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For: Janssen RJ, Mourão-miranda J, Schnack HG. Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2018;3:798-808. [DOI: 10.1016/j.bpsc.2018.04.004] [Cited by in Crossref: 31] [Cited by in F6Publishing: 46] [Article Influence: 7.8] [Reference Citation Analysis]
Number Citing Articles
1 Zheng S, Zeng W, Xin Q, Ye Y, Xue X, Li E, Liu T, Yan N, Chen W, Yin H. Can cognition help predict suicide risk in patients with major depressive disorder? A machine learning study. BMC Psychiatry 2022;22. [DOI: 10.1186/s12888-022-04223-4] [Reference Citation Analysis]
2 Winter NR, Leenings R, Ernsting J, Sarink K, Fisch L, Emden D, Blanke J, Goltermann J, Opel N, Barkhau C, Meinert S, Dohm K, Repple J, Mauritz M, Gruber M, Leehr EJ, Grotegerd D, Redlich R, Jansen A, Nenadic I, Nöthen MM, Forstner A, Rietschel M, Groß J, Bauer J, Heindel W, Andlauer T, Eickhoff SB, Kircher T, Dannlowski U, Hahn T. Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder Across Neuroimaging Modalities. JAMA Psychiatry 2022. [PMID: 35895072 DOI: 10.1001/jamapsychiatry.2022.1780] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
3 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. Mol Psychiatry 2022. [PMID: 35697759 DOI: 10.1038/s41380-022-01635-2] [Reference Citation Analysis]
4 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]
5 Levine SM. Probing patterns for prognostic potential. Transl Psychiatry 2022;12:167. [PMID: 35449151 DOI: 10.1038/s41398-022-01931-z] [Reference Citation Analysis]
6 Jiménez S, Angeles-Valdez D, Rodríguez-Delgado A, Fresán A, Miranda E, Alcalá-Lozano R, Duque-Alarcón X, Arango de Montis I, Garza-Villarreal EA. Machine learning detects predictors of symptom severity and impulsivity after dialectical behavior therapy skills training group in borderline personality disorder. J Psychiatr Res 2022;151:42-9. [PMID: 35447506 DOI: 10.1016/j.jpsychires.2022.03.063] [Reference Citation Analysis]
7 Baker MR, Padmaja DL, Puviarasi R, Mann S, Panduro-ramirez J, Tiwari M, Samori IA, Koundal D. Implementing Critical Machine Learning (ML) Approaches for Generating Robust Discriminative Neuroimaging Representations Using Structural Equation Model (SEM). Computational and Mathematical Methods in Medicine 2022;2022:1-12. [DOI: 10.1155/2022/6501975] [Reference Citation Analysis]
8 Stout DM, Simmons AN, Nievergelt CM, Biswas N, Maihofer AX, Risbrough VB, Baker DG. Deriving psychiatric symptom-based biomarkers from multivariate relationships between psychophysiological and biochemical measures. Neuropsychopharmacology 2022. [PMID: 35347268 DOI: 10.1038/s41386-022-01303-7] [Reference Citation Analysis]
9 Wang M, Hu K, Fan L, Yan H, Li P, Jiang T, Liu B. Predicting Treatment Response in Schizophrenia With Magnetic Resonance Imaging and Polygenic Risk Score. Front Genet 2022;13:848205. [PMID: 35186051 DOI: 10.3389/fgene.2022.848205] [Reference Citation Analysis]
10 Tubío-fungueiriño M, Cernadas E, Gonçalves ÓF, Segalas C, Bertolín S, Mar-barrutia L, Real E, Fernández-delgado M, Menchón JM, Carvalho S, Alonso P, Carracedo A, Fernández-prieto M. Viability Study of Machine Learning-Based Prediction of COVID-19 Pandemic Impact in Obsessive-Compulsive Disorder Patients. Front Neuroinform 2022;16:807584. [DOI: 10.3389/fninf.2022.807584] [Reference Citation Analysis]
11 Paek AY, Brantley JA, Evans BJ, Contreras-Vidal JL. Concerns in the Blurred Divisions between Medical and Consumer Neurotechnology. IEEE Syst J 2021;15:3069-80. [PMID: 35126800 DOI: 10.1109/jsyst.2020.3032609] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
12 Hofmann LA, Lau S, Kirchebner J. Advantages of Machine Learning in Forensic Psychiatric Research—Uncovering the Complexities of Aggressive Behavior in Schizophrenia. Applied Sciences 2022;12:819. [DOI: 10.3390/app12020819] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
