Natural language processing in at-risk mental states: enhancing the assessment of thought disorders and psychotic traits with semantic dynamics and graph theory.

OBJECTIVE: Verbal communication has key information for mental health evaluation. Researchers have linked psychopathology phenomena to some of their counterparts in natural-language-processing (NLP). We study the characterization of subtle impairments presented in early stages of psychosis, developing new analysis techniques and a comprehensive map associating NLP features with the full range of clinical presentation. METHODS: We used NLP to assess elicited and free-speech of 60 individuals in at-risk-mental-states (ARMS) and 73 controls, screened from 4,500 quota-sampled Portuguese speaking citizens in Sao Paulo, Brazil. Psychotic symptoms were independently assessed with Structured-Interview-for-Psychosis-Risk-Syndromes (SIPS). Speech features (e.g.sentiments, semantic coherence), including novel ones, were correlated with psychotic traits (Spearman's-ρ) and ARMS status (general linear models and machine-learning ensembles). RESULTS: NLP features were informative inputs for classification, which presented 86% balanced accuracy. The NLP features brought forth (e.g. Semantic laminarity as 'perseveration', Semantic recurrence time as 'circumstantiality', average centrality in word repetition graphs) carried most information and also presented direct correlations with psychotic symptoms. Out of the standard measures, grammatical tagging (e.g. use of adjectives) was the most relevant. CONCLUSION: Subtle speech impairments can be grasped by sensitive methods and used for ARMS screening. We sketch a blueprint for speech-based evaluation, pairing features to standard thought disorder psychometric items.

Speech connectedness predicts reading performance three months in advance: a longitudinal experiment.

Aiming to verify the predictive value of oral narrative structure on reading acquisition, we followed 253 children (first and second graders) during an entire school year, assessing oral narratives and reading performances in five sessions. Transcriptions of oral narratives were represented as word-recurrence graphs to measure connectedness attributes. Connectedness predicted performance in phonological awareness, reading comprehension, and word reading accuracy 3-4 months in advance.

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.

Detecting at-risk mental states for psychosis (ARMS) using machine learning ensembles and facial features.

AIMS: Our study aimed to develop a machine learning ensemble to distinguish "at-risk mental states for psychosis" (ARMS) subjects from control individuals from the general population based on facial data extracted from video-recordings. METHODS: 58 non-help-seeking medication-naïve ARMS and 70 healthy subjects were screened from a general population sample. At-risk status was assessed with the Structured Interview for Prodromal Syndromes (SIPS), and "Subject's Overview" section was filmed (5-10 min). Several features were extracted, e.g., eye and mouth aspect ratio, Euler angles, coordinates from 51 facial landmarks. This elicited 649 facial features, which were further selected using Gradient Boosting Machines (AdaBoost combined with Random Forests). Data was split in 70/30 for training, and Monte Carlo cross validation was used. RESULTS: Final model reached 83 % of mean F1-score, and balanced accuracy of 85 %. Mean area under the curve for the receiver operator curve classifier was 93 %. Convergent validity testing showed that two features included in the model were significantly correlated with Avolition (SIPS N2 item) and expression of emotion (SIPS N3 item). CONCLUSION: Our model capitalized on short video-recordings from individuals recruited from the general population, effectively distinguishing between ARMS and controls. Results are encouraging for large-screening purposes in low-resource settings.

Gesticulation in individuals with at risk mental states for psychosis.

Nonverbal communication (NVC) is a complex behavior that involves different modalities that are impaired in the schizophrenia spectrum, including gesticulation. However, there are few studies that evaluate it in individuals with at-risk mental states (ARMS) for psychosis, mostly in developed countries. Given our prior findings of reduced movement during speech seen in Brazilian individuals with ARMS, we now aim to determine if this can be accounted for by reduced gesticulation behavior. Fifty-six medication-naïve ARMS and 64 healthy controls were filmed during speech tasks. The frequency of specifically coded gestures across four categories (and self-stimulatory behaviors) were compared between groups and tested for correlations with prodromal symptoms of the Structured Interview for Prodromal Syndromes (SIPS) and with the variables previously published. ARMS individuals showed a reduction in one gesture category, but it did not survive Bonferroni's correction. Gesture frequency was negatively correlated with prodromal symptoms and positively correlated with the variables of the amount of movement previously analyzed. The lack of significant differences between ARMS and control contradicts literature findings in other cultural context, in which a reduction is usually seen in at-risk individuals. However, gesture frequency might be a visual proxy of prodromal symptoms, and of other movement abnormalities. Results show the importance of analyzing NVC in ARMS and of considering different cultural and sociodemographic contexts in the search for markers of these states.

