Changes in the structure of spontaneous speech predict the disruption of hierarchical brain organization in first-episode psychosis.

Psychosis implicates changes across a broad range of cognitive functions. These functions are cortically organized in the form of a hierarchy ranging from primary sensorimotor (unimodal) to higher-order association cortices, which involve functions such as language (transmodal). Language has long been documented as undergoing structural changes in psychosis. We hypothesized that these changes as revealed in spontaneous speech patterns may act as readouts of alterations in the configuration of this unimodal-to-transmodal axis of cortical organization in psychosis. Results from 29 patients with first-episodic psychosis (FEP) and 29 controls scanned with 7 T resting-state fMRI confirmed a compression of the cortical hierarchy in FEP, which affected metrics of the hierarchical distance between the sensorimotor and default mode networks, and of the hierarchical organization within the semantic network. These organizational changes were predicted by graphs representing semantic and syntactic associations between meaningful units in speech produced during picture descriptions. These findings unite psychosis, language, and the cortical hierarchy in a single conceptual scheme, which helps to situate language within the neurocognition of psychosis and opens the clinical prospect for mental dysfunction to become computationally measurable in spontaneous speech.

Perplexity of utterances in untreated first-episode psychosis: an ultra-high field MRI dynamic causal modelling study of the semantic network.

BACKGROUND: Psychosis involves a distortion of thought content, which is partly reflected in anomalous ways in which words are semantically connected into utterances in speech. We sought to explore how these linguistic anomalies are realized through putative circuit-level abnormalities in the brain's semantic network. METHODS: Using a computational large-language model, Bidirectional Encoder Representations from Transformers (BERT), we quantified the contextual expectedness of a given word sequence (perplexity) across 180 samples obtained from descriptions of 3 pictures by patients with first-episode schizophrenia (FES) and controls matched for age, parental social status, and sex, scanned with 7 T ultra-high field functional magnetic resonance imaging (fMRI). Subsequently, perplexity was used to parametrize a spectral dynamic causal model (DCM) of the effective connectivity within (intrinsic) and between (extrinsic) 4 key regions of the semantic network at rest, namely the anterior temporal lobe, the inferior frontal gyrus (IFG), the posterior middle temporal gyrus (MTG), and the angular gyrus. RESULTS: We included 60 participants, including 30 patients with FES and 30 controls. We observed higher perplexity in the FES group, indicating that speech was less predictable by the preceding context among patients. Results of Bayesian model comparisons showed that a DCM including the group by perplexity interaction best explained the underlying patterns of neural activity. We observed an increase of self-inhibitory effective connectivity within the IFG, as well as reduced self-inhibitory tone within the pMTG, in the FES group. An increase in self-inhibitory tone in the IFG correlated strongly and positively with inter-regional excitation between the IFG and posterior MTG, while self-inhibition of the posterior MTG was negatively correlated with this interregional excitation. LIMITATION: Our design did not address connectivity in the semantic network during tasks that selectively activated the semantic network, which could corroborate findings from this resting-state fMRI study. Furthermore, we do not present a replication study, which would ideally use speech in a different language. CONCLUSION: As an explanation for peculiar speech in psychosis, these results index a shift in the excitatory-inhibitory balance regulating information flow across the semantic network, confined to 2 regions that were previously linked specifically to the executive control of meaning. Based on our approach of combining a large language model with causal connectivity estimates, we propose loss in semantic control as a potential neurocognitive mechanism contributing to disorganization in psychosis.

Lost in translation? Deciphering the role of language differences in the excess risk of psychosis among migrant groups.