13 Shane MS, Denomme WJ. Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer. Personal Neurosci 2021;4:e6. [PMID: 34909565 DOI: 10.1017/pen.2021.2] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
14 Lin E, Lin CH, Lane HY. Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments. Clin Psychopharmacol Neurosci 2021;19:577-88. [PMID: 34690113 DOI: 10.9758/cpn.2021.19.4.577] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
15 Shrot S, Lawson P, Shlomovitz O, Hoffmann C, Shrot A, Ben-Zeev B, Tzadok M. Prediction of tuberous sclerosis-associated neurocognitive disorders and seizures via machine learning of structural magnetic resonance imaging. Neuroradiology 2021. [PMID: 34532765 DOI: 10.1007/s00234-021-02789-6] [Reference Citation Analysis]
16 Smit DJA, Andreassen OA, Boomsma DI, Burwell SJ, Chorlian DB, de Geus EJC, Elvsåshagen T, Gordon RL, Harper J, Hegerl U, Hensch T, Iacono WG, Jawinski P, Jönsson EG, Luykx JJ, Magne CL, Malone SM, Medland SE, Meyers JL, Moberget T, Porjesz B, Sander C, Sisodiya SM, Thompson PM, van Beijsterveldt CEM, van Dellen E, Via M, Wright MJ. Large-scale collaboration in ENIGMA-EEG: A perspective on the meta-analytic approach to link neurological and psychiatric liability genes to electrophysiological brain activity. Brain Behav 2021;:e02188. [PMID: 34291596 DOI: 10.1002/brb3.2188] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
17 Cao X, Wang Z, Chen X, Liu Y, Wang W, Abdoulaye IA, Ju S, Yang X, Wang Y, Guo Y. White matter degeneration in remote brain areas of stroke patients with motor impairment due to basal ganglia lesions. Hum Brain Mapp 2021;42:4750-61. [PMID: 34232552 DOI: 10.1002/hbm.25583] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
18 de Nijs J, Burger TJ, Janssen RJ, Kia SM, van Opstal DPJ, de Koning MB, de Haan L, Cahn W, Schnack HG; GROUP investigators. Individualized prediction of three- and six-year outcomes of psychosis in a longitudinal multicenter study: a machine learning approach. NPJ Schizophr 2021;7:34. [PMID: 34215752 DOI: 10.1038/s41537-021-00162-3] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
19 Hauke DJ, Schmidt A, Studerus E, Andreou C, Riecher-Rössler A, Radua J, Kambeitz J, Ruef A, Dwyer DB, Kambeitz-Ilankovic L, Lichtenstein T, Sanfelici R, Penzel N, Haas SS, Antonucci LA, Lalousis PA, Chisholm K, Schultze-Lutter F, Ruhrmann S, Hietala J, Brambilla P, Koutsouleris N, Meisenzahl E, Pantelis C, Rosen M, Salokangas RKR, Upthegrove R, Wood SJ, Borgwardt S; PRONIA Group. Multimodal prognosis of negative symptom severity in individuals at increased risk of developing psychosis. Transl Psychiatry 2021;11:312. [PMID: 34031362 DOI: 10.1038/s41398-021-01409-4] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
20 Nobukawa S, Shirama A, Takahashi T, Takeda T, Ohta H, Kikuchi M, Iwanami A, Kato N, Toda S. Identification of attention-deficit hyperactivity disorder based on the complexity and symmetricity of pupil diameter. Sci Rep 2021;11:8439. [PMID: 33875772 DOI: 10.1038/s41598-021-88191-x] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
21 Lee EE, Torous J, De Choudhury M, Depp CA, Graham SA, Kim HC, Paulus MP, Krystal JH, Jeste DV. Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom. Biol Psychiatry Cogn Neurosci Neuroimaging 2021;6:856-64. [PMID: 33571718 DOI: 10.1016/j.bpsc.2021.02.001] [Cited by in Crossref: 3] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
22 Vieira S, Gong QY, Pinaya WHL, Scarpazza C, Tognin S, Crespo-Facorro B, Tordesillas-Gutierrez D, Ortiz-García V, Setien-Suero E, Scheepers FE, Van Haren NEM, Marques TR, Murray RM, David A, Dazzan P, McGuire P, Mechelli A. Using Machine Learning and Structural Neuroimaging to Detect First Episode Psychosis: Reconsidering the Evidence. Schizophr Bull 2020;46:17-26. [PMID: 30809667 DOI: 10.1093/schbul/sby189] [Cited by in Crossref: 42] [Cited by in F6Publishing: 33] [Article Influence: 42.0] [Reference Citation Analysis]
