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Neuroinformatics

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https://www.readbyqxmd.com/read/28988388/spicodyn-a-toolbox-for-the-analysis-of-neuronal-network-dynamics-and-connectivity-from-multi-site-spike-signal-recordings
#1
Vito Paolo Pastore, Aleksandar Godjoski, Sergio Martinoia, Paolo Massobrio
We implemented an automated and efficient open-source software for the analysis of multi-site neuronal spike signals. The software package, named SPICODYN, has been developed as a standalone windows GUI application, using C# programming language with Microsoft Visual Studio based on .NET framework 4.5 development environment. Accepted input data formats are HDF5, level 5 MAT and text files, containing recorded or generated time series spike signals data. SPICODYN processes such electrophysiological signals focusing on: spiking and bursting dynamics and functional-effective connectivity analysis...
October 7, 2017: Neuroinformatics
https://www.readbyqxmd.com/read/28980186/chips-an-extensible-toolbox-for-cellular-and-hemodynamic-two-photon-image-analysis
#2
Matthew J P Barrett, Kim David Ferrari, Jillian L Stobart, Martin Holub, Bruno Weber
No abstract text is available yet for this article.
October 4, 2017: Neuroinformatics
https://www.readbyqxmd.com/read/28975511/a-topological-representation-of-branching-neuronal-morphologies
#3
Lida Kanari, Paweł Dłotko, Martina Scolamiero, Ran Levi, Julian Shillcock, Kathryn Hess, Henry Markram
Many biological systems consist of branching structures that exhibit a wide variety of shapes. Our understanding of their systematic roles is hampered from the start by the lack of a fundamental means of standardizing the description of complex branching patterns, such as those of neuronal trees. To solve this problem, we have invented the Topological Morphology Descriptor (TMD), a method for encoding the spatial structure of any tree as a "barcode", a unique topological signature. As opposed to traditional morphometrics, the TMD couples the topology of the branches with their spatial extents by tracking their topological evolution in 3-dimensional space...
October 3, 2017: Neuroinformatics
https://www.readbyqxmd.com/read/28940176/touching-soma-segmentation-based-on-the-rayburst-sampling-algorithm
#4
Tianyu Hu, Qiufeng Xu, Wei Lv, Qian Liu
Neuronal soma segmentation is essential for morphology quantification analysis. Rapid advances in light microscope imaging techniques have generated such massive amounts of data that time-consuming manual methods cannot meet requirements for high throughput. However, touching soma segmentation is still a challenge for automatic segmentation methods. In this paper, we propose a soma segmentation method that combines the Rayburst sampling algorithm and ellipsoid fitting. The improved Rayburst sampling algorithm is used to detect the soma surface; the ellipsoid fitting method then refines jagged sampled soma surface to generate smooth ellipsoidal shapes for efficient analysis...
September 22, 2017: Neuroinformatics
https://www.readbyqxmd.com/read/28879641/3d-atlas-of-the-brain-head-and-neck-in-2953-pieces
#5
Wieslaw L Nowinski
No abstract text is available yet for this article.
September 6, 2017: Neuroinformatics
https://www.readbyqxmd.com/read/28849545/cas-cell-annotation-software-research-on-neuronal-tissue-has-never-been-so-transparent
#6
Karolina Nurzynska, Aleksandr Mikhalkin, Adam Piorkowski
CAS (Cell Annotation Software) is a novel tool for analysis of microscopic images and selection of the cell soma or nucleus, depending on the research objectives in medicine, biology, bioinformatics, etc. It replaces time-consuming and tiresome manual analysis of single images not only with automatic methods for object segmentation based on the Statistical Dominance Algorithm, but also semi-automatic tools for object selection within a marked region of interest. For each image, a broad set of object parameters is computed, including shape features and optical and topographic characteristics, thus giving additional insight into data...
August 29, 2017: Neuroinformatics
https://www.readbyqxmd.com/read/28812221/multimodal-neuroimaging-in-schizophrenia-description-and-dissemination
#7
C J Aine, H J Bockholt, J R Bustillo, J M Cañive, A Caprihan, C Gasparovic, F M Hanlon, J M Houck, R E Jung, J Lauriello, J Liu, A R Mayer, N I Perrone-Bizzozero, S Posse, J M Stephen, J A Turner, V P Clark, Vince D Calhoun
In this paper we describe an open-access collection of multimodal neuroimaging data in schizophrenia for release to the community. Data were acquired from approximately 100 patients with schizophrenia and 100 age-matched controls during rest as well as several task activation paradigms targeting a hierarchy of cognitive constructs. Neuroimaging data include structural MRI, functional MRI, diffusion MRI, MR spectroscopic imaging, and magnetoencephalography. For three of the hypothesis-driven projects, task activation paradigms were acquired on subsets of ~200 volunteers which examined a range of sensory and cognitive processes (e...
