Using connectomics for predictive assessment of brain parcellations

Kristoffer J. Albers, Karen S. Ambrosen, Matthew G. Liptrot, Tim B. Dyrby, Mikkel N. Schmidt, Morten Mørup*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

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Abstract

The organization of the human brain remains elusive, yet is of great importance to the mechanisms of integrative brain function. At the macroscale, its structural and functional interpretation is conventionally assessed at the level of cortical units. However, the definition and validation of such cortical parcellations are problematic due to the absence of a true gold standard. We propose a framework for quantitative evaluation of brain parcellations via statistical prediction of connectomics data. Specifically, we evaluate the extent in which the network representation at the level of cortical units (defined as parcels) accounts for high-resolution brain connectivity. Herein, we assess the pertinence and comparative ranking of ten existing parcellation atlases to account for functional (FC) and structural connectivity (SC) data based on data from the Human Connectome Project (HCP), and compare them to data-driven as well as spatially-homogeneous geometric parcellations including geodesic parcellations with similar size distributions as the atlases. We find substantial discrepancy in parcellation structures that well characterize FC and SC and differences in what well represents an individual's functional connectome when compared against the FC structure that is preserved across individuals. Surprisingly, simple spatial homogenous parcellations generally provide good representations of both FC and SC, but are inferior when their within-parcellation distribution of individual parcel sizes is matched to that of a valid atlas. This suggests that the choice of fine grained and coarse representations used by existing atlases are important. However, we find that resolution is more critical than the exact border location of parcels.

Original languageEnglish
Article number118170
JournalNeuroImage
Volume238
Number of pages18
ISSN1053-8119
DOIs
Publication statusPublished - Sep 2021

Bibliographical note

Funding Information:
This project was supported by the Lundbeck Foundation, grant no. R105-9813. The Tesla K40 GPU card used for the BedpostX calculations was donated by the NVIDIA Corporation. Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. We would like to thank the authors of [Arslan et al. 2018] for making the parcellations considered easy accessible 1225 and providing visualizing code, as utilized in figure 2, available at biomedia.doc.ic.ac.uk/brain-parcellation-survey/.

Funding Information:
This project was supported by the Lundbeck Foundation, grant no. R105-9813. The Tesla K40 GPU card used for the BedpostX calculations was donated by the NVIDIA Corporation. Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. We would like to thank the authors of [Arslan et al., 2018] for making the parcellations considered easy accessible 1225 and providing visualizing code, as utilized in figure 2, available at biomedia.doc.ic.ac.uk/brain-parcellation-survey/.

Funding Information:
Funding: This study was funded by Lundbeckfond, grant number R105-9813. The Tesla K40 GPU card used for data preprocessing was donated by the NVIDIA Foundation. Conflict of Interest: The authors declare that they have no conflict of interest.

Publisher Copyright:
© 2021

Keywords

  • Brain parcellation
  • Diffusion magnetic resonance imaging (dMRI)
  • Functional magnetic resonance imaging (fMRI)
  • Human connectome
  • Link prediction
  • Whole brain connectivity

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