Read by QxMD icon Read


Nicole Labra, Pamela Guevara, Delphine Duclap, Josselin Houenou, Cyril Poupon, Jean-François Mangin, Miguel Figueroa
This paper presents an algorithm for fast segmentation of white matter bundles from massive dMRI tractography datasets using a multisubject atlas. We use a distance metric to compare streamlines in a subject dataset to labeled centroids in the atlas, and label them using a per-bundle configurable threshold. In order to reduce segmentation time, the algorithm first preprocesses the data using a simplified distance metric to rapidly discard candidate streamlines in multiple stages, while guaranteeing that no false negatives are produced...
October 8, 2016: Neuroinformatics
David B Stockton, Fidel Santamaria
Simulations in neuroscience are performed on local servers or High Performance Computing (HPC) facilities. Recently, cloud computing has emerged as a potential computational platform for neuroscience simulation. In this paper we compare and contrast HPC and cloud resources for scientific computation, then report how we deployed NEURON, a widely used simulator of neuronal activity, in three clouds: Chameleon Cloud, a hybrid private academic cloud for cloud technology research based on the OpenStack software; Rackspace, a public commercial cloud, also based on OpenStack; and Amazon Elastic Cloud Computing, based on Amazon's proprietary software...
September 21, 2016: Neuroinformatics
Hancan Zhu, Hewei Cheng, Xuesong Yang, Yong Fan
Automatic and reliable segmentation of hippocampus from MR brain images is of great importance in studies of neurological diseases, such as epilepsy and Alzheimer's disease. In this paper, we proposed a novel metric learning method to fuse segmentation labels in multi-atlas based image segmentation. Different from current label fusion methods that typically adopt a predefined distance metric model to compute a similarity measure between image patches of atlas images and the image to be segmented, we learn a distance metric model from the atlases to keep image patches of the same structure close to each other while those of different structures are separated...
September 16, 2016: Neuroinformatics
A J Cope, P Richmond, S S James, K Gurney, D J Allerton
There is a growing requirement in computational neuroscience for tools that permit collaborative model building, model sharing, combining existing models into a larger system (multi-scale model integration), and are able to simulate models using a variety of simulation engines and hardware platforms. Layered XML model specification formats solve many of these problems, however they are difficult to write and visualise without tools. Here we describe a new graphical software tool, SpineCreator, which facilitates the creation and visualisation of layered models of point spiking neurons or rate coded neurons without requiring the need for programming...
September 15, 2016: Neuroinformatics
Guodong Guo, Keegan Guffey, Wenbin Chen, Paola Pergami
Human gait recognition, an active research topic in computer vision, is generally based on data obtained from images/videos. We applied computer vision technology to classify pathology-related changes in gait in young children using a foot-pressure database collected using the GAITRite walkway system. As foot positioning changes with children's development, we also investigated the possibility of age estimation based on this data. Our results demonstrate that the data collected by the GAITRite system can be used for normal/pathological gait classification...
September 1, 2016: Neuroinformatics
Joseph A Maldjian, Carol A Shively, Michael A Nader, David P Friedman, Christopher T Whitlow
No abstract text is available yet for this article.
September 1, 2016: Neuroinformatics
Ludovica Acciai, Paolo Soda, Giulio Iannello
The reconstruction of neuron morphology allows to investigate how the brain works, which is one of the foremost challenges in neuroscience. This process aims at extracting the neuronal structures from microscopic imaging data. The great advances in microscopic technologies have made a huge amount of data available at the micro-, or even lower, resolution where manual inspection is time consuming, prone to error and utterly impractical. This has motivated the development of methods to automatically trace the neuronal structures, a task also known as neuron tracing...
October 2016: Neuroinformatics
Pankaj Singh, Pooran Negi, Fernanda Laezza, Manos Papadakis, Demetrio Labate
The spatial organization of neurites, the thin processes (i.e., dendrites and axons) that stem from a neuron's soma, conveys structural information required for proper brain function. The alignment, direction and overall geometry of neurites in the brain are subject to continuous remodeling in response to healthy and noxious stimuli. In the developing brain, during neurogenesis or in neuroregeneration, these structural changes are indicators of the ability of neurons to establish axon-to-dendrite connections that can ultimately develop into functional synapses...
