Grammatical impairment in schizophrenia: An exploratory study of the pronominal and sentential domains.

Schizophrenia (SZ) is a severe mental disorder associated with a variety of linguistic deficits, and recently it has been suggested that these deficits are caused by an underlying impairment in the ability to build complex syntactic structures and complex semantic relations. Aiming at contributing to determining the specific linguistic profile of SZ, we investigated the usage of pronominal subjects and sentence types in two corpora of oral dream and waking reports produced by speakers with SZ and participants without SZ (NSZ), both native speakers of Brazilian Portuguese. Narratives of 40 adult participants (20 SZ, and 20 NSZ-sample 1), and narratives of 31 teenage participants (11 SZ undergoing first psychotic episode, and 20 NSZ-sample 2) were annotated and statistically analyzed. Overall, narratives of speakers with SZ presented significantly higher rates of matrix sentences, null pronouns-particularly null 3Person referential pronouns-and lower rates of non-anomalous truncated sentences. The high rate of matrix sentences correlated significantly with the total PANSS scores, suggesting an association between the overuse of simple sentences and SZ symptoms in general. In contrast, the high rate of null pronouns correlated significantly with positive PANSS scores, suggesting an association between the overuse of null pronominal forms and the positive symptoms of SZ. Finally, a cross-group analysis between samples 1 and 2 indicated a higher degree of grammatical impairment in speakers with multiple psychotic episodes. Altogether, the results strengthen the notion that deficits at the pronominal and sentential levels constitute a cross-cultural linguistic marker of SZ.

Speech graph analysis in obsessive-compulsive disorder: The relevance of dream reports.

Obsessive-compulsive disorder (OCD) is a distressing disorder characterized by the presence of intrusive thoughts, images or urges (obsessions) and/or behavioral efforts to reduce the anxiety (compulsions). OCD lifetime prevalence varies between 1% and 3% in the general population and there are no reliable markers that support the diagnosis. In order to fill this gap, Computational Psychiatry employs multiple types of quantitative analyses to improve the understanding, diagnosis, prediction, and treatment of mental illnesses including OCD. One of these computational tools is speech graphs analysis. A graph represents a network of nodes connected by edges: in non-semantic speech graphs, nodes correspond to words and edges correspond to the directed link between consecutive words. Using non-semantic speech graphs, we compared free speech samples from OCD patients and healthy controls (HC), to test whether speech graphs analysis can grasp structural differences in speech between these groups. To this end, 39 OCD patients and 37 HC were interviewed and recorded during six types of speech reports: yesterday, dream, old memory, positive image, negative image and neutral image. Also, the Obsessive-Compulsive Inventory-Revised (OCI-R) and the Yale Brown Obsessive-Compulsive Scale (Y-BOCS) were used to assess symptom severity. The graph-theoretical structural analysis of dream reports showed that OCD patients have significantly smaller lexical diversity, lower speech connectedness and a higher recurrence of words in comparison with HC. The other five report types failed to show differences between the groups, adding to the notion that dream reports are especially informative of speech structure in different psychiatric states. Further investigation is necessary to completely assess the potential of this tool in OCD.

Natural Language Processing Markers for Psychosis and Other Psychiatric Disorders: Emerging Themes and Research Agenda From a Cross-Linguistic Workshop.

This workshop summary on natural language processing (NLP) markers for psychosis and other psychiatric disorders presents some of the clinical and research issues that NLP markers might address and some of the activities needed to move in that direction. We propose that the optimal development of NLP markers would occur in the context of research efforts to map out the underlying mechanisms of psychosis and other disorders. In this workshop, we identified some of the challenges to be addressed in developing and implementing NLP markers-based Clinical Decision Support Systems (CDSSs) in psychiatric practice, especially with respect to psychosis. Of note, a CDSS is meant to enhance decision-making by clinicians by providing additional relevant information primarily through software (although CDSSs are not without risks). In psychiatry, a field that relies on subjective clinical ratings that condense rich temporal behavioral information, the inclusion of computational quantitative NLP markers can plausibly lead to operationalized decision models in place of idiosyncratic ones, although ethical issues must always be paramount.

Syntactic Network Analysis in Schizophrenia-Spectrum Disorders.

