NEUROINFORMATICS

Scope & Guideline

Unlocking the Potential of Neural Data Through Computational Science

Introduction

Delve into the academic richness of NEUROINFORMATICS with our guidelines, detailing its aims and scope. Our resource identifies emerging and trending topics paving the way for new academic progress. We also provide insights into declining or waning topics, helping you stay informed about changing research landscapes. Evaluate highly cited topics and recent publications within these guidelines to align your work with influential scholarly trends.
LanguageEnglish
ISSN1539-2791
PublisherHUMANA PRESS INC
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 2003 to 2024
AbbreviationNEUROINFORMATICS / Neuroinformatics
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address999 RIVERVIEW DRIVE SUITE 208, TOTOWA, NJ 07512

Aims and Scopes

The journal 'NEUROINFORMATICS' primarily focuses on the intersection of neuroscience and informatics, emphasizing the development and application of computational tools and methods to understand brain function and structure. It aims to advance the field by integrating various data types, promoting data sharing, and enhancing reproducibility in neuroimaging and neuroinformatics research.
  1. Computational Neuroscience Tools:
    Development and application of computational models and tools for simulating and analyzing neural processes, including neural network models and machine learning approaches.
  2. Neuroimaging Techniques:
    Utilization and advancement of neuroimaging modalities such as fMRI, EEG, and MRI for studying brain structure and function, including the development of new imaging techniques and analysis methods.
  3. Data Integration and Analysis:
    Focus on integrating multimodal neuroimaging data and biological data, enabling comprehensive analyses that facilitate better understanding of brain connectivity and dynamics.
  4. Automated Processing and Machine Learning:
    Application of machine learning and automated algorithms to enhance data processing, classification, and analysis in neuroimaging studies, aimed at improving diagnostic accuracy and research efficiency.
  5. Neuroscience Data Sharing and FAIR Principles:
    Promotion of open science practices, including data sharing, standardization, and the implementation of FAIR (Findable, Accessible, Interoperable, Reusable) principles in neuroscience research.
In recent years, 'NEUROINFORMATICS' has seen a rise in several trending and emerging themes that reflect the evolving landscape of neuroscience research, particularly in the areas of data science and technology integration.
  1. Artificial Intelligence and Machine Learning:
    An increasing number of papers focus on the application of AI and machine learning for analyzing neuroimaging data, indicating a trend towards utilizing advanced computational techniques to enhance diagnostic and analytical capabilities.
  2. Integration of Multimodal Data:
    There is a growing emphasis on integrating various types of neuroimaging and biological data, enabling researchers to draw more comprehensive insights and foster a deeper understanding of brain function and pathology.
  3. Real-Time Neuroimaging Analysis:
    Emerging research is increasingly focused on real-time analysis of neuroimaging data, which is crucial for applications in clinical settings and for enhancing the understanding of dynamic brain processes.
  4. Neuroinformatics Education and Training:
    A trend towards developing educational frameworks and resources in neuroinformatics suggests a commitment to fostering a new generation of researchers skilled in computational methods and data management.
  5. Open Science and Data Sharing Initiatives:
    There is a notable increase in publications advocating for open science practices and data sharing initiatives, reflecting a broader movement towards transparency and collaboration in neuroscience research.

Declining or Waning

While 'NEUROINFORMATICS' continues to thrive in many areas, certain themes have shown a decline in prominence over recent years, indicating a potential shift in focus or interest within the research community.
  1. Traditional Statistical Methods:
    There is a noticeable decrease in the use of traditional statistical methodologies in favor of more advanced machine learning techniques, suggesting a shift towards data-driven approaches in neuroinformatics.
  2. Basic Neuroanatomy Studies:
    Research focusing solely on basic neuroanatomy without computational tools or data integration is becoming less frequent, as the field increasingly emphasizes the application of informatics in understanding complex brain structures.
  3. Single-Modal Neuroimaging Studies:
    Publications centered around single-modal neuroimaging approaches are declining as there is a growing trend towards multimodal studies that combine various imaging techniques for a more holistic understanding of brain function.
  4. Manual Data Processing Techniques:
    Manual and semi-automated data processing methods are being phased out in favor of fully automated solutions, reflecting the demand for efficiency and reproducibility in neuroinformatics research.

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