such as multiphoton excitations and Raman scattering, have been applied to molecule-specific histopathology. Nevertheless, these techniques provide limited qualitative information and require a pulsed laser, which is difficult to use for pathologists with no laser training. Here, we present a label-free optical imaging of mouse brain tissues for addressing structural alteration in Alzheimer's disease. To achieve the mesoscopic, unlabeled tissue images with high contrast and sub-micrometer lateral resolution, we employed holographic microscopy and an automated scanning platform. From the acquired hologram of the brain tissues, we could retrieve scattering coefficients and anisotropies according to the modified scattering-phase theorem. This label-free imaging technique enabled direct access to structural information throughout the tissues with a sub-micrometer lateral resolution and presented a unique means to investigate the structural changes in the optical properties of biological tissues.

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.

In vivo mapping of current density distribution in brain tissues during deep brain stimulation (DBS)

NASA Astrophysics Data System (ADS)

Sajib, Saurav Z. K.; Oh, Tong In; Kim, Hyung Joong; Kwon, Oh In; Woo, Eung Je

2017-01-01

New methods for in vivo mapping of brain responses during deep brain stimulation (DBS) are indispensable to secure clinical applications. Assessment of current density distribution, induced by internally injected currents, may provide an alternative method for understanding the therapeutic effects of electrical stimulation. The current flow and pathway are affected by internal conductivity, and can be imaged using magnetic resonance-based conductivity imaging methods. Magnetic resonance electrical impedance tomography (MREIT) is an imaging method that can enable highly resolved mapping of electromagnetic tissue properties such as current density and conductivity of living tissues. In the current study, we experimentally imaged current density distribution of in vivo canine brains by applying MREIT to electrical stimulation. The current density maps of three canine brains were calculated from the measured magnetic flux density data. The absolute current density values of brain tissues, including gray matter, white matter, and cerebrospinal fluid were compared to assess the active regions during DBS. The resulting current density in different tissue types may provide useful information about current pathways and volume activation for adjusting surgical planning and understanding the therapeutic effects of DBS.

Childhood Brain and Spinal Cord Tumors Treatment Overview (PDQ®)—Health Professional Version

Cancer.gov

Pediatric primary brain and CNS tumors are a diverse group of diseases that together constitute the most common solid tumor of childhood. Get detailed information about the diagnosis, classification, prognosis, and treatment of childhood brain and spinal cord tumors in this comprehensive summary for clinicians.

Effects of positive end-expiratory pressure on brain tissue oxygen pressure of severe traumatic brain injury patients with acute respiratory distress syndrome: A pilot study.

PubMed

Nemer, Sérgio Nogueira; Caldeira, Jefferson B; Santos, Ricardo G; Guimarães, Bruno L; Garcia, João Márcio; Prado, Darwin; Silva, Ricardo T; Azeredo, Leandro M; Faria, Eduardo R; Souza, Paulo Cesar P

2015-12-01

To verify whether high positive end-expiratory pressure levels can increase brain tissue oxygen pressure, and also their effects on pulse oxygen saturation, intracranial pressure, and cerebral perfusion pressure. Twenty traumatic brain injury patients with acute respiratory distress syndrome were submitted to positive end-expiratory pressure levels of 5, 10, and 15 cm H2O progressively. The 3 positive end-expiratory pressure levels were used during 20 minutes for each one, whereas brain tissue oxygen pressure, oxygen saturation, intracranial pressure, and cerebral perfusion pressure were recorded. Brain tissue oxygen pressure and oxygen saturation increased significantly with increasing positive end-expiratory pressure from 5 to 10 and from 10 to 15 cm H2O (P=.0001 and P=.0001 respectively). Intracranial pressure and cerebral perfusion pressure did not differ significantly with increasing positive end-expiratory pressure from 5 to 10 and from 10 to 15 cm H2O (P=.16 and P=.79 respectively). High positive end-expiratory pressure levels increased brain tissue oxygen pressure and oxygen saturation, without increase in intracranial pressure or decrease in cerebral perfusion pressure. High positive end-expiratory pressure levels can be used in severe traumatic brain injury patients with acute respiratory distress syndrome as a safe alternative to improve brain oxygenation. Copyright © 2015 Elsevier Inc. All rights reserved.

Pharmacokinetics and brain penetration of carbapenems in mice.

PubMed

Matsumoto, Kazuaki; Kurihara, Yuji; Kuroda, Yuko; Hori, Seiji; Kizu, Junko

2016-05-01

An adverse effect associated with the administration of carbapenems is central nervous system (CNS) toxicity, with higher brain concentrations of carbapenems being linked to an increased risk of seizures. However, the pharmacokinetics and brain penetration of carbapenems have not yet been examined. Thus, the aim of this in vivo investigation was to determine the pharmacokinetics and brain penetration of carbapenems in mice. Blood samples and brain tissue samples were obtained 10, 20, 30, 60, and 120 min after the subcutaneous administration of carbapenems (91 mg/kg). We obtained the following values for the pharmacokinetic parameters of carbapenems in mice: 1.20-1.71 L/h/kg for CLtotal/F, 1.41-2.03 h(-1) for Ke, 0.34-0.51 h for T1/2, 0.66-0.95 L/kg for Vss/F, 0.49-0.73 h for MRT, 83.46-110.58 μg/mL for Cmax, plasma, and 0.28-0.83 μg/g for Cmax, brain tissue. The AUC0-∞ of the carbapenems tested in plasma were in the following order: doripenem > meropenem > biapenem > imipenem, and in brain tissue were: imipenem > doripenem > meropenem > biapenem. The degrees of brain tissue penetration, defined as the AUC0-∞, brain tissue/fAUC0-∞, plasma ratio, were 0.016 for imipenem, 0.004 for meropenem, 0.002 for biapenem, and 0.008 for doripenem. The results of the present study demonstrated that, of the carbapenems examined, imipenem penetrated brain tissue to the greatest extent. Copyright © 2015 Japanese Society of Chemotherapy and The Japanese Association for Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

Correspondence of DNA Methylation Between Blood and Brain Tissue and Its Application to Schizophrenia Research.

PubMed

Walton, Esther; Hass, Johanna; Liu, Jingyu; Roffman, Joshua L; Bernardoni, Fabio; Roessner, Veit; Kirsch, Matthias; Schackert, Gabriele; Calhoun, Vince; Ehrlich, Stefan

2016-03-01

Given the difficulty of procuring human brain tissue, a key question in molecular psychiatry concerns the extent to which epigenetic signatures measured in more accessible tissues such as blood can serve as a surrogate marker for the brain. Here, we aimed (1) to investigate the blood-brain correspondence of DNA methylation using a within-subject design and (2) to identify changes in DNA methylation of brain-related biological pathways in schizophrenia.We obtained paired blood and temporal lobe biopsy samples simultaneously from 12 epilepsy patients during neurosurgical treatment. Using the Infinium 450K methylation array we calculated similarity of blood and brain DNA methylation for each individual separately. We applied our findings by performing gene set enrichment analyses (GSEA) of peripheral blood DNA methylation data (Infinium 27K) of 111 schizophrenia patients and 122 healthy controls and included only Cytosine-phosphate-Guanine (CpG) sites that were significantly correlated across tissues.Only 7.9% of CpG sites showed a statistically significant, large correlation between blood and brain tissue, a proportion that although small was significantly greater than predicted by chance. GSEA analysis of schizophrenia data revealed altered methylation profiles in pathways related to precursor metabolites and signaling peptides.Our findings indicate that most DNA methylation markers in peripheral blood do not reliably predict brain DNA methylation status. However, a subset of peripheral data may proxy methylation status of brain tissue. Restricting the analysis to these markers can identify meaningful epigenetic differences in schizophrenia and potentially other brain disorders. © The Author 2015. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Detection of Human Brain Cancer Infiltration ex vivo and in vivo Using Quantitative Optical Coherence Tomography*

PubMed Central

Kut, Carmen; Chaichana, Kaisorn L.; Xi, Jiefeng; Raza, Shaan M.; Ye, Xiaobu; McVeigh, Elliot R.; Rodriguez, Fausto J.; Quinones-Hinojosa, Alfredo; Li, Xingde

2015-01-01

More complete brain cancer resection can prolong survival and delay recurrence. However, it is challenging to distinguish cancer from non-cancer tissues intraoperatively, especially at the transitional, infiltrative zones. This is especially critical in eloquent regions (e.g. speech and motor areas). This study tested the feasibility of label-free, quantitative optical coherence tomography (OCT) for differentiating cancer from non-cancer in human brain tissues. Fresh ex vivo human brain tissues were obtained from 32 patients with grades II-IV brain cancer and 5 patients with non-cancer brain pathologies. Based on volumetric OCT imaging data, pathologically confirmed brain cancer tissues (both high-grade and low-grade) had significantly lower optical attenuation values at both cancer core and infiltrated zones when compared with non-cancer white matter, and OCT achieved high sensitivity and specificity at an attenuation threshold of 5.5 mm-1 for brain cancer patients. We also used this attenuation threshold to confirm the intraoperative feasibility of performing in vivo OCT-guided surgery using a murine model harboring human brain cancer. Our OCT system was capable of processing and displaying a color-coded optical property map in real time at a rate of 110-215 frames per second, or 1.2-2.4 seconds for an 8-16 mm3 tissue volume, thus providing direct visual cues for cancer versus non-cancer areas. Our study demonstrates the translational and practical potential of OCT in differentiating cancer from non-cancer tissue. Its intraoperative use may facilitate safe and extensive resection of infiltrative brain cancers and consequently lead to improved outcomes when compared with current clinical standards. PMID:26084803

Endocannabinoids as a Target for the Treatment of Traumatic Brain Injury

DTIC Science & Technology

2012-11-01

DATES COVERED 4 October 2011- 3 October 2012 4. TITLE AND SUBTITLE Endocannabinoids as a Target for the Treatment of Traumatic Brain Injury 5a...interventions aimed at modulation of the endocannabinoid (EC) system targeting degradation of 20arachidonoyl glycerlol (2- AG) and N-arachidonoyl...percussion, traumatic brain injury, blood brain barrier, neuroinflammination, neurological dysfunction, endocannabinoids . 16. SECURITY CLASSIFICATION

Radiological Society of North America

MedlinePlus

... Courses Electronic Education Exhibits RSNA Journals RSNA/AAPM Physics Modules RadioGraphics ABR Diagnostic Radiology Core Exam Study ... Brain Tumor Classification System In 2016, the World Health Organization (WHO) released an update to its brain ...

21 CFR 882.4100 - Ventricular catheter.

Code of Federal Regulations, 2012 CFR

2012-04-01

...) Identification. A ventricular catheter is a device used to gain access to the cavities of the brain for injection of material into, or removal of material from, the brain. (b) Classification. Class II (performance...

21 CFR 882.4100 - Ventricular catheter.

Code of Federal Regulations, 2013 CFR

2013-04-01

...) Identification. A ventricular catheter is a device used to gain access to the cavities of the brain for injection of material into, or removal of material from, the brain. (b) Classification. Class II (performance...

21 CFR 882.4100 - Ventricular catheter.

Code of Federal Regulations, 2011 CFR

2011-04-01

...) Identification. A ventricular catheter is a device used to gain access to the cavities of the brain for injection of material into, or removal of material from, the brain. (b) Classification. Class II (performance...

21 CFR 882.4100 - Ventricular catheter.

Code of Federal Regulations, 2014 CFR

2014-04-01

...) Identification. A ventricular catheter is a device used to gain access to the cavities of the brain for injection of material into, or removal of material from, the brain. (b) Classification. Class II (performance...

21 CFR 882.4100 - Ventricular catheter.

Code of Federal Regulations, 2010 CFR

2010-04-01

...) Identification. A ventricular catheter is a device used to gain access to the cavities of the brain for injection of material into, or removal of material from, the brain. (b) Classification. Class II (performance...

Sub-Network Kernels for Measuring Similarity of Brain Connectivity Networks in Disease Diagnosis.

PubMed

Jie, Biao; Liu, Mingxia; Zhang, Daoqiang; Shen, Dinggang

2018-05-01

As a simple representation of interactions among distributed brain regions, brain networks have been widely applied to automated diagnosis of brain diseases, such as Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment (MCI). In brain network analysis, a challenging task is how to measure the similarity between a pair of networks. Although many graph kernels (i.e., kernels defined on graphs) have been proposed for measuring the topological similarity of a pair of brain networks, most of them are defined using general graphs, thus ignoring the uniqueness of each node in brain networks. That is, each node in a brain network denotes a particular brain region, which is a specific characteristics of brain networks. Accordingly, in this paper, we construct a novel sub-network kernel for measuring the similarity between a pair of brain networks and then apply it to brain disease classification. Different from current graph kernels, our proposed sub-network kernel not only takes into account the inherent characteristic of brain networks, but also captures multi-level (from local to global) topological properties of nodes in brain networks, which are essential for defining the similarity measure of brain networks. To validate the efficacy of our method, we perform extensive experiments on subjects with baseline functional magnetic resonance imaging data obtained from the Alzheimer's disease neuroimaging initiative database. Experimental results demonstrate that the proposed method outperforms several state-of-the-art graph-based methods in MCI classification.

Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning.

PubMed

Formisano, Elia; De Martino, Federico; Valente, Giancarlo

2008-09-01

Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms "learn" a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set ("brain reading"). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.

Classification of Parkinsonian syndromes from FDG-PET brain data using decision trees with SSM/PCA features.

PubMed

Mudali, D; Teune, L K; Renken, R J; Leenders, K L; Roerdink, J B T M

2015-01-01

Medical imaging techniques like fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to aid in the differential diagnosis of neurodegenerative brain diseases. In this study, the objective is to classify FDG-PET brain scans of subjects with Parkinsonian syndromes (Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy) compared to healthy controls. The scaled subprofile model/principal component analysis (SSM/PCA) method was applied to FDG-PET brain image data to obtain covariance patterns and corresponding subject scores. The latter were used as features for supervised classification by the C4.5 decision tree method. Leave-one-out cross validation was applied to determine classifier performance. We carried out a comparison with other types of classifiers. The big advantage of decision tree classification is that the results are easy to understand by humans. A visual representation of decision trees strongly supports the interpretation process, which is very important in the context of medical diagnosis. Further improvements are suggested based on enlarging the number of the training data, enhancing the decision tree method by bagging, and adding additional features based on (f)MRI data.

Support Vector Machine Classification of Major Depressive Disorder Using Diffusion-Weighted Neuroimaging and Graph Theory

PubMed Central

Sacchet, Matthew D.; Prasad, Gautam; Foland-Ross, Lara C.; Thompson, Paul M.; Gotlib, Ian H.

2015-01-01

Recently, there has been considerable interest in understanding brain networks in major depressive disorder (MDD). Neural pathways can be tracked in the living brain using diffusion-weighted imaging (DWI); graph theory can then be used to study properties of the resulting fiber networks. To date, global abnormalities have not been reported in tractography-based graph metrics in MDD, so we used a machine learning approach based on “support vector machines” to differentiate depressed from healthy individuals based on multiple brain network properties. We also assessed how important specific graph metrics were for this differentiation. Finally, we conducted a local graph analysis to identify abnormal connectivity at specific nodes of the network. We were able to classify depression using whole-brain graph metrics. Small-worldness was the most useful graph metric for classification. The right pars orbitalis, right inferior parietal cortex, and left rostral anterior cingulate all showed abnormal network connectivity in MDD. This is the first use of structural global graph metrics to classify depressed individuals. These findings highlight the importance of future research to understand network properties in depression across imaging modalities, improve classification results, and relate network alterations to psychiatric symptoms, medication, and comorbidities. PMID:25762941

Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory.

PubMed

Sacchet, Matthew D; Prasad, Gautam; Foland-Ross, Lara C; Thompson, Paul M; Gotlib, Ian H

2015-01-01

Recently, there has been considerable interest in understanding brain networks in major depressive disorder (MDD). Neural pathways can be tracked in the living brain using diffusion-weighted imaging (DWI); graph theory can then be used to study properties of the resulting fiber networks. To date, global abnormalities have not been reported in tractography-based graph metrics in MDD, so we used a machine learning approach based on "support vector machines" to differentiate depressed from healthy individuals based on multiple brain network properties. We also assessed how important specific graph metrics were for this differentiation. Finally, we conducted a local graph analysis to identify abnormal connectivity at specific nodes of the network. We were able to classify depression using whole-brain graph metrics. Small-worldness was the most useful graph metric for classification. The right pars orbitalis, right inferior parietal cortex, and left rostral anterior cingulate all showed abnormal network connectivity in MDD. This is the first use of structural global graph metrics to classify depressed individuals. These findings highlight the importance of future research to understand network properties in depression across imaging modalities, improve classification results, and relate network alterations to psychiatric symptoms, medication, and comorbidities.

[Acquired brain injury: a proposal for its definition, diagnostic criteria and classification].

PubMed

Castellanos-Pinedo, Fernando; Cid-Gala, Manuel; Duque, Pablo; Ramirez-Moreno, José M; Zurdo-Hernández, José M

2012-03-16

Acquired brain injury is a heterogeneous clinical concept that goes beyond the limits of the classical medical view, which tends to define processes and diseases on the grounds of a single causation. Although in the medical literature it appears fundamentally associated to traumatic brain injury, there are many other causes and management is similar in all of them, during the post-acute and chronic phases, as regards the measures to be taken concerning rehabilitation and attention to dependence. Yet, despite being an important health issue, today we do not have a set of diagnostic criteria or a classification for this condition. This is a serious handicap when it comes to carrying out epidemiological studies, designing specific care programmes and comparing results among different programmes and centres. Accordingly, the Extremadura Acquired Brain Injury Health Care Plan working group has drawn up these proposed diagnostic criteria, definition and classification. The proposal is intended to be essentially practical, its main purpose being to allow correct identification of the cases that must be attended to and to optimise the use of neurorehabilitation and attention to dependence resources, thereby ensuring attention is provided on a fair basis.

