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For: 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]
Number Citing Articles
1 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]
2 Suh JS, Minuzzi L, Raamana PR, Davis A, Hall GB, Harris J, Hassel S, Zamyadi M, Arnott SR, Alders GL, Sassi RB, Milev R, Lam RW, MacQueen GM, Strother SC, Kennedy SH, Frey BN. An investigation of cortical thickness and antidepressant response in major depressive disorder: A CAN-BIND study report. Neuroimage Clin 2020;25:102178. [PMID: 32036277 DOI: 10.1016/j.nicl.2020.102178] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Suh JS, Schneider MA, Minuzzi L, MacQueen GM, Strother SC, Kennedy SH, Frey BN. Cortical thickness in major depressive disorder: A systematic review and meta-analysis. Prog Neuropsychopharmacol Biol Psychiatry 2019;88:287-302. [PMID: 30118825 DOI: 10.1016/j.pnpbp.2018.08.008] [Cited by in Crossref: 42] [Cited by in F6Publishing: 37] [Article Influence: 10.5] [Reference Citation Analysis]
4 Kim YK. Recent advances and challenges in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2021;111:110403. [PMID: 34293412 DOI: 10.1016/j.pnpbp.2021.110403] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Mousavi SM, Abdullah S, Niaki STA, Banihashemi S. An intelligent hybrid classification algorithm integrating fuzzy rule-based extraction and harmony search optimization: Medical diagnosis applications. Knowledge-Based Systems 2021;220:106943. [DOI: 10.1016/j.knosys.2021.106943] [Cited by in Crossref: 9] [Cited by in F6Publishing: 6] [Article Influence: 9.0] [Reference Citation Analysis]
6 Shaikh TA, Ali R. Automated atrophy assessment for Alzheimer's disease diagnosis from brain MRI images. Magnetic Resonance Imaging 2019;62:167-73. [DOI: 10.1016/j.mri.2019.06.019] [Cited by in Crossref: 10] [Cited by in F6Publishing: 2] [Article Influence: 3.3] [Reference Citation Analysis]
7 Zhang W, Nery FG, Tallman MJ, Patino LR, Adler CM, Strawn JR, Fleck DE, Barzman DH, Sweeney JA, Strakowski SM, Lui S, DelBello MP. Individual prediction of symptomatic converters in youth offspring of bipolar parents using proton magnetic resonance spectroscopy. Eur Child Adolesc Psychiatry 2021;30:55-64. [PMID: 32008167 DOI: 10.1007/s00787-020-01483-x] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
8 Jun E, Na KS, Kang W, Lee J, Suk HI, Ham BJ. Identifying resting-state effective connectivity abnormalities in drug-naïve major depressive disorder diagnosis via graph convolutional networks. Hum Brain Mapp 2020;41:4997-5014. [PMID: 32813309 DOI: 10.1002/hbm.25175] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
9 Lin X, Li X. Image Based Brain Segmentation: From Multi-Atlas Fusion to Deep Learning. CMIR 2019;15:443-52. [DOI: 10.2174/1573405614666180817125454] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
10 Stolicyn A, Harris MA, Shen X, Barbu MC, Adams MJ, Hawkins EL, de Nooij L, Yeung HW, Murray AD, Lawrie SM, Steele JD, McIntosh AM, Whalley HC. Automated classification of depression from structural brain measures across two independent community-based cohorts. Hum Brain Mapp 2020;41:3922-37. [PMID: 32558996 DOI: 10.1002/hbm.25095] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
11 Ben Chaabene W, Flah M, Nehdi ML. Machine learning prediction of mechanical properties of concrete: Critical review. Construction and Building Materials 2020;260:119889. [DOI: 10.1016/j.conbuildmat.2020.119889] [Cited by in Crossref: 66] [Cited by in F6Publishing: 16] [Article Influence: 33.0] [Reference Citation Analysis]
