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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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NeAT: a Nonlinear Analysis Toolbox for Neuroimaging. Neuroinformatics (IF 3.3) Pub Date : 2020-03-24 Adrià Casamitjana,Verónica Vilaplana,Santi Puch,Asier Aduriz,Carlos López,Grégory Operto,Raffaele Cacciaglia,Carles Falcón,José Luis Molinuevo,Juan Domingo Gispert,
NeAT is a modular, flexible and user-friendly neuroimaging analysis toolbox for modeling linear and nonlinear effects overcoming the limitations of the standard neuroimaging methods which are solely based on linear models. NeAT provides a wide range of statistical and machine learning non-linear methods for model estimation, several metrics based on curve fitting and complexity for model inference
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GeodesicSlicer: a Slicer Toolbox for Targeting Brain Stimulation. Neuroinformatics (IF 3.3) Pub Date : 2020-03-03 F Briend,E Leroux,C Nathou,N Delcroix,S Dollfus,O Etard
NonInvasive Brain Stimulation (NIBS) is a potential therapeutic tool with growing interest, but neuronavigation-guided software and tools available for the target determination are mostly either expensive or closed proprietary applications. To address these limitations, we propose GeodesicSlicer, a customizable, free, and open-source NIBS therapy research toolkit. GeodesicSlicer is implemented as an
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NeuroPath2Path: Classification and elastic morphing between neuronal arbors using path-wise similarity. Neuroinformatics (IF 3.3) Pub Date : 2020-02-27 Tamal Batabyal,Barry Condron,Scott T Acton
Neuron shape and connectivity affect function. Modern imaging methods have proven successful at extracting morphological information. One potential path to achieve analysis of this morphology is through graph theory. Encoding by graphs enables the use of high throughput informatic methods to extract and infer brain function. However, the application of graph-theoretic methods to neuronal morphology
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Regularized Partial Least Square Regression for Continuous Decoding in Brain-Computer Interfaces. Neuroinformatics (IF 3.3) Pub Date : 2020-02-27 Reza Foodeh,Saeed Ebadollahi,Mohammad Reza Daliri
Continuous decoding is a crucial step in many types of brain-computer interfaces (BCIs). Linear regression techniques have been widely used to determine a linear relation between the input and desired output. A serious issue in this technique is the over-fitting phenomenon. Partial least square (PLS) is a well-known and popular method which tries to overcome this problem. PLS calculates a set of latent
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Ontological Dimensions of Cognitive-Neural Mappings. Neuroinformatics (IF 3.3) Pub Date : 2020-02-18 Taylor Bolt,Jason S Nomi,Rachel Arens,Shruti G Vij,Michael Riedel,Taylor Salo,Angela R Laird,Simon B Eickhoff,Lucina Q Uddin
The growing literature reporting results of cognitive-neural mappings has increased calls for an adequate organizing ontology, or taxonomy, of these mappings. This enterprise is non-trivial, as relevant dimensions that might contribute to such an ontology are not yet agreed upon. We propose that any candidate dimensions should be evaluated on their ability to explain observed differences in functional
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Automated White Matter Hyperintensity Segmentation Using Bayesian Model Selection: Assessment and Correlations with Cognitive Change. Neuroinformatics (IF 3.3) Pub Date : 2020-02-15 Cassidy M Fiford,Carole H Sudre,Hugh Pemberton,Phoebe Walsh,Emily Manning,Ian B Malone,Jennifer Nicholas,Willem H Bouvy,Owen T Carmichael,Geert Jan Biessels,M Jorge Cardoso,Josephine Barnes,
Accurate, automated white matter hyperintensity (WMH) segmentations are needed for large-scale studies to understand contributions of WMH to neurological diseases. We evaluated Bayesian Model Selection (BaMoS), a hierarchical fully-unsupervised model selection framework for WMH segmentation. We compared BaMoS segmentations to semi-automated segmentations, and assessed whether they predicted longitudinal
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Understanding Computational Costs of Cellular-Level Brain Tissue Simulations Through Analytical Performance Models. Neuroinformatics (IF 3.3) Pub Date : 2020-02-13 Francesco Cremonesi,Felix Schürmann
Computational modeling and simulation have become essential tools in the quest to better understand the brain’s makeup and to decipher the causal interrelations of its components. The breadth of biochemical and biophysical processes and structures in the brain has led to the development of a large variety of model abstractions and specialized tools, often times requiring high performance computing
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Automatic Brain Extraction for Rodent MRI Images. Neuroinformatics (IF 3.3) Pub Date : 2020-01-27 Yikang Liu,Hayreddin Said Unsal,Yi Tao,Nanyin Zhang
Rodent models are increasingly important in translational neuroimaging research. In rodent neuroimaging, particularly magnetic resonance imaging (MRI) studies, brain extraction is a critical data preprocessing component. Current brain extraction methods for rodent MRI usually require manual adjustment of input parameters due to widely different image qualities and/or contrasts. Here we propose a novel
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Automatic Adaptation of Model Neurons and Connections to Build Hybrid Circuits with Living Networks. Neuroinformatics (IF 3.3) Pub Date : 2020-01-13 Manuel Reyes-Sanchez,Rodrigo Amaducci,Irene Elices,Francisco B Rodriguez,Pablo Varona