Characterization and classification of zebrafish brain morphology mutants

PubMed Central

Lowery, Laura Anne; De Rienzo, Gianluca; Gutzman, Jennifer H.; Sive, Hazel

2010-01-01

The mechanisms by which the vertebrate brain achieves its three-dimensional structure are clearly complex, requiring the functions of many genes. Using the zebrafish as a model, we have begun to define genes required for brain morphogenesis, including brain ventricle formation, by studying 16 mutants previously identified as having embryonic brain morphology defects. We report the phenotypic characterization of these mutants at several time-points, using brain ventricle dye injection, imaging, and immunohistochemistry with neuronal markers. Most of these mutants display early phenotypes, affecting initial brain shaping, while others show later phenotypes, affecting brain ventricle expansion. In the early phenotype group, we further define four phenotypic classes and corresponding functions required for brain morphogenesis. Although we did not use known genotypes for this classification, basing it solely on phenotypes, many mutants with defects in functionally related genes clustered in a single class. In particular, class 1 mutants show midline separation defects, corresponding to epithelial junction defects; class 2 mutants show reduced brain ventricle size; class 3 mutants show midbrain-hindbrain abnormalities, corresponding to basement membrane defects; and class 4 mutants show absence of ventricle lumen inflation, corresponding to defective ion pumping. Later brain ventricle expansion requires the extracellular matrix, cardiovascular circulation, and transcription/splicing-dependent events. We suggest that these mutants define processes likely to be used during brain morphogenesis throughout the vertebrates. PMID:19051268

Extracellular Nucleotides in Exercise: Possible Effect on Brain Metabolism.

ERIC Educational Resources Information Center

Forrester, Tom

1979-01-01

A review of experiments which demonstrate the release of ATP from skeletal muscle, cardiac muscle, and active brain tissue. Effects of exogenously applied ATP to brain tissue are discussed in relation to whole body exercise. (Author/SA)

A spatiotemporal atlas of MR intensity, tissue probability and shape of the fetal brain with application to segmentation

PubMed Central

Habas, Piotr A.; Kim, Kio; Corbett-Detig, James M.; Rousseau, Francois; Glenn, Orit A.; Barkovich, A. James; Studholme, Colin

2010-01-01

Modeling and analysis of MR images of the developing human brain is a challenge due to rapid changes in brain morphology and morphometry. We present an approach to the construction of a spatiotemporal atlas of the fetal brain with temporal models of MR intensity, tissue probability and shape changes. This spatiotemporal model is created from a set of reconstructed MR images of fetal subjects with different gestational ages. Groupwise registration of manual segmentations and voxelwise nonlinear modeling allow us to capture the appearance, disappearance and spatial variation of brain structures over time. Applying this model to atlas-based segmentation, we generate age-specific MR templates and tissue probability maps and use them to initialize automatic tissue delineation in new MR images. The choice of model parameters and the final performance are evaluated using clinical MR scans of young fetuses with gestational ages ranging from 20.57 to 24.71 weeks. Experimental results indicate that quadratic temporal models can correctly capture growth-related changes in the fetal brain anatomy and provide improvement in accuracy of atlas-based tissue segmentation. PMID:20600970

Chemical Probes for Visualizing Intact Animal and Human Brain Tissue.

PubMed

Lai, Hei Ming; Ng, Wai-Lung; Gentleman, Steve M; Wu, Wutian

2017-06-22

Newly developed tissue clearing techniques can be used to render intact tissues transparent. When combined with fluorescent labeling technologies and optical sectioning microscopy, this allows visualization of fine structure in three dimensions. Gene-transfection techniques have proved very useful in visualizing cellular structures in animal models, but they are not applicable to human brain tissue. Here, we discuss the characteristics of an ideal chemical fluorescent probe for use in brain and other cleared tissues, and offer a comprehensive overview of currently available chemical probes. We describe their working principles and compare their performance with the goal of simplifying probe selection for neuropathologists and stimulating probe development by chemists. We propose several approaches for the development of innovative chemical labeling methods which, when combined with tissue clearing, have the potential to revolutionize how we study the structure and function of the human brain. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

Sparse Multivariate Autoregressive Modeling for Mild Cognitive Impairment Classification

PubMed Central

Li, Yang; Wee, Chong-Yaw; Jie, Biao; Peng, Ziwen

2014-01-01

Brain connectivity network derived from functional magnetic resonance imaging (fMRI) is becoming increasingly prevalent in the researches related to cognitive and perceptual processes. The capability to detect causal or effective connectivity is highly desirable for understanding the cooperative nature of brain network, particularly when the ultimate goal is to obtain good performance of control-patient classification with biological meaningful interpretations. Understanding directed functional interactions between brain regions via brain connectivity network is a challenging task. Since many genetic and biomedical networks are intrinsically sparse, incorporating sparsity property into connectivity modeling can make the derived models more biologically plausible. Accordingly, we propose an effective connectivity modeling of resting-state fMRI data based on the multivariate autoregressive (MAR) modeling technique, which is widely used to characterize temporal information of dynamic systems. This MAR modeling technique allows for the identification of effective connectivity using the Granger causality concept and reducing the spurious causality connectivity in assessment of directed functional interaction from fMRI data. A forward orthogonal least squares (OLS) regression algorithm is further used to construct a sparse MAR model. By applying the proposed modeling to mild cognitive impairment (MCI) classification, we identify several most discriminative regions, including middle cingulate gyrus, posterior cingulate gyrus, lingual gyrus and caudate regions, in line with results reported in previous findings. A relatively high classification accuracy of 91.89 % is also achieved, with an increment of 5.4 % compared to the fully-connected, non-directional Pearson-correlation-based functional connectivity approach. PMID:24595922

In vitro terahertz spectroscopy of gelatin-embedded human brain tumors: a pilot study

NASA Astrophysics Data System (ADS)

Chernomyrdin, N. V.; Gavdush, A. A.; Beshplav, S.-I. T.; Malakhov, K. M.; Kucheryavenko, A. S.; Katyba, G. M.; Dolganova, I. N.; Goryaynov, S. A.; Karasik, V. E.; Spektor, I. E.; Kurlov, V. N.; Yurchenko, S. O.; Komandin, G. A.; Potapov, A. A.; Tuchin, V. V.; Zaytsev, K. I.

2018-04-01

We have performed the in vitro terahertz (THz) spectroscopy of human brain tumors. In order to fix tissues for the THz measurements, we have applied the gelatin embedding. It allows for preserving tissues from hydration/dehydration and sustaining their THz response similar to that of the freshly-excised tissues for a long time after resection. We have assembled an experimental setup for the reflection-mode measurements of human brain tissues based on the THz pulsed spectrometer. We have used this setup to study in vitro the refractive index and the amplitude absorption coefficient of 2 samples of malignant glioma (grade IV), 1 sample of meningioma (grade I), and samples of intact tissues. We have observed significant differences between the THz responses of normal and pathological tissues of the brain. The results of this paper highlight the potential of the THz technology in the intraoperative neurodiagnosis of tumors relying on the endogenous labels of tumorous tissues.

Modern classification and outcome predictors of surgery in patients with brain arteriovenous malformations.

PubMed

Tayebi Meybodi, Ali; Lawton, Michael T

2018-02-23

Brain arteriovenous malformations (bAVM) are challenging lesions. Part of this challenge stems from the infinite diversity of these lesions regarding shape, location, anatomy, and physiology. This diversity has called on a variety of treatment modalities for these lesions, of which microsurgical resection prevails as the mainstay of treatment. As such, outcome prediction and managing strategy mainly rely on unraveling the nature of these complex tangles and ways each lesion responds to various therapeutic modalities. This strategy needs the ability to decipher each lesion through accurate and efficient categorization. Therefore, classification schemes are essential parts of treatment planning and outcome prediction. This article summarizes different surgical classification schemes and outcome predictors proposed for bAVMs.

The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia.

PubMed

Diamond, James; Anderson, Neil H; Bartels, Peter H; Montironi, Rodolfo; Hamilton, Peter W

2004-09-01

Quantitative examination of prostate histology offers clues in the diagnostic classification of lesions and in the prediction of response to treatment and prognosis. To facilitate the collection of quantitative data, the development of machine vision systems is necessary. This study explored the use of imaging for identifying tissue abnormalities in prostate histology. Medium-power histological scenes were recorded from whole-mount radical prostatectomy sections at x 40 objective magnification and assessed by a pathologist as exhibiting stroma, normal tissue (nonneoplastic epithelial component), or prostatic carcinoma (PCa). A machine vision system was developed that divided the scenes into subregions of 100 x 100 pixels and subjected each to image-processing techniques. Analysis of morphological characteristics allowed the identification of normal tissue. Analysis of image texture demonstrated that Haralick feature 4 was the most suitable for discriminating stroma from PCa. Using these morphological and texture measurements, it was possible to define a classification scheme for each subregion. The machine vision system is designed to integrate these classification rules and generate digital maps of tissue composition from the classification of subregions; 79.3% of subregions were correctly classified. Established classification rates have demonstrated the validity of the methodology on small scenes; a logical extension was to apply the methodology to whole slide images via scanning technology. The machine vision system is capable of classifying these images. The machine vision system developed in this project facilitates the exploration of morphological and texture characteristics in quantifying tissue composition. It also illustrates the potential of quantitative methods to provide highly discriminatory information in the automated identification of prostatic lesions using computer vision.

Pulsed terahertz imaging of breast cancer in freshly excised murine tumors

NASA Astrophysics Data System (ADS)

Bowman, Tyler; Chavez, Tanny; Khan, Kamrul; Wu, Jingxian; Chakraborty, Avishek; Rajaram, Narasimhan; Bailey, Keith; El-Shenawee, Magda

2018-02-01

This paper investigates terahertz (THz) imaging and classification of freshly excised murine xenograft breast cancer tumors. These tumors are grown via injection of E0771 breast adenocarcinoma cells into the flank of mice maintained on high-fat diet. Within 1 h of excision, the tumor and adjacent tissues are imaged using a pulsed THz system in the reflection mode. The THz images are classified using a statistical Bayesian mixture model with unsupervised and supervised approaches. Correlation with digitized pathology images is conducted using classification images assigned by a modal class decision rule. The corresponding receiver operating characteristic curves are obtained based on the classification results. A total of 13 tumor samples obtained from 9 tumors are investigated. The results show good correlation of THz images with pathology results in all samples of cancer and fat tissues. For tumor samples of cancer, fat, and muscle tissues, THz images show reasonable correlation with pathology where the primary challenge lies in the overlapping dielectric properties of cancer and muscle tissues. The use of a supervised regression approach shows improvement in the classification images although not consistently in all tissue regions. Advancing THz imaging of breast tumors from mice and the development of accurate statistical models will ultimately progress the technique for the assessment of human breast tumor margins.

Gene expression profiles help identify the tissue of origin for metastatic brain cancers.

PubMed

Wu, Alan H B; Drees, Julia C; Wang, Hangpin; VandenBerg, Scott R; Lal, Anita; Henner, William D; Pillai, Raji

2010-04-26

Metastatic brain cancers are the most common intracranial tumor and occur in about 15% of all cancer patients. In up to 10% of these patients, the primary tumor tissue remains unknown, even after a time consuming and costly workup. The Pathwork Tissue of Origin Test (Pathwork Diagnostics, Redwood City, CA, USA) is a gene expression test to aid in the diagnosis of metastatic, poorly differentiated and undifferentiated tumors. It measures the expression pattern of 1,550 genes in these tumors and compares it to the expression pattern of a panel of 15 known tumor types. The purpose of this study was to evaluate the performance of the Tissue of Origin Test in the diagnosis of primary sites for metastatic brain cancer patients. Fifteen fresh-frozen metastatic brain tumor specimens of known origins met specimen requirements. These specimens were entered into the study and processed using the Tissue of Origin Test. Results were compared to the known primary site and the agreement between the two results was assessed. Fourteen of the fifteen specimens produced microarray data files that passed all quality metrics. One originated from a tissue type that was off-panel. Among the remaining 13 cases, the Tissue of Origin Test accurately predicted the available diagnosis in 12/13 (92.3%) cases. This study demonstrates the accuracy of the Tissue of Origin Test when applied to predict the tissue of origin of metastatic brain tumors. This test could be a very useful tool for pathologists as they classify metastatic brain cancers.

Gene expression profiles help identify the Tissue of Origin for metastatic brain cancers

PubMed Central

2010-01-01

Background Metastatic brain cancers are the most common intracranial tumor and occur in about 15% of all cancer patients. In up to 10% of these patients, the primary tumor tissue remains unknown, even after a time consuming and costly workup. The Pathwork® Tissue of Origin Test (Pathwork Diagnostics, Redwood City, CA, USA) is a gene expression test to aid in the diagnosis of metastatic, poorly differentiated and undifferentiated tumors. It measures the expression pattern of 1,550 genes in these tumors and compares it to the expression pattern of a panel of 15 known tumor types. The purpose of this study was to evaluate the performance of the Tissue of Origin Test in the diagnosis of primary sites for metastatic brain cancer patients. Methods Fifteen fresh-frozen metastatic brain tumor specimens of known origins met specimen requirements. These specimens were entered into the study and processed using the Tissue of Origin Test. Results were compared to the known primary site and the agreement between the two results was assessed. Results Fourteen of the fifteen specimens produced microarray data files that passed all quality metrics. One originated from a tissue type that was off-panel. Among the remaining 13 cases, the Tissue of Origin Test accurately predicted the available diagnosis in 12/13 (92.3%) cases. Discussion This study demonstrates the accuracy of the Tissue of Origin Test when applied to predict the tissue of origin of metastatic brain tumors. This test could be a very useful tool for pathologists as they classify metastatic brain cancers. PMID:20420692

Effect of baculovirus P35 protein on apoptosis in brain tissue of rats with acute cerebral infarction.

PubMed

Ji, J F; Ma, X H

2015-08-10

We explored the effect of baculovirus P35 protein on apoptosis in the brain tissue of rats with acute cerebral infarction (ACI). A rat model of middle cerebral artery infarction was created. The rats were randomly divided into sham, model, and treatment groups. Baculovirus P35 protein was injected into the intracranial arteries of the treatment group rats. The rats in the model group were given an equal volume of phosphate-buffered saline. The rats were sacrificed after 72 h and the brain tissue was separated. The levels of caspase-3, Bcl-2, and Bax mRNA, the brain cell apoptosis index, and the infarct size were determined. After 72 h, the levels of caspase-3 and Bax mRNA in the model and treatment groups were significantly greater than in the sham group, and the levels of Bcl-2 mRNA were significantly smaller (P < 0.05). The levels of caspase-3 and Bax mRNA were significantly lower in the treatment group than in the model group, and the level of Bcl-2 mRNA was significantly greater (P < 0.05). Compared with the sham group, the brain tissue apoptosis index and the cerebral infarction area increased significantly in the model and treatment groups (P < 0.05). The brain tissue apoptosis index and cerebral infarction area in the treatment group were significantly lower than in the model group (P < 0.05). Baculovirus P35 protein can effectively inhibit brain cell apoptosis in rats with ACI. It delayed apoptosis and necrosis in subjects with ACI tissue and had a protective effect on brain tissue.

Quantitative assessment of brain tissue oxygenation in porcine models of cardiac arrest and cardiopulmonary resuscitation using hyperspectral near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Lotfabadi, Shahin S.; Toronov, Vladislav; Ramadeen, Andrew; Hu, Xudong; Kim, Siwook; Dorian, Paul; Hare, Gregory M. T.

2014-03-01

Near-infrared spectroscopy (NIRS) is a non-invasive tool to measure real-time tissue oxygenation in the brain. In an invasive animal experiment we were able to directly compare non-invasive NIRS measurements on the skull with invasive measurements directly on the brain dura matter. We used a broad-band, continuous-wave hyper-spectral approach to measure tissue oxygenation in the brain of pigs under the conditions of cardiac arrest, cardiopulmonary resuscitation (CPR), and defibrillation. An additional purpose of this research was to find a correlation between mortality due to cardiac arrest and inadequacy of the tissue perfusion during attempts at resuscitation. Using this technique we measured the changes in concentrations of oxy-hemoglobin [HbO2] and deoxy-hemoglobin [HHb] to quantify the tissue oxygenation in the brain. We also extracted cytochrome c oxidase changes Δ[Cyt-Ox] under the same conditions to determine increase or decrease in cerebral oxygen delivery. In this paper we proved that applying CPR, [HbO2] concentration and tissue oxygenation in the brain increase while [HHb] concentration decreases which was not possible using other measurement techniques. We also discovered a similar trend in changes of both [Cyt-Ox] concentration and tissue oxygen saturation (StO2). Both invasive and non-invasive measurements showed similar results.

Divide and Conquer: Sub-Grouping of ASD Improves ASD Detection Based on Brain Morphometry.

PubMed

Katuwal, Gajendra J; Baum, Stefi A; Cahill, Nathan D; Michael, Andrew M

2016-01-01

Low success (<60%) in autism spectrum disorder (ASD) classification using brain morphometry from the large multi-site ABIDE dataset and inconsistent findings on brain morphometric abnormalities in ASD can be attributed to the ASD heterogeneity. In this study, we show that ASD brain morphometry is highly heterogeneous, and demonstrate that the heterogeneity can be mitigated and classification improved if autism severity (AS), verbal IQ (VIQ) and age are used with morphometric features. Morphometric features from structural MRIs (sMRIs) of 734 males (ASD: 361, controls: 373) of ABIDE were derived using FreeSurfer. Applying the Random Forest classifier, an AUC of 0.61 was achieved. Adding VIQ and age to morphometric features, AUC improved to 0.68. Sub-grouping the subjects by AS, VIQ and age improved the classification with the highest AUC of 0.8 in the moderate-AS sub-group (AS = 7-8). Matching subjects on age and/or VIQ in each sub-group further improved the classification with the highest AUC of 0.92 in the low AS sub-group (AS = 4-5). AUC decreased with AS and VIQ, and was the lowest in the mid-age sub-group (13-18 years). The important features were mainly from the frontal, temporal, ventricular, right hippocampal and left amygdala regions. However, they highly varied with AS, VIQ and age. The curvature and folding index features from frontal, temporal, lingual and insular regions were dominant in younger subjects suggesting their importance for early detection. When the experiments were repeated using the Gradient Boosting classifier similar results were obtained. Our findings suggest that identifying brain biomarkers in sub-groups of ASD can yield more robust and insightful results than searching across the whole spectrum. Further, it may allow identification of sub-group specific brain biomarkers that are optimized for early detection and monitoring, increasing the utility of sMRI as an important tool for early detection of ASD.

Divide and Conquer: Sub-Grouping of ASD Improves ASD Detection Based on Brain Morphometry

PubMed Central

Baum, Stefi A.; Cahill, Nathan D.; Michael, Andrew M.

