{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T09:48:46Z","timestamp":1773222526682,"version":"3.50.1"},"posted":{"date-parts":[[2026,3,5]]},"group-title":"Neuroscience","reference-count":41,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T00:00:00Z","timestamp":1772668800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["K99EB016689"],"award-info":[{"award-number":["K99EB016689"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["R21NS081437"],"award-info":[{"award-number":["R21NS081437"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["R01EB000461"],"award-info":[{"award-number":["R01EB000461"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["R00EB016689"],"award-info":[{"award-number":["R00EB016689"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["R01EB027779"],"award-info":[{"award-number":["R01EB027779"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["R21EB031211"],"award-info":[{"award-number":["R21EB031211"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,3,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Over the past two decades, open-source research software such as SPM, AFNI and FSL formed the substrate for advancements in the brain functional magnetic resonance imaging (fMRI) field. The spinal cord fMRI field has matured substantially over the past decade, yet there is limited research software tailored for processing cord fMRI data that has distinct noise sources, unique challenges, niche processing requirements and special needs. Spinal cord fMRI data analysis is a \u2018different beast\u2019, involving specialized pre- and post-processing steps due to the cord\u2019s unique anatomy and higher distortions\/physiological noise, thus requiring extensive and careful quality assessment. Building upon 10+ years of research and development, we present Neptune \u2013 a user-interface-based MATLAB toolbox. With 30,000+ lines of in-house code, it is designed to be easy to use and does not require programming knowledge. Neptune builds on our previously published 15-step pre-processing pipeline (Barry et al., 2016) and presents a 19-step pipeline with new processing steps, and enhancements to existing steps. Neptune has a 4-step post-processing pipeline aimed at fMRI connectivity modeling. It generates extensive and novel quality control visuals to enable a thorough assessment of data quality, and displays them in an elegant webpage format. We demonstrate the utility of Neptune on our 7T data. Certain features of the popular Spinal Cord Toolbox (SCT) are integrated into Neptune, and users can import\/export between Neptune and other software such as FSL and SPM. The availability of this open-source, easy-to-use software will benefit the spinal cord fMRI community, and also tip the cost-benefit balance for brain fMRI researchers to invest in learning new software to conduct important neuroscientific and clinical research using spinal cord fMRI.<\/jats:p>","DOI":"10.64898\/2026.03.03.709443","type":"posted-content","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T05:30:25Z","timestamp":1772775025000},"source":"Crossref","is-referenced-by-count":0,"title":["Neptune: a toolbox for spinal cord functional MRI data processing and quality assurance"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9553-1354","authenticated-orcid":false,"given":"D","family":"Rangaprakash","sequence":"first","affiliation":[{"name":"Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6271-9414","authenticated-orcid":false,"given":"Robert L","family":"Barry","sequence":"additional","affiliation":[{"name":"Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA"},{"name":"Harvard-Massachusetts Institute of Technology Division of Health Sciences & Technology, Cambridge, Massachusetts, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2026031005300808000_2026.03.03.709443v1.1","unstructured":"K. J. Friston , J. Ashburner , S. J. Kiebel , T. E. Nichols and W. D. Penny , Statistical Parametric Mapping: The Analysis of Functional Brain Images, Academic Press, 2007."},{"key":"2026031005300808000_2026.03.03.709443v1.2","doi-asserted-by":"publisher","DOI":"10.1006\/cbmr.1996.0014"},{"key":"2026031005300808000_2026.03.03.709443v1.3","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2004.07.051"},{"issue":"6","key":"2026031005300808000_2026.03.03.709443v1.4","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1177\/10738584221101827","article-title":"Spinal Cord fMRI: A New Window into the Central Nervous System","volume":"29","year":"2023","journal-title":"The Neuroscientist"},{"key":"2026031005300808000_2026.03.03.709443v1.5","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awy083"},{"key":"2026031005300808000_2026.03.03.709443v1.6","doi-asserted-by":"crossref","first-page":"694271","DOI":"10.3389\/fneur.2021.694271","article-title":"Spinal Cord Resting State Activity in Individuals With Fibromyalgia Who Take Opioids","volume":"12","year":"2021","journal-title":"Frontiers in neurology"},{"key":"2026031005300808000_2026.03.03.709443v1.7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2009.12.122"},{"key":"2026031005300808000_2026.03.03.709443v1.8","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.02812"},{"key":"2026031005300808000_2026.03.03.709443v1.9","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1414293111"},{"key":"2026031005300808000_2026.03.03.709443v1.10","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2016.02.058"},{"key":"2026031005300808000_2026.03.03.709443v1.11","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pbio.3000789"},{"issue":"2","key":"2026031005300808000_2026.03.03.709443v1.12","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1002\/mrm.27388","article-title":"Dynamic per slice shimming for simultaneous brain and spinal cord fMRI","volume":"81","year":"2019","journal-title":"Magnetic resonance in medicine"},{"key":"2026031005300808000_2026.03.03.709443v1.13","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.04.021"},{"key":"2026031005300808000_2026.03.03.709443v1.14","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.07.003."