Example of Human Brain Mapping format
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Example of Human Brain Mapping format Example of Human Brain Mapping format Example of Human Brain Mapping format Example of Human Brain Mapping format Example of Human Brain Mapping format Example of Human Brain Mapping format Example of Human Brain Mapping format
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Example of Human Brain Mapping format Example of Human Brain Mapping format Example of Human Brain Mapping format Example of Human Brain Mapping format Example of Human Brain Mapping format Example of Human Brain Mapping format Example of Human Brain Mapping 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
recommended Recommended

Human Brain Mapping — Template for authors

Publisher: Wiley
Categories Rank Trend in last 3 yrs
Anatomy #2 of 37 -
Radiology, Nuclear Medicine and Imaging #20 of 288 down down by 11 ranks
Neurology (clinical) #33 of 343 down down by 8 ranks
Neurology #17 of 156 down down by 5 ranks
Radiological and Ultrasound Technology #7 of 51 down down by 5 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 1538 Published Papers | 12888 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 09/06/2020
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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.

4.421

3% from 2018

Impact factor for Human Brain Mapping from 2016 - 2019
Year Value
2019 4.421
2018 4.554
2017 4.927
2016 4.53
graph view Graph view
table view Table view

8.4

6% from 2019

CiteRatio for Human Brain Mapping from 2016 - 2020
Year Value
2020 8.4
2019 7.9
2018 8.3
2017 9.5
2016 9.9
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 3% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased 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.

2.005

10% from 2019

SJR for Human Brain Mapping from 2016 - 2020
Year Value
2020 2.005
2019 2.216
2018 2.447
2017 2.664
2016 2.804
graph view Graph view
table view Table view

1.476

4% from 2019

SNIP for Human Brain Mapping from 2016 - 2020
Year Value
2020 1.476
2019 1.419
2018 1.38
2017 1.413
2016 1.342
graph view Graph view
table view Table view

insights Insights

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

insights Insights

  • SNIP of this journal has increased by 4% in last years.
  • This journal’s SNIP is in the top 10 percentile category.
Human Brain Mapping

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Wiley

Human Brain Mapping

Human Brain Mapping publishes peer-reviewed basic, clinical, technical, and theoretical research in the interdisciplinary and rapidly expanding field of human brain mapping. The journal features research derived from non-invasive brain imaging modalities used to explore the sp...... Read More

Medicine

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Last updated on
09 Jun 2020
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ISSN
1065-9471
i
Impact Factor
High - 1.728
i
Open Access
Yes
i
Sherpa RoMEO Archiving Policy
Yellow faq
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Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
apa
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Citation Type
Numbered
[25]
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Bibliography Example
Beenakker, C.W.J. (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: 10.1002/HBM.460020402
Statistical parametric maps in functional imaging: A general linear approach
01 Jan 1994 - Human Brain Mapping

Abstract:

+ Abstract: Statistical parametric maps are spatially extended statistical processes that are used to test hypotheses about regionally specific effects in neuroimaging data. The most established sorts of statistical parametric maps (e.g., Friston et al. (1991): J Cereb Blood Flow Metab 11:690-699; Worsley et al. 119921: J Cer... + Abstract: Statistical parametric maps are spatially extended statistical processes that are used to test hypotheses about regionally specific effects in neuroimaging data. The most established sorts of statistical parametric maps (e.g., Friston et al. (1991): J Cereb Blood Flow Metab 11:690-699; Worsley et al. 119921: J Cereb Blood Flow Metab 12:YOO-918) are based on linear models, for example ANCOVA, correlation coefficients and t tests. In the sense that these examples are all special cases of the general linear model it should be possible to implement them (and many others) within a unified framework. We present here a general approach that accommodates most forms of experimental layout and ensuing analysis (designed experiments with fixed effects for factors, covariates and interaction of factors). This approach brings together two well established bodies of theory (the general linear model and the theory of Gaussian fields) to provide a complete and simple framework for the analysis of imaging data. The importance of this framework is twofold: (i) Conceptual and mathematical simplicity, in that the same small number of operational equations is used irrespective of the complexity of the experiment or nature of the statistical model and (ii) the generality of the framework provides for great latitude in experimental design and analysis. read more read less