BACKGROUND: Migration is a well-established risk factor for psychotic disorders, and migrant language has been proposed as a novel factor that may improve our understanding of this relationship. Our objective was to explore the association between indicators of linguistic distance and the risk of psychotic disorders among first-generation migrant groups. METHODS: Using linked health administrative data, we constructed a retrospective cohort of first-generation migrants to Ontario over a 20-year period (1992-2011). Linguistic distance of the first language was categorized using several approaches, including language family classifications, estimated acquisition time, syntax-based distance scores, and lexical-based distance scores. Incident cases of non-affective psychotic disorder were identified over a 5- to 25-year period. We used Poisson regression to estimate incidence rate ratios (IRR) for each language variable, after adjustment for knowledge of English at arrival and other factors. RESULTS: Our cohort included 1 863 803 first-generation migrants. Migrants whose first language was in a different language family than English had higher rates of psychotic disorders (IRR = 1.08, 95% CI 1.01-1.16), relative to those whose first language was English. Similarly, migrants in the highest quintile of linguistic distance based on lexical similarity had an elevated risk of psychotic disorder (IRR = 1.15, 95% CI 1.06-1.24). Adjustment for knowledge of English at arrival had minimal effect on observed estimates. CONCLUSION: We found some evidence that linguistic factors that impair comprehension may play a role in the excess risk of psychosis among migrant groups; however, the magnitude of effect is small and unlikely to fully explain the elevated rates of psychotic disorder across migrant groups.

Navigating the semantic space: Unraveling the structure of meaning in psychosis using different computational language models.

Speech in psychosis has long been ascribed as involving 'loosening of associations'. We pursued the aim to elucidate its underlying cognitive mechanisms by analysing picture descriptions from 94 subjects (29 healthy controls, 18 participants at clinical high risk, 29 with first-episode psychosis, and 18 with chronic schizophrenia), using five language models with different computational architectures: FastText, which represents meaning non-contextually/statically; BERT, which represents contextual meaning sensitive to grammar and context; Infersent and SBERT, which provide sentential representations; and CLIP, which evaluates speech relative to a visual stimulus. These models were used to quantify semantic distances crossed between successive tokens/sentences, and semantic perplexity indicating unexpectedness in continuations. Results showed that, among patients, semantic similarity increased when measured with FastText, Infersent, and SBERT, while it decreased with CLIP and BERT. Higher perplexity was observed in first-episode psychosis. Static semantic measures were associated with clinically measured impoverishment of thought and referential semantic measures with disorganization. These patterns indicate a shrinking conceptual semantic space as represented by static language models, which co-occurs with a widening in the referential semantic space as represented by contextual models. This duality underlines the need to separate these two forms of meaning for understanding mechanisms involved in semantic change in psychosis.

Language network self-inhibition and semantic similarity in first-episode schizophrenia: A computational-linguistic and effective connectivity approach.

INTRODUCTION: A central feature of schizophrenia is the disorganization and impoverishment of language. Recently, we observed higher semantic similarity in first-episode-schizophrenia (FES) patients. In this study, we investigate if this aberrant similarity relates to the 'causal' connectivity between two key nodes of the word production system: inferior frontal gyrus (IFG) and the semantic-hub at the ventral anterior temporal lobe (vATL). METHODS: Resting-state fMRI scans were collected from 60 participants (30 untreated FES and 30 healthy controls). The semantic distance was measured with the CoVec semantic tool based on GloVe. A spectral dynamic causal model with Parametrical Empirical Bayes was constructed modelling the intrinsic self-inhibitory and extrinsic-excitatory connections within the brain regions. We estimated the parameters of a fully connected model with the semantic distance as a covariate. RESULTS: FES patients chose words with higher semantic similarity when describing the pictures compared to the HC group. Among patients, an increased semantic similarity was related with an increase in intrinsic connections within both the vATL and IFG, suggesting that reduced 'synaptic gain' in these regions likely contribute to aberrant sampling of the semantic space during discourse in schizophrenia. CONCLUSIONS: Lexical impoverishment relates to increased self-inhibition in both the IFG and vATL. The associated reduction in synaptic gain may relate to reduced precision of locally generated neural activity, forcing the choice of words that are already 'activated' in a lexical network. One approach to improve word sampling may be via promoting synaptic gain via supra-physiological stimulation within the Broca's-vATL network; this proposal needs verification.