August 15, 2017: Neuroinformatics
https://www.readbyqxmd.com/read/28770487/integrating-the-allen-brain-institute-cell-types-database-into-automated-neuroscience-workflow
#8
David B Stockton, Fidel Santamaria
We developed software tools to download, extract features, and organize the Cell Types Database from the Allen Brain Institute (ABI) in order to integrate its whole cell patch clamp characterization data into the automated modeling/data analysis cycle. To expand the potential user base we employed both Python and MATLAB. The basic set of tools downloads selected raw data and extracts cell, sweep, and spike features, using ABI's feature extraction code. To facilitate data manipulation we added a tool to build a local specialized database of raw data plus extracted features...
August 2, 2017: Neuroinformatics
https://www.readbyqxmd.com/read/28748393/the-validate29-mri-based-multi-channel-atlas-of-the-squirrel-monkey-brain
#9
Kurt G Schilling, Yurui Gao, Iwona Stepniewska, Tung-Lin Wu, Feng Wang, Bennett A Landman, John C Gore, Li Min Chen, Adam W Anderson
We describe the development of the first digital atlas of the normal squirrel monkey brain and present the resulting product, VALiDATe29. The VALiDATe29 atlas is based on multiple types of magnetic resonance imaging (MRI) contrast acquired on 29 squirrel monkeys, and is created using unbiased, nonlinear registration techniques, resulting in a population-averaged stereotaxic coordinate system. The atlas consists of multiple anatomical templates (proton density, T1, and T2* weighted), diffusion MRI templates (fractional anisotropy and mean diffusivity), and ex vivo templates (fractional anisotropy and a structural MRI)...
July 26, 2017: Neuroinformatics
https://www.readbyqxmd.com/read/28710672/automated-3-d-detection-of-dendritic-spines-from-in-vivo-two-photon-image-stacks
#10
P K Singh, P Hernandez-Herrera, D Labate, M Papadakis
Despite the significant advances in the development of automated image analysis algorithms for the detection and extraction of neuronal structures, current software tools still have numerous limitations when it comes to the detection and analysis of dendritic spines. The problem is especially challenging in in vivo imaging, where the difficulty of extracting morphometric properties of spines is compounded by lower image resolution and contrast levels native to two-photon laser microscopy. To address this challenge, we introduce a new computational framework for the automated detection and quantitative analysis of dendritic spines in vivo multi-photon imaging...
July 14, 2017: Neuroinformatics
https://www.readbyqxmd.com/read/28748392/mobile-monitoring-of-traumatic-brain-injury-in-older-adults-challenges-and-opportunities
#11
EDITORIAL
Andrei Irimia, Susan Wei, Nanshu Lu, Constance M Moore, David N Kennedy
No abstract text is available yet for this article.
July 2017: Neuroinformatics
https://www.readbyqxmd.com/read/28608010/transcriptome-architecture-of-adult-mouse-brain-revealed-by-sparse-coding-of-genome-wide-in-situ-hybridization-images
#12
Yujie Li, Hanbo Chen, Xi Jiang, Xiang Li, Jinglei Lv, Meng Li, Hanchuan Peng, Joe Z Tsien, Tianming Liu
Highly differentiated brain structures with distinctly different phenotypes are closely correlated with the unique combination of gene expression patterns. Using a genome-wide in situ hybridization image dataset released by Allen Mouse Brain Atlas, we present a data-driven method of dictionary learning and sparse coding. Our results show that sparse coding can elucidate patterns of transcriptome organization of mouse brain. A collection of components obtained from sparse coding display robust region-specific molecular signatures corresponding to the canonical neuroanatomical subdivisions including fiber tracts and ventricular systems...
July 2017: Neuroinformatics
https://www.readbyqxmd.com/read/28555371/hierarchical-high-order-functional-connectivity-networks-and-selective-feature-fusion-for-mci-classification
#13
Xiaobo Chen, Han Zhang, Seong-Whan Lee, Dinggang Shen
Conventional Functional connectivity (FC) analysis focuses on characterizing the correlation between two brain regions, whereas the high-order FC can model the correlation between two brain region pairs. To reduce the number of brain region pairs, clustering is applied to group all the brain region pairs into a small number of clusters. Then, a high-order FC network can be constructed based on the clustering result. By varying the number of clusters, multiple high-order FC networks can be generated and the one with the best overall performance can be finally selected...