October 2016: Neuroinformatics
Laura Anton-Sanchez, Concha Bielza, Ruth Benavides-Piccione, Javier DeFelipe, Pedro Larrañaga
The way in which a neuronal tree expands plays an important role in its functional and computational characteristics. We aimed to study the existence of an optimal neuronal design for different types of cortical GABAergic neurons. To do this, we hypothesized that both the axonal and dendritic trees of individual neurons optimize brain connectivity in terms of wiring length. We took the branching points of real three-dimensional neuronal reconstructions of the axonal and dendritic trees of different types of cortical interneurons and searched for the minimal wiring arborization structure that respects the branching points...
October 2016: Neuroinformatics
Xiaoke Hao, Xiaohui Yao, Jingwen Yan, Shannon L Risacher, Andrew J Saykin, Daoqiang Zhang, Li Shen
Neuroimaging genetics has attracted growing attention and interest, which is thought to be a powerful strategy to examine the influence of genetic variants (i.e., single nucleotide polymorphisms (SNPs)) on structures or functions of human brain. In recent studies, univariate or multivariate regression analysis methods are typically used to capture the effective associations between genetic variants and quantitative traits (QTs) such as brain imaging phenotypes. The identified imaging QTs, although associated with certain genetic markers, may not be all disease specific...
October 2016: Neuroinformatics
Huiyuan Huang, Zhongxiang Ding, Dewang Mao, Jianhua Yuan, Fangmei Zhu, Shuda Chen, Yan Xu, Lin Lou, Xiaoyan Feng, Le Qi, Wusi Qiu, Han Zhang, Yu-Feng Zang
The main goal of brain tumor surgery is to maximize tumor resection while minimizing the risk of irreversible postoperative functional sequelae. Eloquent functional areas should be delineated preoperatively, particularly for patients with tumors near eloquent areas. Functional magnetic resonance imaging (fMRI) is a noninvasive technique that demonstrates great promise for presurgical planning. However, specialized data processing toolkits for presurgical planning remain lacking. Based on several functions in open-source software such as Statistical Parametric Mapping (SPM), Resting-State fMRI Data Analysis Toolkit (REST), Data Processing Assistant for Resting-State fMRI (DPARSF) and Multiple Independent Component Analysis (MICA), here, we introduce an open-source MATLAB toolbox named PreSurgMapp...
October 2016: Neuroinformatics
Žiga Lesjak, Franjo Pernuš, Boštjan Likar, Žiga Špiclin
Changes of white-matter lesions (WMLs) are good predictors of the progression of neurodegenerative diseases like multiple sclerosis (MS). Based on longitudinal magnetic resonance (MR) imaging the changes can be monitored, while the need for their accurate and reliable quantification led to the development of several automated MR image analysis methods. However, an objective comparison of the methods is difficult, because publicly unavailable validation datasets with ground truth and different sets of performance metrics were used...
October 2016: Neuroinformatics
Siqi Liu, Donghao Zhang, Sidong Liu, Dagan Feng, Hanchuan Peng, Weidong Cai
The digital reconstruction of single neurons from 3D confocal microscopic images is an important tool for understanding the neuron morphology and function. However the accurate automatic neuron reconstruction remains a challenging task due to the varying image quality and the complexity in the neuronal arborisation. Targeting the common challenges of neuron tracing, we propose a novel automatic 3D neuron reconstruction algorithm, named Rivulet, which is based on the multi-stencils fast-marching and iterative back-tracking...
October 2016: Neuroinformatics
Márton Gulyás, Norbert Bencsik, Szilvia Pusztai, Hanna Liliom, Katalin Schlett
No abstract text is available yet for this article.