2016-01-01

Low success (<60%) in autism spectrum disorder (ASD) classification using brain morphometry from the large multi-site ABIDE dataset and inconsistent findings on brain morphometric abnormalities in ASD can be attributed to the ASD heterogeneity. In this study, we show that ASD brain morphometry is highly heterogeneous, and demonstrate that the heterogeneity can be mitigated and classification improved if autism severity (AS), verbal IQ (VIQ) and age are used with morphometric features. Morphometric features from structural MRIs (sMRIs) of 734 males (ASD: 361, controls: 373) of ABIDE were derived using FreeSurfer. Applying the Random Forest classifier, an AUC of 0.61 was achieved. Adding VIQ and age to morphometric features, AUC improved to 0.68. Sub-grouping the subjects by AS, VIQ and age improved the classification with the highest AUC of 0.8 in the moderate-AS sub-group (AS = 7–8). Matching subjects on age and/or VIQ in each sub-group further improved the classification with the highest AUC of 0.92 in the low AS sub-group (AS = 4–5). AUC decreased with AS and VIQ, and was the lowest in the mid-age sub-group (13–18 years). The important features were mainly from the frontal, temporal, ventricular, right hippocampal and left amygdala regions. However, they highly varied with AS, VIQ and age. The curvature and folding index features from frontal, temporal, lingual and insular regions were dominant in younger subjects suggesting their importance for early detection. When the experiments were repeated using the Gradient Boosting classifier similar results were obtained. Our findings suggest that identifying brain biomarkers in sub-groups of ASD can yield more robust and insightful results than searching across the whole spectrum. Further, it may allow identification of sub-group specific brain biomarkers that are optimized for early detection and monitoring, increasing the utility of sMRI as an important tool for early detection of ASD. PMID:27065101

Multimodality instrument for tissue characterization

NASA Technical Reports Server (NTRS)

Mah, Robert W. (Inventor); Andrews, Russell J. (Inventor)

2004-01-01

A system with multimodality instrument for tissue identification includes a computer-controlled motor driven heuristic probe with a multisensory tip. For neurosurgical applications, the instrument is mounted on a stereotactic frame for the probe to penetrate the brain in a precisely controlled fashion. The resistance of the brain tissue being penetrated is continually monitored by a miniaturized strain gauge attached to the probe tip. Other modality sensors may be mounted near the probe tip to provide real-time tissue characterizations and the ability to detect the proximity of blood vessels, thus eliminating errors normally associated with registration of pre-operative scans, tissue swelling, elastic tissue deformation, human judgement, etc., and rendering surgical procedures safer, more accurate, and efficient. A neural network program adaptively learns the information on resistance and other characteristic features of normal brain tissue during the surgery and provides near real-time modeling. A fuzzy logic interface to the neural network program incorporates expert medical knowledge in the learning process. Identification of abnormal brain tissue is determined by the detection of change and comparison with previously learned models of abnormal brain tissues. The operation of the instrument is controlled through a user friendly graphical interface. Patient data is presented in a 3D stereographics display. Acoustic feedback of selected information may optionally be provided. Upon detection of the close proximity to blood vessels or abnormal brain tissue, the computer-controlled motor immediately stops probe penetration. The use of this system will make surgical procedures safer, more accurate, and more efficient. Other applications of this system include the detection, prognosis and treatment of breast cancer, prostate cancer, spinal diseases, and use in general exploratory surgery.

Therapeutic Ultrasound Enhancement of Drug Delivery to Soft Tissues

NASA Astrophysics Data System (ADS)

Lewis, George; Wang, Peng; Lewis, George; Olbricht, William

2009-04-01

Effects of exposure to 1.58 MHz focused ultrasound on transport of Evans Blue Dye (EBD) in soft tissues are investigated when an external pressure gradient is applied to induce convective flow through the tissue. The magnitude of the external pressure gradient is chosen to simulate conditions in brain parenchyma during convection-enhanced drug delivery (CED) to the brain. EBD uptake and transport are measured in equine brain, avian muscle and agarose brain-mimicking phantoms. Results show that ultrasound enhances EBD uptake and transport, and the greatest enhancement occurs when the external pressure gradient is applied. The results suggest that exposure of the brain parenchyma to ultrasound could enhance penetration of material infused into the brain during CED therapy.

Development of an experimental model of brain tissue heterotopia in the lung

PubMed Central

Quemelo, Paulo Roberto Veiga; Sbragia, Lourenço; Peres, Luiz Cesar

2007-01-01

Summary The presence of heterotopic brain tissue in the lung is a rare abnormality. The cases reported thus far are usually associated with neural tube defects (NTD). As there are no reports of experimental models of NTD that present this abnormality, the objective of the present study was to develop a surgical method of brain tissue heterotopia in the lung. We used 24 pregnant Swiss mice divided into two groups of 12 animals each, denoted 17GD and 18GD according to the gestational day (GD) when caesarean section was performed to collect the fetuses. Surgery was performed on the 15th GD, one fetus was removed by hysterectomy and its brain tissue was cut into small fragments and implanted in the lung of its litter mates. Thirty-four live fetuses were obtained from the 17GD group. Of these, eight (23.5%) were used as control (C), eight (23.5%) were sham operated (S) and 18 (52.9%) were used for pulmonary brain tissue implantation (PBI). Thirty live fetuses were obtained from the females of the 18GD group. Of these, eight (26.6%) were C, eight (26.6%) S and 14 (46.6%) were used for PBI. Histological examination of the fetal trunks showed implantation of GFAP-positive brain tissue in 85% of the fetuses of the 17GD group and in 100% of those of the 18GD group, with no significant difference between groups for any of the parameters analysed. The experimental model proved to be efficient and of relatively simple execution, showing complete integration of the brain tissue with pulmonary and pleural tissue and thus representing a model that will permit the study of different aspects of cell implantation and interaction. PMID:17877535

Biochemical Fractionation and Stable Isotope Dilution Liquid Chromatography-mass Spectrometry for Targeted and Microdomain-specific Protein Quantification in Human Postmortem Brain Tissue*

PubMed Central

MacDonald, Matthew L.; Ciccimaro, Eugene; Prakash, Amol; Banerjee, Anamika; Seeholzer, Steven H.; Blair, Ian A.; Hahn, Chang-Gyu

2012-01-01

Synaptic architecture and its adaptive changes require numerous molecular events that are both highly ordered and complex. A majority of neuropsychiatric illnesses are complex trait disorders, in which multiple etiologic factors converge at the synapse via many signaling pathways. Investigating the protein composition of synaptic microdomains from human patient brain tissues will yield valuable insights into the interactions of risk genes in many disorders. These types of studies in postmortem tissues have been limited by the lack of proper study paradigms. Thus, it is necessary not only to develop strategies to quantify protein and post-translational modifications at the synapse, but also to rigorously validate them for use in postmortem human brain tissues. In this study we describe the development of a liquid chromatography-selected reaction monitoring method, using a stable isotope-labeled neuronal proteome standard prepared from the brain tissue of a stable isotope-labeled mouse, for the multiplexed quantification of target synaptic proteins in mammalian samples. Additionally, we report the use of this method to validate a biochemical approach for the preparation of synaptic microdomain enrichments from human postmortem prefrontal cortex. Our data demonstrate that a targeted mass spectrometry approach with a true neuronal proteome standard facilitates accurate and precise quantification of over 100 synaptic proteins in mammalian samples, with the potential to quantify over 1000 proteins. Using this method, we found that protein enrichments in subcellular fractions prepared from human postmortem brain tissue were strikingly similar to those prepared from fresh mouse brain tissue. These findings demonstrate that biochemical fractionation methods paired with targeted proteomic strategies can be used in human brain tissues, with important implications for the study of neuropsychiatric disease. PMID:22942359

Segmentation and classification of brain images using firefly and hybrid kernel-based support vector machine

NASA Astrophysics Data System (ADS)

Selva Bhuvaneswari, K.; Geetha, P.

2017-05-01

Magnetic resonance imaging segmentation refers to a process of assigning labels to set of pixels or multiple regions. It plays a major role in the field of biomedical applications as it is widely used by the radiologists to segment the medical images input into meaningful regions. In recent years, various brain tumour detection techniques are presented in the literature. The entire segmentation process of our proposed work comprises three phases: threshold generation with dynamic modified region growing phase, texture feature generation phase and region merging phase. by dynamically changing two thresholds in the modified region growing approach, the first phase of the given input image can be performed as dynamic modified region growing process, in which the optimisation algorithm, firefly algorithm help to optimise the two thresholds in modified region growing. After obtaining the region growth segmented image using modified region growing, the edges can be detected with edge detection algorithm. In the second phase, the texture feature can be extracted using entropy-based operation from the input image. In region merging phase, the results obtained from the texture feature-generation phase are combined with the results of dynamic modified region growing phase and similar regions are merged using a distance comparison between regions. After identifying the abnormal tissues, the classification can be done by hybrid kernel-based SVM (Support Vector Machine). The performance analysis of the proposed method will be carried by K-cross fold validation method. The proposed method will be implemented in MATLAB with various images.

Automated tissue classification of pediatric brains from magnetic resonance images using age-specific atlases

NASA Astrophysics Data System (ADS)

Metzger, Andrew; Benavides, Amanda; Nopoulos, Peg; Magnotta, Vincent

2016-03-01

The goal of this project was to develop two age appropriate atlases (neonatal and one year old) that account for the rapid growth and maturational changes that occur during early development. Tissue maps from this age group were initially created by manually correcting the resulting tissue maps after applying an expectation maximization (EM) algorithm and an adult atlas to pediatric subjects. The EM algorithm classified each voxel into one of ten possible tissue types including several subcortical structures. This was followed by a novel level set segmentation designed to improve differentiation between distal cortical gray matter and white matter. To minimize the req uired manual corrections, the adult atlas was registered to the pediatric scans using high -dimensional, symmetric image normalization (SyN) registration. The subject images were then mapped to an age specific atlas space, again using SyN registration, and the resulting transformation applied to the manually corrected tissue maps. The individual maps were averaged in the age specific atlas space and blurred to generate the age appropriate anatomical priors. The resulting anatomical priors were then used by the EM algorithm to re-segment the initial training set as well as an independent testing set. The results from the adult and age-specific anatomical priors were compared to the manually corrected results. The age appropriate atlas provided superior results as compared to the adult atlas. The image analysis pipeline used in this work was built using the open source software package BRAINSTools.

Cranial irradiation increases tumor growth in experimental breast cancer brain metastasis.

PubMed

Hamilton, Amanda M; Wong, Suzanne M; Wong, Eugene; Foster, Paula J

2018-05-01

Whole-brain radiotherapy is the standard of care for patients with breast cancer with multiple brain metastases and, although this treatment has been essential in the management of existing brain tumors, there are many known negative consequences associated with the irradiation of normal brain tissue. In our study, we used in vivo magnetic resonance imaging analysis to investigate the influence of radiotherapy-induced damage of healthy brain on the arrest and growth of metastatic breast cancer cells in a mouse model of breast cancer brain metastasis. We observed that irradiated, but otherwise healthy, neural tissue had an increased propensity to support metastatic growth compared with never-irradiated controls. The elucidation of the impact of irradiation on normal neural tissue could have implications in clinical patient management, particularly in patients with residual systemic disease or with residual radio-resistant brain cancer. Copyright © 2018 John Wiley & Sons, Ltd.

Within-brain classification for brain tumor segmentation.

PubMed

Havaei, Mohammad; Larochelle, Hugo; Poulin, Philippe; Jodoin, Pierre-Marc

2016-05-01

In this paper, we investigate a framework for interactive brain tumor segmentation which, at its core, treats the problem of interactive brain tumor segmentation as a machine learning problem. This method has an advantage over typical machine learning methods for this task where generalization is made across brains. The problem with these methods is that they need to deal with intensity bias correction and other MRI-specific noise. In this paper, we avoid these issues by approaching the problem as one of within brain generalization. Specifically, we propose a semi-automatic method that segments a brain tumor by training and generalizing within that brain only, based on some minimum user interaction. We investigate how adding spatial feature coordinates (i.e., i, j, k) to the intensity features can significantly improve the performance of different classification methods such as SVM, kNN and random forests. This would only be possible within an interactive framework. We also investigate the use of a more appropriate kernel and the adaptation of hyper-parameters specifically for each brain. As a result of these experiments, we obtain an interactive method whose results reported on the MICCAI-BRATS 2013 dataset are the second most accurate compared to published methods, while using significantly less memory and processing power than most state-of-the-art methods.

Effects of acupuncture on tissue oxygenation of the rat brain.

PubMed

Chen, G S; Erdmann, W

1978-04-01

Acupuncture has been claimed to be effective in restoring consciousness in some comatose patients. Possible mechanisms to explain alleged acupuncture-induced arousal may include vasodilatory effects caused by smypathetic stimulation which leads to an augmentation of cerebral microcirculation and thereby improves oxygen supply to the brain tissue. Experiments were performed in ten albino rats (Wistar) employing PO2 microelectrodes which were inserted into the cortex through small burholes. Brain tissue PO2 was continuously recorded before, during, and after acupuncture. Stimulation of certain acupuncture points (Go-26) resulted in immediate increase of PO2 in the frontal cortex of the rat brain. This effect was reproducible and was comparable to that obtained with increase of inspiratory CO2 known to induce arterial vasodilatation and thus capillary perfusion pressure. The effect was more significant as compared to tissue PO2 increases obtained after increase in inspiratory oxygen concentration from 21% to 100%. It appears that acupuncture causes increased brain tissue perfusion which may be, at least in part, responsible for arousal of unconscious patients.

Classification of change detection and change blindness from near-infrared spectroscopy signals

NASA Astrophysics Data System (ADS)

Tanaka, Hirokazu; Katura, Takusige

2011-08-01

Using a machine-learning classification algorithm applied to near-infrared spectroscopy (NIRS) signals, we classify a success (change detection) or a failure (change blindness) in detecting visual changes for a change-detection task. Five subjects perform a change-detection task, and their brain activities are continuously monitored. A support-vector-machine algorithm is applied to classify the change-detection and change-blindness trials, and correct classification probability of 70-90% is obtained for four subjects. Two types of temporal shapes in classification probabilities are found: one exhibiting a maximum value after the task is completed (postdictive type), and another exhibiting a maximum value during the task (predictive type). As for the postdictive type, the classification probability begins to increase immediately after the task completion and reaches its maximum in about the time scale of neuronal hemodynamic response, reflecting a subjective report of change detection. As for the predictive type, the classification probability shows an increase at the task initiation and is maximal while subjects are performing the task, predicting the task performance in detecting a change. We conclude that decoding change detection and change blindness from NIRS signal is possible and argue some future applications toward brain-machine interfaces.

Metals in tissues of migrant semipalmated sandpipers (Calidris pusilla) from Delaware Bay, New Jersey

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

Burger, Joanna, E-mail: burger@biology.rutgers.edu; Environmental and Occupational Health Sciences Institute; Gochfeld, Michael

2014-08-15

There is an abundance of field data on levels of metals for feathers in a variety of birds, but relatively few data for tissues, especially for migrant species from one location. In this paper we examine the levels of arsenic, cadmium, chromium, lead, manganese, mercury and selenium in muscle, liver, brain, fat and breast feathers from migrant semipalmated sandpipers (Calidris pusilla) collected from Delaware Bay, New Jersey. Our primary objectives were to (1) examine variation as a function of tissue, (2) determine the relationship of metal levels among tissues, and (3) determine the selenium:mercury molar ratio in different tissues sincemore » selenium is thought to protect against mercury toxicity. We were also interested in whether the large physiological changes that occur while shorebirds are on Delaware Bay (e.g. large weight gains in 2–3 weeks) affected metal levels, especially in the brain. There were significant differences among tissues for all metals. The brain had the lowest levels of arsenic and cadmium, and was tied for the lowest levels of all other metals except lead and selenium. Correlations among metals in tissues were varied, with mercury levels being positively correlated for muscle and brain, and for liver and breast feathers. Weights vary among individuals at the Delaware Bay stopover, as they arrive light, and gain weight prior to migration north. Bird weight and levels of arsenic, cadmium, and selenium in the brain were negatively correlated, while they were positively correlated for lead. There was no positive correlation for mercury in the brain as a function of body weight. The selenium:mercury molar ratio varied significantly among tissues, with brain (ratio of 141) and fat having the highest ratios, and liver and breast feathers having the lowest. In all cases, the ratio was above 21, suggesting the potential for amelioration of mercury toxicity. - Highlights: • Metal levels were examined for migrant semipalmated sandpipers. • There were differences in metal levels among internal tissues. • Brain had the lowest levels of arsenic and cadmium. • Bird weight and arsenic, cadmium, and selenium levels in brain were negatively correlated. • Selenium:mercury molar ratio varied among tissues (21–141, suggesting protection)« less

Towards brain-activity-controlled information retrieval: Decoding image relevance from MEG signals.

PubMed

Kauppi, Jukka-Pekka; Kandemir, Melih; Saarinen, Veli-Matti; Hirvenkari, Lotta; Parkkonen, Lauri; Klami, Arto; Hari, Riitta; Kaski, Samuel

2015-05-15

We hypothesize that brain activity can be used to control future information retrieval systems. To this end, we conducted a feasibility study on predicting the relevance of visual objects from brain activity. We analyze both magnetoencephalographic (MEG) and gaze signals from nine subjects who were viewing image collages, a subset of which was relevant to a predetermined task. We report three findings: i) the relevance of an image a subject looks at can be decoded from MEG signals with performance significantly better than chance, ii) fusion of gaze-based and MEG-based classifiers significantly improves the prediction performance compared to using either signal alone, and iii) non-linear classification of the MEG signals using Gaussian process classifiers outperforms linear classification. These findings break new ground for building brain-activity-based interactive image retrieval systems, as well as for systems utilizing feedback both from brain activity and eye movements. Copyright © 2015 Elsevier Inc. All rights reserved.

Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface

PubMed Central

Khan, M. Jawad; Hong, Melissa Jiyoun; Hong, Keum-Shik

2014-01-01

The hybrid brain-computer interface (BCI)'s multimodal technology enables precision brain-signal classification that can be used in the formulation of control commands. In the present study, an experimental hybrid near-infrared spectroscopy-electroencephalography (NIRS-EEG) technique was used to extract and decode four different types of brain signals. The NIRS setup was positioned over the prefrontal brain region, and the EEG over the left and right motor cortex regions. Twelve subjects participating in the experiment were shown four direction symbols, namely, “forward,” “backward,” “left,” and “right.” The control commands for forward and backward movement were estimated by performing arithmetic mental tasks related to oxy-hemoglobin (HbO) changes. The left and right directions commands were associated with right and left hand tapping, respectively. The high classification accuracies achieved showed that the four different control signals can be accurately estimated using the hybrid NIRS-EEG technology. PMID:24808844

Compression stiffening of brain and its effect on mechanosensing by glioma cells

NASA Astrophysics Data System (ADS)

Pogoda, Katarzyna; Chin, LiKang; Georges, Penelope C.; Byfield, FitzRoy J.; Bucki, Robert; Kim, Richard; Weaver, Michael; Wells, Rebecca G.; Marcinkiewicz, Cezary; Janmey, Paul A.

