{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T19:05:48Z","timestamp":1790363148738,"version":"4.1.0"},"reference-count":33,"publisher":"Optica Publishing Group","issue":"7","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/doi.org\/10.1364\/OA_License_v2#VOR-OA"},{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/opg.optica.org\/policies\/opg-tdm-policy.json"}],"content-domain":{"domain":["opg.optica.org"],"crossmark-restriction":false},"short-container-title":["Biomed. Opt. Express"],"published-print":{"date-parts":[[2026,7,1]]},"abstract":"<jats:p>Continuous-wave functional near-infrared spectroscopy (CW-fNIRS) traditionally utilizes fixed-geometry optode configurations, which introduce significant anatomical bias due to inter-subject variations in skull thickness and phenotypic barriers such as dense hair. To address these limitations, we propose an Optical Adaptive Depth Steering (O-ADS) framework that mathematically synthesizes a \u201cVirtual Optode\u201d using a high-density multi-distance array (8\u201332\u2005mm) and a linearly constrained minimum variance (LCMV) spatial beamformer. We computationally validated this framework using high-fidelity Monte Carlo simulations on layered cranial slabs and a 3D anatomical atlas (Colin27). Results demonstrate that O-ADS exhibits high computational resilience to anatomical bias, achieving up to a 39-fold improvement in relative brain sensitivity for thick-skull morphologies compared to traditional 32\u2005mm static sensors. Furthermore, under realistic spatiotemporal noise conditions involving heterogeneous scalp hemodynamics and simulated phenotypic barriers (8\u2005mm reference channel failure), O-ADS maintained a significantly higher mean signal recovery purity (38.3%\u2009\u00b1\u200922.1%) than standard Multi-Distance Regression (MDR) (21.8%\u2009\u00b1\u200915.4%). This software-defined approach provides a scalable solution for optical neuroimaging, reducing the dependence of sensor performance on individual anatomical and phenotypic variance to facilitate more inclusive cerebral monitoring under challenging physical constraints.<\/jats:p>","DOI":"10.1364\/boe.604286","type":"journal-article","created":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T07:00:09Z","timestamp":1782370809000},"page":"3971","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Computational validation of optical adaptive depth steering for continuous-wave fNIRS"],"prefix":"10.1364","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8084-6916","authenticated-orcid":true,"given":"Murad","family":"Althobaiti","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/038cy8j79","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"key":"boe-17-7-3971-R1","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1016\/j.neuroimage.2013.05.004","type":"journal-article","volume":"85","author":"Scholkmann","year":"2014","journal-title":"Neuroimage"},{"key":"boe-17-7-3971-R2","doi-asserted-by":"publisher","first-page":"921","DOI":"10.1016\/j.neuroimage.2012.03.049","type":"journal-article","volume":"63","author":"Ferrari","year":"2012","journal-title":"Neuroimage"},{"key":"boe-17-7-3971-R3","doi-asserted-by":"publisher","first-page":"055005","DOI":"10.1088\/2057-1976\/aba102","type":"journal-article","volume":"6","author":"Almulla","year":"2020","journal-title":"Biomed. 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