journal
MENU ▼
Read by QxMD icon Read
search

Frontiers in Neuroinformatics

journal
https://www.readbyqxmd.com/read/29089883/resting-state-fmri-functional-connectivity-based-classification-using-a-convolutional-neural-network-architecture
#1
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
https://www.readbyqxmd.com/read/28983246/sleep-an-open-source-python-software-for-visualization-analysis-and-staging-of-sleep-data
#2
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
https://www.readbyqxmd.com/read/28979202/classification-of-fixed-point-network-dynamics-from-multiple-node-timeseries-data
#3
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
https://www.readbyqxmd.com/read/28970792/parkinson-disease-detection-from-speech-articulation-neuromechanics
#4
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
https://www.readbyqxmd.com/read/28943848/multimodal-discrimination-of-schizophrenia-using-hybrid-weighted-feature-concatenation-of-brain-functional-connectivity-and-anatomical-features-with-an-extreme-learning-machine
#5
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
https://www.readbyqxmd.com/read/28932191/mri-mouse-brain-data-of-ischemic-lesion-after-transient-middle-cerebral-artery-occlusion
#6
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
https://www.readbyqxmd.com/read/28919854/pranas-a-new-platform-for-retinal-analysis-and-simulation
#7
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 (https://pranas.inria.fr), 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
https://www.readbyqxmd.com/read/28912709/extending-xnat-platform-with-an-incremental-semantic-framework
#8
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
https://www.readbyqxmd.com/read/28912708/remodeling-pearson-s-correlation-for-functional-brain-network-estimation-and-autism-spectrum-disorder-identification
#9
Weikai Li, Zhengxia Wang, Limei Zhang, Lishan Qiao, Dinggang Shen
Functional brain network (FBN) has been becoming an increasingly important way to model the statistical dependence among neural time courses of brain, and provides effective imaging biomarkers for diagnosis of some neurological or psychological disorders. Currently, Pearson's Correlation (PC) is the simplest and most widely-used method in constructing FBNs. Despite its advantages in statistical meaning and calculated performance, the PC tends to result in a FBN with dense connections. Therefore, in practice, the PC-based FBN needs to be sparsified by removing weak (potential noisy) connections...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28868000/fiberweb-diffusion-visualization-and-processing-in-the-browser
#10
Louis-Philippe Ledoux, Felix C Morency, Martin Cousineau, Jean-Christophe Houde, Kevin Whittingstall, Maxime Descoteaux
Data visualization is one of the most important tool to explore the brain as we know it. In this work, we introduce a novel browser-based solution for medical imaging data visualization and interaction with diffusion-weighted magnetic resonance imaging (dMRI) and tractography data: Fiberweb. It uses a recent technology, WebGL, that has yet to be fully explored for medical imaging purposes. There are currently very few software tools that allow medical imaging data visualization in the browser, and none of these tools support efficient data interaction and processing, such as streamlines selection and real-time deterministic and probabilistic tractography (RTT)...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28860985/sorting-overlapping-spike-waveforms-from-electrode-and-tetrode-recordings
#11
Yasamin Mokri, Rodrigo F Salazar, Baldwin Goodell, Jonathan Baker, Charles M Gray, Shih-Cheng Yen
One of the outstanding problems in the sorting of neuronal spike trains is the resolution of overlapping spikes. Resolving these spikes can significantly improve a range of analyses, such as response variability, correlation, and latency. In this paper, we describe a partially automated method that is capable of resolving overlapping spikes. After constructing template waveforms for well-isolated and distinct single units, we generated pair-wise combinations of those templates at all possible time shifts from each other...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28860984/early-seizure-detection-by-applying-frequency-based-algorithm-derived-from-the-principal-component-analysis
#12
Jiseon Lee, Junhee Park, Sejung Yang, Hani Kim, Yun Seo Choi, Hyeon Jin Kim, Hyang Woon Lee, Byung-Uk Lee
The use of automatic electrical stimulation in response to early seizure detection has been introduced as a new treatment for intractable epilepsy. For the effective application of this method as a successful treatment, improving the accuracy of the early seizure detection is crucial. In this paper, we proposed the application of a frequency-based algorithm derived from principal component analysis (PCA), and demonstrated improved efficacy for early seizure detection in a pilocarpine-induced epilepsy rat model...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28848418/approximate-subject-specific-pseudo-mri-from-an-available-mri-dataset-for-meg-source-imaging
#13
Bakul Gohel, Sanghyun Lim, Min-Young Kim, Hyukchan Kwon, Kiwoong Kim
Computation of headmodel and sourcemodel from the subject's MRI scan is an essential step for source localization of magnetoencephalography (MEG) (or EEG) sensor signals. In the absence of a real MRI scan, pseudo MRI (i.e., associated headmodel and sourcemodel) is often approximated from an available standard MRI template or pool of MRI scans considering the subject's digitized head surface. In the present study, we approximated two types of pseudo MRI (i.e., associated headmodel and sourcemodel) using an available pool of MRI scans with the focus on MEG source imaging...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28785214/the-gridcat-a-toolbox-for-automated-analysis-of-human-grid-cell-codes-in-fmri
#14
Matthias Stangl, Jonathan Shine, Thomas Wolbers
Human functional magnetic resonance imaging (fMRI) studies examining the putative firing of grid cells (i.e., the grid code) suggest that this cellular mechanism supports not only spatial navigation, but also more abstract cognitive processes. Despite increased interest in this research, there remain relatively few human grid code studies, perhaps due to the complex analysis methods, which are not included in standard fMRI analysis packages. To overcome this, we have developed the Matlab-based open-source Grid Code Analysis Toolbox (GridCAT), which performs all analyses, from the estimation and fitting of the grid code in the general linear model (GLM), to the generation of grid code metrics and plots...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28769780/automated-functional-analysis-of-astrocytes-from-chronic-time-lapse-calcium-imaging-data
#15
Yinxue Wang, Guilai Shi, David J Miller, Yizhi Wang, Congchao Wang, Gerard Broussard, Yue Wang, Lin Tian, Guoqiang Yu
Recent discoveries that astrocytes exert proactive regulatory effects on neural information processing and that they are deeply involved in normal brain development and disease pathology have stimulated broad interest in understanding astrocyte functional roles in brain circuit. Measuring astrocyte functional status is now technically feasible, due to recent advances in modern microscopy and ultrasensitive cell-type specific genetically encoded Ca(2+) indicators for chronic imaging. However, there is a big gap between the capability of generating large dataset via calcium imaging and the availability of sophisticated analytical tools for decoding the astrocyte function...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28744212/personalized-offline-and-pseudo-online-bci-models-to-detect-pedaling-intent
#16
Marisol Rodríguez-Ugarte, Eduardo Iáñez, Mario Ortíz, Jose M Azorín
The aim of this work was to design a personalized BCI model to detect pedaling intention through EEG signals. The approach sought to select the best among many possible BCI models for each subject. The choice was between different processing windows, feature extraction algorithms and electrode configurations. Moreover, data was analyzed offline and pseudo-online (in a way suitable for real-time applications), with a preference for the latter case. A process for selecting the best BCI model was described in detail...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28713260/automated-detection-of-epileptic-biomarkers-in-resting-state-interictal-meg-data
#17
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
#18
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
#19
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
#20
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
journal
journal
42054
1
2
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"