Frontiers in Neuroinformatics Journal Impact Factor & Information

Publisher: Frontiers Research Foundation, Frontiers

Journal description

Current impact factor: 0.00

Impact Factor Rankings

Additional details

5-year impact 0.00
Cited half-life 0.00
Immediacy index 0.00
Eigenfactor 0.00
Article influence 0.00
ISSN 1662-5196
OCLC 250621701
Material type Document, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details


  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author cannot archive a post-print version
  • Conditions
    • On open access repositories
    • Authors retain copyright
    • Creative Commons Attribution License
    • Published source must be acknowledged
    • Publisher's version/PDF must be used for post-print
    • Set statement to accompany [This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permission.]
    • Articles are placed in PubMed Central immediately on behalf of authors.
    • Publisher last contacted on 04/10/2013
    • All titles are open access journals
  • Classification
    ​ green

Publications in this journal

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    ABSTRACT: Data-driven neuroscience research is providing new insights in progression of neurological disorders and supporting the development of improved treatment approaches. However, the volume, velocity, and variety of neuroscience data generated from sophisticated recording instruments and acquisition methods have exacerbated the limited scalability of existing neuroinformatics tools. This makes it difficult for neuroscience researchers to effectively leverage the growing multi-modal neuroscience data to advance research in serious neurological disorders, such as epilepsy. We describe the development of the Cloudwave data flow that uses new data partitioning techniques to store and analyze electrophysiological signal in distributed computing infrastructure. The Cloudwave data flow uses MapReduce parallel programming algorithm to implement an integrated signal data processing pipeline that scales with large volume of data generated at high velocity. Using an epilepsy domain ontology together with an epilepsy focused extensible data representation format called Cloudwave Signal Format (CSF), the data flow addresses the challenge of data heterogeneity and is interoperable with existing neuroinformatics data representation formats, such as HDF5. The scalability of the Cloudwave data flow is evaluated using a 30-node cluster installed with the open source Hadoop software stack. The results demonstrate that the Cloudwave data flow can process increasing volume of signal data by leveraging Hadoop Data Nodes to reduce the total data processing time. The Cloudwave data flow is a template for developing highly scalable neuroscience data processing pipelines using MapReduce algorithms to support a variety of user applications.
    Frontiers in Neuroinformatics 03/2015; 9:4. DOI:10.3389/fninf.2015.00004
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    ABSTRACT: This paper presents a system for declaratively transforming medical subjects' data into a common data model representation. Our work is part of the "GAAIN" project on Alzheimer's disease data federation across multiple data providers. We present a general purpose data transformation system that we have developed by leveraging the existing state-of-the-art in data integration and query rewriting. In this work we have further extended the current technology with new formalisms that facilitate expressing a broader range of data transformation tasks, plus new execution methodologies to ensure efficient data transformation for disease datasets.
    Frontiers in Neuroinformatics 02/2015; 9:1. DOI:10.3389/fninf.2015.00001
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    Frontiers in Neuroinformatics 01/2015; 9:5. DOI:10.3389/fninf.2015.00005
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    Frontiers in Neuroinformatics 12/2014; 8:86. DOI:10.3389/fninf.2014.00086
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    ABSTRACT: The claustrum seems to have been waiting for the science of connectomics. Due to its tiny size, the structure has remained remarkably difficult to study until modern technological and mathematical advancements like graph theory, connectomics, diffusion tensor imaging, HARDI, and excitotoxic lesioning. That does not mean, however, that early methods allowed researchers to assess micro-connectomics. In fact, the claustrum is such an enigma that the only things known for certain about it are its histology, and that it is extraordinarily well connected. In this literature review, we provide background details on the claustrum and the history of its study in the human and in other animal species. By providing an explanation of the neuroimaging and histology methods have been undertaken to study the claustrum thus far-and the conclusions these studies have drawn-we illustrate this example of how the shift from micro-connectomics to macro-connectomics advances the field of neuroscience and improves our capacity to understand the brain.
    Frontiers in Neuroinformatics 11/2014; 8:83. DOI:10.3389/fninf.2014.00083
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    ABSTRACT: Fluorescence imaging is a powerful method for monitoring dynamic signals in the nervous system. However, analysis of dynamic fluorescence imaging data remains burdensome, in part due to the shortage of available software tools. To address this need, we have developed SIMA, an open source Python package that facilitates common analysis tasks related to fluorescence imaging. Functionality of this package includes correction of motion artifacts occurring during in vivo imaging with laser-scanning microscopy, segmentation of imaged fields into regions of interest (ROIs), and extraction of signals from the segmented ROIs. We have also developed a graphical user interface (GUI) for manual editing of the automatically segmented ROIs and automated registration of ROIs across multiple imaging datasets. This software has been designed with flexibility in mind to allow for future extension with different analysis methods and potential integration with other packages. Software, documentation, and source code for the SIMA package and ROI Buddy GUI are freely available at
    Frontiers in Neuroinformatics 09/2014; 8:80. DOI:10.3389/fninf.2014.00080
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    ABSTRACT: Background / Purpose: Benign childhood epilepsy with centrotemporal spikes (BCECTS) is the most common idiopathic epileptic syndrome with prevalence of approximately 15% in children with seizures. Cognitive impairments in memory and language are often associated with BCECTS. Functional connectivity analysis may provide key information to better understand the functional interactions of neural dynamics in patients with BCECTS. Main conclusion: BCECTS patients were characterized with altered resting state brain dynamics and impaired functional connectivity.
    Neuroinformatics 2014; 09/2014