Erratum: Prediction models of functional outcomes for individuals in the clinical high-risk state for psychosis or with recent-onset depression: A multimodal, multisite machine learning analysis (JAMA Psychiatry (2018) 75:11 (1156-1172) DOI: 10.1001/jamapsychiatry.2018.2165);Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or with Recent-Onset Depression: A Multimodal, Multisite Machine Learning Analysis

JAMA Psychiatry

Volumn 75, Issue 11, 2018, Pages 1156-1172

  Koutsouleris, Nikolaos ^a   Kambeitz Ilankovic, Lana ^a   Ruhrmann, Stephan ^b   Rosen, Marlene ^b   Ruef, Anne ^a   Dwyer, Dominic B ^a   Paolini, Marco ^a   Chisholm, Katharine ^c   Kambeitz, Joseph ^a   Haidl, Theresa ^b   Schmidt, André ^d   Gillam, John ^e,f   Schultze Lutter, Frauke ^g   Falkai, Peter ^a   Reiser, Maximilian ^h   Riecher Rössler, Anita ^d   Upthegrove, Rachel ^c   Hietala, Jarmo ⁱ   Salokangas, Raimo K R ⁱ   Pantelis, Christos ^j,k   Meisenzahl, Eva ^g   Wood, Stephen J ^c,e,f   Beque, Dirk ^l   Brambilla, Paolo ^m   Borgwardt, Stefan ^d   more..

^a LUDWIG MAXIMILIANS UNIVERSITY   (Germany)

^b UNIVERSITY OF COLOGNE   (Germany)

^c UNIVERSITY OF BIRMINGHAM   (United Kingdom)

^d UNIVERSITY OF BASEL   (Switzerland)

^e THE NATIONAL CENTRE OF EXCELLENCE IN YOUTH MENTAL HEALTH   (Australia)

^f UNIVERSITY OF MELBOURNE   (Australia)

^g Department of Orthopedic Surgery   (Germany)

^h UNIVERSITY OF MUNICH   (Germany)

ⁱ UNIVERSITY OF TURKU   (Finland)

^j UNIVERSITY OF MELBOURNE   (Australia)

^k MELBOURNE HEALTH   (Australia)

^l GE Corporation   (Germany)

^m UNIVERSITY OF MILAN   (Italy)

more..

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; AGED; AMYGDALA; ANHEDONIA; ARTICLE; BECK DEPRESSION INVENTORY; BRAIN SIZE; DEPRESSION; DORSAL ANTERIOR CINGULATE CORTEX; EMOTION; FEMALE; FOLLOW UP; GLOBAL ASSESSMENT OF FUNCTIONING; GRANDIOSE DELUSION; GRAY MATTER; HIPPOCAMPUS; HUMAN; INFERIOR TEMPORAL CORTEX; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MIDDLE AGED; NEUROIMAGING; POSITIVE AND NEGATIVE SYNDROME SCALE; PREDICTION; PREDICTIVE VALUE; PROGNOSIS; PSYCHOSIS; SCHIZOTYPAL PERSONALITY DISORDER; SOCIAL INTERACTION; THALAMUS; CASE CONTROL STUDY; DIAGNOSTIC IMAGING; NEUROPSYCHOLOGICAL TEST; PATHOLOGY; PSYCHOLOGY; SOCIAL ADAPTATION; YOUNG ADULT;

ADULT; CASE-CONTROL STUDIES; DEPRESSION; DEPRESSIVE DISORDER; FEMALE; GRAY MATTER; HUMANS; MACHINE LEARNING; MALE; NEUROIMAGING; NEUROPSYCHOLOGICAL TESTS; PSYCHOTIC DISORDERS; SOCIAL ADJUSTMENT; YOUNG ADULT;

EID: 85054155287     PISSN: 2168622X     EISSN: None     Source Type: Journal    
DOI: 10.1001/jamapsychiatry.2019.0618     Document Type: Erratum

References (76)