Syntactic complexity of spoken language in the diagnosis of schizophrenia: A probabilistic Bayes network model.

In the clinical linguistics of schizophrenia, syntactic complexity has received much attention. In this study, we address whether syntactic complexity deteriorates within the six months following the first episode of psychosis in those who develop a diagnosis of schizophrenia. We collected data from a cohort of twenty-six first-episode psychosis and 12 healthy control subjects using the Thought and Language Index interview in response to three pictures from the Thematic Apperception Test at first assessment and after six months (the time of consensus diagnosis). An automated labeling (part-of-speech tagging) for specific syntactic elements calculated large and granular syntactic complexity indices with a focus on clause complexity as a particular case from this spoken language data. Probabilistic reasoning leveraging the conditional independence properties of Bayes networks revealed that consensus diagnosis of schizophrenia predicted a decrease in nominal subjects per clause among individuals with first episode psychosis. From the entire sample, we estimate a 95.4 % probability that a 50 % decrease in mean nominal subjects per clause after six months is explained by the presence of first episode psychosis. Among those with psychosis, a 30 % decrease in this clause-complexity index after six months of experiencing the first episode predicted with 95 % probability a consensus diagnosis of schizophrenia, representing a conditional relationship between a longitudinal decrease in syntactic complexity and a diagnosis of schizophrenia. We conclude that an early drift towards linguistic disorganization/impoverishment of clause complexity-at the granular level of nominal subject per clause-is a distinctive feature of schizophrenia that decreases longitudinally, thus differentiating schizophrenia from other psychotic illnesses with shared phenomenology.

The efficacy of the picture version of the free and cued selective reminding test to detect significant neurocognitive deficits in a Chilean population.

The picture version of the Free and cued selective reminding test with immediate recall is a test adept at measuring the memory encoding and the effect of semantic cues. Furthermore, it is sensitive to detect early dementia stages. This study aimed to obtain psychometric properties of visual Buschke and Grober The Free and Cued Selective Reminding Test (FCSRT) in healthy older adults, mild neurocognitive disorders, and major neurocognitive subjects on a Chilean population. METHOD: 226 participants were included, 113 healthy older adults (HOA), 65 mild neurocognitive disorder (NCD) subjects, and 48 major NCD. Each individual was assessed with the same protocol. RESULTS: The observed area under the curve (AUC) was higher than .90 in all the FCSRT measures in the major cognitive disorders and healthy older people. CONCLUSION: according to the AUCs, it was shown that Free Recall, Sensitivity to Cueing Index, and Delay Recall of the FCSRT are suitable to detect major neurocognitive disorders.

[Development of Normative Data for the Mini Mental State Examination (MMSE) in the Elderly Population of Chile: A Multi-City Study]. / Nuevos datos normativos para el Mini Mental State Examination (MMSE) en la población de personas mayores en Chile.

BACKGROUND: The Mini-Mental State Examination (MMSE) is one of the most widely used tests to assess the global cognitive deterioration of older adults. There are many versions of this Test in Spanish. A validation study was previously conducted in Chile, but to date, no standards computed from a large population sample are available. AIM: to develop normative scores according to age and educational level for older people living in Chile. METHOD: Data were collected from a sample of 1199 subjects between 50 and 91 years of age from various research centers. RESULTS: We present the normative scores in percentiles calculated according to age (50-64, 65-74, 75-91 years of age) and educational level (1-12 years and more than 13 years of formal schooling). CONCLUSION: This article provides normative scores for the MMSE that should be useful in clinical practice as they may help better interpret the performances of Chilean older adults who seek consultation for cognitive impairment.

Progressive changes in descriptive discourse in First Episode Schizophrenia: a longitudinal computational semantics study.

