Towards a rational ECT treatment approach for severe and treatment resistant schizophrenia using neuroimaging biomarkers: A focused review.
Authors
Affiliations (3)
Affiliations (3)
- University of Cincinnati College of Medicine, 3230 Eden Avenue, PO Box 670555, Cincinnati, OH 45267-0555, USA. Electronic address: [email protected].
- Department of Psychiatry, University of Cincinnati College of Medicine, 260 Stetson Street, Cincinnati, OH 45219, USA. Electronic address: [email protected].
- Department of Psychiatry, University of Cincinnati College of Medicine, 260 Stetson Street, Cincinnati, OH 45219, USA. Electronic address: [email protected].
Abstract
Treatment resistant schizophrenia (TRS) is a major challenge in psychiatry, and its management remains an unmet need. Given the relatively high prevalence of resistance to first line antipsychotics (35%) and to clozapine (55%) as a last resort agent, electroconvulsive therapy (ECT) represents an important alternative modality for management of psychosis in TRS and is usually used only after non-response to clozapine, despite evidence suggesting earlier intervention with ECT may be more beneficial in TRS. Here, we discuss recent developments in the use of machine learning models and radiomics to employ neuroimaging markers for predicting ECT treatment response in patients with TRS. The orbitofrontal gyrus, temporal lobe, and limbic structures are regions implicated in the neuropathology of schizophrenia and areas of interest that overlap between these machine learning models. The successful development of these experimental models represents a significant step towards the development of clinical tools that may one day guide treatment decisions for patients identified to have TRS, matching them to effective treatments such as ECT. These experimental models may assist in the development of more personalized treatment protocols, which in turn may decrease duration of untreated psychosis and result in better outcomes for patients with TRS.