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Related Experiment Video

Updated: May 16, 2026

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array
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The UCLA multimodal connectivity database: a web-based platform for brain connectivity matrix sharing and analysis.

Jesse A Brown1, Jeffrey D Rudie, Anita Bandrowski

  • 1Center for Cognitive Neuroscience, University of California Los Angeles Los Angeles, CA, USA ; Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles Los Angeles, CA, USA ; Interdepartmental Program in Neuroscience, University of California Los Angeles Los Angeles, CA, USA.

Frontiers in Neuroinformatics
|December 11, 2012
PubMed
Summary

Researchers can now share and analyze brain connectivity data using the UCLA Multimodal Connectivity Database. This resource facilitates comparisons across different imaging methods and studies for advanced brain network analysis.

Keywords:
data sharingdiffusion-weighted MRIfunctional connectivitygraph theoryresting-state fMRIstructural connectivity

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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Data Science

Background:

  • Brain connectomics research utilizes functional MRI (fMRI) and diffusion-weighted MRI (dwMRI) to generate connectivity matrices (CMs).
  • Connectivity matrices distill complex brain network data, enabling topological analysis and capturing individual differences.
  • Existing data sharing practices can be fragmented, limiting large-scale comparative studies.

Purpose of the Study:

  • Introduce the UCLA Multimodal Connectivity Database (UMCD) as an open-access platform for brain network analysis and data sharing.
  • Provide a centralized repository for researchers to upload, share, and analyze connectivity matrices.
  • Demonstrate the utility of the UMCD for comparing brain networks derived from different imaging modalities and populations.

Main Methods:

  • Developed and launched the UCLA Multimodal Connectivity Database website (http://umcd.humanconnectomeproject.org).
  • Collected and curated over 2000 connectivity matrices from various studies and imaging modalities (fMRI, dwMRI).
  • Derived functional and structural connectivity matrices from 60 subjects' rs-fMRI and dwMRI data for demonstration purposes, uploading them to the UMCD.

Main Results:

  • The UMCD hosts over 2000 contributed connectivity matrices, supporting diverse research needs.
  • Analysis of demonstration data revealed low correspondence between global and nodal graph theoretical measures for functional and structural brain networks.
  • The platform successfully enabled the computation and visualization of graph theory metrics for uploaded connectivity matrices.

Conclusions:

  • The UCLA Multimodal Connectivity Database enhances data sharing and comparability in brain connectomics.
  • The platform facilitates large-scale meta-analyses by enabling comparisons across imaging modalities, age groups, and disease states.
  • The findings underscore the importance of multimodal data integration and standardized analysis for a comprehensive understanding of brain networks.