Example of Behavioral and Brain Functions format
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Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format
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Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format Example of Behavioral and Brain Functions format
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open access Open Access

Behavioral and Brain Functions — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Behavioral Neuroscience #24 of 78 down down by 4 ranks
Cognitive Neuroscience #35 of 96 down down by 2 ranks
Biological Psychiatry #23 of 38 down down by 5 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 52 Published Papers | 254 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 22/06/2020
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Related Journals

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Quality:  
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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.

2.125

14% from 2018

Impact factor for Behavioral and Brain Functions from 2016 - 2019
Year Value
2019 2.125
2018 2.457
2017 2.449
2016 2.207
graph view Graph view
table view Table view

4.9

8% from 2019

CiteRatio for Behavioral and Brain Functions from 2016 - 2020
Year Value
2020 4.9
2019 5.3
2018 5.1
2017 5.0
2016 4.3
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 14% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has decreased by 8% 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.

0.896

13% from 2019

SJR for Behavioral and Brain Functions from 2016 - 2020
Year Value
2020 0.896
2019 1.025
2018 0.998
2017 0.986
2016 1.04
graph view Graph view
table view Table view

0.9

4% from 2019

SNIP for Behavioral and Brain Functions from 2016 - 2020
Year Value
2020 0.9
2019 0.864
2018 0.776
2017 0.94
2016 0.864
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 13% 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.

Behavioral and Brain Functions

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Springer

Behavioral and Brain Functions

Approved by publishing and review experts on SciSpace, this template is built as per for Behavioral and Brain Functions formatting guidelines as mentioned in Springer author instructions. The current version was created on and has been used by 536 authors to write and format their manuscripts to this journal.

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Last updated on
22 Jun 2020
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ISSN
1606-8610
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Open Access
Yes
i
Sherpa RoMEO Archiving Policy
White faq
i
Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Citation Type
Numbered
[25]
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Bibliography Example
Blonder, G.E., Tinkham, M., Klapwijk, T.M.: Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys. Rev. B 25(7), 4515–4532 (1982)

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1186/1744-9081-6-24
Dopamine signals for reward value and risk: basic and recent data
Wolfram Schultz1

Abstract:

Previous lesion, electrical self-stimulation and drug addiction studies suggest that the midbrain dopamine systems are parts of the reward system of the brain. This review provides an updated overview about the basic signals of dopamine neurons to environmental stimuli. The described experiments used standard behavioral and n... Previous lesion, electrical self-stimulation and drug addiction studies suggest that the midbrain dopamine systems are parts of the reward system of the brain. This review provides an updated overview about the basic signals of dopamine neurons to environmental stimuli. The described experiments used standard behavioral and neurophysiological methods to record the activity of single dopamine neurons in awake monkeys during specific behavioral tasks. Dopamine neurons show phasic activations to external stimuli. The signal reflects reward, physical salience, risk and punishment, in descending order of fractions of responding neurons. Expected reward value is a key decision variable for economic choices. The reward response codes reward value, probability and their summed product, expected value. The neurons code reward value as it differs from prediction, thus fulfilling the basic requirement for a bidirectional prediction error teaching signal postulated by learning theory. This response is scaled in units of standard deviation. By contrast, relatively few dopamine neurons show the phasic activation following punishers and conditioned aversive stimuli, suggesting a lack of relationship of the reward response to general attention and arousal. Large proportions of dopamine neurons are also activated by intense, physically salient stimuli. This response is enhanced when the stimuli are novel; it appears to be distinct from the reward value signal. Dopamine neurons show also unspecific activations to non-rewarding stimuli that are possibly due to generalization by similar stimuli and pseudoconditioning by primary rewards. These activations are shorter than reward responses and are often followed by depression of activity. A separate, slower dopamine signal informs about risk, another important decision variable. The prediction error response occurs only with reward; it is scaled by the risk of predicted reward. Neurophysiological studies reveal phasic dopamine signals that transmit information related predominantly but not exclusively to reward. Although not being entirely homogeneous, the dopamine signal is more restricted and stereotyped than neuronal activity in most other brain structures involved in goal directed behavior. read more read less