Hybrid circuits built by creating mono- or bi-directional interactions among living cells and model neurons and synapses are an effective way to study neuron, synaptic and neural network dynamics. However, hybrid circuit technology has been largely underused in the context of neuroscience studies mainly because of the inherent difficulty in implementing and tuning this type of interactions. In this
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A Rational Approach to Understanding and Evaluating Responsive Neurostimulation. Neuroinformatics (IF 3.3) Pub Date : 2020-01-09 Nathaniel D Sisterson,Thomas A Wozny,Vasileios Kokkinos,Anto Bagic,Alexandra P Urban,R Mark Richardson
Closed-loop brain stimulation is increasingly used in level 4 epilepsy centers without an understanding of how the device behaves on a daily basis. This lack of insight is a barrier to improving closed-loop therapy and ultimately understanding why some patients never achieve seizure reduction. We aimed to quantify the accuracy of closed-loop seizure detection and stimulation on the RNS device through
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Porthole and Stormcloud: Tools for Visualisation of Spatiotemporal M/EEG Statistics. Neuroinformatics (IF 3.3) Pub Date : 2020-01-05 Jeremy A Taylor,Marta I Garrido
Electro- and magneto-encephalography are functional neuroimaging modalities characterised by their ability to quantify dynamic spatiotemporal activity within the brain. However, the visualisation techniques used to illustrate these effects are currently limited to single- or multi-channel time series plots, topographic scalp maps and orthographic cross-sections of the spatiotemporal data structure
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Personode: A Toolbox for ICA Map Classification and Individualized ROI Definition. Neuroinformatics (IF 3.3) Pub Date : 2020-01-03 Gustavo S P Pamplona,Bruno H Vieira,Frank Scharnowski,Carlos E G Salmon
Canonical resting state networks (RSNs) can be obtained through independent component analysis (ICA). RSNs are reproducible across subjects but also present inter-individual differences, which can be used to individualize regions-of-interest (ROI) definition, thus making fMRI analyses more accurate. Unfortunately, no automatic tool for defining subject-specific ROIs exists, making the classification
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FCN Based Label Correction for Multi-Atlas Guided Organ Segmentation. Neuroinformatics (IF 3.3) Pub Date : 2020-01-02 Hancan Zhu,Ehsan Adeli,Feng Shi,Dinggang Shen,
Segmentation of medical images using multiple atlases has recently gained immense attention due to their augmented robustness against variabilities across different subjects. These atlas-based methods typically comprise of three steps: atlas selection, image registration, and finally label fusion. Image registration is one of the core steps in this process, accuracy of which directly affects the final
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Web Application for Quantification of Traumatic Brain Injury-Induced Cortical Lesions in Adult Mice. Neuroinformatics (IF 3.3) Pub Date : 2019-12-04 Robert Ciszek,Pedro Andrade,Jesse Tapiala,Asla Pitkänen,Xavier Ekolle Ndode-Ekane
Disabilities resulting from traumatic brain injury (TBI) strongly correlate with the cytoarchitectonic part of the brain damaged, lesion area, and type of lesion. We developed a Web application to estimate the location of the lesion on mouse cerebral cortex caused by TBI induced by lateral fluid-percussion injury. The application unfolds user-determined TBI lesion measurements, e.g., from histologic
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Functional Parcellation of Individual Cerebral Cortex Based on Functional MRI. Neuroinformatics (IF 3.3) Pub Date : 2019-12-04 Jiajia Zhao,Chao Tang,Jingxin Nie
The human brain atlas assists us to enhance our scientific understanding of brain structure and function. The typical anatomical atlases are mainly based on brain morphometry which cannot ensure the consistency of structure and function, and are also hard to cover individual functional differences especially in cerebral cortex. Thus, in recent years, functional atlases for individuals have captured
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Diverse Community Structures in the Neuronal-Level Connectome of the Drosophila Brain. Neuroinformatics (IF 3.3) Pub Date : 2019-12-03 Chi-Tin Shih,Yen-Jen Lin,Cheng-Te Wang,Ting-Yuan Wang,Chih-Chen Chen,Ta-Shun Su,Chung-Chuang Lo,Ann-Shyn Chiang
Drosophila melanogaster is one of the most important model animals in neurobiology owing to its manageable brain size, complex behaviour, and extensive genetic tools. However, without a comprehensive map of the brain-wide neural network, our ability to investigate brain functions at the systems level is seriously limited. In this study, we constructed a neuron-to-neuron network of the Drosophila brain
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A Novel 2D Standard Cartesian Representation for the Human Sensorimotor Cortex. Neuroinformatics (IF 3.3) Pub Date : 2019-12-03 Mark L C M Bruurmijn,Wouter Schellekens,Mathijs A H Raemaekers,Nick F Ramsey
For some experimental approaches in brain imaging, the existing normalization techniques are not always sufficient. This may be the case if the anatomical shape of the region of interest varies substantially across subjects, or if one needs to compare the left and right hemisphere in the same subject. Here we propose a new standard representation, building upon existing normalization methods: Cgrid
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