Example of Neuroinformatics format
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Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format
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Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format Example of Neuroinformatics format
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This content is only for preview purposes. The original open access content can be found here.
open access Open Access

Neuroinformatics — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Information Systems #65 of 329 down down by 31 ranks
Software #101 of 389 down down by 49 ranks
Neuroscience (all) #37 of 110 down down by 12 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 137 Published Papers | 815 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 11/07/2020
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Related Journals

open access Open Access
recommended Recommended

Taylor and Francis

Quality:  
High
CiteRatio: 6.8
SJR: 1.321
SNIP: 1.764
open access Open Access
recommended Recommended

IEEE

Quality:  
High
CiteRatio: 10.8
SJR: 1.075
SNIP: 2.756
open access Open Access

Frontiers Media

Quality:  
High
CiteRatio: 6.2
SJR: 0.427
SNIP: 1.319

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

3.3

36% from 2018

Impact factor for Neuroinformatics from 2016 - 2019
Year Value
2019 3.3
2018 5.127
2017 3.852
2016 3.2
graph view Graph view
table view Table view

5.9

28% from 2019

CiteRatio for Neuroinformatics from 2016 - 2020
Year Value
2020 5.9
2019 8.2
2018 7.2
2017 6.4
2016 5.4
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 36% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has decreased by 28% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

0.929

53% from 2019

SJR for Neuroinformatics from 2016 - 2020
Year Value
2020 0.929
2019 1.984
2018 1.988
2017 1.586
2016 1.358
graph view Graph view
table view Table view

1.41

37% from 2019

SNIP for Neuroinformatics from 2016 - 2020
Year Value
2020 1.41
2019 2.238
2018 1.785
2017 1.616
2016 1.047
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 53% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 37% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Neuroinformatics

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Springer

Neuroinformatics

Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and method...... Read More

Information Systems

Software

General Neuroscience

Computer Science

i
Last updated on
11 Jul 2020
i
ISSN
1539-2791
i
Impact Factor
High - 1.109
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
SPBASIC
i
Citation Type
Author Year
(Blonder et al, 1982)
i
Bibliography Example
Beenakker CWJ (2006) Specular andreev reflection in graphene. Phys Rev Lett 97(6):067,007, URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

Journal Article DOI: 10.1007/S12021-016-9299-4
DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging.
Chao-Gan Yan1, Xin-Di Wang2, Xi-Nian Zuo1, Yu-Feng Zang3
01 Jul 2016 - Neuroinformatics

Abstract:

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 ... 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. DPABI incorporates recent research advances on head motion control and measurement standardization, thus allowing users to evaluate results using stringent control strategies. DPABI also emphasizes test-retest reliability and quality control of data processing. Furthermore, DPABI provides a user-friendly pipeline analysis toolkit for rat/monkey R-fMRI data analysis to reflect the rapid advances in animal imaging. In addition, DPABI includes preprocessing modules for task-based fMRI, voxel-based morphometry analysis, statistical analysis and results viewing. DPABI is designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies. We anticipate this open-source toolbox will assist novices and expert users alike and continue to support advancing R-fMRI methodology and its application to clinical translational studies. read more read less
View PDF
2,179 Citations
Journal Article DOI: 10.1385/NI:2:2:145
The Small World of the Cerebral Cortex
Olaf Sporns1, Jonathan D. Zwi1
01 Jan 2004 - Neuroinformatics

Abstract:

While much information is available on the structural connectivity of the cerebral cortex, especially in the primate, the main organizational principles of the connection patterns linking brain areas, columns and individual cells have remained elusive. We attempt to characterize a wide variety of cortical connectivity data se... While much information is available on the structural connectivity of the cerebral cortex, especially in the primate, the main organizational principles of the connection patterns linking brain areas, columns and individual cells have remained elusive. We attempt to characterize a wide variety of cortical connectivity data sets using a specific set of graph theory methods. We measure global aspects of cortical graphs including the abundance of small structural motifs such as cycles, the degree of local clustering of connections and the average path length. We examine large-scale cortical connection matrices obtained from neuroanatomical data bases, as well as probabilistic connection matrices at the level of small cortical neuronal populations linked by intra-areal and interareal connections. All cortical connection matrices examined in this study exhibit “small-world” attributes, characterized by the presence of abundant clustering of connections combined with short average distances between neuronal elements. We discuss the significance of these universal organizational features of cortex in light of functional brain anatomy. Supplementary materials are at www.indiana.edu/∼cortex/lab.htm. read more read less

Topics:

Cortex (anatomy) (52%)52% related to the paper
1,265 Citations
Journal Article DOI: 10.1385/NI:3:1:065
BrainMap: the social evolution of a human brain mapping database.
Angela R. Laird1, Jack L. Lancaster, Peter T. Fox
01 Jan 2005 - Neuroinformatics

Abstract:

Human brain mapping is an experimental discipline that establishes structure-function correspondences in the brain through the combined application of experimental psychology, human neuroscience, and noninvasive neuroimaging. A deep and diverse literature on the functional organization of the human brain is emerging, which ha... Human brain mapping is an experimental discipline that establishes structure-function correspondences in the brain through the combined application of experimental psychology, human neuroscience, and noninvasive neuroimaging. A deep and diverse literature on the functional organization of the human brain is emerging, which has pushed neuroimaging squarely into the scientific mainstream. Because of this rapid growth, there is a great need to effectively collect and synthesize the body of literature in this field. The BrainMap database was created in response to this need as an electronic environment for modeling the human brain through quantitative meta-analysis of the brain mapping literature. BrainMap was originally conceived in 1987 and has received continuous funding from 1988 to 2004. During this time, BrainMap has consistently evolved to meet the challenges of an ever-changing field and continues to strive toward higher levels of applicability. In this article, we discuss BrainMap's structure and utility, and relate its progress and development as a neuroinformatics tool. read more read less

Topics:

Neuroinformatics (54%)54% related to the paper, Functional neuroimaging (50%)50% related to the paper
493 Citations
open accessOpen access Journal Article DOI: 10.1007/S12021-008-9041-Y
PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data
28 Jan 2009 - Neuroinformatics

Abstract:

Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions bas... Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python’s ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability. read more read less

Topics:

Univariate (57%)57% related to the paper, Statistical learning theory (52%)52% related to the paper, Python (programming language) (51%)51% related to the paper
View PDF
480 Citations
open accessOpen access Journal Article DOI: 10.1007/S12021-011-9109-Y
An Open Source Multivariate Framework for n-Tissue Segmentation with Evaluation on Public Data
Brian B. Avants1, Nicholas J. Tustison2, Jue Wu1, Philip A. Cook1, James C. Gee1
05 Mar 2011 - Neuroinformatics

Abstract:

We introduce Atropos, an ITK-based multivariate n-class open source segmentation algorithm distributed with ANTs (http://www.picsl.upenn.edu/ANTs). The Bayesian formulation of the segmentation problem is solved using the Expectation Maximization (EM) algorithm with the modeling of the class intensities based on either paramet... We introduce Atropos, an ITK-based multivariate n-class open source segmentation algorithm distributed with ANTs (http://www.picsl.upenn.edu/ANTs). The Bayesian formulation of the segmentation problem is solved using the Expectation Maximization (EM) algorithm with the modeling of the class intensities based on either parametric or non-parametric finite mixtures. Atropos is capable of incorporating spatial prior probability maps (sparse), prior label maps and/or Markov Random Field (MRF) modeling. Atropos has also been efficiently implemented to handle large quantities of possible labelings (in the experimental section, we use up to 69 classes) with a minimal memory footprint. This work describes the technical and implementation aspects of Atropos and evaluates its performance on two different ground-truth datasets. First, we use the BrainWeb dataset from Montreal Neurological Institute to evaluate three-tissue segmentation performance via (1) K-means segmentation without use of template data; (2) MRF segmentation with initialization by prior probability maps derived from a group template; (3) Prior-based segmentation with use of spatial prior probability maps derived from a group template. We also evaluate Atropos performance by using spatial priors to drive a 69-class EM segmentation problem derived from the Hammers atlas from University College London. These evaluation studies, combined with illustrative examples that exercise Atropos options, demonstrate both performance and wide applicability of this new platform-independent open source segmentation tool. read more read less

Topics:

Scale-space segmentation (65%)65% related to the paper, Image segmentation (60%)60% related to the paper, Segmentation (54%)54% related to the paper, Prior probability (53%)53% related to the paper, Markov random field (52%)52% related to the paper
471 Citations
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SciSpace is a very innovative solution to the formatting problem and existing providers, such as Mendeley or Word did not really evolve in recent years.

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With SciSpace, you do not need a word template for Neuroinformatics.

It automatically formats your research paper to Springer formatting guidelines and citation style.

You can download a submission ready research paper in pdf, LaTeX and docx formats.

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Time taken to format a paper and Compliance with guidelines

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Neuroinformatics format uses SPBASIC citation style.

Automatically format and order your citations and bibliography in a click.

SciSpace allows imports from all reference managers like Mendeley, Zotero, Endnote, Google Scholar etc.

Frequently asked questions

1. Can I write Neuroinformatics in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Neuroinformatics guidelines and auto format it.

2. Do you follow the Neuroinformatics guidelines?

Yes, the template is compliant with the Neuroinformatics guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Neuroinformatics?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Neuroinformatics citation style.

4. Can I use the Neuroinformatics templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Neuroinformatics.

5. Can I use a manuscript in Neuroinformatics that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Neuroinformatics that you can download at the end.

6. How long does it usually take you to format my papers in Neuroinformatics?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Neuroinformatics.

7. Where can I find the template for the Neuroinformatics?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Neuroinformatics's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Neuroinformatics's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. Neuroinformatics an online tool or is there a desktop version?

SciSpace's Neuroinformatics is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Neuroinformatics?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like Neuroinformatics?”

11. What is the output that I would get after using Neuroinformatics?

After writing your paper autoformatting in Neuroinformatics, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Neuroinformatics's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Neuroinformatics?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Neuroinformatics. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Neuroinformatics?

The 5 most common citation types in order of usage for Neuroinformatics are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the Neuroinformatics?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Neuroinformatics's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Neuroinformatics in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Neuroinformatics Endnote style according to Elsevier guidelines.

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I spent hours with MS word for reformatting. It was frustrating - plain and simple. With SciSpace, I can draft my manuscripts and once it is finished I can just submit. In case, I have to submit to another journal it is really just a button click instead of an afternoon of reformatting.

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