BACKGROUND: Language anomalies are a hallmark feature of schizophrenia-spectrum disorders (SSD). Here, we used network analysis to examine possible differences in syntactic relations between patients with SSD and healthy controls. Moreover, we assessed their relationship with sociodemographic factors, psychotic symptoms, and cognitive functioning, and we evaluated whether the quantification of syntactic network measures has diagnostic value. STUDY DESIGN: Using a semi-structured interview, we collected speech samples from 63 patients with SSD and 63 controls. Per sentence, a syntactic representation (ie, parse tree) was obtained and used as input for network analysis. The resulting syntactic networks were analyzed for 11 local and global network measures, which were compared between groups using multivariate analysis of covariance, considering the effects of age, sex, and education. RESULTS: Patients with SSD and controls significantly differed on most syntactic network measures. Sex had a significant effect on syntactic measures, and there was a significant interaction between sex and group, as the anomalies in syntactic relations were most pronounced in women with SSD. Syntactic measures were correlated with negative symptoms (Positive and Negative Syndrome Scale) and cognition (Brief Assessment of Cognition in Schizophrenia). A random forest classifier based on the best set of network features distinguished patients from controls with 74% cross-validated accuracy. CONCLUSIONS: Examining syntactic relations from a network perspective revealed robust differences between patients with SSD and healthy controls, especially in women. Our results support the validity of linguistic network analysis in SSD and have the potential to be used in combination with other automated language measures as a marker for SSD.

Reading and writing habits compensate for aging effects in speech connectedness.

We investigate the association of short- and long-range recurrences (speech connectedness) with age, education, and reading and writing habits (RWH) in typical aging using an oral narrative production task. Oral narrative transcriptions were represented as word-graphs to measure short- and long-range recurrences. Speech connectedness was explained by the combination of age, education, and RWH, and the strength of RWH's coefficient reflects the aging effect.

From Womb to Neighborhood: A Racial Analysis of Social Determinants of Psychosis in the United States.

The authors examine U.S.-based evidence that connects characteristics of the social environment with outcomes across the psychosis continuum, from psychotic experiences to schizophrenia. The notion that inequitable social and economic systems of society significantly influence psychosis risk through proxies, such as racial minority and immigrant statuses, has been studied more extensively in European countries. While there are existing international reviews of social determinants of psychosis, none to the authors' knowledge focus on factors in the U.S. context specifically-an omission that leaves domestic treatment development and prevention efforts incomplete and underinformed. In this review, the authors first describe how a legacy of structural racism in the United States has shaped the social gradient, highlighting consequential racial inequities in environmental conditions. The authors offer a hypothesized model linking structural racism with psychosis risk through interwoven intermediary factors based on existing theoretical models and a review of the literature. Neighborhood factors, cumulative trauma and stress, and prenatal and perinatal complications were three key areas selected for review because they reflect social and environmental conditions that may affect psychosis risk through a common pathway shaped by structural racism. The authors describe evidence showing that Black and Latino people in the United States suffer disproportionately from risk factors within these three key areas, in large part as a result of racial discrimination and social disadvantage. This broad focus on individual and community factors is intended to provide a consolidated space to review this growing body of research and to guide continued inquiries into social determinants of psychosis in U.S. contexts.

Selective Inhibition of Mirror Invariance for Letters Consolidated by Sleep Doubles Reading Fluency.

Mirror invariance is a visual mechanism that enables a prompt recognition of mirror images. This visual capacity emerges early in human development, is useful to recognize objects, faces, and places from both left and right perspectives, and is also present in primates, pigeons, and cephalopods. Notwithstanding, the same visual mechanism has been suspected to be the source of a specific difficulty for a relatively recent human invention-reading-by creating confusion between mirror letters (e.g., b-d in the Latin alphabet). Using an ecologically valid school-based design, we show here that mirror invariance represents indeed a major leash for reading fluency acquisition in first graders. Our causal approach, which specifically targeted mirror invariance inhibition for letters, in a synergic combination with post-training sleep to increase learning consolidation, revealed unprecedented improvement in reading fluency, which became two-times faster. This gain was obtained with as little as 7.5 h of multisensory-motor training to distinguish mirror letters, such as "b" versus "d." The magnitude, automaticity, and duration of this mirror discrimination learning were greatly enhanced by sleep, which keeps the gains perfectly intact even after 4 months. The results were consistently replicated in three randomized controlled trials. They not only reveal an extreme case of cognitive plasticity in humans (i.e., the inhibition in just 3 weeks of a ∼25-million-year-old visual mechanism), that allows adaptation to a cultural activity (reading), but at the same time also show a simple and cost-effective way to unleash the reading fluency potential of millions of children worldwide.

