{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:21:26Z","timestamp":1792783286190,"version":"4.5.0"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Universidade Federal De Santa Catarina","id":[{"id":"https:\/\/ror.org\/041akq887","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neuroinform"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    We present a validated, containerised pipeline for Linear Mixed-Effects (LME) analysis of task fMRI, built for reproducibility: the fitting backend must be declared explicitly, outputs are versioned, and every step records a manifest of software, hardware, and parameters. The implementation is cross-validated against the R reference standard  (mean\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\varvec{|\\Delta \\text {ICC}| = 2.2\\times 10^{-6}}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    across 21 regions; significance concordance 21\/21) and benchmarked at\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\varvec{19\\times }$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    the per-voxel cost of the GLM, completing a whole-brain analysis in under six hours with 100% convergence. In a case study on emotion task data from 142 HCP subjects (283 runs), voxelwise agreement with the conventional General Linear Model (GLM) is high (\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\varvec{r = 0.94}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ), with LME more conservative under FDR correction (74,004 vs. 108,627 significant voxels at\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\varvec{q &lt; 0.05}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ; Dice\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\varvec{= 0.77}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ). An intermediate LME omitting the run fixed effect is almost indistinguishable from the GLM (Dice\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\varvec{= 0.99}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ), locating the sensitivity difference in the run covariate rather than in hierarchical variance decomposition. Whole-brain ICC mapping shows heterogeneous within-subject variability (mean ICC\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\varvec{= 0.199}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ), and run effects are significant in 13\/21 ROIs, surviving adjustment for head motion. This decomposition strategy offers a reusable diagnostic for sensitivity differences between statistical approaches.\n                  <\/jats:p>","DOI":"10.1007\/s12021-026-09820-2","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T11:34:20Z","timestamp":1790681660000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Beyond the General Linear Model: Linear Mixed-Effects Modeling of fMRI Data from the Human Connectome Project"],"prefix":"10.1007","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0697-7380","authenticated-orcid":false,"given":"Daniele","family":"Orzechowski","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronaldo","family":"Martins da Costa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,29]]},"reference":[{"key":"9820_CR1","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/j.neuroimage.2013.05.033","volume":"80","author":"DM Barch","year":"2013","unstructured":"Barch, D. 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