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Frontiers in Neuroinformatics

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https://www.readbyqxmd.com/read/28713260/automated-detection-of-epileptic-biomarkers-in-resting-state-interictal-meg-data
#1
Miguel C Soriano, Guiomar Niso, Jillian Clements, Silvia Ortín, Sira Carrasco, María Gudín, Claudio R Mirasso, Ernesto Pereda
Certain differences between brain networks of healthy and epilectic subjects have been reported even during the interictal activity, in which no epileptic seizures occur. Here, magnetoencephalography (MEG) data recorded in the resting state is used to discriminate between healthy subjects and patients with either idiopathic generalized epilepsy or frontal focal epilepsy. Signal features extracted from interictal periods without any epileptiform activity are used to train a machine learning algorithm to draw a diagnosis...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28706482/samuroi-a-python-based-software-tool-for-visualization-and-analysis-of-dynamic-time-series-imaging-at-multiple-spatial-scales
#2
Martin Rueckl, Stephen C Lenzi, Laura Moreno-Velasquez, Daniel Parthier, Dietmar Schmitz, Sten Ruediger, Friedrich W Johenning
The measurement of activity in vivo and in vitro has shifted from electrical to optical methods. While the indicators for imaging activity have improved significantly over the last decade, tools for analysing optical data have not kept pace. Most available analysis tools are limited in their flexibility and applicability to datasets obtained at different spatial scales. Here, we present SamuROI (Structured analysis of multiple user-defined ROIs), an open source Python-based analysis environment for imaging data...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28701946/the-nest-dry-run-mode-efficient-dynamic-analysis-of-neuronal-network-simulation-code
#3
Susanne Kunkel, Wolfram Schenck
NEST is a simulator for spiking neuronal networks that commits to a general purpose approach: It allows for high flexibility in the design of network models, and its applications range from small-scale simulations on laptops to brain-scale simulations on supercomputers. Hence, developers need to test their code for various use cases and ensure that changes to code do not impair scalability. However, running a full set of benchmarks on a supercomputer takes up precious compute-time resources and can entail long queuing times...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28694776/visualization-interaction-and-tractometry-dealing-with-millions-of-streamlines-from-diffusion-mri-tractography
#4
Francois Rheault, Jean-Christophe Houde, Maxime Descoteaux
Recently proposed tractography and connectomics approaches often require a very large number of streamlines, in the order of millions. Generating, storing and interacting with these datasets is currently quite difficult, since they require a lot of space in memory and processing time. Compression is a common approach to reduce data size. Recently such an approach has been proposed consisting in removing collinear points in the streamlines. Removing points from streamlines results in files that cannot be robustly post-processed and interacted with existing tools, which are for the most part point-based...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28690513/feature-selection-methods-for-zero-shot-learning-of-neural-activity
#5
Carlos A Caceres, Matthew J Roos, Kyle M Rupp, Griffin Milsap, Nathan E Crone, Michael E Wolmetz, Christopher R Ratto
Dimensionality poses a serious challenge when making predictions from human neuroimaging data. Across imaging modalities, large pools of potential neural features (e.g., responses from particular voxels, electrodes, and temporal windows) have to be related to typically limited sets of stimuli and samples. In recent years, zero-shot prediction models have been introduced for mapping between neural signals and semantic attributes, which allows for classification of stimulus classes not explicitly included in the training set...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28690512/bioinspired-architecture-selection-for-multitask-learning
#6
Andrés Bueno-Crespo, Rosa-María Menchón-Lara, Raquel Martínez-España, José-Luis Sancho-Gómez
Faced with a new concept to learn, our brain does not work in isolation. It uses all previously learned knowledge. In addition, the brain is able to isolate the knowledge that does not benefit us, and to use what is actually useful. In machine learning, we do not usually benefit from the knowledge of other learned tasks. However, there is a methodology called Multitask Learning (MTL), which is based on the idea that learning a task along with other related tasks produces a transfer of information between them, what can be advantageous for learning the first one...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28690511/neurotessmesh-a-tool-for-the-generation-and-visualization-of-neuron-meshes-and-adaptive-on-the-fly-refinement