Topics:

Reward system (62%)62% related to the paper, Addiction (54%)54% related to the paper, PVLV (54%)54% related to the paper, Dopamine (52%)52% related to the paper
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624 Citations
open accessOpen access Journal Article DOI: 10.1186/1744-9081-7-30
Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals

Abstract:

Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component Analysis (ICA) to clean EE... Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component Analysis (ICA) to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts. We propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM). The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT) study, n = 12) that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP) paradigm, n = 18; motor imagery BCI paradigm, n = 80) that used data with different channel setups and from new subjects. Based on six features only, the optimized linear classifier performed on level with the inter-expert disagreement (< 10% Mean Squared Error (MSE)) on the RT data. On data of the auditory ERP study, the same pre-calculated classifier generalized well and achieved 15% MSE. On data of the motor imagery paradigm, we demonstrate that the discriminant information used for BCI is preserved when removing up to 60% of the most artifactual source components. We propose a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data. Based on linear methods, it is applicable for different electrode placements and supports the introspection of results. Trained on expert ratings of large data sets, it is not restricted to the detection of eye- and muscle artifacts. Its performance and generalization ability is demonstrated on data of different EEG studies. read more read less

Topics:

Linear classifier (56%)56% related to the paper, Independent component analysis (52%)52% related to the paper
View PDF
530 Citations
open accessOpen access Journal Article DOI: 10.1186/1744-9081-3-7
The face-specific N170 component is modulated by emotional facial expression
Vera C. Blau1, Urs Maurer1, Nim Tottenham1, Bruce D. McCandliss1

Abstract:

According to the traditional two-stage model of face processing, the face-specific N170 event-related potential (ERP) is linked to structural encoding of face stimuli, whereas later ERP components are thought to reflect processing of facial affect. This view has recently been challenged by reports of N170 modulations by emoti... According to the traditional two-stage model of face processing, the face-specific N170 event-related potential (ERP) is linked to structural encoding of face stimuli, whereas later ERP components are thought to reflect processing of facial affect. This view has recently been challenged by reports of N170 modulations by emotional facial expression. This study examines the time-course and topography of the influence of emotional expression on the N170 response to faces. Dense-array ERPs were recorded in response to a set (n = 16) of fear and neutral faces. Stimuli were normalized on dimensions of shape, size and luminance contrast distribution. To minimize task effects related to facial or emotional processing, facial stimuli were irrelevant to a primary task of learning associative pairings between a subsequently presented visual character and a spoken word. N170 to faces showed a strong modulation by emotional facial expression. A split half analysis demonstrates that this effect was significant both early and late in the experiment and was therefore not associated with only the initial exposures of these stimuli, demonstrating a form of robustness against habituation. The effect of emotional modulation of the N170 to faces did not show significant interaction with the gender of the face stimulus, or hemisphere of recording sites. Subtracting the fear versus neutral topography provided a topography that itself was highly similar to the face N170. The face N170 response can be influenced by emotional expressions contained within facial stimuli. The topography of this effect is consistent with the notion that fear stimuli exaggerates the N170 response itself. This finding stands in contrast to previous models suggesting that N170 processes linked to structural analysis of faces precede analysis of emotional expression, and instead may reflect early top-down modulation from neural systems involved in rapid emotional processing. read more read less

Topics:

Emotional expression (66%)66% related to the paper
View PDF
388 Citations
open accessOpen access Journal Article DOI: 10.1186/1744-9081-8-33
Gender differences in mathematics anxiety and the relation to mathematics performance while controlling for test anxiety
Amy Devine1, Kayleigh Fawcett1, Dénes Szűcs1, Ann Dowker2

Abstract:

Background Mathematics anxiety (MA), a state of discomfort associated with performing mathematical tasks, is thought to affect a notable proportion of the school age population. Some research has indicated that MA negatively affects mathematics performance and that girls may report higher levels of MA than boys. On the other... Background Mathematics anxiety (MA), a state of discomfort associated with performing mathematical tasks, is thought to affect a notable proportion of the school age population. Some research has indicated that MA negatively affects mathematics performance and that girls may report higher levels of MA than boys. On the other hand some research has indicated that boys’ mathematics performance is more negatively affected by MA than girls’ performance is. The aim of the current study was to measure girls’ and boys’ mathematics performance as well as their levels of MA while controlling for test anxiety (TA) a construct related to MA but which is typically not controlled for in MA studies. read more read less

Topics:

Test anxiety (59%)59% related to the paper, Anxiety (54%)54% related to the paper, Test Anxiety Scale (52%)52% related to the paper
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373 Citations
open accessOpen access Journal Article DOI: 10.1186/1744-9081-1-2
Methylphenidate improves prefrontal cortical cognitive function through α2 adrenoceptor and dopamine D1 receptor actions: Relevance to therapeutic effects in Attention Deficit Hyperactivity Disorder
Amy F.T. Arnsten1, Anne G Dudley1

Abstract:

BACKGROUND: Methylphenidate (MPH) is the classic treatment for Attention Deficit Hyperactivity Disorder (ADHD), yet the mechanisms underlying its therapeutic actions remain unclear. Recent studies have identified an oral, MPH dose regimen which when given to rats produces drug plasma levels similar to those measured in humans... BACKGROUND: Methylphenidate (MPH) is the classic treatment for Attention Deficit Hyperactivity Disorder (ADHD), yet the mechanisms underlying its therapeutic actions remain unclear. Recent studies have identified an oral, MPH dose regimen which when given to rats produces drug plasma levels similar to those measured in humans. The current study examined the effects of these low, orally-administered doses of MPH in rats performing a delayed alternation task dependent on prefrontal cortex (PFC), a brain region that is dysfunctional in ADHD, and is highly sensitive to levels of catecholamines. The receptor mechanisms underlying the enhancing effects of MPH were explored by challenging the MPH response with the noradrenergic alpha2 adrenoceptor antagonist, idazoxan, and the dopamine D1 antagonist, SCH23390. RESULTS: MPH produced an inverted U dose response whereby moderate doses (1.0-2.0 mg/kg, p.o.) significantly improved delayed alternation performance, while higher doses (2.0-3.0 mg/kg, p.o.) produced perseverative errors in many animals. The enhancing effects of MPH were blocked by co-administration of either the alpha2 adrenoceptor antagonist, idazoxan, or the dopamine D1 antagonist, SCH23390, in doses that had no effect on their own. CONCLUSION: The administration of low, oral doses of MPH to rats has effects on PFC cognitive function similar to those seen in humans and patients with ADHD. The rat can thus be used as a model for examination of neural mechanisms underlying the therapeutic effects of MPH on executive functions in humans. The efficacy of idazoxan and SCH23390 in reversing the beneficial effects of MPH indicate that both noradrenergic alpha2 adrenoceptor and dopamine D1 receptor stimulation contribute to cognitive-enhancing effects of MPH. read more read less

Topics:

Idazoxan (60%)60% related to the paper, Methylphenidate (54%)54% related to the paper, Dopamine receptor D1 (52%)52% related to the paper, Dopamine (52%)52% related to the paper, Attention deficit hyperactivity disorder (51%)51% related to the paper
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366 Citations
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Frequently asked questions

1. Can I write Behavioral and Brain Functions in LaTeX?

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

2. Do you follow the Behavioral and Brain Functions guidelines?

Yes, the template is compliant with the Behavioral and Brain Functions 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 Behavioral and Brain Functions?

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 Behavioral and Brain Functions 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 Behavioral and Brain Functions.

5. Can I use a manuscript in Behavioral and Brain Functions 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 Behavioral and Brain Functions that you can download at the end.

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12. Is Behavioral and Brain Functions'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 Behavioral and Brain Functions?

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 Behavioral and Brain Functions. 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 Behavioral and Brain Functions?

The 5 most common citation types in order of usage for Behavioral and Brain Functions are:.

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

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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 Behavioral and Brain Functions Endnote style according to Elsevier guidelines.

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