Advancing the sensori-/psychomotor domain in schizophrenia spectrum disorders: from clinical observation to translational innovation - Hirjak, Dusan; Volkmer, Sebastian; Pokorny, Victor; Mittal, Vijay A. (Schizophrenia research, 2025)
Sensori-/psychomotor dysfunction has historically been an underrecognized domain in schizophrenia spectrum disorders (SSD), often overshadowed by a narrow focus on medication-induced side effects such as acute extrapyramidal motor symptoms, akathisia, dystonia, parkinsonism, and tardive dyskinesia. Two decades ago, research in this area was largely confined to these pharmacologically related phenomena, as well as to neurological soft signs (NSS) and catatonia. In the last ten years, however, the sensori-/psychomotor domain has garnered renewed interest as a core feature of SSD—relevant not only for symptom profiling but also for early detection, prognostic stratification, and individualized treatment planning across the lifespan. This narrative review synthesizes major advances from the past five years across Psychiatry, Neuroscience, Human Movement Science, and Affective Computing. It highlights a paradigm shift from traditional hand-based sensori-/psychomotor assessments toward novel, scalable approaches for investigating sensori-/psychomotor dysfunction. Emerging tools—such as actigraphy, 3D motion capture systems, standardized sensorimotor tasks, and multimodal neuroimaging—now allow for more objective, multimodal assessment of sensori-/psychomotor behavior, including gesture dynamics and patterns of physical inactivity. In parallel, computational innovations have enabled the large-scale analysis of sensori-/psychomotor abnormalities, including retrospective mining of unstructured clinical notes through natural language processing and machine learning techniques. Together, these developments underscore the shifting view of sensori-/psychomotor dysfunction as more than behavioral epiphenomena or drug effects. We argue that the integration of emerging technologies and refined methodologies into clinical workflows is essential for translating research findings into personalized, real-world care of mental disorders across the lifespan.