Predicting psychosis-onset through online assessment of speech organisation

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Psychosis affects approximately 0.7% of the UK population (NICE 2016), a mental health condition where an individual perceives and interprets the world so differently they have lost touch with reality and may experience symptoms such as hallucinations, delusions, and cognitive difficulties. This significantly impacts quality of life and everyday functioning with individuals being 6-7 times more likely to be unemployed than the general population (NICE 2015) and nearly a third being homeless at some point (Bebbington et al., 2005). Over half of individuals experiencing first-episode psychosis (FEP) will later relapse within three years (Alvarez-Jimenez et al., 2012). This is estimated to cost England alone £11.8 billion a year (using 2012 data) in direct costs such as NHS care and indirect costs such as unpaid care (Ride et al., 2020).Psychosis-onset typically occurs in early adulthood, however the longer the delay in detection of psychosis, and thus the delay beginning treatment, the more negative the long-term outcomes (Oliver et al., 2018). Therefore, there is increasing focus on detecting psychosis earlier, which has led to identifying clinical high-risk for psychosis groups (CHR-P), to recognise people potentially in the prodromal phase of psychosis (Fusar-Poli, 2017). This allows a key opportunity to prevent or delay the onset of psychosis. However, there is large heterogeneity in clinical outcomes amongst CHR-P groups, with around 20% of this group later developing a psychotic disorder and it is difficult to predict who within this group will transition (Fusar-Poli et al., 2012 and 2016). One way to improve this is to develop prognostic markers based upon mechanisms known to underlie psychosis, to help stratify these CHR-P groups to better direct early intervention and treatment allocation for those who will most benefit.Formal thought disorder (FTD), a cognitive symptom of psychosis where people have a disorganised way of thinking, has been shown to appear in an attenuated form in the prodromal phase (Morgan et al., 2021). Since disorganised thinking leads to abnormal language that can be analysed, speech could be a potential prognostic marker. Mota et al. (2012) have shown that speech can be quantified based upon graph theory, whereby each word is a node and their edges correspond to the semantic and grammatic connections. This graph structure can be analysed for example, by its "connectedness". Using this technique, Mota et al. were able to predict a schizophrenia diagnosis up to 6 months in advance in a small sample of FEP patients (2017). Building upon this, the supervisors of this project have shown that it is possible to differentiate between CHR-P, FEP and healthy controls when studying semantic networks (Nettekoven et al, 2023) and non-semantic networks of speech (Spencer et al., 2021), and can be used to predict clinical outcomes (Morgan et al., 2021).Importantly speech sampling is non-invasive and could be measured remotely using inexpensive devices such as mobile phones. This enables large datasets to be collected and given that most of the target population already owns mobile phones, this would help to reduce sampling bias.