July 2017: Neuroinformatics
https://www.readbyqxmd.com/read/28447297/generating-neuron-geometries-for-detailed-three-dimensional-simulations-using-anamorph
#14
Konstantin Mörschel, Markus Breit, Gillian Queisser
Generating realistic and complex computational domains for numerical simulations is often a challenging task. In neuroscientific research, more and more one-dimensional morphology data is becoming publicly available through databases. This data, however, only contains point and diameter information not suitable for detailed three-dimensional simulations. In this paper, we present a novel framework, AnaMorph, that automatically generates water-tight surface meshes from one-dimensional point-diameter files. These surface triangulations can be used to simulate the electrical and biochemical behavior of the underlying cell...
July 2017: Neuroinformatics
https://www.readbyqxmd.com/read/28378263/improved-automatic-segmentation-of-white-matter-hyperintensities-in-mri-based-on-multilevel-lesion-features
#15
M Rincón, E Díaz-López, P Selnes, K Vegge, M Altmann, T Fladby, A Bjørnerud
Brain white matter hyperintensities (WMHs) are linked to increased risk of cerebrovascular and neurodegenerative diseases among the elderly. Consequently, detection and characterization of WMHs are of significant clinical importance. We propose a novel approach for WMH segmentation from multi-contrast MRI where both voxel-based and lesion-based information are used to improve overall performance in both volume-oriented and object-oriented metrics. Our segmentation method (AMOS-2D) consists of four stages following a "generate-and-test" approach: pre-processing, Gaussian white matter (WM) modelling, hierarchical multi-threshold WMH segmentation and object-based WMH filtering using support vector machines...
July 2017: Neuroinformatics
https://www.readbyqxmd.com/read/28417316/a-web-resource-for-levodopa-induced-dyskinesia-genetics-in-parkinson-s-disease
#16
Hagen Blankenburg, Marika Falla, Christine Schwienbacher, Giovanni Fabbrini, Alfredo Berardelli, Peter P Pramstaller, Francisco S Domingues
No abstract text is available yet for this article.
April 17, 2017: Neuroinformatics
https://www.readbyqxmd.com/read/28500465/the-information-sharing-statement-grows-some-teeth
#17
EDITORIAL
David N Kennedy
No abstract text is available yet for this article.
April 2017: Neuroinformatics
https://www.readbyqxmd.com/read/28316055/a-comparison-of-accelerated-and-non-accelerated-mri-scans-for-brain-volume-and-boundary-shift-integral-measures-of-volume-change-evidence-from-the-adni-dataset
#18
Emily N Manning, Kelvin K Leung, Jennifer M Nicholas, Ian B Malone, M Jorge Cardoso, Jonathan M Schott, Nick C Fox, Josephine Barnes
The aim of this study was to assess whether the use of accelerated MRI scans in place of non-accelerated scans influenced brain volume and atrophy rate measures in controls and subjects with mild cognitive impairment and Alzheimer's disease. We used data from 861 subjects at baseline, 573 subjects at 6 months and 384 subjects at 12 months from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We calculated whole-brain, ventricular and hippocampal atrophy rates using the k-means boundary shift integral (BSI)...
April 2017: Neuroinformatics
https://www.readbyqxmd.com/read/28210983/multi-view-ensemble-classification-of-brain-connectivity-images-for-neurodegeneration-type-discrimination
#19
Michele Fratello, Giuseppina Caiazzo, Francesca Trojsi, Antonio Russo, Gioacchino Tedeschi, Roberto Tagliaferri, Fabrizio Esposito
Brain connectivity analyses using voxels as features are not robust enough for single-patient classification because of the inter-subject anatomical and functional variability. To construct more robust features, voxels can be aggregated into clusters that are maximally coherent across subjects. Moreover, combining multi-modal neuroimaging and multi-view data integration techniques allows generating multiple independent connectivity features for the same patient. Structural and functional connectivity features were extracted from multi-modal MRI images with a clustering technique, and used for the multi-view classification of different phenotypes of neurodegeneration by an ensemble learning method (random forest)...
April 2017: Neuroinformatics
https://www.readbyqxmd.com/read/27928657/multi-domain-transfer-learning-for-early-diagnosis-of-alzheimer-s-disease
#20
Bo Cheng, Mingxia Liu, Dinggang Shen, Zuoyong Li, Daoqiang Zhang
Recently, transfer learning has been successfully applied in early diagnosis of Alzheimer's Disease (AD) based on multi-domain data. However, most of existing methods only use data from a single auxiliary domain, and thus cannot utilize the intrinsic useful correlation information from multiple domains. Accordingly, in this paper, we consider the joint learning of tasks in multi-auxiliary domains and the target domain, and propose a novel Multi-Domain Transfer Learning (MDTL) framework for early diagnosis of AD...
April 2017: Neuroinformatics
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