{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T23:04:55Z","timestamp":1789427095058,"version":"build-2803163510"},"posted":{"date-parts":[[2026,9,14]]},"group-title":"Neuroscience","reference-count":0,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"NIH","award":["1P50DA044121-01A1"],"award-info":[{"award-number":["1P50DA044121-01A1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,9,14]]},"abstract":"<jats:p>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(\u03bb1)) and the slope of the log10-transformed spectrum (\u03b2). 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.\nOur key findings include: (1) log10(\u03bb1) and \u03b2 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(\u03bb1) 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(\u03bb1) reflected the proportion of functional connectivity edges significantly associated with motion, indicating that higher log10(\u03bb1) reflects more widespread motion-related contamination across large-scale functional networks; and (5) global signal regression consistently reduced log10(\u03bb1), whereas spike regression had dataset-dependent effects.\nTogether, 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.\nThe full procedure is implemented in R, and the corresponding script is available at: https:\/\/github.com\/lejianhuang\/EigenvalueSignature.<\/jats:p>","DOI":"10.64898\/2026.09.08.750194","type":"posted-content","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T22:40:15Z","timestamp":1789425615000},"source":"Crossref","is-referenced-by-count":0,"title":["Eigenvalue Signatures Reveal Residual Motion Effects Across Resting-State fMRI Denoising Strategies"],"prefix":"10.64898","author":[{"given":"Lejian","family":"Huang","sequence":"first","affiliation":[{"name":"Northwestern;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew D.","family":"Vigotsky","sequence":"additional","affiliation":[{"name":"Northwestern University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A Vania","family":"Apkarian","sequence":"additional","affiliation":[{"name":"Northwestern University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.64898\/2026.09.08.750194","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T22:40:16Z","timestamp":1789425616000},"score":1,"resource":{"primary":{"URL":"http:\/\/biorxiv.org\/lookup\/doi\/10.64898\/2026.09.08.750194"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,14]]},"references-count":0,"URL":"https:\/\/doi.org\/10.64898\/2026.09.08.750194","relation":{},"subject":[],"published":{"date-parts":[[2026,9,14]]},"subtype":"preprint"}}