#7
Juan J Garcia-Cantero, Juan P Brito, Susana Mata, Sofia Bayona, Luis Pastor
Gaining a better understanding of the human brain continues to be one of the greatest challenges for science, largely because of the overwhelming complexity of the brain and the difficulty of analyzing the features and behavior of dense neural networks. Regarding analysis, 3D visualization has proven to be a useful tool for the evaluation of complex systems. However, the large number of neurons in non-trivial circuits, together with their intricate geometry, makes the visualization of a neuronal scenario an extremely challenging computational problem...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28620293/a-manual-segmentation-tool-for-three-dimensional-neuron-datasets
#8
Chiara Magliaro, Alejandro L Callara, Nicola Vanello, Arti Ahluwalia
To date, automated or semi-automated software and algorithms for segmentation of neurons from three-dimensional imaging datasets have had limited success. The gold standard for neural segmentation is considered to be the manual isolation performed by an expert. To facilitate the manual isolation of complex objects from image stacks, such as neurons in their native arrangement within the brain, a new Manual Segmentation Tool (ManSegTool) has been developed. ManSegTool allows user to load an image stack, scroll down the images and to manually draw the structures of interest stack-by-stack...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28616008/corrigendum-toolconnect-a-functional-connectivity-toolbox-for-in-vitro-networks
#9
Vito P Pastore, Daniele Poli, Aleksandar Godjoski, Sergio Martinoia, Paolo Massobrio
[This corrects the article on p. 13 in vol. 10, PMID: 27065841.].
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28611620/atpp-a-pipeline-for-automatic-tractography-based-brain-parcellation
#10
Hai Li, Lingzhong Fan, Junjie Zhuo, Jiaojian Wang, Yu Zhang, Zhengyi Yang, Tianzi Jiang
There is a longstanding effort to parcellate brain into areas based on micro-structural, macro-structural, or connectional features, forming various brain atlases. Among them, connectivity-based parcellation gains much emphasis, especially with the considerable progress of multimodal magnetic resonance imaging in the past two decades. The Brainnetome Atlas published recently is such an atlas that follows the framework of connectivity-based parcellation. However, in the construction of the atlas, the deluge of high resolution multimodal MRI data and time-consuming computation poses challenges and there is still short of publically available tools dedicated to parcellation...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28596730/integration-of-continuous-time-dynamics-in-a-spiking-neural-network-simulator
#11
Jan Hahne, David Dahmen, Jannis Schuecker, Andreas Frommer, Matthias Bolten, Moritz Helias, Markus Diesmann
Contemporary modeling approaches to the dynamics of neural networks include two important classes of models: biologically grounded spiking neuron models and functionally inspired rate-based units. We present a unified simulation framework that supports the combination of the two for multi-scale modeling, enables the quantitative validation of mean-field approaches by spiking network simulations, and provides an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28559808/constructing-neuronal-network-models-in-massively-parallel-environments
#12
Tammo Ippen, Jochen M Eppler, Hans E Plesser, Markus Diesmann
Recent advances in the development of data structures to represent spiking neuron network models enable us to exploit the complete memory of petascale computers for a single brain-scale network simulation. In this work, we investigate how well we can exploit the computing power of such supercomputers for the creation of neuronal networks. Using an established benchmark, we divide the runtime of simulation code into the phase of network construction and the phase during which the dynamical state is advanced in time...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28522970/automatic-optimization-of-the-computation-graph-in-the-nengo-neural-network-simulator
#13
Jan Gosmann, Chris Eliasmith
One critical factor limiting the size of neural cognitive models is the time required to simulate such models. To reduce simulation time, specialized hardware is often used. However, such hardware can be costly, not readily available, or require specialized software implementations that are difficult to maintain. Here, we present an algorithm that optimizes the computational graph of the Nengo neural network simulator, allowing simulations to run more quickly on commodity hardware. This is achieved by merging identical operations into single operations and restructuring the accessed data in larger blocks of sequential memory...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28507515/reusable-client-side-javascript-modules-for-immersive-web-based-real-time-collaborative-neuroimage-visualization