2014-07-01

Many cell types, including neurons, astrocytes and other cells of the central nervous system, respond to changes in the extracellular matrix or substrate viscoelasticity, and increased tissue stiffness is a hallmark of several disease states, including fibrosis and some types of cancers. Whether the malignant tissue in brain, an organ that lacks the protein-based filamentous extracellular matrix of other organs, exhibits the same macroscopic stiffening characteristic of breast, colon, pancreatic and other tumors is not known. In this study we show that glioma cells, like normal astrocytes, respond strongly in vitro to substrate stiffness in the range of 100 to 2000 Pa, but that macroscopic (mm to cm) tissue samples isolated from human glioma tumors have elastic moduli in the order of 200 Pa that are indistinguishable from those of normal brain. However, both normal brain and glioma tissues increase their shear elastic moduli under modest uniaxial compression, and glioma tissue stiffens more strongly under compression than normal brain. These findings suggest that local tissue stiffness has the potential to alter glial cell function, and that stiffness changes in brain tumors might arise not from increased deposition or crosslinking of the collagen-rich extracellular matrix, but from pressure gradients that form within the tumors in vivo.

Mary Jane Hogue (1883-1962): A pioneer in human brain tissue culture.

PubMed

Zottoli, Steven J; Seyfarth, Ernst-August

2018-05-16

The ability to maintain human brain explants in tissue culture was a critical step in the use of these cells for the study of central nervous system disorders. Ross G. Harrison (1870-1959) was the first to successfully maintain frog medullary tissue in culture in 1907, but it took another 38 years before successful culture of human brain tissue was accomplished. One of the pioneers in this achievement was Mary Jane Hogue (1883-1962). Hogue was born into a Quaker family in 1883 in West Chester, Pennsylvania, and received her undergraduate degree from Goucher College in Baltimore, Maryland. Research with the developmental biologist Theodor Boveri (1862-1915) in Würzburg, Germany, resulted in her Ph.D. (1909). Hogue transitioned from studying protozoa to the culture of human brain tissue in the 1940s and 1950s, when she was one of the first to culture cells from human fetal, infant, and adult brain explants. We review Hogue's pioneering contributions to the study of human brain cells in culture, her putative identification of progenitor neuroblast and/or glioblast cells, and her use of the cultures to study the cytopathogenic effects of poliovirus. We also put Hogue's work in perspective by discussing how other women pioneers in tissue culture influenced Hogue and her research.

Mimicking brain tissue binding in an in vitro model of the blood-brain barrier illustrates differences between in vitro and in vivo methods for assessing the rate of brain penetration.

PubMed

Heymans, Marjolein; Sevin, Emmanuel; Gosselet, Fabien; Lundquist, Stefan; Culot, Maxime

2018-06-01

Assessing the rate of drug delivery to the central nervous system (CNS) in vitro has been used for decades to predict whether CNS drug candidates are likely to attain their pharmacological targets, located within the brain parenchyma, at an effective dose. The predictive value of in vitro blood-brain barrier (BBB) models is therefore frequently assessed by comparing in vitro BBB permeability, usually quoted as the endothelial permeability coefficient (P e ) or apparent permeability (P app ), to their rate of BBB permeation measured in vivo, the latter being commonly assessed in rodents. In collaboration with AstraZeneca (DMPK department, Södertälje, Sweden), the in vitro BBB permeability (P app and P e ) of 27 marketed CNS drugs has been determined using a bovine in vitro BBB model and compared to their in vivo permeability (P vivo ), obtained by rat in-situ brain perfusion. The latter was taken from published data from Summerfield et al. (2007). This comparison confirmed previous reports, showing a strong in vitro/in vivo correlation for hydrophilic compounds, characterized by low brain tissue binding and a weak correlation for lipophilic compounds, characterized by high brain tissue binding. This observation can be explained by the influence of brain tissue binding on the uptake of drugs into the CNS in vivo and the absence of possible brain tissue binding in vitro. The use of glial cells (GC) in the in vitro BBB model to mimic brain tissue binding and the introduction of a new calculation method for in vitro BBB permeability (P vitro ) resulted in a strong correlation between the in vitro and in vivo rate of BBB permeation for the whole set of compounds. These findings might facilitate further in vitro to in vivo extrapolation for CNS drug candidates. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Development and Validation of a Method for Alcohol Analysis in Brain Tissue by Headspace Gas Chromatography with Flame Ionization Detector

PubMed Central

Chun, Hao-Jung; Poklis, Justin L.; Poklis, Alphonse; Wolf, Carl E.

2016-01-01

Ethanol is the most widely used and abused drug. While blood is the preferred specimen for analysis, tissue specimens such as brain serve as alternative specimens for alcohol analysis in post-mortem cases where blood is unavailable or contaminated. A method was developed using headspace gas chromatography with flame ionization detection (HS-GC-FID) for the detection and quantification of ethanol, acetone, isopropanol, methanol and n-propanol in brain tissue specimens. Unfixed volatile-free brain tissue specimens were obtained from the Department of Pathology at Virginia Commonwealth University. Calibrators and controls were prepared from 4-fold diluted homogenates of these brain tissue specimens, and were analyzed using t-butanol as the internal standard. The chromatographic separation was performed with a Restek BAC2 column. A linear calibration was generated for all analytes (mean r2 > 0.9992) with the limits of detection and quantification of 100–110 mg/kg. Matrix effect from the brain tissue was determined by comparing the slopes of matrix prepared calibration curves with those of aqueous calibration curves; no significant differences were observed for ethanol, acetone, isopropanol, methanol and n-propanol. The bias and the CVs for all volatile controls were ≤10%. The method was also evaluated for carryover, selectivity, interferences, bench-top stability and freeze-thaw stability. The HS-GC-FID method was determined to be reliable and robust for the analysis of ethanol, acetone, isopropanol, methanol and n-propanol concentrations in brain tissue, effectively expanding the specimen options for post-mortem alcohol analysis. PMID:27488829

Hyper- and viscoelastic modeling of needle and brain tissue interaction.

PubMed

Lehocky, Craig A; Yixing Shi; Riviere, Cameron N

2014-01-01

Deep needle insertion into brain is important for both diagnostic and therapeutic clinical interventions. We have developed an automated system for robotically steering flexible needles within the brain to improve targeting accuracy. In this work, we have developed a finite element needle-tissue interaction model that allows for the investigation of safe parameters for needle steering. The tissue model implemented contains both hyperelastic and viscoelastic properties to simulate the instantaneous and time-dependent responses of brain tissue. Several needle models were developed with varying parameters to study the effects of the parameters on tissue stress, strain and strain rate during needle insertion and rotation. The parameters varied include needle radius, bevel angle, bevel tip fillet radius, insertion speed, and rotation speed. The results will guide the design of safe needle tips and control systems for intracerebral needle steering.

Numerical analysis of the diffusive mass transport in brain tissues with applications to optical sensors

NASA Astrophysics Data System (ADS)

Neculae, Adrian P.; Otte, Andreas; Curticapean, Dan

2013-03-01

In the brain-cell microenvironment, diffusion plays an important role: apart from delivering glucose and oxygen from the vascular system to brain cells, it also moves informational substances between cells. The brain is an extremely complex structure of interwoven, intercommunicating cells, but recent theoretical and experimental works showed that the classical laws of diffusion, cast in the framework of porous media theory, can deliver an accurate quantitative description of the way molecules are transported through this tissue. The mathematical modeling and the numerical simulations are successfully applied in the investigation of diffusion processes in tissues, replacing the costly laboratory investigations. Nevertheless, modeling must rely on highly accurate information regarding the main parameters (tortuosity, volume fraction) which characterize the tissue, obtained by structural and functional imaging. The usual techniques to measure the diffusion mechanism in brain tissue are the radiotracer method, the real time iontophoretic method and integrative optical imaging using fluorescence microscopy. A promising technique for obtaining the values for characteristic parameters of the transport equation is the direct optical investigation using optical fibers. The analysis of these parameters also reveals how the local geometry of the brain changes with time or under pathological conditions. This paper presents a set of computations concerning the mass transport inside the brain tissue, for different types of cells. By measuring the time evolution of the concentration profile of an injected substance and using suitable fitting procedures, the main parameters characterizing the tissue can be determined. This type of analysis could be an important tool in understanding the functional mechanisms of effective drug delivery in complex structures such as the brain tissue. It also offers possibilities to realize optical imaging methods for in vitro and in vivo measurements using optical fibers. The model also may help in radiotracer biomarker models for the understanding of the mechanism of action of new chemical entities.

The effect of nimodipine on cerebral oxygenation in patients with poor-grade subarachnoid hemorrhage.

PubMed

Stiefel, Michael F; Heuer, Gregory G; Abrahams, John M; Bloom, Stephanie; Smith, Michelle J; Maloney-Wilensky, Eileen; Grady, M Sean; LeRoux, Peter D

2004-10-01

Nimodipine has been shown to improve neurological outcome after subarachnoid hemorrhage (SAH); the mechanism of this improvement, however, is uncertain. In addition, adverse systemic effects such as hypotension have been described. The authors investigated the effect of nimodipine on brain tissue PO2. Patients in whom Hunt and Hess Grade IV or V SAH had occurred who underwent aneurysm occlusion and had stable blood pressure were prospectively evaluated using continuous brain tissue PO2 monitoring. Nimodipine (60 mg) was delivered through a nasogastric or Dobhoff tube every 4 hours. Data were obtained from 11 patients and measurements of brain tissue PO2, intracranial pressure (ICP), mean arterial blood pressure (MABP), and cerebral perfusion pressure (CPP) were recorded every 15 minutes. Nimodipine resulted in a significant reduction in brain tissue PO2 in seven (64%) of 11 patients. The baseline PO2 before nimodipine administration was 38.4+/-10.9 mm Hg. The baseline MABP and CPP were 90+/-20 and 84+/-19 mm Hg, respectively. The greatest reduction in brain tissue PO2 occurred 15 minutes after administration, when the mean pressure was 26.9+/-7.7 mm Hg (p < 0.05). The PO2 remained suppressed at 30 minutes (27.5+/-7.7 mm Hg [p < 0.05]) and at 60 minutes (29.7+/-11.1 mm Hg [p < 0.05]) after nimodipine administration but returned to baseline levels 2 hours later. In the seven patients in whom brain tissue PO2 decreased, other physiological variables such as arterial saturation, end-tidal CO2, heart rate, MABP, ICP, and CPP did not demonstrate any association with the nimodipine-induced reduction in PO2. In four patients PO2 remained stable and none of these patients had a significant increase in brain tissue PO2. Although nimodipine use is associated with improved outcome following SAH, in some patients it can temporarily reduce brain tissue PO2.

Advantages of analyzing postmortem brain samples in routine forensic drug screening-Case series of three non-natural deaths tested positive for lysergic acid diethylamide (LSD).

PubMed

Mardal, Marie; Johansen, Sys Stybe; Thomsen, Ragnar; Linnet, Kristian

2017-09-01

Three case reports are presented, including autopsy findings and toxicological screening results, which were tested positive for the potent hallucinogenic drug lysergic acid diethylamide (LSD). LSD and its main metabolites were quantified in brain tissue and femoral blood, and furthermore hematoma and urine when available. LSD, its main metabolite 2-oxo-3-hydroxy-LSD (oxo-HO-LSD), and iso-LSD were quantified in biological samples according to a previously published procedure involving liquid-liquid extraction and ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). LSD was measured in the brain tissue of all presented cases at a concentration level from 0.34-10.8μg/kg. The concentration level in the target organ was higher than in peripheral blood. Additional psychoactive compounds were quantified in blood and brain tissue, though all below toxic concentration levels. The cause of death in case 1 was collision-induced brain injury, while it was drowning in case 2 and 3 and thus not drug intoxication. However, the toxicological findings could help explain the decedent's inability to cope with brain injury or drowning incidents. The presented findings could help establish reference concentrations in brain samples and assist in interpretation of results from forensic drug screening in brain tissue. This is to the author's knowledge the first report of LSD, iso-LSD, and oxo-HO-LSD measured in brain tissue samples. Copyright © 2017 Elsevier B.V. All rights reserved.

Targeting therapeutics across the blood brain barrier (BBB), prerequisite towards thrombolytic therapy for cerebrovascular disorders-an overview and advancements.

PubMed

Pulicherla, K K; Verma, Mahendra Kumar

2015-04-01

Cerebral tissues possess highly selective and dynamic protection known as blood brain barrier (BBB) that regulates brain homeostasis and provides protection against invading pathogens and various chemicals including drug molecules. Such natural protection strictly monitors entry of drug molecules often required for the management of several diseases and disorders including cerebral vascular and neurological disorders. However, in recent times, the ischemic cerebrovascular disease and clinical manifestation of acute arterial thrombosis are the most common causes of mortality and morbidity worldwide. The management of cerebral Ischemia requires immediate infusion of external thrombolytic into systemic circulation and must cross the blood brain barrier. The major challenge with available thrombolytic is their poor affinity towards the blood brain barrier and cerebral tissue subsequently. In the clinical practice, a high dose of thrombolytic often prescribed to deliver drugs across the blood brain barrier which results in drug dependent toxicity leading to damage of neuronal tissues. In recent times, more emphasis was given to utilize blood brain barrier transport mechanism to deliver drugs in neuronal tissue. The blood brain barrier expresses a series of receptor on membrane became an ideal target for selective drug delivery. In this review, the author has given more emphasis molecular biology of receptor on blood brain barrier and their potential as a carrier for drug molecules to cerebral tissues. Further, the use of nanoscale design and real-time monitoring for developed therapeutic to encounter drug dependent toxicity has been reviewed in this study.

Better diet quality relates to larger brain tissue volumes: The Rotterdam Study.

PubMed

Croll, Pauline H; Voortman, Trudy; Ikram, M Arfan; Franco, Oscar H; Schoufour, Josje D; Bos, Daniel; Vernooij, Meike W

2018-05-16

To investigate the relation of diet quality with structural brain tissue volumes and focal vascular lesions in a dementia-free population. From the population-based Rotterdam Study, 4,447 participants underwent dietary assessment and brain MRI scanning between 2005 and 2015. We excluded participants with an implausible energy intake, prevalent dementia, or cortical infarcts, leaving 4,213 participants for the current analysis. A diet quality score (0-14) was calculated reflecting adherence to Dutch dietary guidelines. Brain MRI was performed to obtain information on brain tissue volumes, white matter lesion volume, lacunes, and cerebral microbleeds. The associations of diet quality score and separate food groups with brain structures were assessed using multivariable linear and logistic regression. We found that better diet quality related to larger brain volume, gray matter volume, white matter volume, and hippocampal volume. Diet quality was not associated with white matter lesion volume, lacunes, or microbleeds. High intake of vegetables, fruit, whole grains, nuts, dairy, and fish and low intake of sugar-containing beverages were associated with larger brain volumes. A better diet quality is associated with larger brain tissue volumes. These results suggest that the effect of nutrition on neurodegeneration may act via brain structure. More research, in particular longitudinal research, is needed to unravel direct vs indirect effects between diet quality and brain health. © 2018 American Academy of Neurology.

Permeabilization of brain tissue in situ enables multiregion analysis of mitochondrial function in a single mouse brain.

PubMed

Herbst, Eric A F; Holloway, Graham P

2015-02-15

Mitochondrial function in the brain is traditionally assessed through analysing respiration in isolated mitochondria, a technique that possesses significant tissue and time requirements while also disrupting the cooperative mitochondrial reticulum. We permeabilized brain tissue in situ to permit analysis of mitochondrial respiration with the native mitochondrial morphology intact, removing the need for isolation time and minimizing tissue requirements to ∼2 mg wet weight. The permeabilized brain technique was validated against the traditional method of isolated mitochondria and was then further applied to assess regional variation in the mouse brain with ischaemia-reperfusion injuries. A transgenic mouse model overexpressing catalase within mitochondria was applied to show the contribution of mitochondrial reactive oxygen species to ischaemia-reperfusion injuries in different brain regions. This technique enhances the accessibility of addressing physiological questions in small brain regions and in applying transgenic mouse models to assess mechanisms regulating mitochondrial function in health and disease. Mitochondria function as the core energy providers in the brain and symptoms of neurodegenerative diseases are often attributed to their dysregulation. Assessing mitochondrial function is classically performed in isolated mitochondria; however, this process requires significant isolation time, demand for abundant tissue and disruption of the cooperative mitochondrial reticulum, all of which reduce reliability when attempting to assess in vivo mitochondrial bioenergetics. Here we introduce a method that advances the assessment of mitochondrial respiration in the brain by permeabilizing existing brain tissue to grant direct access to the mitochondrial reticulum in situ. The permeabilized brain preparation allows for instant analysis of mitochondrial function with unaltered mitochondrial morphology using significantly small sample sizes (∼2 mg), which permits the analysis of mitochondrial function in multiple subregions within a single mouse brain. Here this technique was applied to assess regional variation in brain mitochondrial function with acute ischaemia-reperfusion injuries and to determine the role of reactive oxygen species in exacerbating dysfunction through the application of a transgenic mouse model overexpressing catalase within mitochondria. Through creating accessibility to small regions for the investigation of mitochondrial function, the permeabilized brain preparation enhances the capacity for examining regional differences in mitochondrial regulation within the brain, as the majority of genetic models used for unique approaches exist in the mouse model. © 2014 The Authors. The Journal of Physiology © 2014 The Physiological Society.

Uncommon localizations of hydatid cyst. Review of the literature.

