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For: 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]
Number Citing Articles
1 Brzezicki MA, Kobetić MD, Neumann S. Frideswide - An artificial intelligence deep learning algorithm for audits and quality improvement in the neurosurgical practice. Int J Surg 2017;43:56-7. [PMID: 28552810 DOI: 10.1016/j.ijsu.2017.05.038] [Cited by in Crossref: 3] [Article Influence: 0.6] [Reference Citation Analysis]
2 Takahashi T, Suzuki M. Brain morphologic changes in early stages of psychosis: Implications for clinical application and early intervention. Psychiatry Clin Neurosci 2018;72:556-71. [DOI: 10.1111/pcn.12670] [Cited by in Crossref: 35] [Cited by in F6Publishing: 30] [Article Influence: 8.8] [Reference Citation Analysis]
3 Zaroug A, Proud JK, Lai DTH, Mudie K, Billing D, Begg R. Overview of Computational Intelligence (CI) Techniques for Powered Exoskeletons. In: Mishra BB, Dehuri S, Panigrahi BK, Nayak AK, Mishra BSP, Das H, editors. Computational Intelligence in Sensor Networks. Berlin: Springer Berlin Heidelberg; 2019. pp. 353-83. [DOI: 10.1007/978-3-662-57277-1_15] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 1.3] [Reference Citation Analysis]
4 Schwarz E, Doan NT, Pergola G, Westlye LT, Kaufmann T, Wolfers T, Brecheisen R, Quarto T, Ing AJ, Di Carlo P, Gurholt TP, Harms RL, Noirhomme Q, Moberget T, Agartz I, Andreassen OA, Bellani M, Bertolino A, Blasi G, Brambilla P, Buitelaar JK, Cervenka S, Flyckt L, Frangou S, Franke B, Hall J, Heslenfeld DJ, Kirsch P, McIntosh AM, Nöthen MM, Papassotiropoulos A, de Quervain DJ, Rietschel M, Schumann G, Tost H, Witt SH, Zink M, Meyer-Lindenberg A; IMAGEMEND Consortium, Karolinska Schizophrenia Project (KaSP) Consortium. Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder. Transl Psychiatry 2019;9:12. [PMID: 30664633 DOI: 10.1038/s41398-018-0225-4] [Cited by in Crossref: 20] [Cited by in F6Publishing: 12] [Article Influence: 6.7] [Reference Citation Analysis]
5 Fusar-Poli P, Stringer D, M S Durieux A, Rutigliano G, Bonoldi I, De Micheli A, Stahl D. Clinical-learning versus machine-learning for transdiagnostic prediction of psychosis onset in individuals at-risk. Transl Psychiatry 2019;9:259. [PMID: 31624229 DOI: 10.1038/s41398-019-0600-9] [Cited by in Crossref: 17] [Cited by in F6Publishing: 18] [Article Influence: 5.7] [Reference Citation Analysis]
6 Salagre E, Arango C, Artigas F, Ayuso-mateos JL, Bernardo M, Castro-fornieles J, Bobes J, Desco M, Fañanás L, González-pinto A, Haro JM, Leza JC, Mckenna PJ, Meana JJ, Menchón JM, Micó JA, Palomo T, Pazos Á, Pérez V, Saiz-ruiz J, Sanjuán J, Tabarés-seisdedos R, Crespo-facorro B, Casas M, Vilella E, Palao D, Olivares JM, Rodriguez-jimenez R, Vieta E. CIBERSAM: Ten years of collaborative translational research in mental disorders. Revista de Psiquiatría y Salud Mental (English Edition) 2019;12:1-8. [DOI: 10.1016/j.rpsmen.2018.10.001] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
7 Zang J, Huang Y, Kong L, Lei B, Ke P, Li H, Zhou J, Xiong D, Li G, Chen J, Li X, Xiang Z, Ning Y, Wu F, Wu K. Effects of Brain Atlases and Machine Learning Methods on the Discrimination of Schizophrenia Patients: A Multimodal MRI Study. Front Neurosci 2021;15:697168. [PMID: 34385901 DOI: 10.3389/fnins.2021.697168] [Reference Citation Analysis]
