TY - GEN
T1 - Brain functional mapping and network connectivity of reconstructed magnetic susceptibility data
AU - Chen, Zikuan
AU - Calhoun, Vince
N1 - Publisher Copyright:
© 2018 SPIE.
PY - 2018
Y1 - 2018
N2 - Traditionally, brain function analysis is based on the magnitude data of complex-valued spatiotemporal (4D) functional magnetic resonance imaging (fMRI). Since an MRI signal is formed from the underlying brain tissue magnetic property through a cascade of transformations (such as dipole magnetization), the fMRI data (either magnitude or phase) do not directly capture the original magnetic source. In principle, upon solving an inverse fMRI problem, we can reconstruct the magnetic source (specifically magnetic susceptibility, denoted by χ and analyze brain function in the reconstructed χ dataspace at a stage closer to the origin of brain function neurophysiology. Our recent research has shown that the magnetic χ source can be reconstructed from the fMRI phase through a computational inverse MRI solution (CIMRI). Together with the fMRI output data, we can compare three aspects of the data, the magnitude, the phase, and the susceptibility, each of which provides a different perspective. Given a 4D dataset, we analyze the data via independent component analysis (ICA), applicable to both single-subject and multi-subject data. In this study, we addressed the following points: 1) brain function ICA decomposition of magnitude (mICA), phase (pICA), and susceptibility (χICA); 2) comparison of brain function network connectivity matrices (FC) for each of these, namely {mFC, pFC, and χFC} matrices; and 3) applications to a task fMRI experiment (fingertapping, 20 subjects). In theory, we show that the fMRI phase is approximately linearly related to the reconstructed χ source data (different by a spatial dipole convolution), while fMRI magnitude has a nonlinear relationship. Therefore, we conclude that pFC is more similar to χFC than mFC. Through experimental data analyses, we have verified this conclusion.
AB - Traditionally, brain function analysis is based on the magnitude data of complex-valued spatiotemporal (4D) functional magnetic resonance imaging (fMRI). Since an MRI signal is formed from the underlying brain tissue magnetic property through a cascade of transformations (such as dipole magnetization), the fMRI data (either magnitude or phase) do not directly capture the original magnetic source. In principle, upon solving an inverse fMRI problem, we can reconstruct the magnetic source (specifically magnetic susceptibility, denoted by χ and analyze brain function in the reconstructed χ dataspace at a stage closer to the origin of brain function neurophysiology. Our recent research has shown that the magnetic χ source can be reconstructed from the fMRI phase through a computational inverse MRI solution (CIMRI). Together with the fMRI output data, we can compare three aspects of the data, the magnitude, the phase, and the susceptibility, each of which provides a different perspective. Given a 4D dataset, we analyze the data via independent component analysis (ICA), applicable to both single-subject and multi-subject data. In this study, we addressed the following points: 1) brain function ICA decomposition of magnitude (mICA), phase (pICA), and susceptibility (χICA); 2) comparison of brain function network connectivity matrices (FC) for each of these, namely {mFC, pFC, and χFC} matrices; and 3) applications to a task fMRI experiment (fingertapping, 20 subjects). In theory, we show that the fMRI phase is approximately linearly related to the reconstructed χ source data (different by a spatial dipole convolution), while fMRI magnitude has a nonlinear relationship. Therefore, we conclude that pFC is more similar to χFC than mFC. Through experimental data analyses, we have verified this conclusion.
KW - BOLD fMRI
KW - Functional neuroimaging
KW - computed inverse MRI (CIMRI)
KW - dipole inversion
KW - functional network connectivity (FC)
KW - independent component analysis (ICA)
KW - spatial correlation
KW - task fMRI
KW - temporal correlation
UR - http://www.scopus.com/inward/record.url?scp=85049569081&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049569081&partnerID=8YFLogxK
U2 - 10.1117/12.2292988
DO - 10.1117/12.2292988
M3 - Conference contribution
AN - SCOPUS:85049569081
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2018
A2 - Gimi, Barjor
A2 - Krol, Andrzej
PB - SPIE
T2 - Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging
Y2 - 11 February 2018 through 13 February 2018
ER -