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Correction to: A Standards Organization for Open and FAIR Neuroscience: the International Neuroinformatics Coordinating Facility Neuroinformatics (IF 3.3) Pub Date : 2021-04-13 Mathew Birdsall Abrams, Jan G. Bjaalie, Samir Das, Gary F. Egan, Satrajit S. Ghosh, Wojtek J. Goscinski, Jeffrey S. Grethe, Jeanette Hellgren Kotaleski, Eric Tatt Wei Ho, David N. Kennedy, Linda J. Lanyon, Trygve B. Leergaard, Helen S. Mayberg, Luciano Milanesi, Roman Mouček, J. B. Poline, Prasun K. Roy, Stephen C. Strother, Tong Boon Tang, Paul Tiesinga, Thomas Wachtler, Daniel K. Wójcik, Maryann
A Correction to this paper has been published: https://doi.org/10.1007/s12021-021-09522-x
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A Fast Transform for Brain Connectivity Difference Evaluation Neuroinformatics (IF 3.3) Pub Date : 2021-04-12 Massimiliano Zanin, Ilinka Ivanoska, Bahar Güntekin, Görsev Yener, Tatjana Loncar-Turukalo, Niksa Jakovljevic, Olivera Sveljo, David Papo
Anatomical and dynamical connectivity are essential to healthy brain function. However, quantifying variations in connectivity across conditions or between patient populations and appraising their functional significance are highly non-trivial tasks. Here we show that link ranking differences induce specific geometries in a convenient auxiliary space that are often easily recognisable at mere eye inspection
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Volume Reduction Techniques for the Classification of Independent Components of rs-fMRI Data: a Study with Convolutional Neural Networks Neuroinformatics (IF 3.3) Pub Date : 2021-04-07 Leonel Mera Jiménez, John F. Ochoa Gómez
In the last decade, neurosciences have had an increasing interest in resting state functional magnetic resonance imaging (rs-fMRI) as a result of its advantages, such as high spatial resolution, compared to other brain exploration techniques. To improve the technique, the elimination of artifacts through Independent Components Analysis (ICA) has been proposed, as this can separate neural signal and
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Biomarkers Based on Comprehensive Hierarchical EEG Coherence Analysis: Example Application to Social Competence in Autism (Preliminary Results) Neuroinformatics (IF 3.3) Pub Date : 2021-03-30 Mo Modarres, David Cochran, David N. Kennedy, Richard Schmidt, Paula Fitzpatrick, Jean A. Frazier
Electroencephalography (EEG) coherence analysis, based on measurement of synchronous oscillations of neuronal clusters, has been used extensively to evaluate functional connectivity in brain networks. EEG coherence studies have used a variety of analysis variables (e.g., time and frequency resolutions corresponding to the analysis time period and frequency bandwidth), regions of the brain (e.g., connectivity
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A Comparative Analysis of MRI Automated Segmentation of Subcortical Brain Volumes in a Large Dataset of Elderly Subjects Neuroinformatics (IF 3.3) Pub Date : 2021-03-30 Jaime Gomez-Ramirez, Javier Quilis-Sancho, Miguel A. Fernandez-Blazquez
In this study, we perform a comparative analysis of automated image segmentation of subcortical structures in the elderly brain. Manual segmentation is very time-consuming and automated methods are gaining importance as a clinical tool for diagnosis. The two most commonly used software libraries for brain segmentation -FreeSurfer and FSL- are put to work in a large dataset of 4,028 magnetic resonance
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Uncovering Statistical Links Between Gene Expression and Structural Connectivity Patterns in the Mouse Brain Neuroinformatics (IF 3.3) Pub Date : 2021-03-11 Nestor Timonidis, Alberto Llera, Paul H. E. Tiesinga
Finding links between genes and structural connectivity is of the utmost importance for unravelling the underlying mechanism of the brain connectome. In this study we identify links between the gene expression and the axonal projection density in the mouse brain, by applying a modified version of the Linked ICA method to volumetric data from the Allen Institute for Brain Science for identifying independent
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Identification of Neuronal Polarity by Node-Based Machine Learning Neuroinformatics (IF 3.3) Pub Date : 2021-03-05 Chen-Zhi Su, Kuan-Ting Chou, Hsuan-Pei Huang, Chiau-Jou Li, Ching-Che Charng, Chung-Chuan Lo, Daw-Wei Wang
Identifying the direction of signal flows in neural networks is important for understanding the intricate information dynamics of a living brain. Using a dataset of 213 projection neurons distributed in more than 15 neuropils of a Drosophila brain, we develop a powerful machine learning algorithm: node-based polarity identifier of neurons (NPIN). The proposed model is trained only by information specific
