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For: Koutsouleris N, Meisenzahl EM, Borgwardt S, Riecher-Rössler A, Frodl T, Kambeitz J, Köhler Y, Falkai P, Möller HJ, Reiser M, Davatzikos C. Individualized differential diagnosis of schizophrenia and mood disorders using neuroanatomical biomarkers. Brain 2015;138:2059-73. [PMID: 25935725 DOI: 10.1093/brain/awv111] [Cited by in Crossref: 90] [Cited by in F6Publishing: 76] [Article Influence: 12.9] [Reference Citation Analysis]
Number Citing Articles
1 Fu CHY, Fan Y, Davatzikos C. Widespread Morphometric Abnormalities in Major Depression: Neuroplasticity and Potential for Biomarker Development. Neuroimaging Clin N Am 2020;30:85-95. [PMID: 31759575 DOI: 10.1016/j.nic.2019.09.008] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.7] [Reference Citation Analysis]
2 Ashar YK, Andrews-Hanna JR, Dimidjian S, Wager TD. Empathic Care and Distress: Predictive Brain Markers and Dissociable Brain Systems. Neuron 2017;94:1263-1273.e4. [PMID: 28602689 DOI: 10.1016/j.neuron.2017.05.014] [Cited by in Crossref: 80] [Cited by in F6Publishing: 58] [Article Influence: 16.0] [Reference Citation Analysis]
3 Stoyanov D, Kandilarova S, Paunova R, Barranco Garcia J, Latypova A, Kherif F. Cross-Validation of Functional MRI and Paranoid-Depressive Scale: Results From Multivariate Analysis. Front Psychiatry. 2019;10:869. [PMID: 31824359 DOI: 10.3389/fpsyt.2019.00869] [Cited by in Crossref: 11] [Cited by in F6Publishing: 8] [Article Influence: 3.7] [Reference Citation Analysis]
4 Kambeitz J, Cabral C, Sacchet MD, Gotlib IH, Zahn R, Serpa MH, Walter M, Falkai P, Koutsouleris N. Detecting Neuroimaging Biomarkers for Depression: A Meta-analysis of Multivariate Pattern Recognition Studies. Biol Psychiatry 2017;82:330-8. [PMID: 28110823 DOI: 10.1016/j.biopsych.2016.10.028] [Cited by in Crossref: 81] [Cited by in F6Publishing: 67] [Article Influence: 13.5] [Reference Citation Analysis]
5 Koutsouleris N, Pantelis C, Velakoulis D, McGuire P, Dwyer DB, Urquijo-Castro MF, Paul R, Dong S, Popovic D, Oeztuerk O, Kambeitz J, Salokangas RKR, Hietala J, Bertolino A, Brambilla P, Upthegrove R, Wood SJ, Lencer R, Borgwardt S, Maj C, Nöthen M, Degenhardt F, Polyakova M, Mueller K, Villringer A, Danek A, Fassbender K, Fliessbach K, Jahn H, Kornhuber J, Landwehrmeyer B, Anderl-Straub S, Prudlo J, Synofzik M, Wiltfang J, Riedl L, Diehl-Schmid J, Otto M, Meisenzahl E, Falkai P, Schroeter ML; International FTD-Genetics Consortium (IFGC), the German Frontotemporal Lobar Degeneration (FTLD) Consortium, and the PRONIA Consortium. Exploring Links Between Psychosis and Frontotemporal Dementia Using Multimodal Machine Learning: Dementia Praecox Revisited. JAMA Psychiatry 2022. [PMID: 35921104 DOI: 10.1001/jamapsychiatry.2022.2075] [Reference Citation Analysis]
6 Michielse S, Lange I, Bakker J, Goossens L, Verhagen S, Wichers M, Lieverse R, Schruers K, van Amelsvoort T, van Os J, Marcelis M. White matter microstructure and network-connectivity in emerging adults with subclinical psychotic experiences. Brain Imaging Behav 2020;14:1876-88. [PMID: 31183775 DOI: 10.1007/s11682-019-00129-0] [Reference Citation Analysis]
7 Doan NT, Engvig A, Persson K, Alnæs D, Kaufmann T, Rokicki J, Córdova-Palomera A, Moberget T, Brækhus A, Barca ML, Engedal K, Andreassen OA, Selbæk G, Westlye LT. Dissociable diffusion MRI patterns of white matter microstructure and connectivity in Alzheimer's disease spectrum. Sci Rep 2017;7:45131. [PMID: 28338052 DOI: 10.1038/srep45131] [Cited by in Crossref: 28] [Cited by in F6Publishing: 23] [Article Influence: 5.6] [Reference Citation Analysis]
