De-noising, phase ambiguity correction and visualization techniques for complex-valued ICA of group fMRI data

Abstract

Analysis of functional magnetic resonance imaging (fMRI) data in its native, complex form has been shown to increase the sensitivity both for data-driven techniques, such as independent component analysis (ICA), and for model-driven techniques. The promise of an increase in sensitivity and specificity in clinical studies, provides a powerful motivation for utilizing both the phase and magnitude data; however, the unknown and noisy nature of the phase poses a challenge. In addition, many complex-valued analysis algorithms, such as ICA, suffer from an inherent phase ambiguity, which introduces additional difficulty for group analysis. We present solutions for these issues, which have been among the main reasons phase information has been traditionally discarded, and show their effectiveness when used as part of a complex-valued group ICA algorithm application. The methods we present thus allow the development of new fully complex data-driven and semi-blind methods to process, analyze, and visualize fMRI data. We first introduce a phase ambiguity correction scheme that can be either applied subsequent to ICA of fMRI data or can be incorporated into the ICA algorithm in the form of prior information to eliminate the need for further processing for phase correction. We also present a Mahalanobis distance-based thresholding method, which incorporates both magnitude and phase information into a single threshold, that can be used to increase the sensitivity in the identification of voxels of interest. This method shows particular promise for identifying voxels with significant susceptibility changes but that are located in low magnitude (i.e. activation) areas. We demonstrate the performance gain of the introduced methods on actual fMRI data.

Adaptive independent vector analysis for multi-subject complex-valued fMRI data

“…This confirms the fact that phase fMRI data also include useful and unique brain information Adali, 2012a, 2012b;Rodriguez et al, 2011Rodriguez et al, , 2012Li et al, 2010Li et al, , 2011Yu et al, 2015), and that complex-valued fMRI data can provide additional insights beyond magnitudeonly data. Furthermore, phase fMRI data have higher noise levels than magnitude-only fMRI data, and thus a de-noising stage is required for IVA of complex-valued fMRI data.…”

“…Other candidates to accomplish this are pre-ICA de-noising methods such as quality map phase denoising (QMPD) (Rodriguez et al, , 2012 or fast Fourier transform (FFT) filtering (Cong et al, 2014;Kuang et al, 2016). When omitting the post-IVA phase de-noising, all values of the IVA algorithms decreased, while the proposed method still provided the best performance for both simulated and experimental fMRI data.…”

“…Phase fMRI data contain useful and unique information such as blood oxygenation levels during functional activation (Hoogenraad et al, 1998;Arja et al, 2010), the effects of macro-and micro-vessels (Menon, 2002;Tomasi and Caparelli, 2007), and the orientation of large blood vessels (Klassen and Menon, 2005). Analysis of complex-valued fMRI data provides additional insights beyond magnitude-only fMRI data (Rowe, 2005;Adali and Calhoun, 2007;Adali, 2012a, 2012b;Rodriguez et al, 2011Rodriguez et al, , 2012Li et al, 2010Li et al, , 2011Yu et al, 2015). The complex-valued method with pre-ICA de-noising (using observed phase images to identify and remove noisy voxels in original fMRI data) achieved higher sensitivity and specificity than the magnitude-only method (Rodriguez et al, , 2012Li et al, 2011).…”

“…Analysis of complex-valued fMRI data provides additional insights beyond magnitude-only fMRI data (Rowe, 2005;Adali and Calhoun, 2007;Adali, 2012a, 2012b;Rodriguez et al, 2011Rodriguez et al, , 2012Li et al, 2010Li et al, , 2011Yu et al, 2015). The complex-valued method with pre-ICA de-noising (using observed phase images to identify and remove noisy voxels in original fMRI data) achieved higher sensitivity and specificity than the magnitude-only method (Rodriguez et al, , 2012Li et al, 2011). By using post-ICA de-noising (using SM phase information to identify and remove noisy voxels in ICA estimates), the complex-valued method extracts more contiguous and reasonable activations than the magnitude-only method (Yu et al, 2015).…”

Brain decoding: Opportunities and challenges for pattern recognition

“…Feature selection is studied using clustered random sampling [4], which shows that distributed yet local patterns are most informative, or by hierarchical clustering [5] to construct and take advantage of spatial relationships. Rodriguez et al [6] extend independent component analysis (ICA) to take into account the phase information of fMRI data; ICA can be used as feature extraction based on the training data or in a semi-supervised way. Cabral et al [7] investigate ensembles of classifiers for decoding of visual information, and Olivetti et al [8] study the statistical significance of classification results evaluated within the Bayesian framework.…”