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Excavating FAIR Data: the Case of the Multicenter Animal Spinal Cord Injury Study (MASCIS), Blood Pressure, and Neuro-Recovery Neuroinformatics (IF 3.3) Pub Date : 2021-03-02 Carlos A. Almeida, Abel Torres-Espin, J. Russell Huie, Dongming Sun, Linda J. Noble-Haeusslein, Wise Young, Michael S. Beattie, Jacqueline C. Bresnahan, Jessica L. Nielson, Adam R. Ferguson
Meta-analyses suggest that the published literature represents only a small minority of the total data collected in biomedical research, with most becoming ‘dark data’ unreported in the literature. Dark data is due to publication bias toward novel results that confirm investigator hypotheses and omission of data that do not. Publication bias contributes to scientific irreproducibility and failures
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Transfer, Collection and Organisation of Electrophysiological and Imaging Data for Multicentre Studies Neuroinformatics (IF 3.3) Pub Date : 2021-02-10 Nicolas Roehri, Samuel Medina Villalon, Aude Jegou, Bruno Colombet, Bernard Giusiano, Aurélie Ponz, Fabrice Bartolomei, Christian-George Bénar
Multicentre studies are of utmost importance to confirm hypotheses. The lack of established standards and the ensuing complexity of their data management often hamper their implementation. The Brain Imaging Data Structure (BIDS) is an initiative for organizing and describing neuroimaging and electrophysiological data. Building on BIDS, we have developed two software programs: BIDS Manager and BIDS
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Deep Learning‐based Classification of Resting‐state fMRI Independent‐component Analysis Neuroinformatics (IF 3.3) Pub Date : 2021-02-05 Victor Nozais, Philippe Boutinaud, Violaine Verrecchia, Marie-Fateye Gueye, Pierre-Yves Hervé, Christophe Tzourio, Bernard Mazoyer, Marc Joliot
Functional connectivity analyses of fMRI data have shown that the activity of the brain at rest is spatially organized into resting-state networks (RSNs). RSNs appear as groups of anatomically distant but functionally tightly connected brain regions. Inter-RSN intrinsic connectivity analyses may provide an optimal spatial level of integration to analyze the variability of the functional connectome
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Improved Segmentation of the Intracranial and Ventricular Volumes in Populations with Cerebrovascular Lesions and Atrophy Using 3D CNNs Neuroinformatics (IF 3.3) Pub Date : 2021-02-01 Emmanuel E. Ntiri, Melissa F. Holmes, Parisa M. Forooshani, Joel Ramirez, Fuqiang Gao, Miracle Ozzoude, Sabrina Adamo, Christopher J. M. Scott, Dar Dowlatshahi, Jane M. Lawrence-Dewar, Donna Kwan, Anthony E. Lang, Sean Symons, Robert Bartha, Stephen Strother, Jean-Claude Tardif, Mario Masellis, Richard H. Swartz, Alan Moody, Sandra E. Black, Maged Goubran
Successful segmentation of the total intracranial vault (ICV) and ventricles is of critical importance when studying neurodegeneration through neuroimaging. We present iCVMapper and VentMapper, robust algorithms that use a convolutional neural network (CNN) to segment the ICV and ventricles from both single and multi-contrast MRI data. Our models were trained on a large dataset from two multi-site
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A Standards Organization for Open and FAIR Neuroscience: the International Neuroinformatics Coordinating Facility Neuroinformatics (IF 3.3) Pub Date : 2021-01-27 Mathew Birdsall Abrams, Jan G. Bjaalie, Samir Das, Gary F. Egan, Satrajit S. Ghosh, Wojtek J. Goscinski, Jeffrey S. Grethe, Jeanette Hellgren Kotaleski, Eric Tatt Wei Ho, David N. Kennedy, Linda J. Lanyon, Trygve B. Leergaard, Helen S. Mayberg, Luciano Milanesi, Roman Mouček, J. B. Poline, Prasun K. Roy, Stephen C. Strother, Tong Boon Tang, Paul Tiesinga, Thomas Wachtler, Daniel K. Wójcik, Maryann
There is great need for coordination around standards and best practices in neuroscience to support efforts to make neuroscience a data-centric discipline. Major brain initiatives launched around the world are poised to generate huge stores of neuroscience data. At the same time, neuroscience, like many domains in biomedicine, is confronting the issues of transparency, rigor, and reproducibility. Widely
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Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction Neuroinformatics (IF 3.3) Pub Date : 2021-01-27 Gaia Amaranta Taberna, Jessica Samogin, Dante Mantini
In the last years, technological advancements for the analysis of electroencephalography (EEG) recordings have permitted to investigate neural activity and connectivity in the human brain with unprecedented precision and reliability. A crucial element for accurate EEG source reconstruction is the construction of a realistic head model, incorporating information on electrode positions and head tissue
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Signaleeg Neuroinformatics (IF 3.3) Pub Date : 2021-01-21 Joaquim Massana, Òscar Raya, Jaume Gauchola, Beatriz López
Due to the proliferation of brain and neurological disorders (World Health Organization 2006), EEG (Blinowska and Durka 2006) is gaining attention as a support for decision making in the fields of neurology, psychology, and psychiatry. But EEG data are not always easy to understand. Therefore, extracting the desired information from EEG data in different contexts is an important requirement. This article
