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Frontiers in Neuroinformatics

Tarek Lajnef, Christian O'Reilly, Etienne Combrisson, Sahbi Chaibi, Jean-Baptiste Eichenlaub, Perrine M Ruby, Pierre-Emmanuel Aguera, Mounir Samet, Abdennaceur Kachouri, Sonia Frenette, Julie Carrier, Karim Jerbi
Sleep spindles and K-complexes are among the most prominent micro-events observed in electroencephalographic (EEG) recordings during sleep. These EEG microstructures are thought to be hallmarks of sleep-related cognitive processes. Although tedious and time-consuming, their identification and quantification is important for sleep studies in both healthy subjects and patients with sleep disorders. Therefore, procedures for automatic detection of spindles and K-complexes could provide valuable assistance to researchers and clinicians in the field...
2017: Frontiers in Neuroinformatics
Alejandro O Blenkmann, Holly N Phillips, Juan P Princich, James B Rowe, Tristan A Bekinschtein, Carlos H Muravchik, Silvia Kochen
The localization of intracranial electrodes is a fundamental step in the analysis of invasive electroencephalography (EEG) recordings in research and clinical practice. The conclusions reached from the analysis of these recordings rely on the accuracy of electrode localization in relationship to brain anatomy. However, currently available techniques for localizing electrodes from magnetic resonance (MR) and/or computerized tomography (CT) images are time consuming and/or limited to particular electrode types or shapes...
2017: Frontiers in Neuroinformatics
Zhuo Sun, Martijn van de Giessen, Boudewijn P F Lelieveldt, Marius Staring
Mild Cognitive Impairment (MCI) is an intermediate stage between healthy and Alzheimer's disease (AD). To enable early intervention it is important to identify the MCI subjects that will convert to AD in an early stage. In this paper, we provide a new method to distinguish between MCI patients that either convert to Alzheimer's Disease (MCIc) or remain stable (MCIs), using only longitudinal T1-weighted MRI. Currently, most longitudinal studies focus on volumetric comparison of a few anatomical structures, thereby ignoring more detailed development inside and outside those structures...
2017: Frontiers in Neuroinformatics
Sijia Wang, Daniel J Peterson, J C Gatenby, Wenbin Li, Thomas J Grabowski, Tara M Madhyastha
Correction of echo planar imaging (EPI)-induced distortions (called "unwarping") improves anatomical fidelity for diffusion magnetic resonance imaging (MRI) and functional imaging investigations. Commonly used unwarping methods require the acquisition of supplementary images during the scanning session. Alternatively, distortions can be corrected by nonlinear registration to a non-EPI acquired structural image. In this study, we compared reliability using two methods of unwarping: (1) nonlinear registration to a structural image using symmetric normalization (SyN) implemented in Advanced Normalization Tools (ANTs); and (2) unwarping using an acquired field map...
2017: Frontiers in Neuroinformatics
Tiina Manninen, Riikka Havela, Marja-Leena Linne
The scientific community across all disciplines faces the same challenges of ensuring accessibility, reproducibility, and efficient comparability of scientific results. Computational neuroscience is a rapidly developing field, where reproducibility and comparability of research results have gained increasing interest over the past years. As the number of computational models of brain functions is increasing, we chose to address reproducibility using four previously published computational models of astrocyte excitability as an example...
2017: Frontiers in Neuroinformatics
Chaoyi Qin, Zheng Tan, Yali Pan, Yanyan Li, Lin Wang, Liankun Ren, Wenjing Zhou, Liang Wang
Object: Subdural or deep intracerebral electrodes are essential in order to precisely localize epileptic zone in patients with medically intractable epilepsy. Precise localization of the implanted electrodes is critical to clinical diagnosing and treatment as well as for scientific studies. In this study, we sought to automatically and precisely extract intracranial electrodes using pre-operative MRI and post-operative CT images. Method: The subdural and depth intracranial electrodes were readily detected using clustering-based segmentation...
2017: Frontiers in Neuroinformatics
Ulf Großekathöfer, Nikolay V Manyakov, Vojkan Mihajlović, Gahan Pandina, Andrew Skalkin, Seth Ness, Abigail Bangerter, Matthew S Goodwin
A number of recent studies using accelerometer features as input to machine learning classifiers show promising results for automatically detecting stereotypical motor movements (SMM) in individuals with Autism Spectrum Disorder (ASD). However, replicating these results across different types of accelerometers and their position on the body still remains a challenge. We introduce a new set of features in this domain based on recurrence plot and quantification analyses that are orientation invariant and able to capture non-linear dynamics of SMM...
2017: Frontiers in Neuroinformatics
Nicholas J Matiasz, Justin Wood, Wei Wang, Alcino J Silva, William Hsu
Computers help neuroscientists to analyze experimental results by automating the application of statistics; however, computer-aided experiment planning is far less common, due to a lack of similar quantitative formalisms for systematically assessing evidence and uncertainty. While ontologies and other Semantic Web resources help neuroscientists to assimilate required domain knowledge, experiment planning requires not only ontological but also epistemological (e.g., methodological) information regarding how knowledge was obtained...
2017: 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
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