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For: 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]
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11 Yu S, Xie M, Liu S, Guo X, Tian J, Wei W, Zhang Q, Zeng F, Liang F, Yang J. Resting-State Functional Connectivity Patterns Predict Acupuncture Treatment Response in Primary Dysmenorrhea. Front Neurosci 2020;14:559191. [PMID: 33013312 DOI: 10.3389/fnins.2020.559191] [Reference Citation Analysis]
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13 Saif Alghawli A, Taloba AI, Liu H. An Enhanced Ant Colony Optimization Mechanism for the Classification of Depressive Disorders. Computational Intelligence and Neuroscience 2022;2022:1-12. [DOI: 10.1155/2022/1332664] [Reference Citation Analysis]
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15 Lai CH. Fronto-limbic neuroimaging biomarkers for diagnosis and prediction of treatment responses in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2021;107:110234. [PMID: 33370569 DOI: 10.1016/j.pnpbp.2020.110234] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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17 Luo N, Sui J, Abrol A, Chen J, Turner JA, Damaraju E, Fu Z, Fan L, Lin D, Zhuo C, Xu Y, Glahn DC, Rodrigue AL, Banich MT, Pearlson GD, Calhoun VD. Structural Brain Architectures Match Intrinsic Functional Networks and Vary across Domains: A Study from 15 000+ Individuals. Cereb Cortex 2020;30:5460-70. [PMID: 32488253 DOI: 10.1093/cercor/bhaa127] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
18 Yeung HW, Shen X, Stolicyn A, de Nooij L, Harris MA, Romaniuk L, Buchanan CR, Waiter GD, Sandu AL, McNeil CJ, Murray A, Steele JD, Campbell A, Porteous D, Lawrie SM, McIntosh AM, Cox SR, Smith KM, Whalley HC. Spectral clustering based on structural magnetic resonance imaging and its relationship with major depressive disorder and cognitive ability. Eur J Neurosci 2021. [PMID: 34390586 DOI: 10.1111/ejn.15423] [Reference Citation Analysis]
19 Kelley ME, Choi KS, Rajendra JK, Craighead WE, Rakofsky JJ, Dunlop BW, Mayberg HS. Establishing Evidence for Clinical Utility of a Neuroimaging Biomarker in Major Depressive Disorder: Prospective Testing and Implementation Challenges. Biol Psychiatry 2021;90:236-42. [PMID: 33896622 DOI: 10.1016/j.biopsych.2021.02.966] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 6.0] [Reference Citation Analysis]
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21 Huang Y, Talwar A, Chatterjee S, Aparasu RR. Application of machine learning in predicting hospital readmissions: a scoping review of the literature. BMC Med Res Methodol 2021;21:96. [PMID: 33952192 DOI: 10.1186/s12874-021-01284-z] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
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23 Li Y, Lu W, Li J, Li M, Fang J, Xu T, Yuan T, Qian D, Shi H, Yin S. Electroencephalography Microstate Alterations in Otogenic Vertigo: A Potential Disease Marker. Front Aging Neurosci 2022;14:914920. [DOI: 10.3389/fnagi.2022.914920] [Reference Citation Analysis]
24 Luján M, Jimeno M, Mateo Sotos J, Ricarte J, Borja A. A Survey on EEG Signal Processing Techniques and Machine Learning: Applications to the Neurofeedback of Autobiographical Memory Deficits in Schizophrenia. Electronics 2021;10:3037. [DOI: 10.3390/electronics10233037] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
25 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]
26 Zhuo C, Li G, Lin X, Jiang D, Xu Y, Tian H, Wang W, Song X. The rise and fall of MRI studies in major depressive disorder. Transl Psychiatry 2019;9:335. [PMID: 31819044 DOI: 10.1038/s41398-019-0680-6] [Cited by in Crossref: 25] [Cited by in F6Publishing: 15] [Article Influence: 8.3] [Reference Citation Analysis]
27 Xu J, Xie H, Liu L, Shen Z, Yang L, Wei W, Guo X, Liang F, Yu S, Yang J. Brain Mechanism of Acupuncture Treatment of Chronic Pain: An Individual-Level Positron Emission Tomography Study. Front Neurol 2022;13:884770. [DOI: 10.3389/fneur.2022.884770] [Reference Citation Analysis]
