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Advances in Astrocyte Computational Models: From Metabolic Reconstructions to Multi-omic Approaches. Front. Neuroinform. (IF 2.649) Pub Date : 2020-08-07 Janneth González,Andrés Pinzón,Andrea Angarita-Rodríguez,Andrés Felipe Aristizabal,George E Barreto,Cynthia Martín-Jiménez
The growing importance of astrocytes in the field of neuroscience has led to a greater number of computational models devoted to the study of astrocytic functions and their metabolic interactions with neurons. The modeling of these interactions demands a combined understanding of brain physiology and the development of computational frameworks based on genomic-scale reconstructions, system biology
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A Quantitative EEG Toolbox for the MNI Neuroinformatics Ecosystem: Normative SPM of EEG Source Spectra. Front. Neuroinform. (IF 2.649) Pub Date : 2020-08-07 Jorge Bosch-Bayard,Eduardo Aubert-Vazquez,Shawn T Brown,Christine Rogers,Gregory Kiar,Tristan Glatard,Lalet Scaria,Lidice Galan-Garcia,Maria L Bringas-Vega,Trinidad Virues-Alba,Armin Taheri,Samir Das,Cecile Madjar,Zia Mohaddes,Leigh MacIntyre,,Alan C Evans,Pedro A Valdes-Sosa
The Tomographic Quantitative Electroencephalography (qEEGt) toolbox is integrated with the Montreal Neurological Institute (MNI) Neuroinformatics Ecosystem as a docker into the Canadian Brain Imaging Research Platform (CBRAIN). qEEGt produces age-corrected normative Statistical Parametric Maps of EEG log source spectra testing compliance to a normative database. This toolbox was developed at the Cuban
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Sparse Logistic Regression With L 1/2 Penalty for Emotion Recognition in Electroencephalography Classification. Front. Neuroinform. (IF 2.649) Pub Date : 2020-08-07 Dong-Wei Chen,Rui Miao,Zhao-Yong Deng,Yue-Yue Lu,Yong Liang,Lan Huang
Emotion recognition based on electroencephalography (EEG) signals is a current focus in brain-computer interface research. However, the classification of EEG is difficult owing to large amounts of data and high levels of noise. Therefore, it is important to determine how to effectively extract features that include important information. Regularization, one of the effective methods for EEG signal processing
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Experimental Pipeline (Expipe): A Lightweight Data Management Platform to Simplify the Steps From Experiment to Data Analysis. Front. Neuroinform. (IF 2.649) Pub Date : 2020-07-24 Mikkel Elle Lepperød,Svenn-Arne Dragly,Alessio Paolo Buccino,Milad Hobbi Mobarhan,Anders Malthe-Sørenssen,Torkel Hafting,Marianne Fyhn
As experimental neuroscience is moving toward more integrative approaches, with a variety of acquisition techniques covering multiple spatiotemporal scales, data management is becoming increasingly challenging for neuroscience laboratories. Often, datasets are too large to practically be stored on a laptop or a workstation. The ability to query metadata collections without retrieving complete datasets
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Deciphering the Morphology of Motor Evoked Potentials. Front. Neuroinform. (IF 2.649) Pub Date : 2020-07-14 Jan Yperman,Thijs Becker,Dirk Valkenborg,Niels Hellings,Melissa Cambron,Dominique Dive,Guy Laureys,Veronica Popescu,Bart Van Wijmeersch,Liesbet M Peeters
Motor Evoked Potentials (MEPs) are used to monitor disability progression in multiple sclerosis (MS). Their morphology plays an important role in this process. Currently, however, there is no clear definition of what constitutes a normal or abnormal morphology. To address this, five experts independently labeled the morphology (normal or abnormal) of the same set of 1,000 MEPs. The intra- and inter-rater
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Pycabnn: Efficient and Extensible Software to Construct an Anatomical Basis for a Physiologically Realistic Neural Network Model. Front. Neuroinform. (IF 2.649) Pub Date : 2020-07-07 Ines Wichert,Sanghun Jee,Erik De Schutter,Sungho Hong
Physiologically detailed models of neural networks are an important tool for studying how biophysical mechanisms impact neural information processing. An important, fundamental step in constructing such a model is determining where neurons are placed and how they connect to each other, based on known anatomical properties and constraints given by experimental data. Here we present an open-source software
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A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features. Front. Neuroinform. (IF 2.649) Pub Date : 2020-06-11 Gloria Castellazzi,Maria Giovanna Cuzzoni,Matteo Cotta Ramusino,Daniele Martinelli,Federica Denaro,Antonio Ricciardi,Paolo Vitali,Nicoletta Anzalone,Sara Bernini,Fulvia Palesi,Elena Sinforiani,Alfredo Costa,Giuseppe Micieli,Egidio D'Angelo,Giovanni Magenes,Claudia A M Gandini Wheeler-Kingshott
