{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T15:46:38Z","timestamp":1790955998590,"version":"4.1.0"},"posted":{"date-parts":[[2026,8,12]]},"group-title":"Neuroscience","reference-count":29,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.biorxiv.org\/about\/FAQ#license"}],"funder":[{"award":["1R01EB006841"],"award-info":[{"award-number":["1R01EB006841"]}],"id":[{"id":"https:\/\/ror.org\/01cwqze88","id-type":"ROR","asserted-by":"publisher"}]},{"award":["1R01EB005846"],"award-info":[{"award-number":["1R01EB005846"]}],"id":[{"id":"https:\/\/ror.org\/01cwqze88","id-type":"ROR","asserted-by":"publisher"}]},{"award":["R01AG090597"],"award-info":[{"award-number":["R01AG090597"]}],"id":[{"id":"https:\/\/ror.org\/01cwqze88","id-type":"ROR","asserted-by":"publisher"}]},{"name":"National Science Foundation","award":["2112455"],"award-info":[{"award-number":["2112455"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,8,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Whole-brain tractography reconstructs the major white matter pathways as millions of individual streamlines, offering an exceptionally rich description of neural geometry. Yet the statistical methods used to compare these reconstructions across individuals inevitably discard key information. Voxel-based analyses sacrifice pathway continuity, trajectory-based methods rarely support population-level statistical decomposition, and connectome models largely abstract away the underlying geometry. No existing framework jointly characterizes the population-level statistical organization of white matter and the three-dimensional geometry of the pathways from which that organization is expressed.<\/jats:p>\n                <jats:p>We introduce streamline independent component analysis (SILICA), a framework that links group-level voxel-space statistical decomposition to subject-specific trajectories through a sparse streamline-by-voxel fingerprint. Each streamline is represented by its physical path length within a common anatomical voxel grid while retaining an explicit index-level link to its original trajectory. A two-stage dimensionality reduction reconciles tractograms of differing size and enables continuous component loadings to be back-reconstructed for every original streamline. These subject-specific loadings support weighted trajectory visualization and can be projected into voxel space to generate track-weighted component maps for conventional image-based visualization and future voxel-wise analysis. Separately, the learned group spatial components can be expressed on an independently reconstructed representative whole-brain tractogram to generate a compact trajectory-resolved atlas for group-level visualization. SILICA is a single decomposition expressed simultaneously in statistical and geometric form.<\/jats:p>\n                <jats:p>SILICA was evaluated in diffusion MRI tractograms from 30 healthy adults. The recovered spatial patterns correspond to recognizable commissural, projection, and association systems. Back-reconstructions preserved individual trajectory variation while isolating components shared across the group, and their projection into voxel and trajectory space yielded interpretable maps and atlases. As a proof of concept, SILICA has not yet been validated against anatomical reference standards or evaluated for reproducibility and performance relative to established methods. Nevertheless, these results establish a coherent foundation for analyzing white matter in a framework that jointly represents population-level statistical structure and streamline geometry.<\/jats:p>","DOI":"10.64898\/2026.08.06.743368","type":"posted-content","created":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:50:12Z","timestamp":1786582212000},"source":"Crossref","is-referenced-by-count":1,"title":["SILICA: Streamline Independent Component Analysis for Trajectory-Resolved White Matter Decomposition"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9705-1829","authenticated-orcid":false,"given":"Lei","family":"Wu","sequence":"first","affiliation":[{"name":"Tri-institutional center for Translational Research in Neuroimaging and Data Science (TReNDS) Center, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia 30303"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9058-0747","authenticated-orcid":false,"given":"Vince D.","family":"Calhoun","sequence":"additional","affiliation":[{"name":"Tri-institutional center for Translational Research in Neuroimaging and Data Science (TReNDS) Center, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia 30303"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026081416150532000_2026.08.06.743368v1.1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2010.09.025"},{"key":"2026081416150532000_2026.08.06.743368v1.2","doi-asserted-by":"publisher","DOI":"10.1002\/1522-2594(200010)44:4<625::AID-MRM17>3.0.CO;2-O"},{"key":"2026081416150532000_2026.08.06.743368v1.3","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.10609"},{"issue":"4","key":"2026081416150532000_2026.08.06.743368v1.4","first-page":"317","article-title":"Track-weighted imaging methods: Extracting information from a streamlines tractogram. 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