8 Reavis EA, Lee J, Wynn JK, Engel SA, Cohen MS, Nuechterlein KH, Glahn DC, Altshuler LL, Green MF. Assessing neural tuning for object perception in schizophrenia and bipolar disorder with multivariate pattern analysis of fMRI data. Neuroimage Clin 2017;16:491-7. [PMID: 28932681 DOI: 10.1016/j.nicl.2017.08.023] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 1.2] [Reference Citation Analysis]
9 Koike S, Tanaka SC, Okada T, Aso T, Yamashita A, Yamashita O, Asano M, Maikusa N, Morita K, Okada N, Fukunaga M, Uematsu A, Togo H, Miyazaki A, Murata K, Urushibata Y, Autio J, Ose T, Yoshimoto J, Araki T, Glasser MF, Van Essen DC, Maruyama M, Sadato N, Kawato M, Kasai K, Okamoto Y, Hanakawa T, Hayashi T; Brain/MINDS Beyond Human Brain MRI Group. Brain/MINDS beyond human brain MRI project: A protocol for multi-level harmonization across brain disorders throughout the lifespan. Neuroimage Clin 2021;30:102600. [PMID: 33741307 DOI: 10.1016/j.nicl.2021.102600] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
10 Blitz R, Storck M, Baune BT, Dugas M, Opel N. Design and Implementation of an Informatics Infrastructure for Standardized Data Acquisition, Transfer, Storage, and Export in Psychiatric Clinical Routine: Feasibility Study. JMIR Ment Health 2021;8:e26681. [PMID: 34106072 DOI: 10.2196/26681] [Reference Citation Analysis]
11 Kropf E, Syan SK, Minuzzi L, Frey BN. From anatomy to function: the role of the somatosensory cortex in emotional regulation. Braz J Psychiatry 2019;41:261-9. [PMID: 30540029 DOI: 10.1590/1516-4446-2018-0183] [Cited by in Crossref: 32] [Cited by in F6Publishing: 21] [Article Influence: 8.0] [Reference Citation Analysis]
12 Mikolas P, Vahid A, Bernardoni F, Süß M, Martini J, Beste C, Bluschke A. Training a machine learning classifier to identify ADHD based on real-world clinical data from medical records. Sci Rep 2022;12:12934. [PMID: 35902654 DOI: 10.1038/s41598-022-17126-x] [Reference Citation Analysis]
13 Wang R, Chaudhari P, Davatzikos C. Embracing the disharmony in medical imaging: A Simple and effective framework for domain adaptation. Med Image Anal 2021;76:102309. [PMID: 34871931 DOI: 10.1016/j.media.2021.102309] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
14 Arbabshirani MR, Plis S, Sui J, Calhoun VD. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage 2017;145:137-65. [PMID: 27012503 DOI: 10.1016/j.neuroimage.2016.02.079] [Cited by in Crossref: 398] [Cited by in F6Publishing: 307] [Article Influence: 66.3] [Reference Citation Analysis]
15 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]
16 Chen J, Patil KR, Yeo BT, Eickhoff SB. Leveraging machine learning for gaining neurobiological and nosological insights in psychiatric research. Biological Psychiatry 2022. [DOI: 10.1016/j.biopsych.2022.07.025] [Reference Citation Analysis]
17 Opel N, Goltermann J, Hermesdorf M, Berger K, Baune BT, Dannlowski U. Cross-Disorder Analysis of Brain Structural Abnormalities in Six Major Psychiatric Disorders: A Secondary Analysis of Mega- and Meta-analytical Findings From the ENIGMA Consortium. Biol Psychiatry 2020;88:678-86. [PMID: 32646651 DOI: 10.1016/j.biopsych.2020.04.027] [Cited by in Crossref: 52] [Cited by in F6Publishing: 30] [Article Influence: 26.0] [Reference Citation Analysis]
18 Teixeira AL, Colpo GD, Fries GR, Bauer IE, Selvaraj S. Biomarkers for bipolar disorder: current status and challenges ahead. Expert Rev Neurother 2019;19:67-81. [PMID: 30451546 DOI: 10.1080/14737175.2019.1550361] [Cited by in Crossref: 22] [Cited by in F6Publishing: 17] [Article Influence: 5.5] [Reference Citation Analysis]
19 Squarcina L, Castellani U, Bellani M, Perlini C, Lasalvia A, Dusi N, Bonetto C, Cristofalo D, Tosato S, Rambaldelli G, Alessandrini F, Zoccatelli G, Pozzi-Mucelli R, Lamonaca D, Ceccato E, Pileggi F, Mazzi F, Santonastaso P, Ruggeri M, Brambilla P; GET UP Group. Classification of first-episode psychosis in a large cohort of patients using support vector machine and multiple kernel learning techniques. Neuroimage 2017;145:238-45. [PMID: 26690803 DOI: 10.1016/j.neuroimage.2015.12.007] [Cited by in Crossref: 35] [Cited by in F6Publishing: 22] [Article Influence: 5.0] [Reference Citation Analysis]
