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Neuroscience Needs Network Science

  • Dániel L. Barabási*
  • , Ginestra Bianconi
  • , Ed Bullmore
  • , Mark Burgess
  • , Sue Yeon Chung
  • , Tina Eliassi-Rad
  • , Dileep George
  • , István A. Kovács
  • , Hernán Makse
  • , Thomas E. Nichols
  • , Christos Papadimitriou
  • , Olaf Sporns
  • , Kim Stachenfeld
  • , Zoltán Toroczkai
  • , Emma K. Towlson
  • , Anthony M. Zador
  • , Hongkui Zeng
  • , Albert László Barabási
  • , Amy Bernard
  • , György Buzsáki*
  • *Corresponding author for this work
  • Harvard University
  • Queen Mary University of London
  • Alan Turing Institute
  • University of Cambridge
  • ChiTek-i AS
  • New York University
  • Simons Foundation
  • Northeastern University
  • Santa Fe Institute
  • Alphabet Inc.
  • Northwestern University
  • City University of New York
  • University of Oxford
  • Columbia University
  • Indiana University Bloomington
  • University of Notre Dame
  • University of Calgary
  • Cold Spring Harbor Laboratory
  • Allen Institute for Brain Science
  • Kavli Foundation

Research output: Contribution to journalArticlepeer-review

Abstract (may include machine translation)

The brain is a complex system comprising a myriad of interacting neurons, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such interconnected systems, offering a framework for integrating multiscale data and complexity. To date, network methods have significantly advanced functional imaging studies of the human brain and have facilitated the development of control theory-based applications for directing brain activity. Here, we discuss emerging frontiers for network neuroscience in the brain atlas era, addressing the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease. We underscore the importance of fostering interdisciplinary opportunities through workshops, conferences, and funding initiatives, such as supporting students and postdoctoral fellows with interests in both disciplines. By bringing together the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way toward a deeper understanding of the brain and its functions, as well as offering new challenges for network science.

Original languageEnglish
Pages (from-to)5989-5995
Number of pages7
JournalJournal of Neuroscience
Volume43
Issue number34
DOIs
StatePublished - 23 Aug 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Connectomics
  • Network Neuroscience
  • Network Science
  • NeuroAI
  • Neurodevelopment
  • Systems Neuroscience

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