After more than a century of neuroscience research, reproducible, clinically relevant biomarkers for schizophrenia have not yet been established . While early clinical diagnosis or relapse of a schizophrenia-spectrum disorder can be rather straightforward if there is a good working alliance between patient and psychiatrist, lack in trust, little disease insight and failing motivation may result in insufficient anamnestic information. In these situations, an objective quantitative biomarker to aid the diagnostic or prognostic process would be most welcome. However, blood-based and neuroimaging biomarkers for schizophrenia fail to reach clinically applicable levels [2–4], with diagnostic accuracies varying between 60 and 90%. A rich source of information that has so far rarely been used, is spoken language. Recent advances in the field of computational linguistics afford the clinician to turn to language output as a novel biomarker that is low-cost, time efficient and noninvasive in nature . Language as a biomarker has important advantages over traditional biomarkers such as blood markers or imaging, because it can be reproducibly quantified without special training.
It has long been observed that schizophrenia is characterized by disturbed language, with Kraepelin describing a subgroup of patients with ‘schizophasia’ , and Bleuler who stressed the importance of aberrant language as a feature of schizophrenia . Pioneers in this line of research applied manual linguistic analyses to spoken language to evaluate its use in the diagnostic or prognostic process in schizophrenia-spectrum disorders [8–10].
Here, we reviewed the use of computational language analysis in schizophrenia-spectrum disorders with an emphasis on how recent translational research contributes to the development of diagnostic and prognostic tools. Much of the recent literature relates to advances in methodological and analytic tools which may facilitate diagnosis and prognosis of schizophrenia-spectrum disorders.
LANGUAGE DISTURBANCES IN SCHIZOPHRENIA
Impaired verbal communication is one of the key diagnostic features of schizophrenia-spectrum disorders. For reviews on this topic refer to [11–16]. Overall, patients with schizophrenia display a broad range of semantic (i.e. meaning in language) processing disturbances; including difficulties with lexical selection and retrieval , disturbances in priming  and reduced proactive inhibition [12,19]. On a discourse level, they show difficulties with coherently generating a narrative, which is thought to reflect an underlying disturbance in taking viewpoints or perspectives [20▪]. Other related disturbances in schizophrenic language include: neologisms, word approximation , disturbances in cohesion , vague references, missing information and confused references [15,22]. At a syntactic (i.e. grammatical) level, patients with a schizophrenia-spectrum disorder produce sentences with reduced syntactic complexity , less dependents and embedded clauses [24▪], and use fewer connective markers . Furthermore, syntactic priming appears to be reduced . Spontaneous abnormal morphology (i.e. using abnormal word forms) in schizophrenia is quite rare . In a test setting, however, patients make more morphological errors than controls . Schizophrenic speech usually has normal segmental phonology (i.e. the articulation of segments such as syllables), although compared with normal speech it contains more hesitations and pauses and longer pauses [28,29], and the intonation is flat (monotonous) .
It has been suggested that language disturbances in schizophrenia arise from abnormal semantic and phonological processing [30–33]. Indeed, neuroimaging data implicate altered frontotemporal semantic and phonological networks in schizophrenia. These include abnormalities in the structure of Broca's, Wernicke's and other frontotemporal regions [34,35], abnormal white matter language tracts [36–40] and altered functional MRI activation patterns in a variety of language tasks [41–44]. White matter language tract alterations were found in individuals at clinical high-risk (CHR) for psychosis [45,46], suggesting that these abnormalities precede schizophrenia onset. Indeed, retrospective studies suggest childhood language delays in people who later developed schizophrenia [47,48]. Previous reports have indicated that genetic alternations underlie the neurodevelopment of language abnormalities in schizophrenia [49,50]. The first identified gene involved in language was the FOXP2 gene . Preliminary association studies on FOXP2 polymorphisms and schizophrenia have delivered inconsistent results [52–54], although epigenetic data do suggest that FOXP2 may be involved in language disorders in schizophrenia . Furthermore, variations in another gene, dysbindin 1 (DTNBP1) have been associated with neural correlates of language production . However, further research is needed to confirm these preliminary results.
Summarizing, biological correlates of language disturbances in schizophrenia have been found in both neuroimaging and genetic studies. Previous research into aberrant language in schizophrenia-spectrum disorders has investigated difficulties arising at the semantic, syntactic and phonological levels of language production. Correspondingly, computational language analyses have focused on these aspects of language output (i.e. semantics, syntax and phonology).
COMPUTATIONAL LANGUAGE ANALYSIS IN SCHIZOPHRENIA
Content analyses: meaning, structure and coherence
An often used method to examine meaning and coherence in language is that of semantic space models. Semantic space models, of which latent semantic analysis (LSA)  is the most commonly used tool, aim to capture word meaning by representing words as so called ‘vectors’ in a ‘semantic space’. These vectors contain word features (i.e. aspects of word meaning); ‘furry’, ‘pet’ and ‘purring’ might be features attempting to grasp the meaning of ‘cat’. The distance between words in a semantic space indicates word interrelatedness or coherence; the word ‘furry’ will be more closely related to ‘pet’ than to ‘banana’, by virtue of what concepts these words are taken to represent. A sentence with low internal coherence will consist of words reflecting relatively more separated concepts. So-called distributed models like Word2vec aim at capturing both semantic as well as syntactic information [58,59].