20 Dwyer DB, Cabral C, Kambeitz-Ilankovic L, Sanfelici R, Kambeitz J, Calhoun V, Falkai P, Pantelis C, Meisenzahl E, Koutsouleris N. Brain Subtyping Enhances The Neuroanatomical Discrimination of Schizophrenia. Schizophr Bull 2018;44:1060-9. [PMID: 29529270 DOI: 10.1093/schbul/sby008] [Cited by in Crossref: 40] [Cited by in F6Publishing: 32] [Article Influence: 13.3] [Reference Citation Analysis]
21 DeLisi LE, Fleischhacker WW. How precise is precision medicine for schizophrenia? Curr Opin Psychiatry 2016;29:187-9. [PMID: 26998940 DOI: 10.1097/YCO.0000000000000245] [Cited by in Crossref: 8] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]
22 Baldinger-Melich P, Urquijo Castro MF, Seiger R, Ruef A, Dwyer DB, Kranz GS, Klöbl M, Kambeitz J, Kaufmann U, Windischberger C, Kasper S, Falkai P, Lanzenberger R, Koutsouleris N. Sex Matters: A Multivariate Pattern Analysis of Sex- and Gender-Related Neuroanatomical Differences in Cis- and Transgender Individuals Using Structural Magnetic Resonance Imaging. Cereb Cortex 2020;30:1345-56. [PMID: 31368487 DOI: 10.1093/cercor/bhz170] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 7.0] [Reference Citation Analysis]
23 Wenzel J, Haas SS, Dwyer DB, Ruef A, Oeztuerk OF, Antonucci LA, von Saldern S, Bonivento C, Garzitto M, Ferro A, Paolini M, Blautzik J, Borgwardt S, Brambilla P, Meisenzahl E, Salokangas RKR, Upthegrove R, Wood SJ, Kambeitz J, Koutsouleris N, Kambeitz-Ilankovic L; PRONIA consortium. Cognitive subtypes in recent onset psychosis: distinct neurobiological fingerprints? Neuropsychopharmacology 2021;46:1475-83. [PMID: 33723384 DOI: 10.1038/s41386-021-00963-1] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
24 Wu Z, Luo Q, Wu H, Wu Z, Zheng Y, Yang Y, He J, Ding Y, Yu R, Peng H. Amplitude of Low-Frequency Oscillations in Major Depressive Disorder With Childhood Trauma. Front Psychiatry 2020;11:596337. [PMID: 33551867 DOI: 10.3389/fpsyt.2020.596337] [Reference Citation Analysis]
25 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]
26 Doan NT, Engvig A, Zaske K, Persson K, Lund MJ, Kaufmann T, Cordova-Palomera A, Alnæs D, Moberget T, Brækhus A, Barca ML, Nordvik JE, Engedal K, Agartz I, Selbæk G, Andreassen OA, Westlye LT; Alzheimer's Disease Neuroimaging Initiative. Distinguishing early and late brain aging from the Alzheimer's disease spectrum: consistent morphological patterns across independent samples. Neuroimage 2017;158:282-95. [PMID: 28666881 DOI: 10.1016/j.neuroimage.2017.06.070] [Cited by in Crossref: 22] [Cited by in F6Publishing: 16] [Article Influence: 4.4] [Reference Citation Analysis]
27 Shang J, Fisher P, Bäuml JG, Daamen M, Baumann N, Zimmer C, Bartmann P, Boecker H, Wolke D, Sorg C, Koutsouleris N, Dwyer DB. A machine learning investigation of volumetric and functional MRI abnormalities in adults born preterm. Hum Brain Mapp 2019;40:4239-52. [PMID: 31228329 DOI: 10.1002/hbm.24698] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 2.3] [Reference Citation Analysis]
28 Kang SK, Shin SA, Seo S, Byun MS, Lee DY, Kim YK, Lee DS, Lee JS. Deep learning-Based 3D inpainting of brain MR images. Sci Rep 2021;11:1673. [PMID: 33462321 DOI: 10.1038/s41598-020-80930-w] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 5.0] [Reference Citation Analysis]
29 Fleck DE, Ernest N, Adler CM, Cohen K, Eliassen JC, Norris M, Komoroski RA, Chu WJ, Welge JA, Blom TJ, DelBello MP, Strakowski SM. Prediction of lithium response in first-episode mania using the LITHium Intelligent Agent (LITHIA): Pilot data and proof-of-concept. Bipolar Disord 2017;19:259-72. [PMID: 28574156 DOI: 10.1111/bdi.12507] [Cited by in Crossref: 22] [Cited by in F6Publishing: 17] [Article Influence: 4.4] [Reference Citation Analysis]