Among dementia-like diseases, Alzheimer disease (AD) and vascular dementia (VD) are two of the most frequent. AD and VD may share multiple neurological symptoms that may lead to controversial diagnoses when using conventional clinical and MRI criteria. Therefore, other approaches are needed to overcome this issue. Machine learning (ML) combined with magnetic resonance imaging (MRI) has been shown to
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Editorial: Reproducibility and Rigour in Computational Neuroscience. Front. Neuroinform. (IF 2.649) Pub Date : 2020-05-27 Sharon M Crook,Andrew P Davison,Robert A McDougal,Hans Ekkehard Plesser
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NICA: A Novel Toolbox for Near-Infrared Spectroscopy Calculations and Analyses. Front. Neuroinform. (IF 2.649) Pub Date : 2020-04-29 Philipp Raggam,Günther Bauernfeind,Selina C Wriessnegger
Functional near-infrared spectroscopy (fNIRS) measures the functional activity of the cerebral cortex. The concentration changes of oxygenated (oxy-Hb) and deoxygenated hemoglobin (deoxy-Hb) can be detected and associated with activation of the cortex in the investigated area (neurovascular coupling). Recorded signals of hemodynamic responses may contain influences from physiological signals (systemic
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Sammba-MRI: A Library for Processing SmAll-MaMmal BrAin MRI Data in Python. Front. Neuroinform. (IF 2.649) Pub Date : 2020-04-23 Marina Celestine,Nachiket A Nadkarni,Clément M Garin,Salma Bougacha,Marc Dhenain
Small-mammal neuroimaging offers incredible opportunities to investigate structural and functional aspects of the brain. Many tools have been developed in the last decade to analyse small animal data, but current softwares are less mature than the available tools that process human brain data. The Python package Sammba-MRI (SmAll-MaMmal BrAin MRI in Python; http://sammba-mri.github.io) allows flexible
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A Multicenter Preclinical MRI Study: Definition of Rat Brain Relaxometry Reference Maps. Front. Neuroinform. (IF 2.649) Pub Date : 2020-04-21 Tristan Deruelle,Frank Kober,Adriana Perles-Barbacaru,Thierry Delzescaux,Vincent Noblet,Emmanuel L Barbier,Michel Dojat
Similarly to human population imaging, there are several well-founded motivations for animal population imaging, the most notable being the improvement of the validity of statistical results by pooling a sufficient number of animal data provided by different imaging centers. In this paper, we demonstrate the feasibility of such a multicenter animal study, sharing raw data from forty rats and processing
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OPETH: Open Source Solution for Real-Time Peri-Event Time Histogram Based on Open Ephys. Front. Neuroinform. (IF 2.649) Pub Date : 2020-04-17 András Széll,Sergio Martínez-Bellver,Panna Hegedüs,Balázs Hangya
Single cell electrophysiology remains one of the most widely used approaches of systems neuroscience. Decisions made by the experimenter during electrophysiology recording largely determine recording quality, duration of the project and value of the collected data. Therefore, online feedback aiding these decisions can lower monetary and time investment, and substantially speed up projects as well as
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Small Animal Shanoir (SAS) A Cloud-Based Solution for Managing Preclinical MR Brain Imaging Studies. Front. Neuroinform. (IF 2.649) Pub Date : 2020-04-16 Michael Kain,Marjolaine Bodin,Simon Loury,Yao Chi,Julien Louis,Mathieu Simon,Julien Lamy,Christian Barillot,Michel Dojat
Clinical multicenter imaging studies are frequent and rely on a wide range of existing tools for sharing data and processing pipelines. This is not the case for preclinical (small animal) studies. Animal population imaging is still in infancy, especially because a complete standardization and control of initial conditions in animal models across labs is still difficult and few studies aim at standardization
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Beware (Surprisingly Common) Left-Right Flips in Your MRI Data: An Efficient and Robust Method to Check MRI Dataset Consistency Using AFNI. Front. Neuroinform. (IF 2.649) Pub Date : 2020-04-14 Daniel R Glen,Paul A Taylor,Bradley R Buchsbaum,Robert W Cox,Richard C Reynolds
Knowing the difference between left and right is generally assumed throughout the brain MRI research community. However, we note widespread occurrences of left-right orientation errors in MRI open database repositories where volumes have contained systematic left-right flips between subject EPIs and anatomicals, due to having incorrect or missing file header information. Here we present a simple method
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Primary Categorizing and Masking Cerebral Small Vessel Disease Based on "Deep Learning System". Front. Neuroinform. (IF 2.649) Pub Date : 2020-03-31 Yunyun Duan,Wei Shan,Liying Liu,Qun Wang,Zhenzhou Wu,Pan Liu,Jiahao Ji,Yaou Liu,Kunlun He,Yongjun Wang
