Example of Frontiers in Systems Neuroscience format
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Example of Frontiers in Systems Neuroscience format Example of Frontiers in Systems Neuroscience format Example of Frontiers in Systems Neuroscience format Example of Frontiers in Systems Neuroscience format Example of Frontiers in Systems Neuroscience format Example of Frontiers in Systems Neuroscience format Example of Frontiers in Systems Neuroscience format Example of Frontiers in Systems Neuroscience format Example of Frontiers in Systems Neuroscience format Example of Frontiers in Systems Neuroscience format Example of Frontiers in Systems Neuroscience format Example of Frontiers in Systems Neuroscience format Example of Frontiers in Systems Neuroscience format Example of Frontiers in Systems Neuroscience format
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Example of Frontiers in Systems Neuroscience format Example of Frontiers in Systems Neuroscience format Example of Frontiers in Systems Neuroscience format Example of Frontiers in Systems Neuroscience format Example of Frontiers in Systems Neuroscience format Example of Frontiers in Systems Neuroscience format Example of Frontiers in Systems Neuroscience format Example of Frontiers in Systems Neuroscience format Example of Frontiers in Systems Neuroscience format Example of Frontiers in Systems Neuroscience format Example of Frontiers in Systems Neuroscience format Example of Frontiers in Systems Neuroscience format Example of Frontiers in Systems Neuroscience format Example of Frontiers in Systems Neuroscience format
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open access Open Access

Frontiers in Systems Neuroscience — Template for authors

Publisher: Frontiers Media
Categories Rank Trend in last 3 yrs
Neuroscience (miscellaneous) #7 of 24 down down by 5 ranks
Cognitive Neuroscience #37 of 96 down down by 28 ranks
Developmental Neuroscience #15 of 35 down down by 9 ranks
Cellular and Molecular Neuroscience #57 of 88 down down by 42 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 319 Published Papers | 1545 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 09/07/2020
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Related Journals

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Frontiers Media

Quality:  
High
CiteRatio: 5.7
SJR: 2.036
SNIP: 1.066
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Quality:  
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CiteRatio: 6.0
SJR: 1.959
SNIP: 1.174
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open access Open Access

Springer

Quality:  
High
CiteRatio: 6.8
SJR: 1.582
SNIP: 1.194

Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

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

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.

4.8

30% from 2019

CiteRatio for Frontiers in Systems Neuroscience from 2016 - 2020
Year Value
2020 4.8
2019 6.9
2018 7.7
2017 8.0
2016 6.3
graph view Graph view
table view Table view

1.65

18% from 2019

SJR for Frontiers in Systems Neuroscience from 2016 - 2020
Year Value
2020 1.65
2019 2.003
2018 2.067
2017 2.207
2016 2.07
graph view Graph view
table view Table view

0.99

21% from 2019

SNIP for Frontiers in Systems Neuroscience from 2016 - 2020
Year Value
2020 0.99
2019 1.255
2018 1.211
2017 1.236
2016 1.081
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has decreased by 30% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has decreased by 18% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 21% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Frontiers in Systems Neuroscience

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Frontiers Media

Frontiers in Systems Neuroscience

Approved by publishing and review experts on SciSpace, this template is built as per for Frontiers in Systems Neuroscience formatting guidelines as mentioned in Frontiers Media author instructions. The current version was created on 09 Jul 2020 and has been used by 589 authors to write and format their manuscripts to this journal.

Neuroscience

i
Last updated on
09 Jul 2020
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ISSN
1662-5137
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Impact Factor
High - 1.003
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
frontiersinSCNS_ENG_HUMS
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Citation Type
Numbered
[25]
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Bibliography Example
Blonder GE, Tinkham M, Klapwijk TM. Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys. Rev. B 25 (1982) 4515–4532.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.3389/NEURO.06.004.2008
Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience
Nikolaus Kriegeskorte1, Marieke Mur, Peter A. Bandettini

Abstract:

A fundamental challenge for systems neuroscience is to quantitatively relate its three major branches of research: brain-activity measurement, behavioral measurement, and computational modeling. Using measured brain-activity patterns to evaluate computational network models is complicated by the need to define the corresponde... A fundamental challenge for systems neuroscience is to quantitatively relate its three major branches of research: brain-activity measurement, behavioral measurement, and computational modeling. Using measured brain-activity patterns to evaluate computational network models is complicated by the need to define the correspondency between the units of the model and the channels of the brain-activity data, e.g. single-cell recordings or voxels from functional magnetic resonance imaging (fMRI). Similar correspondency problems complicate relating activity patterns between different modalities of brain-activity measurement, and between subjects and species. In order to bridge these divides, we suggest abstracting from the activity patterns themselves and computing representational dissimilarity matrices, which characterize the information carried by a given representation in a brain or model. We propose a new experimental and data-analytical framework called representational similarity analysis (RSA), in which multi-channel measures of neural activity are quantitatively related to each other and to computational theory and behavior by comparing representational dissimilarity matrices. We demonstrate RSA by relating representations of visual objects as measured with fMRI to computational models spanning a wide range of complexities. We argue that these ideas, which have deep roots in psychology and neuroscience, will allow the integrated quantitative analysis of data from all three branches, thus contributing to a more unified systems neuroscience. read more read less

Topics:

Computational model (54%)54% related to the paper, Similarity (psychology) (51%)51% related to the paper
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2,723 Citations
open accessOpen access Journal Article DOI: 10.3389/FNSYS.2010.00013
DPARSF: A MATLAB Toolbox for “Pipeline” Data Analysis of Resting-State fMRI
Yan Chao-Gan1, Zang Yu-Feng1

