Publication

Neurobiological Divergence of the Positive and Negative Schizophrenia Subtypes Identified on a New Factor Structure of Psychopathology Using Non-negative Factorization: An International Machine Learning Study

Pharmacotherapy Monitoring, Chen, J., Patil, K. R., Weis, S., Sim, K., Nickl-Jockschat, T., Zhou, J., Aleman, A., Sommer, I. E., Liemburg, E. J., Hoffstaedter, F., Habel, U., Derntl, B., Liu, X., Fischer, J. M., Kogler, L., Regenbogen, C., Diwadkar, V. A., Stanley, J. A., Riedl, V., Jardri, R., Gruber, O., Sotiras, A., Davatzikos, C., Eickhoff, S. B. & Jorg, F., 1-Feb-2020, In : Biological Psychiatry. 87, 3, p. 282-293 12 p.

Research output: Contribution to journalArticleAcademicpeer-review

APA

Pharmacotherapy Monitoring, Chen, J., Patil, K. R., Weis, S., Sim, K., Nickl-Jockschat, T., Zhou, J., Aleman, A., Sommer, I. E., Liemburg, E. J., Hoffstaedter, F., Habel, U., Derntl, B., Liu, X., Fischer, J. M., Kogler, L., Regenbogen, C., Diwadkar, V. A., Stanley, J. A., ... Jorg, F. (2020). Neurobiological Divergence of the Positive and Negative Schizophrenia Subtypes Identified on a New Factor Structure of Psychopathology Using Non-negative Factorization: An International Machine Learning Study. Biological Psychiatry, 87(3), 282-293. https://doi.org/10.1016/j.biopsych.2019.08.031

Author

Pharmacotherapy Monitoring ; Chen, Ji ; Patil, Kaustubh R. ; Weis, Susanne ; Sim, Kang ; Nickl-Jockschat, Thomas ; Zhou, Juan ; Aleman, Andre ; Sommer, Iris E. ; Liemburg, Edith J. ; Hoffstaedter, Felix ; Habel, Ute ; Derntl, Birgit ; Liu, Xiaojin ; Fischer, Jona M. ; Kogler, Lydia ; Regenbogen, Christina ; Diwadkar, Vaibhav A. ; Stanley, Jeffrey A. ; Riedl, Valentin ; Jardri, Renaud ; Gruber, Oliver ; Sotiras, Aristeidis ; Davatzikos, Christos ; Eickhoff, Simon B. ; Jorg, Frederike. / Neurobiological Divergence of the Positive and Negative Schizophrenia Subtypes Identified on a New Factor Structure of Psychopathology Using Non-negative Factorization : An International Machine Learning Study. In: Biological Psychiatry. 2020 ; Vol. 87, No. 3. pp. 282-293.

Harvard

Pharmacotherapy Monitoring, Chen, J, Patil, KR, Weis, S, Sim, K, Nickl-Jockschat, T, Zhou, J, Aleman, A, Sommer, IE, Liemburg, EJ, Hoffstaedter, F, Habel, U, Derntl, B, Liu, X, Fischer, JM, Kogler, L, Regenbogen, C, Diwadkar, VA, Stanley, JA, Riedl, V, Jardri, R, Gruber, O, Sotiras, A, Davatzikos, C, Eickhoff, SB & Jorg, F 2020, 'Neurobiological Divergence of the Positive and Negative Schizophrenia Subtypes Identified on a New Factor Structure of Psychopathology Using Non-negative Factorization: An International Machine Learning Study', Biological Psychiatry, vol. 87, no. 3, pp. 282-293. https://doi.org/10.1016/j.biopsych.2019.08.031

Standard

Neurobiological Divergence of the Positive and Negative Schizophrenia Subtypes Identified on a New Factor Structure of Psychopathology Using Non-negative Factorization : An International Machine Learning Study. / Pharmacotherapy Monitoring; Chen, Ji; Patil, Kaustubh R.; Weis, Susanne; Sim, Kang; Nickl-Jockschat, Thomas; Zhou, Juan; Aleman, Andre; Sommer, Iris E.; Liemburg, Edith J.; Hoffstaedter, Felix; Habel, Ute; Derntl, Birgit; Liu, Xiaojin; Fischer, Jona M.; Kogler, Lydia; Regenbogen, Christina; Diwadkar, Vaibhav A.; Stanley, Jeffrey A.; Riedl, Valentin; Jardri, Renaud; Gruber, Oliver; Sotiras, Aristeidis; Davatzikos, Christos; Eickhoff, Simon B.; Jorg, Frederike.

In: Biological Psychiatry, Vol. 87, No. 3, 01.02.2020, p. 282-293.