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29 Andersson S, Bathula DR, Iliadis SI, Walter M, Skalkidou A. Predicting women with depressive symptoms postpartum with machine learning methods. Sci Rep 2021;11:7877. [PMID: 33846362 DOI: 10.1038/s41598-021-86368-y] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
30 Ho CS, Chan Y, Tan TW, Tay GW, Tang T. Improving the diagnostic accuracy for major depressive disorder using machine learning algorithms integrating clinical and near-infrared spectroscopy data. Journal of Psychiatric Research 2022;147:194-202. [DOI: 10.1016/j.jpsychires.2022.01.026] [Reference Citation Analysis]
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32 Bhadra S, Kumar CJ. An insight into diagnosis of depression using machine learning techniques: a systematic review. Curr Med Res Opin 2022;:1-62. [PMID: 35129401 DOI: 10.1080/03007995.2022.2038487] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
33 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]
34 Geerts H, Barrett JE. Neuronal Circuit-Based Computer Modeling as a Phenotypic Strategy for CNS R&D. Front Neurosci 2019;13:723. [PMID: 31379482 DOI: 10.3389/fnins.2019.00723] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.7] [Reference Citation Analysis]
35 Chen X, Xu L, Pan Z. Design and Preliminary Realization of a Screening and Early Warning Health Management System for Populations at High Risk for Depression. Int J Environ Res Public Health 2022;19:3599. [PMID: 35329284 DOI: 10.3390/ijerph19063599] [Reference Citation Analysis]
36 Stolicyn A, Steele JD, Seriès P. Prediction of depression symptoms in individual subjects with face and eye movement tracking. Psychol Med 2020;:1-9. [PMID: 33161920 DOI: 10.1017/S0033291720003608] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
37 Tran BX, McIntyre RS, Latkin CA, Phan HT, Vu GT, Nguyen HLT, Gwee KK, Ho CSH, Ho RCM. The Current Research Landscape on the Artificial Intelligence Application in the Management of Depressive Disorders: A Bibliometric Analysis. Int J Environ Res Public Health 2019;16:E2150. [PMID: 31216619 DOI: 10.3390/ijerph16122150] [Cited by in Crossref: 29] [Cited by in F6Publishing: 18] [Article Influence: 9.7] [Reference Citation Analysis]
38 Richter T, Fishbain B, Richter-Levin G, Okon-Singer H. Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions. J Pers Med 2021;11:957. [PMID: 34683098 DOI: 10.3390/jpm11100957] [Reference Citation Analysis]
39 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]
40 Lee HJ, Kim SH, Lee MS. Understanding Mood Disorders in Children. Adv Exp Med Biol 2019;1192:251-61. [PMID: 31705498 DOI: 10.1007/978-981-32-9721-0_12] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
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46 Zhao GF, Sun LF, Ni T. Depression Identification of Students Based on Campus Social Platform Data and Deep Learning. Scientific Programming 2022;2022:1-8. [DOI: 10.1155/2022/6532384] [Reference Citation Analysis]
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50 Sui J, Jiang R, Bustillo J, Calhoun V. Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises. Biol Psychiatry 2020;88:818-28. [PMID: 32336400 DOI: 10.1016/j.biopsych.2020.02.016] [Cited by in Crossref: 73] [Cited by in F6Publishing: 50] [Article Influence: 36.5] [Reference Citation Analysis]
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59 Stoyanov D, Kandilarova S, Aryutova K, Paunova R, Todeva-Radneva A, Latypova A, Kherif F. Multivariate Analysis of Structural and Functional Neuroimaging Can Inform Psychiatric Differential Diagnosis. Diagnostics (Basel). 2020;11. [PMID: 33374207 DOI: 10.3390/diagnostics11010019] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 0.5] [Reference Citation Analysis]
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