Structural differences between REM and non-REM dream reports assessed by graph analysis.

Dream reports collected after rapid eye movement sleep (REM) awakenings are, on average, longer, more vivid, bizarre, emotional and story-like compared to those collected after non-REM. However, a comparison of the word-to-word structural organization of dream reports is lacking, and traditional measures that distinguish REM and non-REM dreaming may be confounded by report length. This problem is amenable to the analysis of dream reports as non-semantic directed word graphs, which provide a structural assessment of oral reports, while controlling for individual differences in verbosity. Against this background, the present study had two main aims: Firstly, to investigate differences in graph structure between REM and non-REM dream reports, and secondly, to evaluate how non-semantic directed word graph analysis compares to the widely used measure of report length in dream analysis. To do this, we analyzed a set of 133 dream reports obtained from 20 participants in controlled laboratory awakenings from REM and N2 sleep. We found that: (1) graphs from REM sleep possess a larger connectedness compared to those from N2; (2) measures of graph structure can predict ratings of dream complexity, where increases in connectedness and decreases in randomness are observed in relation to increasing dream report complexity; and (3) measures of the Largest Connected Component of a graph can improve a model containing report length in predicting sleep stage and dream report complexity. These results indicate that dream reports sampled after REM awakening have on average a larger connectedness compared to those sampled after N2 (i.e. words recur with a longer range), a difference which appears to be related to underlying differences in dream complexity. Altogether, graph analysis represents a promising method for dream research, due to its automated nature and potential to complement report length in dream analysis.

Post-class naps boost declarative learning in a naturalistic school setting.

Laboratory evidence of a positive effect of sleep on declarative memory consolidation suggests that naps can be used to boost school learning in a scalable, low-cost manner. The few direct investigations of this hypothesis have so far upheld it, but departed from the naturalistic setting by testing non-curricular contents presented by experimenters instead of teachers. Furthermore, nap and non-nap groups were composed of different children. Here we assessed the effect of post-class naps on the retention of Science and History curricular contents presented by the regular class teacher to 24 students from 5th grade. Retention was repeatedly measured 3-4 days after content learning, with weekly group randomization over 6 consecutive weeks. Contents followed by long naps (>30 min), but not short naps (<30 min), were significantly more retained than contents followed by waking (Cohen's d = 0.7962). The results support the use of post-class morning naps to enhance formal education.

Thought disorder measured as random speech structure classifies negative symptoms and schizophrenia diagnosis 6 months in advance.

In chronic psychotic patients, word graph analysis shows potential as complementary psychiatric assessment. This analysis relies mostly on connectedness, a structural feature of speech that is anti-correlated with negative symptoms. Here we aimed to verify whether speech disorganization during the first clinical contact, as measured by graph connectedness, can correctly classify negative symptoms and the schizophrenia diagnosis 6 months in advance. Positive and negative syndrome scale scores and memory reports were collected from 21 patients undergoing first clinical contact for recent-onset psychosis, followed for 6 months to establish diagnosis, and compared to 21 well-matched healthy subjects. Each report was represented as a word-trajectory graph. Connectedness was measured by number of edges, number of nodes in the largest connected component and number of nodes in the largest strongly connected component. Similarities to random graphs were estimated. All connectedness attributes were combined into a single Disorganization Index weighted by the correlation with the positive and negative syndrome scale negative subscale, and used for classifications. Random-like connectedness was more prevalent among schizophrenia patients (64 × 5% in Control group, p = 0.0002). Connectedness from two kinds of memory reports (dream and negative image) explained 88% of negative symptoms variance (p < 0.0001). The Disorganization Index classified low vs. high severity of negative symptoms with 100% accuracy (area under the receiver operating characteristic curve = 1), and schizophrenia diagnosis with 91.67% accuracy (area under the receiver operating characteristic curve = 0.85). The index was validated in an independent cohort of chronic psychotic patients and controls (N = 60) (85% accuracy). Thus, speech disorganization during the first clinical contact correlates tightly with negative symptoms, and is quite discriminative of the schizophrenia diagnosis.

