Multimodal workflows optimally predict response to repetitive transcranial magnetic stimulation in patients with schizophrenia: a multisite machine learning analysis
The response variability to repetitive transcranial magnetic stimulation (rTMS) challenges the effective use of this treatment option in patients with schizophrenia. This variability may be deciphered by leveraging predictive information in structural MRI, clinical, sociodemographic, and genetic dat...
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| Hauptverfasser: | , , , , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
25 April 2024
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| In: |
Translational Psychiatry
Year: 2024, Jahrgang: 14, Pages: 1-11 |
| ISSN: | 2158-3188 |
| DOI: | 10.1038/s41398-024-02903-1 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41398-024-02903-1 Verlag, kostenfrei, Volltext: http://www.nature.com/articles/s41398-024-02903-1 
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| Verfasserangaben: | Mark Sen Dong, Jaroslav Rokicki, Dominic Dwyer, Sergi Papiol, Fabian Streit, Marcella Rietschel, Thomas Wobrock, Bertram Müller-Myhsok, Peter Falkai, Lars Tjelta Westlye, Ole A. Andreassen, Lena Palaniyappan, Thomas Schneider-Axmann, Alkomiet Hasan, Emanuel Schwarz and Nikolaos Koutsouleris |
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| 245 | 1 | 0 | |a Multimodal workflows optimally predict response to repetitive transcranial magnetic stimulation in patients with schizophrenia |b a multisite machine learning analysis |c Mark Sen Dong, Jaroslav Rokicki, Dominic Dwyer, Sergi Papiol, Fabian Streit, Marcella Rietschel, Thomas Wobrock, Bertram Müller-Myhsok, Peter Falkai, Lars Tjelta Westlye, Ole A. Andreassen, Lena Palaniyappan, Thomas Schneider-Axmann, Alkomiet Hasan, Emanuel Schwarz and Nikolaos Koutsouleris |
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| 520 | |a The response variability to repetitive transcranial magnetic stimulation (rTMS) challenges the effective use of this treatment option in patients with schizophrenia. This variability may be deciphered by leveraging predictive information in structural MRI, clinical, sociodemographic, and genetic data using artificial intelligence. We developed and cross-validated rTMS response prediction models in patients with schizophrenia drawn from the multisite RESIS trial. The models incorporated pre-treatment sMRI, clinical, sociodemographic, and polygenic risk score (PRS) data. Patients were randomly assigned to receive active (N = 45) or sham (N = 47) rTMS treatment. The prediction target was individual response, defined as ≥20% reduction in pre-treatment negative symptom sum scores of the Positive and Negative Syndrome Scale. Our multimodal sequential prediction workflow achieved a balanced accuracy (BAC) of 94% (non-responders: 92%, responders: 95%) in the active-treated group and 50% in the sham-treated group. The clinical, clinical + PRS, and sMRI-based classifiers yielded BACs of 65%, 76%, and 80%, respectively. Apparent sadness, inability to feel, educational attainment PRS, and unemployment were most predictive of non-response in the clinical + PRS model, while grey matter density reductions in the default mode, limbic networks, and the cerebellum were most predictive in the sMRI model. Our sequential modelling approach provided superior predictive performance while minimising the diagnostic burden in the clinical setting. Predictive patterns suggest that rTMS responders may have higher levels of brain grey matter in the default mode and salience networks which increases their likelihood of profiting from plasticity-inducing brain stimulation methods, such as rTMS. The future clinical implementation of our models requires findings to be replicated at the international scale using stratified clinical trial designs. | ||
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