ICA of full complex-valued fMRI data using phase information of spatial maps

Yu, Mou Chuan; Qiu, Lin; Kuang, Li; Xiao, Gong; Cong, Fengyu; Calhoun, Vince D.

doi:10.1016/j.jneumeth.2015.03.036

Cited by 34 publications

(64 citation statements)

References 45 publications

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“…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). This supports the potential of identifying useful brain information from complex-valued fMRI data beyond magnitude-only fMRI data.…”

Section: Introductionmentioning

confidence: 59%

“…For the IVA-generated SMs and TCs, we employed post-IVA de-noising based on SM phase information to remove noisy voxels introduced by phase fMRI data (Yu et al, 2015). More precisely, we first used the phase de-ambiguity approach based on TC estimates to eliminate the phase ambiguity of subject-specific SMs and TCs, and then retained only voxels with phase values within ⁄ .…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…We selected 12 components which had higher mean correlations with their magnitude SM references (Yu et al, 2015;Smith et al, 2009) for the ten subjects. These components included the task-related component, DMN, supplementary motor area, medial and occipital pole visual areas, auditory cortex, cerebellum, left and right frontoparietal areas, posterior cingulate cortex, inferior parietal lobule (IPL), and a noise component with a higher shape parameter than the other 11 components.…”

Section: Simulated Fmri Datamentioning

confidence: 99%

“…The smaller the mean correlations between SM components within an SCV or between their corresponding TCs, the larger the inter-subject spatial or temporal Each component consisted of an SM and a corresponding TC; each SM contained 53 × 63 × 46 voxels while non-brain voxels were all zeroes; and each TC included 165 time points with TR = 2 seconds. In the brain, these selected SMs and TCs were all phase corrected using the phase de-ambiguity approach based on TC estimates (Yu et al, 2015), thus the blood oxygenation-level dependent related voxels had phase values within [-π/4, π/4]. We computed the mean magnitude and phase images across all subjects of complex-valued fMRI data to generate baseline images, and then generated no-noise simulated fMRI datasets by adding the product of SMs and TCs to the baseline images for each subject.…”

Section: Simulated Fmri Datamentioning

confidence: 99%

“…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).…”

Section: Introductionmentioning

confidence: 99%

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

Kuang

Qiu

Xiao

et al. 2017

Journal of Neuroscience Methods

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3 Highlights:An adaptive IVA algorithm was proposed for multi-subject complex-valued fMRI data.An MGGD-based nonlinear function was exploited to match varying SCV distributions.The MGGD shape parameter was estimated using maximum likelihood estimation.Subspace de-noising, post-IVA phase de-noising, and noncircularity were utilized.Our method detected more contiguous activations than magnitude-only methods. 4 Abstract BackgroundComplex-valued fMRI data can provide additional insights beyond magnitude-only data. However, independent vector analysis (IVA), which has exhibited great potential for group analysis of magnitudeonly fMRI data, has rarely been applied to complex-valued fMRI data. The main challenges in this application include the extremely noisy nature and large variability of the source component vector (SCV) distribution. New MethodTo address these challenges, we propose an adaptive fixed-point IVA algorithm for analyzing multiplesubject complex-valued fMRI data. We exploited a multivariate generalized Gaussian distribution (MGGD)-based nonlinear function to match varying SCV distributions in which the MGGD shape parameter was estimated using maximum likelihood estimation. To achieve our de-noising goal, we updated the MGGD-based nonlinearity in the dominant SCV subspace, and employed a post-IVA denoising strategy based on phase information in the IVA estimates. We also incorporated the pseudocovariance matrix of fMRI data into the algorithm to emphasize the noncircularity of complex-valued fMRI sources. ResultsResults from simulated and experimental fMRI data demonstrated the efficacy of our method. Comparison with Existing Method(s)Our approach exhibited significant improvements over typical complex-valued IVA algorithms, especially during higher noise levels and larger spatial and temporal changes. As expected, the proposed complex-valued IVA algorithm detected more contiguous and reasonable activations than the magnitudeonly method for task-related (393%) and default mode (301%) spatial maps. ConclusionsThe proposed approach is suitable for decomposing multi-subject complex-valued fMRI data, and has great potential for capturing additional subject variability.

