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Journal of Neuroscience Methods
H-index 37

Journal of Neuroscience Methods

Ranking & Metrics

Discipline name Position Best Scientists Publications D-Index
Neuroscience 74 372 335 31

Additional Metrics

Number of Best Scientists*: 617
Documents by Best Scientists*: 503
Top 100 Ranked Scientists*: 19
SCIMAGO H-index: 180
SCIMAGO SJR: 0.726
Impact Factor: 2.3

Overview

Top Research Topics at Journal of Neuroscience Methods?

Journal of Neuroscience Methods mainly tackles studies in Neuroscience, Artificial intelligence, Anatomy, Pattern recognition and Electrophysiology. Journal of Neuroscience Methods focused on Neuroscience research but expanded to cover Biophysics. In Journal of Neuroscience Methods, Machine learning, Software, Computer vision and Electroencephalography are investigated in conjunction with one another to address concerns in Artificial intelligence research.

The Electroencephalography study featured in the journal draws connections with the study of Speech recognition. The studies on Anatomy discussed can also contribute to research in the domains of Biomedical engineering and Spinal cord. It focuses on Biomedical engineering research which is adjacent to topics in Electrode.

  • Neuroscience (21.79%)
  • Artificial intelligence (16.33%)
  • Anatomy (9.04%)

What are the most cited papers published in the journal?

  • EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. (13028 citations)
  • Developments of a water-maze procedure for studying spatial learning in the rat (5653 citations)
  • Quantitative assessment of tactile allodynia in the rat paw. (5423 citations)

Research areas of the most cited articles at Journal of Neuroscience Methods:

The journal publications mainly tackle studies in Neuroscience, Artificial intelligence, Electrophysiology, Anatomy and Biomedical engineering. The Neuroscience research tackled in the most cited papers is interrelated with Biophysics which concerns subjects like Biochemistry. The journal papers tackle studies in Electroencephalography and the interrelated subject of Speech recognition to gain insights into Artificial intelligence.

What topics the last edition of the journal is best known for?

  • Internal medicine
  • Gene
  • Artificial intelligence

The previous edition focused in particular on these issues:

The aim of Journal of Neuroscience Methods is to expand the discussion of research in Artificial intelligence, Pattern recognition, Food intake, Food science and Food preference. The journal addresses concerns in Artificial intelligence which are intertwined with other disciplines, such as Stereoelectroencephalography and Brain tissue. In addition to Pattern recognition research, the journal aims to explore topics under Frequency domain and Motor imagery, Brain–computer interface.

It connects research in Food intake with the related topic of Food choice.

The most cited articles from the last journal are:

  • Brain tissue classification from stereoelectroencephalographic recordings (0 citations)
  • Feasibility of ultrasound-induced blood-brain barrier disruption with a single-element transducer under three different frequencies in two non-human primates in vivo: Case report. (0 citations)
  • Mouse model of voluntary movement deficits induced by needlestick injuries to the primary motor cortex (0 citations)

Papers citation over time

A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

The top authors publishing in Journal of Neuroscience Methods (based on the number of publications) are:

  • Vince D. Calhoun (40 papers) absent at the last edition,
  • Greg A. Gerhardt (30 papers) absent at the last edition,
  • Floris G. Wouterlood (19 papers) absent at the last edition,
  • Fengyu Cong (16 papers) absent at the last edition,
  • Dario Farina (16 papers) absent at the last edition.

The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.

Only papers with recognized affiliations are considered

The top affiliations publishing in Journal of Neuroscience Methods (based on the number of publications) are:

  • Max Planck Society (131 papers) absent at the last edition,
  • National Institutes of Health (119 papers) absent at the last edition,
  • Harvard University (119 papers) absent at the last edition,
  • French Institute of Health and Medical Research (100 papers) absent at the last edition,
  • Centre national de la recherche scientifique (94 papers) absent at the last edition.

The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2022 edition, 0.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 0.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 0.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 0.00% of all publications and 100.00% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

  • Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
  • Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
  • Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
  • Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
  • Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Career Opportunities and Progression:

The dynamic and interdisciplinary nature of Neuroscience and its associated fields like Artificial Intelligence and Anatomy provide a considerable scope of career opportunities. These range from research, academia, to healthcare and technology. Gaining deeper knowledge and experience in any of these areas can lead to varied professions like a neurologist, neuroscience researcher or a speech-language pathologist.

In the light of the mentioned disciplines, one intriguing profession is that of a speech-language pathologist. They work with people who have various speech-related conditions and use their knowledge of anatomy, neurology, and linguistics to provide effective therapy.

Typically, becoming a speech-pathologist requires a bachelor's degree in a related field, followed by a master's in speech pathology. Some individuals might choose to further specialize with a Ph.D. or clinical certification. If you are particularly interested in this profession, you might want to explore how to be a speech therapist in Connecticut. This link provides an in-depth guide on the educational path, licensure requirements, and job outlook for speech-language pathologists in Connecticut.

Overall, the broad spectrum of research areas covered by the Journal of Neuroscience Methods not only contributes to scientific knowledge but also opens up numerous pathways for personal career advancement in these fields.

Top Publications

  • Osprey: Open-source processing, reconstruction & estimation of magnetic resonance spectroscopy data

    Georg Oeltzschner;Georg Oeltzschner;Helge J. Zöllner;Steve C.N. Hui;Mark Mikkelsen

    (2020)
    224 Citations
  • The rt-TEP tool: real-time visualization of TMS-Evoked Potentials to maximize cortical activation and minimize artifacts

    Unknown

    (2021)
    165 Citations
  • TAAC - TMS Adaptable Auditory Control: A universal tool to mask TMS clicks

    Unknown

    (2021)
    156 Citations
  • Deep residual learning for neuroimaging: An application to predict progression to Alzheimer's disease.

    Anees Abrol;Anees Abrol;Anees Abrol;Manish Bhattarai;Manish Bhattarai;Alex Fedorov;Alex Fedorov;Alex Fedorov;Yuhui Du;Yuhui Du;Yuhui Du

    (2020)
    122 Citations
  • High-pass filtering artifacts in multivariate classification of neural time series data.

    Joram van Driel;Christian N.L. Olivers;Johannes J. Fahrenfort

    (2021)
    109 Citations
  • Animal models of pain: Diversity and benefits.

    Cynthia Abboud;Alexia Duveau;Rabia Bouali-Benazzouz;Karine Massé

    (2021)
    90 Citations
  • TMS-induced silent periods: A review of methods and call for consistency.

    K.E. Hupfeld;C.W. Swanson;B.W. Fling;R.D. Seidler

    (2020)
    75 Citations
  • The sensitivity of diffusion MRI to microstructural properties and experimental factors.

    Maryam Afzali;Tomasz Pieciak;Tomasz Pieciak;Sharlene Newman;Eleftherios Garyfallidis

    (2021)
    73 Citations
  • A convolutional-recurrent neural network approach to resting-state EEG classification in Parkinson's disease.

    Soojin Lee;Soojin Lee;Ramy Hussein;Rabab Ward;Z. Jane Wang

    (2021)
    70 Citations
  • Efficient whole brain transduction by systemic infusion of minimally purified AAV-PHP.eB.

    Ayumu Konno;Hirokazu Hirai

    (2020)
    57 Citations

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Best Scientists Contributing to This Journal

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