Robust intra-individual estimation of structural connectivity by Principal Component Analysis

Lidia Konopleva; Kamil A Il'yasov; Shi Jia Teo; Volker A Coenen; Christoph P Kaller; Marco Reisert

doi:10.1016/j.neuroimage.2020.117483

Robust intra-individual estimation of structural connectivity by Principal Component Analysis

Neuroimage. 2021 Feb 1:226:117483. doi: 10.1016/j.neuroimage.2020.117483. Epub 2020 Dec 1.

Authors

Lidia Konopleva¹, Kamil A Il'yasov², Shi Jia Teo³, Volker A Coenen⁴, Christoph P Kaller⁵, Marco Reisert⁶

Affiliations

¹ Institute of Physics, Kazan (Volga Region) Federal University, Russia. Electronic address: lidia.konopleva@gmail.com.
² Institute of Physics, Kazan (Volga Region) Federal University, Russia.
³ Medical Physics, Department of Radiology, Medical Center, University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany.
⁴ Department of Stereotaxy and Functional Neurosurgery, Medical Center, University of Freiburg, Germany.
⁵ Department of Neuroradiology, Medical Center, University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany.
⁶ Department of Stereotaxy and Functional Neurosurgery, Medical Center, University of Freiburg, Germany; Medical Physics, Department of Radiology, Medical Center, University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany.

PMID: 33271269
DOI: 10.1016/j.neuroimage.2020.117483

Abstract

Fiber tractography based on diffusion-weighted MRI provides a non-invasive characterization of the structural connectivity of the human brain at the macroscopic level. Quantification of structural connectivity strength is challenging and mainly reduced to "streamline counting" methods. These are however highly dependent on the topology of the connectome and the particular specifications for seeding and filtering, which limits their intra-subject reproducibility across repeated measurements and, in consequence, also confines their validity. Here we propose a novel method for increasing the intra-subject reproducibility of quantitative estimates of structural connectivity strength. To this end, the connectome is described by a large matrix in positional-orientational space and reduced by Principal Component Analysis to obtain the main connectivity "modes". It was found that the proposed method is quite robust to structural variability of the data.

Keywords: Connectivity matrix; Diffusion MRI; Fiber tracking; Human connectome.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Brain / anatomy & histology*
Connectome / methods*
Diffusion Tensor Imaging / methods
Humans
Image Processing, Computer-Assisted / methods*
Neural Pathways / anatomy & histology*
Principal Component Analysis / methods