Vocal expression in schizophrenia: Less than meets the ear.

Abstract

Abnormalities in nonverbal communication are a hallmark of schizophrenia. Results from studies using symptom rating scales suggest that these abnormalities are profound (i.e., 3–5 standard deviations) and occur across virtually every channel of vocal expression. Computerized acoustic analytic technologies, employed to overcome practical and psychometric limitations with symptom rating scales, have found much more benign and isolated abnormalities. In order to better understand vocal deficits in schizophrenia and to advance acoustic analytic technologies for clinical and research applications, we examined archived speech samples from five separate studies, each employing different speaking tasks (patient N = 309; control N = 117). We sought to: a) employ Principal Component Analysis (PCA) to identify independent vocal expression measures from a large set of variables, b) quantify how patients with schizophrenia are abnormal with respect to these variables, c) evaluate the impact of demographic and contextual factors (e.g., study site, speaking task), and d) examine the relationship between clinically–rated psychiatric symptoms and vocal variables. PCA identified seven independent markers of vocal expression. Most of these vocal variables varied considerably as a function of context and many were associated with demographic factors. After controlling for context and demographics, there were no meaningful differences in vocal expression between patients and controls. Within patients, vocal variables were associated with a range of psychiatric symptoms – though only pause length was significantly associated with clinically-rated negative symptoms. The discussion centers on explaining the apparent discordance between clinical and computerized speech measures.

Voice Patterns in Schizophrenia: A systematic Review and Bayesian Meta-Analysis

“…Individuals with schizophrenia display atypical voice patterns, qualitatively described in terms of poverty of speech, increased pauses, distinctive tone and intensity of voice (Alpert et al, 2000;Andreasen et al, 1985;Cohen et al, 2016Cohen et al, , 2012bGalynker et al, 2000;Hoekert et al, 2007;Trémeau et al, 2005). Voice atypicalities have been reported since the first definitions of the disorder (Bleuler, 1911;Kraepelin, 1919), are used in the clinical assessment process, and assume an even stronger relevance in the light of growing findings associating voice patterns to cognitive function, emotional states, and social engagement Cohen and Hong, 2011;Faurholt-Jepsen et al, 2016;Nevler et al, 2017;Pisanski et al, 2016;Trigeorgis et al, 2016;Tsanas et al, 2011;Wang et al, 2015;Williams et al, 2014;Yin et al, 2007).…”

“…This, in turn, may result in social withdrawal and the further aggravation of social cognitive impairments (Bambini et al, 2016;Bowie and Harvey, 2008;Del-Monte et al, 2013;Sparks et al, 2010;Tan et al, 2014;Thoma et al, 2009). Voice atypicalities may thus represent an important marker that parallels both clinical features and social cognitive functioning of individuals with schizophrenia over time (Cohen et al, 2016;Rapcan et al, 2010;Tahir et al, 2019).…”

Can RDoC Help Find Order in Thought Disorder?

“…[23][24][25] Symbolic expression using "nonverbal" media involves many of the same neural substrates as verbal language (eg, music, 26 gestures, 27 and math 28 ), a notion formalized in theories about the origins of thought disorder in schizophrenia. 29 Moreover, clinical measures of thought disorder are often associated with measures of bizarre behavior (ie, nonverbal expression), 30,31 and measures of cognitive disorganization have been associated with increased creativity within the general population. 32 In sum, the RDoC language construct can be helpful for understanding thought disorder as an extreme variant of a potentially broad range of linguistic functions, which may help provide insight into its maladaptive and adaptive nature.…”

The effects of oxytocin and galantamine on objectively-defined vocal and facial expression: Data from the CIDAR study

“…Unfortunately, data from the CIDAR trial failed to find significant improvements in negative symptom severity or cognitive functioning with either galantamine or oxytocin compared to placebo using standard clinical rating scales or neuropsychological test batteries (Buchanan et al, 2016). The present study reanalyzed these data using computerized measures of natural speech and facial expressions as the dependent variables; measures with potentially superior reliability, sensitivity and precision than clinical rating scales for measuring blunted affect and alogia (Cohen and Elvevag, 2014; Cohen et al, 2016; Cohen et al, 2013). …”

“…The Computerized assessment of Affect from Natural Speech protocol (Cohen et al, 2016) and FaceReader version 4.0, a commercially-available program developed by Noldus Information Technology (2010), were used to measure vocal and facial expressions respectively. Summary variables, selected based on recent studies (Cohen et al, 2016; Cohen et al, 2013), included: mean pause time (i.e., average voiceless epoch bounded by speech > 150ms in length), intonation (i.e., standard deviation of the fundamental frequency values computed within a voiced epoch [i.e., an “utterance”], then averaged across utterances), and emphasis (i.e., standard deviation of the volume computed within an utterance, then averaged across utterances), as were neutral , happy and negative (sum of sad, anger, scared) facial expressions . Extreme values were “winsorized” (i.e., replaced with values 3.5 SD from the overall means).…”