Psychosis and the Control of Lucid Dreaming.

Dreaming and psychosis share important features, such as intrinsic sense perceptions independent of external stimulation, and a general lack of criticism that is associated with reduced frontal cerebral activity. Awareness of dreaming while a dream is happening defines lucid dreaming (LD), a state in which the prefrontal cortex is more active than during regular dreaming. For this reason, LD has been proposed to be potentially therapeutic for psychotic patients. According to this view, psychotic patients would be expected to report LD less frequently, and with lower control ability, than healthy subjects. Furthermore, psychotic patients able to experience LD should present milder psychiatric symptoms, in comparison with psychotic patients unable to experience LD. To test these hypotheses, we investigated LD features (occurrence, control abilities, frequency, and affective valence) and psychiatric symptoms (measure by PANSS, BPRS, and automated speech analysis) in 45 subjects with psychotic symptoms [25 with Schizophrenia (S) and 20 with Bipolar Disorder (B) diagnosis] versus 28 non-psychotic control (C) subjects. Psychotic lucid dreamers reported control of their dreams more frequently (67% of S and 73% of B) than non-psychotic lucid dreamers (only 23% of C; S > C with p = 0.0283, B > C with p = 0.0150). Importantly, there was no clinical advantage for lucid dreamers among psychotic patients, even for the diagnostic question specifically related to lack of judgment and insight. Despite some limitations (e.g., transversal design, large variation of medications), these preliminary results support the notion that LD is associated with psychosis, but falsify the hypotheses that we set out to test. A possible explanation is that psychosis enhances the experience of internal reality in detriment of external reality, and therefore lucid dreamers with psychotic symptoms would be more able to control their internal reality than non-psychotic lucid dreamers. Training dream lucidity is likely to produce safe psychological strengthening in a non-psychotic population, but in a psychotic population LD practice may further empower deliria and hallucinations, giving internal reality the appearance of external reality.

Automated analysis of free speech predicts psychosis onset in high-risk youths.

BACKGROUND/OBJECTIVES: Psychiatry lacks the objective clinical tests routinely used in other specializations. Novel computerized methods to characterize complex behaviors such as speech could be used to identify and predict psychiatric illness in individuals. AIMS: In this proof-of-principle study, our aim was to test automated speech analyses combined with Machine Learning to predict later psychosis onset in youths at clinical high-risk (CHR) for psychosis. METHODS: Thirty-four CHR youths (11 females) had baseline interviews and were assessed quarterly for up to 2.5 years; five transitioned to psychosis. Using automated analysis, transcripts of interviews were evaluated for semantic and syntactic features predicting later psychosis onset. Speech features were fed into a convex hull classification algorithm with leave-one-subject-out cross-validation to assess their predictive value for psychosis outcome. The canonical correlation between the speech features and prodromal symptom ratings was computed. RESULTS: Derived speech features included a Latent Semantic Analysis measure of semantic coherence and two syntactic markers of speech complexity: maximum phrase length and use of determiners (e.g., which). These speech features predicted later psychosis development with 100% accuracy, outperforming classification from clinical interviews. Speech features were significantly correlated with prodromal symptoms. CONCLUSIONS: Findings support the utility of automated speech analysis to measure subtle, clinically relevant mental state changes in emergent psychosis. Recent developments in computer science, including natural language processing, could provide the foundation for future development of objective clinical tests for psychiatry.

Graph analysis of verbal fluency test discriminate between patients with Alzheimer's disease, mild cognitive impairment and normal elderly controls.