23 Tognin S, van Hell HH, Merritt K, Winter-van Rossum I, Bossong MG, Kempton MJ, Modinos G, Fusar-Poli P, Mechelli A, Dazzan P, Maat A, de Haan L, Crespo-Facorro B, Glenthøj B, Lawrie SM, McDonald C, Gruber O, van Amelsvoort T, Arango C, Kircher T, Nelson B, Galderisi S, Bressan R, Kwon JS, Weiser M, Mizrahi R, Sachs G, Maatz A, Kahn R, McGuire P; PSYSCAN Consortium. Towards Precision Medicine in Psychosis: Benefits and Challenges of Multimodal Multicenter Studies-PSYSCAN: Translating Neuroimaging Findings From Research into Clinical Practice. Schizophr Bull 2020;46:432-41. [PMID: 31424555 DOI: 10.1093/schbul/sbz067] [Cited by in Crossref: 29] [Cited by in F6Publishing: 34] [Article Influence: 29.0] [Reference Citation Analysis]
24 Abbas A, Schultebraucks K, Galatzer-levy IR. Digital Measurement of Mental Health: Challenges, Promises, and Future Directions. Psychiatric Annals 2021;51:14-20. [DOI: 10.3928/00485713-20201207-01] [Cited by in Crossref: 8] [Cited by in F6Publishing: 2] [Article Influence: 8.0] [Reference Citation Analysis]
25 Rodrigue AL, Mastrovito D, Esteban O, Durnez J, Koenis MMG, Janssen R, Alexander-Bloch A, Knowles EM, Mathias SR, Mollon J, Pearlson GD, Frangou S, Blangero J, Poldrack RA, Glahn DC. Searching for Imaging Biomarkers of Psychotic Dysconnectivity. Biol Psychiatry Cogn Neurosci Neuroimaging 2020:S2451-9022(20)30372-4. [PMID: 33622655 DOI: 10.1016/j.bpsc.2020.12.002] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
26 Griffiths SL, Birchwood M. A Synthetic Literature Review on the Management of Emerging Treatment Resistance in First Episode Psychosis: Can We Move towards Precision Intervention and Individualised Care? Medicina (Kaunas) 2020;56:E638. [PMID: 33255489 DOI: 10.3390/medicina56120638] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
27 Zhao M, Feng Z. Machine Learning Methods to Evaluate the Depression Status of Chinese Recruits: A Diagnostic Study. Neuropsychiatr Dis Treat 2020;16:2743-52. [PMID: 33209029 DOI: 10.2147/NDT.S275620] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
28 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] [Cited by in F6Publishing: 8] [Reference Citation Analysis]
29 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]
30 Kottaram A, Johnston LA, Tian Y, Ganella EP, Laskaris L, Cocchi L, McGorry P, Pantelis C, Kotagiri R, Cropley V, Zalesky A. Predicting individual improvement in schizophrenia symptom severity at 1-year follow-up: Comparison of connectomic, structural, and clinical predictors. Hum Brain Mapp 2020;41:3342-57. [PMID: 32469448 DOI: 10.1002/hbm.25020] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
31 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: 10] [Article Influence: 3.0] [Reference Citation Analysis]
32 Scarpazza C, Ha M, Baecker L, Garcia-Dias R, Pinaya WHL, Vieira S, Mechelli A. Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders. Transl Psychiatry 2020;10:107. [PMID: 32313006 DOI: 10.1038/s41398-020-0798-6] [Cited by in Crossref: 13] [Cited by in F6Publishing: 8] [Article Influence: 6.5] [Reference Citation Analysis]
33 Kamarajan C, Ardekani BA, Pandey AK, Chorlian DB, Kinreich S, Pandey G, Meyers JL, Zhang J, Kuang W, Stimus AT, Porjesz B. Random Forest Classification of Alcohol Use Disorder Using EEG Source Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures. Behav Sci (Basel) 2020;10:E62. [PMID: 32121585 DOI: 10.3390/bs10030062] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
34 Kim C, Lee SH, Lim JS, Kim Y, Jang MU, Oh MS, Jung S, Lee JH, Yu KH, Lee BC. Impact of 25-Hydroxyvitamin D on the Prognosis of Acute Ischemic Stroke: Machine Learning Approach. Front Neurol 2020;11:37. [PMID: 32082247 DOI: 10.3389/fneur.2020.00037] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
