Resting state functional magnetic resonance imaging is used to study how brain regions are functionally connected by measuring temporal correlation of the fMRI signals, when a subject is at rest. Sparse dictionary learning is used to estimate a dictionary of resting state networks by decomposing the whole brain signals into several temporal features (atoms), each being shared by a set of voxels associated to a network. Recently, we proposed and validated a new method entitled Sparsity-based Analysis of Reliable K-hubness (SPARK), suggesting that connector hubs of brain networks participating in inter-network communication can be identified by counting the number of atoms involved in each voxel (sparse number k). However, such hub analysis can be corrupted by the presence of noise-related atoms, where physiological fluctuations in cardiorespiratory processes may remain even after band-pass filtering and regression of confound signals from the white matter and cerebrospinal fluid. Handling this issue might require manual classification of noisy atoms, which is a time-consuming and subjective task. Motivated by the fact that the physiological fluctuations are often localized in tissues close to large vasculatures, i.e. sagittal sinus, we propose an automatic classification of physiological noise-related atoms for SPARK using spatial priors and a stepwise regression procedure. We measured the degree to which the noise-characteristic time-courses within the mask are explained by each atom, and classified noise-related atoms using a subject-specific threshold estimated using a bootstrap resampling based strategy. Using real data from healthy subjects (N = 25), manual classification of the atoms by two independent reviewers showed the presence of sagittal sinus related noise in 65% of the runs. Applying the same manual classification after the proposed automatic removal method reduced this rate to 19%. A 10-fold cross-validation on real data showed good specificity and accuracy of the proposed automated method in classifying the target noise (area under the ROC curve= 0.89), when compared to the manual classification considered as the reference. We demonstrated decrease in k-hubness values in the voxels involved in the sagittal sinus at both individual and group levels, suggesting a significant improvement of SPARK, which is particularly important when considering clinical applications.
Keywords: Classification; Denoising; Functional MRI; Physiological noise; Sparse dictionary learning; Stepwise regression.
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