The first to introduce semantic space models in schizophrenia were Elvevåg et al., who used LSA to show that schizophrenia patients could successfully be distinguished from healthy controls based solely on their spoken language output (achieving correct classification of patients and controls with an accuracy of 82.4%). Furthermore, this study showed that patients with formal thought disorder (FTD) could be distinguished from patients with low FTD scores (with an accuracy of 87.5%). LSA thus appears to be an accurate tool for detecting FTD. Significantly, clinical raters achieved slightly lower classification scores (84%) than the LSA models. This research was later expanded on by classifying patients with schizophrenia and their healthy family members . Using cross-validation, 85.7% of patients with schizophrenia could be correctly distinguished from their family members, indicating that LSA is sensitive to subtle phenomena, as patients are taken to resemble family members more than nonfamily controls.
In their seminal study, Bedi et al. used LSA and two measures of language complexity [maximum phrase length and the use of determiners (e.g. that)] on spoken language samples, to predict later psychosis onset in youths at CHR for psychosis. Combined, these language measures predicted psychosis development with 100% accuracy, outperforming clinical ratings (yielding an accuracy of 79%). However, in their sample of 34 CHR youths, only five transitioned to psychosis. This model was adapted and validated in a larger sample, and across cohorts in a larger sample [63▪▪]. Using decreased semantic coherence, greater variance in coherence and reduced use of possessive pronouns; 83% accuracy was achieved within the main cohort (79% across cohorts).
Using a pretrained set of vectors (fastText , Bar et al.[65▪]) examined patients with schizophrenia and controls with a special emphasis on their use of adjectives and adverbs. Their results show that patients with schizophrenia use adjectives and adverbs that are less common (i.e. lower frequency words), which can be used to distinguish them from healthy controls with machine learning models (accuracies depending on the model ranging from 70.4 to 81.5%).
In a recent meta-analysis of the diagnostic and prognostic value of semantic space models , a large effect size was found for diagnosing schizophrenia-spectrum disorders using semantic space (Hedges’ g = 0.96, P = 0.003). Semantic space models perform better on (semi) spontaneous language or sentences, than they do on lists of single words (e.g. words produced during a verbal fluency task). Pooling all studies in a meta-analysis of diagnostic test accuracy in schizophrenia-spectrum patients, an overall sensitivity of 71% and specificity of 91% was found.
Another influential approach to model coherence in language is the use of speech graphs [67–69]. Using graph-based tools to visualize connectedness in language, patients with schizophrenia could be distinguished from manic patients with a sensitivity and specificity of 94% .
Posts on social media have been analysed to examine written language in schizophrenia-spectrum disorders in several studies. Using content on the social media platform Reddit, conversion to psychosis was shown to be signalled by low semantic density, a measure developed to quantify sentence richness (calculated using Word2vec). Combined with writing about voices and sounds, these variables predicted conversion to psychosis with 93% accuracy [70▪].
In a similar study, Twitter content of self-proclaimed schizophrenia patients was analysed using the semantic space model Latent Dirichlet Allocation , in addition to part-of-speech, pragmatic analyses and syntactic dependency measures . Combined, these measures were used to classify schizophrenia patients and matched controls using machine learning (support vector machine), which resulted in an area under the curve of 82.6; indicating 83% of cases could be successfully distinguished from controls.
Further, Facebook content and behaviour analysis of patients with recent onset psychosis was used to predict relapse hospitalization [73▪]. The increased use of first and second-person pronouns, swear words and words related to anger and death, as well as decreased use of words related to work, friends and health, were predictive of relapse. Combined with other behaviour on Facebook, relapse could be predicted with 71% specificity, however, sensitivity was low (38%).
Nonverbal and phonetic analyses
Computerized analyses of phonetic features (i.e. speech sounds) have also been used to objectively evaluate (especially negative) symptoms in schizophrenia-spectrum disorders. For instance, schizophrenia patients with clinically rated aprosody were shown to differ from controls in pitch variation . Nonverbal language measures (e.g. turn duration, percentage of time speaking) were used to classify patients with schizophrenia and healthy controls, with an accuracy of 81.3% [75▪▪]. A similar study  measured prosodic and phonetic cues (prosodic peaks, syllabic dynamics) while reading the first paragraph of ‘Don Quixote’ to classify patients with schizophrenia and controls, reaching a sensitivity of 95.6% and a specificity of 91.4%, with an overall accuracy of 93.8%.
