TY - GEN
T1 - High resolution robust and smooth precision matrices to capture functional connectivity
AU - Honnorat, Nicolas
AU - Davatzikos, Christos
N1 - Publisher Copyright:
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
PY - 2018
Y1 - 2018
N2 - Resting-state functional MRI (fMRI) provides a crucial insight into brain organization, by offering a mean to measure the functional connectivity between brain regions. A popular measure, the effective functional connectivity, is derived from the precision matrix obtained by inverting the correlations between brain regions fMRI signals. This approach has been widely adopted to build brain connectomes for large populations. For small populations and single fMRI scans, however, the significant amount of noise in the fMRI scans reduces the quality of the precision matrices, and the non-invertibility of the correlation matrices calls for more sophisticated precision estimators. These issues are especially dramatic at full brain resolution. In this work, we investigate several approaches to improve full resolution precision matrices derived from single fMRI scans. First, we compare three approaches for the computation of the correlation matrix. Then, we investigate two regularized inversions, in combination with a correlation shrinkage and two spatial smoothing strategies. During these experiments, the quality of precision matrices obtained for random fMRI half scans was measured by their generalization: their fit to the unseen time points. Our experiments, using ten high resolutions scans of the Human Connectome Project, indicate that correlation shrinkage strongly improves precision generalization. The two regularizations are associated with similar generalization. Smoothing the fMRI signal before the inversion deteriorates the generalization whereas a penalty directly improving the smoothness of the precision matrix can improve the generalization, in particular for short time series and in combination with shrinkage.
AB - Resting-state functional MRI (fMRI) provides a crucial insight into brain organization, by offering a mean to measure the functional connectivity between brain regions. A popular measure, the effective functional connectivity, is derived from the precision matrix obtained by inverting the correlations between brain regions fMRI signals. This approach has been widely adopted to build brain connectomes for large populations. For small populations and single fMRI scans, however, the significant amount of noise in the fMRI scans reduces the quality of the precision matrices, and the non-invertibility of the correlation matrices calls for more sophisticated precision estimators. These issues are especially dramatic at full brain resolution. In this work, we investigate several approaches to improve full resolution precision matrices derived from single fMRI scans. First, we compare three approaches for the computation of the correlation matrix. Then, we investigate two regularized inversions, in combination with a correlation shrinkage and two spatial smoothing strategies. During these experiments, the quality of precision matrices obtained for random fMRI half scans was measured by their generalization: their fit to the unseen time points. Our experiments, using ten high resolutions scans of the Human Connectome Project, indicate that correlation shrinkage strongly improves precision generalization. The two regularizations are associated with similar generalization. Smoothing the fMRI signal before the inversion deteriorates the generalization whereas a penalty directly improving the smoothness of the precision matrix can improve the generalization, in particular for short time series and in combination with shrinkage.
KW - covariance shrinkage
KW - effective connectivity
KW - functional MRI
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U2 - 10.1117/12.2293318
DO - 10.1117/12.2293318
M3 - Conference contribution
AN - SCOPUS:85047311793
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2018
A2 - Angelini, Elsa D.
A2 - Angelini, Elsa D.
A2 - Landman, Bennett A.
PB - SPIE
T2 - Medical Imaging 2018: Image Processing
Y2 - 11 February 2018 through 13 February 2018
ER -