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Frontiers in Neuroinformatics

László Szécsi, Ágota Kacsó, Günther Zeck, Péter Hantz
Light stimulation with precise and complex spatial and temporal modulation is demanded by a series of research fields like visual neuroscience, optogenetics, ophthalmology, and visual psychophysics. We developed a user-friendly and flexible stimulus generating framework (GEARS GPU-based Eye And Retina Stimulation Software), which offers access to GPU computing power, and allows interactive modification of stimulus parameters during experiments. Furthermore, it has built-in support for driving external equipment, as well as for synchronization tasks, via USB ports...
2017: Frontiers in Neuroinformatics
Sreenath P Kyathanahally, Yun Wang, Vince D Calhoun, Gopikrishna Deshpande
Previous work using simultaneously acquired electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data has shown that the slow temporal dynamics of resting state brain networks (RSNs), e.g., default mode network (DMN), visual network (VN), obtained from fMRI are correlated with smoothed and down sampled versions of various EEG features such as microstates and band-limited power envelopes. Therefore, even though the down sampled and smoothed envelope of EEG gamma band power is correlated with fMRI fluctuations in the RSNs, it does not mean that the electrical substrates of the RSNs fluctuate with periods <100 ms...
2017: Frontiers in Neuroinformatics
Claudio Román, Miguel Guevara, Ronald Valenzuela, Miguel Figueroa, Josselin Houenou, Delphine Duclap, Cyril Poupon, Jean-François Mangin, Pamela Guevara
Human brain connectivity is extremely complex and variable across subjects. While long association and projection bundles are stable and have been deeply studied, short association bundles present higher intersubject variability, and few studies have been carried out to adequately describe the structure, shape, and reproducibility of these bundles. However, their analysis is crucial to understand brain function and better characterize the human connectome. In this study, we propose an automatic method to identify reproducible short association bundles of the superficial white matter, based on intersubject hierarchical clustering...
2017: Frontiers in Neuroinformatics
Christoforos Christoforou, Timothy C Papadopoulos, Fofi Constantinidou, Maria Theodorou
The ability to anticipate the population-wide response of a target audience to a new movie or TV series, before its release, is critical to the film industry. Equally important is the ability to understand the underlying factors that drive or characterize viewer's decision to watch a movie. Traditional approaches (which involve pilot test-screenings, questionnaires, and focus groups) have reached a plateau in their ability to predict the population-wide responses to new movies. In this study, we develop a novel computational approach for extracting neurophysiological electroencephalography (EEG) and eye-gaze based metrics to predict the population-wide behavior of movie goers...
2017: Frontiers in Neuroinformatics
Andoni Mujika, Peter Leškovský, Roberto Álvarez, Miguel A Otaduy, Gorka Epelde
This paper focusses on the simulation of the neural network of the Caenorhabditis elegans living organism, and more specifically in the modeling of the stimuli applied within behavioral experiments and the stimuli that is generated in the interaction of the C. elegans with the environment. To the best of our knowledge, all efforts regarding stimuli modeling for the C. elegansare focused on a single type of stimulus, which is usually tested with a limited subnetwork of the C. elegansneural system. In this paper, we follow a different approach where we model a wide-range of different stimuli, with more flexible neural network configurations and simulations in mind...
2017: Frontiers in Neuroinformatics
Danilo Benozzo, Emanuele Olivetti, Paolo Avesani
Brain effective connectivity aims to detect causal interactions between distinct brain units and it is typically studied through the analysis of direct measurements of the neural activity, e.g., magneto/electroencephalography (M/EEG) signals. The literature on methods for causal inference is vast. It includes model-based methods in which a generative model of the data is assumed and model-free methods that directly infer causality from the probability distribution of the underlying stochastic process. Here, we firstly focus on the model-based methods developed from the Granger criterion of causality, which assumes the autoregressive model of the data...
2017: Frontiers in Neuroinformatics
James F Cavanagh, Arthur Napolitano, Christopher Wu, Abdullah Mueen
Electroencephalographic (EEG) recordings are thought to reflect the network-wide operations of canonical neural computations, making them a uniquely insightful measure of brain function. As evidence of these virtues, numerous candidate biomarkers of different psychiatric and neurological diseases have been advanced. Presumably, we would only need to apply powerful machine-learning methods to validate these ideas and provide novel clinical tools. Yet, the reality of this advancement is more complex: the scale of data required for robust and reliable identification of a clinical biomarker transcends the ability of any single laboratory...
