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TMS in Schizophrenia: Potential Mechanistic Insights via Resting-State Network Analyses

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Abstract

Purpose of review

This review uses a dysconnectivity-based lens for understanding schizophrenia pathophysiology. From this vantage recent studies on the clinical utility of transcranial magnetic stimulation in schizophrenia are consolidated, with particular attention to studies reporting resting-state network functional connectivity (FC) changes after treatment.

Recent findings

In schizophrenia, functional connectivity is predominantly decreased across brain regions and functional networks. Specific networks implicated in symptomatology include the default mode network and central executive network. TMS modalities which increase functional connectivity, particularly when targeting the dorsolateral PFC, have shown relative efficacy in treating negative symptoms of schizophrenia. However, post-treatment changes in specific network connectivity vary by study design and by degree of concordance with symptom improvement.

Summary

Generally, increased functional connectivity between brain networks seems to confer some benefit of TMS in schizophrenia, particularly for negative symptoms. Among studies conducted thus far, there has been substantial variation in both target selection and observed post-treatment connectivity changes. While there is convincing evidence for pathological DMN-CEN connectivity in the disease process of schizophrenia, as well as evidence that dlPFC-targeted TMS modulates the DMN-CEN in depression, restoration of this particular network aberrancy by TMS in schizophrenia has not been observed.

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Funding

Funding support was provided by the University of Maryland/Sheppard Pratt Residency Physician Scientist Training Program (PSTP).

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  1. Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, P.O. Box 21247, Baltimore, MD, USA

    Andrew van der Vaart

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  1. Andrew van der Vaart
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van der Vaart, A. TMS in Schizophrenia: Potential Mechanistic Insights via Resting-State Network Analyses. Curr Behav Neurosci Rep 10, 58–63 (2023). https://doi.org/10.1007/s40473-023-00260-9

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