 A common approach to modeling dynamic connectivity in resting-state whole-brain  fMRI is to compute (across moving temporal windows) the Pearson-correlation between anatomical regions derived from predefined brain parcellation. Due to image acquisition and physiological noise , the direct use of the sample correlation matrices is not reliable for the subsequent modeling of the dynamically-evolving connectivity matrices. To overcome this limitation , we propose a new regression approach that smooths out the noise by exploiting the geometric structure of correlation matrices. We demonstrate that the proposed method is useful for identifying distinct state spacesin the resting-state connectivities of males and females through the transition matrices of the common states .
Background: Recent studies have indicated that functional connectivity is dynamic even during rest. A common approach to modeling the dynamic functional connectivity in whole-brain resting-state fMRI is to compute the correlation between anatomical regions via sliding time windows. However , the direct use of the sample correlation matrices is not reliable due to the image acquisition and processing noises in resting-sate fMRI. New method: To overcome these limitations , we propose a new statistical model that smooths out the noise by exploiting the geometric structure of correlation matrices. The dynamic correlation matrix is modeled as a linear combination of symmetric positive-definite matrices combined with cosine series representation. The resulting smoothed dynamic correlation matrices are clustered into disjoint brain connectivity states using the k-means clustering algorithm. Results: The proposed model preserves the geometric structure of underlying physiological dynamic correlation, eliminates unwanted noise in connectivity and obtains more accurate state spaces. The difference in the estimated dynamic connectivity states between males and females is identified. Comparison with existing methods: We demonstrate that the proposed statistical model has less rapid state changes caused by noise and improves the accuracy in identifying and discriminating different states. Conclusions: We propose a new regression model on dynamically changing correlation matrices that provides better performance over existing windowed correlation and is more reliable for the modeling of dynamic connectivity .
A
Background: Recent studies have indicated that functional connectivity is dynamic even during rest.
R
dynamic connectivity in resting-state
the dynamic functional connectivity in
A
resting-state
R
(across moving temporal windows) the Pearson-correlation
the correlation
R
derived from predefined brain parcellation. Due to image acquisition and physiological noise
via sliding time windows. However
R
for the subsequent modeling of the dynamically-evolving connectivity matrices. To overcome this limitation
due to the image acquisition and processing noises in resting-sate fMRI. New method: To overcome these limitations
R
regression approach
statistical model
R
We demonstrate that the proposed method is useful for identifying distinct state spacesin the resting-state connectivities of
The dynamic correlation matrix is modeled as a linear combination of symmetric positive-definite matrices combined with cosine series representation. The resulting smoothed dynamic correlation matrices are clustered into disjoint brain connectivity states using the k-means clustering algorithm. Results: The proposed model preserves the geometric structure of underlying physiological dynamic correlation, eliminates unwanted noise in connectivity and obtains more accurate state spaces. The difference in the estimated dynamic connectivity states between
R
through the transition matrices of the common states
is identified. Comparison with existing methods: We demonstrate that the proposed statistical model has less rapid state changes caused by noise and improves the accuracy in identifying and discriminating different states. Conclusions: We propose a new regression model on dynamically changing correlation matrices that provides better performance over existing windowed correlation and is more reliable for the modeling of dynamic connectivity