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Decentralized Multisite VBM Analysis During Adolescence Shows Structural Changes Linked to Age, Body Mass Index, and Smoking: a COINSTAC Analysis Neuroinformatics (IF 3.3) Pub Date : 2021-01-18 Harshvardhan Gazula, Bharath Holla, Zuo Zhang, Jiayuan Xu, Eric Verner, Ross Kelly, Sanjeev Jain, Rose Dawn Bharath, Gareth J. Barker, Debasish Basu, Amit Chakrabarti, Kartik Kalyanram, Kalyanaraman Kumaran, Lenin Singh, Rebecca Kuriyan, Pratima Murthy, Vivek Benega, Sergey M. Plis, Anand D. Sarwate, Jessica A. Turner, Gunter Schumann, Vince D. Calhoun
There has been an upward trend in developing frameworks that enable neuroimaging researchers to address challenging questions by leveraging data across multiple sites all over the world. One such open-source framework is the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC) that works on Windows, macOS, and Linux operating systems and leverages containerized
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Reflections on Data Sharing Practices in Spinal Cord Injury Research Neuroinformatics (IF 3.3) Pub Date : 2021-01-16 John C. Gensel, Michael B. Orr
There are few pharmacological therapeutics available for spinal cord injury despite years of preclinical and clinical research. This brief editorial discusses some of the shortcomings of translational research efforts. In addition, we comment on our previous experiences with data curation and highlight evolving efforts by the spinal cord injury research community to improve prospects for future therapeutic
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Data-Theoretical Synthesis of the Early Developmental Process Neuroinformatics (IF 3.3) Pub Date : 2021-01-15 Bradly Alicea, Richard Gordon, Thomas E. Portegys
Biological development is often described as a dynamic, emergent process. This is evident across a variety of phenomena, from the temporal organization of cell types in the embryo to compounding trends that affect large-scale differentiation. To better understand this, we propose combining quantitative investigations of biological development with theory-building techniques. This provides an alternative
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DREAM Neuroinformatics (IF 3.3) Pub Date : 2021-01-07 Zhu-Qing Gong, Peng Gao, Chao Jiang, Xiu-Xia Xing, Hao-Ming Dong, Tonya White, F. Xavier Castellanos, Hai-Fang Li, Xi-Nian Zuo
Rhythms of the brain are generated by neural oscillations across multiple frequencies. These oscillations can be decomposed into distinct frequency intervals associated with specific physiological processes. In practice, the number and ranges of decodable frequency intervals are determined by sampling parameters, often ignored by researchers. To improve the situation, we report on an open toolbox with
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Causal Network Inference for Neural Ensemble Activity Neuroinformatics (IF 3.3) Pub Date : 2021-01-04 Rong Chen
Interactions among cellular components forming a mesoscopic scale brain network (microcircuit) display characteristic neural dynamics. Analysis of microcircuits provides a system-level understanding of the neurobiology of health and disease. Causal discovery aims to detect causal relationships among variables based on observational data. A key barrier in causal discovery is the high dimensionality
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RippleNet: a Recurrent Neural Network for Sharp Wave Ripple (SPW-R) Detection Neuroinformatics (IF 3.3) Pub Date : 2021-01-04 Espen Hagen, Anna R. Chambers, Gaute T. Einevoll, Klas H. Pettersen, Rune Enger, Alexander J. Stasik
Hippocampal sharp wave ripples (SPW-R) have been identified as key bio-markers of important brain functions such as memory consolidation and decision making. Understanding their underlying mechanisms in healthy and pathological brain function and behaviour rely on accurate SPW-R detection. In this multidisciplinary study, we propose a novel, self-improving artificial intelligence (AI) detection method
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Generating Longitudinal Atrophy Evaluation Datasets on Brain Magnetic Resonance Images Using Convolutional Neural Networks and Segmentation Priors Neuroinformatics (IF 3.3) Pub Date : 2021-01-02 Jose Bernal, Sergi Valverde, Kaisar Kushibar, Mariano Cabezas, Arnau Oliver, Xavier Lladó
Brain atrophy quantification plays a fundamental role in neuroinformatics since it permits studying brain development and neurological disorders. However, the lack of a ground truth prevents testing the accuracy of longitudinal atrophy quantification methods. We propose a deep learning framework to generate longitudinal datasets by deforming T1-w brain magnetic resonance imaging scans as requested
