{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T16:07:08Z","timestamp":1753891628068,"version":"3.41.2"},"reference-count":10,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T00:00:00Z","timestamp":1677542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neuroinform."],"DOI":"10.3389\/fninf.2023.1154835","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T17:05:15Z","timestamp":1678122315000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Editorial: Machine learning methods for human brain imaging"],"prefix":"10.3389","volume":"17","author":[{"given":"Fatos Tunay","family":"Yarman Vural","sequence":"first","affiliation":[]},{"given":"Sharlene D.","family":"Newman","sequence":"additional","affiliation":[]},{"given":"Tolga","family":"\u00c7ukur","sequence":"additional","affiliation":[]},{"given":"It\u0131r","family":"\u00d6nal Ertugrul","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2023,2,28]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2301.01241","article-title":"Developing and deploying deep learning models in brain MRI: a review","author":"Aggarwal","year":"2023","journal-title":"arXiv preprint arXiv:2301.01241"},{"key":"B2","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1080\/03007995.2022.2038487","article-title":"An insight into diagnosis of depression using machine learning techniques: a systematic review","volume":"38","author":"Bhadra","year":"2022","journal-title":"Curr. Med. Res. Opin."},{"key":"B3","doi-asserted-by":"publisher","first-page":"2755","DOI":"10.1038\/s41598-022-06651-4","article-title":"Machine learning classification of schizophrenia patients and healthy controls using diverse neuroanatomical markers and Ensemble methods","volume":"12","author":"Chilla","year":"2022","journal-title":"Sci. Rep."},{"key":"B4","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1117\/12.2582286","article-title":"HydraNet: a multi-branch convolutional neural network architecture for MRI denoising,","volume-title":"Medical Imaging 2021: Image Processing, Vol. 11596","author":"Gregory","year":"2021"},{"key":"B5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0065-2458(05)65001-2","article-title":"The state of artificial intelligence","volume":"65","author":"Hopgood","year":"2005","journal-title":"Adv. Comput."},{"key":"B6","doi-asserted-by":"publisher","first-page":"103293","DOI":"10.1016\/j.bspc.2021.103293","article-title":"Machine learning techniques for diagnosis of Alzheimer disease, mild cognitive disorder, and other types of dementia","volume":"72","author":"Mirzaei","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"B7","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1023\/B:MACH.0000035475.85309.1b","article-title":"Learning to decode cognitive states from brain images","volume":"57","author":"Mitchell","year":"2004","journal-title":"Mach. Learn."},{"key":"B8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s40489-021-00299-y","article-title":"Applications of unsupervised machine learning in autism spectrum disorder research: a review","author":"Parlett-Pelleriti","year":"2022","journal-title":"Rev. J. Autism Dev. Disord."},{"key":"B9","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1109\/RBME.2022.3185292","article-title":"Image segmentation for MR brain tumor detection using machine learning: a review","volume":"16","author":"Soomro","year":"2022","journal-title":"IEEE Rev. Biomed. Eng"},{"key":"B10","doi-asserted-by":"publisher","first-page":"108828","DOI":"10.1016\/j.jneumeth.2020.108828","article-title":"Sparse representation of DWI images for fully automated brain tissue segmentation","volume":"343","author":"Wang","year":"2020","journal-title":"J. Neurosci. Methods"}],"container-title":["Frontiers in Neuroinformatics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fninf.2023.1154835\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T17:05:19Z","timestamp":1678122319000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fninf.2023.1154835\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,28]]},"references-count":10,"alternative-id":["10.3389\/fninf.2023.1154835"],"URL":"https:\/\/doi.org\/10.3389\/fninf.2023.1154835","relation":{},"ISSN":["1662-5196"],"issn-type":[{"type":"electronic","value":"1662-5196"}],"subject":[],"published":{"date-parts":[[2023,2,28]]},"article-number":"1154835"}}