},{"key":"2026031005300808000_2026.03.03.709443v1.15","unstructured":"D. Rangaprakash and R. L. Barry , \u201cNeptune: a toolbox for spinal cord functional MRI data processing and quality assurance,\u201d in International Society for Magnetic Resonance in Medicine annual meeting, London, UK, 2022."},{"key":"2026031005300808000_2026.03.03.709443v1.16","unstructured":"D. Rangaprakash and R. Barry , \u201cNeptune: a toolbox for spinal cord fMRI data processing,\u201d 01 03 2026. [Online]. Available: https:\/\/www.mathworks.com\/matlabcentral\/fileexchange\/183329-neptune-a-toolbox-for-spinal-cord-fmri-data-processing."},{"key":"2026031005300808000_2026.03.03.709443v1.17","unstructured":"D. Rangaprakash and R. Barry , \u201cNeptune,\u201d 01 03 2026. [Online]. Available: https:\/\/github.com\/rangadeshpande\/Neptune."},{"key":"2026031005300808000_2026.03.03.709443v1.18","doi-asserted-by":"publisher","DOI":"10.17632\/75nn8v6znt.1"},{"key":"2026031005300808000_2026.03.03.709443v1.19","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2016.10.009."},{"issue":"1","key":"2026031005300808000_2026.03.03.709443v1.20","doi-asserted-by":"crossref","first-page":"5181","DOI":"10.1038\/s41467-021-25431-8","article-title":"Lowering the thermal noise barrier in functional brain mapping with magnetic resonance imaging","volume":"12","year":"2021","journal-title":"Nature communications"},{"key":"2026031005300808000_2026.03.03.709443v1.21","unstructured":"D. Rangaprakash and R. L. Barry , \u201cRealizing sub-second and sub-millimeter spinal cord fMRI at 7 Tesla,\u201d in Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM 2023), Toronto, ON, Canada, 2023."},{"key":"2026031005300808000_2026.03.03.709443v1.22","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2013.01.003"},{"key":"2026031005300808000_2026.03.03.709443v1.23","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2003.11.029"},{"key":"2026031005300808000_2026.03.03.709443v1.24","doi-asserted-by":"crossref","first-page":"934138","DOI":"10.3389\/fnins.2023.934138","article-title":"The confound of hemodynamic response function variability in human resting-state functional MRI studies","volume":"17","year":"2023","journal-title":"Frontiers in neuroscience"},{"key":"2026031005300808000_2026.03.03.709443v1.25","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.27146"},{"key":"2026031005300808000_2026.03.03.709443v1.26","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1016\/j.nicl.2017.07.016","article-title":"Hemodynamic variability in soldiers with trauma: Implications for functional MRI connectivity studies","volume":"16","year":"2017","journal-title":"NeuroImage: Clinical"},{"key":"2026031005300808000_2026.03.03.709443v1.27","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.nicl.2018.04.013","article-title":"Aberrant Hemodynamic Responses in Autism: Implications for Resting State fMRI Functional Connectivity Studies","volume":"19","year":"2018","journal-title":"Neuroimage: Clinical"},{"key":"2026031005300808000_2026.03.03.709443v1.28","unstructured":"D. Rangaprakash and R. L. Barry , \u201cVariability of the hemodynamic response function in the healthy human cervical spinal cord at 3 Tesla,\u201d in Proceedings of the International Society for Magnetic Resonance in Medicine, 2021."},{"key":"2026031005300808000_2026.03.03.709443v1.29","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2014.10.017"},{"key":"2026031005300808000_2026.03.03.709443v1.30","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1016\/j.neuroimage.2016.12.072","article-title":"Investigating resting-state functional connectivity in the cervical spinal cord at 3T","volume":"147","year":"2017","journal-title":"NeuroImage"},{"key":"2026031005300808000_2026.03.03.709443v1.31","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.26606"},{"key":"2026031005300808000_2026.03.03.709443v1.32","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroscience.2016.08.042"},{"key":"2026031005300808000_2026.03.03.709443v1.33","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroscience.2017.10.035"},{"key":"2026031005300808000_2026.03.03.709443v1.34","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2020.07.024"},{"issue":"1","key":"2026031005300808000_2026.03.03.709443v1.35","doi-asserted-by":"crossref","first-page":"16512","DOI":"10.1038\/s41598-018-34841-6","article-title":"A practical protocol for measurements of spinal cord functional connectivity","volume":"8","year":"2018","journal-title":"Scientific Reports"},{"key":"2026031005300808000_2026.03.03.709443v1.36","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2023.119949"},{"key":"2026031005300808000_2026.03.03.709443v1.37","doi-asserted-by":"crossref","first-page":"120011","DOI":"10.1016\/j.neuroimage.2023.120011","article-title":"Improved sensitivity and microvascular weighting of 3T laminar fMRI with GE-BOLD using NORDIC and phase regression","volume":"271","year":"2023","journal-title":"NeuroImage"},{"key":"2026031005300808000_2026.03.03.709443v1.38","first-page":"15","article-title":"Criticality in large-scale brain fMRI dynamics unveiled by a novel point process analysis","volume":"3","year":"2012","journal-title":"Frontiers in Physiology"},{"key":"2026031005300808000_2026.03.03.709443v1.39","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.118591"},{"key":"2026031005300808000_2026.03.03.709443v1.40","doi-asserted-by":"crossref","unstructured":"D. Rangaprakash , O. David , R. L. Barry and G. Deshpande , \u201cComparison of hemodynamic response functions estimated from resting-state functional MRI and invasive electrophysiological recordings in rats,\u201d bioRxiv, p. 2023.02.27.530359, 2023.","DOI":"10.1101\/2023.02.27.530359"},{"key":"2026031005300808000_2026.03.03.709443v1.41","doi-asserted-by":"publisher","DOI":"10.7910\/DVN\/NVBCPS"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.64898\/2026.03.03.709443","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T12:30:19Z","timestamp":1773145819000},"score":1,"resource":{"primary":{"URL":"http:\/\/biorxiv.org\/lookup\/doi\/10.64898\/2026.03.03.709443"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,5]]},"references-count":41,"URL":"https:\/\/doi.org\/10.64898\/2026.03.03.709443","relation":{},"subject":[],"published":{"date-parts":[[2026,3,5]]},"subtype":"preprint"}}