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Improving Covariance Matrices Derived from Tiny Training Datasets for the Classification of Event-Related Potentials with Linear Discriminant Analysis Neuroinformatics (IF 3.3) Pub Date : 2020-12-14 Jan Sosulski, Jan-Philipp Kemmer, Michael Tangermann
Electroencephalogram data used in the domain of brain–computer interfaces typically has subpar signal-to-noise ratio and data acquisition is expensive. An effective and commonly used classifier to discriminate event-related potentials is the linear discriminant analysis which, however, requires an estimate of the feature distribution. While this information is provided by the feature covariance matrix
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Pandora: 4-D White Matter Bundle Population-Based Atlases Derived from Diffusion MRI Fiber Tractography Neuroinformatics (IF 3.3) Pub Date : 2020-11-16 Colin B Hansen, Qi Yang, Ilwoo Lyu, Francois Rheault, Cailey Kerley, Bramsh Qamar Chandio, Shreyas Fadnavis, Owen Williams, Andrea T. Shafer, Susan M. Resnick, David H. Zald, Laurie E Cutting, Warren D Taylor, Brian Boyd, Eleftherios Garyfallidis, Adam W Anderson, Maxime Descoteaux, Bennett A Landman, Kurt G Schilling
Brain atlases have proven to be valuable neuroscience tools for localizing regions of interest and performing statistical inferences on populations. Although many human brain atlases exist, most do not contain information about white matter structures, often neglecting them completely or labelling all white matter as a single homogenous substrate. While few white matter atlases do exist based on diffusion
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An Optimized Mouse Brain Atlas for Automated Mapping and Quantification of Neuronal Activity Using iDISCO+ and Light Sheet Fluorescence Microscopy Neuroinformatics (IF 3.3) Pub Date : 2020-10-16 Johanna Perens, Casper Gravesen Salinas, Jacob Lercke Skytte, Urmas Roostalu, Anders Bjorholm Dahl, Tim B. Dyrby, Franziska Wichern, Pernille Barkholt, Niels Vrang, Jacob Jelsing, Jacob Hecksher-Sørensen
In recent years, the combination of whole-brain immunolabelling, light sheet fluorescence microscopy (LSFM) and subsequent registration of data with a common reference atlas, has enabled 3D visualization and quantification of fluorescent markers or tracers in the adult mouse brain. Today, the common coordinate framework version 3 developed by the Allen’s Institute of Brain Science (AIBS CCFv3), is
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Deep Representational Similarity Learning for Analyzing Neural Signatures in Task-based fMRI Dataset Neuroinformatics (IF 3.3) Pub Date : 2020-10-15 Muhammad Yousefnezhad, Jeffrey Sawalha, Alessandro Selvitella, Daoqiang Zhang
Similarity analysis is one of the crucial steps in most fMRI studies. Representational Similarity Analysis (RSA) can measure similarities of neural signatures generated by different cognitive states. This paper develops Deep Representational Similarity Learning (DRSL), a deep extension of RSA that is appropriate for analyzing similarities between various cognitive tasks in fMRI datasets with a large
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3D Deep Neural Network Segmentation of Intracerebral Hemorrhage: Development and Validation for Clinical Trials. Neuroinformatics (IF 3.3) Pub Date : 2020-09-27 Matthew F Sharrock,W Andrew Mould,Hasan Ali,Meghan Hildreth,Issam A Awad,Daniel F Hanley,John Muschelli
Intracranial hemorrhage (ICH) occurs when a blood vessel ruptures in the brain. This leads to significant morbidity and mortality, the likelihood of which is predicated on the size of the bleeding event. X-ray computed tomography (CT) scans allow clinicians and researchers to qualitatively and quantitatively diagnose hemorrhagic stroke, guide interventions and determine inclusion criteria of patients
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Deep Learning Model for the Automated Detection and Histopathological Prediction of Meningioma. Neuroinformatics (IF 3.3) Pub Date : 2020-09-25 Hua Zhang,Jiajie Mo,Han Jiang,Zhuyun Li,Wenhan Hu,Chao Zhang,Yao Wang,Xiu Wang,Chang Liu,Baotian Zhao,Jianguo Zhang,Kai Zhang
The volumetric assessment and accurate grading of meningiomas before surgery are highly relevant for therapy planning and prognosis prediction. This study was to design a deep learning algorithm and evaluate the performance in detecting meningioma lesions and grade classification. In total, 5088 patients with histopathologically confirmed meningioma were retrospectively included. The pyramid scene
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An MRI-Based, Data-Driven Model of Cortical Laminar Connectivity. Neuroinformatics (IF 3.3) Pub Date : 2020-09-19 Ittai Shamir,Yaniv Assaf
Over the past two centuries, great scientific efforts have been spent on deciphering the structure and function of the cerebral cortex using a wide variety of methods. Since the advent of MRI neuroimaging, significant progress has been made in imaging of global white matter connectivity (connectomics), followed by promising new studies regarding imaging of grey matter laminar compartments. Despite