Objective To supply the attending doctor’s diagnosis of the persisting of cerebral small vessel disease and speed up their work effectively, we developed a “deep learning system (DLS)” for cerebral small vessel disease predication. The reliability and the disease area segmentation accuracy, of the proposed DLS, was also investigated. Methods A deep learning model based on the convolutional neural network
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Simulation of a Human-Scale Cerebellar Network Model on the K Computer. Front. Neuroinform. (IF 2.649) Pub Date : 2020-03-18 Hiroshi Yamaura,Jun Igarashi,Tadashi Yamazaki
Computer simulation of the human brain at an individual neuron resolution is an ultimate goal of computational neuroscience. The Japanese flagship supercomputer, K, provides unprecedented computational capability toward this goal. The cerebellum contains 80% of the neurons in the whole brain. Therefore, computer simulation of the human-scale cerebellum will be a challenge for modern supercomputers
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EEG Signal Reconstruction Using a Generative Adversarial Network With Wasserstein Distance and Temporal-Spatial-Frequency Loss. Front. Neuroinform. (IF 2.649) Pub Date : 2020-03-16 Tian-Jian Luo,Yachao Fan,Lifei Chen,Gongde Guo,Changle Zhou
Applications based on electroencephalography (EEG) signals suffer from the mutual contradiction of high classification performance vs. low cost. The nature of this contradiction makes EEG signal reconstruction with high sampling rates and sensitivity challenging. Conventional reconstruction algorithms lead to loss of the representative details of brain activity and suffer from remaining artifacts because
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MonkeyCBP: A Toolbox for Connectivity-Based Parcellation of Monkey Brain. Front. Neuroinform. (IF 2.649) Pub Date : 2020-03-10 Bin He,Zhengyi Yang,Lingzhong Fan,Bin Gao,Hai Li,Chuyang Ye,Bo You,Tianzi Jiang
Non-human primate models are widely used in studying the brain mechanism underlying brain development, cognitive functions, and psychiatric disorders. Neuroimaging techniques, such as magnetic resonance imaging, play an important role in the examinations of brain structure and functions. As an indispensable tool for brain imaging data analysis, brain atlases have been extensively investigated, and
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Automated Hierarchy Evaluation System of Large Vessel Occlusion in Acute Ischemia Stroke. Front. Neuroinform. (IF 2.649) Pub Date : 2020-03-09 Jia You,Anderson C O Tsang,Philip L H Yu,Eva L H Tsui,Pauline P S Woo,Carrie S M Lui,Gilberto K K Leung
Background The detection of large vessel occlusion (LVO) plays a critical role in the diagnosis and treatment of acute ischemic stroke (AIS). Identifying LVO in the pre-hospital setting or early stage of hospitalization would increase the patients’ chance of receiving appropriate reperfusion therapy and thereby improve neurological recovery. Methods To enable rapid identification of LVO, we established
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Efficient Communication in Distributed Simulations of Spiking Neuronal Networks With Gap Junctions. Front. Neuroinform. (IF 2.649) Pub Date : 2020-03-06 Jakob Jordan,Moritz Helias,Markus Diesmann,Susanne Kunkel
Investigating the dynamics and function of large-scale spiking neuronal networks with realistic numbers of synapses is made possible today by state-of-the-art simulation code that scales to the largest contemporary supercomputers. However, simulations that involve electrical interactions, also called gap junctions, besides chemical synapses scale only poorly due to a communication scheme that collects
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Finite Element Simulation of Ionic Electrodiffusion in Cellular Geometries Front. Neuroinform. (IF 2.649) Pub Date : 2020-03-05 Ada J. Ellingsrud; Andreas Solbrå; Gaute T. Einevoll; Geir Halnes; Marie E. Rognes
Mathematical models for excitable cells are commonly based on cable theory, which considers a homogenized domain and spatially constant ionic concentrations. Although such models provide valuable insight, the effect of altered ion concentrations or detailed cell morphology on the electrical potentials cannot be captured. In this paper, we discuss an alternative approach to detailed modeling of electrodiffusion
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Predicting Body Mass Index From Structural MRI Brain Images Using a Deep Convolutional Neural Network. Front. Neuroinform. (IF 2.649) Pub Date : 2020-03-02 Pál Vakli,Regina J Deák-Meszlényi,Tibor Auer,Zoltán Vidnyánszky
In recent years, deep learning (DL) has become more widespread in the fields of cognitive and clinical neuroimaging. Using deep neural network models to process neuroimaging data is an efficient method to classify brain disorders and identify individuals who are at increased risk of age-related cognitive decline and neurodegenerative disease. Here we investigated, for the first time, whether structural
