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. |
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| 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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| ISSN: | 1097-0193 |
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| DOI: | 10.1002/hbm.70557 |