1. Addington J, Cornblatt BA, Cadenhead KS, At clinical high risk for psychosis: outcome for nonconverters. Am J Psychiatry. 2011; 168 (8): 800-805. doi: 10.1176/appi.ajp.2011.10081191 21498462
2. Salokangas RKR, Nieman DH, Heinimaa M,; EPOS group. Psychosocial outcome in patients at clinical high risk of psychosis: a prospective follow-up. Soc Psychiatry Psychiatr Epidemiol. 2013; 48 (2): 303-311. doi: 10.1007/s00127-012-0545-2 22797132
3. Lin A, Wood SJ, Nelson B, Beavan A, McGorry P, Yung AR. Outcomes of nontransitioned cases in a sample at ultra-high risk for psychosis. Am J Psychiatry. 2015; 172 (3): 249-258. doi: 10.1176/appi.ajp.2014.13030418 25727537
4. McGorry P, Keshavan M, Goldstone S, Biomarkers and clinical staging in psychiatry. World Psychiatry. 2014; 13 (3): 211-223. doi: 10.1002/wps.20144 25273285
5. Schmidt SJ, Schultze-Lutter F, Schimmelmann BG, EPA guidance on the early intervention in clinical high risk states of psychoses. Eur Psychiatry. 2015; 30 (3): 388-404. doi: 10.1016/j.eurpsy.2015.01.013 25749390
6. Fusar-Poli P, Borgwardt S, Bechdolf A, The psychosis high-risk state: a comprehensive state-of-the-art review. JAMA Psychiatry. 2013; 70 (1): 107-120. doi: 10.1001/jamapsychiatry.2013.269 23165428
7. Carrión RE, McLaughlin D, Goldberg TE, Prediction of functional outcome in individuals at clinical high risk for psychosis. JAMA Psychiatry. 2013; 70 (11): 1133-1142. doi: 10.1001/jamapsychiatry.2013.1909 24006090
8. Simon AE, Velthorst E, Nieman DH, Linszen D, Umbricht D, de Haan L. Ultra high-risk state for psychosis and non-transition: a systematic review. Schizophr Res. 2011; 132 (1): 8-17. doi: 10.1016/j.schres.2011.07.002 21784618
9. Michel C, Ruhrmann S, Schimmelmann BG, Klosterkötter J, Schultze-Lutter F. Course of clinical high-risk states for psychosis beyond conversion. Eur Arch Psychiatry Clin Neurosci. 2018; 268 (1): 39-48. doi: 10.1007/s00406-016-0764-8 28054132
10. Cornblatt BA, Auther AM, Niendam T, Preliminary findings for two new measures of social and role functioning in the prodromal phase of schizophrenia. Schizophr Bull. 2007; 33 (3): 688-702. doi: 10.1093/schbul/sbm029 17440198
11. Fusar-Poli P, Byrne M, Valmaggia L,; OASIS Team. Social dysfunction predicts two years clinical outcome in people at ultra high risk for psychosis. J Psychiatr Res. 2010; 44 (5): 294-301. doi: 10.1016/j.jpsychires.2009.08.016 19836755
12. Cannon TD, Yu C, Addington J, An individualized risk calculator for research in prodromal psychosis. Am J Psychiatry. 2016; 173 (10): 980-988. doi: 10.1176/appi.ajp.2016.15070890 27363508
13. Salokangas RK, Heinimaa M, From T,; EPOS group. Short-term functional outcome and premorbid adjustment in clinical high-risk patients: results of the EPOS project. Eur Psychiatry. 2014; 29 (6): 371-380. doi: 10.1016/j.eurpsy.2013.10.003 24315804
14. Bora E, Harrison BJ, Yücel M, Pantelis C. Cognitive impairment in euthymic major depressive disorder: a meta-analysis. Psychol Med. 2013; 43 (10): 2017-2026. doi: 10.1017/S0033291712002085 23098294
15. Fusar-Poli P, Nelson B, Valmaggia L, Yung AR, McGuire PK. Comorbid depressive and anxiety disorders in 509 individuals with an at-risk mental state: impact on psychopathology and transition to psychosis. Schizophr Bull. 2014; 40 (1): 120-131. doi: 10.1093/schbul/sbs136 23180756
