The SPEAK study rationale and design: A linguistic corpus-based approach to understanding thought disorder.

AIM: Psychotic symptoms are typically measured using clinical ratings, but more objective and sensitive metrics are needed. Hence, we will assess thought disorder using the Research Domain Criteria (RDoC) heuristic for language production, and its recommended paradigm of "linguistic corpus-based analyses of language output". Positive thought disorder (e.g., tangentiality and derailment) can be assessed using word-embedding approaches that assess semantic coherence, whereas negative thought disorder (e.g., concreteness, poverty of speech) can be assessed using part-of-speech (POS) tagging to assess syntactic complexity. We aim to establish convergent validity of automated linguistic metrics with clinical ratings, assess normative demographic variance, determine cognitive and functional correlates, and replicate their predictive power for psychosis transition among at-risk youths. METHODS: This study will assess language production in 450 English-speaking individuals in Australia and Canada, who have recent onset psychosis, are at clinical high risk (CHR) for psychosis, or who are healthy volunteers, all well-characterized for cognition, function and symptoms. Speech will be elicited using open-ended interviews. Audio files will be transcribed and preprocessed for automated natural language processing (NLP) analyses of coherence and complexity. Data analyses include canonical correlation, multivariate linear regression with regularization, and machine-learning classification of group status and psychosis outcome. CONCLUSIONS: This prospective study aims to characterize language disturbance across stages of psychosis using computational approaches, including psychometric properties, normative variance and clinical correlates, important for biomarker development. SPEAK will create a large archive of language data available to other investigators, a rich resource for the field.

The neurophenomenology of early psychosis: An integrative empirical study.

BACKGROUND: The integration of various domains or levels of analysis (clinical, neurobiological, genetic, etc.) has been a challenge in schizophrenia research. A promising approach is to use the core phenomenological features of the disorder as an organising principle for other levels of analysis. Minimal self-disturbance (fragility in implicit first-person perspective, presence and agency) is emerging as a strong candidate to play this role. This approach was adopted in a previously described theoretical neurophenomenological model that proposed that source monitoring deficits and aberrant salience may be neurocognitive/neurobiological processes that correlate with minimal self-disturbance on the phenomenological level, together playing an aetiological role in the onset of schizophrenia spectrum disorders. The current paper presents full cross-sectional data from the first empirical test of this model. METHODS: Fifty ultra-high risk for psychosis patients, 39 first episode psychosis patients and 34 healthy controls were assessed with a variety of clinical measures, including the Examination of Anomalous Self-Experience (EASE), and neurocognitive and neurophysiological (EEG) measures of source monitoring deficits and aberrant salience. RESULTS: Linear regression indicated that source monitoring (composite score across neurocognitive and neurophysiological measures), with study group as an interaction term, explained 39.8% of the variance in EASE scores (R2 = 0.41, F(3,85) = 14.78, p < 0.001), whereas aberrant salience (composite score) explained only 6% of the variance in EASE scores (R2 = 0.06, F(3,85) = 1.44, p = 0.93). Aberrant salience measures were more strongly related to general psychopathology measures, particularly to positive psychotic symptoms, than to EASE scores. DISCUSSION: A neurophenomenological model of minimal self-disturbance in schizophrenia spectrum disorders may need to be expanded from source monitoring deficits to encompass other relevant constructs such as temporal processing, intermodal/multisensory integration, and hierarchical predictive processing. The cross-sectional data reported here will be expanded with longitudinal analysis in subsequent reports. These data and other related recent research show an emerging picture of neuro-features of core phenomenological aspects of schizophrenia spectrum disorders beyond surface-level psychotic symptoms.

Dynamic prediction of transition to psychosis using joint modelling.

Considerable research has been conducted seeking risk factors and constructing prediction models for transition to psychosis in individuals at ultra-high risk (UHR). Nearly all such research has only employed baseline predictors, i.e. data collected at the baseline time point, even though longitudinal data on relevant measures such as psychopathology have often been collected at various time points. Dynamic prediction, which is the updating of prediction at a post-baseline assessment using baseline and longitudinal data accumulated up to that assessment, has not been utilized in the UHR context. This study explored the use of dynamic prediction and determined if it could enhance the prediction of frank psychosis onset in UHR individuals. An emerging statistical methodology called joint modelling was used to implement the dynamic prediction. Data from the NEURAPRO study (n = 304 UHR individuals), an intervention study with transition to psychosis study as the primary outcome, were used to investigate dynamic predictors. Compared with the conventional approach of using only baseline predictors, dynamic prediction using joint modelling showed significantly better sensitivity, specificity and likelihood ratios. As dynamic prediction can provide an up-to-date prediction for each individual at each new assessment post entry, it can be a useful tool to help clinicians adjust their prognostic judgements based on the unfolding clinical symptomatology of the patients. This study has shown that a dynamic approach to psychosis prediction using joint modelling has the potential to aid clinicians in making decisions about the provision of timely and personalized treatment to patients concerned.