{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T14:58:10Z","timestamp":1790521090232,"version":"4.1.0"},"posted":{"date-parts":[[2026,9,24]]},"group-title":"Neuroscience","reference-count":42,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"award":["R01MH111417"],"award-info":[{"award-number":["R01MH111417"]}],"id":[{"id":"https:\/\/ror.org\/001d55x84","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,9,24]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                <jats:p>Submillimeter functional magnetic resonance imaging (fMRI) based on blood-oxygenation-level-dependent (BOLD) signal enables the study of brain function at the submillimeter level, uncovering insights into fine-scale organisations like cortical layers and columns. However, its inherently low contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) often limit its reliability and applicability. Noise Reduction with Distribution Corrected Principal Components Analysis (NORDIC PCA) is a locally low-rank denoising algorithm that reduces thermal noise levels in BOLD fMRI in a local patch manner. However, local patches often contain a mixture of signals from multiple tissues that negatively affect the low-rank structure of the patches, which limits the denoising capabilities of the algorithm. We propose an alternative approach for patch formation by gathering similar non-local voxels, dubbed voxel-matching (VM) NORDIC. The results on submillimeter-resolution BOLD fMRI data indicate that VM-NORDIC effectively promotes the low rank of the patches by boosting signal redundancy, allowing for more efficient noise attenuation. Moreover, the method barely affects spatial smoothness due to the non-local voxel selection based on time-series similarity. In particular, VM-NORDIC outperforms standard NORDIC with default local patching (Standard-NORDIC) in terms of temporal SNR (tSNR) (\u223c9-90% larger than Standard-NORDIC; \u223c23-250% larger than the original) and spatial smoothness estimates (\u223c20% of the smoothness induced by Standard-NORDIC). These improvements are fundamental to improving the validity and precision of fMRI studies at submillimeter resolutions.<\/jats:p>","DOI":"10.64898\/2026.09.18.752417","type":"posted-content","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:20:46Z","timestamp":1790295646000},"source":"Crossref","is-referenced-by-count":0,"title":["Voxel-Matching NORDIC: Non-local patch formation by time-series similarity increases tSNR in high-resolution BOLD fMRI"],"prefix":"10.64898","author":[{"given":"Alessandro","family":"Nigi","sequence":"first","affiliation":[{"name":"Department of Radiology, Center for Image Sciences, Advanced MRI Gradient Hardware and Methods Group, University Medical Center Utrecht, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natalia","family":"Petridou","sequence":"additional","affiliation":[{"name":"Department of Radiology, Center for Image Sciences, Translational Neuroimaging Group, University Medical Center Utrecht, The 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