PubMed

Salamone, G; Licari, L; Randisi, B; Falco, N; Tutino, R; Vaglica, A; Gullo, R; Porello, C; Cocorullo, G; Gulotta, G

2016-01-01

Hydatid disease is an endemic anthropozoonosis with usual localization in liver and lungs. Rarely it localizes in uncommon sites as spleen, skeleton, kidney, brain, cardiac muscle, peritoneum, sub cutis. Complications of uncommon localizations are the same that for usual ones. Review of the literature on rare and atypical localization of hydatid cysts in soft tissues. Key-words used on Pub-Med [(echinococ OR hydatid) AND (soft tissue OR subcutaneous OR cutaneous)] without time limit. There were found 282 articles; 242 were excluded because of muscular or bone localizations. 40 were coherent. Different variables are taken into account: age, sex, geographic area, anatomic localization of the cyst, dimension, symptoms, signs, mobility, blood exams and specific serological tests, imaging techniques for diagnosis, existing of septa in the structure, treatment, anaesthesia, spillage, neo-adjuvant and adjuvant treatment, follow-up period, recurrent lesions. It would be useful create an homogeneous and standardized collection of data of these rare and potentially life-threatening conditions in order to create guide-line of diagnostic and therapeutic process and create (or adopt) unique classification of the lesions.

Brain-computer interfacing under distraction: an evaluation study

NASA Astrophysics Data System (ADS)

Brandl, Stephanie; Frølich, Laura; Höhne, Johannes; Müller, Klaus-Robert; Samek, Wojciech

2016-10-01

Objective. While motor-imagery based brain-computer interfaces (BCIs) have been studied over many years by now, most of these studies have taken place in controlled lab settings. Bringing BCI technology into everyday life is still one of the main challenges in this field of research. Approach. This paper systematically investigates BCI performance under 6 types of distractions that mimic out-of-lab environments. Main results. We report results of 16 participants and show that the performance of the standard common spatial patterns (CSP) + regularized linear discriminant analysis classification pipeline drops significantly in this ‘simulated’ out-of-lab setting. We then investigate three methods for improving the performance: (1) artifact removal, (2) ensemble classification, and (3) a 2-step classification approach. While artifact removal does not enhance the BCI performance significantly, both ensemble classification and the 2-step classification combined with CSP significantly improve the performance compared to the standard procedure. Significance. Systematically analyzing out-of-lab scenarios is crucial when bringing BCI into everyday life. Algorithms must be adapted to overcome nonstationary environments in order to tackle real-world challenges.

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

Lowe, Xiu R; Bhattacharya, Sanchita; Marchetti, Francesco

Understanding the cognitive and behavioral consequences of brain exposures to low-dose ionizing radiation has broad relevance for health risks from medical radiation diagnostic procedures, radiotherapy, environmental nuclear contamination, as well as earth orbit and space missions. Analyses of transcriptome profiles of murine brain tissue after whole-body radiation showed that low-dose exposures (10 cGy) induced genes not affected by high dose (2 Gy), and low-dose genes were associated with unique pathways and functions. The low-dose response had two major components: pathways that are consistently seen across tissues, and pathways that were brain tissue specific. Low-dose genes clustered into a saturated networkmore » (p < 10{sup -53}) containing mostly down-regulated genes involving ion channels, long-term potentiation and depression, vascular damage, etc. We identified 9 neural signaling pathways that showed a high degree of concordance in their transcriptional response in mouse brain tissue after low-dose radiation, in the aging human brain (unirradiated), and in brain tissue from patients with Alzheimer's disease. Mice exposed to high-dose radiation did not show these effects and associations. Our findings indicate that the molecular response of the mouse brain within a few hours after low-dose irradiation involves the down-regulation of neural pathways associated with cognitive dysfunctions that are also down regulated in normal human aging and Alzheimer's disease.« less

Automated classification and visualization of healthy and pathological dental tissues based on near-infrared hyper-spectral imaging

NASA Astrophysics Data System (ADS)

Usenik, Peter; Bürmen, Miran; Vrtovec, Tomaž; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan

2011-03-01

Despite major improvements in dental healthcare and technology, dental caries remains one of the most prevalent chronic diseases of modern society. The initial stages of dental caries are characterized by demineralization of enamel crystals, commonly known as white spots which are difficult to diagnose. If detected early enough, such demineralization can be arrested and reversed by non-surgical means through well established dental treatments (fluoride therapy, anti-bacterial therapy, low intensity laser irradiation). Near-infrared (NIR) hyper-spectral imaging is a new promising technique for early detection of demineralization based on distinct spectral features of healthy and pathological dental tissues. In this study, we apply NIR hyper-spectral imaging to classify and visualize healthy and pathological dental tissues including enamel, dentin, calculus, dentin caries, enamel caries and demineralized areas. For this purpose, a standardized teeth database was constructed consisting of 12 extracted human teeth with different degrees of natural dental lesions imaged by NIR hyper-spectral system, X-ray and digital color camera. The color and X-ray images of teeth were presented to a clinical expert for localization and classification of the dental tissues, thereby obtaining the gold standard. Principal component analysis was used for multivariate local modeling of healthy and pathological dental tissues. Finally, the dental tissues were classified by employing multiple discriminant analysis. High agreement was observed between the resulting classification and the gold standard with the classification sensitivity and specificity exceeding 85 % and 97 %, respectively. This study demonstrates that NIR hyper-spectral imaging has considerable diagnostic potential for imaging hard dental tissues.

Assessing Amide Proton Transfer (APT) MRI Contrast Origins in 9 L Gliosarcoma in the Rat Brain Using Proteomic Analysis.

PubMed

Yan, Kun; Fu, Zongming; Yang, Chen; Zhang, Kai; Jiang, Shanshan; Lee, Dong-Hoon; Heo, Hye-Young; Zhang, Yi; Cole, Robert N; Van Eyk, Jennifer E; Zhou, Jinyuan

2015-08-01

To investigate the biochemical origin of the amide photon transfer (APT)-weighted hyperintensity in brain tumors. Seven 9 L gliosarcoma-bearing rats were imaged at 4.7 T. Tumor and normal brain tissue samples of equal volumes were prepared with a coronal rat brain matrix and a tissue biopsy punch. The total tissue protein and the cytosolic subproteome were extracted from both samples. Protein samples were analyzed using two-dimensional gel electrophoresis, and the proteins with significant abundance changes were identified by mass spectrometry. There was a significant increase in the cytosolic protein concentration in the tumor, compared to normal brain regions, but the total protein concentrations were comparable. The protein profiles of the tumor and normal brain tissue differed significantly. Six cytosolic proteins, four endoplasmic reticulum proteins, and five secreted proteins were considerably upregulated in the tumor. Our experiments confirmed an increase in the cytosolic protein concentration in tumors and identified several key proteins that may cause APT-weighted hyperintensity.

Multi-fractal detrended texture feature for brain tumor classification

NASA Astrophysics Data System (ADS)

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

2015-03-01

We propose a novel non-invasive brain tumor type classification using Multi-fractal Detrended Fluctuation Analysis (MFDFA) [1] in structural magnetic resonance (MR) images. This preliminary work investigates the efficacy of the MFDFA features along with our novel texture feature known as multifractional Brownian motion (mBm) [2] in classifying (grading) brain tumors as High Grade (HG) and Low Grade (LG). Based on prior performance, Random Forest (RF) [3] is employed for tumor grading using two different datasets such as BRATS-2013 [4] and BRATS-2014 [5]. Quantitative scores such as precision, recall, accuracy are obtained using the confusion matrix. On an average 90% precision and 85% recall from the inter-dataset cross-validation confirm the efficacy of the proposed method.

Dexamethasone increases production of C-type natriuretic peptide in the sheep brain.

PubMed

Wilson, Michele O; McNeill, Bryony A; Barrell, Graham K; Prickett, Timothy C R; Espiner, Eric A

2017-10-01

Although C-type natriuretic peptide (CNP) has high abundance in brain tissues and cerebrospinal fluid (CSF), the source and possible factors regulating its secretion within the central nervous system (CNS) are unknown. Here we report the dynamic effects of a single IV bolus of dexamethasone or saline solution on plasma, CSF, CNS and pituitary tissue content of CNP products in adult sheep, along with changes in CNP gene expression in selected tissues. Both CNP and NTproCNP (the amino-terminal product of proCNP) in plasma and CSF showed dose-responsive increases lasting 12-16 h after dexamethasone, whereas other natriuretic peptides were unaffected. CNS tissue concentrations of CNP and NTproCNP were increased by dexamethasone in all of the 12 regions examined. Abundance was highest in limbic tissues, pons and medulla oblongata. Relative to controls, CNP gene expression ( NPPC ) was upregulated by dexamethasone in 5 of 7 brain tissues examined. Patterns of responses differed in pituitary tissue. Whereas the abundance of CNP in both lobes of the pituitary gland greatly exceeded that of brain tissues, neither CNP nor NTproCNP concentration was affected by dexamethasone, despite an increase in NPPC expression. This is the first report of enhanced production and secretion of CNP in brain tissues in response to a corticosteroid. Activation of CNP secretion within CNS tissues by dexamethasone, not exhibited by other natriuretic peptides, suggests an important role for CNP in settings of acute stress. Differential findings in pituitary tissues likely relate to altered processing of proCNP storage and secretion. © 2017 Society for Endocrinology.

Brain-computer interface design using alpha wave

NASA Astrophysics Data System (ADS)

Zhao, Hai-bin; Wang, Hong; Liu, Chong; Li, Chun-sheng

2010-01-01

A brain-computer interface (BCI) is a novel communication system that translates brain activity into commands for a computer or other electronic devices. BCI system based on non-invasive scalp electroencephalogram (EEG) has become a hot research area in recent years. BCI technology can help improve the quality of life and restore function for people with severe motor disabilities. In this study, we design a real-time asynchronous BCI system using Alpha wave. The basic theory of this BCI system is alpha wave-block phenomenon. Alpha wave is the most prominent wave in the whole realm of brain activity. This system includes data acquisition, feature selection and classification. The subject can use this system easily and freely choose anyone of four commands with only short-time training. The results of the experiment show that this BCI system has high classification accuracy, and has potential application for clinical engineering and is valuable for further research.

A new classification method for MALDI imaging mass spectrometry data acquired on formalin-fixed paraffin-embedded tissue samples.

PubMed

Boskamp, Tobias; Lachmund, Delf; Oetjen, Janina; Cordero Hernandez, Yovany; Trede, Dennis; Maass, Peter; Casadonte, Rita; Kriegsmann, Jörg; Warth, Arne; Dienemann, Hendrik; Weichert, Wilko; Kriegsmann, Mark

2017-07-01

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) shows a high potential for applications in histopathological diagnosis, and in particular for supporting tumor typing and subtyping. The development of such applications requires the extraction of spectral fingerprints that are relevant for the given tissue and the identification of biomarkers associated with these spectral patterns. We propose a novel data analysis method based on the extraction of characteristic spectral patterns (CSPs) that allow automated generation of classification models for spectral data. Formalin-fixed paraffin embedded (FFPE) tissue samples from N=445 patients assembled on 12 tissue microarrays were analyzed. The method was applied to discriminate primary lung and pancreatic cancer, as well as adenocarcinoma and squamous cell carcinoma of the lung. A classification accuracy of 100% and 82.8%, resp., could be achieved on core level, assessed by cross-validation. The method outperformed the more conventional classification method based on the extraction of individual m/z values in the first application, while achieving a comparable accuracy in the second. LC-MS/MS peptide identification demonstrated that the spectral features present in selected CSPs correspond to peptides relevant for the respective classification. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann. Copyright © 2016 Elsevier B.V. All rights reserved.

A comparative evaluation of Raman and fluorescence spectroscopy for optical diagnosis of oral neoplasia

NASA Astrophysics Data System (ADS)

Majumder, S. K.; Krishna, H.; Sidramesh, M.; Chaturvedi, P.; Gupta, P. K.

2011-08-01

We report the results of a comparative evaluation of in vivo fluorescence and Raman spectroscopy for diagnosis of oral neoplasia. The study carried out at Tata Memorial Hospital, Mumbai, involved 26 healthy volunteers and 138 patients being screened for neoplasm of oral cavity. Spectral measurements were taken from multiple sites of abnormal as well as apparently uninvolved contra-lateral regions of the oral cavity in each patient. The different tissue sites investigated belonged to one of the four histopathology categories: 1) squamous cell carcinoma (SCC), 2) oral sub-mucous fibrosis (OSMF), 3) leukoplakia (LP) and 4) normal squamous tissue. A probability based multivariate statistical algorithm utilizing nonlinear Maximum Representation and Discrimination Feature for feature extraction and Sparse Multinomial Logistic Regression for classification was developed for direct multi-class classification in a leave-one-patient-out cross validation mode. The results reveal that the performance of Raman spectroscopy is considerably superior to that of fluorescence in stratifying the oral tissues into respective histopathologic categories. The best classification accuracy was observed to be 90%, 93%, 94%, and 89% for SCC, SMF, leukoplakia, and normal oral tissues, respectively, on the basis of leave-one-patient-out cross-validation, with an overall accuracy of 91%. However, when a binary classification was employed to distinguish spectra from all the SCC, SMF and leukoplakik tissue sites together from normal, fluorescence and Raman spectroscopy were seen to have almost comparable performances with Raman yielding marginally better classification accuracy of 98.5% as compared to 94% of fluorescence.

Information spreading by a combination of MEG source estimation and multivariate pattern classification.

PubMed

Sato, Masashi; Yamashita, Okito; Sato, Masa-Aki; Miyawaki, Yoichi

2018-01-01

To understand information representation in human brain activity, it is important to investigate its fine spatial patterns at high temporal resolution. One possible approach is to use source estimation of magnetoencephalography (MEG) signals. Previous studies have mainly quantified accuracy of this technique according to positional deviations and dispersion of estimated sources, but it remains unclear how accurately MEG source estimation restores information content represented by spatial patterns of brain activity. In this study, using simulated MEG signals representing artificial experimental conditions, we performed MEG source estimation and multivariate pattern analysis to examine whether MEG source estimation can restore information content represented by patterns of cortical current in source brain areas. Classification analysis revealed that the corresponding artificial experimental conditions were predicted accurately from patterns of cortical current estimated in the source brain areas. However, accurate predictions were also possible from brain areas whose original sources were not defined. Searchlight decoding further revealed that this unexpected prediction was possible across wide brain areas beyond the original source locations, indicating that information contained in the original sources can spread through MEG source estimation. This phenomenon of "information spreading" may easily lead to false-positive interpretations when MEG source estimation and classification analysis are combined to identify brain areas that represent target information. Real MEG data analyses also showed that presented stimuli were able to be predicted in the higher visual cortex at the same latency as in the primary visual cortex, also suggesting that information spreading took place. These results indicate that careful inspection is necessary to avoid false-positive interpretations when MEG source estimation and multivariate pattern analysis are combined.

Information spreading by a combination of MEG source estimation and multivariate pattern classification

PubMed Central

Sato, Masashi; Yamashita, Okito; Sato, Masa-aki

2018-01-01

To understand information representation in human brain activity, it is important to investigate its fine spatial patterns at high temporal resolution. One possible approach is to use source estimation of magnetoencephalography (MEG) signals. Previous studies have mainly quantified accuracy of this technique according to positional deviations and dispersion of estimated sources, but it remains unclear how accurately MEG source estimation restores information content represented by spatial patterns of brain activity. In this study, using simulated MEG signals representing artificial experimental conditions, we performed MEG source estimation and multivariate pattern analysis to examine whether MEG source estimation can restore information content represented by patterns of cortical current in source brain areas. Classification analysis revealed that the corresponding artificial experimental conditions were predicted accurately from patterns of cortical current estimated in the source brain areas. However, accurate predictions were also possible from brain areas whose original sources were not defined. Searchlight decoding further revealed that this unexpected prediction was possible across wide brain areas beyond the original source locations, indicating that information contained in the original sources can spread through MEG source estimation. This phenomenon of “information spreading” may easily lead to false-positive interpretations when MEG source estimation and classification analysis are combined to identify brain areas that represent target information. Real MEG data analyses also showed that presented stimuli were able to be predicted in the higher visual cortex at the same latency as in the primary visual cortex, also suggesting that information spreading took place. These results indicate that careful inspection is necessary to avoid false-positive interpretations when MEG source estimation and multivariate pattern analysis are combined. PMID:29912968

Neuroprotective effects of vagus nerve stimulation on traumatic brain injury

PubMed Central

Zhou, Long; Lin, Jinhuang; Lin, Junming; Kui, Guoju; Zhang, Jianhua; Yu, Yigang

2014-01-01

Previous studies have shown that vagus nerve stimulation can improve the prognosis of traumatic brain injury. The aim of this study was to elucidate the mechanism of the neuroprotective effects of vagus nerve stimulation in rabbits with brain explosive injury. Rabbits with brain explosive injury received continuous stimulation (10 V, 5 Hz, 5 ms, 20 minutes) of the right cervical vagus nerve. Tumor necrosis factor-α, interleukin-1β and interleukin-10 concentrations were detected in serum and brain tissues, and water content in brain tissues was measured. Results showed that vagus nerve stimulation could reduce the degree of brain edema, decrease tumor necrosis factor-α and interleukin-1β concentrations, and increase interleukin-10 concentration after brain explosive injury in rabbits. These data suggest that vagus nerve stimulation may exert neuroprotective effects against explosive injury via regulating the expression of tumor necrosis factor-α, interleukin-1β and interleukin-10 in the serum and brain tissue. PMID:25368644

MEASUREMENT OF SMALL MECHANICAL VIBRATIONS OF BRAIN TISSUE EXPOSED TO EXTREMELY-LOW-FREQUENCY ELECTRIC FIELDS

EPA Science Inventory

Electromagnetic fields can interact with biological tissue both electrically and mechanically. This study investigated the mechanical interaction between brain tissue and an extremely-low-frequency (ELF) electric field by measuring the resultant vibrational amplitude. The exposur...

A clinical perspective on the 2016 WHO brain tumor classification and routine molecular diagnostics.

PubMed

van den Bent, Martin J; Weller, Michael; Wen, Patrick Y; Kros, Johan M; Aldape, Ken; Chang, Susan

2017-05-01

The 2007 World Health Organization (WHO) classification of brain tumors did not use molecular abnormalities as diagnostic criteria. Studies have shown that genotyping allows a better prognostic classification of diffuse glioma with improved treatment selection. This has resulted in a major revision of the WHO classification, which is now for adult diffuse glioma centered around isocitrate dehydrogenase (IDH) and 1p/19q diagnostics. This revised classification is reviewed with a focus on adult brain tumors, and includes a recommendation of genes of which routine testing is clinically useful. Apart from assessment of IDH mutational status including sequencing of R132H-immunohistochemistry negative cases and testing for 1p/19q, several other markers can be considered for routine testing, including assessment of copy number alterations of chromosome 7 and 10 and of TERT promoter, BRAF, and H3F3A mutations. For "glioblastoma, IDH mutated" the term "astrocytoma grade IV" could be considered. It should be considered to treat IDH wild-type grades II and III diffuse glioma with polysomy of chromosome 7 and loss of 10q as glioblastoma. New developments must be more quickly translated into further revised diagnostic categories. Quality control and rapid integration of molecular findings into the final diagnosis and the communication of the final diagnosis to clinicians require systematic attention. © The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Application of machine learning on brain cancer multiclass classification

NASA Astrophysics Data System (ADS)

Panca, V.; Rustam, Z.

