Efficient Blind Source Separation Method for fMRI Using Autoencoder and Spatiotemporal Sparsity Constraints

Khalid, Muhammad Usman; Khawaja, Bilal A.; Nauman, Malik Muhammad

doi:10.1109/access.2023.3277543

Cited by 5 publications

(1 citation statement)

References 77 publications

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“…The ssBSS method 49 proposed the following optimization model by considering that dataset Y m can be decomposed into temporal source matrix T m ∈ R N×P and spatial source matrix S m ∈ R P×V as where T m = T p C m accounts for the smoothness of the BOLD signal by storing DCT bases in T p ∈ R N×K p , and c m,p is the p-th column of the sparse representation matrix C m , P < K p < N . .…”

Section: Proposed Preliminariesmentioning

confidence: 99%

A novel subject-wise dictionary learning approach using multi-subject fMRI spatial and temporal components

Khalid,

Nauman

2023

Sci Rep

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The conventional dictionary learning (DL) algorithms aim to adapt the dictionary/sparse code to individual functional magnetic resonance imaging (fMRI) data. Thus, lacking the capability to consolidate the spatiotemporal diversities offered by other subjects. Considering that subject-wise (sw) data matrix can be decomposed into the sparse linear combination of multi-subject (MS) time courses and MS spatial maps, two new algorithms, sw sequential DL (swsDL) and sw block DL (swbDL), have been proposed. They are based on the novel framework, defined by the mixing model, where base matrices prepared by operating a computationally fast sparse spatiotemporal blind source separation method over multiple subjects are employed to adapt the mixing matrices to sw training data. They solve the optimization models formulated using $$l_0$$ l 0 /$$l_1$$ l 1 -norm penalization/constraints through dictionary/sparse code pair update and alternating minimization approach. They are unique because no existing sparse DL method can incorporate MS spatiotemporal components while updating sw atoms/sparse codes, which can eventually be assembled using neuroscience knowledge to extract group-level dynamics. Various fMRI datasets are used to evaluate and compare the performance of the proposed algorithms with existing state-of-the-art algorithms. Specifically, overall, a $$14\%$$ 14 % increase in the mean correlation value and $$39\%$$ 39 % reduction in the mean computation time exhibited by swsDL and swbDL, respectively, over the adaptive consistent sequential dictionary algorithm.

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Section: Proposed Preliminariesmentioning

confidence: 99%

A novel subject-wise dictionary learning approach using multi-subject fMRI spatial and temporal components

Khalid,

Nauman

2023

Sci Rep

Self Cite

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A novel physiological signal denoising method coupled with multispectral adaptive wavelet denoising(MAWD) and unsupervised source counting algorithm(USCA)

Wang

Xiang

Zhi

et al. 2024

Journal of Engineering Research

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Three layered sparse dictionary learning algorithm for enhancing the subject wise segregation of brain networks

Khalid,

Nauman,

Akram

et al. 2024

Sci Rep

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Independent component analysis (ICA) and dictionary learning (DL) are the most successful blind source separation (BSS) methods for functional magnetic resonance imaging (fMRI) data analysis. However, ICA to higher and DL to lower extent may suffer from performance degradation by the presence of anomalous observations in the recovered time courses (TCs) and high overlaps among spatial maps (SMs). This paper addressed both problems using a novel three-layered sparse DL (TLSDL) algorithm that incorporated prior information in the dictionary update process and recovered full-rank outlier-free TCs from highly corrupted measurements. The associated sequential DL model involved factorizing each subject’s data into a multi-subject (MS) dictionary and MS sparse code while imposing a low-rank and a sparse matrix decomposition restriction on the dictionary matrix. It is derived by solving three layers of feature extraction and component estimation. The first and second layers captured brain regions with low and moderate spatial overlaps, respectively. The third layer that segregated regions with significant spatial overlaps solved a sequence of vector decomposition problems using the proximal alternating linearized minimization (PALM) method and solved a decomposition restriction using the alternating directions method (ALM). It learned outlier-free dynamics that integrate spatiotemporal diversities across brains and external information. It differs from existing DL methods owing to its unique optimization model, which incorporates prior knowledge, subject-wise/multi-subject representation matrices, and outlier handling. The TLSDL algorithm was compared with existing dictionary learning algorithms using experimental and synthetic fMRI datasets to verify its performance. Overall, the mean correlation value was found to be higher for the TLSDL than for the state-of-the-art subject-wise sequential DL (swsDL) technique.

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Efficient Blind Source Separation Method for fMRI Using Autoencoder and Spatiotemporal Sparsity Constraints

Cited by 5 publications

References 77 publications

A novel subject-wise dictionary learning approach using multi-subject fMRI spatial and temporal components

A novel subject-wise dictionary learning approach using multi-subject fMRI spatial and temporal components

A novel physiological signal denoising method coupled with multispectral adaptive wavelet denoising(MAWD) and unsupervised source counting algorithm(USCA)

Three layered sparse dictionary learning algorithm for enhancing the subject wise segregation of brain networks

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