Model order effects on ICA of resting-state complex-valued fMRI data: Application to schizophrenia

Kuang, Li; Qiu, Lin; Xiao, Gong; Cong, Fengyu; Sui, Jing; Calhoun, Vince D.

doi:10.1016/j.jneumeth.2018.02.013

Cited by 31 publications

(22 citation statements)

References 70 publications

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“…More precisely, the proposed algorithm detects 73.9% more contiguous voxels in RPMA+SMA (932 vs. 536) and 746.5% more contiguous voxels in LPMA (838 vs. 99) than ICA-sCPD. In summary, our proposed method extracts 178.7% more contiguous task-related activations than ICA-sCPD, consistent with the previous finding that phase data provides additional brain information [15], [18], [20], [35], [39].…”

Section: B Experimental Fmri Datasupporting

confidence: 89%

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Shift-Invariant Canonical Polyadic Decomposition of Complex-Valued Multi-Subject fMRI Data With a Phase Sparsity Constraint

Kuang

Lin

Gong

et al. 2020

IEEE Trans. Med. Imaging

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Section: B Experimental Fmri Datasupporting

confidence: 89%

“…For each of shared SM estimate, i.e., s n , phase de-ambiguity is first performed using (17) and (18) to obtain θ(s v,n ), phase de-noising is then performed based on θ(s v,n ). Specifically, a binary mask…”

Section: Experimental Methodsmentioning

confidence: 99%

Shift-Invariant Canonical Polyadic Decomposition of Complex-Valued Multi-Subject fMRI Data With a Phase Sparsity Constraint

Kuang

Lin

Gong

et al. 2020

IEEE Trans. Med. Imaging

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“…We next select the best run of

{\underset{false¯}{bolds}}_{r, *}^{k}

from all R runs ( r = 1, … , R ) for subject k using the approach proposed in Kuang et al ():

r^{*} = \arg \max_{r = 1, \dots, R} ∣ corr ((),, {\underset{s}{true}}_{r, *}^{k}, s_{ref}^{k}) ∣,

where “|∙|” denotes the magnitude calculation,

s_{ref}^{k} = ({}, s_{ref}^{k} ((), v)) \in

ℝ V is a reference generated by combining cross‐run averaging and a one‐sample t ‐test:

s_{ref}^{k} ((), v) = ({, \begin{array}{l} ([], {\underset{false¯}{s}}_{1, *}^{k} ((), v) + \dots + {\underset{false¯}{s}}_{R, *}^{k} ((), v)) / R, if normalp_ttest ((),,, {\underset{false¯}{s}}_{1, *}^{k} ((), v), \dots, {\underset{false¯}{s}}_{R, *}^{k} ((), v)) < p_{th} \\ 0, otherwise \end{array}),

p_ttest(∙) denotes the p ‐value of the one‐sample t ‐test, and p th is a p ‐value threshold ( p th = 0.05).…”

Section: Methodsmentioning

confidence: 99%

“…We next select the best run of s k r, * from all R runs (r = 1, … , R) for subject k using the approach proposed in Kuang et al (2018):…”

Section: Post-ica Processing For Spatial Source Phasementioning

confidence: 99%

“…Multiple publications have provided evidences that phase data plays a complementary role to magnitude data in extracting contiguous and meaningful brain activations (Kuang et al, ; Kuang, Lin, Gong, Cong, & Calhoun, ; Rodriguez, Calhoun, & Adalı, ; Rodriguez, Correa, Eichele, Calhoun, & Adalı, ; Yu et al, ). Moreover, the incorporation of fMRI phase data better preserves the integrity of larger networks (Kuang et al, ; Kuang et al, ), and the intact group default mode network (DMN) at higher model orders shows significant difference between patients with schizophrenia (SZs) and healthy controls (HCs; Kuang et al, ).…”

Section: Introductionmentioning

confidence: 99%

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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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Model order effects on ICA of resting-state complex-valued fMRI data: Application to schizophrenia

Abstract: These results demonstrate the potential of complex-valued fMRI data to contribute generally and specifically to brain network analysis in identification of schizophrenia-related changes.

Cited by 31 publications

References 70 publications

Shift-Invariant Canonical Polyadic Decomposition of Complex-Valued Multi-Subject fMRI Data With a Phase Sparsity Constraint

Shift-Invariant Canonical Polyadic Decomposition of Complex-Valued Multi-Subject fMRI Data With a Phase Sparsity Constraint

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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