Computational semantics, a branch of computational linguistics, involves automated meaning analysis that relies on how words occur together in natural language. This offers a promising tool to study schizophrenia. At present, we do not know if these word-level choices in speech are sensitive to the illness stage (i.e., acute untreated vs. stable established state), track cognitive deficits in major domains (e.g., cognitive control, processing speed) or relate to established dimensions of formal thought disorder. In this study, we collected samples of descriptive discourse in patients experiencing an untreated first episode of schizophrenia and healthy control subjects (246 samples of 1-minute speech; n = 82, FES = 46, HC = 36) and used a co-occurrence based vector embedding of words to quantify semantic similarity in speech. We obtained six-month follow-up data in a subsample (99 speech samples, n = 33, FES = 20, HC = 13). At baseline, semantic similarity was evidently higher in patients compared to healthy individuals, especially when social functioning was impaired; but this was not related to the severity of clinically ascertained thought disorder in patients. Across the study sample, higher semantic similarity at baseline was related to poorer Stroop performance and processing speed. Over time, while semantic similarity was stable in healthy subjects, it increased in patients, especially when they had an increasing burden of negative symptoms. Disruptions in word-level choices made by patients with schizophrenia during short 1-min descriptions are sensitive to interindividual differences in cognitive and social functioning at first presentation and persist over the early course of the illness.

Montreal Cognitive Assessment as Screening Measure for Mild and Major Neurocognitive Disorder in a Chilean Population.

BACKGROUND: The Montreal Cognitive Assessment (MoCA) is a sensitive screening instrument for mild neurocognitive disorder (mild NCD). However, cut-off scores and accuracy indices should be established using representative samples of the population. In this context, the aim of this study was to update the normative values, and diagnostic efficiency statistics of the MoCA to detect mild NCD in the Chilean population. METHODS: This study included 226 participants from the north, center, and south of the country, classified into 3 groups: healthy elderly (HE; n = 113), mild NCD (n = 65), and major neurocognitive disorder (major NCD; n = 48). RESULTS: The optimal cut-off score to discriminate mild NCD from HE participants was 20 points with a sensitivity of 82.8% and a specificity of 84.1%. The observed balance between sensitivity and specificity shows a good test performance either to confirm or discard a diagnosis. The cut-off between mild NCD and major NCD from HE participants was 19 points with 85.6% of sensitivity and 90.3% of specificity. CONCLUSION: Overall diagnostic accuracy can be considered as outstanding (AUC ≥0.904) when discriminating HE from both mild NCD and major NCD. These results showed that the MoCA is a suitable tool to identify mild NCD and major NCD.

Diferencias temporales y espaciales en el procesamiento de incongruencias léxicas: un estudio de potenciales relacionados a eventos / Temporal and spatial differences in the processing of lexical incongruities: A study of event-related potentials

Resumen Introducción. La comprensión del lenguaje está determinada por diversos procesos, entre los que se encuentra el reconocimiento léxico. Según el modelo conexionista, este reconocimiento se genera por activación mediante el emparejamiento de la información acumulada y por la inhibición de las entradas léxicas que compiten por activación. Objetivo. Determinar las diferencias temporales y espaciales de procesamiento entre las incongruencias léxicas semánticamente relacionadas a un contexto lingüístico oracional y las no relacionadas a través de mediciones electrofisiológicas de potenciales relacionados a eventos (PRE). Materiales y métodos. Se realizó la medición de los PRE en 10 sujetos sanos por medio de un paradigma de 240 oraciones en español agrupadas de la siguiente manera: 80 oraciones congruentes, 80 con incongruencias dentro del campo léxico y 80 con incongruencias fuera del campo léxico. Resultados. Se observó una diferencia estadística en la latencia de aparición del componente N400 entre las dos condiciones. Por su parte, se encontró una mayor activación del precúneo, del giro orbitofrontal, del giro angular y del giro supramarginal en la condición de incongruencia fuera del campo léxico. Conclusión. Se identificaron diferencias temporales y espaciales (activación del precúneo, del giro orbitofrontal, del giro angular y del giro supramarginal) entre el procesamiento de las incongruencias léxicas y no léxicas.

