Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer
Scheinost D, Pollatou A, Dufford A, Jiang R, Farruggia M, Rosenblatt M, Peterson H, Rodriguez R, Dadashkarimi J, Liang Q, Dai W, Foster M, Camp C, Tejavibulya L, Adkinson B, Sun H, Ye J, Cheng Q, Spann M, Rolison M, Noble S, Westwater M. Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer. Biological Psychiatry 2022, 93: 893-904. PMID: 36759257, PMCID: PMC10259670, DOI: 10.1016/j.biopsych.2022.10.014.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsA Neuroimaging Signature of Cognitive Aging from Whole‐Brain Functional Connectivity
Jiang R, Scheinost D, Zuo N, Wu J, Qi S, Liang Q, Zhi D, Luo N, Chung Y, Liu S, Xu Y, Sui J, Calhoun V. A Neuroimaging Signature of Cognitive Aging from Whole‐Brain Functional Connectivity. Advanced Science 2022, 9: 2201621. PMID: 35811304, PMCID: PMC9403648, DOI: 10.1002/advs.202201621.Peer-Reviewed Original ResearchConceptsCognitive declineNormal agingFunctional connectivitySimilar neural correlatesWhole-brain functional connectivityDorsal attention networkBrain network organizationNeural dedifferentiationFluid intelligenceCognitive agingCognitive abilitiesNeural correlatesAttention networkCognitive functionNetwork organizationHuman ageNeuroimaging signaturesCognitionUnique patternAgingConnectivityIntelligenceCorrelatesConstructsHealthy cohortPredicting the future of neuroimaging predictive models in mental health
Tejavibulya L, Rolison M, Gao S, Liang Q, Peterson H, Dadashkarimi J, Farruggia MC, Hahn CA, Noble S, Lichenstein SD, Pollatou A, Dufford AJ, Scheinost D. Predicting the future of neuroimaging predictive models in mental health. Molecular Psychiatry 2022, 27: 3129-3137. PMID: 35697759, PMCID: PMC9708554, DOI: 10.1038/s41380-022-01635-2.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus Statements