Ethical Implications of the Use of Language Analysis Technologies for the Diagnosis and Prediction of Psychiatric Disorders.

Recent developments in artificial intelligence technologies have come to a point where machine learning algorithms can infer mental status based on someone's photos and texts posted on social media. More than that, these algorithms are able to predict, with a reasonable degree of accuracy, future mental illness. They potentially represent an important advance in mental health care for preventive and early diagnosis initiatives, and for aiding professionals in the follow-up and prognosis of their patients. However, important issues call for major caution in the use of such technologies, namely, privacy and the stigma related to mental disorders. In this paper, we discuss the bioethical implications of using such technologies to diagnose and predict future mental illness, given the current scenario of swiftly growing technologies that analyze human language and the online availability of personal information given by social media. We also suggest future directions to be taken to minimize the misuse of such important technologies.

Imagetic and affective measures of memory reverberation diverge at sleep onset in association with theta rhythm.

The 'day residue' - the presence of waking memories into dreams - is a century-old concept that remains controversial in neuroscience. Even at the psychological level, it remains unclear how waking imagery cedes into dreams. Are visual and affective residues enhanced, modified, or erased at sleep onset? Are they linked, or dissociated? What are the neural correlates of these transformations? To address these questions we combined quantitative semantics, sleep EEG markers, visual stimulation, and multiple awakenings to investigate visual and affect residues in hypnagogic imagery at sleep onset. Healthy adults were repeatedly stimulated with an affective image, allowed to sleep and awoken seconds to minutes later, during waking (WK), N1 or N2 sleep stages. 'Image Residue' was objectively defined as the formal semantic similarity between oral reports describing the last image visualized before closing the eyes ('ground image'), and oral reports of subsequent visual imagery ('hypnagogic imagery). Similarly, 'Affect Residue' measured the proximity of affective valences between 'ground image' and 'hypnagogic imagery'. We then compared these grounded measures of two distinct aspects of the 'day residue', calculated within participants, to randomly generated values calculated across participants. The results show that Image Residue persisted throughout the transition to sleep, increasing during N1 in proportion to the time spent in this stage. In contrast, the Affect Residue was gradually neutralized as sleep progressed, decreasing in proportion to the time spent in N1 and reaching a minimum during N2. EEG power in the theta band (4.5-6.5 Hz) was inversely correlated with the Image Residue during N1. The results show that the visual and affective aspects of the 'day residue' in hypnagogic imagery diverge at sleep onset, possibly decoupling visual contents from strong negative emotions, in association with increased theta rhythm.

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.

A Brazilian bottom-up strategy to address mental health in a diverse population over a large territorial area - an inspiration for the use of digital mental health.

Brazil is a continental country with a history of massive immigration waves from around the world. Consequently, the Brazilian population is rich in ethnic, cultural, and religious diversity, but suffers from tremendous socioeconomic inequality. Brazil has a documented history of categorizing individuals with culturally specific behaviors as mentally ill, which has led to psychiatric institutionalization for reasons that were more social than clinical. To address this, a "network for psychosocial care" was created in Brazil, that included mental health clinics and community services distributed throughout the country. This generates local support for mental health rehabilitation, integrating psychiatric care, family support and education/work opportunities. These clinics and community services are tailored to provide care for each specific area, and are more attuned to regional culture, values and neighborhood infrastructure. Here we review existing reports about the Brazilian experience, including advances in public policy on mental health, and challenges posed by the large diversity to the psychosocial rehabilitation.  In addition, we show how new digital technologies in general, and computational speech analysis in particular, can contribute to unbiased assessments, resulting in decreased stigma and more effective diagnosis of the mental diseases, with methods that are free of gender, ethnic, or socioeconomic biases.

Low-dose LSD and the stream of thought: Increased Discontinuity of Mind, Deep Thoughts and abstract flow.

