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

Frontiers in Human Neuroscience — Template for authors

Publisher: Frontiers Media
Categories Rank Trend in last 3 yrs
Neuropsychology and Physiological Psychology #12 of 60 down down by 2 ranks
Psychiatry and Mental Health #100 of 502 down down by 28 ranks
Behavioral Neuroscience #20 of 78 down down by 8 ranks
Neurology #57 of 156 down down by 18 ranks
Biological Psychiatry #21 of 38 down down by 5 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 2090 Published Papers | 10661 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 01/06/2020
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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.

2.673

7% from 2018

Impact factor for Frontiers in Human Neuroscience from 2016 - 2019
Year Value
2019 2.673
2018 2.87
2017 2.871
2016 3.209
graph view Graph view
table view Table view

5.1

6% from 2019

CiteRatio for Frontiers in Human Neuroscience from 2016 - 2020
Year Value
2020 5.1
2019 5.4
2018 4.7
2017 5.6
2016 6.7
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 7% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has decreased by 6% 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.

1.128

9% from 2019

SJR for Frontiers in Human Neuroscience from 2016 - 2020
Year Value
2020 1.128
2019 1.241
2018 1.284
2017 1.471
2016 1.794
graph view Graph view
table view Table view

1.221

9% from 2019

SNIP for Frontiers in Human Neuroscience from 2016 - 2020
Year Value
2020 1.221
2019 1.122
2018 1.137
2017 1.137
2016 1.134
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 9% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 9% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Frontiers in Human Neuroscience

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Frontiers Media

Frontiers in Human Neuroscience

Frontiers in Human Neuroscience is a first-tier electronic journal devoted to understanding the brain mechanisms supporting cognitive and social behavior in humans, and how these mechanisms might be altered in disease states. The last 25 years have seen an explosive growth in ...... Read More

Neuroscience

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Last updated on
01 Jun 2020
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ISSN
1662-5161
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Impact Factor
Medium - 0.968
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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/FNHUM.2010.00186
Shaping functional architecture by oscillatory alpha activity : gating by inhibition
Ole Jensen1, Ali Mazaheri1

Abstract:

In order to understand the working brain as a network, it is essential to identify the mechanisms by which information is gated between regions. We here propose that information is gated by inhibiting task-irrelevant regions, thus routing information to task-relevant regions. The functional inhibition is reflected in oscillat... In order to understand the working brain as a network, it is essential to identify the mechanisms by which information is gated between regions. We here propose that information is gated by inhibiting task-irrelevant regions, thus routing information to task-relevant regions. The functional inhibition is reflected in oscillatory activity in the alpha band (8-13 Hz). From a physiological perspective the alpha activity provides pulsed inhibition reducing the processing capabilities of a given area. Active processing in the engaged areas is reflected by neuronal synchronization in the gamma band (30-100 Hz) accompanied by an alpha band decrease. According to this framework the brain should be studied as a network by investigating cross-frequency interactions between gamma and alpha activity. Specifically the framework predicts that optimal task performance will correlate with alpha activity in task-irrelevant areas. In this review we will discuss the empirical support for this framework. Given that alpha activity is by far the strongest signal recorded by EEG and MEG, we propose that a major part of the electrophysiological activity detected from the working brain reflects gating by inhibition. read more read less

Topics:

Magnetoencephalography (52%)52% related to the paper, Gating (52%)52% related to the paper, Alpha (ethology) (50%)50% related to the paper
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2,448 Citations
open accessOpen access Journal Article DOI: 10.3389/FNHUM.2014.00213
ERPLAB: an open-source toolbox for the analysis of event-related potentials
Javier Lopez-Calderon1, Steven J. Luck1

Abstract:

ERPLAB Toolbox is a freely available, open-source toolbox for processing and analyzing event-related potential (ERP) data in the MATLAB environment. ERPLAB is closely integrated with EEGLAB, a popular open-source toolbox that provides many EEG preprocessing steps and an excellent user interface design. ERPLAB adds to EEGLAB’s... ERPLAB Toolbox is a freely available, open-source toolbox for processing and analyzing event-related potential (ERP) data in the MATLAB environment. ERPLAB is closely integrated with EEGLAB, a popular open-source toolbox that provides many EEG preprocessing steps and an excellent user interface design. ERPLAB adds to EEGLAB’s EEG processing functions, providing additional tools for filtering, artifact detection, re-referencing, and sorting of events, among others. ERPLAB also provides robust tools for averaging EEG segments together to create averaged ERPs, for creating difference waves and other recombinations of ERP waveforms through algebraic expressions, for filtering and re-referencing the averaged ERPs, for plotting ERP waveforms and scalp maps, and for quantifying several types of amplitudes and latencies. ERPLAB’s tools can be accessed either from an easy-to-learn graphical user interface or from MATLAB scripts, and a command history function makes it easy for users with no programming experience to write scripts. Consequently, ERPLAB provides both ease of use and virtually unlimited power and flexibility, making it appropriate for the analysis of both simple and complex ERP experiments. Several forms of documentation are available, including a detailed user’s guide, a step-by-step tutorial, a scripting guide, and a set of video-based demonstrations. read more read less

Topics:

EEGLAB (64%)64% related to the paper, Toolbox (54%)54% related to the paper, User interface design (52%)52% related to the paper, Graphical user interface (51%)51% related to the paper
View PDF
1,726 Citations
open accessOpen access Journal Article DOI: 10.3389/NEURO.09.031.2009
The Human Brain in Numbers: A Linearly Scaled-up Primate Brain
Suzana Herculano-Houzel1

Abstract:

The human brain has often been viewed as outstanding among mammalian brains: the most cognitively able, the largest-than-expected from body size, endowed with an overdeveloped cerebral cortex that represents over 80% of brain mass, and purportedly containing 100 billion neurons and 10× more glial cells. Such uniqueness was se... The human brain has often been viewed as outstanding among mammalian brains: the most cognitively able, the largest-than-expected from body size, endowed with an overdeveloped cerebral cortex that represents over 80% of brain mass, and purportedly containing 100 billion neurons and 10× more glial cells. Such uniqueness was seemingly necessary to justify the superior cognitive abilities of humans over larger-brained mammals such as elephants and whales. However, our recent studies using a novel method to determine the cellular composition of the brain of humans and other primates as well as of rodents and insectivores show that, since different cellular scaling rules apply to the brains within these orders, brain size can no longer be considered a proxy for the number of neurons in the brain. These studies also showed that the human brain is not exceptional in its cellular composition, as it was found to contain as many neuronal and non-neuronal cells as would be expected of a primate brain of its size. Additionally, the so-called overdeveloped human cerebral cortex holds only 19% of all brain neurons, a fraction that is similar to that found in other mammals. In what regards absolute numbers of neurons, however, the human brain does have two advantages compared to other mammalian brains: compared to rodents, and probably to whales and elephants as well, it is built according to the very economical, space-saving scaling rules that apply to other primates; and, among economically built primate brains, it is the largest, hence containing the most neurons. These findings argue in favor of a view of cognitive abilities that is centered on absolute numbers of neurons, rather than on body size or encephalization, and call for a re-examination of several concepts related to the exceptionality of the human brain. read more read less

Topics:

Human brain (59%)59% related to the paper, Brain size (58%)58% related to the paper, Encephalization (57%)57% related to the paper, Brain Mass (52%)52% related to the paper
View PDF
1,241 Citations
open accessOpen access Journal Article DOI: 10.3389/FNHUM.2010.00215
Attention, Uncertainty, and Free-Energy
Harriet Feldman1, Karl J. Friston1

Abstract:

We suggested recently that attention can be understood as inferring the level of uncertainty or precision during hierarchical perception. In this paper, we try to substantiate this claim using neuronal simulations of directed spatial attention and biased competition. These simulations assume that neuronal activity encodes a p... We suggested recently that attention can be understood as inferring the level of uncertainty or precision during hierarchical perception. In this paper, we try to substantiate this claim using neuronal simulations of directed spatial attention and biased competition. These simulations assume that neuronal activity encodes a probabilistic representation of the world that optimizes free-energy in a Bayesian fashion. Because free-energy bounds surprise or the (negative) log-evidence for internal models of the world, this optimization can be regarded as evidence accumulation or (generalized) predictive coding. Crucially, both predictions about the state of the world generating sensory data and the precision of those data have to be optimized. Here, we show that if the precision depends on the states, one can explain many aspects of attention. We illustrate this in the context of the Posner paradigm, using the simulations to generate both psychophysical and electrophysiological responses. These simulated responses are consistent with attentional bias or gating, competition for attentional resources, attentional capture and associated speed-accuracy trade-offs. Furthermore, if we present both attended and non-attended stimuli simultaneously, biased competition for neuronal representation emerges as a principled and straightforward property of Bayes-optimal perception. read more read less

Topics:

Attentional bias (52%)52% related to the paper
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1,015 Citations
open accessOpen access Journal Article DOI: 10.3389/FNHUM.2015.00386
GRETNA: a graph theoretical network analysis toolbox for imaging connectomics
Jinhui Wang1, Jinhui Wang2, Xindi Wang1, Mingrui Xia1, Xuhong Liao1, Alan C. Evans3, Yong-Min He3, Yong-Min He1

Abstract:

Recent studies have suggested that the brain’s structural and functional networks (i.e., connectomics) can be constructed by various imaging technologies (e.g., EEG/MEG; structural, diffusion and functional MRI) and further characterized by graph theory. Given the huge complexity of network construction, analysis and statisti... Recent studies have suggested that the brain’s structural and functional networks (i.e., connectomics) can be constructed by various imaging technologies (e.g., EEG/MEG; structural, diffusion and functional MRI) and further characterized by graph theory. Given the huge complexity of network construction, analysis and statistics, toolboxes incorporating these functions are largely lacking. Here, we developed the GRaph thEoreTical Network Analysis (GRETNA) toolbox for imaging connectomics. The GRETNA contains several key features as follows: (i) an open-source, Matlab-based, cross-platform (Windows and UNIX OS) package with a graphical user interface; (ii) allowing topological analyses of global and local network properties with parallel computing ability, independent of imaging modality and species; (iii) providing flexible manipulations in several key steps during network construction and analysis, which include network node definition, network connectivity processing, network type selection and choice of thresholding procedure; (iv) allowing statistical comparisons of global, nodal and connectional network metrics and assessments of relationship between these network metrics and clinical or behavioral variables of interest; and (v) including functionality in image preprocessing and network construction based on resting-state functional MRI (R-fMRI) data. After applying the GRETNA to a publicly released R-fMRI dataset of 54 healthy young adults, we demonstrated that human brain functional networks exhibit efficient small-world, assortative, hierarchical and modular organizations and possess highly connected hubs and that these findings are robust against different analytical strategies. With these efforts, we anticipate that GRETNA will accelerate imaging connectomics in an easy, quick and flexible manner. GRETNA is freely available on the NITRC website (http://www.nitrc.org/projects/gretna/). read more read less

Topics:

Connectomics (57%)57% related to the paper, Local area network (51%)51% related to the paper, Network analysis (50%)50% related to the paper, Graph (abstract data type) (50%)50% related to the paper
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884 Citations
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Frontiers in Human Neuroscience format uses frontiersinSCNS_ENG_HUMS citation style.

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Frequently asked questions

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

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Yes, the template is compliant with the Frontiers in Human 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 Human 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 Human Neuroscience citation style.

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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 Human Neuroscience.

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

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

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13. What is Sherpa RoMEO Archiving Policy for Frontiers in Human 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 Human 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 Human Neuroscience?

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

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

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