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A Smart Region-Growing Algorithm for Single-Neuron Segmentation From Confocal and 2-Photon Datasets Front. Neuroinform. (IF 2.649) Pub Date : 2020-02-26 Alejandro Luis Callara; Chiara Magliaro; Arti Ahluwalia; Nicola Vanello
Accurately digitizing the brain at the micro-scale is crucial for investigating brain structure-function relationships and documenting morphological alterations due to neuropathies. Here we present a new Smart Region Growing algorithm (SmRG) for the segmentation of single neurons in their intricate 3D arrangement within the brain. Its Region Growing procedure is based on a homogeneity predicate determined
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Robust Monte-Carlo Simulations in Diffusion-MRI: Effect of the Substrate Complexity and Parameter Choice on the Reproducibility of Results Front. Neuroinform. (IF 2.649) Pub Date : 2020-02-20 Jonathan Rafael-Patino; David Romascano; Alonso Ramirez-Manzanares; Erick Jorge Canales-Rodríguez; Gabriel Girard; Jean-Philippe Thiran
Monte-Carlo Diffusion Simulations (MCDS) have been used extensively as a ground truth tool for the validation of microstructure models for Diffusion-Weighted MRI. However, methodological pitfalls in the design of the biomimicking geometrical configurations and the simulation parameters can lead to approximation biases. Such pitfalls affect the reliability of the estimated signal, as well as its validity
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A Standardized Protocol for Efficient and Reliable Quality Control of Brain Registration in Functional MRI Studies Front. Neuroinform. (IF 2.649) Pub Date : 2020-02-12 Yassine Benhajali; AmanPreet Badhwar; Helen Spiers; Sebastian Urchs; Jonathan Armoza; Thomas Ong; Daniel Pérusse; Pierre Bellec
Automatic alignment of brain anatomy in a standard space is a key step when processing magnetic resonance imaging for group analyses. Such brain registration is prone to failure, and the results are therefore typically reviewed visually to ensure quality. There is however no standard, validated protocol available to perform this visual quality control (QC). We propose here a standardized QC protocol
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Optimizing Computer-Brain Interface Parameters for Non-invasive Brain-to-Brain Interface. Front. Neuroinform. (IF 2.649) Pub Date : 2020-02-07 John LaRocco,Dong-Guk Paeng
A non-invasive, brain-to-brain interface (BBI) requires precision neuromodulation and high temporal resolution as well as portability to increase accessibility. A BBI is a combination of the brain-computer interface (BCI) and the computer-brain interface (CBI). The optimization of BCI parameters has been extensively researched, but CBI has not. Parameters taken from the BCI and CBI literature were
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Clustering of Brain Function Network Based on Attribute and Structural Information and Its Application in Brain Diseases. Front. Neuroinform. (IF 2.649) Pub Date : 2020-02-07 Xiaohong Cui,Jihai Xiao,Hao Guo,Bin Wang,Dandan Li,Yan Niu,Jie Xiang,Junjie Chen
At present, the diagnosis of brain disease is mainly based on the self-reported symptoms and clinical signs of the patient, which can easily lead to psychiatrists' bias. The purpose of this study is to develop a brain network clustering model to accurately identify brain diseases based on resting state functional magnetic resonance imaging (fMRI) in the absence of clinical information. We use cosine
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Developmental Designs and Adult Functions of Cortical Maps in Multiple Modalities: Perception, Attention, Navigation, Numbers, Streaming, Speech, and Cognition. Front. Neuroinform. (IF 2.649) Pub Date : 2020-02-06 Stephen Grossberg
This article unifies neural modeling results that illustrate several basic design principles and mechanisms that are used by advanced brains to develop cortical maps with multiple psychological functions. One principle concerns how brains use a strip map that simultaneously enables one feature to be represented throughout its extent, as well as an ordered array of another feature at different positions
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Stimulus Onset Hub: an Open-Source, Low Latency, and Opto-Isolated Trigger Box for Neuroscientific Research Replicability and Beyond. Front. Neuroinform. (IF 2.649) Pub Date : 2020-02-06 Charles E Davis,Jacob G Martin,Simon J Thorpe
Accurate stimulus onset timing is critical to almost all behavioral research. Auditory, visual, or manual response time stimulus onsets are typically sent through wires to various machines that record data such as: eye gaze positions, electroencephalography, stereo electroencephalography, and electrocorticography. These stimulus onsets are collated and analyzed according to experimental condition.
