Network Neuroscience - MIT Press | 2025 Impact Factor:3.4 | Cite Score:7.2 | SCOPUS Q1

Impact Factor and Journal Rank of Network Neuroscience

• About: Network Neuroscience is a peer-reviewed journal published by MIT Press. It focuses on research at the intersection of neuroscience, network science, and complex systems. The journal provides a platform for scholars, researchers, and practitioners to publish original research articles, reviews, and case studies that advance the understanding of brain networks and their dynamics.
• Objective: The primary objective of Network Neuroscience is to promote research and innovation in the field of network neuroscience. The journal aims to explore theories, methodologies, and applications that enhance our understanding of brain connectivity and its relationship to cognitive function and behavior. By publishing high-quality research, the journal contributes to the advancement of network neuroscience and its implications for neuroscience, psychology, and medicine.
• Interdisciplinary Focus: Network Neuroscience adopts an interdisciplinary approach, welcoming contributions from various fields related to neuroscience, network science, and complex systems, including but not limited to, Brain Connectivity, Functional Brain Networks, Structural Brain Networks, Network Dynamics, Graph Theory, Computational Neuroscience, Cognitive Neuroscience, Clinical Neuroscience, Neuroimaging, Brain Mapping. This interdisciplinary perspective fosters collaboration and innovation, leading to the development of advanced methodologies and models that elucidate the complex organization of the brain.
• Global Reach and Impact: With a broad international readership and authorship, Network Neuroscience has a global reach and impact. Its publications contribute to the dissemination of knowledge and advancements in network neuroscience worldwide. The journal content influences both academic research and practical applications, driving progress in areas such as understanding brain disorders, developing diagnostic tools, and designing interventions for neurological conditions.
• High Standards and Rigorous Review: Maintaining high academic standards, Network Neuroscience conducts a rigorous peer-review process. Each submitted manuscript undergoes thorough evaluation by experts in the field to ensure the quality, originality, and scientific rigor of the research. This stringent review process upholds the integrity and reputation of the journal, ensuring that only high-quality and impactful research is published.
• Significance: Network Neuroscience plays a significant role in advancing research and practice in the field of network neuroscience. By providing a platform for the publication of cutting-edge research findings, the journal contributes to the growth of knowledge and innovation in understanding the brain as a complex network. It serves as an essential resource for researchers, practitioners, and policymakers seeking to leverage network neuroscience to unravel the mysteries of brain function and dysfunction, ultimately leading to improved treatments and interventions for neurological and psychiatric disorders.

• Journal Home:  Journal Homepage

• Editor-in-Chief:  Olaf Sporns

• Scope: The Network Neuroscience journal, published by MIT Press, is a prominent peer-reviewed publication dedicated to advancing the interdisciplinary field of network neuroscience. It provides a crucial platform for researchers, neuroscientists, physicists, computer scientists, and experts from various disciplines to share their latest research findings, methodologies, and insights into the complex network organization of the brain. Here is an overview of its key focus areas and scope:
• 1. Brain Network Analysis:
  Research on the analysis and modeling of brain networks using techniques from graph theory, network science, and computational neuroscience.
  Studies on structural and functional brain connectivity, including the organization of brain networks at multiple spatial and temporal scales.
• 2. Network Dynamics and Function:
  Advancements in understanding the dynamic properties and functional significance of brain networks in cognition, perception, and behavior.
  Research on the dynamics of brain network activity, network resilience, and the role of network motifs in information processing.
• 3. Connectomics and Brain Mapping:
  Exploration of connectomic approaches for mapping the structural and functional connections within the brain.
  Research on brain imaging techniques, such as diffusion MRI, functional MRI (fMRI), and electroencephalography (EEG), for characterizing brain network architecture.
• 4. Network Neuroscience Methods:
  Advancements in methodological approaches for analyzing, visualizing, and interpreting complex brain networks.
  Research on network inference techniques, data-driven modeling, and validation methods for studying brain connectivity.
• 5. Computational Modeling of Brain Networks:
  Exploration of computational models and simulations of brain network dynamics, including spiking neural networks, neural mass models, and large-scale brain simulations.
  Research on the role of network topology, synaptic plasticity, and neural dynamics in shaping brain function and behavior.
• 6. Brain Network Disorders:
  Advancements in understanding the alterations of brain networks in neurological and psychiatric disorders, such as Alzheimer disease, schizophrenia, and epilepsy.
  Research on network biomarkers, diagnostic tools, and therapeutic interventions targeting dysfunctional brain networks.
• 7. Network Neuroscience and Machine Learning:
  Exploration of the intersection between network neuroscience and machine learning techniques for brain network analysis and prediction.
  Research on deep learning approaches, graph neural networks, and network-based classifiers for decoding brain activity and identifying biomarkers of brain disorders.
• 8. Network Neuroscience and Artificial Intelligence:
  Advancements in leveraging insights from network neuroscience to inspire new algorithms and architectures in artificial intelligence (AI) and computational neuroscience.
  Research on brain-inspired computing, neuromorphic engineering, and the development of AI systems with human-like learning and decision-making capabilities.
• 9. Network Neuroscience and Clinical Applications:
  Exploration of translational research applications of network neuroscience for clinical diagnosis, prognosis, and treatment planning.
  Research on personalized medicine approaches, therapeutic targeting of brain networks, and interventions to modulate brain connectivity.
• 10. Ethical, Legal, and Societal Implications:
  Advancements in addressing ethical considerations, privacy concerns, and societal impacts of network neuroscience research and applications.
  Research on responsible data sharing practices, informed consent, and public engagement in neuroscience research.
• Latest Research Topics for PhD in Artificial Intelligence
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• Print ISSN:  24721751

  Electronic ISSN:  

• Abstracting and Indexing:  Scopus, Science Citation Index Expanded

• Imapct Factor 2025:  3.4

• Subject Area and Category:  Computer Science,Artificial Intelligence,Computer Science Applications ,Mathematics,Applied Mathematics,Neuroscience,Neuroscience (miscellaneous)

• Publication Frequency:  

• H Index:  44

• Best Quartile:

  Q1:  Applied Mathematics

  Q2:  

  Q3:  

  Q4:  

• Cite Score:  7.2

• SNIP:  1.024

• Journal Rank(SJR):  1.450

• Latest Articles:   Latest Articles in Network Neuroscience

• Guidelines for Authors: Network Neuroscience Author Guidelines

• Paper Submissions: Paper Submissions in Network Neuroscience

• Publisher:  MIT Press Journals

  Country:  United States

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