2017-07-01

Classification of brain cancer is a problem of multiclass classification. One approach to solve this problem is by first transforming it into several binary problems. The microarray gene expression dataset has the two main characteristics of medical data: extremely many features (genes) and only a few number of samples. The application of machine learning on microarray gene expression dataset mainly consists of two steps: feature selection and classification. In this paper, the features are selected using a method based on support vector machine recursive feature elimination (SVM-RFE) principle which is improved to solve multiclass classification, called multiple multiclass SVM-RFE. Instead of using only the selected features on a single classifier, this method combines the result of multiple classifiers. The features are divided into subsets and SVM-RFE is used on each subset. Then, the selected features on each subset are put on separate classifiers. This method enhances the feature selection ability of each single SVM-RFE. Twin support vector machine (TWSVM) is used as the method of the classifier to reduce computational complexity. While ordinary SVM finds single optimum hyperplane, the main objective Twin SVM is to find two non-parallel optimum hyperplanes. The experiment on the brain cancer microarray gene expression dataset shows this method could classify 71,4% of the overall test data correctly, using 100 and 1000 genes selected from multiple multiclass SVM-RFE feature selection method. Furthermore, the per class results show that this method could classify data of normal and MD class with 100% accuracy.

Individual Patient Diagnosis of AD and FTD via High-Dimensional Pattern Classification of MRI

PubMed Central

Davatzikos, C.; Resnick, S. M.; Wu, X.; Parmpi, P.; Clark, C. M.

2008-01-01

The purpose of this study is to determine the diagnostic accuracy of MRI-based high-dimensional pattern classification in differentiating between patients with Alzheimer’s Disease (AD), Frontotemporal Dementia (FTD), and healthy controls, on an individual patient basis. MRI scans of 37 patients with AD and 37 age-matched cognitively normal elderly individuals, as well as 12 patients with FTD and 12 age-matched cognitively normal elderly individuals, were analyzed using voxel-based analysis and high-dimensional pattern classification. Diagnostic sensitivity and specificity of spatial patterns of regional brain atrophy found to be characteristic of AD and FTD were determined via cross-validation and via split-sample methods. Complex spatial patterns of relatively reduced brain volumes were identified, including temporal, orbitofrontal, parietal and cingulate regions, which were predominantly characteristic of either AD or FTD. These patterns provided 100% diagnostic accuracy, when used to separate AD or FTD from healthy controls. The ability to correctly distinguish AD from FTD averaged 84.3%. All estimates of diagnostic accuracy were determined via cross-validation. In conclusion, AD- and FTD-specific patterns of brain atrophy can be detected with high accuracy using high-dimensional pattern classification of MRI scans obtained in a typical clinical setting. PMID:18474436

Molecular Pathological Classification of Neurodegenerative Diseases: Turning towards Precision Medicine.

PubMed

Kovacs, Gabor G

2016-02-02

Neurodegenerative diseases (NDDs) are characterized by selective dysfunction and loss of neurons associated with pathologically altered proteins that deposit in the human brain but also in peripheral organs. These proteins and their biochemical modifications can be potentially targeted for therapy or used as biomarkers. Despite a plethora of modifications demonstrated for different neurodegeneration-related proteins, such as amyloid-β, prion protein, tau, α-synuclein, TAR DNA-binding protein 43 (TDP-43), or fused in sarcoma protein (FUS), molecular classification of NDDs relies on detailed morphological evaluation of protein deposits, their distribution in the brain, and their correlation to clinical symptoms together with specific genetic alterations. A further facet of the neuropathology-based classification is the fact that many protein deposits show a hierarchical involvement of brain regions. This has been shown for Alzheimer and Parkinson disease and some forms of tauopathies and TDP-43 proteinopathies. The present paper aims to summarize current molecular classification of NDDs, focusing on the most relevant biochemical and morphological aspects. Since the combination of proteinopathies is frequent, definition of novel clusters of patients with NDDs needs to be considered in the era of precision medicine. Optimally, neuropathological categorizing of NDDs should be translated into in vivo detectable biomarkers to support better prediction of prognosis and stratification of patients for therapy trials.

Molecular Pathological Classification of Neurodegenerative Diseases: Turning towards Precision Medicine

PubMed Central

Kovacs, Gabor G.

2016-01-01

Neurodegenerative diseases (NDDs) are characterized by selective dysfunction and loss of neurons associated with pathologically altered proteins that deposit in the human brain but also in peripheral organs. These proteins and their biochemical modifications can be potentially targeted for therapy or used as biomarkers. Despite a plethora of modifications demonstrated for different neurodegeneration-related proteins, such as amyloid-β, prion protein, tau, α-synuclein, TAR DNA-binding protein 43 (TDP-43), or fused in sarcoma protein (FUS), molecular classification of NDDs relies on detailed morphological evaluation of protein deposits, their distribution in the brain, and their correlation to clinical symptoms together with specific genetic alterations. A further facet of the neuropathology-based classification is the fact that many protein deposits show a hierarchical involvement of brain regions. This has been shown for Alzheimer and Parkinson disease and some forms of tauopathies and TDP-43 proteinopathies. The present paper aims to summarize current molecular classification of NDDs, focusing on the most relevant biochemical and morphological aspects. Since the combination of proteinopathies is frequent, definition of novel clusters of patients with NDDs needs to be considered in the era of precision medicine. Optimally, neuropathological categorizing of NDDs should be translated into in vivo detectable biomarkers to support better prediction of prognosis and stratification of patients for therapy trials. PMID:26848654

The adult brain tissue response to hollow fiber membranes of varying surface architecture with or without cotransplanted cells

NASA Astrophysics Data System (ADS)

Zhang, Ning

A variety of biomaterials have been chronically implanted into the central nervous system (CNS) for repair or therapeutic purposes. Regardless of the application, chronic implantation of materials into the CNS induces injury and elicits a wound healing response, eventually leading to the formation of a dense extracellular matrix (ECM)-rich scar tissue that is associated with the segregation of implanted materials from the surrounding normal tissue. Often this reaction results in impaired performance of indwelling CNS devices. In order to enhance the performance of biomaterial-based implantable devices in the CNS, this thesis investigated whether adult brain tissue response to implanted biomaterials could be manipulated by changing biomaterial surface properties or further by utilizing the biology of co-transplanted cells. Specifically, the adult rat brain tissue response to chronically implanted poly(acrylonitrile-vinylchloride) (PAN-PVC) hollow fiber membranes (HFMs) of varying surface architecture were examined temporally at 2, 4, and 12 weeks postimplantation. Significant differences were discovered in the brain tissue response to the PAN-PVC HFMs of varying surface architecture at 4 and 12 weeks. To extend this work, whether the soluble factors derived from a co-transplanted cellular component further affect the brain tissue response to an implanted HFM in a significant way was critically exploited. The cells used were astrocytes, whose ability to influence scar formation process following CNS injury by physical contact with the host tissue had been documented in the literature. Data indicated for the first time that astrocyte-derived soluble factors ameliorate the adult brain tissue reactivity toward HFM implants in an age-dependent manner. While immature astrocytes secreted soluble factors that suppressed the brain tissue reactivity around the implants, mature astrocytes secreted factors that enhanced the gliotic response. These findings prove the feasibility of ameliorating the CNS tissue reactivity toward biomaterials implants by varying biomaterial surface properties or incorporating scar-reductive factors derived from functional cells into implant constructs, therefore, provide guidance in the design of more integrative biomaterial-based implantable devices for CNS repair.

Classification of Foreign Body Reactions due to Industrial Silicone Injection.

PubMed

Harlim, Ago; Kanoko, Mpu; Aisah, Siti

2018-05-02

A foreign body reaction (FBR) is a typical tissue response to a biomaterial that has been injected or implanted in human body tissue. There has been a lack of data on the classification of foreign body reaction to silicone injection, which can describe the pattern of body tissue responses to silicone. Determine the foreign body reaction to silicone injection. We modified the classification proposed by Duranti and colleagues, which has categorized a FBR to hyaluronic acid injection into a new classification of an FBR to silicone injection. A cohort study of 31 women suffering from silicone-induced granulomas on their chin was conducted. Granulomatous tissue and submental skin were stained with hematoxylin-eosin and evaluated. Our data revealed that there were at least 7 categories of FBRs to silicone injection that could be developed. Categories 1 to 4 showed inflammatory activity, and categories 5 to 8 showed tissue repair by fibrosis. Using histopathological staining, we are able to sequence the steps of body reactions to silicone injection. Initial inflammatory reaction is then replaced by fibrosis process repairing the damaged tissues. The process depends on the host immune tolerance.This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

Detection of radiation-induced brain necrosis in live rats using label-free time-resolved fluorescence spectroscopy (TRFS) (Conference Presentation)

NASA Astrophysics Data System (ADS)

Hartl, Brad A.; Ma, Htet S. W.; Sridharan, Shamira; Hansen, Katherine; Klich, Melanie; Perks, Julian; Kent, Michael; Kim, Kyoungmi; Fragoso, Ruben; Marcu, Laura

2017-02-01

Differentiating radiation-induced necrosis from recurrent tumor in the brain remains a significant challenge to the neurosurgeon. Clinical imaging modalities are not able to reliably discriminate the two tissue types, making biopsy location selection and surgical management difficult. Label-free fluorescence lifetime techniques have previously been shown to be able to delineate human brain tumor from healthy tissues. Thus, fluorescence lifetime techniques represent a potential means to discriminate the two tissues in real-time during surgery. This study aims to characterize the endogenous fluorescence lifetime signatures from radiation induced brain necrosis in a tumor-free rat model. Fischer rats received a single fraction of 60 Gy of radiation to the right hemisphere using a linear accelerator. Animals underwent a terminal live surgery after gross necrosis had developed, as verified with MRI. During surgery, healthy and necrotic brain tissue was measured with a fiber optic needle connected to a multispectral fluorescence lifetime system. Measurements of the necrotic tissue showed a 48% decrease in intensity and 20% increase in lifetimes relative to healthy tissue. Using a support vector machine classifier and leave-one-out validation technique, the necrotic tissue was correctly classified with 94% sensitivity and 97% specificity. Spectral contribution analysis also confirmed that the primary source of fluorescence contrast lies within the redox and bound-unbound population shifts of nicotinamide adenine dinucleotide. A clinical trial is presently underway to measure these tissue types in humans. These results show for the first time that radiation-induced necrotic tissue in the brain contains significantly different metabolic signatures that are detectable with label-free fluorescence lifetime techniques.

Light interference as a possible stressor altering HSP70 and its gene expression levels in brain and hepatic tissues of golden spiny mice.

PubMed

Ashkenazi, Lilach; Haim, Abraham

2012-11-15

Light at night and light interference (LI) disrupt the natural light:dark cycle, causing alterations at physiological and molecular levels, partly by suppressing melatonin (MLT) secretion at night. Heat shock proteins (HSPs) can be activated in response to environmental changes. We assessed changes in gene expression and protein level of HSP70 in brain and hepatic tissues of golden spiny mice (Acomys russatus) acclimated to LI for two (SLI), seven (MLI) and 21 nights (LLI). The effect of MLT treatment on LI-mice was also assessed. HSP70 levels increased in brain and hepatic tissues after SLI, whereas after MLI and LLI, HSP70 decreased to control levels. Changes in HSP70 levels as a response to MLT occurred after SLI only in hepatic tissue. However, hsp70 expression following SLI increased in brain tissue, but not in hepatic tissue. MLT treatment and SLI caused a decrease in hsp70 levels in brain tissue and an increase in hsp70 in hepatic tissue. SLI acclimation elicited a stress response in A. russatus, as expressed by increased HSP70 levels and gene expression. Longer acclimation decreases protein and gene expression to their control levels. We conclude that for brain and hepatic tissues of A. russatus, LI is a short-term stressor. Our results also revealed that A. russatus can acclimate to LI, possibly because of its circadian system plasticity, which allows it to behave both as a nocturnal and as a diurnal rodent. To the best of our knowledge, this is the first study showing the effect of LI as a stressor at the cellular level, by activating HSP70.

Distribution of lead in the brain tissues from DNTC patients using synchrotron radiation microbeams

NASA Astrophysics Data System (ADS)

Ide-Ektessabi, Ari; Ota, Yukihide; Ishihara, Ryoko; Mizuno, Yutaka; Takeuchi, Tohru

2005-12-01

Diffuse neurofibrillary tangles with calcification (DNTC) is a form of dementia with certain characteristics. Its pathology is characterized by cerebrum atrophy, calcification on globus pallidus and dentate nucleus and diffuse neurofibrillary tangles without senile plaques. In the present study brain tissues were prepared from patients with patients DNTC, calcified and non-calcified Alzheimer's disease (AD) patients. The brain tissues were examined non-destructively by X-ray fluorescence (XRF) spectroscopy using synchrotron radiation (SR) microbeams for trace metallic elements Ca, Fe, Cu, Zn and Pb. The XRF analysis showed that there were Pb concentrations in the calcified areas in the brain tissues with both DNTC and AD but there was none in those with non-calcified AD.

Brain Sex Matters: estrogen in cognition and Alzheimer’s disease

PubMed Central

Li, Rena; Cui, Jie; Shen, Yong

2014-01-01

Estrogens are the primary female sex hormones and play important roles in both reproductive and non-reproductive systems. Estrogens can be synthesized in non-reproductive tissues such as liver, heart, muscle, bone and the brain. During the past decade, increasing evidence suggests that brain estrogen can not only be synthesized by neurons, but also by astrocytes. Brain estrogen also works locally at the site of synthesis in paracrine and/or intracrine fashion to maintain important tissue-specific functions. Here, we will focus on the biology of brain estrogen and its impact on cognitive function and Alzheimer’s disease. This comprehensive review provides new insights into brain estrogens by presenting a better understanding of the tissue-specific estrogen effects and their roles in healthy ageing and cognitive function. PMID:24418360

Expression of hypoxia-inducible carbonic anhydrases in brain tumors

PubMed Central

Proescholdt, Martin A.; Mayer, Christina; Kubitza, Marion; Schubert, Thomas; Liao, Shu-Yuan; Stanbridge, Eric J.; Ivanov, Sergey; Oldfield, Edward H.; Brawanski, Alexander; Merrill, Marsha J.

2005-01-01

Malignant brain tumors exhibit distinct metabolic characteristics. Despite high levels of lactate, the intracellular pH of brain tumors is more alkaline than normal brain. Additionally, with increasing malignancy, brain tumors display intratumoral hypoxia. Carbonic anhydrase (CA) IX and XII are transmembrane isoenzymes that are induced by tissue hypoxia. They participate in regulation of pH homeostasis by catalyzing the reversible hydration of carbon dioxide. The aim of our study was to investigate whether brain tumors of different histology and grade of malignancy express elevated levels of CA IX and XII as compared to normal brain. We analyzed 120 tissue specimens from brain tumors (primary and metastatic) and normal brain for CA IX and XII expression by immunohistochemistry, Western blot, and in situ hybridization. Whereas normal brain tissue showed minimal levels of CA IX and XII expression, expression in tumors was found to be upregulated with increased level of malignancy. Hemangioblastomas, from patients with von Hippel–Lindau disease, also displayed high levels of CA IX and XII expression. Comparison of CA IX and XII staining with HIF-1α staining revealed a similar microanatomical distribution, indicating hypoxia as a major, but not the only, induction factor. The extent of CA IX and XII staining correlated with cell proliferation, as indicated by Ki67 labeling. The results demonstrate that CA IX and XII are upregulated in intrinsic and metastatic brain tumors as compared to normal brain tissue. This may contribute to the management of tumor-specific acid load and provide a therapeutic target. PMID:16212811

Dye-enhanced multimodal confocal imaging as a novel approach to intraoperative diagnosis of brain tumors.

PubMed

Snuderl, Matija; Wirth, Dennis; Sheth, Sameer A; Bourne, Sarah K; Kwon, Churl-Su; Ancukiewicz, Marek; Curry, William T; Frosch, Matthew P; Yaroslavsky, Anna N

2013-01-01

Intraoperative diagnosis plays an important role in accurate sampling of brain tumors, limiting the number of biopsies required and improving the distinction between brain and tumor. The goal of this study was to evaluate dye-enhanced multimodal confocal imaging for discriminating gliomas from nonglial brain tumors and from normal brain tissue for diagnostic use. We investigated a total of 37 samples including glioma (13), meningioma (7), metastatic tumors (9) and normal brain removed for nontumoral indications (8). Tissue was stained in 0.05 mg/mL aqueous solution of methylene blue (MB) for 2-5 minutes and multimodal confocal images were acquired using a custom-built microscope. After imaging, tissue was formalin fixed and paraffin embedded for standard neuropathologic evaluation. Thirteen pathologists provided diagnoses based on the multimodal confocal images. The investigated tumor types exhibited distinctive and complimentary characteristics in both the reflectance and fluorescence responses. Images showed distinct morphological features similar to standard histology. Pathologists were able to distinguish gliomas from normal brain tissue and nonglial brain tumors, and to render diagnoses from the images in a manner comparable to haematoxylin and eosin (H&E) slides. These results confirm the feasibility of multimodal confocal imaging for intravital intraoperative diagnosis. © 2012 The Authors; Brain Pathology © 2012 International Society of Neuropathology.