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Magia: Robust Automated Image Processing and Kinetic Modeling Toolbox for PET Neuroinformatics. Front. Neuroinform. (IF 2.649) Pub Date : 2020-02-04 Tomi Karjalainen,Jouni Tuisku,Severi Santavirta,Tatu Kantonen,Marco Bucci,Lauri Tuominen,Jussi Hirvonen,Jarmo Hietala,Juha O Rinne,Lauri Nummenmaa
Processing of positron emission tomography (PET) data typically involves manual work, causing inter-operator variance. Here we introduce the Magia toolbox that enables processing of brain PET data with minimal user intervention. We investigated the accuracy of Magia with four tracers: [11C]carfentanil, [11C]raclopride, [11C]MADAM, and [11C]PiB. We used data from 30 control subjects for each tracer
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Prediction of Pilot's Reaction Time Based on EEG Signals. Front. Neuroinform. (IF 2.649) Pub Date : 2020-01-24 Bartosz Binias,Dariusz Myszor,Henryk Palus,Krzysztof A Cyran
The main hypothesis of this work is that the time of delay in reaction to an unexpected event can be predicted on the basis of the brain activity recorded prior to that event. Such mental activity can be represented by electroencephalographic data. To test this hypothesis, we conducted a novel experiment involving 19 participants that took part in a 2-h long session of simulated aircraft flights. An
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Animal Functional Magnetic Resonance Imaging: Trends and Path Toward Standardization. Front. Neuroinform. (IF 2.649) Pub Date : 2020-01-22 Francesca Mandino,Domenic H Cerri,Clement M Garin,Milou Straathof,Geralda A F van Tilborg,M Mallar Chakravarty,Marc Dhenain,Rick M Dijkhuizen,Alessandro Gozzi,Andreas Hess,Shella D Keilholz,Jason P Lerch,Yen-Yu Ian Shih,Joanes Grandjean
Animal whole-brain functional magnetic resonance imaging (fMRI) provides a non-invasive window into brain activity. A collection of associated methods aims to replicate observations made in humans and to identify the mechanisms underlying the distributed neuronal activity in the healthy and disordered brain. Animal fMRI studies have developed rapidly over the past years, fueled by the development of
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An Automated Open-Source Workflow for Standards-Compliant Integration of Small Animal Magnetic Resonance Imaging Data. Front. Neuroinform. (IF 2.649) Pub Date : 2020-01-16 Horea-Ioan Ioanas,Markus Marks,Clément M Garin,Marc Dhenain,Mehmet Fatih Yanik,Markus Rudin
Large-scale research integration is contingent on seamless access to data in standardized formats. Standards enable researchers to understand external experiment structures, pool results, and apply homogeneous preprocessing and analysis workflows. Particularly, they facilitate these features without the need for numerous potentially confounding compatibility add-ons. In small animal magnetic resonance
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Unsupervised Cerebrovascular Segmentation of TOF-MRA Images Based on Deep Neural Network and Hidden Markov Random Field Model. Front. Neuroinform. (IF 2.649) Pub Date : 2020-01-10 Shengyu Fan,Yueyan Bian,Hao Chen,Yan Kang,Qi Yang,Tao Tan
Automated cerebrovascular segmentation of time-of-flight magnetic resonance angiography (TOF-MRA) images is an important technique, which can be used to diagnose abnormalities in the cerebrovascular system, such as vascular stenosis and malformation. Automated cerebrovascular segmentation can direct show the shape, direction and distribution of blood vessels. Although deep neural network (DNN)-based
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Analysis of Decision-Making Process Using Methods of Quantitative Electroencephalography and Machine Learning Tools. Front. Neuroinform. (IF 2.649) Pub Date : 2019-11-27 Grzegorz M Wojcik,Jolanta Masiak,Andrzej Kawiak,Lukasz Kwasniewicz,Piotr Schneider,Filip Postepski,Anna Gajos-Balinska
The electroencephalographic activity of particular brain areas during the decision making process is still little known. This paper presents results of experiments on the group of 30 patients with a wide range of psychiatric disorders and 41 members of the control group. All subjects were performing the Iowa Gambling Task that is often used for decision process investigations. The electroencephalographical
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ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data. Front. Neuroinform. (IF 2.649) Pub Date : 2019-11-27 Taban Eslami,Vahid Mirjalili,Alvis Fong,Angela R Laird,Fahad Saeed
Heterogeneous mental disorders such as Autism Spectrum Disorder (ASD) are notoriously difficult to diagnose, especially in children. The current psychiatric diagnostic process is based purely on the behavioral observation of symptomology (DSM-5/ICD-10) and may be prone to misdiagnosis. In order to move the field toward more quantitative diagnosis, we need advanced and scalable machine learning infrastructure
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Knowing What You Know in Brain Segmentation Using Bayesian Deep Neural Networks. Front. Neuroinform. (IF 2.649) Pub Date : 2019-11-22 Patrick McClure,Nao Rho,John A Lee,Jakub R Kaczmarzyk,Charles Y Zheng,Satrajit S Ghosh,Dylan M Nielson,Adam G Thomas,Peter Bandettini,Francisco Pereira
