{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T01:20:44Z","timestamp":1778635244473,"version":"3.51.4"},"reference-count":33,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2020,11,13]],"date-time":"2020-11-13T00:00:00Z","timestamp":1605225600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000145","name":"Division of Information and Intelligent Systems","doi-asserted-by":"publisher","award":["1910306"],"award-info":[{"award-number":["1910306"]}],"id":[{"id":"10.13039\/100000145","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006785","name":"Google","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006785","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"publisher","award":["F32 MH114325"],"award-info":[{"award-number":["F32 MH114325"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"publisher","award":["MH059883"],"award-info":[{"award-number":["MH059883"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"publisher","award":["MH084826"],"award-info":[{"award-number":["MH084826"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"publisher","award":["MH104234"],"award-info":[{"award-number":["MH104234"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"publisher","award":["MH106438"],"award-info":[{"award-number":["MH106438"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"publisher","award":["MH119546"],"award-info":[{"award-number":["MH119546"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007297","name":"Office of Naval Research Global","doi-asserted-by":"publisher","award":["N000141812485"],"award-info":[{"award-number":["N000141812485"]}],"id":[{"id":"10.13039\/100007297","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Human Brain Mapping"],"published-print":{"date-parts":[[2021,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Previous work using logistic regression suggests that cognitive control\u2010related frontoparietal activation in early psychosis can predict symptomatic improvement after 1\u2009year of coordinated specialty care with 66% accuracy. Here, we evaluated the ability of six machine learning (ML) algorithms and deep learning (DL) to predict \u201cImprover\u201d status (&gt;20% improvement on Brief Psychiatric Rating Scale [BPRS] total score at 1\u2010year follow\u2010up vs. baseline) and continuous change in BPRS score using the same functional magnetic resonance imaging\u2010based features (frontoparietal activations during the AX\u2010continuous performance task) in the same sample (individuals with either schizophrenia (\n                    <jats:italic>n\u2009=<\/jats:italic>\n                    65, 49M\/16F, mean age 20.8\u2009years) or Type I bipolar disorder (\n                    <jats:italic>n<\/jats:italic>\n                    =\u200917, 9M\/8F, mean age 21.6\u2009years)). 138 healthy controls were included as a reference group. \u201cShallow\u201d ML methods included Naive Bayes, support vector machine, K Star, AdaBoost, J48 decision tree, and random forest. DL included an explainable artificial intelligence (XAI) procedure for understanding results. The best overall performances (70% accuracy for the binary outcome and root mean square error = 9.47 for the continuous outcome) were achieved using DL. XAI revealed left DLPFC activation was the strongest feature used to make binary classification decisions, with a classification activation threshold (adjusted beta = .017) intermediate to the healthy control mean (adjusted beta = .15, 95% CI = \u22120.02 to 0.31) and patient mean (adjusted beta = \u2212.13, 95% CI = \u22120.37 to 0.11). Our results suggest DL is more powerful than shallow ML methods for predicting symptomatic improvement. The left DLPFC may be a functional target for future biomarker development as its activation was particularly important for predicting improvement.\n                  <\/jats:p>","DOI":"10.1002\/hbm.25286","type":"journal-article","created":{"date-parts":[[2020,11,13]],"date-time":"2020-11-13T07:19:59Z","timestamp":1605251999000},"page":"1197-1205","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Comparing machine and deep\n                    <scp>learning\u2010based<\/scp>\n                    algorithms for prediction of clinical improvement in psychosis with functional magnetic resonance imaging"],"prefix":"10.1002","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5656-7987","authenticated-orcid":false,"given":"Jason","family":"Smucny","sequence":"first","affiliation":[{"name":"Department of Psychiatry University of California  Davis California USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ian","family":"Davidson","sequence":"additional","affiliation":[{"name":"Department of Computer Science University of California  Davis California USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cameron S.","family":"Carter","sequence":"additional","affiliation":[{"name":"Department of Psychiatry University of California  Davis California USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2020,11,13]]},"reference":[{"key":"e_1_2_8_2_1","volume-title":"Introduction to machine learning","author":"Alpaydin E.","year":"2004"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0808187106"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_8_5_1","first-page":"210","article-title":"Region\u2010of\u2010interest analysis using an SPM toolbox. Presented at the 8th International Conference on Functional Mapping of the Human Brain. Sendai, Japan","volume":"13","author":"Brett M.","year":"2002","journal-title":"NeuroImage"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009715923555"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1037\/0021-843X.108.1.120"},{"key":"e_1_2_8_8_1","unstructured":"Davidson I. Gourru A. &Ravi S. S.(2018).The cluster description problem\u2010complexity results formulations and approximations. Paper presented at the Advances in Neural Information Processing Systems Montreal Canada."},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1176\/appi.ajp.2012.12010144"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41380-019-0365-9"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1177\/0956797620916786"},{"key":"e_1_2_8_12_1","volume-title":"Structured clinical interview for DSM\u2010IV\u2010TR axis I disorders, research version, patient edition","author":"First M. B.","year":"2002"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1968.1054155"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1093\/schbul\/sbr172"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1176\/appi.ajp.2016.16050503"},{"key":"e_1_2_8_16_1","unstructured":"Krizhevsky A. Sutskever I. &Hinton G. E.(2012).ImageNet classification with deep convolutional neural networks. Paper presented at the Neural Information Processing Systems Lake Tahoe NV."},{"key":"e_1_2_8_17_1","doi-asserted-by":"crossref","unstructured":"Le Q. V. Ranzato M. Monga R. Devin M. Chen K. Corrado G. S. \u2026Ng A. Y.(2012).Building high\u2010level features using large scale unsupervised learning. 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