RATIONALE: Stream of thought describes the nature of the mind when it is freely roaming, a mental state that is continuous and highly dynamic as in mind-wandering or free association. Classic serotonergic psychedelics are known to profoundly impact perception, cognition and language, yet their influence on the stream of thought remains largely unexplored. OBJECTIVE: To elucidate the effects of LSD on the stream of thought. METHODS: In a randomized, double-blind, placebo-controlled, crossover study, 24 healthy participants received 50 µg lysergic acid diethylamide (LSD) or inactive placebo. Mind-wandering was measured by the Amsterdam Resting State Questionnaire (ARSQ), free association by the Forward Flow Task (FFT) for three seed word types (animals, objects, abstract words). ARSQ and FFT were assessed at +0 h, +2 h, +4 h, +6 h, +8 h and +24 h after drug administration, respectively. RESULTS: LSD, compared to placebo, induced different facets of mind-wandering we conceptualized as "chaos" (Discontinuity of Mind, decreased Sleepiness, Planning, Thoughts under Control, Thoughts about Work and Thoughts about Past), "meaning" (Deep Thoughts, Not Sharing Thoughts) and "sensation" (Thoughts about Odours, Thoughts about Sounds). LSD increased the FFT for abstract words reflecting an "abstract flow" under free association. Overall, chaos was strongest pronounced (+2 h to +6 h), followed by meaning (+2 h to +4 h), sensation (+2 h) and abstract flow (+4 h). CONCLUSIONS: LSD affects the stream of thought within several levels (active, passive), facets (chaos, meaning, sensation, abstractness) and time points (from +2 h to +6 h). Increased chaos, meaning and abstract flow at +4 h indicate the utility of a late therapeutic window in psycholytic therapy.

Nonsemantic word graphs of texts spanning ∼ 4500 years, including pre-literate Amerindian oral narratives.

Non-semantic word graphs obtained from oral reports are useful to describe cognitive decline in psychiatric conditions such as Schizophrenia, as well as education-related gains in discourse structure during typical development. Here we provide non-semantic word graph attributes of texts spanning approximately 4500 years of history, and pre-literate Amerindian oral narratives. The dataset assessed comprises 707 literary texts representative of 9 different Afro-Eurasian traditions (Syro-Mesopotamian, Egyptian, Hinduist, Persian, Judeo-Christian, Greek-Roman, Medieval, Modern and Contemporary), and Amerindian narratives (N = 39) obtained from a single ethnic group from South America (Kalapalo, N = 18), or from a mixed ethnic group from South, Central and North America (non-Kalapalo, N = 21). The present article provides detailed information about each text or narrative, including measurements of four graph attributes of interest: number of nodes (lexical diversity), repeated edges (short-range recurrence), largest strongly connected component (long-range recurrence), and average shortest path (graph length).

Low Speech Connectedness in Alzheimer's Disease is Associated with Poorer Semantic Memory Performance.

Connected speech is an everyday activity. We aimed to investigate whether connected speech can differentiate oral narrative production between adults with Alzheimer's disease (AD; n = 24) and cognitively healthy older adults (n = 48). We used graph attributes analysis to represent connected speech. Participants produced oral narratives and performed semantic, episodic, and working memory tasks. AD patients produced less connected narratives than cognitively healthy older adults. Connectedness was associated with semantic memory in AD and with episodic memory in controls. Word-graphs connectedness represents a practical tool to assess cognitive impairment in AD patients.

Network analysis of narrative discourse and attention-deficit hyperactivity symptoms in adults.

Previous research investigating language in attention-deficit hyperactivity disorder (ADHD) has demonstrated several deficits in many aspects. However, no previous study employed quantitative methodology providing objective measures that could be compared among different studies with diverse samples. To fill this gap, we used network analysis to investigate how ADHD symptomatology impacts narrative discourse, a complex linguistic task considered to be an ecological measure of language. Fifty-eight adults (34 females and 24 males) with a mean age of 26 years old and a mean of 17 years of educational level were administered the Adult Self-Rating Scale for ADHD symptomatology. They also completed the State-Trait Anxiety Inventory, the Beck Depression Inventory and the Urgency, Premeditation, Perseverance, Sensation Seeking Behavior Scale. Intelligence quotient was calculated. Individuals were asked to tell a story based on a wordless picture book. Speech was recorded and transcribed as an input to SpeechGraphs software. Parameters were total number of words (TNW), number of loops of one node (L1), repeated edges (RE), largest strongly connected component (LSC) and average shortest path (ASP). Verbosity was controlled. Statistical analysis was corrected for multiples comparisons and partial correlations were performed for confounding variables. After controlling for anxiety, depression, IQ, and impulsiveness ADHD symptomatology was positively correlated with L1 and negatively correlated with LSC. TNW was positively correlated with ADHD symptoms. In a subdomain analysis, both inattention and hyperactivity-impulsivity were negatively correlated with LSC. Only hyperactivity-impulsivity positively correlated with TNW and L1. Results indicated a correlation between ADHD symptoms and lower connectedness in narrative discourse (as indicated by higher L1 and lower LSC), as well as higher total number of words (TNW). Our results suggest that the higher the number of ADHD symptoms, the less connectivity among words, and a higher number of words in narrative discourse.

