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Frontiers in Neuroinformatics

Weiliang Chen, Erik De Schutter
Stochastic, spatial reaction-diffusion simulations have been widely used in systems biology and computational neuroscience. However, the increasing scale and complexity of models and morphologies have exceeded the capacity of any serial implementation. This led to the development of parallel solutions that benefit from the boost in performance of modern supercomputers. In this paper, we describe an MPI-based, parallel operator-splitting implementation for stochastic spatial reaction-diffusion simulations with irregular tetrahedral meshes...
2017: Frontiers in Neuroinformatics
Francisco Naveros, Jesus A Garrido, Richard R Carrillo, Eduardo Ros, Niceto R Luque
Modeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity)...
2017: Frontiers in Neuroinformatics
Juan García-Prieto, Ricardo Bajo, Ernesto Pereda
Functional Connectivity has demonstrated to be a key concept for unraveling how the brain balances functional segregation and integration properties while processing information. This work presents a set of open-source tools that significantly increase computational efficiency of some well-known connectivity indices and Graph-Theory measures. PLV, PLI, ImC, and wPLI as Phase Synchronization measures, Mutual Information as an information theory based measure, and Generalized Synchronization indices are computed much more efficiently than prior open-source available implementations...
2017: Frontiers in Neuroinformatics
Hana Uhlirova, Peifang Tian, Kıvılcım Kılıç, Martin Thunemann, Vishnu B Sridhar, Hauke Bartsch, Anders M Dale, Anna Devor, Payam A Saisan
No abstract text is available yet for this article.
2017: Frontiers in Neuroinformatics
Marc Cavazza, Gabor Aranyi, Fred Charles
The ability to develop Brain-Computer Interfaces (BCI) to Intelligent Systems would offer new perspectives in terms of human supervision of complex Artificial Intelligence (AI) systems, as well as supporting new types of applications. In this article, we introduce a basic mechanism for the control of heuristic search through fNIRS-based BCI. The rationale is that heuristic search is not only a basic AI mechanism but also one still at the heart of many different AI systems. We investigate how users' mental disposition can be harnessed to influence the performance of heuristic search algorithm through a mechanism of precision-complexity exchange...
2017: Frontiers in Neuroinformatics
Tom Mingasson, Tanguy Duval, Nikola Stikov, Julien Cohen-Adad
HIGHLIGHTS AxonPacking: Open-source software for simulating white matter microstructure.Validation on a theoretical disk packing problem.Reproducible and stable for various densities and diameter distributions.Can be used to study interplay between myelin/fiber density and restricted fraction. Quantitative Magnetic Resonance Imaging (MRI) can provide parameters that describe white matter microstructure, such as the fiber volume fraction (FVF), the myelin volume fraction (MVF) or the axon volume fraction (AVF) via the fraction of restricted water (fr)...
2017: Frontiers in Neuroinformatics
Inge A Mulder, Artem Khmelinskii, Oleh Dzyubachyk, Sebastiaan de Jong, Nathalie Rieff, Marieke J H Wermer, Mathias Hoehn, Boudewijn P F Lelieveldt, Arn M J M van den Maagdenberg
Magnetic resonance imaging (MRI) has become increasingly important in ischemic stroke experiments in mice, especially because it enables longitudinal studies. Still, quantitative analysis of MRI data remains challenging mainly because segmentation of mouse brain lesions in MRI data heavily relies on time-consuming manual tracing and thresholding techniques. Therefore, in the present study, a fully automated approach was developed to analyze longitudinal MRI data for quantification of ischemic lesion volume progression in the mouse brain...
2017: Frontiers in Neuroinformatics
Ahmed Serag, Alastair G Wilkinson, Emma J Telford, Rozalia Pataky, Sarah A Sparrow, Devasuda Anblagan, Gillian Macnaught, Scott I Semple, James P Boardman
Quantitative volumes from brain magnetic resonance imaging (MRI) acquired across the life course may be useful for investigating long term effects of risk and resilience factors for brain development and healthy aging, and for understanding early life determinants of adult brain structure. Therefore, there is an increasing need for automated segmentation tools that can be applied to images acquired at different life stages. We developed an automatic segmentation method for human brain MRI, where a sliding window approach and a multi-class random forest classifier were applied to high-dimensional feature vectors for accurate segmentation...
2017: Frontiers in Neuroinformatics
David Alexander Dickie, Susan D Shenkin, Devasuda Anblagan, Juyoung Lee, Manuel Blesa Cabez, David Rodriguez, James P Boardman, Adam Waldman, Dominic E Job, Joanna M Wardlaw
Brain MRI atlases may be used to characterize brain structural changes across the life course. Atlases have important applications in research, e.g., as registration and segmentation targets to underpin image analysis in population imaging studies, and potentially in future in clinical practice, e.g., as templates for identifying brain structural changes out with normal limits, and increasingly for use in surgical planning. However, there are several caveats and limitations which must be considered before successfully applying brain MRI atlases to research and clinical problems...
2017: Frontiers in Neuroinformatics
Samir Das, Tristan Glatard, Christine Rogers, John Saigle, Santiago Paiva, Leigh MacIntyre, Mouna Safi-Harab, Marc-Etienne Rousseau, Jordan Stirling, Najmeh Khalili-Mahani, David MacFarlane, Penelope Kostopoulos, Pierre Rioux, Cecile Madjar, Xavier Lecours-Boucher, Sandeep Vanamala, Reza Adalat, Zia Mohaddes, Vladimir S Fonov, Sylvain Milot, Ilana Leppert, Clotilde Degroot, Thomas M Durcan, Tara Campbell, Jeremy Moreau, Alain Dagher, D Louis Collins, Jason Karamchandani, Amit Bar-Or, Edward A Fon, Rick Hoge, Sylvain Baillet, Guy Rouleau, Alan C Evans
Data sharing is becoming more of a requirement as technologies mature and as global research and communications diversify. As a result, researchers are looking for practical solutions, not only to enhance scientific collaborations, but also to acquire larger amounts of data, and to access specialized datasets. In many cases, the realities of data acquisition present a significant burden, therefore gaining access to public datasets allows for more robust analyses and broadly enriched data exploration. To answer this demand, the Montreal Neurological Institute has announced its commitment to Open Science, harnessing the power of making both clinical and research data available to the world (Owens, 2016a,b)...
2016: 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
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