8 Salagre E, Arango C, Artigas F, Ayuso-Mateos JL, Bernardo M, Castro-Fornieles J, Bobes J, Desco M, Fañanás L, González-Pinto A, Haro JM, Leza JC, Mckenna PJ, Meana JJ, Menchón JM, Micó JA, Palomo T, Pazos Á, Pérez V, Saiz-Ruiz J, Sanjuán J, Tabarés-Seisdedos R, Crespo-Facorro B, Casas M, Vilella E, Palao D, Olivares JM, Rodriguez-Jimenez R, Vieta E. CIBERSAM: Ten years of collaborative translational research in mental disorders. Rev Psiquiatr Salud Ment (Engl Ed) 2019;12:1-8. [PMID: 30416047 DOI: 10.1016/j.rpsm.2018.10.001] [Cited by in Crossref: 35] [Cited by in F6Publishing: 31] [Article Influence: 8.8] [Reference Citation Analysis]
9 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: 55] [Cited by in F6Publishing: 36] [Article Influence: 13.8] [Reference Citation Analysis]
10 Shen X, Zhang F, Lv H, Liu J, Liu H. Prediction of Entering Percentage into Expressway Service Areas Based on Wavelet Neural Networks and Genetic Algorithms. IEEE Access 2019;7:54562-74. [DOI: 10.1109/access.2019.2913177] [Cited by in Crossref: 9] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
11 Mikolas P, Hlinka J, Skoch A, Pitra Z, Frodl T, Spaniel F, Hajek T. Machine learning classification of first-episode schizophrenia spectrum disorders and controls using whole brain white matter fractional anisotropy. BMC Psychiatry 2018;18:97. [PMID: 29636016 DOI: 10.1186/s12888-018-1678-y] [Cited by in Crossref: 15] [Cited by in F6Publishing: 11] [Article Influence: 3.8] [Reference Citation Analysis]
12 Dündar-coecke S. Future avenues for education and neuroenhancement. New Ideas in Psychology 2021;63:100875. [DOI: 10.1016/j.newideapsych.2021.100875] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Zaninotto L, Qian J, Sun Y, Bassi G, Solmi M, Salcuni S. Gender, Personality Traits and Experience With Psychiatric Patients as Predictors of Stigma in Italian Psychology Students. Front Public Health 2018;6:362. [PMID: 30619803 DOI: 10.3389/fpubh.2018.00362] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]
14 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] [Reference Citation Analysis]
15 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]
16 Ediri Arachchi W, Peng Y, Zhang X, Qin W, Zhuo C, Yu C, Liang M. A Systematic Characterization of Structural Brain Changes in Schizophrenia. Neurosci Bull 2020;36:1107-22. [PMID: 32495122 DOI: 10.1007/s12264-020-00520-8] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
17 Jeffries CD, Ford JR, Tilson JL, Perkins DO, Bost DM, Filer DL, Wilhelmsen KC. A greedy regression algorithm with coarse weights offers novel advantages. Sci Rep 2022;12:5440. [PMID: 35361850 DOI: 10.1038/s41598-022-09415-2] [Reference Citation Analysis]
18 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]
19 de Filippis R, Carbone EA, Gaetano R, Bruni A, Pugliese V, Segura-Garcia C, De Fazio P. Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review. Neuropsychiatr Dis Treat 2019;15:1605-27. [PMID: 31354276 DOI: 10.2147/NDT.S202418] [Cited by in Crossref: 30] [Cited by in F6Publishing: 16] [Article Influence: 10.0] [Reference Citation Analysis]
20 Di Carlo P, Pergola G, Antonucci LA, Bonvino A, Mancini M, Quarto T, Rampino A, Popolizio T, Bertolino A, Blasi G. Multivariate patterns of gray matter volume in thalamic nuclei are associated with positive schizotypy in healthy individuals. Psychol Med 2020;50:1501-9. [DOI: 10.1017/s0033291719001430] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 2.3] [Reference Citation Analysis]
21 Guo Y, Qiu J, Lu W. Support Vector Machine-Based Schizophrenia Classification Using Morphological Information from Amygdaloid and Hippocampal Subregions. Brain Sci 2020;10:E562. [PMID: 32824267 DOI: 10.3390/brainsci10080562] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
