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Efficient Deep Learning Models for Predicting Individualized Task Activation From Resting-State Functional Connectivity.

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Title: Efficient Deep Learning Models for Predicting Individualized Task Activation From Resting-State Functional Connectivity.
Authors: Madsen SJ; Department of Psychiatry, Stanford University, Stanford, California, USA., Lee YE; Department of Psychiatry, Stanford University, Stanford, California, USA., Quah SKL; Department of Psychiatry, Stanford University, Stanford, California, USA., Uddin LQ; Department of Psychiatry, University of California, Los Angeles, California, USA., Mumford JA; Department of Psychology, Stanford University, Stanford, California, USA., Barch DM; Department of Psychology, Washington University in St. Louis, St. Louis, Missouri, USA., Fair DA; Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota, USA., Gotlib IH; Department of Psychology, Stanford University, Stanford, California, USA., Poldrack RA; Department of Psychology, Stanford University, Stanford, California, USA., Kuceyeski A; Department of Radiology, Weill Cornell Medicine, New York, New York, USA., Saggar M; Department of Psychiatry, Stanford University, Stanford, California, USA.
Source: Human brain mapping [Hum Brain Mapp] 2026 Jun 15; Vol. 47 (9), pp. e70557.
Publication Type: Journal Article
Journal Info: Publisher: Wiley Country of Publication: United States NLM ID: 9419065 Publication Model: Print Cited Medium: Internet ISSN: 1097-0193 (Electronic) Linking ISSN: 10659471 NLM ISO Abbreviation: Hum Brain Mapp Subsets: MEDLINE
Database: MEDLINE Ultimate
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              Text: 2026 Jun 15
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