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Section: Introductionmentioning

confidence: 59%

Section: Proposed Algorithmmentioning

confidence: 99%

Section: Simulated Fmri Datamentioning

confidence: 99%

Section: Simulated Fmri Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Kuang

Qiu

Xiao

et al. 2017

Journal of Neuroscience Methods

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Spatial source phase: A new feature for identifying spatial differences based on complex‐valued resting‐state fMRI data

Qiu

Lin

Kuang

et al. 2019

Human Brain Mapping

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Spatial source phase, the phase information of spatial maps extracted from functional magnetic resonance imaging (fMRI) data by data‐driven methods such as independent component analysis (ICA), has rarely been studied. While the observed phase has been shown to convey unique brain information, the role of spatial source phase in representing the intrinsic activity of the brain is yet not clear. This study explores the spatial source phase for identifying spatial differences between patients with schizophrenia (SZs) and healthy controls (HCs) using complex‐valued resting‐state fMRI data from 82 individuals. ICA is first applied to preprocess fMRI data, and post‐ICA phase de‐ambiguity and denoising are then performed. The ability of spatial source phase to characterize spatial differences is examined by the homogeneity of variance test (voxel‐wise F‐test) with false discovery rate correction. Resampling techniques are performed to ensure that the observations are significant and reliable. We focus on two components of interest widely used in analyzing SZs, including the default mode network (DMN) and auditory cortex. Results show that the spatial source phase exhibits more significant variance changes and higher sensitivity to the spatial differences between SZs and HCs in the anterior areas of DMN and the left auditory cortex, compared to the magnitude of spatial activations. Our findings show that the spatial source phase can potentially serve as a new brain imaging biomarker and provide a novel perspective on differences in SZs compared to HCs, consistent with but extending previous work showing increased variability in patient data.

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Denoising brain networks using a fixed mathematical phase change in independent component analysis of magnitude‐only fMRI data

Zhang,

Lin,

Niu

et al. 2023

Human Brain Mapping

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Brain networks extracted by independent component analysis (ICA) from magnitude‐only fMRI data are usually denoised using various amplitude‐based thresholds. By contrast, spatial source phase (SSP) or the phase information of ICA brain networks extracted from complex‐valued fMRI data, has provided a simple yet effective way to perform the denoising using a fixed phase change. In this work, we extend the approach to magnitude‐only fMRI data to avoid testing various amplitude thresholds for denoising magnitude maps extracted by ICA, as most studies do not save the complex‐valued data. The main idea is to generate a mathematical SSP map for a magnitude map using a mapping framework, and the mapping framework is built using complex‐valued fMRI data with a known SSP map. Here we leverage the fact that the phase map derived from phase fMRI data has similar phase information to the SSP map. After verifying the use of the magnitude data of complex‐valued fMRI, this framework is generalized to work with magnitude‐only data, allowing use of our approach even without the availability of the corresponding phase fMRI datasets. We test the proposed method using both simulated and experimental fMRI data including complex‐valued data from University of New Mexico and magnitude‐only data from Human Connectome Project. The results provide evidence that the mathematical SSP denoising with a fixed phase change is effective for denoising spatial maps from magnitude‐only fMRI data in terms of retaining more BOLD‐related activity and fewer unwanted voxels, compared with amplitude‐based thresholding. The proposed method provides a unified and efficient SSP approach to denoise ICA brain networks in fMRI data.

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ICA of full complex-valued fMRI data using phase information of spatial maps

Abstract: The TC-based phase de-ambiguity is essential to prepare the SM phases. The SM phases provide a new post-ICA index for reliably identifying and suppressing the unwanted voxels.

Cited by 34 publications

References 45 publications

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

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

Spatial source phase: A new feature for identifying spatial differences based on complex‐valued resting‐state fMRI data

Denoising brain networks using a fixed mathematical phase change in independent component analysis of magnitude‐only fMRI data

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