In this paper, we describe a Bayesian deep neural network (DNN) for predicting FreeSurfer segmentations of structural MRI volumes, in minutes rather than hours. The network was trained and evaluated on a large dataset (n = 11,480), obtained by combining data from more than a hundred different sites, and also evaluated on another completely held-out dataset (n = 418). The network was trained using a
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ShuTu: Open-Source Software for Efficient and Accurate Reconstruction of Dendritic Morphology. Front. Neuroinform. (IF 2.649) Pub Date : 2019-11-19 Dezhe Z Jin,Ting Zhao,David L Hunt,Rachel P Tillage,Ching-Lung Hsu,Nelson Spruston
Neurons perform computations by integrating inputs from thousands of synapses-mostly in the dendritic tree-to drive action potential firing in the axon. One fruitful approach to studying this process is to record from neurons using patch-clamp electrodes, fill the recorded neurons with a substance that allows subsequent staining, reconstruct the three-dimensional architectures of the dendrites, and
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The Dmipy Toolbox: Diffusion MRI Multi-Compartment Modeling and Microstructure Recovery Made Easy. Front. Neuroinform. (IF 2.649) Pub Date : 2019-11-05 Rutger H J Fick,Demian Wassermann,Rachid Deriche
Non-invasive estimation of brain microstructure features using diffusion MRI (dMRI)-known as Microstructure Imaging-has become an increasingly diverse and complicated field over the last decades. Multi-compartment (MC)-models, representing the measured diffusion signal as a linear combination of signal models of distinct tissue types, have been developed in many forms to estimate these features. However
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Exploring EEG Effective Connectivity Network in Estimating Influence of Color on Emotion and Memory. Front. Neuroinform. (IF 2.649) Pub Date : 2019-10-28 Meei Tyng Chai,Hafeez Ullah Amin,Lila Iznita Izhar,Mohamad Naufal Mohamad Saad,Mohammad Abdul Rahman,Aamir Saeed Malik,Tong Boon Tang
Color is a perceptual stimulus that has a significant impact on improving human emotion and memory. Studies have revealed that colored multimedia learning materials (MLMs) have a positive effect on learner's emotion and learning where it was assessed by subjective/objective measurements. This study aimed to quantitatively assess the influence of colored MLMs on emotion, cognitive processes during learning
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CoreNEURON : An Optimized Compute Engine for the NEURON Simulator. Front. Neuroinform. (IF 2.649) Pub Date : 2019-10-17 Pramod Kumbhar,Michael Hines,Jeremy Fouriaux,Aleksandr Ovcharenko,James King,Fabien Delalondre,Felix Schürmann
The NEURON simulator has been developed over the past three decades and is widely used by neuroscientists to model the electrical activity of neuronal networks. Large network simulation projects using NEURON have supercomputer allocations that individually measure in the millions of core hours. Supercomputer centers are transitioning to next generation architectures and the work accomplished per core
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odMLtables: A User-Friendly Approach for Managing Metadata of Neurophysiological Experiments. Front. Neuroinform. (IF 2.649) Pub Date : 2019-10-16 Julia Sprenger,Lyuba Zehl,Jana Pick,Michael Sonntag,Jan Grewe,Thomas Wachtler,Sonja Grün,Michael Denker
An essential aspect of scientific reproducibility is a coherent and complete acquisition of metadata along with the actual data of an experiment. The high degree of complexity and heterogeneity of neuroscience experiments requires a rigorous management of the associated metadata. The odML framework represents a solution to organize and store complex metadata digitally in a hierarchical format that
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A Performant Web-Based Visualization, Assessment, and Collaboration Tool for Multidimensional Biosignals. Front. Neuroinform. (IF 2.649) Pub Date : 2019-10-15 Maximilian Beier,Thomas Penzel,Dagmar Krefting
Biosignal-based research is often multidisciplinary and benefits greatly from multi-site collaboration. This requires appropriate tooling that supports collaboration, is easy to use, and is accessible. However, current software tools do not provide the necessary functionality, usability, and ubiquitous availability. The latter is particularly crucial in environments, such as hospitals, which often
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Recommendations for Processing Head CT Data. Front. Neuroinform. (IF 2.649) Pub Date : 2019-09-26 John Muschelli
Many research applications of neuroimaging use magnetic resonance imaging (MRI). As such, recommendations for image analysis and standardized imaging pipelines exist. Clinical imaging, however, relies heavily on X-ray computed tomography (CT) scans for diagnosis and prognosis. Currently, there is only one image processing pipeline for head CT, which focuses mainly on head CT data with lesions. We present