Research output: Contribution to journalArticleAcademicpeer-review

Vancouver

Pharmacotherapy Monitoring, Chen J, Patil KR, Weis S, Sim K, Nickl-Jockschat T et al. Neurobiological Divergence of the Positive and Negative Schizophrenia Subtypes Identified on a New Factor Structure of Psychopathology Using Non-negative Factorization: An International Machine Learning Study. Biological Psychiatry. 2020 Feb 1;87(3):282-293. https://doi.org/10.1016/j.biopsych.2019.08.031


BibTeX

@article{90d104a86cb94cc898af942f8f5e0c59,
title = "Neurobiological Divergence of the Positive and Negative Schizophrenia Subtypes Identified on a New Factor Structure of Psychopathology Using Non-negative Factorization: An International Machine Learning Study",
abstract = "BACKGROUND: Disentangling psychopathological heterogeneity in schizophrenia is challenging, and previous results remain inconclusive. We employed advanced machine learning to identify a stable and generalizable factorization of the Positive and Negative Syndrome Scale and used it to identify psychopathological subtypes as well as their neurobiological differentiations.METHODS: Positive and Negative Syndrome Scale data from the Pharmacotherapy Monitoring and Outcome Survey cohort (1545 patients; 586 followed up after 1.35 +/- 0.70 years) were used for learning the factor structure by an orthonormal projective non-negative factorization. An international sample, pooled from 9 medical centers across Europe, the United States, and Asia (490 patients), was used for validation. Patients were clustered into psychopathological subtypes based on the identified factor structure, and the neurobiological divergence between the subtypes was assessed by classification analysis on functional magnetic resonance imaging connectivity patterns.RESULTS: A 4-factor structure representing negative, positive, affective, and cognitive symptoms was identified as the most stable and generalizable representation of psychopathology. It showed higher internal consistency than the original Positive and Negative Syndrome Scale subscales and previously proposed factor models. Based on this representation, the positive-negative dichotomy was confirmed as the (only) robust psychopathological subtypes, and these subtypes were longitudinally stable in about 80% of the repeatedly assessed patients. Finally, the individual subtype could be predicted with good accuracy from functional connectivity profiles of the ventromedial frontal cortex, temporoparietal junction, and precuneus.CONCLUSIONS: Machine learning applied to multisite data with cross-validation yielded a factorization generalizable across populations and medical systems. Together with subtyping and the demonstrated ability to predict subtype membership from neuroimaging data, this work further disentangles the heterogeneity in schizophrenia.",
keywords = "Brain imaging, Machine learning, Multivariate classification, Non-negative factorization, Schizophrenia, Subtyping, SYNDROME SCALE PANSS, 5-FACTOR MODEL, PATTERNS, SYMPTOMS, DISORDERS, STABILITY, CLASSIFICATION, MEDICATION, SUBGROUPS, MULTISITE",
author = "{Pharmacotherapy Monitoring} and Ji Chen and Patil, {Kaustubh R.} and Susanne Weis and Kang Sim and Thomas Nickl-Jockschat and Juan Zhou and Andre Aleman and Sommer, {Iris E.} and Liemburg, {Edith J.} and Felix Hoffstaedter and Ute Habel and Birgit Derntl and Xiaojin Liu and Fischer, {Jona M.} and Lydia Kogler and Christina Regenbogen and Diwadkar, {Vaibhav A.} and Stanley, {Jeffrey A.} and Valentin Riedl and Renaud Jardri and Oliver Gruber and Aristeidis Sotiras and Christos Davatzikos and Eickhoff, {Simon B.} and Bartels-Velthuis, {Agna A.} and Richard Bruggeman and Stynke Castelein and Frederike Jorg and Pijnenborg, {Gerdina H. M.} and Henderikus Knegtering and Ellen Visser",
year = "2020",
month = feb,
day = "1",
doi = "10.1016/j.biopsych.2019.08.031",
language = "English",
volume = "87",
pages = "282--293",
journal = "Biological Psychiatry",
issn = "1873-2402",
publisher = "ELSEVIER SCIENCE INC",
number = "3",

}

RIS

TY - JOUR

T1 - Neurobiological Divergence of the Positive and Negative Schizophrenia Subtypes Identified on a New Factor Structure of Psychopathology Using Non-negative Factorization

T2 - An International Machine Learning Study

AU - Pharmacotherapy Monitoring

AU - Chen, Ji

AU - Patil, Kaustubh R.

AU - Weis, Susanne

AU - Sim, Kang

AU - Nickl-Jockschat, Thomas

AU - Zhou, Juan

AU - Aleman, Andre

AU - Sommer, Iris E.

AU - Liemburg, Edith J.

AU - Hoffstaedter, Felix

AU - Habel, Ute

AU - Derntl, Birgit

AU - Liu, Xiaojin

AU - Fischer, Jona M.

AU - Kogler, Lydia

AU - Regenbogen, Christina

AU - Diwadkar, Vaibhav A.