2017: Frontiers in Neuroinformatics
Diego Castillo-Barnes, Ignacio Peis, Francisco J Martínez-Murcia, Fermín Segovia, Ignacio A Illán, Juan M Górriz, Javier Ramírez, Diego Salas-Gonzalez
A wide range of segmentation approaches assumes that intensity histograms extracted from magnetic resonance images (MRI) have a distribution for each brain tissue that can be modeled by a Gaussian distribution or a mixture of them. Nevertheless, intensity histograms of White Matter and Gray Matter are not symmetric and they exhibit heavy tails. In this work, we present a hidden Markov random field model with expectation maximization (EM-HMRF) modeling the components using the α-stable distribution. The proposed model is a generalization of the widely used EM-HMRF algorithm with Gaussian distributions...
2017: Frontiers in Neuroinformatics
Antonio Fernández-Caballero, Elena Navarro, Patricia Fernández-Sotos, Pascual González, Jorge J Ricarte, José M Latorre, Roberto Rodriguez-Jimenez
This perspective paper faces the future of alternative treatments that take advantage of a social and cognitive approach with regards to pharmacological therapy of auditory verbal hallucinations (AVH) in patients with schizophrenia. AVH are the perception of voices in the absence of auditory stimulation and represents a severe mental health symptom. Virtual/augmented reality (VR/AR) and brain computer interfaces (BCI) are technologies that are growing more and more in different medical and psychological applications...
2017: Frontiers in Neuroinformatics
Francisco J Martinez-Murcia, Juan M Górriz, Javier Ramírez, Ignacio A Illán, Fermín Segovia, Diego Castillo-Barnes, Diego Salas-Gonzalez
The rise of neuroimaging in research and clinical practice, together with the development of new machine learning techniques has strongly encouraged the Computer Aided Diagnosis (CAD) of different diseases and disorders. However, these algorithms are often tested in proprietary datasets to which the access is limited and, therefore, a direct comparison between CAD procedures is not possible. Furthermore, the sample size is often small for developing accurate machine learning methods. Multi-center initiatives are currently a very useful, although limited, tool in the recruitment of large populations and standardization of CAD evaluation...
2017: Frontiers in Neuroinformatics
Tara M Madhyastha, Natalie Koh, Trevor K M Day, Moises Hernández-Fernández, Austin Kelley, Daniel J Peterson, Sabreena Rajan, Karl A Woelfer, Jonathan Wolf, Thomas J Grabowski
The contribution of this paper is to identify and describe current best practices for using Amazon Web Services (AWS) to execute neuroimaging workflows "in the cloud." Neuroimaging offers a vast set of techniques by which to interrogate the structure and function of the living brain. However, many of the scientists for whom neuroimaging is an extremely important tool have limited training in parallel computation. At the same time, the field is experiencing a surge in computational demands, driven by a combination of data-sharing efforts, improvements in scanner technology that allow acquisition of images with higher image resolution, and by the desire to use statistical techniques that stress processing requirements...
2017: Frontiers in Neuroinformatics
Liberty S Hamilton, David L Chang, Morgan B Lee, Edward F Chang
In this article, we introduce img_pipe, our open source python package for preprocessing of imaging data for use in intracranial electrocorticography (ECoG) and intracranial stereo-EEG analyses. The process of electrode localization, labeling, and warping for use in ECoG currently varies widely across laboratories, and it is usually performed with custom, lab-specific code. This python package aims to provide a standardized interface for these procedures, as well as code to plot and display results on 3D cortical surface meshes...
2017: Frontiers in Neuroinformatics
Regina J Meszlényi, Krisztian Buza, Zoltán Vidnyánszky
Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups...
