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For: 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]
Number Citing Articles
1 Dwyer D, Koutsouleris N. Annual Research Review: Translational machine learning for child and adolescent psychiatry. J Child Psychol Psychiatry 2022. [PMID: 35040130 DOI: 10.1111/jcpp.13545] [Cited by in Crossref: 8] [Cited by in F6Publishing: 4] [Article Influence: 8.0] [Reference Citation Analysis]
2 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]
3 Miranda L, Paul R, Pütz B, Koutsouleris N, Müller-Myhsok B. Systematic Review of Functional MRI Applications for Psychiatric Disease Subtyping. Front Psychiatry 2021;12:665536. [PMID: 34744805 DOI: 10.3389/fpsyt.2021.665536] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
4 Soldatos RF, Cearns M, Nielsen MØ, Kollias C, Xenaki LA, Stefanatou P, Ralli I, Dimitrakopoulos S, Hatzimanolis A, Kosteletos I, Vlachos II, Selakovic M, Foteli S, Nianiakas N, Mantonakis L, Triantafyllou TF, Ntigridaki A, Ermiliou V, Voulgaraki M, Psarra E, Sørensen ME, Bojesen KB, Tangmose K, Sigvard AM, Ambrosen KS, Meritt T, Syeda W, Glenthøj BY, Koutsouleris N, Pantelis C, Ebdrup BH, Stefanis N. Prediction of Early Symptom Remission in Two Independent Samples of First-Episode Psychosis Patients Using Machine Learning. Schizophr Bull 2021:sbab107. [PMID: 34535800 DOI: 10.1093/schbul/sbab107] [Reference Citation Analysis]
5 Feng C, Thompson WK, Paulus MP. Effect sizes of associations between neuroimaging measures and affective symptoms: A meta-analysis. Depress Anxiety 2021. [PMID: 34516701 DOI: 10.1002/da.23215] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
6 Leehr EJ, Roesmann K, Böhnlein J, Dannlowski U, Gathmann B, Herrmann MJ, Junghöfer M, Schwarzmeier H, Seeger FR, Siminski N, Straube T, Lueken U, Hilbert K. Clinical predictors of treatment response towards exposure therapy in virtuo in spider phobia: A machine learning and external cross-validation approach. J Anxiety Disord 2021;83:102448. [PMID: 34298236 DOI: 10.1016/j.janxdis.2021.102448] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
7 Arbabyazd L, Shen K, Wang Z, Hofmann-Apitius M, Ritter P, McIntosh AR, Battaglia D, Jirsa V. Virtual Connectomic Datasets in Alzheimer's Disease and Aging Using Whole-Brain Network Dynamics Modelling. eNeuro 2021;8:ENEURO. [PMID: 34045210 DOI: 10.1523/ENEURO.0475-20.2021] [Cited by in F6Publishing: 5] [Reference Citation Analysis]
8 Yang J, Pu W, Wu G, Chen E, Lee E, Liu Z, Palaniyappan L. Connectomic Underpinnings of Working Memory Deficits in Schizophrenia: Evidence From a replication fMRI study. Schizophr Bull 2020;46:916-26. [PMID: 32016430 DOI: 10.1093/schbul/sbz137] [Cited by in Crossref: 6] [Cited by in F6Publishing: 10] [Article Influence: 6.0] [Reference Citation Analysis]
9 Eitel F, Schulz MA, Seiler M, Walter H, Ritter K. Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research. Exp Neurol 2021;339:113608. [PMID: 33513353 DOI: 10.1016/j.expneurol.2021.113608] [Cited by in F6Publishing: 5] [Reference Citation Analysis]
10 Todeva-Radneva A, Paunova R, Kandilarova S, St Stoyanov D. The Value of Neuroimaging Techniques in the Translation and Transdiagnostic Validation of Psychiatric Diagnoses - Selective Review. Curr Top Med Chem 2020;20:540-53. [PMID: 32003690 DOI: 10.2174/1568026620666200131095328] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
11 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]
12 Itani S, Rossignol M. At the Crossroads Between Psychiatry and Machine Learning: Insights Into Paradigms and Challenges for Clinical Applicability. Front Psychiatry 2020;11:552262. [PMID: 33192664 DOI: 10.3389/fpsyt.2020.552262] [Reference Citation Analysis]
13 Hamaker EL, Mulder JD, van IJzendoorn MH. Description, prediction and causation: Methodological challenges of studying child and adolescent development. Dev Cogn Neurosci 2020;46:100867. [PMID: 33186867 DOI: 10.1016/j.dcn.2020.100867] [Cited by in Crossref: 5] [Cited by in F6Publishing: 10] [Article Influence: 2.5] [Reference Citation Analysis]
14 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]
15 Chen L, Xia C, Sun H. Recent advances of deep learning in psychiatric disorders. Precision Clinical Medicine 2020;3:202-13. [DOI: 10.1093/pcmedi/pbaa029] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
16 Sacher J, Chechko N, Dannlowski U, Walter M, Derntl B. The peripartum human brain: Current understanding and future perspectives. Front Neuroendocrinol 2020;59:100859. [PMID: 32771399 DOI: 10.1016/j.yfrne.2020.100859] [Cited by in Crossref: 4] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
17 Burkhardt G, Adorjan K, Kambeitz J, Kambeitz-Ilankovic L, Falkai P, Eyer F, Koller G, Pogarell O, Koutsouleris N, Dwyer DB. A machine learning approach to risk assessment for alcohol withdrawal syndrome. Eur Neuropsychopharmacol 2020;35:61-70. [PMID: 32418843 DOI: 10.1016/j.euroneuro.2020.03.016] [Reference Citation Analysis]
18 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: 4] [Article Influence: 1.0] [Reference Citation Analysis]
19 Lin E, Lin CH, Lane HY. Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches. Int J Mol Sci 2020;21:E969. [PMID: 32024055 DOI: 10.3390/ijms21030969] [Cited by in Crossref: 23] [Cited by in F6Publishing: 33] [Article Influence: 11.5] [Reference Citation Analysis]
20 Martani A, Geneviève LD, Pauli-Magnus C, McLennan S, Elger BS. Regulating the Secondary Use of Data for Research: Arguments Against Genetic Exceptionalism. Front Genet 2019;10:1254. [PMID: 31956328 DOI: 10.3389/fgene.2019.01254] [Cited by in Crossref: 9] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
21 Schwarzmeier H, Leehr EJ, Böhnlein J, Seeger FR, Roesmann K, Gathmann B, Herrmann MJ, Siminski N, Junghöfer M, Straube T, Grotegerd D, Dannlowski U. Theranostic markers for personalized therapy of spider phobia: Methods of a bicentric external cross-validation machine learning approach. Int J Methods Psychiatr Res 2020;29:e1812. [PMID: 31814209 DOI: 10.1002/mpr.1812] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.7] [Reference Citation Analysis]
22 [DOI: 10.1109/tiptekno.2019.8895222] [Cited by in Crossref: 9] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]