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Frontiers in Neuroinformatics

Ricardo A Pizarro, Xi Cheng, Alan Barnett, Herve Lemaitre, Beth A Verchinski, Aaron L Goldman, Ena Xiao, Qian Luo, Karen F Berman, Joseph H Callicott, Daniel R Weinberger, Venkata S Mattay
High-resolution three-dimensional magnetic resonance imaging (3D-MRI) is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming...
2016: Frontiers in Neuroinformatics
Marieke Musegaas, Bas J Dietzenbacher, Peter E M Borm
We consider the problem of computing the influence of a neuronal structure in a brain network. Abraham et al. (2006) computed this influence by using the Shapley value of a coalitional game corresponding to a directed network as a rating. Kötter et al. (2007) applied this rating to large-scale brain networks, in particular to the macaque visual cortex and the macaque prefrontal cortex. Our aim is to improve upon the above technique by measuring the importance of subgroups of neuronal structures in a different way...
2016: Frontiers in Neuroinformatics
Kristian Loewe, Sarah E Donohue, Mircea A Schoenfeld, Rudolf Kruse, Christian Borgelt
The functioning of the human brain relies on the interplay and integration of numerous individual units within a complex network. To identify network configurations characteristic of specific cognitive tasks or mental illnesses, functional connectomes can be constructed based on the assessment of synchronous fMRI activity at separate brain sites, and then analyzed using graph-theoretical concepts. In most previous studies, relatively coarse parcellations of the brain were used to define regions as graphical nodes...
2016: Frontiers in Neuroinformatics
Natalia Y Bilenko, Jack L Gallant
In this article we introduce Pyrcca, an open-source Python package for performing canonical correlation analysis (CCA). CCA is a multivariate analysis method for identifying relationships between sets of variables. Pyrcca supports CCA with or without regularization, and with or without linear, polynomial, or Gaussian kernelization. We first use an abstract example to describe Pyrcca functionality. We then demonstrate how Pyrcca can be used to analyze neuroimaging data. Specifically, we use Pyrcca to implement cross-subject comparison in a natural movie functional magnetic resonance imaging (fMRI) experiment by finding a data-driven set of functional response patterns that are similar across individuals...
2016: Frontiers in Neuroinformatics
Kathleen M Gates, Teague Henry, Doug Steinley, Damien A Fair
The past decade has been marked with a proliferation of community detection algorithms that aim to organize nodes (e.g., individuals, brain regions, variables) into modular structures that indicate subgroups, clusters, or communities. Motivated by the emergence of big data across many fields of inquiry, these methodological developments have primarily focused on the detection of communities of nodes from matrices that are very large. However, it remains unknown if the algorithms can reliably detect communities in smaller graph sizes (i...
2016: Frontiers in Neuroinformatics
Oliver Rübel, Max Dougherty, Prabhat, Peter Denes, David Conant, Edward F Chang, Kristofer Bouchard
Neuroscience continues to experience a tremendous growth in data; in terms of the volume and variety of data, the velocity at which data is acquired, and in turn the veracity of data. These challenges are a serious impediment to sharing of data, analyses, and tools within and across labs. Here, we introduce BRAINformat, a novel data standardization framework for the design and management of scientific data formats. The BRAINformat library defines application-independent design concepts and modules that together create a general framework for standardization of scientific data...
2016: Frontiers in Neuroinformatics
Kamal Shadi, Saideh Bakhshi, David A Gutman, Helen S Mayberg, Constantine Dovrolis
Recent progress in diffusion MRI and tractography algorithms as well as the launch of the Human Connectome Project (HCP) have provided brain research with an abundance of structural connectivity data. In this work, we describe and evaluate a method that can infer the structural brain network that interconnects a given set of Regions of Interest (ROIs) from probabilistic tractography data. The proposed method, referred to as Minimum Asymmetry Network Inference Algorithm (MANIA), does not determine the connectivity between two ROIs based on an arbitrary connectivity threshold...
2016: Frontiers in Neuroinformatics
Jian Zhang, Chong Li, Tianzi Jiang
Granger causality analysis, as a time series analysis technique derived from econometrics, has been applied in an ever-increasing number of publications in the field of neuroscience, including fMRI, EEG/MEG, and fNIRS. The present study mainly focuses on the validity of "signed path coefficient Granger causality," a Granger-causality-derived analysis method that has been adopted by many fMRI researches in the last few years. This method generally estimates the causality effect among the time series by an order-1 autoregression, and defines a positive or negative coefficient as an "excitatory" or "inhibitory" influence...
2016: Frontiers in Neuroinformatics
Gonzalo M Rojas, Jorge A Fuentes, Marcelo Gálvez
Multiple functional MRI (fMRI)-based functional connectivity networks were obtained by Yeo et al. (2011), and the visualization of these complex networks is a difficult task. Also, the combination of functional connectivity networks determined by fMRI with electroencephalography (EEG) data could be a very useful tool. Mobile devices are becoming increasingly common among users, and for this reason, we describe here two applications for Android and iOS mobile devices: one that shows in an interactive way the seven Yeo functional connectivity networks, and another application that shows the relative position of 10-20 EEG electrodes with Yeo's seven functional connectivity networks...
2016: Frontiers in Neuroinformatics
Nima Bigdely-Shamlo, Jeremy Cockfield, Scott Makeig, Thomas Rognon, Chris La Valle, Makoto Miyakoshi, Kay A Robbins
Real-world brain imaging by EEG requires accurate annotation of complex subject-environment interactions in event-rich tasks and paradigms. This paper describes the evolution of the Hierarchical Event Descriptor (HED) system for systematically describing both laboratory and real-world events. HED version 2, first described here, provides the semantic capability of describing a variety of subject and environmental states. HED descriptions can include stimulus presentation events on screen or in virtual worlds, experimental or spontaneous events occurring in the real world environment, and events experienced via one or multiple sensory modalities...
2016: Frontiers in Neuroinformatics
Sergio E Galindo, Pablo Toharia, Oscar D Robles, Luis Pastor
After decades of independent morphological and functional brain research, a key point in neuroscience nowadays is to understand the combined relationships between the structure of the brain and its components and their dynamics on multiple scales, ranging from circuits of neurons at micro or mesoscale to brain regions at macroscale. With such a goal in mind, there is a vast amount of research focusing on modeling and simulating activity within neuronal structures, and these simulations generate large and complex datasets which have to be analyzed in order to gain the desired insight...
2016: 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
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