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.

The entropic tongue: Disorganization of natural language under LSD.

Serotonergic psychedelics have been suggested to mirror certain aspects of psychosis, and, more generally, elicit a state of consciousness underpinned by increased entropy of on-going neural activity. We investigated the hypothesis that language produced under the effects of lysergic acid diethylamide (LSD) should exhibit increased entropy and reduced semantic coherence. Computational analysis of interviews conducted at two different time points after 75 µg of intravenous LSD verified this prediction. Non-semantic analysis of speech organization revealed increased verbosity and a reduced lexicon, changes that are more similar to those observed during manic psychoses than in schizophrenia, which was confirmed by direct comparison with reference samples. Importantly, features related to language organization allowed machine learning classifiers to identify speech under LSD with accuracy comparable to that obtained by examining semantic content. These results constitute a quantitative and objective characterization of disorganized natural speech as a landmark feature of the psychedelic state.

The History of Writing Reflects the Effects of Education on Discourse Structure: Implications for Literacy, Orality, Psychosis and the Axial Age.

BACKGROUND: Graph analysis detects psychosis and literacy acquisition. Bronze Age literature has been proposed to contain childish or psychotic features, which would only have matured during the Axial Age (∼800-200 BC), a putative boundary for contemporary mentality. METHOD: Graph analysis of literary texts spanning ∼4,500 years shows remarkable asymptotic changes over time. RESULTS: While lexical diversity, long-range recurrence and graph length increase away from randomness, short-range recurrence declines towards random levels. Bronze Age texts are structurally similar to oral reports from literate typical children and literate psychotic adults, but distinct from poetry, and from narratives by preliterate preschoolers or Amerindians. Text structure reconstitutes the "arrow-of-time", converging to educated adult levels at the Axial Age onset. CONCLUSION: The educational pathways of oral and literate traditions are structurally divergent, with a decreasing range of recurrence in the former, and an increasing range of recurrence in the latter. Education is seemingly the driving force underlying discourse maturation.

Lowering costs for large-scale screening in psychosis: a systematic review and meta-analysis of performance and value of information for speech-based psychiatric evaluation

Objective: Obstacles for computational tools in psychiatry include gathering robust evidence and keeping implementation costs reasonable. We report a systematic review of automated speech evaluation for the psychosis spectrum and analyze the value of information for a screening program in a healthcare system with a limited number of psychiatrists (Maputo, Mozambique). Methods: Original studies on speech analysis for forecasting of conversion in individuals at clinical high risk (CHR) for psychosis, diagnosis of manifested psychotic disorder, and first-episode psychosis (FEP) were included in this review. Studies addressing non-verbal components of speech (e.g., pitch, tone) were excluded. Results: Of 168 works identified, 28 original studies were included. Valuable speech features included direct measures (e.g., relative word counting) and mathematical embeddings (e.g.: word-to-vector, graphs). Accuracy estimates reported for schizophrenia diagnosis and CHR conversion ranged from 71 to 100% across studies. Studies used structured interviews, directed tasks, or prompted free speech. Directed-task protocols were faster while seemingly maintaining performance. The expected value of perfect information is USD 9.34 million. Imperfect tests would nevertheless yield high value. Conclusion: Accuracy for screening and diagnosis was high. Larger studies are needed to enhance precision of classificatory estimates. Automated analysis presents itself as a feasible, low-cost method which should be especially useful for regions in which the physician pool is insufficient to meet demand.

Dreaming during the Covid-19 pandemic: Computational assessment of dream reports reveals mental suffering related to fear of contagion.

The current global threat brought on by the Covid-19 pandemic has led to widespread social isolation, posing new challenges in dealing with metal suffering related to social distancing, and in quickly learning new social habits intended to prevent contagion. Neuroscience and psychology agree that dreaming helps people to cope with negative emotions and to learn from experience, but can dreaming effectively reveal mental suffering and changes in social behavior? To address this question, we applied natural language processing tools to study 239 dream reports by 67 individuals, made either before the Covid-19 outbreak or during the months of March and April, 2020, when lockdown was imposed in Brazil following the WHO's declaration of the pandemic. Pandemic dreams showed a higher proportion of anger and sadness words, and higher average semantic similarities to the terms "contamination" and "cleanness". These features seem to be associated with mental suffering linked to social isolation, as they explained 40% of the variance in the PANSS negative subscale related to socialization (p = 0.0088). These results corroborate the hypothesis that pandemic dreams reflect mental suffering, fear of contagion, and important changes in daily habits that directly impact socialization.

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.