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For: Serpa MH, Ou Y, Schaufelberger MS, Doshi J, Ferreira LK, Machado-Vieira R, Menezes PR, Scazufca M, Davatzikos C, Busatto GF, Zanetti MV. Neuroanatomical classification in a population-based sample of psychotic major depression and bipolar I disorder with 1 year of diagnostic stability. Biomed Res Int 2014;2014:706157. [PMID: 24575411 DOI: 10.1155/2014/706157] [Cited by in Crossref: 30] [Cited by in F6Publishing: 31] [Article Influence: 3.8] [Reference Citation Analysis]
Number Citing Articles
1 Kambeitz J, Cabral C, Sacchet MD, Gotlib IH, Zahn R, Serpa MH, Walter M, Falkai P, Koutsouleris N. Detecting Neuroimaging Biomarkers for Depression: A Meta-analysis of Multivariate Pattern Recognition Studies. Biol Psychiatry 2017;82:330-8. [PMID: 28110823 DOI: 10.1016/j.biopsych.2016.10.028] [Cited by in Crossref: 68] [Cited by in F6Publishing: 53] [Article Influence: 11.3] [Reference Citation Analysis]
2 Han W, Sorg C, Zheng C, Yang Q, Zhang X, Ternblom A, Mawuli CB, Gao L, Luo C, Yao D, Li T, Liang S, Shao J. Low-rank network signatures in the triple network separate schizophrenia and major depressive disorder. Neuroimage Clin 2019;22:101725. [PMID: 30798168 DOI: 10.1016/j.nicl.2019.101725] [Cited by in Crossref: 9] [Cited by in F6Publishing: 8] [Article Influence: 3.0] [Reference Citation Analysis]
3 Koutsouleris N, Meisenzahl EM, Borgwardt S, Riecher-Rössler A, Frodl T, Kambeitz J, Köhler Y, Falkai P, Möller HJ, Reiser M, Davatzikos C. Individualized differential diagnosis of schizophrenia and mood disorders using neuroanatomical biomarkers. Brain 2015;138:2059-73. [PMID: 25935725 DOI: 10.1093/brain/awv111] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
4 Nunes A, Schnack HG, Ching CRK, Agartz I, Akudjedu TN, Alda M, Alnæs D, Alonso-Lana S, Bauer J, Baune BT, Bøen E, Bonnin CDM, Busatto GF, Canales-Rodríguez EJ, Cannon DM, Caseras X, Chaim-Avancini TM, Dannlowski U, Díaz-Zuluaga AM, Dietsche B, Doan NT, Duchesnay E, Elvsåshagen T, Emden D, Eyler LT, Fatjó-Vilas M, Favre P, Foley SF, Fullerton JM, Glahn DC, Goikolea JM, Grotegerd D, Hahn T, Henry C, Hibar DP, Houenou J, Howells FM, Jahanshad N, Kaufmann T, Kenney J, Kircher TTJ, Krug A, Lagerberg TV, Lenroot RK, López-Jaramillo C, Machado-Vieira R, Malt UF, McDonald C, Mitchell PB, Mwangi B, Nabulsi L, Opel N, Overs BJ, Pineda-Zapata JA, Pomarol-Clotet E, Redlich R, Roberts G, Rosa PG, Salvador R, Satterthwaite TD, Soares JC, Stein DJ, Temmingh HS, Trappenberg T, Uhlmann A, van Haren NEM, Vieta E, Westlye LT, Wolf DH, Yüksel D, Zanetti MV, Andreassen OA, Thompson PM, Hajek T; ENIGMA Bipolar Disorders Working Group. Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group. Mol Psychiatry 2020;25:2130-43. [PMID: 30171211 DOI: 10.1038/s41380-018-0228-9] [Cited by in Crossref: 36] [Cited by in F6Publishing: 23] [Article Influence: 9.0] [Reference Citation Analysis]
5 Librenza-Garcia D, Kotzian BJ, Yang J, Mwangi B, Cao B, Pereira Lima LN, Bermudez MB, Boeira MV, Kapczinski F, Passos IC. The impact of machine learning techniques in the study of bipolar disorder: A systematic review. Neurosci Biobehav Rev 2017;80:538-54. [PMID: 28728937 DOI: 10.1016/j.neubiorev.2017.07.004] [Cited by in Crossref: 76] [Cited by in F6Publishing: 49] [Article Influence: 15.2] [Reference Citation Analysis]