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Fast Estimation of L1-Regularized Linear Models in the Mass-Univariate Setting. Neuroinformatics (IF 3.3) Pub Date : 2020-09-15 Holger Mohr,Hannes Ruge
In certain modeling approaches, activation analyses of task-based fMRI data can involve a relatively large number of predictors. For example, in the encoding model approach, complex stimuli are represented in a high-dimensional feature space, resulting in design matrices with many predictors. Similarly, single-trial models and finite impulse response models may also encompass a large number of predictors
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Musclesense: a Trained, Artificial Neural Network for the Anatomical Segmentation of Lower Limb Magnetic Resonance Images in Neuromuscular Diseases. Neuroinformatics (IF 3.3) Pub Date : 2020-09-05 Baris Kanber,Jasper M Morrow,Uros Klickovic,Stephen Wastling,Sachit Shah,Pietro Fratta,Amy R McDowell,Matt G Hall,Chris A Clark,Francesco Muntoni,Mary M Reilly,Michael G Hanna,Daniel C Alexander,Tarek Yousry,John S Thornton
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Tractography Processing with the Sparse Closest Point Transform. Neuroinformatics (IF 3.3) Pub Date : 2020-08-29 Ryan P Cabeen,Arthur W Toga,David H Laidlaw
We propose a novel approach for processing diffusion MRI tractography datasets using the sparse closest point transform (SCPT). Tractography enables the 3D geometry of white matter pathways to be reconstructed; however, algorithms for processing them are often highly customized, and thus, do not leverage the existing wealth of machine learning (ML) algorithms. We investigated a vector-space tractography
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Cross-Sectional Volumes and Trajectories of the Human Brain, Gray Matter, White Matter and Cerebrospinal Fluid in 9473 Typically Aging Adults. Neuroinformatics (IF 3.3) Pub Date : 2020-08-27 Andrei Irimia
Accurate knowledge of adult human brain volume (BV) is critical for studies of aging- and disease-related brain alterations, and for monitoring the trajectories of neural and cognitive functions in conditions like Alzheimer’s disease and traumatic brain injury. This scoping meta-analysis aggregates normative reference values for BV and three related volumetrics—gray matter volume (GMV), white matter
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Addressing Pitfalls in Phase-Amplitude Coupling Analysis with an Extended Modulation Index Toolbox. Neuroinformatics (IF 3.3) Pub Date : 2020-08-26 Gabriela J Jurkiewicz,Mark J Hunt,Jarosław Żygierewicz
Phase-amplitude coupling (PAC) is proposed to play an essential role in coordinating the processing of information on local and global scales. In recent years, the methods able to reveal trustworthy PAC has gained considerable interest. However, the intrinsic features of some signals can lead to the identification of spurious or waveform-dependent coupling. This prompted us to develop an easily accessible
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GTree: an Open-source Tool for Dense Reconstruction of Brain-wide Neuronal Population. Neuroinformatics (IF 3.3) Pub Date : 2020-08-25 Hang Zhou,Shiwei Li,Anan Li,Qing Huang,Feng Xiong,Ning Li,Jiacheng Han,Hongtao Kang,Yijun Chen,Yun Li,Huimin Lin,Yu-Hui Zhang,Xiaohua Lv,Xiuli Liu,Hui Gong,Qingming Luo,Shaoqun Zeng,Tingwei Quan
Recent technological advancements have facilitated the imaging of specific neuronal populations at the single-axon level across the mouse brain. However, the digital reconstruction of neurons from a large dataset requires months of manual effort using the currently available software. In this study, we develop an open-source software called GTree (global tree reconstruction system) to overcome the
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Neuroimaging PheWAS (Phenome-Wide Association Study): A Free Cloud-Computing Platform for Big-Data, Brain-Wide Imaging Association Studies. Neuroinformatics (IF 3.3) Pub Date : 2020-08-21 Lu Zhao,Ishaan Batta,William Matloff,Caroline O'Driscoll,Samuel Hobel,Arthur W Toga
Large-scale, case-control genome-wide association studies (GWASs) have revealed genetic variations associated with diverse neurological and psychiatric disorders. Recent advances in neuroimaging and genomic databases of large healthy and diseased cohorts have empowered studies to characterize effects of the discovered genetic factors on brain structure and function, implicating neural pathways and
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DeepMapi: a Fully Automatic Registration Method for Mesoscopic Optical Brain Images Using Convolutional Neural Networks. Neuroinformatics (IF 3.3) Pub Date : 2020-08-04 Hong Ni,Zhao Feng,Yue Guan,Xueyan Jia,Wu Chen,Tao Jiang,Qiuyuan Zhong,Jing Yuan,Miao Ren,Xiangning Li,Hui Gong,Qingming Luo,Anan Li
The extreme complexity of mammalian brains requires a comprehensive deconstruction of neuroanatomical structures. Scientists normally use a brain stereotactic atlas to determine the locations of neurons and neuronal circuits. However, different brain images are normally not naturally aligned even when they are imaged with the same setup, let alone under the differing resolutions and dataset sizes used
