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Use of Machine Learning to Determine Deviance in Neuroanatomical Maturity Associated With Future Psychosis in Youths at Clinically High Risk.
Chung Y, Addington J, Bearden CE, Cadenhead K, Cornblatt B, Mathalon DH, McGlashan T, Perkins D, Seidman LJ, Tsuang M, Walker E, Woods SW, McEwen S, van Erp TGM, Cannon TD; North American Prodrome Longitudinal Study (NAPLS) Consortium and the Pediatric Imaging, Neurocognition, and Genetics (PING) Study Consortium. Chung Y, et al. JAMA Psychiatry. 2018 Sep 1;75(9):960-968. doi: 10.1001/jamapsychiatry.2018.1543. JAMA Psychiatry. 2018. PMID: 29971330 Free PMC article.
DESIGN, SETTING, AND PARTICIPANTS: In this prospective longitudinal multicenter study, a neuroanatomical-based age prediction model was developed using a supervised machine learning technique with T1-weighted magnetic resonance imaging scans of 953 healthy controls 3 to 21 …
DESIGN, SETTING, AND PARTICIPANTS: In this prospective longitudinal multicenter study, a neuroanatomical-based age prediction model was deve …
Gray matter maturation and cognition in children with different APOE ε genotypes.
Chang L, Douet V, Bloss C, Lee K, Pritchett A, Jernigan TL, Akshoomoff N, Murray SS, Frazier J, Kennedy DN, Amaral DG, Gruen J, Kaufmann WE, Casey BJ, Sowell E, Ernst T; Pediatric Imaging, Neurocognition, and Genetics (PING) Study Consortium. Chang L, et al. Neurology. 2016 Aug 9;87(6):585-94. doi: 10.1212/WNL.0000000000002939. Epub 2016 Jul 13. Neurology. 2016. PMID: 27412137 Free PMC article.
METHODS: A total of 1,187 healthy children (aged 3-20 years, 52.1% boys, 47.9% girls) with acceptable data from the cross-sectional Pediatric Imaging Neurocognition and Genetics Study were evaluated for the effects of 6 APOE epsilon genotypes on macroscopic a …
METHODS: A total of 1,187 healthy children (aged 3-20 years, 52.1% boys, 47.9% girls) with acceptable data from the cross-sectional Pedia …