Independent vector analysis for common subspace analysis: Application to multi-subject fMRI data yields meaningful subgroups of schizophrenia

Long, Qunfang; Bhinge, Suchita; Calhoun, Vince D.

doi:10.1016/j.neuroimage.2020.116872

Cited by 29 publications

(43 citation statements)

References 61 publications

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“…Ma et al, [1] and Bhinge et al, [22]- [23] used resting state fMRI data with IVA to study dynamics using a windowed approach. One issue with the application of IVA for dynamics studies is the fact that as the number of datasets increases, precision of IVA can be compromised [23]- [24]. In addition, study of dynamics primarily consider use of resting state data [1], [22]- [23] while cognitive tasks require responses from participants and a dynamic analysis can show how brain networks evolve with the task [19] and provides synchrony among subjects.…”

Section: Introductionmentioning

confidence: 99%

Data-driven spatio-temporal dynamic brain connectivity analysis using fALFF: Application to sensorimotor task data

Hossain

Bhinge

Long

et al. 2022

2022 56th Annual Conference on Information Sciences and Systems (CISS)

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Dynamic functional connectivity (dFC) analysis enables us to capture the time-varying interactions between brain regions and can lead to powerful biomarkers. Most dFC studies are focused on the study of temporal dynamics and require significant postprocessing to summarize the results of the dynamics analysis. In this paper, we introduce an effective framework that makes use of independent vector analysis (IVA) with fractional amplitude of low frequency fluctuation (fALFF) features extracted from task functional magnetic resonance imaging (fMRI) data. Our approach, which is based on IVA with fALLF features as input, (IVA-fALLF) produces an effective summary of the dynamics also greatly facilitating the study of both spatial and temporal dynamics in a more concise manner. IVA-fALLF captures the spatial and temporal dynamics of sensorimotor task data and identifies a component with significant difference in dynamic behavior between healthy controls (HC) and patients with schizophrenia (SZ). Finally, our post analysis using behavioral scores finds significant correlation between brain imaging data and the associated behavioral scores, increasing confidence on our results. Our results are consistent with the previous data-driven dFC analysis as we find similar brain networks showing abnormal behavior in patients with SZ. Moreover, our analysis identifies component behavior in task and rest windows separately and provides additional confirmation of results through correlation with behavioral scores.

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Section: Introductionmentioning

confidence: 99%

Data-driven spatio-temporal dynamic brain connectivity analysis using fALFF: Application to sensorimotor task data

Hossain

Bhinge

Long

et al. 2022

2022 56th Annual Conference on Information Sciences and Systems (CISS)

Self Cite

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“…Subject-specific temporal and spatial patterns are identified on a per window basis. This allows study of both temporal and spatial patterns of dynamics, however, the complexity of the model grows with the number of time windows, which negatively affects the performance of IVA (Long et al, 2020 ). Our approach in this article makes use of the synchrony across subjects in the task, and decreases the dimensionality of the problem by collapsing the time dimension through the use of fALFF as features for each time window (see section 2.2.4 for more details).…”

Section: Methodsmentioning

confidence: 99%

Tracing Evolving Networks Using Tensor Factorizations vs. ICA-Based Approaches

et al. 2022

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Analysis of time-evolving data is crucial to understand the functioning of dynamic systems such as the brain. For instance, analysis of functional magnetic resonance imaging (fMRI) data collected during a task may reveal spatial regions of interest, and how they evolve during the task. However, capturing underlying spatial patterns as well as their change in time is challenging. The traditional approach in fMRI data analysis is to assume that underlying spatial regions of interest are static. In this article, using fractional amplitude of low-frequency fluctuations (fALFF) as an effective way to summarize the variability in fMRI data collected during a task, we arrange time-evolving fMRI data as a subjects by voxels by time windows tensor, and analyze the tensor using a tensor factorization-based approach called a PARAFAC2 model to reveal spatial dynamics. The PARAFAC2 model jointly analyzes data from multiple time windows revealing subject-mode patterns, evolving spatial regions (also referred to as networks) and temporal patterns. We compare the PARAFAC2 model with matrix factorization-based approaches relying on independent components, namely, joint independent component analysis (ICA) and independent vector analysis (IVA), commonly used in neuroimaging data analysis. We assess the performance of the methods in terms of capturing evolving networks through extensive numerical experiments demonstrating their modeling assumptions. In particular, we show that (i) PARAFAC2 provides a compact representation in all modes, i.e., subjects, time, and voxels, revealing temporal patterns as well as evolving spatial networks, (ii) joint ICA is as effective as PARAFAC2 in terms of revealing evolving networks but does not reveal temporal patterns, (iii) IVA's performance depends on sample size, data distribution and covariance structure of underlying networks. When these assumptions are satisfied, IVA is as accurate as the other methods, (iv) when subject-mode patterns differ from one time window to another, IVA is the most accurate. Furthermore, we analyze real fMRI data collected during a sensory motor task, and demonstrate that a component indicating statistically significant group difference between patients with schizophrenia and healthy controls is captured, which includes primary and secondary motor regions, cerebellum, and temporal lobe, revealing a meaningful spatial map and its temporal change.

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“…Then, using these correlation values, we can analyze how close the components within each SCV are to each other. We assume that all the correlation values for the target SCV are sufficiently high, and we then determine the target SCV [31] by (1) counting the number of correlation values greater than an empirically determined threshold, (2) calculating the ratio of this number to the total number of correlation values for each SCV, and (3) choosing the maximum of this ratio. The procedure is shown diagrammatically in Figure 3(b).…”

Section: Methodsmentioning

confidence: 99%

Noncontact Heart Rate Measurement Using a Webcam, Based on Joint Blind Source Separation and a Skin Reflection Model: For a Wide Range of Imaging Conditions

Zhang

et al. 2021

Journal of Sensors

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Remote photoplethysmography (rPPG) can be used for noncontact and continuous measurement of the heart rate (HR). Currently, the main factors affecting the accuracy and robustness of rPPG-based HR measurement methods are the subject’s skin tone, body movement, exercise recovery, and variable or inadequate illumination. In response to these challenges, this study is aimed at investigating a rPPG-based HR measurement method that is effective under a wide range of conditions by only using a webcam. We propose a new approach, which combines joint blind source separation (JBSS) and a projection process based on a skin reflection model, so as to eliminate the interference of background illumination and enhance the extraction of pulse rate information. Three datasets derived from subjects with different skin tones considering six environmental scenarios are used to validate the proposed method against three other state-of-the-art methods. The results show that the proposed method can provide more accurate and robust HR measurement for all three datasets and is therefore more applicable to a wide range of scenarios.

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Independent vector analysis for common subspace analysis: Application to multi-subject fMRI data yields meaningful subgroups of schizophrenia

Cited by 29 publications

References 61 publications

Data-driven spatio-temporal dynamic brain connectivity analysis using fALFF: Application to sensorimotor task data

Data-driven spatio-temporal dynamic brain connectivity analysis using fALFF: Application to sensorimotor task data

Tracing Evolving Networks Using Tensor Factorizations vs. ICA-Based Approaches

Noncontact Heart Rate Measurement Using a Webcam, Based on Joint Blind Source Separation and a Skin Reflection Model: For a Wide Range of Imaging Conditions

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