16. Bodatsch M, Ruhrmann S, Wagner M, Prediction of psychosis by mismatch negativity. Biol Psychiatry. 2011; 69 (10): 959-966. doi: 10.1016/j.biopsych.2010.09.057 21167475
17. Koutsouleris N, Riecher-Rössler A, Meisenzahl EM, Detecting the psychosis prodrome across high-risk populations using neuroanatomical biomarkers. Schizophr Bull. 2015; 41 (2): 471-482. doi: 10.1093/schbul/sbu078 24914177
18. Michel C, Ruhrmann S, Schimmelmann BG, Klosterkötter J, Schultze-Lutter F. A stratified model for psychosis prediction in clinical practice. Schizophr Bull. 2014; 40 (6): 1533-1542. doi: 10.1093/schbul/sbu025 24609300
19. Ruhrmann S, Schultze-Lutter F, Salokangas RKR, Prediction of psychosis in adolescents and young adults at high risk: results from the prospective European prediction of psychosis study. Arch Gen Psychiatry. 2010; 67 (3): 241-251. doi: 10.1001/archgenpsychiatry.2009.206 20194824
20. Nieman DH, Ruhrmann S, Dragt S, Psychosis prediction: stratification of risk estimation with information-processing and premorbid functioning variables. Schizophr Bull. 2014; 40 (6): 1482-1490. doi: 10.1093/schbul/sbt145 24142369
21. Fusar-Poli P, Rutigliano G, Stahl D, Development and validation of a clinically based risk calculator for the transdiagnostic prediction of psychosis. JAMA Psychiatry. 2017; 74 (5): 493-500. doi: 10.1001/jamapsychiatry.2017.0284 28355424
22. Chekroud AM, Zotti RJ, Shehzad Z, Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry. 2016; 3 (3): 243-250. doi: 10.1016/S2215-0366(15)00471-X 26803397
23. Kessler RC, van Loo HM, Wardenaar KJ, Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports. Mol Psychiatry. 2016; 21 (10): 1366-1371. doi: 10.1038/mp.2015.198 26728563
24. Koutsouleris N, Kahn RS, Chekroud AM, Multisite prediction of 4-week and 52-week treatment outcomes in patients with first-episode psychosis: a machine learning approach. Lancet Psychiatry. 2016; 3 (10): 935-946. doi: 10.1016/S2215-0366(16)30171-7 27569526
25. Kambeitz-Ilankovic L, Meisenzahl EM, Cabral C, Prediction of outcome in the psychosis prodrome using neuroanatomical pattern classification. Schizophr Res. 2016; 173 (3): 159-165. doi: 10.1016/j.schres.2015.03.005 25819936
26. de Wit S, Ziermans TB, Nieuwenhuis M, Individual prediction of long-term outcome in adolescents at ultra-high risk for psychosis: applying machine learning techniques to brain imaging data. Hum Brain Mapp. 2017; 38 (2): 704-714. doi: 10.1002/hbm.23410 27699911
27. Velthorst E, Nieman DH, Linszen D, Disability in people clinically at high risk of psychosis. Br J Psychiatry. 2010; 197 (4): 278-284. doi: 10.1192/bjp.bp.109.075036 20884950
28. Cornblatt BA, Carrión RE, Addington J, Risk factors for psychosis: impaired social and role functioning. Schizophr Bull. 2012; 38 (6): 1247-1257. doi: 10.1093/schbul/sbr136 22080497
29. Studerus E, Ramyead A, Riecher-Rössler A. Prediction of transition to psychosis in patients with a clinical high risk for psychosis: a systematic review of methodology and reporting. Psychol Med. 2017; 47 (7): 1163-1178. doi: 10.1017/S0033291716003494 28091343
30. Schmidt A, Cappucciati M, Radua J, Improving prognostic accuracy in subjects at clinical high risk for psychosis: systematic review of predictive models and meta-analytical sequential testing simulation. Schizophr Bull. 2017; 43 (2): 375-388. 27535081