30 Minuzzi L, Syan SK, Smith M, Hall A, Hall GB, Frey BN. Structural and functional changes in the somatosensory cortex in euthymic females with bipolar disorder. Aust N Z J Psychiatry 2018;52:1075-83. [DOI: 10.1177/0004867417746001] [Cited by in Crossref: 16] [Cited by in F6Publishing: 13] [Article Influence: 3.2] [Reference Citation Analysis]
31 Librenza-Garcia D, Kotzian BJ, Yang J, Mwangi B, Cao B, Pereira Lima LN, Bermudez MB, Boeira MV, Kapczinski F, Passos IC. The impact of machine learning techniques in the study of bipolar disorder: A systematic review. Neurosci Biobehav Rev 2017;80:538-54. [PMID: 28728937 DOI: 10.1016/j.neubiorev.2017.07.004] [Cited by in Crossref: 76] [Cited by in F6Publishing: 49] [Article Influence: 15.2] [Reference Citation Analysis]
32 Gifford G, Crossley N, Fusar-poli P, Schnack HG, Kahn RS, Koutsouleris N, Cannon TD, Mcguire P. Using neuroimaging to help predict the onset of psychosis. NeuroImage 2017;145:209-17. [DOI: 10.1016/j.neuroimage.2016.03.075] [Cited by in Crossref: 36] [Cited by in F6Publishing: 27] [Article Influence: 7.2] [Reference Citation Analysis]
33 Newton R, Rouleau A, Nylander AG, Loze JY, Resemann HK, Steeves S, Crespo-Facorro B. Diverse definitions of the early course of schizophrenia-a targeted literature review. NPJ Schizophr 2018;4:21. [PMID: 30323274 DOI: 10.1038/s41537-018-0063-7] [Cited by in Crossref: 25] [Cited by in F6Publishing: 16] [Article Influence: 6.3] [Reference Citation Analysis]
34 Davatzikos C. Machine learning in neuroimaging: Progress and challenges. Neuroimage 2019;197:652-6. [PMID: 30296563 DOI: 10.1016/j.neuroimage.2018.10.003] [Cited by in Crossref: 56] [Cited by in F6Publishing: 39] [Article Influence: 14.0] [Reference Citation Analysis]
35 Singh J, Goyal G. Decoding depressive disorder using computer vision. Multimed Tools Appl 2021;80:8189-212. [DOI: 10.1007/s11042-020-10128-9] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
36 Brandl F, Avram M, Weise B, Shang J, Simões B, Bertram T, Hoffmann Ayala D, Penzel N, Gürsel DA, Bäuml J, Wohlschläger AM, Vukadinovic Z, Koutsouleris N, Leucht S, Sorg C. Specific Substantial Dysconnectivity in Schizophrenia: A Transdiagnostic Multimodal Meta-analysis of Resting-State Functional and Structural Magnetic Resonance Imaging Studies. Biological Psychiatry 2019;85:573-83. [DOI: 10.1016/j.biopsych.2018.12.003] [Cited by in Crossref: 56] [Cited by in F6Publishing: 46] [Article Influence: 18.7] [Reference Citation Analysis]
37 Upthegrove R, Lalousis P, Mallikarjun P, Chisholm K, Griffiths SL, Iqbal M, Pelton M, Reniers R, Stainton A, Rosen M, Ruef A, Dwyer DB, Surman M, Haidl T, Penzel N, Kambeitz-Llankovic L, Bertolino A, Brambilla P, Borgwardt S, Kambeitz J, Lencer R, Pantelis C, Ruhrmann S, Schultze-Lutter F, Salokangas RKR, Meisenzahl E, Wood SJ, Koutsouleris N; PRONIA Consortium. The Psychopathology and Neuroanatomical Markers of Depression in Early Psychosis. Schizophr Bull 2021;47:249-58. [PMID: 32634220 DOI: 10.1093/schbul/sbaa094] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
38 Dukart J, Smieskova R, Harrisberger F, Lenz C, Schmidt A, Walter A, Huber C, Riecher-Rössler A, Simon A, Lang UE, Fusar-Poli P, Borgwardt S. Age-related brain structural alterations as an intermediate phenotype of psychosis. J Psychiatry Neurosci 2017;42:307-19. [PMID: 28459416 DOI: 10.1503/jpn.160179] [Cited by in Crossref: 20] [Cited by in F6Publishing: 12] [Article Influence: 5.0] [Reference Citation Analysis]
39 Gowen CL, Khwaounjoo P, Cakmak YO. EMG-Free Monitorization of the Acoustic Startle Reflex with a Mobile Phone: Implications of Sound Parameters with Posture Related Responses. Sensors (Basel) 2020;20:E5996. [PMID: 33105890 DOI: 10.3390/s20215996] [Reference Citation Analysis]