6 Koutsouleris N, Meisenzahl EM, Borgwardt S, Riecher-Rössler A, Frodl T, Kambeitz J, Köhler Y, Falkai P, Möller HJ, Reiser M, Davatzikos C. Individualized differential diagnosis of schizophrenia and mood disorders using neuroanatomical biomarkers. Brain 2015;138:2059-73. [PMID: 25935725 DOI: 10.1093/brain/awv111] [Cited by in Crossref: 90] [Cited by in F6Publishing: 76] [Article Influence: 12.9] [Reference Citation Analysis]
7 Kambeitz J, Koutsouleris N. [Neuroimaging in psychiatry: multivariate analysis techniques for diagnosis and prognosis]. Nervenarzt 2014;85:714-9. [PMID: 24849118 DOI: 10.1007/s00115-014-4022-x] [Cited by in Crossref: 3] [Article Influence: 0.4] [Reference Citation Analysis]
8 Fleck DE, Ernest N, Adler CM, Cohen K, Eliassen JC, Norris M, Komoroski RA, Chu WJ, Welge JA, Blom TJ, DelBello MP, Strakowski SM. Prediction of lithium response in first-episode mania using the LITHium Intelligent Agent (LITHIA): Pilot data and proof-of-concept. Bipolar Disord 2017;19:259-72. [PMID: 28574156 DOI: 10.1111/bdi.12507] [Cited by in Crossref: 22] [Cited by in F6Publishing: 17] [Article Influence: 4.4] [Reference Citation Analysis]
9 Hilbert K, Lueken U, Muehlhan M, Beesdo-Baum K. Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI data: A multimodal machine learning study. Brain Behav 2017;7:e00633. [PMID: 28293473 DOI: 10.1002/brb3.633] [Cited by in Crossref: 23] [Cited by in F6Publishing: 21] [Article Influence: 4.6] [Reference Citation Analysis]
10 Kang SG, Cho SE. Neuroimaging Biomarkers for Predicting Treatment Response and Recurrence of Major Depressive Disorder. Int J Mol Sci 2020;21:E2148. [PMID: 32245086 DOI: 10.3390/ijms21062148] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 4.5] [Reference Citation Analysis]
11 Arbabshirani MR, Plis S, Sui J, Calhoun VD. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage 2017;145:137-65. [PMID: 27012503 DOI: 10.1016/j.neuroimage.2016.02.079] [Cited by in Crossref: 398] [Cited by in F6Publishing: 307] [Article Influence: 66.3] [Reference Citation Analysis]
12 Najafpour Z, Fatemi A, Goudarzi Z, Goudarzi R, Shayanfard K, Noorizadeh F. Cost-effectiveness of neuroimaging technologies in management of psychiatric and insomnia disorders: A meta-analysis and prospective cost analysis. J Neuroradiol 2021;48:348-58. [PMID: 33383065 DOI: 10.1016/j.neurad.2020.12.003] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Weiss RJ, Bates SV, Song Y, Zhang Y, Herzberg EM, Chen YC, Gong M, Chien I, Zhang L, Murphy SN, Gollub RL, Grant PE, Ou Y. Mining multi-site clinical data to develop machine learning MRI biomarkers: application to neonatal hypoxic ischemic encephalopathy. J Transl Med 2019;17:385. [PMID: 31752923 DOI: 10.1186/s12967-019-2119-5] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
14 Sankar A, Zhang T, Gaonkar B, Doshi J, Erus G, Costafreda SG, Marangell L, Davatzikos C, Fu CH. Diagnostic potential of structural neuroimaging for depression from a multi-ethnic community sample. BJPsych Open 2016;2:247-54. [PMID: 27703783 DOI: 10.1192/bjpo.bp.115.002493] [Cited by in Crossref: 13] [Cited by in F6Publishing: 9] [Article Influence: 2.2] [Reference Citation Analysis]
15 MacQueen GM, Hassel S, Arnott SR, Jean A, Bowie CR, Bray SL, Davis AD, Downar J, Foster JA, Frey BN, Goldstein BI, Hall GB, Harkness KL, Harris J, Lam RW, Lebel C, Milev R, Müller DJ, Parikh SV, Rizvi S, Rotzinger S, Sharma GB, Soares CN, Turecki G, Vila-Rodriguez F, Yu J, Zamyadi M, Strother SC, Kennedy SH; CAN-BIND Investigator Team. The Canadian Biomarker Integration Network in Depression (CAN-BIND): magnetic resonance imaging protocols. J Psychiatry Neurosci 2019;44:223-36. [PMID: 30840428 DOI: 10.1503/jpn.180036] [Cited by in Crossref: 19] [Cited by in F6Publishing: 17] [Article Influence: 9.5] [Reference Citation Analysis]
