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Purpose: Communication atypicalities are considered promising markers of a broad range of clinical conditions. However, little is known about the mechanisms and confounders underlying them. Medications might have a crucial, relatively unknown role both as potential confounders and offering an insight on the mechanisms at work. The integration of regulatory documents with disproportionality analyses provides a more comprehensive picture to account for in future investigations of communication-related markers. The aim of this study was to identify a list of drugs potentially associated with communicative atypicalities within psychotic and affective disorders.
Method: We developed a query using the Medical Dictionary for Regulatory Activities to search for communicative atypicalities within the FDA Adverse Event Reporting System (updated June 2021). A Bonferroni-corrected disproportionality analysis (reporting odds ratio) was separately performed on spontaneous reports involving psychotic, affective, and non-neuropsychiatric disorders, to account for the confounding role of different underlying conditions. Drug-adverse event associations not already reported in the Side Effect Resource database of labeled adverse drug reactions (unexpected) were subjected to further robustness analyses to account for expected biases.
Results: A list of 291 expected and 91 unexpected potential confounding medications was identified, including drugs that may irritate (inhalants) or desiccate (anticholinergics) the larynx, impair speech motor control (antipsychotics), or induce nodules (acitretin) or necrosis (vascular endothelial growth factor receptor inhibitors) on vocal cords; sedatives and stimulants; neurotoxic agents (anti-infectives); and agents acting on neurotransmitter pathways (dopamine agonists).
Conclusions: We provide a list of medications to account for in future studies of communication-related markers in affective and psychotic disorders. The current test case illustrates rigorous procedures for digital phenotyping, and the methodological tools implemented for large-scale disproportionality analyses can be considered a road map for investigations of communication-related markers in other clinical populations.
Supplemental Material: https://doi.org/10.23641/asha.23721345.
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http://dx.doi.org/10.1044/2023_JSLHR-22-00739 | DOI Listing |
BJPsych Open
September 2025
Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Population Health Sciences, Bristol Medical School, Bristol, UK.
Background: Some psychotic experiences in the general population show associations with higher schizophrenia and other mental health-related polygenic risk scores (PRSs), but studies have not usually included interviewer-rated positive, negative and disorganised dimensions, which show distinct associations in clinical samples.
Aims: To investigate associations of these psychotic experience dimensions primarily with schizophrenia PRS and, secondarily, with other relevant PRSs.
Method: Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort participants were assessed for positive, negative and disorganised psychotic experience dimensions from interviews, and for self-rated negative symptoms, at 24 years of age.
Neurosci Biobehav Rev
September 2025
Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Ospedale San Raffaele, Milano, Italy; University Vita-Salute San Raffaele, Milano, Italy.
Machine learning (ML) could be useful in identifying reliable predictors of treatment response in affective and not affective psychoses, potentially helping to propose personalized interventions. In this systematic review and meta-analysis, we evaluated studies exploiting ML algorithms to predict the improvement of psychotic symptoms, cognition and quality of life in psychoses related to different treatments. We searched MEDLINE (PubMed), Web of Science, and PsycINFO databases updated until February 2024, identifying 64 articles published in English in peer-reviewed journals.
View Article and Find Full Text PDFEur Arch Psychiatry Clin Neurosci
September 2025
Department of Psychiatry, University of Pittsburgh, 121 Meyran Avenue, Pittsburgh, PA, 15213, USA.
Psychotic-like experiences (PLEs) -subclinical experiences or symptoms that resemble psychosis, such as hallucinations and delusional thoughts-often emerge during adolescence and are predictive of serious psychopathology. Understanding PLEs during adolescence is crucial due to co-occurring developmental changes in neural reward systems that heighten the risk for psychotic-related and affective psychopathology, especially in those with a family history of severe mental illness (SMI). We examined associations among PLEs, clinical symptoms, and neural reward function during this critical developmental period.
View Article and Find Full Text PDFObjective: This study examines the feasibility and differential inter-rater reliability of Operationalized Psychodynamic Diagnosis 3 (OPD-3) for patients experiencing psychosis.
Method: We conducted OPD interviews with n₁ = 40 patients with SCID-diagnosed nonpsychotic disorders and n₂ = 40 patients with non-affective or affective psychoses. Each patient's video-recorded interview was independently rated by two trained OPD-3 raters, selected from a rater pool of five.
Front Psychiatry
August 2025
Department of Psychiatry & Behavioral Sciences, Division of Child & Adolescent Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, United States.