35 Kamarajan C, Ardekani BA, Pandey AK, Kinreich S, Pandey G, Chorlian DB, Meyers JL, Zhang J, Bermudez E, Stimus AT, Porjesz B. Random Forest Classification of Alcohol Use Disorder Using fMRI Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures. Brain Sci 2020;10:E115. [PMID: 32093319 DOI: 10.3390/brainsci10020115] [Cited by in Crossref: 6] [Cited by in F6Publishing: 11] [Article Influence: 3.0] [Reference Citation Analysis]
36 Na KS, Cho SE, Geem ZW, Kim YK. Predicting future onset of depression among community dwelling adults in the Republic of Korea using a machine learning algorithm. Neurosci Lett 2020;721:134804. [PMID: 32014516 DOI: 10.1016/j.neulet.2020.134804] [Cited by in Crossref: 4] [Cited by in F6Publishing: 10] [Article Influence: 2.0] [Reference Citation Analysis]
37 Giorgio J, Landau SM, Jagust WJ, Tino P, Kourtzi Z; Alzheimer's Disease Neuroimaging Initiative. Modelling prognostic trajectories of cognitive decline due to Alzheimer's disease. Neuroimage Clin 2020;26:102199. [PMID: 32106025 DOI: 10.1016/j.nicl.2020.102199] [Cited by in Crossref: 7] [Cited by in F6Publishing: 19] [Article Influence: 3.5] [Reference Citation Analysis]
38 Heinrichs B, Eickhoff SB. Your evidence? Machine learning algorithms for medical diagnosis and prediction. Hum Brain Mapp 2020;41:1435-44. [PMID: 31804003 DOI: 10.1002/hbm.24886] [Cited by in Crossref: 15] [Cited by in F6Publishing: 23] [Article Influence: 5.0] [Reference Citation Analysis]
39 Ivleva EI, Turkozer HB, Sweeney JA. Imaging-Based Subtyping for Psychiatric Syndromes. Neuroimaging Clin N Am 2020;30:35-44. [PMID: 31759570 DOI: 10.1016/j.nic.2019.09.005] [Cited by in Crossref: 4] [Cited by in F6Publishing: 6] [Article Influence: 1.3] [Reference Citation Analysis]
40 Graham S, Depp C, Lee EE, Nebeker C, Tu X, Kim HC, Jeste DV. Artificial Intelligence for Mental Health and Mental Illnesses: an Overview. Curr Psychiatry Rep 2019;21:116. [PMID: 31701320 DOI: 10.1007/s11920-019-1094-0] [Cited by in Crossref: 48] [Cited by in F6Publishing: 84] [Article Influence: 16.0] [Reference Citation Analysis]
41 Paulus MP, Thompson WK. Computational approaches and machine learning for individual-level treatment predictions. Psychopharmacology (Berl) 2021;238:1231-9. [PMID: 31134293 DOI: 10.1007/s00213-019-05282-4] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 1.7] [Reference Citation Analysis]
42 Portugal LCL, Schrouff J, Stiffler R, Bertocci M, Bebko G, Chase H, Lockovitch J, Aslam H, Graur S, Greenberg T, Pereira M, Oliveira L, Phillips M, Mourão-Miranda J. Predicting anxiety from wholebrain activity patterns to emotional faces in young adults: a machine learning approach. Neuroimage Clin 2019;23:101813. [PMID: 31082774 DOI: 10.1016/j.nicl.2019.101813] [Cited by in Crossref: 12] [Cited by in F6Publishing: 10] [Article Influence: 4.0] [Reference Citation Analysis]
43 Walter M, Alizadeh S, Jamalabadi H, Lueken U, Dannlowski U, Walter H, Olbrich S, Colic L, Kambeitz J, Koutsouleris N, Hahn T, Dwyer DB. Translational machine learning for psychiatric neuroimaging. Progress in Neuro-Psychopharmacology and Biological Psychiatry 2019;91:113-21. [DOI: 10.1016/j.pnpbp.2018.09.014] [Cited by in Crossref: 26] [Cited by in F6Publishing: 22] [Article Influence: 8.7] [Reference Citation Analysis]
44 Rakesh G, Morey RA, Zannas AS, Malik Z, Marx CE, Clausen AN, Kritzer MD, Szabo ST. Resilience as a translational endpoint in the treatment of PTSD. Mol Psychiatry 2019;24:1268-83. [PMID: 30867558 DOI: 10.1038/s41380-019-0383-7] [Cited by in Crossref: 26] [Cited by in F6Publishing: 21] [Article Influence: 8.7] [Reference Citation Analysis]
45 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: 19] [Article Influence: 3.8] [Reference Citation Analysis]
46 Fornito A, Zalesky A. Computational Approaches to Understanding Mental Dysfunction: Progress, Challenges, and New Frontiers. Biol Psychiatry Cogn Neurosci Neuroimaging 2018;3:728-30. [PMID: 30170710 DOI: 10.1016/j.bpsc.2018.07.005] [Reference Citation Analysis]