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PyParadigm-A Python Library to Build Screens in a Declarative Way. Front. Neuroinform. (IF 2.649) Pub Date : 2019-08-29 Felix G Knorr,Johannes Petzold,Michael Marxen
In experimental psychology, subjects are often confronted with computer-based experimental paradigms. Creating such paradigms can require a lot of effort. PyParadigm is a newly developed Python library to ease the development of such paradigms by employing a declarative approach to build user interfaces (UIs). Paradigm specifications in this approach requires much less code and training than in alternative
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Novel Technological Solutions for Assessment, Treatment, and Assistance in Mild Cognitive Impairment. Front. Neuroinform. (IF 2.649) Pub Date : 2019-08-29 Gianmaria Mancioppi,Laura Fiorini,Marco Timpano Sportiello,Filippo Cavallo
Alzheimer's disease, and dementia, represent a common cause of disability and one of the most relevant challenges in the health world. In addition, these conditions do not have, at moment, a pharmacological treatment that can stop the pathological progress. Mild cognitive impairment (MCI), defined as the borderline between normal aging and early dementia, represents a meaningful field of study because
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The LONI QC System: A Semi-Automated, Web-Based and Freely-Available Environment for the Comprehensive Quality Control of Neuroimaging Data. Front. Neuroinform. (IF 2.649) Pub Date : 2019-08-28 Hosung Kim,Andrei Irimia,Samuel M Hobel,Mher Pogosyan,Haoteng Tang,Petros Petrosyan,Rita Esquivel Castelo Blanco,Ben A Duffy,Lu Zhao,Karen L Crawford,Sook-Lei Liew,Kristi Clark,Meng Law,Pratik Mukherjee,Geoffrey T Manley,John D Van Horn,Arthur W Toga
Quantifying, controlling, and monitoring image quality is an essential prerequisite for ensuring the validity and reproducibility of many types of neuroimaging data analyses. Implementation of quality control (QC) procedures is the key to ensuring that neuroimaging data are of high-quality and their validity in the subsequent analyses. We introduce the QC system of the Laboratory of Neuro Imaging (LONI):
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Curated Model Development Using NEUROiD: A Web-Based NEUROmotor Integration and Design Platform. Front. Neuroinform. (IF 2.649) Pub Date : 2019-08-24 Raghu Sesha Iyengar,Madhav Vinodh Pithapuram,Avinash Kumar Singh,Mohan Raghavan
Decades of research on neuromotor circuits and systems has provided valuable information on neuronal control of movement. Computational models of several elements of the neuromotor system have been developed at various scales, from sub-cellular to system. While several small models abound, their structured integration is the key to building larger and more biologically realistic models which can predict
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Post-hoc Labeling of Arbitrary M/EEG Recordings for Data-Efficient Evaluation of Neural Decoding Methods. Front. Neuroinform. (IF 2.649) Pub Date : 2019-08-21 Sebastián Castaño-Candamil,Andreas Meinel,Michael Tangermann
Many cognitive, sensory and motor processes have correlates in oscillatory neural source activity, which is embedded as a subspace in the recorded brain signals. Decoding such processes from noisy magnetoencephalogram/electroencephalogram (M/EEG) signals usually requires data-driven analysis methods. The objective evaluation of such decoding algorithms on experimental raw signals, however, is a challenge:
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Revealing the Synaptic Hodology of Mammalian Neural Circuits With Multiscale Neurocartography. Front. Neuroinform. (IF 2.649) Pub Date : 2019-08-21 Erik B Bloss,David L Hunt
The functional features of neural circuits are determined by a combination of properties that range in scale from projections systems across the whole brain to molecular interactions at the synapse. The burgeoning field of neurocartography seeks to map these relevant features of brain structure-spanning a volume ∼20 orders of magnitude-to determine how neural circuits perform computations supporting
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Comparing Open-Source Toolboxes for Processing and Analysis of Spike and Local Field Potentials Data. Front. Neuroinform. (IF 2.649) Pub Date : 2019-08-17 Valentina A Unakafova,Alexander Gail
Analysis of spike and local field potential (LFP) data is an essential part of neuroscientific research. Today there exist many open-source toolboxes for spike and LFP data analysis implementing various functionality. Here we aim to provide a practical guidance for neuroscientists in the choice of an open-source toolbox best satisfying their needs. We overview major open-source toolboxes for spike
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A Comparison of Shallow and Deep Learning Methods for Predicting Cognitive Performance of Stroke Patients From MRI Lesion Images. Front. Neuroinform. (IF 2.649) Pub Date : 2019-08-17 Sucheta Chauhan,Lovekesh Vig,Michele De Filippo De Grazia,Maurizio Corbetta,Shandar Ahmad,Marco Zorzi