12 Nwanosike EM, Conway BR, Merchant HA, Hasan SS. Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review. Int J Med Inform 2021;159:104679. [PMID: 34990939 DOI: 10.1016/j.ijmedinf.2021.104679] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
13 Chaabene WB, Nehdi ML. Novel soft computing hybrid model for predicting shear strength and failure mode of SFRC beams with superior accuracy. Composites Part C: Open Access 2020;3:100070. [DOI: 10.1016/j.jcomc.2020.100070] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
14 Han KM, De Berardis D, Fornaro M, Kim YK. Differentiating between bipolar and unipolar depression in functional and structural MRI studies. Prog Neuropsychopharmacol Biol Psychiatry 2019;91:20-7. [PMID: 29601896 DOI: 10.1016/j.pnpbp.2018.03.022] [Cited by in Crossref: 60] [Cited by in F6Publishing: 51] [Article Influence: 15.0] [Reference Citation Analysis]
15 Ding X, Yue X, Zheng R, Bi C, Li D, Yao G. Classifying major depression patients and healthy controls using EEG, eye tracking and galvanic skin response data. Journal of Affective Disorders 2019;251:156-61. [DOI: 10.1016/j.jad.2019.03.058] [Cited by in Crossref: 15] [Cited by in F6Publishing: 8] [Article Influence: 5.0] [Reference Citation Analysis]
16 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]
17 Na KS, Cho SE, Hong JP, Lee JY, Chang SM, Jeon HJ, Cho SJ. Association between personality traits and suicidality by age groups in a nationally representative Korean sample. Medicine (Baltimore) 2020;99:e19161. [PMID: 32311919 DOI: 10.1097/MD.0000000000019161] [Reference Citation Analysis]
18 Gong B, Naveed S, Hafeez DM, Afzal KI, Majeed S, Abele J, Nicolaou S, Khosa F. Neuroimaging in psychiatric disorders: A bibliometric analysis of the 100 most highly cited articles. J Neuroimaging. 2019;29:14-33. [PMID: 30311320 DOI: 10.1111/jon.12570] [Cited by in Crossref: 15] [Cited by in F6Publishing: 10] [Article Influence: 3.8] [Reference Citation Analysis]
19 Aydin O, Unal Aydin P, Arslan A. Development of Neuroimaging-Based Biomarkers in Psychiatry. Adv Exp Med Biol 2019;1192:159-95. [PMID: 31705495 DOI: 10.1007/978-981-32-9721-0_9] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]
20 Niu H, Li W, Wang G, Hu Q, Hao R, Li T, Zhang F, Cheng T. Performances of whole-brain dynamic and static functional connectivity fingerprinting in machine learning-based classification of major depressive disorder. Front Psychiatry 2022;13:973921. [DOI: 10.3389/fpsyt.2022.973921] [Reference Citation Analysis]
21 Jan Z, Ai-Ansari N, Mousa O, Abd-Alrazaq A, Ahmed A, Alam T, Househ M. The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review. J Med Internet Res 2021;23:e29749. [PMID: 34806996 DOI: 10.2196/29749] [Reference Citation Analysis]
22 Tian X, Zhang G, Shao Y, Yang Z. Towards enhanced metabolomic data analysis of mass spectrometry image: Multivariate Curve Resolution and Machine Learning. Anal Chim Acta 2018;1037:211-9. [PMID: 30292295 DOI: 10.1016/j.aca.2018.02.031] [Cited by in Crossref: 11] [Cited by in F6Publishing: 13] [Article Influence: 2.8] [Reference Citation Analysis]
23 Liu G, Gao Y, Liu Y, Guo Y, Yan Z, Ou Z, Zhong L, Xie C, Zeng J, Zhang W, Peng K, Lv Q. Machine Learning for Predicting Individual Severity of Blepharospasm Using Diffusion Tensor Imaging. Front Neurosci 2021;15:670475. [PMID: 34054417 DOI: 10.3389/fnins.2021.670475] [Reference Citation Analysis]
24 Solanes A, Radua J. Advances in Using MRI to Estimate the Risk of Future Outcomes in Mental Health - Are We Getting There? Front Psychiatry 2022;13:fpsyt-13-826111. [PMID: 35492715 DOI: 10.3389/fpsyt.2022.826111] [Reference Citation Analysis]