40 Bodén R, Bengtsson J, Thörnblom E, Struckmann W, Persson J. Dorsomedial prefrontal theta burst stimulation to treat anhedonia, avolition, and blunted affect in schizophrenia or depression - a randomized controlled trial. J Affect Disord 2021;290:308-15. [PMID: 34020205 DOI: 10.1016/j.jad.2021.04.053] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
41 Fusar-Poli P, Meyer-Lindenberg A. Forty years of structural imaging in psychosis: promises and truth. Acta Psychiatr Scand 2016;134:207-24. [PMID: 27404479 DOI: 10.1111/acps.12619] [Cited by in Crossref: 37] [Cited by in F6Publishing: 31] [Article Influence: 6.2] [Reference Citation Analysis]
42 Cousins A, Nakano L, Schofield E, Kabaila R. A neural network approach to optimising treatments for depression using data from specialist and community psychiatric services in Australia, New Zealand and Japan. Neural Comput Appl 2022;:1-20. [PMID: 35039718 DOI: 10.1007/s00521-021-06710-3] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
43 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]
44 Brosch K, Stein F, Schmitt S, Pfarr JK, Ringwald KG, Thomas-Odenthal F, Meller T, Steinsträter O, Waltemate L, Lemke H, Meinert S, Winter A, Breuer F, Thiel K, Grotegerd D, Hahn T, Jansen A, Dannlowski U, Krug A, Nenadić I, Kircher T. Reduced hippocampal gray matter volume is a common feature of patients with major depression, bipolar disorder, and schizophrenia spectrum disorders. Mol Psychiatry 2022. [PMID: 35840798 DOI: 10.1038/s41380-022-01687-4] [Reference Citation Analysis]
45 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: 19] [Article Influence: 8.7] [Reference Citation Analysis]
46 Gupta S, Ranganathan M, D'Souza DC. The early identification of psychosis: can lessons be learnt from cardiac stress testing? Psychopharmacology (Berl) 2016;233:19-37. [PMID: 26566609 DOI: 10.1007/s00213-015-4143-3] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 0.7] [Reference Citation Analysis]
47 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]
48 Baecker L, Dafflon J, da Costa PF, Garcia-Dias R, Vieira S, Scarpazza C, Calhoun VD, Sato JR, Mechelli A, Pinaya WHL. Brain age prediction: A comparison between machine learning models using region- and voxel-based morphometric data. Hum Brain Mapp 2021;42:2332-46. [PMID: 33738883 DOI: 10.1002/hbm.25368] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
49 Li P, Jing RX, Zhao RJ, Ding ZB, Shi L, Sun HQ, Lin X, Fan TT, Dong WT, Fan Y, Lu L. Electroconvulsive therapy-induced brain functional connectivity predicts therapeutic efficacy in patients with schizophrenia: a multivariate pattern recognition study. NPJ Schizophr 2017;3:21. [PMID: 28560267 DOI: 10.1038/s41537-017-0023-7] [Cited by in Crossref: 15] [Cited by in F6Publishing: 15] [Article Influence: 3.0] [Reference Citation Analysis]
50 Niu M, Wang Y, Jia Y, Wang J, Zhong S, Lin J, Sun Y, Zhao L, Liu X, Huang L, Huang R. Common and Specific Abnormalities in Cortical Thickness in Patients with Major Depressive and Bipolar Disorders. EBioMedicine 2017;16:162-71. [PMID: 28109831 DOI: 10.1016/j.ebiom.2017.01.010] [Cited by in Crossref: 36] [Cited by in F6Publishing: 31] [Article Influence: 7.2] [Reference Citation Analysis]
51 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]
52 Yamamoto M, Bagarinao E, Kushima I, Takahashi T, Sasabayashi D, Inada T, Suzuki M, Iidaka T, Ozaki N. Support vector machine-based classification of schizophrenia patients and healthy controls using structural magnetic resonance imaging from two independent sites. PLoS One 2020;15:e0239615. [PMID: 33232334 DOI: 10.1371/journal.pone.0239615] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
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