#14
Jorge L Bernal-Rusiel, Nicolas Rannou, Randy L Gollub, Steve Pieper, Shawn Murphy, Richard Robertson, Patricia E Grant, Rudolph Pienaar
In this paper we present a web-based software solution to the problem of implementing real-time collaborative neuroimage visualization. In both clinical and research settings, simple and powerful access to imaging technologies across multiple devices is becoming increasingly useful. Prior technical solutions have used a server-side rendering and push-to-client model wherein only the server has the full image dataset. We propose a rich client solution in which each client has all the data and uses the Google Drive Realtime API for state synchronization...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28496407/unsupervised-idealization-of-ion-channel-recordings-by-minimum-description-length-application-to-human-piezo1-channels
#15
Radhakrishnan Gnanasambandam, Morten S Nielsen, Christopher Nicolai, Frederick Sachs, Johannes P Hofgaard, Jakob K Dreyer
Researchers can investigate the mechanistic and molecular basis of many physiological phenomena in cells by analyzing the fundamental properties of single ion channels. These analyses entail recording single channel currents and measuring current amplitudes and transition rates between conductance states. Since most electrophysiological recordings contain noise, the data analysis can proceed by idealizing the recordings to isolate the true currents from the noise. This de-noising can be accomplished with threshold crossing algorithms and Hidden Markov Models, but such procedures generally depend on inputs and supervision by the user, thus requiring some prior knowledge of underlying processes...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28496406/neural-correlates-of-phrase-rhythm-an-eeg-study-of-bipartite-vs-rondo-sonata-form
#16
Arturo Martínez-Rodrigo, Alicia Fernández-Sotos, José Miguel Latorre, José Moncho-Bogani, Antonio Fernández-Caballero
This paper introduces the neural correlates of phrase rhythm. In short, phrase rhythm is the rhythmic aspect of phrase construction and the relationships between phrases. For the sake of establishing the neural correlates, a musical experiment has been designed to induce music-evoked stimuli related to phrase rhythm. Brain activity is monitored through electroencephalography (EEG) by using a brain-computer interface. The power spectral value of each EEG channel is estimated to obtain how power variance distributes as a function of frequency...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28491032/topological-filtering-of-dynamic-functional-brain-networks-unfolds-informative-chronnectomics-a-novel-data-driven-thresholding-scheme-based-on-orthogonal-minimal-spanning-trees-omsts
#17
Stavros I Dimitriadis, Christos Salis, Ioannis Tarnanas, David E Linden
The human brain is a large-scale system of functionally connected brain regions. This system can be modeled as a network, or graph, by dividing the brain into a set of regions, or "nodes," and quantifying the strength of the connections between nodes, or "edges," as the temporal correlation in their patterns of activity. Network analysis, a part of graph theory, provides a set of summary statistics that can be used to describe complex brain networks in a meaningful way. The large-scale organization of the brain has features of complex networks that can be quantified using network measures from graph theory...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28469570/a-framework-for-collaborative-curation-of-neuroscientific-literature
#18
Christian O'Reilly, Elisabetta Iavarone, Sean L Hill
Large models of complex neuronal circuits require specifying numerous parameters, with values that often need to be extracted from the literature, a tedious and error-prone process. To help establishing shareable curated corpora of annotations, we have developed a literature curation framework comprising an annotation format, a Python API (NeuroAnnotation Toolbox; NAT), and a user-friendly graphical interface (NeuroCurator). This framework allows the systematic annotation of relevant statements and model parameters...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28443014/the-topographical-mapping-in-drosophila-central-complex-network-and-its-signal-routing
#19
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
#20
Alexandre M Savio, Michael Schutte, Manuel Graña, Igor Yakushev
No abstract text is available yet for this article.
2017: Frontiers in Neuroinformatics
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