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Neuroinformatics

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https://www.readbyqxmd.com/read/28812221/multimodal-neuroimaging-in-schizophrenia-description-and-dissemination
#1
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
#2
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
#3
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/28748392/mobile-monitoring-of-traumatic-brain-injury-in-older-adults-challenges-and-opportunities
#4
EDITORIAL
Andrei Irimia, Susan Wei, Nanshu Lu, Constance M Moore, David N Kennedy
No abstract text is available yet for this article.
July 26, 2017: Neuroinformatics
https://www.readbyqxmd.com/read/28710672/automated-3-d-detection-of-dendritic-spines-from-in-vivo-two-photon-image-stacks
#5
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/28608010/transcriptome-architecture-of-adult-mouse-brain-revealed-by-sparse-coding-of-genome-wide-in-situ-hybridization-images
#6
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
#7
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
#8
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
#9
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
#10
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
#11
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
#12
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
#13
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
#14
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
https://www.readbyqxmd.com/read/28185058/ensemble-neuron-tracer-for-3d-neuron-reconstruction
#15
Ching-Wei Wang, Yu-Ching Lee, Hilmil Pradana, Zhi Zhou, Hanchuan Peng
Tracing of neuron paths is important in neuroscience. Recent studies have shown that it is possible to segment and reconstruct three-dimensional morphology of axons and dendrites using fully automatic neuron tracing methods. A specific tracer may be better than others for a specific dataset, but another tracer could perform better for some other datasets. Ensemble of learners is an effective way to improve learning accuracy in machine learning. We developed automatic ensemble neuron tracers, which consistently perform well on 57 datasets of 5 species collected from 7 laboratories worldwide...
February 9, 2017: Neuroinformatics
https://www.readbyqxmd.com/read/28132187/combining-a-patch-based-approach-with-a-non-rigid-registration-based-label-fusion-method-for-the-hippocampal-segmentation-in-alzheimer-s-disease
#16
Carlos Platero, M Carmen Tobar
We provide and evaluate an open-source software solution for automatically hippocampal segmentation from T1-weighted (T1w) magnetic resonance imaging (MRI). The method is applied for measuring the hippocampal volume, which allows discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls (NC). The method is based on a fast patch-based label fusion method, whose selected patches and their weights are calculated from a combination of similarity measures between patches using intensity-based distances and labeling-based distances...
January 28, 2017: Neuroinformatics
https://www.readbyqxmd.com/read/28063108/validation-of-18-f-fdg-pet-single-subject-optimized-spm-procedure-with-different-pet-scanners
#17
Luca Presotto, Tommaso Ballarini, Silvia Paola Caminiti, Valentino Bettinardi, Luigi Gianolli, Daniela Perani
(18)F-fluoro-deoxy-glucose Positron Emission Tomography (FDG-PET) allows early identification of neurodegeneration in dementia. The use of an optimized method based on the SPM software package highly improves diagnostic accuracy. However, the impact of different scanners for data acquisition on the SPM results and the effects of different pools of healthy subjects on the statistical comparison have not been investigated yet. Images from 144 AD patients acquired using six different PET scanners were analysed with an optimized single-subject SPM procedure to identify the typical AD hypometabolism pattern at single subject level...
January 6, 2017: Neuroinformatics
https://www.readbyqxmd.com/read/28058618/time-to-bring-single-neuron-modeling-into-3d
#18
EDITORIAL
Weiliang Chen, Erik De Schutter
No abstract text is available yet for this article.
January 5, 2017: Neuroinformatics
https://www.readbyqxmd.com/read/27873151/a-stereotactic-probabilistic-atlas-for-the-major-cerebral-arteries
#19
Tora Dunås, Anders Wåhlin, Khalid Ambarki, Laleh Zarrinkoob, Jan Malm, Anders Eklund
Improved whole brain angiographic and velocity-sensitive MRI is pushing the boundaries of noninvasively obtained cerebral vascular flow information. The complexity of the information contained in such datasets calls for automated algorithms and pipelines, thus reducing the need of manual analyses by trained radiologists. The objective of this work was to lay the foundation for such automated pipelining by constructing and evaluating a probabilistic atlas describing the shape and location of the major cerebral arteries...
January 2017: Neuroinformatics
https://www.readbyqxmd.com/read/27837401/collection-of-simulated-data-from-a-thalamocortical-network-model
#20
Helena Głąbska, Chaitanya Chintaluri, Daniel K Wójcik
A major challenge in experimental data analysis is the validation of analytical methods in a fully controlled scenario where the justification of the interpretation can be made directly and not just by plausibility. In some sciences, this could be a mathematical proof, yet biological systems usually do not satisfy assumptions of mathematical theorems. One solution is to use simulations of realistic models to generate ground truth data. In neuroscience, creating such data requires plausible models of neural activity, access to high performance computers, expertise and time to prepare and run the simulations, and to process the output...
January 2017: Neuroinformatics
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