Assessing psychosis risk using quantitative markers of disorganised speech

Abstract

Recent work has suggested that disorganised speech might be a powerful predictor of later psychotic illness in clinical high risk subjects. To that end, several automated measures to quantify disorganisation of transcribed speech have been proposed. However, it remains unclear which measures are most predictive of psychosis-onset, how different measures relate to each other and what the best strategies are to elicit disorganised speech from participants. Here, we assessed the ability of twelve automated Natural Language Processing markers to differentiate transcribed speech excerpts from subjects at clinical high risk for psychosis (N=25), first episode psychosis patients (N=16) and healthy control subjects (N=13; N=54 in total). In-line with previous work, several of these measures showed significant differences between groups, including semantic coherence and speech graph connectivity. We also proposed two additional measures of repetition and whether speech was on topic, the latter of which exhibited significant group differences and outperformed the prior, related measure of tangentiality. Most measures examined were only weakly related to each other, suggesting they provide complementary information and that combining different measures could provide additional power to predict the onset of psychotic illness. Finally, we compared the ability of transcribed speech generated using different tasks to differentiate the groups. Speech generated from picture descriptions of the Thematic Apperception Test and a story re-telling task outperformed free speech, suggesting that choice of speech generation method may be an important consideration. Overall, quantitative speech markers represent a promising direction for future diagnostic applications for psychosis risk.

Speech disturbances in schizophrenia: assessing cross-linguistic generalizability of NLP automated measures of coherence

“…However, this assumption has only inconsistently been supported by the literature, with highly varying statistical significance and size of the effects found across studies: while some studies found a strong correlation between semantic coherence and measures of formal thought disorder (Bilgrami et al, 2022;Elvevåg et al, 2007), most others reported uncertain results (Bedi et al, 2015;Haas et al, 2020;Just et al, 2020;Morgan et al, 2021;Pauselli et al, 2018;Sarzynska-Wawer et al, 2021;Tang et al, 2021). Our study emphasized the lack of a clear picture.…”

“…However, a critical obstacle to any concrete use of these findings is that it is not clear whether the findings would replicate and generalize to new samples and populations, an overarching problem for clinical and social sciences (Hitczenko et al, 2021;Parola et al, 2020;Rocca & Yarkoni, 2021;Rybner et al, 2021). Indeed, a closer look reveals clearly contradictory results: linguistic measures are inconsistently associated with symptoms, and findings vary across different rating scales and samples (Bedi et al, 2015;Corcoran et al, 2018;Haas et al, 2020;Morgan et al, 2021;Pauselli et al, 2018;Sarzynska-Wawer et al, 2021;Tang et al, 2021).…”

DASentimental: Detecting Depression, Anxiety, and Stress in Texts via Emotional Recall, Cognitive Networks, and Machine Learning

“…Recognition starts with diving deep into an individual's mindset and understanding their emotions. Psychological research [8,15,22,25,26] has found that people's mental states relate to how they communicate; written and spoken language can thus reveal psychological states. A person's emotional state can be anticipated through their communication [22,27]identifying a quantitative coexistence and correlation among emotional words used by an individual can unveil crucial insights into their emotional state [28,29].…”

Exploring the relationship between frequent cannabis use, vocal changes and language use

“…Speech analysis is an emerging field in mental health research that holds great potential for improving the diagnosis and treatment of various mental disorders Cummins et al, 2015;De Boer et al, 2021;Morgan et al, 2021;Teferra et al, 2022;Wanderley Espinola et al, 2022). By analyzing patterns in language use, such as the use of specific words or the rate of speech, researchers can gain insights into a patient's cognitive and emotional functioning, as well as neural development (Mota, Natália Bezerra, Weissheimer, Finger, Ribeiro, Malcorra, & Hübner, 2023).…”

“…Most common approaches include the measurement of speech connectivity, which is the way that speech flows and connects to form thoughts and helps evaluate both coherence and syntactic complexity (Mota, Natália B. et al, 2017;Mota, Natália Bezerra et al, 2023;Spencer et al, 2021a) as well as measuring markers of semantic coherence (Bedi et al, 2015;. Evaluating semantic coherence is based on Latent Semantic Analysis (LSA), an approach that captures the distance between words that have been represented in a vectorized space based on their co-occurence in a big text corpus while connectivity is measured applying graph theory on speech, representing all words as nodes and the connection between words as edges (see Methods for more detailed explanation) (Bedi et al, 2015;Morgan et al, 2021;Nettekoven et al, 2023;Spencer et al, 2021b). Former studies utilized these automated markers to identify psychotic disorders, relapse and vulnerability as well as to quantify changes in speechhowever, as did studies did not take cannabis use into account, the effect of cannabis use on these observed changes and classifications remained unexplored.…”