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Evolution of Human Brain Atlases in Terms of Content, Applications, Functionality, and Availability. Neuroinformatics (IF 3.3) Pub Date : 2020-07-29 Wieslaw L Nowinski
Human brain atlases have been evolving tremendously, propelled recently by brain big projects, and driven by sophisticated imaging techniques, advanced brain mapping methods, vast data, analytical strategies, and powerful computing. We overview here this evolution in four categories: content, applications, functionality, and availability, in contrast to other works limited mostly to content. Four atlas
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RT-NET: real-time reconstruction of neural activity using high-density electroencephalography. Neuroinformatics (IF 3.3) Pub Date : 2020-07-28 Roberto Guarnieri,Mingqi Zhao,Gaia Amaranta Taberna,Marco Ganzetti,Stephan P Swinnen,Dante Mantini
High-density electroencephalography (hdEEG) has been successfully used for large-scale investigations of neural activity in the healthy and diseased human brain. Because of their high computational demand, analyses of source-projected hdEEG data are typically performed offline. Here, we present a real-time noninvasive electrophysiology toolbox, RT-NET, which has been specifically developed for online
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Constructing Connectome Atlas by Graph Laplacian Learning. Neuroinformatics (IF 3.3) Pub Date : 2020-07-25 Minjeong Kim,Chenggang Yan,Defu Yang,Peipeng Liang,Daniel I Kaufer,Guorong Wu
The recent development of neuroimaging technology and network theory allows us to visualize and characterize the whole-brain functional connectivity in vivo. The importance of conventional structural image atlas widely used in population-based neuroimaging studies has been well verified. Similarly, a “common” brain connectivity map (also called connectome atlas) across individuals can open a new pathway
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Supervised Multidimensional Scaling and its Application in MRI-Based Individual Age Predictions. Neuroinformatics (IF 3.3) Pub Date : 2020-07-16 Xuyu Cao,Chen Chen,Lixia Tian
It has been a popular trend to decode individuals’ demographic and cognitive variables based on MRI. Features extracted from MRI data are usually of high dimensionality, and dimensionality reduction (DR) is an effective way to deal with these high-dimensional features. Despite many supervised DR techniques for classification purposes, there is still a lack of supervised DR techniques for regression
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MEArec: A Fast and Customizable Testbench Simulator for Ground-truth Extracellular Spiking Activity. Neuroinformatics (IF 3.3) Pub Date : 2020-07-09 Alessio Paolo Buccino,Gaute Tomas Einevoll
When recording neural activity from extracellular electrodes, both in vivo and in vitro, spike sorting is a required and very important processing step that allows for identification of single neurons’ activity. Spike sorting is a complex algorithmic procedure, and in recent years many groups have attempted to tackle this problem, resulting in numerous methods and software packages. However, validation
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DeepDicomSort: An Automatic Sorting Algorithm for Brain Magnetic Resonance Imaging Data. Neuroinformatics (IF 3.3) Pub Date : 2020-07-05 Sebastian R van der Voort,Marion Smits,Stefan Klein,
With the increasing size of datasets used in medical imaging research, the need for automated data curation is arising. One important data curation task is the structured organization of a dataset for preserving integrity and ensuring reusability. Therefore, we investigated whether this data organization step can be automated. To this end, we designed a convolutional neural network (CNN) that automatically
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SHYBRID: A Graphical Tool for Generating Hybrid Ground-Truth Spiking Data for Evaluating Spike Sorting Performance. Neuroinformatics (IF 3.3) Pub Date : 2020-07-02 Jasper Wouters,Fabian Kloosterman,Alexander Bertrand
Spike sorting is the process of retrieving the spike times of individual neurons that are present in an extracellular neural recording. Over the last decades, many spike sorting algorithms have been published. In an effort to guide a user towards a specific spike sorting algorithm, given a specific recording setting (i.e., brain region and recording device), we provide an open-source graphical tool
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DeepNeuro: an open-source deep learning toolbox for neuroimaging. Neuroinformatics (IF 3.3) Pub Date : 2020-06-23 Andrew Beers,James Brown,Ken Chang,Katharina Hoebel,Jay Patel,K Ina Ly,Sara M Tolaney,Priscilla Brastianos,Bruce Rosen,Elizabeth R Gerstner,Jayashree Kalpathy-Cramer