NASA Robotic Neurosurgery Testbed

NASA Technical Reports Server (NTRS)

Mah, Robert

1997-01-01

The detection of tissue interface (e.g., normal tissue, cancer, tumor) has been limited clinically to tactile feedback, temperature monitoring, and the use of a miniature ultrasound probe for tissue differentiation during surgical operations, In neurosurgery, the needle used in the standard stereotactic CT or MRI guided brain biopsy provides no information about the tissue being sampled. The tissue sampled depends entirely upon the accuracy with which the localization provided by the preoperative CT or MRI scan is translated to the intracranial biopsy site. In addition, no information about the tissue being traversed by the needle (e.g., a blood vessel) is provided. Hemorrhage due to the biopsy needle tearing a blood vessel within the brain is the most devastating complication of stereotactic CT/MRI guided brain biopsy. A robotic neurosurgery testbed has been developed at NASA Ames Research Center as a spin-off of technologies from space, aeronautics and medical programs. The invention entitled "Robotic Neurosurgery Leading to Multimodality Devices for Tissue Identification" is nearing a state ready for commercialization. The devices will: 1) improve diagnostic accuracy and precision of general surgery, with near term emphasis on stereotactic brain biopsy, 2) automate tissue identification, with near term emphasis on stereotactic brain biopsy, to permit remote control of the procedure, and 3) reduce morbidity for stereotactic brain biopsy. The commercial impact from this work is the potential development of a whole new generation of smart surgical tools to increase the safety, accuracy and efficiency of surgical procedures. Other potential markets include smart surgical tools for tumor ablation in neurosurgery, general exploratory surgery, prostate cancer surgery, and breast cancer surgery.

NASA Robotic Neurosurgery Testbed

NASA Technical Reports Server (NTRS)

Mah, Robert

1997-01-01

The detection of tissue interface (e.g., normal tissue, cancer, tumor) has been limited clinically to tactile feedback, temperature monitoring, and the use of a miniature ultrasound probe for tissue differentiation during surgical operations. In neurosurgery, the needle used in the standard stereotactic CT (Computational Tomography) or MRI (Magnetic Resonance Imaging) guided brain biopsy provides no information about the tissue being sampled. The tissue sampled depends entirely upon the accuracy with which the localization provided by the preoperative CT or MRI scan is translated to the intracranial biopsy site. In addition, no information about the tissue being traversed by the needle (e.g., a blood vessel) is provided. Hemorrhage due to the biopsy needle tearing a blood vessel within the brain is the most devastating complication of stereotactic CT/MRI guided brain biopsy. A robotic neurosurgery testbed has been developed at NASA Ames Research Center as a spin-off of technologies from space, aeronautics and medical programs. The invention entitled 'Robotic Neurosurgery Leading to Multimodality Devices for Tissue Identification' is nearing a state ready for commercialization. The devices will: 1) improve diagnostic accuracy and precision of general surgery, with near term emphasis on stereotactic brain biopsy, 2) automate tissue identification, with near term emphasis on stereotactic brain biopsy, to permit remote control of the procedure, and 3) reduce morbidity for stereotactic brain biopsy. The commercial impact from this work is the potential development of a whole new generation of smart surgical tools to increase the safety, accuracy and efficiency of surgical procedures. Other potential markets include smart surgical tools for tumor ablation in neurosurgery, general exploratory surgery, prostate cancer surgery, and breast cancer surgery.

Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder

PubMed Central

Zhao, Guangjun; Wang, Xuchu; Niu, Yanmin; Tan, Liwen; Zhang, Shao-Xiang

2016-01-01

Cryosection brain images in Chinese Visible Human (CVH) dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel). Fast and accurate segmentation of these images into white matter, gray matter, and cerebrospinal fluid plays a critical role in analyzing and measuring the anatomical structures of human brain. However, most existing automated segmentation methods are designed for computed tomography or magnetic resonance imaging data, and they may not be applicable for cryosection images due to the imaging difference. In this paper, we propose a supervised learning-based CVH brain tissues segmentation method that uses stacked autoencoder (SAE) to automatically learn the deep feature representations. Specifically, our model includes two successive parts where two three-layer SAEs take image patches as input to learn the complex anatomical feature representation, and then these features are sent to Softmax classifier for inferring the labels. Experimental results validated the effectiveness of our method and showed that it outperformed four other classical brain tissue detection strategies. Furthermore, we reconstructed three-dimensional surfaces of these tissues, which show their potential in exploring the high-resolution anatomical structures of human brain. PMID:27057543

Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder.

PubMed

Zhao, Guangjun; Wang, Xuchu; Niu, Yanmin; Tan, Liwen; Zhang, Shao-Xiang

2016-01-01

Cryosection brain images in Chinese Visible Human (CVH) dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel). Fast and accurate segmentation of these images into white matter, gray matter, and cerebrospinal fluid plays a critical role in analyzing and measuring the anatomical structures of human brain. However, most existing automated segmentation methods are designed for computed tomography or magnetic resonance imaging data, and they may not be applicable for cryosection images due to the imaging difference. In this paper, we propose a supervised learning-based CVH brain tissues segmentation method that uses stacked autoencoder (SAE) to automatically learn the deep feature representations. Specifically, our model includes two successive parts where two three-layer SAEs take image patches as input to learn the complex anatomical feature representation, and then these features are sent to Softmax classifier for inferring the labels. Experimental results validated the effectiveness of our method and showed that it outperformed four other classical brain tissue detection strategies. Furthermore, we reconstructed three-dimensional surfaces of these tissues, which show their potential in exploring the high-resolution anatomical structures of human brain.

[Effects of Geometrical Dimensions and Material Properties on the Rotation Characteristics of Head].

PubMed

Chen, Yue; Cui, Shihai; Li, Haiyan; Ruan, Shijie

2016-08-01

The validated finite element head model(FEHM)of a 3-year-old child,a 6-year-old child and a 50 th percentile adult were used to investigate the effects of head dimension and material parameters of brain tissues on the head rotational responses based on experimental design.Results showed that the effects of head dimension and directions of rotation on the head rotational responses were not significant under the same rotational loading condition,and the same results appeared in the viscoelastic material parameters of brain tissues.However,the head rotational responses were most sensitive to the shear modulus(G)of brain tissues relative to decay constant(β)and bulk modulus(K).Therefore,the selection of material parameters of brain tissues is most important to the accuracy of simulation results,especially in the study of brain injury criterion under the rotational loading conditions.

The biochemical, nanomechanical and chemometric signatures of brain cancer

NASA Astrophysics Data System (ADS)

Abramczyk, Halina; Imiela, Anna

2018-01-01

Raman spectroscopy and imaging combined with AFM topography and mechanical indentation by AFM have been shown to be an effective tool for analysis and discrimination of human brain tumors from normal structures. Raman methods have potential to be applied in clinical practice as they allow for identification of tumor margins during surgery. In this study, we investigate medulloblastoma (grade IV WHO) (n = 5) and the tissue from the negative margins used as normal controls. We compare a high grade medulloblastoma (IV grade), and non-tumor samples from human central nervous system (CNS) tissue. Based on the properties of the Raman vibrational spectra and Raman images we provide a real-time feedback that is label-free method to monitor tumor metabolism that reveals reprogramming of biosynthesis of lipids, and proteins. We have found that the high-grade tumors of central nervous system (medulloblastoma) exhibit enhanced level of β-sheet conformation and down-regulated level of α-helix conformation when comparing against normal tissue. We have shown that the ratio of Raman intensities I2930/I2845 at 2930 and 2845 cm- 1 is a good source of information on the ratio of lipid and protein contents. We have found that the ratio reflects the lipid and protein contents of tumorous brain tissue compared to the non-tumor tissue. Almost all brain tumors have the Raman intensity ratios significantly higher (1.99 ± 0.026) than that found in non-tumor brain tissue, which is 1.456 ± 0.02, and indicates that the relative amount of lipids compared to proteins is significantly higher in the normal brain tissue. Mechanical indentation using AFM on sliced human brain tissues (medulloblastoma, grade IV) revealed that the mechanical properties of this tissue are strongly heterogeneous, between 1.8 and 75.7 kPa, and the mean of 27.16 kPa. The sensitivity and specificity obtained directly from PLSDA and cross validation gives a sensitivity and specificity of 98.5% and 96% and 96.3% and 92% for cross-validation, respectively. The high sensitivity and specificity demonstrates usefulness for a proper decision for a Raman diagnostic test on biochemical alterations monitored by Raman spectroscopy related to brain cancer development.

Examining Brain Morphometry Associated with Self-Esteem in Young Adults Using Multilevel-ROI-Features-Based Classification Method

PubMed Central

Peng, Bo; Lu, Jieru; Saxena, Aditya; Zhou, Zhiyong; Zhang, Tao; Wang, Suhong; Dai, Yakang

2017-01-01

Purpose: This study is to exam self-esteem related brain morphometry on brain magnetic resonance (MR) images using multilevel-features-based classification method. Method: The multilevel region of interest (ROI) features consist of two types of features: (i) ROI features, which include gray matter volume, white matter volume, cerebrospinal fluid volume, cortical thickness, and cortical surface area, and (ii) similarity features, which are based on similarity calculation of cortical thickness between ROIs. For each feature type, a hybrid feature selection method, comprising of filter-based and wrapper-based algorithms, is used to select the most discriminating features. ROI features and similarity features are integrated by using multi-kernel support vector machines (SVMs) with appropriate weighting factor. Results: The classification performance is improved by using multilevel ROI features with an accuracy of 96.66%, a specificity of 96.62%, and a sensitivity of 95.67%. The most discriminating ROI features that are related to self-esteem spread over occipital lobe, frontal lobe, parietal lobe, limbic lobe, temporal lobe, and central region, mainly involving white matter and cortical thickness. The most discriminating similarity features are distributed in both the right and left hemisphere, including frontal lobe, occipital lobe, limbic lobe, parietal lobe, and central region, which conveys information of structural connections between different brain regions. Conclusion: By using ROI features and similarity features to exam self-esteem related brain morphometry, this paper provides a pilot evidence that self-esteem is linked to specific ROIs and structural connections between different brain regions. PMID:28588470

Examining Brain Morphometry Associated with Self-Esteem in Young Adults Using Multilevel-ROI-Features-Based Classification Method.

PubMed

Peng, Bo; Lu, Jieru; Saxena, Aditya; Zhou, Zhiyong; Zhang, Tao; Wang, Suhong; Dai, Yakang

2017-01-01

Purpose: This study is to exam self-esteem related brain morphometry on brain magnetic resonance (MR) images using multilevel-features-based classification method. Method: The multilevel region of interest (ROI) features consist of two types of features: (i) ROI features, which include gray matter volume, white matter volume, cerebrospinal fluid volume, cortical thickness, and cortical surface area, and (ii) similarity features, which are based on similarity calculation of cortical thickness between ROIs. For each feature type, a hybrid feature selection method, comprising of filter-based and wrapper-based algorithms, is used to select the most discriminating features. ROI features and similarity features are integrated by using multi-kernel support vector machines (SVMs) with appropriate weighting factor. Results: The classification performance is improved by using multilevel ROI features with an accuracy of 96.66%, a specificity of 96.62%, and a sensitivity of 95.67%. The most discriminating ROI features that are related to self-esteem spread over occipital lobe, frontal lobe, parietal lobe, limbic lobe, temporal lobe, and central region, mainly involving white matter and cortical thickness. The most discriminating similarity features are distributed in both the right and left hemisphere, including frontal lobe, occipital lobe, limbic lobe, parietal lobe, and central region, which conveys information of structural connections between different brain regions. Conclusion: By using ROI features and similarity features to exam self-esteem related brain morphometry, this paper provides a pilot evidence that self-esteem is linked to specific ROIs and structural connections between different brain regions.

Age dependence of dielectric properties of bovine brain and ocular tissues in the frequency range of 400 MHz to 18 GHz

NASA Astrophysics Data System (ADS)

Schmid, Gernot; Überbacher, Richard

2005-10-01

In order to identify possible age-dependent dielectric properties of brain and eye tissues in the frequency range of 400 MHz to 18 GHz, measurements on bovine grey and white matter as well as on cornea, lens (cortical) and the vitreous body were performed using a commercially available open-ended coaxial probe and a computer-controlled vector network analyser. Freshly excised tissues of 52 animals of two age groups (42 adult animals, i.e. 16-24 month old and 10 young animals, i.e. 4-6 month old calves) were examined within 8 min (brain tissue) and 15 min (eye tissue), respectively, of the animals' death. Tissue temperatures for the measurements were 32 ± 1 °C and 25 ± 1 °C for brain and eye tissues, respectively. Statistical analysis of the measured data revealed significant differences in the dielectric properties of white matter and cortical lens tissue between the adult and the young group. In the case of white matter the mean values of conductivity and permittivity of young tissue were 15%-22% and 12%-15%, respectively, higher compared to the adult tissue in the considered frequency range. Similarly, young cortical lens tissue was 25%-76% higher in conductivity and 27%-39% higher in permittivity than adult cortical lens tissue.

Zika Virus RNA Replication and Persistence in Brain and Placental Tissue

PubMed Central

Rabeneck, Demi B.; Martines, Roosecelis B.; Reagan-Steiner, Sarah; Ermias, Yokabed; Estetter, Lindsey B.C.; Suzuki, Tadaki; Ritter, Jana; Keating, M. Kelly; Hale, Gillian; Gary, Joy; Muehlenbachs, Atis; Lambert, Amy; Lanciotti, Robert; Oduyebo, Titilope; Meaney-Delman, Dana; Bolaños, Fernando; Saad, Edgar Alberto Parra; Shieh, Wun-Ju; Zaki, Sherif R.

2017-01-01

Zika virus is causally linked with congenital microcephaly and may be associated with pregnancy loss. However, the mechanisms of Zika virus intrauterine transmission and replication and its tropism and persistence in tissues are poorly understood. We tested tissues from 52 case-patients: 8 infants with microcephaly who died and 44 women suspected of being infected with Zika virus during pregnancy. By reverse transcription PCR, tissues from 32 (62%) case-patients (brains from 8 infants with microcephaly and placental/fetal tissues from 24 women) were positive for Zika virus. In situ hybridization localized replicative Zika virus RNA in brains of 7 infants and in placentas of 9 women who had pregnancy losses during the first or second trimester. These findings demonstrate that Zika virus replicates and persists in fetal brains and placentas, providing direct evidence of its association with microcephaly. Tissue-based reverse transcription PCR extends the time frame of Zika virus detection in congenital and pregnancy-associated infections. PMID:27959260

Single-Trial Classification of Multi-User P300-Based Brain-Computer Interface Using Riemannian Geometry.

PubMed

Korczowski, L; Congedo, M; Jutten, C

2015-08-01

The classification of electroencephalographic (EEG) data recorded from multiple users simultaneously is an important challenge in the field of Brain-Computer Interface (BCI). In this paper we compare different approaches for classification of single-trials Event-Related Potential (ERP) on two subjects playing a collaborative BCI game. The minimum distance to mean (MDM) classifier in a Riemannian framework is extended to use the diversity of the inter-subjects spatio-temporal statistics (MDM-hyper) or to merge multiple classifiers (MDM-multi). We show that both these classifiers outperform significantly the mean performance of the two users and analogous classifiers based on the step-wise linear discriminant analysis. More importantly, the MDM-multi outperforms the performance of the best player within the pair.

Gene expression changes with age in skin, adipose tissue, blood and brain.

PubMed

Glass, Daniel; Viñuela, Ana; Davies, Matthew N; Ramasamy, Adaikalavan; Parts, Leopold; Knowles, David; Brown, Andrew A; Hedman, Asa K; Small, Kerrin S; Buil, Alfonso; Grundberg, Elin; Nica, Alexandra C; Di Meglio, Paola; Nestle, Frank O; Ryten, Mina; Durbin, Richard; McCarthy, Mark I; Deloukas, Panagiotis; Dermitzakis, Emmanouil T; Weale, Michael E; Bataille, Veronique; Spector, Tim D

2013-07-26

Previous studies have demonstrated that gene expression levels change with age. These changes are hypothesized to influence the aging rate of an individual. We analyzed gene expression changes with age in abdominal skin, subcutaneous adipose tissue and lymphoblastoid cell lines in 856 female twins in the age range of 39-85 years. Additionally, we investigated genotypic variants involved in genotype-by-age interactions to understand how the genomic regulation of gene expression alters with age. Using a linear mixed model, differential expression with age was identified in 1,672 genes in skin and 188 genes in adipose tissue. Only two genes expressed in lymphoblastoid cell lines showed significant changes with age. Genes significantly regulated by age were compared with expression profiles in 10 brain regions from 100 postmortem brains aged 16 to 83 years. We identified only one age-related gene common to the three tissues. There were 12 genes that showed differential expression with age in both skin and brain tissue and three common to adipose and brain tissues. Skin showed the most age-related gene expression changes of all the tissues investigated, with many of the genes being previously implicated in fatty acid metabolism, mitochondrial activity, cancer and splicing. A significant proportion of age-related changes in gene expression appear to be tissue-specific with only a few genes sharing an age effect in expression across tissues. More research is needed to improve our understanding of the genetic influences on aging and the relationship with age-related diseases.

Tissue-like Neural Probes for Understanding and Modulating the Brain.

PubMed

Hong, Guosong; Viveros, Robert D; Zwang, Theodore J; Yang, Xiao; Lieber, Charles M

2018-03-19

Electrophysiology tools have contributed substantially to understanding brain function, yet the capabilities of conventional electrophysiology probes have remained limited in key ways because of large structural and mechanical mismatches with respect to neural tissue. In this Perspective, we discuss how the general goal of probe design in biochemistry, that the probe or label have a minimal impact on the properties and function of the system being studied, can be realized by minimizing structural, mechanical, and topological differences between neural probes and brain tissue, thus leading to a new paradigm of tissue-like mesh electronics. The unique properties and capabilities of the tissue-like mesh electronics as well as future opportunities are summarized. First, we discuss the design of an ultraflexible and open mesh structure of electronics that is tissue-like and can be delivered in the brain via minimally invasive syringe injection like molecular and macromolecular pharmaceuticals. Second, we describe the unprecedented tissue healing without chronic immune response that leads to seamless three-dimensional integration with a natural distribution of neurons and other key cells through these tissue-like probes. These unique characteristics lead to unmatched stable long-term, multiplexed mapping and modulation of neural circuits at the single-neuron level on a year time scale. Last, we offer insights on several exciting future directions for the tissue-like electronics paradigm that capitalize on their unique properties to explore biochemical interactions and signaling in a "natural" brain environment.

In vivo multiphoton tomography and fluorescence lifetime imaging of human brain tumor tissue.