FOOD FOR THOUGHT
Language versus speech
Two important and notably distinct concepts in this line of research are ‘language’ and ‘speech’. Language is the term used for the mental system underlying verbal behaviour, which includes meaning, grammar and form. Speech is the term used for the spoken output or the medium of the language, the way it is produced by the speech organs. Language can of course also be produced in writing or in gestures (sign language), which still requires similar cognitive processes to formulate sentences, without the use of the vocal tract (i.e. without articulation). Although communication difficulties in schizophrenia are currently described as ‘disorganized speech’, the literature discussed in this review clearly demonstrates that patients with schizophrenia display a wide variety of language disorders including broad disturbances in semantics, pragmatics and grammatical structures [12,15]. ‘Disorganized speech’  would therefore, better be described as ‘disturbed language’, which may include, but is not limited to, speech.
The term biomarker is classically used for analytes of a human biological system (e.g. plasma, urine, cerebrospinal fluid) or for biological properties (i.e. mass concentration). However, the Biomarkers Definitions Working Group and other initiatives have advocated a broader, less ambiguous, definition of biomarkers, namely: ‘a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes or pharmacological response’ [78,79]. Language output fully adheres to this definition and can thus serve as a true biomarker for schizophrenia-spectrum disorders.
Current state of research
Of note, in most classification models discussed in this review, the final model included one or several variables which are nonspecific to language. Examples of such general features are task duration (reading 400 words aloud)  and response time to a question [75▪▪]. These variables are most likely based on general cognitive deficits such as reductions in attention, working memory or general fatigue, which are common in schizophrenia . The decision to add less specific measures to a model is presumably motivated by the aspiration of models with high diagnostic or prognostic accuracy and the pursuit of developing clinically valuable tools. However, whereas general cognitive measures may have high discriminatory power, employing them in an early stage forecloses improvement of our knowledge of language-related disturbances in schizophrenia. Further, including nonspecific measures in classification models reduces their power to detect early or subtle symptoms in spoken language that are specific to schizophrenia and may be used for differential diagnosis. While we endorse the ultimate goal of developing highly accurate diagnostic and prognostic tools, the aim to assess the value of purely linguistic measures should not be neglected. To this end, results of models with only linguistic features should be reported as well, even if they are less accurate than models that in addition include nonspecific factors.
A related point of discussion is that in extensive machine learning and deep learning models, features become abstract and an abounding number of features is fed to the model (e.g. 40 526 speech features were used in a model to detect post-traumatic stress disorder ), which renders it difficult to retrace a classification model to clinically recognizable symptoms or signs. A word of caution for this development is, therefore, in order. In an extreme example such tendencies could lead to a model that bases its classification of patients and healthy controls only on their use of antipsychotic medication. This of course would lead to (near) perfect classification scores, but such a model would have no diagnostic value. Similarly, algorithms might ‘overfit’ predictions due to for example multicollinearity or correlated predictors, producing unstable estimates. Such problems can be overcome by validation in a truly independent dataset; problems in the model fitting stage will show up as poor performance in a validation process. However, of the studies reviewed here, most use cross-validation to assess the generalizability of their models, which does not fully overcome this risk of overfitting. Few studies validated their models in a separate subset of their data [70▪] or in an independent dataset [63▪▪]; the latter of which should become the standard in this field of research.
The value of computational language analyses as biomarkers in schizophrenia-spectrum disorders is increasing as a result of rapidly advancing linguistic techniques. Language technology evolves quickly and analytic techniques such as machine learning allow for the application of complex features to a clinically relevant goal. Language analyses show potential for a range of applications in schizophrenia; for example in identifying at risk groups on social media [82,83], monitoring psychosis relapse through smartphone applications  or predicting treatment response. Recent work using computational semantic tools such as semantic space and graph analysis, as well as phonetic acoustic markers, have proved successful in both diagnosis and prognosis of schizophrenia-spectrum disorders. Accuracy scores in differentiating patients from healthy controls, family members or at risk groups range from 80 to 90%, often outperforming clinical raters. Even the clinically difficult differentiation between psychosis and mania showed high specificity and sensitivity with language analysis (both 94%).
Further longitudinal studies across a broader range of ages, disease severity and illness durations will be needed to understand the trajectory of language disturbances in schizophrenia-spectrum disorders. Future research is needed to fully appraise the potential of language as a diagnostic or prognostic tool. For example, a variety of language characteristics could be targeted by combining disparate computational tools. This may improve the predictive power substantially; since the most often used tools (semantic space and acoustic measures) are thought to be a reflection of a different set of symptoms. Semantic incoherence is often associated with FTD or disorganized language [24▪,60,85], while acoustic measures are often used to objectify negative symptoms [29,75▪▪,86,87]. Bringing these methods together acknowledges the heterogeneity of symptoms associated with schizophrenia-spectrum disorders. Combining several quantifiable aspects of language may also pave the road towards cross-diagnostic analyses. Finally, researchers in this field should aim to do cross-linguistic analyses, to examine whether these models hold for the great diversity of languages in the world.
We acknowledge the valuable contribution of authors we have been unable to cite due to space constraints.
Financial support and sponsorship
I.S. received a TOP grant from The Netherlands Organization for Health Research and Development (ZonMW, project: 91213009).
Conflicts of interest
I.S. is a consultant to Gabather, received research support from Janssen Pharmaceuticals Inc. and Sunovion Pharmaceuticals Inc.
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