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Frontiers in Neuroinformatics

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https://www.readbyqxmd.com/read/28443014/the-topographical-mapping-in-drosophila-central-complex-network-and-its-signal-routing
#1
Po-Yen Chang, Ta-Shun Su, Chi-Tin Shih, Chung-Chuan Lo
Neural networks regulate brain functions by routing signals. Therefore, investigating the detailed organization of a neural circuit at the cellular levels is a crucial step toward understanding the neural mechanisms of brain functions. To study how a complicated neural circuit is organized, we analyzed recently published data on the neural circuit of the Drosophila central complex, a brain structure associated with a variety of functions including sensory integration and coordination of locomotion. We discovered that, except for a small number of "atypical" neuron types, the network structure formed by the identified 194 neuron types can be described by only a few simple mathematical rules...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28443013/pypes-workflows-for-processing-multimodal-neuroimaging-data
#2
Alexandre M Savio, Michael Schutte, Manuel Graña, Igor Yakushev
No abstract text is available yet for this article.
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28428750/applying-an-archetype-based-approach-to-electroencephalography-event-related-potential-experiments-in-the-eegbase-resource
#3
Václav Papež, Roman Mouček
PURPOSE: The purpose of this study is to investigate the feasibility of applying openEHR (an archetype-based approach for electronic health records representation) to modeling data stored in EEGBase, a portal for experimental electroencephalography/event-related potential (EEG/ERP) data management. The study evaluates re-usage of existing openEHR archetypes and proposes a set of new archetypes together with the openEHR templates covering the domain. The main goals of the study are to (i) link existing EEGBase data/metadata and openEHR archetype structures and (ii) propose a new openEHR archetype set describing the EEG/ERP domain since this set of archetypes currently does not exist in public repositories...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28424607/multivariate-analysis-of-18-f-dmfp-pet-data-to-assist-the-diagnosis-of-parkinsonism
#4
Fermín Segovia, Juan M Górriz, Javier Ramírez, Francisco J Martínez-Murcia, Johannes Levin, Madeleine Schuberth, Matthias Brendel, Axel Rominger, Kai Bötzel, Gaëtan Garraux, Christophe Phillips
An early and differential diagnosis of parkinsonian syndromes still remains a challenge mainly due to the similarity of their symptoms during the onset of the disease. Recently, (18)F-Desmethoxyfallypride (DMFP) has been suggested to increase the diagnostic precision as it is an effective radioligand that allows us to analyze post-synaptic dopamine D2/3 receptors. Nevertheless, the analysis of these data is still poorly covered and its use limited. In order to address this challenge, this paper shows a novel model to automatically distinguish idiopathic parkinsonism from non-idiopathic variants using DMFP data...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28408879/comparison-of-in-vivo-and-ex-vivo-mri-for-the-detection-of-structural-abnormalities-in-a-mouse-model-of-tauopathy
#5
Holly E Holmes, Nick M Powell, Da Ma, Ozama Ismail, Ian F Harrison, Jack A Wells, Niall Colgan, James M O'Callaghan, Ross A Johnson, Tracey K Murray, Zeshan Ahmed, Morten Heggenes, Alice Fisher, M Jorge Cardoso, Marc Modat, Michael J O'Neill, Emily C Collins, Elizabeth M C Fisher, Sébastien Ourselin, Mark F Lythgoe
With increasingly large numbers of mouse models of human disease dedicated to MRI studies, compromises between in vivo and ex vivo MRI must be fully understood in order to inform the choice of imaging methodology. We investigate the application of high resolution in vivo and ex vivo MRI, in combination with tensor-based morphometry (TBM), to uncover morphological differences in the rTg4510 mouse model of tauopathy. The rTg4510 mouse also offers a novel paradigm by which the overexpression of mutant tau can be regulated by the administration of doxycycline, providing us with a platform on which to investigate more subtle alterations in morphology with morphometry...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28396633/the-open-anatomy-browser-a-collaborative-web-based-viewer-for-interoperable-anatomy-atlases
#6
Michael Halle, Valentin Demeusy, Ron Kikinis
The Open Anatomy Browser (OABrowser) is an open source, web-based, zero-installation anatomy atlas viewer based on current web browser technologies and evolving anatomy atlas interoperability standards. OABrowser displays three-dimensional anatomical models, image cross-sections of labeled structures and source radiological imaging, and a text-based hierarchy of structures. The viewer includes novel collaborative tools: users can save bookmarks of atlas views for later access and exchange those bookmarks with other users, and dynamic shared views allow groups of users can participate in a collaborative interactive atlas viewing session...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28381997/reproducible-large-scale-neuroimaging-studies-with-the-openmole-workflow-management-system
#7
Jonathan Passerat-Palmbach, Romain Reuillon, Mathieu Leclaire, Antonios Makropoulos, Emma C Robinson, Sarah Parisot, Daniel Rueckert
