BPARC: A novel spatio-temporal (4D) data-driven brain parcellation scheme based on deep residual networks

Behnam Kazemivash, Vince D. Calhoun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Brain parcellation plays a significant role in computational neuroimaging by dividing the brain into meaningful anatomical sub-regions which can be used to study broad brain functionalities and structures. Most ongoing brain parcellation research has focused on fixed regions that do not vary across individuals or over time. However, brain functional organization is by its very nature dynamic and ignoring this can lead to misleading results. In this work, we have tried to address this shortcoming in fMRI-based brain parcellation by proposing a novel 4D approach using a deep residual network structure, trained to predict probabilities of voxels in each volume belonging to independent components extracted from fMRI images. Results show that the presented approach not only provides informative 4D spatiotemporal networks which are individualized but also linked across subjects, providing an important tool for further study of the human brain.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1071-1076
Number of pages6
ISBN (Electronic)9781728195742
DOIs
StatePublished - Oct 2020
Event20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020 - Virtual, Cincinnati, United States
Duration: Oct 26 2020Oct 28 2020

Publication series

NameProceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020

Conference

Conference20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020
CountryUnited States
CityVirtual, Cincinnati
Period10/26/2010/28/20

Keywords

  • Brain Parcellation
  • fMRI
  • Independent Component Analysis
  • Neuroimaging
  • Residual Deep Network

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Molecular Biology
  • Artificial Intelligence
  • Computer Science Applications
  • Biomedical Engineering
  • Modeling and Simulation
  • Health Informatics

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