PubMed

Kantelhardt, Sven R; Kalasauskas, Darius; König, Karsten; Kim, Ella; Weinigel, Martin; Uchugonova, Aisada; Giese, Alf

2016-05-01

High resolution multiphoton tomography and fluorescence lifetime imaging differentiates glioma from adjacent brain in native tissue samples ex vivo. Presently, multiphoton tomography is applied in clinical dermatology and experimentally. We here present the first application of multiphoton and fluorescence lifetime imaging for in vivo imaging on humans during a neurosurgical procedure. We used a MPTflex™ Multiphoton Laser Tomograph (JenLab, Germany). We examined cultured glioma cells in an orthotopic mouse tumor model and native human tissue samples. Finally the multiphoton tomograph was applied to provide optical biopsies during resection of a clinical case of glioblastoma. All tissues imaged by multiphoton tomography were sampled and processed for conventional histopathology. The multiphoton tomograph allowed fluorescence intensity- and fluorescence lifetime imaging with submicron spatial resolution and 200 picosecond temporal resolution. Morphological fluorescence intensity imaging and fluorescence lifetime imaging of tumor-bearing mouse brains and native human tissue samples clearly differentiated tumor and adjacent brain tissue. Intraoperative imaging was found to be technically feasible. Intraoperative image quality was comparable to ex vivo examinations. To our knowledge we here present the first intraoperative application of high resolution multiphoton tomography and fluorescence lifetime imaging of human brain tumors in situ. It allowed in vivo identification and determination of cell density of tumor tissue on a cellular and subcellular level within seconds. The technology shows the potential of rapid intraoperative identification of native glioma tissue without need for tissue processing or staining.

Evidence for brain glucose dysregulation in Alzheimer's disease.

PubMed

An, Yang; Varma, Vijay R; Varma, Sudhir; Casanova, Ramon; Dammer, Eric; Pletnikova, Olga; Chia, Chee W; Egan, Josephine M; Ferrucci, Luigi; Troncoso, Juan; Levey, Allan I; Lah, James; Seyfried, Nicholas T; Legido-Quigley, Cristina; O'Brien, Richard; Thambisetty, Madhav

2018-03-01

It is unclear whether abnormalities in brain glucose homeostasis are associated with Alzheimer's disease (AD) pathogenesis. Within the autopsy cohort of the Baltimore Longitudinal Study of Aging, we measured brain glucose concentration and assessed the ratios of the glycolytic amino acids, serine, glycine, and alanine to glucose. We also quantified protein levels of the neuronal (GLUT3) and astrocytic (GLUT1) glucose transporters. Finally, we assessed the relationships between plasma glucose measured before death and brain tissue glucose. Higher brain tissue glucose concentration, reduced glycolytic flux, and lower GLUT3 are related to severity of AD pathology and the expression of AD symptoms. Longitudinal increases in fasting plasma glucose levels are associated with higher brain tissue glucose concentrations. Impaired glucose metabolism due to reduced glycolytic flux may be intrinsic to AD pathogenesis. Abnormalities in brain glucose homeostasis may begin several years before the onset of clinical symptoms. Copyright © 2017 the Alzheimer's Association. All rights reserved.

Assessment of sexual orientation using the hemodynamic brain response to visual sexual stimuli.

PubMed

Ponseti, Jorge; Granert, Oliver; Jansen, Olav; Wolff, Stephan; Mehdorn, Hubertus; Bosinski, Hartmut; Siebner, Hartwig

2009-06-01

The assessment of sexual orientation is of importance to the diagnosis and treatment of sex offenders and paraphilic disorders. Phallometry is considered gold standard in objectifying sexual orientation, yet this measurement has been criticized because of its intrusiveness and limited reliability. To evaluate whether the spatial response pattern to sexual stimuli as revealed by a change in blood oxygen level-dependent (BOLD) signal can be used for individual classification of sexual orientation. We used a preexisting functional MRI (fMRI) data set that had been acquired in a nonclinical sample of 12 heterosexual men and 14 homosexual men. During fMRI, participants were briefly exposed to pictures of same-sex and opposite-sex genitals. Data analysis involved four steps: (i) differences in the BOLD response to female and male sexual stimuli were calculated for each subject; (ii) these contrast images were entered into a group analysis to calculate whole-brain difference maps between homosexual and heterosexual participants; (iii) a single expression value was computed for each subject expressing its correspondence to the group result; and (iv) based on these expression values, Fisher's linear discriminant analysis and the kappa-nearest neighbor classification method were used to predict the sexual orientation of each subject. Sensitivity and specificity of the two classification methods in predicting individual sexual orientation. Both classification methods performed well in predicting individual sexual orientation with a mean accuracy of >85% (Fisher's linear discriminant analysis: 92% sensitivity, 85% specificity; kappa-nearest neighbor classification: 88% sensitivity, 92% specificity). Despite the small sample size, the functional response patterns of the brain to sexual stimuli contained sufficient information to predict individual sexual orientation with high accuracy. These results suggest that fMRI-based classification methods hold promise for the diagnosis of paraphilic disorders (e.g., pedophilia).

Classification of single-trial auditory events using dry-wireless EEG during real and motion simulated flight.

PubMed

Callan, Daniel E; Durantin, Gautier; Terzibas, Cengiz

2015-01-01

Application of neuro-augmentation technology based on dry-wireless EEG may be considerably beneficial for aviation and space operations because of the inherent dangers involved. In this study we evaluate classification performance of perceptual events using a dry-wireless EEG system during motion platform based flight simulation and actual flight in an open cockpit biplane to determine if the system can be used in the presence of considerable environmental and physiological artifacts. A passive task involving 200 random auditory presentations of a chirp sound was used for evaluation. The advantage of this auditory task is that it does not interfere with the perceptual motor processes involved with piloting the plane. Classification was based on identifying the presentation of a chirp sound vs. silent periods. Evaluation of Independent component analysis (ICA) and Kalman filtering to enhance classification performance by extracting brain activity related to the auditory event from other non-task related brain activity and artifacts was assessed. The results of permutation testing revealed that single trial classification of presence or absence of an auditory event was significantly above chance for all conditions on a novel test set. The best performance could be achieved with both ICA and Kalman filtering relative to no processing: Platform Off (83.4% vs. 78.3%), Platform On (73.1% vs. 71.6%), Biplane Engine Off (81.1% vs. 77.4%), and Biplane Engine On (79.2% vs. 66.1%). This experiment demonstrates that dry-wireless EEG can be used in environments with considerable vibration, wind, acoustic noise, and physiological artifacts and achieve good single trial classification performance that is necessary for future successful application of neuro-augmentation technology based on brain-machine interfaces.

Laser Desorption/Ionization Mass Spectrometric Imaging of Endogenous Lipids from Rat Brain Tissue Implanted with Silver Nanoparticles

NASA Astrophysics Data System (ADS)

Muller, Ludovic; Baldwin, Kathrine; Barbacci, Damon C.; Jackson, Shelley N.; Roux, Aurélie; Balaban, Carey D.; Brinson, Bruce E.; McCully, Michael I.; Lewis, Ernest K.; Schultz, J. Albert; Woods, Amina S.

2017-08-01

Mass spectrometry imaging (MSI) of tissue implanted with silver nanoparticulate (AgNP) matrix generates reproducible imaging of lipids in rodent models of disease and injury. Gas-phase production and acceleration of size-selected 8 nm AgNP is followed by controlled ion beam rastering and soft landing implantation of 500 eV AgNP into tissue. Focused 337 nm laser desorption produces high quality images for most lipid classes in rat brain tissue (in positive mode: galactoceramides, diacylglycerols, ceramides, phosphatidylcholines, cholesteryl ester, and cholesterol, and in negative ion mode: phosphatidylethanolamides, sulfatides, phosphatidylinositol, and sphingomyelins). Image reproducibility in serial sections of brain tissue is achieved within <10% tolerance by selecting argentated instead of alkali cationized ions. The imaging of brain tissues spotted with pure standards was used to demonstrate that Ag cationized ceramide and diacylglycerol ions are from intact, endogenous species. In contrast, almost all Ag cationized fatty acid ions are a result of fragmentations of numerous lipid types having the fatty acid as a subunit. Almost no argentated intact fatty acid ions come from the pure fatty acid standard on tissue.

In vivo three-photon microscopy of subcortical structures within an intact mouse brain

NASA Astrophysics Data System (ADS)

Horton, Nicholas G.; Wang, Ke; Kobat, Demirhan; Clark, Catharine G.; Wise, Frank W.; Schaffer, Chris B.; Xu, Chris

2013-03-01

Two-photon fluorescence microscopy enables scientists in various fields including neuroscience, embryology and oncology to visualize in vivo and ex vivo tissue morphology and physiology at a cellular level deep within scattering tissue. However, tissue scattering limits the maximum imaging depth of two-photon fluorescence microscopy to the cortical layer within mouse brain, and imaging subcortical structures currently requires the removal of overlying brain tissue or the insertion of optical probes. Here, we demonstrate non-invasive, high-resolution, in vivo imaging of subcortical structures within an intact mouse brain using three-photon fluorescence microscopy at a spectral excitation window of 1,700 nm. Vascular structures as well as red fluorescent protein-labelled neurons within the mouse hippocampus are imaged. The combination of the long excitation wavelength and the higher-order nonlinear excitation overcomes the limitations of two-photon fluorescence microscopy, enabling biological investigations to take place at a greater depth within tissue.

The national DBS brain tissue network pilot study: need for more tissue and more standardization.

PubMed

Vedam-Mai, V; Krock, N; Ullman, M; Foote, K D; Shain, W; Smith, K; Yachnis, A T; Steindler, D; Reynolds, B; Merritt, S; Pagan, F; Marjama-Lyons, J; Hogarth, P; Resnick, A S; Zeilman, P; Okun, M S

2011-08-01

Over 70,000 DBS devices have been implanted worldwide; however, there remains a paucity of well-characterized post-mortem DBS brains available to researchers. We propose that the overall understanding of DBS can be improved through the establishment of a Deep Brain Stimulation-Brain Tissue Network (DBS-BTN), which will further our understanding of DBS and brain function. The objectives of the tissue bank are twofold: (a) to provide a complete (clinical, imaging and pathological) database for DBS brain tissue samples, and (b) to make available DBS tissue samples to researchers, which will help our understanding of disease and underlying brain circuitry. Standard operating procedures for processing DBS brains were developed as part of the pilot project. Complete data files were created for individual patients and included demographic information, clinical information, imaging data, pathology, and DBS lead locations/settings. 19 DBS brains were collected from 11 geographically dispersed centers from across the U.S. The average age at the time of death was 69.3 years (51-92, with a standard deviation or SD of 10.13). The male:female ratio was almost 3:1. Average post-mortem interval from death to brain collection was 10.6 h (SD of 7.17). The DBS targets included: subthalamic nucleus, globus pallidus interna, and ventralis intermedius nucleus of the thalamus. In 16.7% of cases the clinical diagnosis failed to match the pathological diagnosis. We provide neuropathological findings from the cohort, and perilead responses to DBS. One of the most important observations made in this pilot study was the missing data, which was approximately 25% of all available data fields. Preliminary results demonstrated the feasibility and utility of creating a National DBS-BTN resource for the scientific community. We plan to improve our techniques to remedy omitted clinical/research data, and expand the Network to include a larger donor pool. We will enhance sample preparation to facilitate advanced molecular studies and progenitor cell retrieval.

Organization of brain tissue - Is the brain a noisy processor.

NASA Technical Reports Server (NTRS)

Adey, W. R.

1972-01-01

This paper presents some thoughts on functional organization in cerebral tissue. 'Spontaneous' wave and unit firing are considered as essential phenomena in the handling of information. Various models are discussed which have been suggested to describe the pseudorandom behavior of brain cells, leading to a view of the brain as an information processor and its role in learning, memory, remembering and forgetting.

Epstein Barr Virus and Blood Brain Barrier in Multiple Sclerosis

DTIC Science & Technology

2013-07-01

Distribution Unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT Multiple sclerosis (MS) is a chronic, autoimmune neurodegenerative disease . Epstein - Barr ...of EBV in MS disease . 15. SUBJECT TERMS Blood-brain-barrier, Epstein - Barr virus ; EBV; BBB; MS, Multiple sclerosis 16. SECURITY CLASSIFICATION OF...AD_________________ Award Number: W81XWH-12-1-0225 TITLE: Epstein Barr virus and blood brain

Spectral areas and ratios classifier algorithm for pancreatic tissue classification using optical spectroscopy

NASA Astrophysics Data System (ADS)

Chandra, Malavika; Scheiman, James; Simeone, Diane; McKenna, Barbara; Purdy, Julianne; Mycek, Mary-Ann

2010-01-01

Pancreatic adenocarcinoma is one of the leading causes of cancer death, in part because of the inability of current diagnostic methods to reliably detect early-stage disease. We present the first assessment of the diagnostic accuracy of algorithms developed for pancreatic tissue classification using data from fiber optic probe-based bimodal optical spectroscopy, a real-time approach that would be compatible with minimally invasive diagnostic procedures for early cancer detection in the pancreas. A total of 96 fluorescence and 96 reflectance spectra are considered from 50 freshly excised tissue sites-including human pancreatic adenocarcinoma, chronic pancreatitis (inflammation), and normal tissues-on nine patients. Classification algorithms using linear discriminant analysis are developed to distinguish among tissues, and leave-one-out cross-validation is employed to assess the classifiers' performance. The spectral areas and ratios classifier (SpARC) algorithm employs a combination of reflectance and fluorescence data and has the best performance, with sensitivity, specificity, negative predictive value, and positive predictive value for correctly identifying adenocarcinoma being 85, 89, 92, and 80%, respectively.

Implementation of several mathematical algorithms to breast tissue density classification

NASA Astrophysics Data System (ADS)

Quintana, C.; Redondo, M.; Tirao, G.

2014-02-01

The accuracy of mammographic abnormality detection methods is strongly dependent on breast tissue characteristics, where a dense breast tissue can hide lesions causing cancer to be detected at later stages. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. This paper presents the implementation and the performance of different mathematical algorithms designed to standardize the categorization of mammographic images, according to the American College of Radiology classifications. These mathematical techniques are based on intrinsic properties calculations and on comparison with an ideal homogeneous image (joint entropy, mutual information, normalized cross correlation and index Q) as categorization parameters. The algorithms evaluation was performed on 100 cases of the mammographic data sets provided by the Ministerio de Salud de la Provincia de Córdoba, Argentina—Programa de Prevención del Cáncer de Mama (Department of Public Health, Córdoba, Argentina, Breast Cancer Prevention Program). The obtained breast classifications were compared with the expert medical diagnostics, showing a good performance. The implemented algorithms revealed a high potentiality to classify breasts into tissue density categories.

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.

Relationship between concentrations of lutein and StARD3 among pediatric and geriatric human brain tissue

USDA-ARS?s Scientific Manuscript database

Lutein, a dietary carotenoid, selectively accumulates in human retina and brain. While many epidemiological studies show evidence of a relationship between lutein status and cognitive health, lutein's selective uptake in human brain tissue and its potential function in early neural development and c...

Time-resolved fluorescence spectroscopy of human brain tumors

NASA Astrophysics Data System (ADS)

Marcu, Laura; Thompson, Reid C.; Garde, Smita; Sedrak, Mark; Black, Keith L.; Yong, William H.

2002-05-01

Fluorescence spectroscopy of the endogenous emission of brain tumors has been researched as a potentially important method for the intraoperative localization of brain tumor margins. In this study, we investigate the use of time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) for demarcation of primary brain tumors by studying the time-resolved spectra of gliomas of different histologic grades. Time-resolved fluorescence (3 ns, 337 nm excitation) from excised human brain tumor show differences between the time-resolved emission of malignant glioma and normal brain tissue (gray and white matter). Our findings suggest that brain tumors can be differentiated from normal brain tissue based upon unique time-resolved fluorescence signature.

Medical diagnosis imaging systems: image and signal processing applications aided by fuzzy logic

NASA Astrophysics Data System (ADS)

Hata, Yutaka

2010-04-01

First, we describe an automated procedure for segmenting an MR image of a human brain based on fuzzy logic for diagnosing Alzheimer's disease. The intensity thresholds for segmenting the whole brain of a subject are automatically determined by finding the peaks of the intensity histogram. After these thresholds are evaluated in a region growing, the whole brain can be identified. Next, we describe a procedure for decomposing the obtained whole brain into the left and right cerebral hemispheres, the cerebellum and the brain stem. Our method then identified the whole brain, the left cerebral hemisphere, the right cerebral hemisphere, the cerebellum and the brain stem. Secondly, we describe a transskull sonography system that can visualize the shape of the skull and brain surface from any point to examine skull fracture and some brain diseases. We employ fuzzy signal processing to determine the skull and brain surface. The phantom model, the animal model with soft tissue, the animal model with brain tissue, and a human subjects' forehead is applied in our system. The all shapes of the skin surface, skull surface, skull bottom, and brain tissue surface are successfully determined.

Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer

PubMed Central

2012-01-01

Background Automated classification of histopathology involves identification of multiple classes, including benign, cancerous, and confounder categories. The confounder tissue classes can often mimic and share attributes with both the diseased and normal tissue classes, and can be particularly difficult to identify, both manually and by automated classifiers. In the case of prostate cancer, they may be several confounding tissue types present in a biopsy sample, posing as major sources of diagnostic error for pathologists. Two common multi-class approaches are one-shot classification (OSC), where all classes are identified simultaneously, and one-versus-all (OVA), where a “target” class is distinguished from all “non-target” classes. OSC is typically unable to handle discrimination of classes of varying similarity (e.g. with images of prostate atrophy and high grade cancer), while OVA forces several heterogeneous classes into a single “non-target” class. In this work, we present a cascaded (CAS) approach to classifying prostate biopsy tissue samples, where images from different classes are grouped to maximize intra-group homogeneity while maximizing inter-group heterogeneity. Results We apply the CAS approach to categorize 2000 tissue samples taken from 214 patient studies into seven classes: epithelium, stroma, atrophy, prostatic intraepithelial neoplasia (PIN), and prostate cancer Gleason grades 3, 4, and 5. A series of increasingly granular binary classifiers are used to split the different tissue classes until the images have been categorized into a single unique class. Our automatically-extracted image feature set includes architectural features based on location of the nuclei within the tissue sample as well as texture features extracted on a per-pixel level. The CAS strategy yields a positive predictive value (PPV) of 0.86 in classifying the 2000 tissue images into one of 7 classes, compared with the OVA (0.77 PPV) and OSC approaches (0.76 PPV). Conclusions Use of the CAS strategy increases the PPV for a multi-category classification system over two common alternative strategies. In classification problems such as histopathology, where multiple class groups exist with varying degrees of heterogeneity, the CAS system can intelligently assign class labels to objects by performing multiple binary classifications according to domain knowledge. PMID:23110677