AU - Stanley, Jeffrey A.

AU - Riedl, Valentin

AU - Jardri, Renaud

AU - Gruber, Oliver

AU - Sotiras, Aristeidis

AU - Davatzikos, Christos

AU - Eickhoff, Simon B.

AU - Bartels-Velthuis, Agna A.

AU - Bruggeman, Richard

AU - Castelein, Stynke

AU - Jorg, Frederike

AU - Pijnenborg, Gerdina H. M.

AU - Knegtering, Henderikus

AU - Visser, Ellen

PY - 2020/2/1

Y1 - 2020/2/1

N2 - BACKGROUND: Disentangling psychopathological heterogeneity in schizophrenia is challenging, and previous results remain inconclusive. We employed advanced machine learning to identify a stable and generalizable factorization of the Positive and Negative Syndrome Scale and used it to identify psychopathological subtypes as well as their neurobiological differentiations.METHODS: Positive and Negative Syndrome Scale data from the Pharmacotherapy Monitoring and Outcome Survey cohort (1545 patients; 586 followed up after 1.35 +/- 0.70 years) were used for learning the factor structure by an orthonormal projective non-negative factorization. An international sample, pooled from 9 medical centers across Europe, the United States, and Asia (490 patients), was used for validation. Patients were clustered into psychopathological subtypes based on the identified factor structure, and the neurobiological divergence between the subtypes was assessed by classification analysis on functional magnetic resonance imaging connectivity patterns.RESULTS: A 4-factor structure representing negative, positive, affective, and cognitive symptoms was identified as the most stable and generalizable representation of psychopathology. It showed higher internal consistency than the original Positive and Negative Syndrome Scale subscales and previously proposed factor models. Based on this representation, the positive-negative dichotomy was confirmed as the (only) robust psychopathological subtypes, and these subtypes were longitudinally stable in about 80% of the repeatedly assessed patients. Finally, the individual subtype could be predicted with good accuracy from functional connectivity profiles of the ventromedial frontal cortex, temporoparietal junction, and precuneus.CONCLUSIONS: Machine learning applied to multisite data with cross-validation yielded a factorization generalizable across populations and medical systems. Together with subtyping and the demonstrated ability to predict subtype membership from neuroimaging data, this work further disentangles the heterogeneity in schizophrenia.

AB - BACKGROUND: Disentangling psychopathological heterogeneity in schizophrenia is challenging, and previous results remain inconclusive. We employed advanced machine learning to identify a stable and generalizable factorization of the Positive and Negative Syndrome Scale and used it to identify psychopathological subtypes as well as their neurobiological differentiations.METHODS: Positive and Negative Syndrome Scale data from the Pharmacotherapy Monitoring and Outcome Survey cohort (1545 patients; 586 followed up after 1.35 +/- 0.70 years) were used for learning the factor structure by an orthonormal projective non-negative factorization. An international sample, pooled from 9 medical centers across Europe, the United States, and Asia (490 patients), was used for validation. Patients were clustered into psychopathological subtypes based on the identified factor structure, and the neurobiological divergence between the subtypes was assessed by classification analysis on functional magnetic resonance imaging connectivity patterns.RESULTS: A 4-factor structure representing negative, positive, affective, and cognitive symptoms was identified as the most stable and generalizable representation of psychopathology. It showed higher internal consistency than the original Positive and Negative Syndrome Scale subscales and previously proposed factor models. Based on this representation, the positive-negative dichotomy was confirmed as the (only) robust psychopathological subtypes, and these subtypes were longitudinally stable in about 80% of the repeatedly assessed patients. Finally, the individual subtype could be predicted with good accuracy from functional connectivity profiles of the ventromedial frontal cortex, temporoparietal junction, and precuneus.CONCLUSIONS: Machine learning applied to multisite data with cross-validation yielded a factorization generalizable across populations and medical systems. Together with subtyping and the demonstrated ability to predict subtype membership from neuroimaging data, this work further disentangles the heterogeneity in schizophrenia.

KW - Brain imaging

KW - Machine learning

KW - Multivariate classification

KW - Non-negative factorization

KW - Schizophrenia

KW - Subtyping

KW - SYNDROME SCALE PANSS

KW - 5-FACTOR MODEL

KW - PATTERNS

KW - SYMPTOMS

KW - DISORDERS

KW - STABILITY

KW - CLASSIFICATION

KW - MEDICATION

KW - SUBGROUPS

KW - MULTISITE

U2 - 10.1016/j.biopsych.2019.08.031

DO - 10.1016/j.biopsych.2019.08.031

M3 - Article

VL - 87

SP - 282

EP - 293

JO - Biological Psychiatry

JF - Biological Psychiatry

SN - 1873-2402

IS - 3

ER -

ID: 107833320