31. Steyerberg EW, Moons KGM, van der Windt DA,; PROGRESS Group. Prognosis research strategy (PROGRESS) 3: prognostic model research. PLoS Med. 2013; 10 (2): e1001381. doi: 10.1371/journal.pmed.1001381 23393430
32. Hahn T, Nierenberg AA, Whitfield-Gabrieli S. Predictive analytics in mental health: applications, guidelines, challenges and perspectives. Mol Psychiatry. 2017; 22 (1): 37-43. doi: 10.1038/mp.2016.201 27843153
33. Chekroud AM, Koutsouleris N. The perilous path from publication to practice. Mol Psychiatry. 2018; 23 (1): 24-25. doi: 10.1038/mp.2017.227 29112192
34. Fowler D, Hodgekins J, French P, Social recovery therapy in combination with early intervention services for enhancement of social recovery in patients with first-episode psychosis (SUPEREDEN3): a single-blind, randomised controlled trial. Lancet Psychiatry. 2018; 5 (1): 41-50. doi: 10.1016/S2215-0366(17)30476-5 29242000
35. De Silva MJ, Cooper S, Li HL, Lund C, Patel V. Effect of psychosocial interventions on social functioning in depression and schizophrenia: meta-analysis. Br J Psychiatry. 2013; 202 (4): 253-260. doi: 10.1192/bjp.bp.112.118018 23549941
36. McFarlane WR, Levin B, Travis L, Clinical and functional outcomes after 2 years in the early detection and intervention for the prevention of psychosis multisite effectiveness trial. Schizophr Bull. 2015; 41 (1): 30-43. doi: 10.1093/schbul/sbu108 25065017
37. Seidman LJ, Nordentoft M. New targets for prevention of schizophrenia: is it time for interventions in the premorbid phase? Schizophr Bull. 2015; 41 (4): 795-800. doi: 10.1093/schbul/sbv050 25925393
38. Wolpert DH. Stacked generalization. Neural Netw. 1992; 5: 241-259. doi: 10.1016/S0893-6080(05)80023-1
39. Gaonkar B, T Shinohara R, Davatzikos C; Alzheimers Disease Neuroimaging Initiative. Interpreting support vector machine models for multivariate group wise analysis in neuroimaging. Med Image Anal. 2015; 24 (1): 190-204. doi: 10.1016/j.media.2015.06.008 26210913
40. Ding C, Li T, Peng W, Park H. Orthogonal Nonnegative Matrix T-factorizations for Clustering. In: Proceedings of the 12th Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining International Conference, Philadelphia, PA; August 20-23, 2006; 126-135.
41. Gustavsson A, Svensson M, Jacobi F,; CDBE2010Study Group. Cost of disorders of the brain in Europe 2010 [published correction appears in Eur Neuropsychopharmacol. 2012;22(3):237-8]. Eur Neuropsychopharmacol. 2011; 21 (10): 718-779. doi: 10.1016/j.euroneuro.2011.08.008 21924589
42. Huys QJ, Maia TV, Frank MJ. Computational psychiatry as a bridge from neuroscience to clinical applications. Nat Neurosci. 2016; 19 (3): 404-413. doi: 10.1038/nn.4238 26906507
43. Paulus MP, Huys QJ, Maia TV. A roadmap for the development of applied computational psychiatry. Biol Psychiatry Cogn Neurosci Neuroimaging. 2016; 1 (5): 386-392. doi: 10.1016/j.bpsc.2016.05.001 28018986
44. Abi-Dargham A, Horga G. The search for imaging biomarkers in psychiatric disorders. Nat Med. 2016; 22 (11): 1248-1255. doi: 10.1038/nm.4190 27783066
45. Eack SM, Newhill CE, Keshavan MS. Cognitive enhancement therapy improves resting-state functional connectivity in early course schizophrenia. J Soc Social Work Res. 2016; 7 (2): 211-230. doi: 10.1086/686538 27713804
46. Eack SM, Hogarty GE, Cho RY, Neuroprotective effects of cognitive enhancement therapy against gray matter loss in early schizophrenia: results from a 2-year randomized controlled trial. Arch Gen Psychiatry. 2010; 67 (7): 674-682. doi: 10.1001/archgenpsychiatry.2010.63 20439824
