Neurological soft signs: Effects of trait schizotypy, psychological distress and auditory hallucination predisposition

Abstract

Schizotypy is regarded as a trait vulnerability for psychotic disorders, yet alone is insufficient for development of a diagnosable disorder. Additional symptoms and psychological distress are necessary for help seeking and transition from an at risk mental state to a clinical diagnosis. The present study investigated the interaction between trait schizotypy, state auditory verbal hallucination (AVH) predisposition, distress and handedness for the expression of neurological soft signs (NSS), a neurodevelopmental vulnerability factor for psychosis. Cluster analysis formed schizotypy groups statistically across the dimensions captured by the SPQ. It was hypothesized that schizotypy and AVH predisposition would interact, resulting in significantly greater NSS. Psychological distress and handedness were hypothesized to be significant covariates, accounting for some variance in the expression of NSS between the groups. A sample of University students (n = 327) completed the Schizotypal Personality Questionnaire, Launay-Slade Hallucination Scale, General Health Questionnaire and the Neurological Evaluation Scale (NES). Cluster Analysis revealed four schizotypy groups. Distress was not a significant covariate in any analysis. As expected, those with high overall schizotypy and high AVH predisposition expressed significantly greater Motor-Coordination NSS compared to those with high schizotypy and low AVH predisposition. Within the Mixed Interpersonal and Cognitive-Perceptual Schizotypy cluster, those with low AVH predisposition expressed significantly more Motor-Coordination NSS than those with high AVH predisposition. These findings suggest motor coordination NSS are detectable in schizotypy, and AVH predisposition appears to interact with these traits. This study highlights the importance of considering both trait and subclinical state risk factors when investigating risk for psychosis.

Postural Sway Abnormalities in Schizotypal Personality Disorder

“…This finding of impaired postural control in SPD is consistent with a large body of literature documenting motor abnormalities in both SPD 16,19,52 and psychometrically defined schizotypy [53][54][55][56] ), as well as in schizophrenia. 1,2,23,26,27 To the best of our knowledge, this is the first examination of postural sway in SPD or any other intermediate phenotype of schizophrenia.…”

The positive dimension of schizotypy is associated with a reduced attenuation and precision of self-generated touch

“…motor coordination, graphesthesia) (Buchanan and Heinrichs, 1989;Schröder et al, 1991), that are present with variable severity across the psychosis continuum (Chan et al, 2018;Gaha et al, 2015;Herold et al, 2021;Janssen et al, 2009;Mechri et al, 2010). These neurological soft signs have been repeatedly associated with the negative symptoms of schizophrenia and negative schizotypy in non-clinical individuals (Bombin et al, 2005;Chan et al, 2015;Cvetić et al, 2009;Hembram et al, 2014;Kaczorowski et al, 2009;Prikryl et al, 2012;Theleritis et al, 2012;Tosaro and Dazzan, 2005;Varambally et al, 2006;Whitty et al, 2006;Yazici et al, 2002), and less robustly with the positive and disorganized dimensions (Barkus et al, 2006;de Leede-Smith et al, 2017;Mechri et al, 2010;Ojagbemi et al, 2015). Instead, our data revealed a relationship of somatosensory attenuation and precision only with positive schizotypy, and not with the negative and the disorganized dimensions.…”

Exploring concomitant neuroimaging and genetic alterations in patients with and patients without auditory verbal hallucinations: A pilot study and mini review

“…The development of qualitative diagnostic and therapeutic efficacy prediction models in patients with AVHs who have various diagnoses may improve the accuracy and repeatability of prediction models, and may, ultimately, facilitate the delivery of early, potentially curative treatments. 7 , 52 – 60 …”

“…The development of qualitative diagnostic and therapeutic efficacy prediction models in patients with AVHs who have various diagnoses may improve the accuracy and repeatability of prediction models, and may, ultimately, facilitate the delivery of early, potentially curative treatments. 7,[52][53][54][55][56][57][58][59][60] Data related to genomic characteristics, brain characterizations (endophenotypes), clinical manifestations, and prognostic characteristics (phenotypes) may be submitted to machine learning algorithms to establish disease optimization classifiers that reveal characteristics that are specific to particular diagnoses or common across neurological conditions accompanied by AVHs. This approach may provide a basis for early AVH diagnosis and treatment models in different diagnostic contexts based on imaging and genetic features.…”