OpenMOLE is a scientific workflow engine with a strong emphasis on workload distribution. Workflows are designed using a high level Domain Specific Language (DSL) built on top of Scala. It exposes natural parallelism constructs to easily delegate the workload resulting from a workflow to a wide range of distributed computing environments. OpenMOLE hides the complexity of designing complex experiments thanks to its DSL. Users can embed their own applications and scale their pipelines from a small prototype running on their desktop computer to a large-scale study harnessing distributed computing infrastructures, simply by changing a single line in the pipeline definition...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28360851/neuroimaging-genetics-and-clinical-data-sharing-in-python-using-the-cubicweb-framework
#8
Antoine Grigis, David Goyard, Robin Cherbonnier, Thomas Gareau, Dimitri Papadopoulos Orfanos, Nicolas Chauvat, Adrien Di Mascio, Gunter Schumann, Will Spooren, Declan Murphy, Vincent Frouin
In neurosciences or psychiatry, the emergence of large multi-center population imaging studies raises numerous technological challenges. From distributed data collection, across different institutions and countries, to final data publication service, one must handle the massive, heterogeneous, and complex data from genetics, imaging, demographics, or clinical scores. These data must be both efficiently obtained and downloadable. We present a Python solution, based on the CubicWeb open-source semantic framework, aimed at building population imaging study repositories...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28344551/construction-and-analysis-of-weighted-brain-networks-from-sice-for-the-study-of-alzheimer-s-disease
#9
Jorge Munilla, Andrés Ortiz, Juan M Górriz, Javier Ramírez
Alzheimer's Disease (AD) is the most common neurodegenerative disease in elderly people, and current drugs, unfortunately, do not represent yet a cure but only slow down its progression. This is explained, at least in part, because the understanding of the neurodegenerative process is still incomplete, being sometimes mistaken, particularly at the first steps of the illness, with the natural aging process. A better identification of how the functional activity deteriorates is thus crucial to develop new and more effective treatments...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28303099/meet-spinky-an-open-source-spindle-and-k-complex-detection-toolbox-validated-on-the-open-access-montreal-archive-of-sleep-studies-mass
#10
Tarek Lajnef, Christian O'Reilly, Etienne Combrisson, Sahbi Chaibi, Jean-Baptiste Eichenlaub, Perrine M Ruby, Pierre-Emmanuel Aguera, Mounir Samet, Abdennaceur Kachouri, Sonia Frenette, Julie Carrier, Karim Jerbi
Sleep spindles and K-complexes are among the most prominent micro-events observed in electroencephalographic (EEG) recordings during sleep. These EEG microstructures are thought to be hallmarks of sleep-related cognitive processes. Although tedious and time-consuming, their identification and quantification is important for sleep studies in both healthy subjects and patients with sleep disorders. Therefore, procedures for automatic detection of spindles and K-complexes could provide valuable assistance to researchers and clinicians in the field...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28303098/ielectrodes-a-comprehensive-open-source-toolbox-for-depth-and-subdural-grid-electrode-localization
#11
Alejandro O Blenkmann, Holly N Phillips, Juan P Princich, James B Rowe, Tristan A Bekinschtein, Carlos H Muravchik, Silvia Kochen
The localization of intracranial electrodes is a fundamental step in the analysis of invasive electroencephalography (EEG) recordings in research and clinical practice. The conclusions reached from the analysis of these recordings rely on the accuracy of electrode localization in relationship to brain anatomy. However, currently available techniques for localizing electrodes from magnetic resonance (MR) and/or computerized tomography (CT) images are time consuming and/or limited to particular electrode types or shapes...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28286479/detection-of-conversion-from-mild-cognitive-impairment-to-alzheimer-s-disease-using-longitudinal-brain-mri
#12
Zhuo Sun, Martijn van de Giessen, Boudewijn P F Lelieveldt, Marius Staring
Mild Cognitive Impairment (MCI) is an intermediate stage between healthy and Alzheimer's disease (AD). To enable early intervention it is important to identify the MCI subjects that will convert to AD in an early stage. In this paper, we provide a new method to distinguish between MCI patients that either convert to Alzheimer's Disease (MCIc) or remain stable (MCIs), using only longitudinal T1-weighted MRI. Currently, most longitudinal studies focus on volumetric comparison of a few anatomical structures, thereby ignoring more detailed development inside and outside those structures...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28270762/evaluation-of-field-map-and-nonlinear-registration-methods-for-correction-of-susceptibility-artifacts-in-diffusion-mri
#13
Sijia Wang, Daniel J Peterson, J C Gatenby, Wenbin Li, Thomas J Grabowski, Tara M Madhyastha