Topics:

Statistical model (58%)58% related to the paper, Linear model (57%)57% related to the paper, General linear model (57%)57% related to the paper, Parametric statistics (57%)57% related to the paper, Statistical parametric mapping (54%)54% related to the paper
View PDF
9,254 Citations
open accessOpen access Journal Article DOI: 10.1002/HBM.10062
Fast robust automated brain extraction
Stephen M. Smith1
01 Nov 2002 - Human Brain Mapping

Abstract:

An automated method for segmenting magnetic resonance head images into brain and non-brain has been developed. It is very robust and accurate and has been tested on thousands of data sets from a wide variety of scanners and taken with a wide variety of MR sequences. The method, Brain Extraction Tool (BET), uses a deformable m... An automated method for segmenting magnetic resonance head images into brain and non-brain has been developed. It is very robust and accurate and has been tested on thousands of data sets from a wide variety of scanners and taken with a wide variety of MR sequences. The method, Brain Extraction Tool (BET), uses a deformable model that evolves to fit the brain's surface by the application of a set of locally adaptive model forces. The method is very fast and requires no preregistration or other pre-processing before being applied. We describe the new method and give examples of results and the results of extensive quantitative testing against "gold-standard" hand segmentations, and two other popular automated methods. read more read less

Topics:

Brain segmentation (60%)60% related to the paper
View PDF
8,815 Citations
open accessOpen access Journal Article DOI: 10.1002/HBM.1058
Nonparametric permutation tests for functional neuroimaging: A primer with examples
Thomas E. Nichols1, Andrew P. Holmes2
01 Jan 2002 - Human Brain Mapping

Abstract:

Requiring only minimal assumptions for validity, nonparametric permutation testing provides a flexible and intuitive methodology for the statistical analysis of data from functional neuroimaging experiments, at some computational expense. Introduced into the functional neuroimaging literature by Holmes et al. ([1996]: J Cereb... Requiring only minimal assumptions for validity, nonparametric permutation testing provides a flexible and intuitive methodology for the statistical analysis of data from functional neuroimaging experiments, at some computational expense. Introduced into the functional neuroimaging literature by Holmes et al. ([1996]: J Cereb Blood Flow Metab 16:7-22), the permutation approach readily accounts for the multiple comparisons problem implicit in the standard voxel-by-voxel hypothesis testing framework. When the appropriate assumptions hold, the nonparametric permutation approach gives results similar to those obtained from a comparable Statistical Parametric Mapping approach using a general linear model with multiple comparisons corrections derived from random field theory. For analyses with low degrees of freedom, such as single subject PET/SPECT experiments or multi-subject PET/SPECT or fMRI designs assessed for population effects, the nonparametric approach employing a locally pooled (smoothed) variance estimate can outperform the comparable Statistical Parametric Mapping approach. Thus, these nonparametric techniques can be used to verify the validity of less computationally expensive parametric approaches. Although the theory and relative advantages of permutation approaches have been discussed by various authors, there has been no accessible explication of the method, and no freely distributed software implementing it. Consequently, there have been few practical applications of the technique. This article, and the accompanying MATLAB software, attempts to address these issues. The standard nonparametric randomization and permutation testing ideas are developed at an accessible level, using practical examples from functional neuroimaging, and the extensions for multiple comparisons described. Three worked examples from PET and fMRI are presented, with discussion, and comparisons with standard parametric approaches made where appropriate. Practical considerations are given throughout, and relevant statistical concepts are expounded in appendices. read more read less

Topics:

Statistical parametric mapping (57%)57% related to the paper, Nonparametric statistics (56%)56% related to the paper, Parametric statistics (53%)53% related to the paper, Statistical hypothesis testing (52%)52% related to the paper, Permutation (51%)51% related to the paper
View PDF
5,237 Citations
Journal Article DOI: 10.1002/HBM.460030303
Spatial registration and normalization of images
01 Jan 1995 - Human Brain Mapping

Abstract:

This paper concerns the spatial and intensity transformations that map one image onto another. We present a general technique that facilitates nonlinear spatial (stereotactic) normalization and image realignment. This technique minimizes the sum of squares between two images following nonlinear spatial deformations and transf... This paper concerns the spatial and intensity transformations that map one image onto another. We present a general technique that facilitates nonlinear spatial (stereotactic) normalization and image realignment. This technique minimizes the sum of squares between two images following nonlinear spatial deformations and transformations of the voxel (intensity) values. The spatial and intensity transformations are obtained simultaneously, and explicitly, using a least squares solution and a series of linearising devices. The approach is completely noninteractive (automatic), nonlinear, and noniterative. It can be applied in any number of dimensions. Various applications are considered, including the realignment of functional magnetic resonance imaging (MRI) time-series, the linear (affine) and nonlinear spatial normalization of positron emission tomography (PET) and structural MRI images, the coregistration of PET to structural MRI, and, implicitly, the conjoining of PET and MRI to obtain high resolution functional images. © 1995 Wiley-Liss, Inc. read more read less

Topics:

Spatial normalization (61%)61% related to the paper, Normalization (image processing) (55%)55% related to the paper, Image processing (53%)53% related to the paper, Affine transformation (50%)50% related to the paper, Explained sum of squares (50%)50% related to the paper
3,622 Citations
open accessOpen access Journal Article DOI: 10.1002/1097-0193(200007)10:3<120::AID-HBM30>3.0.CO;2-8
Automated Talairach Atlas labels for functional brain mapping
01 Jul 2000 - Human Brain Mapping

Abstract:

An automated coordinate-based system to retrieve brain labels from the 1988 Talairach Atlas, called the Talairach Daemon (TD), was previously introduced (Lancaster et al., 1997). In the present study, the TD system and its 3-D database of labels for the 1988 Talairach atlas were tested for labeling of functional activation fo... An automated coordinate-based system to retrieve brain labels from the 1988 Talairach Atlas, called the Talairach Daemon (TD), was previously introduced (Lancaster et al., 1997). In the present study, the TD system and its 3-D database of labels for the 1988 Talairach atlas were tested for labeling of functional activation foci. TD system labels were compared with author-designated labels of activation coordinates from over 250 published functional brain-mapping studies and with manual atlas-derived labels from an expert group using a subset of these activation coordinates. Automated labeling by the TD system compared well with authors' labels, with a 70% or greater label match averaged over all locations. Author-label matching improved to greater than 90% within a search range of 65 mm for most sites. An adaptive grey matter (GM) range-search utility was evaluated using individual activations from the M1 mouth region (30 subjects, 52 sites). It provided an 87% label match to Brodmann area labels (B A4&B A 6) within a search range of 65 mm. Using the adaptive GM range search, the TD system's overall match with authors' labels (90%) was better than that of the expert group (80%). When used in concert with authors' deeper knowledge of an experiment, the TD system provides consistent and comprehensive labels for brain activation foci. Additional suggested applications of the TD system include interactive labeling, anatomical grouping of activation foci, lesion-deficit analysis, and neuroanatomy education. Hum. Brain Mapping 10:120 -131, 2000. © 2000 Wiley-Liss, Inc. read more read less
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3,213 Citations
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Human Brain Mapping format uses apa citation style.

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Frequently asked questions

1. Can I write Human Brain Mapping in LaTeX?

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

2. Do you follow the Human Brain Mapping guidelines?

Yes, the template is compliant with the Human Brain Mapping 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 Human Brain Mapping?

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 Human Brain Mapping citation style.

4. Can I use the Human Brain Mapping 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 Human Brain Mapping.

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

6. How long does it usually take you to format my papers in Human Brain Mapping?

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

7. Where can I find the template for the Human Brain Mapping?

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 Human Brain Mapping'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 Human Brain Mapping'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. Human Brain Mapping an online tool or is there a desktop version?

SciSpace's Human Brain Mapping 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 Human Brain Mapping?

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 Human Brain Mapping?”

11. What is the output that I would get after using Human Brain Mapping?

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

12. Is Human Brain Mapping'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 Human Brain Mapping?

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 Human Brain Mapping. 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 Human Brain Mapping?

The 5 most common citation types in order of usage for Human Brain Mapping 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 Human Brain Mapping?

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 Human Brain Mapping's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Human Brain Mapping 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 Human Brain Mapping Endnote style according to Elsevier guidelines.

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