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For: Ranlund S, Rosa MJ, de Jong S, Cole JH, Kyriakopoulos M, Fu CHY, Mehta MA, Dima D. Associations between polygenic risk scores for four psychiatric illnesses and brain structure using multivariate pattern recognition. Neuroimage Clin 2018;20:1026-36. [PMID: 30340201 DOI: 10.1016/j.nicl.2018.10.008] [Cited by in Crossref: 17] [Cited by in F6Publishing: 11] [Article Influence: 4.3] [Reference Citation Analysis]
Number Citing Articles
1 Zhu W, Shen S, Zhang Z, Chiroma H. Improved Multiclassification of Schizophrenia Based on Xgboost and Information Fusion for Small Datasets. Computational and Mathematical Methods in Medicine 2022;2022:1-11. [DOI: 10.1155/2022/1581958] [Reference Citation Analysis]
2 Dimitriadis SI, Perry G, Lancaster TM, Tansey KE, Singh KD, Holmans P, Pocklington A, Davey Smith G, Zammit S, Hall J, O'Donovan MC, Owen MJ, Jones DK, Linden DE. Genetic risk for schizophrenia is associated with increased proportion of indirect connections in brain networks revealed by a semi-metric analysis: evidence from population sample stratified for polygenic risk. Cereb Cortex 2022:bhac256. [PMID: 35830871 DOI: 10.1093/cercor/bhac256] [Reference Citation Analysis]
3 Manca R, Pardiñas AF, Venneri A; Alzheimer’s Disease Neuroimaging Initiative. The neural signatures of psychoses in Alzheimer's disease: a neuroimaging genetics approach. Eur Arch Psychiatry Clin Neurosci 2022. [PMID: 35727357 DOI: 10.1007/s00406-022-01432-6] [Reference Citation Analysis]
4 Rodrigue AL, Mathias SR, Knowles EE, Mollon J, Almasy L, Schultz L, Turner J, Calhoun V, Glahn DC. Specificity of Psychiatric Polygenic Risk Scores and their Effects on Associated Risk Phenotypes. Biological Psychiatry Global Open Science 2022. [DOI: 10.1016/j.bpsgos.2022.05.008] [Reference Citation Analysis]
5 Cattarinussi G, Delvecchio G, Sambataro F, Brambilla P. The effect of polygenic risk scores for major depressive disorder, bipolar disorder and schizophrenia on morphological brain measures: A systematic review of the evidence. J Affect Disord 2022:S0165-0327(22)00509-2. [PMID: 35533776 DOI: 10.1016/j.jad.2022.05.007] [Reference Citation Analysis]
6 Kang J, Jiao Z, Qin Y, Wang Y, Wang J, Jin L, Feng J, Wang F, Tang Y, Gong X. Associations between polygenic risk scores and amplitude of low-frequency fluctuation of inferior frontal gyrus in schizophrenia. J Psychiatr Res 2022;147:4-12. [PMID: 34999338 DOI: 10.1016/j.jpsychires.2021.12.043] [Reference Citation Analysis]
7 Wei Y, de Lange SC, Pijnenburg R, Scholtens LH, Ardesch DJ, Watanabe K, Posthuma D, van den Heuvel MP. Statistical testing in transcriptomic-neuroimaging studies: A how-to and evaluation of methods assessing spatial and gene specificity. Hum Brain Mapp 2021. [PMID: 34862695 DOI: 10.1002/hbm.25711] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
8 Rootes-Murdy K, Zendehrouh E, Calhoun VD, Turner JA. Spatially Covarying Patterns of Gray Matter Volume and Concentration Highlight Distinct Regions in Schizophrenia. Front Neurosci 2021;15:708387. [PMID: 34720851 DOI: 10.3389/fnins.2021.708387] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
9 Sha Z, Schijven D, Francks C. Patterns of brain asymmetry associated with polygenic risks for autism and schizophrenia implicate language and executive functions but not brain masculinization. Mol Psychiatry 2021. [PMID: 34211121 DOI: 10.1038/s41380-021-01204-z] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 6.0] [Reference Citation Analysis]
10 Janiri D, Kotzalidis GD, di Luzio M, Giuseppin G, Simonetti A, Janiri L, Sani G. Genetic neuroimaging of bipolar disorder: a systematic 2017-2020 update. Psychiatr Genet 2021;31:50-64. [PMID: 33492063 DOI: 10.1097/YPG.0000000000000274] [Reference Citation Analysis]
11 Qin Y, Kang J, Jiao Z, Wang Y, Wang J, Wang H, Feng J, Jin L, Wang F, Gong X. Polygenic risk for autism spectrum disorder affects left amygdala activity and negative emotion in schizophrenia. Transl Psychiatry 2020;10:322. [PMID: 32958750 DOI: 10.1038/s41398-020-01001-2] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
12 Taylor JA, Larsen KM, Garrido MI. Multi-dimensional predictions of psychotic symptoms via machine learning. Hum Brain Mapp 2020;41:5151-63. [PMID: 32870535 DOI: 10.1002/hbm.25181] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
13 Wendt FR, Pathak GA, Tylee DS, Goswami A, Polimanti R. Heterogeneity and Polygenicity in Psychiatric Disorders: A Genome-Wide Perspective. Chronic Stress (Thousand Oaks) 2020;4:2470547020924844. [PMID: 32518889 DOI: 10.1177/2470547020924844] [Cited by in Crossref: 4] [Cited by in F6Publishing: 10] [Article Influence: 2.0] [Reference Citation Analysis]
14 Nicholson AA, Harricharan S, Densmore M, Neufeld RWJ, Ros T, McKinnon MC, Frewen PA, Théberge J, Jetly R, Pedlar D, Lanius RA. Classifying heterogeneous presentations of PTSD via the default mode, central executive, and salience networks with machine learning. Neuroimage Clin 2020;27:102262. [PMID: 32446241 DOI: 10.1016/j.nicl.2020.102262] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 4.5] [Reference Citation Analysis]
15 Mallet J, Le Strat Y, Dubertret C, Gorwood P. Polygenic Risk Scores Shed Light on the Relationship between Schizophrenia and Cognitive Functioning: Review and Meta-Analysis. J Clin Med 2020;9:E341. [PMID: 31991840 DOI: 10.3390/jcm9020341] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 6.0] [Reference Citation Analysis]
16 Maglanoc LA, Kaufmann T, van der Meer D, Marquand AF, Wolfers T, Jonassen R, Hilland E, Andreassen OA, Landrø NI, Westlye LT. Brain Connectome Mapping of Complex Human Traits and Their Polygenic Architecture Using Machine Learning. Biol Psychiatry 2020;87:717-26. [PMID: 31858985 DOI: 10.1016/j.biopsych.2019.10.011] [Cited by in Crossref: 15] [Cited by in F6Publishing: 12] [Article Influence: 5.0] [Reference Citation Analysis]
17 Pisanu C, Squassina A. Treatment-Resistant Schizophrenia: Insights From Genetic Studies and Machine Learning Approaches. Front Pharmacol 2019;10:617. [PMID: 31191325 DOI: 10.3389/fphar.2019.00617] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 2.3] [Reference Citation Analysis]