Correction of echo planar imaging (EPI)-induced distortions (called "unwarping") improves anatomical fidelity for diffusion magnetic resonance imaging (MRI) and functional imaging investigations. Commonly used unwarping methods require the acquisition of supplementary images during the scanning session. Alternatively, distortions can be corrected by nonlinear registration to a non-EPI acquired structural image. In this study, we compared reliability using two methods of unwarping: (1) nonlinear registration to a structural image using symmetric normalization (SyN) implemented in Advanced Normalization Tools (ANTs); and (2) unwarping using an acquired field map...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28270761/reproducibility-and-comparability-of-computational-models-for-astrocyte-calcium-excitability
#14
Tiina Manninen, Riikka Havela, Marja-Leena Linne
The scientific community across all disciplines faces the same challenges of ensuring accessibility, reproducibility, and efficient comparability of scientific results. Computational neuroscience is a rapidly developing field, where reproducibility and comparability of research results have gained increasing interest over the past years. As the number of computational models of brain functions is increasing, we chose to address reproducibility using four previously published computational models of astrocyte excitability as an example...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28261083/automatic-and-precise-localization-and-cortical-labeling-of-subdural-and-depth-intracranial-electrodes
#15
Chaoyi Qin, Zheng Tan, Yali Pan, Yanyan Li, Lin Wang, Liankun Ren, Wenjing Zhou, Liang Wang
Object: Subdural or deep intracerebral electrodes are essential in order to precisely localize epileptic zone in patients with medically intractable epilepsy. Precise localization of the implanted electrodes is critical to clinical diagnosing and treatment as well as for scientific studies. In this study, we sought to automatically and precisely extract intracranial electrodes using pre-operative MRI and post-operative CT images. Method: The subdural and depth intracranial electrodes were readily detected using clustering-based segmentation...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28261082/automated-detection-of-stereotypical-motor-movements-in-autism-spectrum-disorder-using-recurrence-quantification-analysis
#16
Ulf Großekathöfer, Nikolay V Manyakov, Vojkan Mihajlović, Gahan Pandina, Andrew Skalkin, Seth Ness, Abigail Bangerter, Matthew S Goodwin
A number of recent studies using accelerometer features as input to machine learning classifiers show promising results for automatically detecting stereotypical motor movements (SMM) in individuals with Autism Spectrum Disorder (ASD). However, replicating these results across different types of accelerometers and their position on the body still remains a challenge. We introduce a new set of features in this domain based on recurrence plot and quantification analyses that are orientation invariant and able to capture non-linear dynamics of SMM...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28243197/computer-aided-experiment-planning-toward-causal-discovery-in-neuroscience
#17
Nicholas J Matiasz, Justin Wood, Wei Wang, Alcino J Silva, William Hsu
Computers help neuroscientists to analyze experimental results by automating the application of statistics; however, computer-aided experiment planning is far less common, due to a lack of similar quantitative formalisms for systematically assessing evidence and uncertainty. While ontologies and other Semantic Web resources help neuroscientists to assimilate required domain knowledge, experiment planning requires not only ontological but also epistemological (e.g., methodological) information regarding how knowledge was obtained...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28239346/parallel-steps-large-scale-stochastic-spatial-reaction-diffusion-simulation-with-high-performance-computers
#18
Weiliang Chen, Erik De Schutter
Stochastic, spatial reaction-diffusion simulations have been widely used in systems biology and computational neuroscience. However, the increasing scale and complexity of models and morphologies have exceeded the capacity of any serial implementation. This led to the development of parallel solutions that benefit from the boost in performance of modern supercomputers. In this paper, we describe an MPI-based, parallel operator-splitting implementation for stochastic spatial reaction-diffusion simulations with irregular tetrahedral meshes...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28223930/event-and-time-driven-techniques-using-parallel-cpu-gpu-co-processing-for-spiking-neural-networks
#19
Francisco Naveros, Jesus A Garrido, Richard R Carrillo, Eduardo Ros, Niceto R Luque
Modeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity)...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28220071/efficient-computation-of-functional-brain-networks-toward-real-time-functional-connectivity
#20
Juan García-Prieto, Ricardo Bajo, Ernesto Pereda
Functional Connectivity has demonstrated to be a key concept for unraveling how the brain balances functional segregation and integration properties while processing information. This work presents a set of open-source tools that significantly increase computational efficiency of some well-known connectivity indices and Graph-Theory measures. PLV, PLI, ImC, and wPLI as Phase Synchronization measures, Mutual Information as an information theory based measure, and Generalized Synchronization indices are computed much more efficiently than prior open-source available implementations...
2017: Frontiers in Neuroinformatics
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