{
  "abstract": "The eigenvalue structure of resting-state fMRI (RS-fMRI) signals provides a compact representation of its variance-covariance structure, yet the information it encodes remains unclear. In this study, we introduce an eigenvalue-based framework to characterize RS-fMRI data using two features derived from the eigenspectrum: the log10-transformed first eigenvalue (log10({lambda}1)) and the slope of the log10-transformed spectrum ({beta}). We systematically evaluated these features across 14 denoising strategies using two independent datasets (China167 (83 males, 84 females; mean age +/- SD: 41 +/- 14 years old) and HCP1200 (425 males, 501 females; mean age +/- SD: 29 +/- 4 years old)). Specifically, we used singular value decomposition to obtain the eigenspectrum of the cortical BOLD signals after preprocessing and nuisance regression, and a linear model to parameterize it. We then examined the relationships between the eigenvalue parameters, head motion, and functional connectivity metrics across subjects and denoising strategies. Our key findings include: (1) log10({lambda}1) and {beta} strongly covaried with one another across subjects, denoising strategies, and datasets, indicating that they capture highly coherent aspects of the eigenspectrum; (2) both features were systematically influenced by denoising strategies; (3) within each denoising strategy, participants with greater log10({lambda}1) had greater mean framewise displacements (mFD), demonstrating sensitivity to residual motion effects, for all strategies in HCP1200 and 12/14 in China167; (4) across denoising strategies, mean log10({lambda}1) reflected the proportion of functional connectivity edges significantly associated with motion, indicating that higher log10({lambda}1) reflects more widespread motion-related contamination across large-scale functional networks; and (5) global signal regression consistently reduced log10({lambda}1), whereas spike regression had dataset-dependent effects. Together, these results establish eigenvalue signatures as robust and sensitive metrics for quantifying residual motion effects in RS-fMRI and provide a framework for evaluating denoising performance based on eigenvalue structure. The full procedure is implemented in R, and the corresponding script is available at: https://github.com/lejianhuang/EigenvalueSignature.",
  "abstract_shown": true,
  "affiliations": [
    "Northwestern"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "L. Huang"
    },
    {
      "name": "A. D. Vigotsky"
    },
    {
      "name": "A. V. Apkarian"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/edv11cp1whsjg.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.08.750194",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.08.750194v1.full",
  "id": "edv11cp1whsjg",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.08.750194v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "neuroscience"
    ],
    [
      "openalex",
      "2805|Functional Brain Connectivity Studies"
    ]
  ],
  "openalex": "W7213113072",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.08.750194v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/edv11cp1whsjg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:21Z",
      "native_id": "10.64898/2026.09.08.750194",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.08.750194v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "Eigenvalue Signatures Reveal Residual Motion Effects Across Resting-State fMRI Denoising Strategies",
  "title_text": "Eigenvalue Signatures Reveal Residual Motion Effects Across Resting-State fMRI Denoising Strategies",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:21Z",
  "url": "https://research.draconitic.com/paper/edv11cp1whsjg",
  "venue": "bioRxiv",
  "versions": []
}