October 2016: Neuroinformatics
Sailesh Conjeti, Sepideh Mesbah, Mohammadreza Negahdar, Philipp L Rautenberg, Shaoting Zhang, Nassir Navab, Amin Katouzian
The steadily growing amounts of digital neuroscientific data demands for a reliable, systematic, and computationally effective retrieval algorithm. In this paper, we present Neuron-Miner, which is a tool for fast and accurate reference-based retrieval within neuron image databases. The proposed algorithm is established upon hashing (search and retrieval) technique by employing multiple unsupervised random trees, collectively called as Hashing Forests (HF). The HF are trained to parse the neuromorphological space hierarchically and preserve the inherent neuron neighborhoods while encoding with compact binary codewords...
October 2016: Neuroinformatics
Eduardo Conde-Sousa, Peter Szücs, Hanchuan Peng, Paulo Aguiar
It is well established that not only electrophysiology but also morphology plays an important role in shaping the functional properties of neurons. In order to properly quantify morphological features it is first necessary to translate observational histological data into 3-dimensional geometric reconstructions of the neuronal structures. This reconstruction process, independently of being manual or (semi-)automatic, requires several preparation steps (e.g. histological processing) before data acquisition using specialized software...
July 13, 2016: Neuroinformatics
Giorgio A Ascoli
No abstract text is available yet for this article.
July 2016: Neuroinformatics
Chao-Gan Yan, Xin-Di Wang, Xi-Nian Zuo, Yu-Feng Zang
Brain imaging efforts are being increasingly devoted to decode the functioning of the human brain. Among neuroimaging techniques, resting-state fMRI (R-fMRI) is currently expanding exponentially. Beyond the general neuroimaging analysis packages (e.g., SPM, AFNI and FSL), REST and DPARSF were developed to meet the increasing need of user-friendly toolboxes for R-fMRI data processing. To address recently identified methodological challenges of R-fMRI, we introduce the newly developed toolbox, DPABI, which was evolved from REST and DPARSF...
July 2016: Neuroinformatics
Grégory Operto, Marie Chupin, Bénédicte Batrancourt, Marie-Odile Habert, Olivier Colliot, Habib Benali, Cyril Poupon, Catherine Champseix, Christine Delmaire, Sullivan Marie, Denis Rivière, Mélanie Pélégrini-Issac, Vincent Perlbarg, Régine Trebossen, Michel Bottlaender, Vincent Frouin, Antoine Grigis, Dimitri Papadopoulos Orfanos, Hugo Dary, Ludovic Fillon, Chabha Azouani, Ali Bouyahia, Clara Fischer, Lydie Edward, Mathilde Bouin, Urielle Thoprakarn, Jinpeng Li, Leila Makkaoui, Sylvain Poret, Carole Dufouil, Vincent Bouteloup, Gaël Chételat, Bruno Dubois, Stéphane Lehéricy, Jean-François Mangin, Yann Cointepas
This paper provides an overview of CATI, a platform dedicated to multicenter neuroimaging. Initiated by the French Alzheimer's plan (2008-2012), CATI is a research project called on to provide service to other projects like an industrial partner. Its core mission is to support the neuroimaging of large populations, providing concrete solutions to the increasing complexity involved in such projects by bringing together a service infrastructure, the know-how of its expert academic teams and a large-scale, harmonized network of imaging facilities...
July 2016: Neuroinformatics
Andrew Melbourne, Nicolas Toussaint, David Owen, Ivor Simpson, Thanasis Anthopoulos, Enrico De Vita, David Atkinson, Sebastien Ourselin
Multi-modal, multi-parametric Magnetic Resonance (MR) Imaging is becoming an increasingly sophisticated tool for neuroimaging. The relationships between parameters estimated from different individual MR modalities have the potential to transform our understanding of brain function, structure, development and disease. This article describes a new software package for such multi-contrast Magnetic Resonance Imaging that provides a unified model-fitting framework. We describe model-fitting functionality for Arterial Spin Labeled MRI, T1 Relaxometry, T2 relaxometry and Diffusion Weighted imaging, providing command line documentation to generate the figures in the manuscript...
July 2016: Neuroinformatics
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"