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

Hossain, Khondoker Murad; Bhinge, Suchita; Long, Qunfang; Calhoun, Vince D.

doi:10.1109/ciss53076.2022.9751190

Cited by 2 publications

(4 citation statements)

References 40 publications

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“…Here, we report the results using R = 40 (see the Supplementary Material for the spatial maps extracted using R = 2, which are also very similar to R = 40 in terms of the component of interest). Only one out of 40 components is statistically significant in most of the time windows, and that is component 5 in Figure 11A (as also shown in Hossain et al, 2022 on the same dataset). Figure 11B shows that except for one time window, component 5 has a p -value ≤ 0.05.…”

Section: Resultssupporting

confidence: 75%

“…Neither joint ICA nor IVA can extract temporal patterns in a compact way. When using IVA, one can focus on how the average subject-mode patterns change from one time window to another (Hossain et al, 2022 ); however, that relies on the assumption that the average would be representative.…”

Section: Resultsmentioning

confidence: 99%

“…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). As such, this provides an attractive formulation for dynamic analysis using IVA (Hossain et al, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

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

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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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Section: Resultssupporting

confidence: 75%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

et al. 2022

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“…In that case, the non-invasive method is preferred due to its flexibility and reduced risk. There are many techniques (Lotte et al, 2018 ) by which the neural activity is recorded, such as magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI) (Acar et al, 2022 ; Hossain et al, 2022 ), and electroencephalography (EEG), and fully functioning near-infrared spectroscopy (fNIRS). The EEG method is preferred due to its robustness and user-friendly approach (Bi et al, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

Status of deep learning for EEG-based brain–computer interface applications

Hossain

Islam²,

Hossain³

et al. 2023

Front. Comput. Neurosci.

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In the previous decade, breakthroughs in the central nervous system bioinformatics and computational innovation have prompted significant developments in brain–computer interface (BCI), elevating it to the forefront of applied science and research. BCI revitalization enables neurorehabilitation strategies for physically disabled patients (e.g., disabled patients and hemiplegia) and patients with brain injury (e.g., patients with stroke). Different methods have been developed for electroencephalogram (EEG)-based BCI applications. Due to the lack of a large set of EEG data, methods using matrix factorization and machine learning were the most popular. However, things have changed recently because a number of large, high-quality EEG datasets are now being made public and used in deep learning-based BCI applications. On the other hand, deep learning is demonstrating great prospects for solving complex relevant tasks such as motor imagery classification, epileptic seizure detection, and driver attention recognition using EEG data. Researchers are doing a lot of work on deep learning-based approaches in the BCI field right now. Moreover, there is a great demand for a study that emphasizes only deep learning models for EEG-based BCI applications. Therefore, we introduce this study to the recent proposed deep learning-based approaches in BCI using EEG data (from 2017 to 2022). The main differences, such as merits, drawbacks, and applications are introduced. Furthermore, we point out current challenges and the directions for future studies. We argue that this review study will help the EEG research community in their future research.

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

Cited by 2 publications

References 40 publications

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

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

Status of deep learning for EEG-based brain–computer interface applications

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