Translating deep learning research from theory into clinical practice has unique challenges, specifically in the field of neuroimaging. In this paper, we present DeepNeuro, a Python-based deep learning framework that puts deep neural networks for neuroimaging into practical usage with a minimum of friction during implementation. We show how this framework can be used to design deep learning pipelines
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SUPFUNSIM: Spatial Filtering Toolbox for EEG. Neuroinformatics (IF 3.3) Pub Date : 2020-06-21 Krzysztof Rykaczewski,Jan Nikadon,Włodzisław Duch,Tomasz Piotrowski
Brain activity pattern recognition from EEG or MEG signal analysis is one of the most important method in cognitive neuroscience. The supFunSim library is a new Matlab toolbox which generates accurate EEG forward model and implements a collection of spatial filters for EEG source reconstruction, including the linearly constrained minimum-variance (LCMV), eigenspace LCMV, nulling (NL), and minimum-variance
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Review of Riemannian Distances and Divergences, Applied to SSVEP-based BCI. Neuroinformatics (IF 3.3) Pub Date : 2020-06-19 S Chevallier,E K Kalunga,Q Barthélemy,E Monacelli
The firstgeneration of brain-computer interfaces (BCI) classifies multi-channel electroencephalographic (EEG) signals, enhanced by optimized spatial filters.The second generation directly classifies covariance matrices estimated on EEG signals, based on straightforward algorithms such as the minimum-distance-to-Riemannian-mean (MDRM). Classification results vary greatly depending on the chosen Riemannian
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NEURO-LEARN: a Solution for Collaborative Pattern Analysis of Neuroimaging Data. Neuroinformatics (IF 3.3) Pub Date : 2020-06-10 Bingye Lei,Fengchun Wu,Jing Zhou,Dongsheng Xiong,Kaixi Wang,Lingyin Kong,Pengfei Ke,Jun Chen,Yuping Ning,Xiaobo Li,Zhiming Xiang,Kai Wu
The development of neuroimaging instrumentation has boosted neuroscience researches. Consequently, both the fineness and the cost of data acquisition have profoundly increased, leading to the main bottleneck of this field: limited sample size and high dimensionality of neuroimaging data. Therefore, the emphasis of ideas of data pooling and research collaboration has increased over the past decade.
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Reproducible Evaluation of Diffusion MRI Features for Automatic Classification of Patients with Alzheimer's Disease. Neuroinformatics (IF 3.3) Pub Date : 2020-06-10 Junhao Wen,Jorge Samper-González,Simona Bottani,Alexandre Routier,Ninon Burgos,Thomas Jacquemont,Sabrina Fontanella,Stanley Durrleman,Stéphane Epelbaum,Anne Bertrand,Olivier Colliot,
Diffusion MRI is the modality of choice to study alterations of white matter. In past years, various works have used diffusion MRI for automatic classification of Alzheimer’s disease. However, classification performance obtained with different approaches is difficult to compare because of variations in components such as input data, participant selection, image preprocessing, feature extraction, feature
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BVAR-Connect: A Variational Bayes Approach to Multi-Subject Vector Autoregressive Models for Inference on Brain Connectivity Networks. Neuroinformatics (IF 3.3) Pub Date : 2020-06-05 Jeong Hwan Kook,Kelly A Vaughn,Dana M DeMaster,Linda Ewing-Cobbs,Marina Vannucci
In this paper we propose BVAR-connect, a variational inference approach to a Bayesian multi-subject vector autoregressive (VAR) model for inference on effective brain connectivity based on resting-state functional MRI data. The modeling framework uses a Bayesian variable selection approach that flexibly integrates multi-modal data, in particular structural diffusion tensor imaging (DTI) data, into
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Regularized Bagged Canonical Component Analysis for Multiclass Learning in Brain Imaging. Neuroinformatics (IF 3.3) Pub Date : 2020-06-05 Carlos Sevilla-Salcedo,Vanessa Gómez-Verdejo,Jussi Tohka,
A fundamental problem of supervised learning algorithms for brain imaging applications is that the number of features far exceeds the number of subjects. In this paper, we propose a combined feature selection and extraction approach for multiclass problems. This method starts with a bagging procedure which calculates the sign consistency of the multivariate analysis (MVA) projection matrix feature-wise
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Neuroinformatics in the Time of Coronavirus. Neuroinformatics (IF 3.3) Pub Date : 2020-06-01 Giorgio A Ascoli
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QFib: Fast and Efficient Brain Tractogram Compression. Neuroinformatics (IF 3.3) Pub Date : 2020-05-30 C Mercier,S Rousseau,P Gori,I Bloch,T Boubekeur
Diffusion MRI fiber tracking datasets can contain millions of 3D streamlines, and their representation can weight tens of gigabytes of memory. These sets of streamlines are called tractograms and are often used for clinical operations or research. Their size makes them difficult to store, visualize, process or exchange over the network. We propose a new compression algorithm well-suited for tractograms
