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
Creators
- Chen, Ji1
- Patil, Kaustubh1
- Weis, Susanne1
- Sim, Kang2
- Nickl-Jockschat, Thomas3
- Zhou, Juan4
- Aleman, André5
- Sommer, Iris5
- Limburg, Edith6
- Hoffstaedter, Felix1
- Habel, Ute7
- Dirndl, Birgit8
- Liu, Xiaojin1
- Fischer, Jona1
- Kogler, Lydia8
- Regenbogen, Christina7
- Diwadkar, Vaibhav9
- Stanley, Jeffrey9
- Riedl, Valentin10
- Jardri, Renaud11
- Gruber, Oliver12
- Sotiras, Aristeidis13
- Davatzikos, Christos14
- Eickhoff, Simon1
- 1. Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- 2. Department of General Psychiatry, Institute of Mental Health, Singapore
- 3. Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, Iowa City, Iowa
- 4. Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke–National
- 5. Department of Neuroscience, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- 6. Rob Giel Research Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- 7. Department of Psychiatry, Psychotherapy and Psychosomatics, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University, Aachen, Germany
- 8. Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
- 9. Department of Psychiatry and Behavioral Neuroscience, Wayne State University, Detroit, Michigan
- 10. Department of Neuroradiology, Rechts der Isar Hospital, Technical University of Munich, Munich, Germany
- 11. University of Lille, National Centre for Scientific Research, UMR 9193, SCALab and CHU Lille, Fontan Hospital, CURE platform, Lille, France
- 12. Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany
- 13. Department of Radiology and Institute for Informatics, School of Medicine, Washington University in St. Louis, St. Louis, Missouri
- 14. Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
Description
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.
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