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Structured dissociative PCA methods for high dimensional neuroimaging signal decomposition
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  • Published: 02 February 2026

Structured dissociative PCA methods for high dimensional neuroimaging signal decomposition

  • Muhammad Usman Khalid1,
  • Malik Muhammad Nauman2,
  • Shafiq Ur Rehman1,
  • Liyanage Chandratilak De Silva3 &
  • …
  • Seyedali Mirjalili4,5 

Scientific Reports , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Computational biology and bioinformatics
  • Mathematics and computing
  • Neuroscience

Abstract

Sparse principal component analysis (SPCA) and independent component analysis (ICA) pipelines are widely used for fMRI blind source separation. However,  applying sparsity and independence constraints in isolation can distort spatial coherence and degrade source recovery when networks overlap or exhibit strong spatial dependencies. To address these shortcomings, we propose a novel unified structured dissociative PCA framework that performs source separation by jointly learning dissociation matrices (unmixers) for singular value decomposition (SVD) and representation matrices (reconstructors) for structured basis representation within a single decomposition. Our approach integrates spatiotemporal priors (temporal DCTs, spatial splines, and hemodynamic response models) into the SVD decomposition and solves it using two algorithms: block coordinate descent (SDPCAG) and coordinate descent (SDPCAC). Both algorithms employ adaptive row-wise sparsity to disentangle overlapping sources and denoise each source through correlation-guided least-squares reconstruction with domain specific constraints. This dual-decomposition strategy, which learned iteratively, allows precise recovery of spatially coherent brain networks while maintaining temporal fidelity. The effectiveness of the proposed algorithms is illustrated on three types of fMRI datasets including synthetic, block-design, and event-related paradigms. Across all scenarios, the proposed methods consistently achieved superior performance compared to existing state-of-the-art techniques, including PMD, ACSDBE, and SICA. Overall, SDPCAG achieved a 22% improvement over ACSDBE in source recovery accuracy. Computationally, SDPCAG achieved 1.6 times faster execution compared to SDPCAC across the experimental datasets while producing comparable results. The source codes are available at https://github.com/usmankhalid06/SDPCA.

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Funding

The work presented in the article is financially supported by Universiti Brunei Darussalam, Brunei Darussalam, through its University Research Grant scheme (UBD/RSCH/1.3/FICBF(b)/2023/020)

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Authors and Affiliations

  1. College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11564, Saudi Arabia

    Muhammad Usman Khalid & Shafiq Ur Rehman

  2. Faculty of Integrated Technologies, Universiti Brunei Darussalam, Bandar Seri Begawan, BE1410, Brunei

    Malik Muhammad Nauman

  3. School of Digital Sciences, Universiti Brunei Darussalam, Bandar Seri Begawan, BE1410, Brunei

    Liyanage Chandratilak De Silva

  4. Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, QLD, Australia

    Seyedali Mirjalili

  5. University Research and Innovation Center , Obuda University, Budapest, Hungary

    Seyedali Mirjalili

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  1. Muhammad Usman Khalid
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Contributions

Muhammad Usman Khalid: Conception, Methodology, data acquisition, data collection, Implementation, Writing, Malik Muhammad Nauman: Management and Funding Acquisition, writing, Shafiq ur Rehman: Visualization, writing, Review and Editing, Liyanage Chandratilak De Silva: Critical Review, writing,  Seyedali Mirjalili: Supervision, writing, Review, editing, All authors reviewed the manuscript.

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Correspondence to Malik Muhammad Nauman.

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Ethical Approval and Data/Code Availability 

This study analyzed synthetic and previously collected data. The publicly available HCP block-design data were used in accordance with its data use terms. Additional datasets are available from the corresponding author upon reasonable request (subject to any applicable data use/sharing restrictions). The source code used in this work is available at https://github.com/usmankhalid06/SDPCA.

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Khalid, M.U., Nauman, M.M., Rehman, S.U. et al. Structured dissociative PCA methods for high dimensional neuroimaging signal decomposition. Sci Rep (2026). https://doi.org/10.1038/s41598-026-35764-3

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  • Received: 07 July 2025

  • Accepted: 08 January 2026

  • Published: 02 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-35764-3

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