22 Yin P, Mao N, Zhao C, Wu J, Chen L, Hong N. A Triple-Classification Radiomics Model for the Differentiation of Primary Chordoma, Giant Cell Tumor, and Metastatic Tumor of Sacrum Based on T2-Weighted and Contrast-Enhanced T1-Weighted MRI: A Triple-Classification Radiomics Model for the Differentiation of Primary Chordoma, Giant Cell Tumor, and Metastatic Tumor of Sacrum. J Magn Reson Imaging 2019;49:752-9. [DOI: 10.1002/jmri.26238] [Cited by in Crossref: 22] [Cited by in F6Publishing: 19] [Article Influence: 5.5] [Reference Citation Analysis]
23 Pitoglou S. Machine Learning in Healthcare, Introduction and Real World Application Considerations: . International Journal of Reliable and Quality E-Healthcare 2018;7:27-36. [DOI: 10.4018/ijrqeh.2018040102] [Cited by in Crossref: 9] [Cited by in F6Publishing: 1] [Article Influence: 2.3] [Reference Citation Analysis]
24 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]
25 Schmidt A, Borgwardt S. Implementing MR Imaging into Clinical Routine Screening in Patients with Psychosis? Neuroimaging Clinics of North America 2020;30:65-72. [DOI: 10.1016/j.nic.2019.09.004] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
26 Pinaya WHL, Mechelli A, Sato JR. Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large-scale multi-sample study. Hum Brain Mapp 2019;40:944-54. [PMID: 30311316 DOI: 10.1002/hbm.24423] [Cited by in Crossref: 32] [Cited by in F6Publishing: 25] [Article Influence: 8.0] [Reference Citation Analysis]
27 McNabb CB, Burgess LG, Fancourt A, Mulligan N, FitzGibbon L, Riddell P, Murayama K. No evidence for a relationship between social closeness and similarity in resting-state functional brain connectivity in schoolchildren. Sci Rep 2020;10:10710. [PMID: 32612156 DOI: 10.1038/s41598-020-67718-8] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
28 Li F, Wu D, Lui S, Gong Q, Sweeney JA. Clinical Strategies and Technical Challenges in Psychoradiology. Neuroimaging Clin N Am 2020;30:1-13. [PMID: 31759566 DOI: 10.1016/j.nic.2019.09.001] [Cited by in Crossref: 4] [Cited by in F6Publishing: 9] [Article Influence: 1.3] [Reference Citation Analysis]
29 Hu M, Qian X, Liu S, Koh AJ, Sim K, Jiang X, Guan C, Zhou JH. Structural and diffusion MRI based schizophrenia classification using 2D pretrained and 3D naive Convolutional Neural Networks. Schizophr Res 2021:S0920-9964(21)00223-1. [PMID: 34210562 DOI: 10.1016/j.schres.2021.06.011] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
30 van den Heuvel MP, Sporns O. A cross-disorder connectome landscape of brain dysconnectivity. Nat Rev Neurosci 2019;20:435-46. [DOI: 10.1038/s41583-019-0177-6] [Cited by in Crossref: 115] [Cited by in F6Publishing: 80] [Article Influence: 38.3] [Reference Citation Analysis]
31 Salvador R, Canales-Rodríguez E, Guerrero-Pedraza A, Sarró S, Tordesillas-Gutiérrez D, Maristany T, Crespo-Facorro B, McKenna P, Pomarol-Clotet E. Multimodal Integration of Brain Images for MRI-Based Diagnosis in Schizophrenia. Front Neurosci 2019;13:1203. [PMID: 31787874 DOI: 10.3389/fnins.2019.01203] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 1.7] [Reference Citation Analysis]
32 de Pierrefeu A, Löfstedt T, Laidi C, Hadj-Selem F, Bourgin J, Hajek T, Spaniel F, Kolenic M, Ciuciu P, Hamdani N, Leboyer M, Fovet T, Jardri R, Houenou J, Duchesnay E. Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity. Acta Psychiatr Scand 2018;138:571-80. [PMID: 30242828 DOI: 10.1111/acps.12964] [Cited by in Crossref: 11] [Cited by in F6Publishing: 6] [Article Influence: 2.8] [Reference Citation Analysis]
