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Meas."],"published-print":{"date-parts":[[2026,11,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    We examined how evaluation design affects comparisons of functional near-infrared spectroscopy short-separation correction strategies.\n                    <jats:italic>Approach.<\/jats:italic>\n                    Nearest-SC, All-SC, SC-PCA and an adapted tCCA-GLM were compared in a multimodal resting-state dataset (\n                    <jats:italic>n<\/jats:italic>\n                    = 14), using semi-nested and correction-stage fully nested cross-validation, boundary purging, symmetric leave-one-modality-out (LOMO) evaluation, circular-shift nulls, injected hemodynamic response function recovery and a second cohort.\n                    <jats:italic>Main results.<\/jats:italic>\n                    In the fixed-seed purged joint-target analysis, the adapted tCCA-GLM had a null-adjusted \u0394\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    of \u22121.1%. Symmetric LOMO, which evaluates reduced tCCA models, yielded no statistically detected method differences (all paired\n                    <jats:italic>p<\/jats:italic>\n                    \u2a7e 0.3685), but did not establish equivalence or separately quantify leakage and input\u2013target overlap. Motion-targeted external validation provided partial support for an evaluation-tier \u00d7 method interaction (Freedman\u2013Lane\n                    <jats:italic>p<\/jats:italic>\n                    &lt; 0.005). SC-PCA had lower mean known-truth normalized root mean square error (nRMSE = 0.037) than the adapted tCCA-GLM (0.121); within-metric Holm-adjusted comparisons were significant for this contrast and for amplitude-bias comparisons with all three SC-based strategies, but not for slopes. Sensitivity analyses included training-fold PCA selection, LOMO boundary\/segment-filtering checks, lagged evaluation and longer test blocks.\n                    <jats:italic>Significance.<\/jats:italic>\n                    Benchmarks should fit learned correction stages within training data, disclose correction\u2013evaluation overlap and assess signal preservation separately. Findings are specific to the evaluated implementations, outcomes and datasets.\n                  <\/jats:p>","DOI":"10.1088\/1361-6579\/aeb8de","type":"journal-article","created":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T22:54:12Z","timestamp":1792796052000},"page":"115001","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Correction-stage information leakage and target\u2013input asymmetry can distort comparisons of fNIRS short-separation channel correction strategies"],"prefix":"10.1088","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7003-8472","authenticated-orcid":true,"given":"Esra","family":"S\u00fczen","sequence":"first","affiliation":[{"name":"Akdeniz University"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"266","published-online":{"date-parts":[[2026,11,3]]},"reference":[{"key":"pmeaaeb8debib1","doi-asserted-by":"publisher","DOI":"10.1117\/1.NPh.2.2.025005","type":"journal-article","article-title":"How short is short? 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