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Frontiers in Neuroinformatics

Angel Lareo, Caroline G Forlim, Reynaldo D Pinto, Pablo Varona, Francisco de Borja Rodriguez
Closed-loop activity-dependent stimulation is a powerful methodology to assess information processing in biological systems. In this context, the development of novel protocols, their implementation in bioinformatics toolboxes and their application to different description levels open up a wide range of possibilities in the study of biological systems. We developed a methodology for studying biological signals representing them as temporal sequences of binary events. A specific sequence of these events (code) is chosen to deliver a predefined stimulation in a closed-loop manner...
2016: Frontiers in Neuroinformatics
Leah B Honor, Christian Haselgrove, Jean A Frazier, David N Kennedy
[This corrects the article on p. 34 in vol. 10, PMID: 27570508.].
2016: Frontiers in Neuroinformatics
Erinç Gökdeniz, Arzucan Özgür, Reşit Canbeyli
Identifying the relations among different regions of the brain is vital for a better understanding of how the brain functions. While a large number of studies have investigated the neuroanatomical and neurochemical connections among brain structures, their specific findings are found in publications scattered over a large number of years and different types of publications. Text mining techniques have provided the means to extract specific types of information from a large number of publications with the aim of presenting a larger, if not necessarily an exhaustive picture...
2016: Frontiers in Neuroinformatics
Robert Meyer, Klaus Obermayer
pypet (Python parameter exploration toolkit) is a new multi-platform Python toolkit for managing numerical simulations. Sampling the space of model parameters is a key aspect of simulations and numerical experiments. pypet is designed to allow easy and arbitrary sampling of trajectories through a parameter space beyond simple grid searches. pypet collects and stores both simulation parameters and results in a single HDF5 file. This collective storage allows fast and convenient loading of data for further analyses...
2016: Frontiers in Neuroinformatics
Diego A Angulo, Cyril Schneider, James H Oliver, Nathalie Charpak, Jose T Hernandez
Brain research typically requires large amounts of data from different sources, and often of different nature. The use of different software tools adapted to the nature of each data source can make research work cumbersome and time consuming. It follows that data is not often used to its fullest potential thus limiting exploratory analysis. This paper presents an ancillary software tool called BRAVIZ that integrates interactive visualization with real-time statistical analyses, facilitating access to multi-facetted neuroscience data and automating many cumbersome and error-prone tasks required to explore such data...
2016: Frontiers in Neuroinformatics
Aldo Zaimi, Tanguy Duval, Alicja Gasecka, Daniel Côté, Nikola Stikov, Julien Cohen-Adad
Segmenting axon and myelin from microscopic images is relevant for studying the peripheral and central nervous system and for validating new MRI techniques that aim at quantifying tissue microstructure. While several software packages have been proposed, their interface is sometimes limited and/or they are designed to work with a specific modality (e.g., scanning electron microscopy (SEM) only). Here we introduce AxonSeg, which allows to perform automatic axon and myelin segmentation on histology images, and to extract relevant morphometric information, such as axon diameter distribution, axon density and the myelin g-ratio...
2016: Frontiers in Neuroinformatics
Leah B Honor, Christian Haselgrove, Jean A Frazier, David N Kennedy
Data sharing and reuse, while widely accepted as good ideas, have been slow to catch on in any concrete and consistent way. One major hurdle within the scientific community has been the lack of widely accepted standards for citing that data, making it difficult to track usage and measure impact. Within the neuroimaging community, there is a need for a way to not only clearly identify and cite datasets, but also to derive new aggregate sets from multiple sources while clearly maintaining lines of attribution...
2016: Frontiers in Neuroinformatics
Eloy Roura, Nicolae Sarbu, Arnau Oliver, Sergi Valverde, Sandra González-Villà, Ricard Cervera, Núria Bargalló, Xavier Lladó
Brain magnetic resonance imaging provides detailed information which can be used to detect and segment white matter lesions (WML). In this work we propose an approach to automatically segment WML in Lupus patients by using T1w and fluid-attenuated inversion recovery (FLAIR) images. Lupus WML appear as small focal abnormal tissue observed as hyperintensities in the FLAIR images. The quantification of these WML is a key factor for the stratification of lupus patients and therefore both lesion detection and segmentation play an important role...
2016: Frontiers in Neuroinformatics
Robert D Vincent, Peter Neelin, Najmeh Khalili-Mahani, Andrew L Janke, Vladimir S Fonov, Steven M Robbins, Leila Baghdadi, Jason Lerch, John G Sled, Reza Adalat, David MacDonald, Alex P Zijdenbos, D Louis Collins, Alan C Evans
It is often useful that an imaging data format can afford rich metadata, be flexible, scale to very large file sizes, support multi-modal data, and have strong inbuilt mechanisms for data provenance. Beginning in 1992, MINC was developed as a system for flexible, self-documenting representation of neuroscientific imaging data with arbitrary orientation and dimensionality. The MINC system incorporates three broad components: a file format specification, a programming library, and a growing set of tools. In the early 2000's the MINC developers created MINC 2...
2016: Frontiers in Neuroinformatics
Antonio Ulloa, Barry Horwitz
A number of recent efforts have used large-scale, biologically realistic, neural models to help understand the neural basis for the patterns of activity observed in both resting state and task-related functional neural imaging data. An example of the former is The Virtual Brain (TVB) software platform, which allows one to apply large-scale neural modeling in a whole brain framework. TVB provides a set of structural connectomes of the human cerebral cortex, a collection of neural processing units for each connectome node, and various forward models that can convert simulated neural activity into a variety of functional brain imaging signals...