16 Tekin Erguzel T, Tas C, Cebi M. A wrapper-based approach for feature selection and classification of major depressive disorder–bipolar disorders. Computers in Biology and Medicine 2015;64:127-37. [DOI: 10.1016/j.compbiomed.2015.06.021] [Cited by in Crossref: 30] [Cited by in F6Publishing: 18] [Article Influence: 4.3] [Reference Citation Analysis]
17 Schmitt A, Rujescu D, Gawlik M, Hasan A, Hashimoto K, Iceta S, Jarema M, Kambeitz J, Kasper S, Keeser D, Kornhuber J, Koutsouleris N, Lanzenberger R, Malchow B, Saoud M, Spies M, Stöber G, Thibaut F, Riederer P, Falkai P; WFSBP Task Force on Biological Markers. Consensus paper of the WFSBP Task Force on Biological Markers: Criteria for biomarkers and endophenotypes of schizophrenia part II: Cognition, neuroimaging and genetics. World J Biol Psychiatry 2016;17:406-28. [PMID: 27311987 DOI: 10.1080/15622975.2016.1183043] [Cited by in Crossref: 18] [Cited by in F6Publishing: 19] [Article Influence: 3.0] [Reference Citation Analysis]
18 Colombo F, Calesella F, Mazza MG, Melloni EMT, Morelli MJ, Scotti GM, Benedetti F, Bollettini I, Vai B. Machine learning approaches for prediction of bipolar disorder based on biological, clinical and neuropsychological markers: a systematic review and meta-analysis. Neurosci Biobehav Rev 2022;:104552. [PMID: 35120970 DOI: 10.1016/j.neubiorev.2022.104552] [Reference Citation Analysis]
19 O'Halloran R, Kopell BH, Sprooten E, Goodman WK, Frangou S. Multimodal Neuroimaging-Informed Clinical Applications in Neuropsychiatric Disorders. Front Psychiatry 2016;7:63. [PMID: 27148092 DOI: 10.3389/fpsyt.2016.00063] [Cited by in Crossref: 12] [Cited by in F6Publishing: 10] [Article Influence: 2.0] [Reference Citation Analysis]
20 Chaim-Avancini TM, Doshi J, Zanetti MV, Erus G, Silva MA, Duran FLS, Cavallet M, Serpa MH, Caetano SC, Louza MR, Davatzikos C, Busatto GF. Neurobiological support to the diagnosis of ADHD in stimulant-naïve adults: pattern recognition analyses of MRI data. Acta Psychiatr Scand 2017;136:623-36. [PMID: 29080396 DOI: 10.1111/acps.12824] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 2.0] [Reference Citation Analysis]
21 Yang J, Zhang M, Ahn H, Zhang Q, Jin TB, Li I, Nemesure M, Joshi N, Jiang H, Miller JM, Ogden RT, Petkova E, Milak MS, Sublette ME, Sullivan GM, Trivedi MH, Weissman M, McGrath PJ, Fava M, Kurian BT, Pizzagalli DA, Cooper CM, McInnis M, Oquendo MA, Mann JJ, Parsey RV, DeLorenzo C. Development and evaluation of a multimodal marker of major depressive disorder. Hum Brain Mapp 2018;39:4420-39. [PMID: 30113112 DOI: 10.1002/hbm.24282] [Cited by in Crossref: 14] [Cited by in F6Publishing: 14] [Article Influence: 3.5] [Reference Citation Analysis]
22 Wang X, Zhang T, Chaim TM, Zanetti MV, Davatzikos C. Classification of MRI under the Presence of Disease Heterogeneity using Multi-Task Learning: Application to Bipolar Disorder. Med Image Comput Comput Assist Interv 2015;9349:125-32. [PMID: 29367958 DOI: 10.1007/978-3-319-24553-9_16] [Cited by in Crossref: 1] [Cited by in F6Publishing: 4] [Article Influence: 0.1] [Reference Citation Analysis]
23 Chen Z, Huang X, Gong Q, Biswal BB. Translational application of neuroimaging in major depressive disorder: a review of psychoradiological studies. Front Med 2021;15:528-40. [PMID: 33511554 DOI: 10.1007/s11684-020-0798-1] [Reference Citation Analysis]
