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Vector Representation of Complex Numbers01:16

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Mapping higher-order relations between brain structure and function with embedded vector representations of

Gideon Rosenthal1,2, František Váša3, Alessandra Griffa4,5

  • 1Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, P.O.B. 653, 8410501, Beer-Sheva, Israel.

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Summary
This summary is machine-generated.

Connectome embeddings (CE) create vector representations of brain networks, revealing region relationships and inferring missing connections. This approach aids in understanding brain structure-function links and simulating network effects.

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Network Science

Background:

  • Connectomics maps brain networks, but individual node context is challenging.
  • Word embedding techniques from natural language processing offer a potential solution.

Purpose of the Study:

  • To develop connectome embeddings (CE) for representing brain networks in a low-dimensional vector space.
  • To infer missing structural connections and model network-wide effects.

Main Methods:

  • Applied word2vec-like algorithms to create vector representations of brain regions.
  • Constructed predictive deep models for functional and structural connectivity.
  • Simulated network lesion effects using the face processing system.

Main Results:

  • Connectome embeddings (CE) effectively characterize relationships among brain regions.
  • CE can infer missing connections, such as inter-hemispheric homotopic connections.
  • Developed predictive models and simulated lesion effects.

Conclusions:

  • Connectome embeddings (CE) provide a novel method for analyzing brain network structure and function.
  • This approach facilitates understanding of structure-function relationships and network dynamics.