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Prediction of a Cell-Class-Specific Mouse Mesoconnectome Using Gene Expression Data. Neuroinformatics (IF 3.3) Pub Date : 2020-05-24 Nestor Timonidis,Rembrandt Bakker,Paul Tiesinga
Reconstructing brain connectivity at sufficient resolution for computational models designed to study the biophysical mechanisms underlying cognitive processes is extremely challenging. For such a purpose, a mesoconnectome that includes laminar and cell-class specificity would be a major step forward. We analyzed the ability of gene expression patterns to predict cell-class and layer-specific projection
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A Systematic Evaluation of Interneuron Morphology Representations for Cell Type Discrimination. Neuroinformatics (IF 3.3) Pub Date : 2020-05-04 Sophie Laturnus,Dmitry Kobak,Philipp Berens
Quantitative analysis of neuronal morphologies usually begins with choosing a particular feature representation in order to make individual morphologies amenable to standard statistics tools and machine learning algorithms. Many different feature representations have been suggested in the literature, ranging from density maps to intersection profiles, but they have never been compared side by side
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Colocalization Colormap -an ImageJ Plugin for the Quantification and Visualization of Colocalized Signals. Neuroinformatics (IF 3.3) Pub Date : 2020-10-01 Adam Gorlewicz,Katarzyna Krawczyk,Andrzej A Szczepankiewicz,Pawel Trzaskoma,Christophe Mulle,Grzegorz M Wilczynski
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Axonal Tree Morphology and Signal Propagation Dynamics Improve Interneuron Classification. Neuroinformatics (IF 3.3) Pub Date : 2020-04-29 Netanel Ofer,Orit Shefi,Gur Yaari
Neurons are diverse and can be differentiated by their morphological, electrophysiological, and molecular properties. Current morphology-based classification approaches largely rely on the dendritic tree structure or on the overall axonal projection layout. Here, we use data from public databases of neuronal reconstructions and membrane properties to study the characteristics of the axonal and dendritic
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FEMfuns: A Volume Conduction Modeling Pipeline that Includes Resistive, Capacitive or Dispersive Tissue and Electrodes. Neuroinformatics (IF 3.3) Pub Date : 2020-04-18 M Vermaas,M C Piastra,T F Oostendorp,N F Ramsey,P H E Tiesinga
Applications such as brain computer interfaces require recordings of relevant neuronal population activity with high precision, for example, with electrocorticography (ECoG) grids. In order to achieve this, both the placement of the electrode grid on the cortex and the electrode properties, such as the electrode size and material, need to be optimized. For this purpose, it is essential to have a reliable
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Human Brain Atlases in Stroke Management. Neuroinformatics (IF 3.3) Pub Date : 2020-04-15 Wieslaw L Nowinski
Stroke is a leading cause of death and a major cause of permanent disability. Its management is demanding because of variety of protocols, imaging modalities, pulse sequences, hemodynamic maps, criteria for treatment, and time constraints to promptly evaluate and treat. To cope with some of these issues, we propose novel, patented solutions in stroke management by employing multiple brain atlases for
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Reducing Inter-Site Variability for Fluctuation Amplitude Metrics in Multisite Resting State BOLD-fMRI Data Neuroinformatics (IF 3.3) Pub Date : 2020-04-13 Xinbo Wang, Qing Wang, Peiwen Zhang, Shufang Qian, Shiyu Liu, Dong-Qiang Liu
It has been reported that resting state fluctuation amplitude (RSFA) exhibits extremely large inter-site variability, which limits its application in multisite studies. Although global normalization (GN) based approaches are efficient in reducing the site effects, they may cause spurious results. In this study, our purpose was to find alternative strategies to minimize the substantial site effects
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Computing Univariate Neurodegenerative Biomarkers with Volumetric Optimal Transportation: A Pilot Study. Neuroinformatics (IF 3.3) Pub Date : 2020-04-06 Yanshuai Tu,Liang Mi,Wen Zhang,Haomeng Zhang,Junwei Zhang,Yonghui Fan,Dhruman Goradia,Kewei Chen,Richard J Caselli,Eric M Reiman,Xianfeng Gu,Yalin Wang,
Changes in cognitive performance due to neurodegenerative diseases such as Alzheimer's disease (AD) are closely correlated to the brain structure alteration. A univariate and personalized neurodegenerative biomarker with strong statistical power based on magnetic resonance imaging (MRI) will benefit clinical diagnosis and prognosis of neurodegenerative diseases. However, few biomarkers of this type
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