2017: Frontiers in Neuroinformatics
Etienne Combrisson, Raphael Vallat, Jean-Baptiste Eichenlaub, Christian O'Reilly, Tarek Lajnef, Aymeric Guillot, Perrine M Ruby, Karim Jerbi
We introduce Sleep, a new Python open-source graphical user interface (GUI) dedicated to visualization, scoring and analyses of sleep data. Among its most prominent features are: (1) Dynamic display of polysomnographic data, spectrogram, hypnogram and topographic maps with several customizable parameters, (2) Implementation of several automatic detection of sleep features such as spindles, K-complexes, slow waves, and rapid eye movements (REM), (3) Implementation of practical signal processing tools such as re-referencing or filtering, and (4) Display of main descriptive statistics including publication-ready tables and figures...
2017: Frontiers in Neuroinformatics
David Blaszka, Elischa Sanders, Jeffrey A Riffell, Eli Shlizerman
Fixed point networks are dynamic networks encoding stimuli via distinct output patterns. Although, such networks are common in neural systems, their structures are typically unknown or poorly characterized. It is thereby valuable to use a supervised approach for resolving how a network encodes inputs of interest and the superposition of those inputs from sampled multiple node time series. In this paper, we show that accomplishing such a task involves finding a low-dimensional state space from supervised noisy recordings...
2017: Frontiers in Neuroinformatics
Pedro Gómez-Vilda, Jiri Mekyska, José M Ferrández, Daniel Palacios-Alonso, Andrés Gómez-Rodellar, Victoria Rodellar-Biarge, Zoltan Galaz, Zdenek Smekal, Ilona Eliasova, Milena Kostalova, Irena Rektorova
Aim: The research described is intended to give a description of articulation dynamics as a correlate of the kinematic behavior of the jaw-tongue biomechanical system, encoded as a probability distribution of an absolute joint velocity. This distribution may be used in detecting and grading speech from patients affected by neurodegenerative illnesses, as Parkinson Disease. Hypothesis: The work hypothesis is that the probability density function of the absolute joint velocity includes information on the stability of phonation when applied to sustained vowels, as well as on fluency if applied to connected speech...
2017: Frontiers in Neuroinformatics
Muhammad Naveed Iqbal Qureshi, Jooyoung Oh, Dongrae Cho, Hang Joon Jo, Boreom Lee
Multimodal features of structural and functional magnetic resonance imaging (MRI) of the human brain can assist in the diagnosis of schizophrenia. We performed a classification study on age, sex, and handedness-matched subjects. The dataset we used is publicly available from the Center for Biomedical Research Excellence (COBRE) and it consists of two groups: patients with schizophrenia and healthy controls. We performed an independent component analysis and calculated global averaged functional connectivity-based features from the resting-state functional MRI data for all the cortical and subcortical anatomical parcellation...
2017: Frontiers in Neuroinformatics
Inge A Mulder, Artem Khmelinskii, Oleh Dzyubachyk, Sebastiaan de Jong, Marieke J H Wermer, Mathias Hoehn, Boudewijn P F Lelieveldt, Arn M J M van den Maagdenberg
No abstract text is available yet for this article.
2017: Frontiers in Neuroinformatics
Bruno Cessac, Pierre Kornprobst, Selim Kraria, Hassan Nasser, Daniela Pamplona, Geoffrey Portelli, Thierry Viéville
The retina encodes visual scenes by trains of action potentials that are sent to the brain via the optic nerve. In this paper, we describe a new free access user-end software allowing to better understand this coding. It is called PRANAS (, standing for Platform for Retinal ANalysis And Simulation. PRANAS targets neuroscientists and modelers by providing a unique set of retina-related tools. PRANAS integrates a retina simulator allowing large scale simulations while keeping a strong biological plausibility and a toolbox for the analysis of spike train population statistics...
2017: Frontiers in Neuroinformatics
Santiago Timón, Mariano Rincón, Rafael Martínez-Tomás
Informatics increases the yield from neuroscience due to improved data. Data sharing and accessibility enable joint efforts between different research groups, as well as replication studies, pivotal for progress in the field. Research data archiving solutions are evolving rapidly to address these necessities, however, distributed data integration is still difficult because of the need of explicit agreements for disparate data models. To address these problems, ontologies are widely used in biomedical research to obtain common vocabularies and logical descriptions, but its application may suffer from scalability issues, domain bias, and loss of low-level data access...
2017: Frontiers in Neuroinformatics
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