2016: Frontiers in Neuroinformatics
Philipp Weidel, Mikael Djurfeldt, Renato C Duarte, Abigail Morrison
In order to properly assess the function and computational properties of simulated neural systems, it is necessary to account for the nature of the stimuli that drive the system. However, providing stimuli that are rich and yet both reproducible and amenable to experimental manipulations is technically challenging, and even more so if a closed-loop scenario is required. In this work, we present a novel approach to solve this problem, connecting robotics and neural network simulators. We implement a middleware solution that bridges the Robotic Operating System (ROS) to the Multi-Simulator Coordinator (MUSIC)...
2016: Frontiers in Neuroinformatics
Jessica L Forbes, Regina E Y Kim, Jane S Paulsen, Hans J Johnson
The creation of high-quality medical imaging reference atlas datasets with consistent dense anatomical region labels is a challenging task. Reference atlases have many uses in medical image applications and are essential components of atlas-based segmentation tools commonly used for producing personalized anatomical measurements for individual subjects. The process of manual identification of anatomical regions by experts is regarded as a so-called gold standard; however, it is usually impractical because of the labor-intensive costs...
2016: Frontiers in Neuroinformatics
José V Manjón, Pierrick Coupé
The amount of medical image data produced in clinical and research settings is rapidly growing resulting in vast amount of data to analyze. Automatic and reliable quantitative analysis tools, including segmentation, allow to analyze brain development and to understand specific patterns of many neurological diseases. This field has recently experienced many advances with successful techniques based on non-linear warping and label fusion. In this work we present a novel and fully automatic pipeline for volumetric brain analysis based on multi-atlas label fusion technology that is able to provide accurate volumetric information at different levels of detail in a short time...
2016: Frontiers in Neuroinformatics
Nikolaas N Oosterhof, Andrew C Connolly, James V Haxby
Recent years have seen an increase in the popularity of multivariate pattern (MVP) analysis of functional magnetic resonance (fMRI) data, and, to a much lesser extent, magneto- and electro-encephalography (M/EEG) data. We present CoSMoMVPA, a lightweight MVPA (MVP analysis) toolbox implemented in the intersection of the Matlab and GNU Octave languages, that treats both fMRI and M/EEG data as first-class citizens. CoSMoMVPA supports all state-of-the-art MVP analysis techniques, including searchlight analyses, classification, correlations, representational similarity analysis, and the time generalization method...
2016: Frontiers in Neuroinformatics
William Grisham, Barbara Lom, Linda Lanyon, Raddy L Ramos
The scale of data being produced in neuroscience at present and in the future creates new and unheralded challenges, outstripping conventional ways of handling, considering, and analyzing data. As neuroinformatics enters into this big data era, a need for a highly trained and perhaps unique workforce is emerging. To determine the staffing needs created by the impending era of big data, a workshop (iNeuro Project) was convened November 13-14, 2014. Participants included data resource providers, bioinformatics/analytics trainers, computer scientists, library scientists, and neuroscience educators...
2016: Frontiers in Neuroinformatics
Lyuba Zehl, Florent Jaillet, Adrian Stoewer, Jan Grewe, Andrey Sobolev, Thomas Wachtler, Thomas G Brochier, Alexa Riehle, Michael Denker, Sonja Grün
To date, non-reproducibility of neurophysiological research is a matter of intense discussion in the scientific community. A crucial component to enhance reproducibility is to comprehensively collect and store metadata, that is, all information about the experiment, the data, and the applied preprocessing steps on the data, such that they can be accessed and shared in a consistent and simple manner. However, the complexity of experiments, the highly specialized analysis workflows and a lack of knowledge on how to make use of supporting software tools often overburden researchers to perform such a detailed documentation...
2016: Frontiers in Neuroinformatics
Haiyong Wu, Geng Chen, Yan Jin, Dinggang Shen, Pew-Thian Yap
Global tractography estimates brain connectivity by organizing signal-generating fiber segments in an optimal configuration that best describes the measured diffusion-weighted data, promising better stability than local greedy methods with respect to imaging noise. However, global tractography is computationally very demanding and requires computation times that are often prohibitive for clinical applications. We present here a reformulation of the global tractography algorithm for fast parallel implementation amendable to acceleration using multi-core CPUs and general-purpose GPUs...
2016: Frontiers in Neuroinformatics
Ruth Pauli, Alexander Bowring, Richard Reynolds, Gang Chen, Thomas E Nichols, Camille Maumet
No abstract text is available yet for this article.
2016: Frontiers in Neuroinformatics
Carmen A Lupascu, Annunziato Morabito, Elisabetta Merenda, Silvia Marinelli, Cristina Marchetti, Rosanna Migliore, Enrico Cherubini, Michele Migliore
Computational modeling of brain circuits requires the definition of many parameters that are difficult to determine from experimental findings. One way to help interpret these data is to fit them using a particular kinetic model. In this paper, we propose a general procedure to fit individual synaptic events recorded from voltage clamp experiments. Starting from any given model description (mod file) in the NEURON simulation environment, the procedure exploits user-defined constraints, dependencies, and rules for the parameters of the model to fit the time course of individual spontaneous synaptic events that are recorded experimentally...
2016: Frontiers in Neuroinformatics
Luz María Alonso-Valerdi
A brain-computer interface (BCI) aims to establish communication between the human brain and a computing system so as to enable the interaction between an individual and his environment without using the brain output pathways. Individuals control a BCI system by modulating their brain signals through mental tasks (e.g., motor imagery or mental calculation) or sensory stimulation (e.g., auditory, visual, or tactile). As users modulate their brain signals at different frequencies and at different levels, the appropriate characterization of those signals is necessary...
2016: Frontiers in Neuroinformatics
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