Abstract:

Resting-state functional magnetic resonance imaging (fMRI) has attracted more and more attention because of its effectiveness, simplicity and non-invasiveness in exploration of the intrinsic functional architecture of the human brain. However, user-friendly toolbox for "pipeline" data analysis of resting-state fMRI is still l... Resting-state functional magnetic resonance imaging (fMRI) has attracted more and more attention because of its effectiveness, simplicity and non-invasiveness in exploration of the intrinsic functional architecture of the human brain. However, user-friendly toolbox for "pipeline" data analysis of resting-state fMRI is still lacking. Based on some functions in Statistical Parametric Mapping (SPM) and Resting-State fMRI Data Analysis Toolkit (REST), we have developed a MATLAB toolbox called Data Processing Assistant for Resting-State fMRI (DPARSF) for "pipeline" data analysis of resting-state fMRI. After the user arranges the DICOM files and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data and results for functional connectivity (FC), regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF), and fractional ALFF (fALFF). DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. In addition, users can also use DPARSF to extract time courses from regions of interest. read more read less

Topics:

Resting state fMRI (54%)54% related to the paper, Statistical parametric mapping (54%)54% related to the paper
View PDF
2,556 Citations
open accessOpen access Journal Article DOI: 10.3389/FNSYS.2011.00002
A Baseline for the Multivariate Comparison of Resting-State Networks

Abstract:

As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both ro... As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease. read more read less

Topics:

Resting state fMRI (52%)52% related to the paper, Multivariate statistics (50%)50% related to the paper
View PDF
1,172 Citations
open accessOpen access Journal Article DOI: 10.3389/FNSYS.2010.00019
Clinical Applications of Resting State Functional Connectivity
Michael D. Fox1, Michael D. Greicius2

Abstract:

During resting conditions the brain remains functionally and metabolically active. One manifestation of this activity that has become an important research tool is spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal of functional magnetic resonance imaging (fMRI). The identification of correlation patte... During resting conditions the brain remains functionally and metabolically active. One manifestation of this activity that has become an important research tool is spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal of functional magnetic resonance imaging (fMRI). The identification of correlation patterns in these spontaneous fluctuations has been termed resting state functional connectivity (fcMRI) and has the potential to greatly increase the translation of fMRI into clinical care. In this article we review the advantages of the resting state signal for clinical applications including detailed discussion of signal to noise considerations. We include guidelines for performing resting state research on clinical populations, outline the different areas for clinical application, and identify important barriers to be addressed to facilitate the translation of resting state fcMRI into the clinical realm. read more read less

Topics:

Resting state fMRI (69%)69% related to the paper
View PDF
1,009 Citations
open accessOpen access Journal Article DOI: 10.3389/FNSYS.2010.00008
Advances and Pitfalls in the Analysis and Interpretation of Resting-State FMRI Data
David M. Cole1, Stephen M. Smith2, Christian F. Beckmann2, Christian F. Beckmann1

Abstract:

The last 15 years have witnessed a steady increase in the number of resting-state functional neuroimaging studies. The connectivity patterns of multiple functional, distributed, large-scale networks of brain dynamics have been recognised for their potential as useful tools in the domain of systems and other neurosciences. The... The last 15 years have witnessed a steady increase in the number of resting-state functional neuroimaging studies. The connectivity patterns of multiple functional, distributed, large-scale networks of brain dynamics have been recognised for their potential as useful tools in the domain of systems and other neurosciences. The application of functional connectivity methods to areas such as cognitive psychology, clinical diagnosis and treatment progression has yielded promising preliminary results, but is yet to be fully realised. This is due, in part, to an array of methodological and interpretative issues that remain to be resolved. We here present a review of the methods most commonly applied in this rapidly advancing field, such as seed-based correlation analysis and independent component analysis, along with examples of their use at the individual subject and group analysis levels and a discussion of practical and theoretical issues arising from this data ‘explosion’. We describe the similarities and differences across these varied statistical approaches to processing resting-state FMRI signals, and conclude that further technical optimisation and experimental refinement is required in order to fully delineate and characterise the gross complexity of the human neural functional architecture. read more read less

Topics:

Resting state fMRI (54%)54% related to the paper, Functional neuroimaging (52%)52% related to the paper
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931 Citations
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Frontiers in Systems Neuroscience format uses frontiersinSCNS_ENG_HUMS citation style.

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Frequently asked questions

1. Can I write Frontiers in Systems Neuroscience in LaTeX?

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

2. Do you follow the Frontiers in Systems Neuroscience guidelines?

Yes, the template is compliant with the Frontiers in Systems Neuroscience 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 Frontiers in Systems Neuroscience?

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 Frontiers in Systems Neuroscience citation style.

4. Can I use the Frontiers in Systems Neuroscience 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 Frontiers in Systems Neuroscience.

5. Can I use a manuscript in Frontiers in Systems Neuroscience 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 Frontiers in Systems Neuroscience that you can download at the end.

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7. Where can I find the template for the Frontiers in Systems Neuroscience?

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SciSpace's Frontiers in Systems Neuroscience 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.

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12. Is Frontiers in Systems Neuroscience'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 Frontiers in Systems Neuroscience?

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 Frontiers in Systems Neuroscience. 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 Frontiers in Systems Neuroscience?

The 5 most common citation types in order of usage for Frontiers in Systems Neuroscience 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 Frontiers in Systems Neuroscience?

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16. Can I download Frontiers in Systems Neuroscience 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 Frontiers in Systems Neuroscience Endnote style according to Elsevier guidelines.

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