33 Lee J, Chon MW, Kim H, Rathi Y, Bouix S, Shenton ME, Kubicki M. Diagnostic value of structural and diffusion imaging measures in schizophrenia. Neuroimage Clin 2018;18:467-74. [PMID: 29876254 DOI: 10.1016/j.nicl.2018.02.007] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 2.5] [Reference Citation Analysis]
34 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: 32] [Article Influence: 42.0] [Reference Citation Analysis]
35 Pratt JA, Morris B, Dawson N. Deconstructing Schizophrenia: Advances in Preclinical Models for Biomarker Identification. In: Pratt J, Hall J, editors. Biomarkers in Psychiatry. Cham: Springer International Publishing; 2018. pp. 295-323. [DOI: 10.1007/7854_2018_48] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 2.0] [Reference Citation Analysis]
36 Yu Y, Wu X, Chen J, Cheng G, Zhang X, Wan C, Hu J, Miao S, Yin Y, Wang Z, Shan T, Jing S, Wang W, Guo J, Hu X, Liu Y. Characterizing Brain Tumor Regions Using Texture Analysis in Magnetic Resonance Imaging. Front Neurosci 2021;15:634926. [PMID: 34149343 DOI: 10.3389/fnins.2021.634926] [Reference Citation Analysis]
37 Ellis JK, Walker EF, Goldsmith DR. Selective Review of Neuroimaging Findings in Youth at Clinical High Risk for Psychosis: On the Path to Biomarkers for Conversion. Front Psychiatry 2020;11:567534. [PMID: 33173516 DOI: 10.3389/fpsyt.2020.567534] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
38 Tulay EE, Metin B, Tarhan N, Arıkan MK. Multimodal Neuroimaging: Basic Concepts and Classification of Neuropsychiatric Diseases. Clin EEG Neurosci 2019;50:20-33. [DOI: 10.1177/1550059418782093] [Cited by in Crossref: 12] [Cited by in F6Publishing: 8] [Article Influence: 3.0] [Reference Citation Analysis]
39 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]
40 Palaniyappan L, Deshpande G, Lanka P, Rangaprakash D, Iwabuchi S, Francis S, Liddle PF. Effective connectivity within a triple network brain system discriminates schizophrenia spectrum disorders from psychotic bipolar disorder at the single-subject level. Schizophrenia Research 2019;214:24-33. [DOI: 10.1016/j.schres.2018.01.006] [Cited by in Crossref: 23] [Cited by in F6Publishing: 23] [Article Influence: 7.7] [Reference Citation Analysis]
41 Madre M, Canales-Rodríguez EJ, Fuentes-Claramonte P, Alonso-Lana S, Salgado-Pineda P, Guerrero-Pedraza A, Moro N, Bosque C, Gomar JJ, Ortíz-Gil J, Goikolea JM, Bonnin CM, Vieta E, Sarró S, Maristany T, McKenna PJ, Salvador R, Pomarol-Clotet E. Structural abnormality in schizophrenia versus bipolar disorder: A whole brain cortical thickness, surface area, volume and gyrification analyses. Neuroimage Clin 2020;25:102131. [PMID: 31911343 DOI: 10.1016/j.nicl.2019.102131] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 1.7] [Reference Citation Analysis]
42 Radua J, Carvalho AF. Route map for machine learning in psychiatry: Absence of bias, reproducibility, and utility. Eur Neuropsychopharmacol 2021;50:115-7. [PMID: 34116365 DOI: 10.1016/j.euroneuro.2021.05.006] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
43 Correia M, Kagenaar E, van Schalkwijk DB, Bourbon M, Gama-Carvalho M. Machine learning modelling of blood lipid biomarkers in familial hypercholesterolaemia versus polygenic/environmental dyslipidaemia. Sci Rep 2021;11:3801. [PMID: 33589716 DOI: 10.1038/s41598-021-83392-w] [Reference Citation Analysis]
44 Ienca M, Ignatiadis K. Artificial Intelligence in Clinical Neuroscience: Methodological and Ethical Challenges. AJOB Neurosci 2020;11:77-87. [PMID: 32228387 DOI: 10.1080/21507740.2020.1740352] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]