24 Claude LA, Houenou J, Duchesnay E, Favre P. Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions. Bipolar Disord 2020;22:334-55. [PMID: 32108409 DOI: 10.1111/bdi.12895] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
25 Busatto Filho G, Rosa PG, Serpa MH, Squarzoni P, Duran FL. Psychiatric neuroimaging research in Brazil: historical overview, current challenges, and future opportunities. Braz J Psychiatry 2020;43:83-101. [PMID: 32520165 DOI: 10.1590/1516-4446-2019-0757] [Reference Citation Analysis]
26 Salvador R, Radua J, Canales-Rodríguez EJ, Solanes A, Sarró S, Goikolea JM, Valiente A, Monté GC, Natividad MDC, Guerrero-Pedraza A, Moro N, Fernández-Corcuera P, Amann BL, Maristany T, Vieta E, McKenna PJ, Pomarol-Clotet E. Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction in psychosis. PLoS One 2017;12:e0175683. [PMID: 28426817 DOI: 10.1371/journal.pone.0175683] [Cited by in Crossref: 47] [Cited by in F6Publishing: 35] [Article Influence: 9.4] [Reference Citation Analysis]
27 Doan NT, Kaufmann T, Bettella F, Jørgensen KN, Brandt CL, Moberget T, Alnæs D, Douaud G, Duff E, Djurovic S, Melle I, Ueland T, Agartz I, Andreassen OA, Westlye LT. Distinct multivariate brain morphological patterns and their added predictive value with cognitive and polygenic risk scores in mental disorders. Neuroimage Clin 2017;15:719-31. [PMID: 28702349 DOI: 10.1016/j.nicl.2017.06.014] [Cited by in Crossref: 35] [Cited by in F6Publishing: 26] [Article Influence: 7.0] [Reference Citation Analysis]
28 Kim Y, Na K. Application of machine learning classification for structural brain MRI in mood disorders: Critical review from a clinical perspective. Progress in Neuro-Psychopharmacology and Biological Psychiatry 2018;80:71-80. [DOI: 10.1016/j.pnpbp.2017.06.024] [Cited by in Crossref: 33] [Cited by in F6Publishing: 16] [Article Influence: 8.3] [Reference Citation Analysis]
29 Squarcina L, Dagnew T, Rivolta M, Bellani M, Sassi R, Brambilla P. Automated cortical thickness and skewness feature selection in bipolar disorder using a semi-supervised learning method. Journal of Affective Disorders 2019;256:416-23. [DOI: 10.1016/j.jad.2019.06.019] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 1.3] [Reference Citation Analysis]
30 Rashid B, Calhoun V. Towards a brain-based predictome of mental illness. Hum Brain Mapp. 2020;41:3468-3535. [PMID: 32374075 DOI: 10.1002/hbm.25013] [Cited by in Crossref: 15] [Cited by in F6Publishing: 18] [Article Influence: 7.5] [Reference Citation Analysis]
31 Gao S, Calhoun VD, Sui J. Machine learning in major depression: From classification to treatment outcome prediction. CNS Neurosci Ther 2018;24:1037-52. [PMID: 30136381 DOI: 10.1111/cns.13048] [Cited by in Crossref: 68] [Cited by in F6Publishing: 55] [Article Influence: 17.0] [Reference Citation Analysis]
32 Wolfers T, Buitelaar JK, Beckmann CF, Franke B, Marquand AF. From estimating activation locality to predicting disorder: A review of pattern recognition for neuroimaging-based psychiatric diagnostics. Neurosci Biobehav Rev 2015;57:328-49. [PMID: 26254595 DOI: 10.1016/j.neubiorev.2015.08.001] [Cited by in Crossref: 172] [Cited by in F6Publishing: 142] [Article Influence: 24.6] [Reference Citation Analysis]
33 Ou Y, Akbari H, Bilello M, Da X, Davatzikos C. Comparative evaluation of registration algorithms in different brain databases with varying difficulty: results and insights. IEEE Trans Med Imaging 2014;33:2039-65. [PMID: 24951685 DOI: 10.1109/TMI.2014.2330355] [Cited by in Crossref: 83] [Cited by in F6Publishing: 43] [Article Influence: 10.4] [Reference Citation Analysis]