47. Keshavan MS, Eack SM, Wojtalik JA, A broad cortical reserve accelerates response to cognitive enhancement therapy in early course schizophrenia. Schizophr Res. 2011; 130 (1-3): 123-129. doi: 10.1016/j.schres.2011.05.001 21645997
48. Burcusa SL, Iacono WG. Risk for recurrence in depression. Clin Psychol Rev. 2007; 27 (8): 959-985. doi: 10.1016/j.cpr.2007.02.005 17448579
49. Evert H, Harvey C, Trauer T, Herrman H. The relationship between social networks and occupational and self-care functioning in people with psychosis. Soc Psychiatry Psychiatr Epidemiol. 2003; 38 (4): 180-188. doi: 10.1007/s00127-003-0617-4 12664228
50. Lidwall U, Bill S, Palmer E, Olsson Bohlin C. Mental disorder sick leave in Sweden: a population study. Work. 2018; 59 (2): 259-272. doi: 10.3233/WOR-172672 29355123
51. Kessler RC, Stein MB, Petukhova MV,; Army STARRS Collaborators. Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). Mol Psychiatry. 2017; 22 (4): 544-551. doi: 10.1038/mp.2016.110 27431294
52. Cotter J, Granger K, Backx R, Hobbs M, Looi CY, Barnett JH. Social cognitive dysfunction as a clinical marker: a systematic review of meta-analyses across 30 clinical conditions. Neurosci Biobehav Rev. 2018; 84: 92-99. doi: 10.1016/j.neubiorev.2017.11.014 29175518
53. Glenthøj LB, Fagerlund B, Hjorthøj C, Social cognition in patients at ultra-high risk for psychosis: what is the relation to social skills and functioning? Schizophr Res Cogn. 2016; 5: 21-27. doi: 10.1016/j.scog.2016.06.004 28740813
54. Simons CJP, Bartels-Velthuis AA, Pijnenborg GHM; Genetic Risk and Outcome of Psychosis (GROUP) Investigators. Cognitive performance and long-term social functioning in psychotic disorder: a three-year follow-up study. PLoS One. 2016; 11 (4): e0151299. doi: 10.1371/journal.pone.0151299 27082629
55. Hua LL, Wilens TE, Martelon M, Wong P, Wozniak J, Biederman J. Psychosocial functioning, familiality, and psychiatric comorbidity in bipolar youth with and without psychotic features. J Clin Psychiatry. 2011; 72 (3): 397-405. doi: 10.4088/JCP.10m06025yel 21450156
56. Koutsouleris N, Davatzikos C, Bottlender R, Early recognition and disease prediction in the at-risk mental states for psychosis using neurocognitive pattern classification. Schizophr Bull. 2012; 38 (6): 1200-1215. doi: 10.1093/schbul/sbr037 21576280
57. Bedi G, Carrillo F, Cecchi GA, Automated analysis of free speech predicts psychosis onset in high-risk youths. NPJ Schizophr. 2015; 1: 15030. doi: 10.1038/npjschz.2015.30 27336038
58. Frank E, Maier D, Pajula J, Platform for systems medicine research and diagnostic applications in psychotic disorders-The METSY project. Eur Psychiatry. 2018; 50: 40-46. doi: 10.1016/j.eurpsy.2017.12.001 29361398
59. Palaniyappan L, Liddle PF. Does the salience network play a cardinal role in psychosis? an emerging hypothesis of insular dysfunction. J Psychiatry Neurosci. 2012; 37 (1): 17-27. doi: 10.1503/jpn.100176 21693094
60. Wang H, Zeng L-L, Chen Y, Yin H, Tan Q, Hu D. Evidence of a dissociation pattern in default mode subnetwork functional connectivity in schizophrenia. Sci Rep. 2015; 5: 14655. doi: 10.1038/srep14655 26419213
61. Fusar-Poli P, Radua J, McGuire P, Borgwardt S. Neuroanatomical maps of psychosis onset: voxel-wise meta-analysis of antipsychotic-naive VBM studies. Schizophr Bull. 2012; 38 (6): 1297-1307. doi: 10.1093/schbul/sbr134 22080494