Stroke causes behavioral deficits in multiple cognitive domains and there is a growing interest in predicting patient performance from neuroimaging data using machine learning techniques. Here, we investigated a deep learning approach based on convolutional neural networks (CNNs) for predicting the severity of language disorder from 3D lesion images from magnetic resonance imaging (MRI) in a heterogeneous
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Asynchronous Branch-Parallel Simulation of Detailed Neuron Models. Front. Neuroinform. (IF 2.649) Pub Date : 2019-08-10 Bruno R C Magalhães,Thomas Sterling,Michael Hines,Felix Schürmann
Simulations of electrical activity of networks of morphologically detailed neuron models allow for a better understanding of the brain. State-of-the-art simulations describe the dynamics of ionic currents and biochemical processes within branching topological representations of the neurons. Acceleration of such simulation is possible in the weak scaling limit by modeling neurons as indivisible computation
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Wavelet Entropy-Based Inter-subject Associative Cortical Source Localization for Sensorimotor BCI. Front. Neuroinform. (IF 2.649) Pub Date : 2019-08-10 Simanto Saha,Md Shakhawat Hossain,Khawza Ahmed,Raqibul Mostafa,Leontios Hadjileontiadis,Ahsan Khandoker,Mathias Baumert
We propose event-related cortical sources estimation from subject-independent electroencephalography (EEG) recordings for motor imagery brain computer interface (BCI). By using wavelet-based maximum entropy on the mean (wMEM), task-specific EEG channels are selected to predict right hand and right foot sensorimotor tasks, employing common spatial pattern (CSP) and regularized common spatial pattern
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Corrigendum: Reconstruction and Simulation of a Scaffold Model of the Cerebellar Network. Front. Neuroinform. (IF 2.649) Pub Date : 2019-07-30 Stefano Casali,Elisa Marenzi,Chaitanya Medini,Claudia Casellato,Egidio D'Angelo
[This corrects the article DOI: 10.3389/fninf.2019.00037.].
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Corrigendum: Complex Dynamics in Simplified Neuronal Models: Reproducing Golgi Cell Electroresponsiveness. Front. Neuroinform. (IF 2.649) Pub Date : 2019-07-30 Alice Geminiani,Claudia Casellato,Francesca Locatelli,Francesca Prestori,Alessandra Pedrocchi,Egidio D'Angelo
[This corrects the article DOI: 10.3389/fninf.2018.00088.].
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An Automatic Estimation of Arterial Input Function Based on Multi-Stream 3D CNN. Front. Neuroinform. (IF 2.649) Pub Date : 2019-07-25 Shengyu Fan,Yueyan Bian,Erling Wang,Yan Kang,Danny J J Wang,Qi Yang,Xunming Ji
Arterial input function (AIF) is estimated from perfusion images as a basic curve for the following deconvolution process to calculate hemodynamic variables to evaluate vascular status of tissues. However, estimation of AIF is currently based on manual annotations with prior knowledge. We propose an automatic estimation of AIF in perfusion images based on a multi-stream 3D CNN, which combined spatial
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Detection of EEG K-Complexes Using Fractal Dimension of Time Frequency Images Technique Coupled With Undirected Graph Features. Front. Neuroinform. (IF 2.649) Pub Date : 2019-07-19 Wessam Al-Salman,Yan Li,Peng Wen
K-complexes identification is a challenging task in sleep research. The detection of k-complexes in electroencephalogram (EEG) signals based on visual inspection is time consuming, prone to errors, and requires well-trained knowledge. Many existing methods for k-complexes detection rely mainly on analyzing EEG signals in time and frequency domains. In this study, an efficient method is proposed to
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Ictal High-Frequency Oscillation for Lateralizing Patients With Suspected Bitemporal Epilepsy Using Wavelet Transform and Granger Causality Analysis. Front. Neuroinform. (IF 2.649) Pub Date : 2019-07-19 Tao Han,Zhexue Xu,Jialin Du,Qilin Zhou,Tao Yu,Chunyan Liu,Yuping Wang
Identifying lateralization of bilateral temporal lobe epilepsy (TLE) is a challenging issue; scalp electroencephalography (EEG) and routine band electrocorticography (ECoG) fail to reveal the epileptogenic focus for further temporal lobectomy treatment. High-frequency oscillations (HFOs) can be utilized as a biomarker for lateralizing the onset zone in suspected bitemporal epilepsy. Except subjective
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Parkinson's Disease Detection Using Isosurfaces-Based Features and Convolutional Neural Networks. Front. Neuroinform. (IF 2.649) Pub Date : 2019-07-18 Andrés Ortiz,Jorge Munilla,Manuel Martínez-Ibañez,Juan M Górriz,Javier Ramírez,Diego Salas-Gonzalez
Computer aided diagnosis systems based on brain imaging are an important tool to assist in the diagnosis of Parkinson's disease, whose ultimate goal is the detection by automatic recognizing of patterns that characterize the disease. In recent times Convolutional Neural Networks (CNN) have proved to be amazingly useful for that task. The drawback, however, is that 3D brain images contain a huge amount
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