Verbal fluency is the ability to produce a satisfying sequence of spoken words during a given time interval. The core of verbal fluency lies in the capacity to manage the executive aspects of language. The standard scores of the semantic verbal fluency test are broadly used in the neuropsychological assessment of the elderly, and different analytical methods are likely to extract even more information from the data generated in this test. Graph theory, a mathematical approach to analyze relations between items, represents a promising tool to understand a variety of neuropsychological states. This study reports a graph analysis of data generated by the semantic verbal fluency test by cognitively healthy elderly (NC), patients with Mild Cognitive Impairment-subtypes amnestic (aMCI) and amnestic multiple domain (a+mdMCI)-and patients with Alzheimer's disease (AD). Sequences of words were represented as a speech graph in which every word corresponded to a node and temporal links between words were represented by directed edges. To characterize the structure of the data we calculated 13 speech graph attributes (SGA). The individuals were compared when divided in three (NC-MCI-AD) and four (NC-aMCI-a+mdMCI-AD) groups. When the three groups were compared, significant differences were found in the standard measure of correct words produced, and three SGA: diameter, average shortest path, and network density. SGA sorted the elderly groups with good specificity and sensitivity. When the four groups were compared, the groups differed significantly in network density, except between the two MCI subtypes and NC and aMCI. The diameter of the network and the average shortest path were significantly different between the NC and AD, and between aMCI and AD. SGA sorted the elderly in their groups with good specificity and sensitivity, performing better than the standard score of the task. These findings provide support for a new methodological frame to assess the strength of semantic memory through the verbal fluency task, with potential to amplify the predictive power of this test. Graph analysis is likely to become clinically relevant in neurology and psychiatry, and may be particularly useful for the differential diagnosis of the elderly.

Graph analysis of dream reports is especially informative about psychosis.

Early psychiatry investigated dreams to understand psychopathologies. Contemporary psychiatry, which neglects dreams, has been criticized for lack of objectivity. In search of quantitative insight into the structure of psychotic speech, we investigated speech graph attributes (SGA) in patients with schizophrenia, bipolar disorder type I, and non-psychotic controls as they reported waking and dream contents. Schizophrenic subjects spoke with reduced connectivity, in tight correlation with negative and cognitive symptoms measured by standard psychometric scales. Bipolar and control subjects were undistinguishable by waking reports, but in dream reports bipolar subjects showed significantly less connectivity. Dream-related SGA outperformed psychometric scores or waking-related data for group sorting. Altogether, the results indicate that online and offline processing, the two most fundamental modes of brain operation, produce nearly opposite effects on recollections: While dreaming exposes differences in the mnemonic records across individuals, waking dampens distinctions. The results also demonstrate the feasibility of the differential diagnosis of psychosis based on the analysis of dream graphs, pointing to a fast, low-cost and language-invariant tool for psychiatric diagnosis and the objective search for biomarkers. The Freudian notion that "dreams are the royal road to the unconscious" is clinically useful, after all.

Speech graphs provide a quantitative measure of thought disorder in psychosis.

BACKGROUND: Psychosis has various causes, including mania and schizophrenia. Since the differential diagnosis of psychosis is exclusively based on subjective assessments of oral interviews with patients, an objective quantification of the speech disturbances that characterize mania and schizophrenia is in order. In principle, such quantification could be achieved by the analysis of speech graphs. A graph represents a network with nodes connected by edges; in speech graphs, nodes correspond to words and edges correspond to semantic and grammatical relationships. METHODOLOGY/PRINCIPAL FINDINGS: To quantify speech differences related to psychosis, interviews with schizophrenics, manics and normal subjects were recorded and represented as graphs. Manics scored significantly higher than schizophrenics in ten graph measures. Psychopathological symptoms such as logorrhea, poor speech, and flight of thoughts were grasped by the analysis even when verbosity differences were discounted. Binary classifiers based on speech graph measures sorted schizophrenics from manics with up to 93.8% of sensitivity and 93.7% of specificity. In contrast, sorting based on the scores of two standard psychiatric scales (BPRS and PANSS) reached only 62.5% of sensitivity and specificity. CONCLUSIONS/SIGNIFICANCE: The results demonstrate that alterations of the thought process manifested in the speech of psychotic patients can be objectively measured using graph-theoretical tools, developed to capture specific features of the normal and dysfunctional flow of thought, such as divergence and recurrence. The quantitative analysis of speech graphs is not redundant with standard psychometric scales but rather complementary, as it yields a very accurate sorting of schizophrenics and manics. Overall, the results point to automated psychiatric diagnosis based not on what is said, but on how it is said.