62. Wood SJ, Yung AR, McGorry PD, Pantelis C. Neuroimaging and treatment evidence for clinical staging in psychotic disorders: from the at-risk mental state to chronic schizophrenia. Biol Psychiatry. 2011; 70 (7): 619-625. doi: 10.1016/j.biopsych.2011.05.034 21762875
63. Douaud G, Mackay C, Andersson J, Schizophrenia delays and alters maturation of the brain in adolescence. Brain. 2009; 132 (pt 9): 2437-2448. doi: 10.1093/brain/awp126 19477963
64. Falkai P, Rossner MJ, Schulze TG, Kraepelin revisited: schizophrenia from degeneration to failed regeneration. Mol Psychiatry. 2015; 20 (6): 671-676. doi: 10.1038/mp.2015.35 25824303
65. Bartholomeusz CF, Cropley VL, Wannan C, Di Biase M, McGorry PD, Pantelis C. Structural neuroimaging across early-stage psychosis: Aberrations in neurobiological trajectories and implications for the staging model. Aust N Z J Psychiatry. 2017; 51 (5): 455-476. doi: 10.1177/0004867416670522 27733710
66. Torres US, Duran FL, Schaufelberger MS, Patterns of regional gray matter loss at different stages of schizophrenia: a multisite, cross-sectional VBM study in first-episode and chronic illness. Neuroimage Clin. 2016; 12: 1-15. doi: 10.1016/j.nicl.2016.06.002 27354958
67. Palaniyappan L. Progressive cortical reorganisation: A framework for investigating structural changes in schizophrenia. Neurosci Biobehav Rev. 2017; 79: 1-13. doi: 10.1016/j.neubiorev.2017.04.028 28501553
68. Ryali S, Supekar K, Chen T, Temporal dynamics and developmental maturation of salience, default and central-executive network interactions revealed by variational Bayes hidden Markov modeling. PLoS Comput Biol. 2016; 12 (12): e1005138. doi: 10.1371/journal.pcbi.1005138 27959921
69. Sherman LE, Rudie JD, Pfeifer JH, Masten CL, McNealy K, Dapretto M. Development of the default mode and central executive networks across early adolescence: a longitudinal study. Dev Cogn Neurosci. 2014; 10: 148-159. doi: 10.1016/j.dcn.2014.08.002 25282602
70. Schmaal L, Veltman DJ, van Erp TG, Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder Working Group. Mol Psychiatry. 2016; 21 (6): 806-812. doi: 10.1038/mp.2015.69 26122586
71. Wang W, Zhao Y, Hu X, Conjoint and dissociated structural and functional abnormalities in first-episode drug-naive patients with major depressive disorder: a multimodal meta-analysis. Sci Rep. 2017; 7 (1): 10401. doi: 10.1038/s41598-017-08944-5 28871117
72. Schmaal L, Hibar DP, Sämann PG, Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group. Mol Psychiatry. 2017; 22 (6): 900-909. doi: 10.1038/mp.2016.60 27137745
73. Stratmann M, Konrad C, Kugel H, Insular and hippocampal gray matter volume reductions in patients with major depressive disorder. PLoS One. 2014; 9 (7): e102692. doi: 10.1371/journal.pone.0102692 25051163
74. Insel T, Cuthbert B, Garvey M, Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry. 2010; 167 (7): 748-751. doi: 10.1176/appi.ajp.2010.09091379 20595427
75. Drysdale AT, Grosenick L, Downar J, Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med. 2017; 23 (1): 28-38. doi: 10.1038/nm.4246 27918562
76. Dwyer DB, Cabral C, Kambeitz-Ilankovic L, Brain subtyping enhances the neuroanatomical discrimination of schizophrenia [published online February 26, 2018]. Schizophr Bull. doi: 10.1093/schbul/sby008 29529270