Abstract Introduction: Language understanding depends on several processes, including lexical recognition. According to the connectionist model, this recognition is generated by activation through the matching of accumulated information and by the inhibition of lexical entries that compete for activation. Objective: To determine, through electrophysiological measurements of event-related potentials (ERP), temporal and spatial processing differences between lexical inconsistencies semantically related to a sentence linguistic context and those that are unrelated. Materials and methods: ERPs were measured in 10 healthy subjects by means of a 240 Spanish sentences paradigm grouped as follows: 80 congruent sentences, 80 sentences with lexical incongruities, and 80 with non-lexical incongruities. Results: A statistical difference was found in the latency of appearance of the N400 component between both conditions. On the other hand, a greater activation of the precuneus, the orbitofrontal gyrus, the angular gyrus and the supramarginal gyrus was observed in the non-lexical incongruity condition. Conclusion: There are temporal and spatial (activation of the precuneus, the orbitofrontal gyrus, the angular gyrus and the supramarginal gyrus) differences between the processing of lexical inconsistencies and the processing of non-lexical inconsistencies.

Fluidez verbal fonológica, morfológica y semántica en sujetos con enfermedad de Parkinson / Phonologic, morphologic and semantic verbal fluency in Parkinson disease subjects

Resumen La Enfermedad de Parkinson (EP) es una patología progresiva con degeneración del sistema dopaminérgico nigro-estriatal con disfunción de proyecciones frontales que genera alteraciones en funciones ejecutivas. Esta condición debería afectar particularmente la capacidad de evocar unidades léxicas y recuperarlas desde la memoria de largo plazo; no obstante, si bien se cuenta con datos acerca de este comportamiento, aún es necesario determinar qué subcomponentes del lenguaje se ven afectados a fin de comprender con mayor especificidad tanto la patología como su manifestación lingüística. En este marco, el objetivo del presente estudio consistió en describir el rendimiento en tareas de fluidez verbal de tipo fonológica, morfosintáctica, semántica y sus combinaciones en participantes con enfermedad de Parkinson (EP). Para ello, se realizó un estudio transversal con 42 sujetos, agrupados en Adulto Mayor Sano (AMS; n = 23) y Adulto Mayor Diagnosticado con EP (EP; n = 19). Cada sujeto realizó un total de 15 tareas de fluidez verbal en las que debió evocar la mayor cantidad de unidades léxicas en 60 segundos, las que fueron, además, medidas en intervalos de 15 segundos. Los resultados permitieron observar diferencias estadísticamente significativas en las subtareas de tipo fonológica de fonema excluido, categoría gramatical, relaciones semánticas y de combinación campo léxico y fonema inicial. Estos datos parecen indicar que los sujetos con EP muestran un rendimiento significativamente inferior en tareas que exigen alto control inhibitorio, dado que las subtareas que combinan niveles de la lengua implican evocación e inhibición.

Abstract Parkinson's disease (PD) is a progressive pathology that implies a degeneration of the nigro-striatal dopaminergic system with dysfunction of frontal projections, which generates alterations in executive functions, specifically in sustained attention, working memory, planning and cognitive flexibility, among others. In fact, this pathology has repercussions on several language components. Particularly, there is evidence indicating that the ability to evoke lexical units and recover them from long-term memory or lexicon is affected. Although these types of tasks seem to be sensitive for the detection of Parkinson's disease, both in patients with mild cognitive impairment and another with dementia, it is still necessary to have evidence to determine which specific subcomponents of the language are affected, since this will allow us to understand with more specificity both the pathology and its linguistic manifestation, at the same time as to design therapies by support teams for this type of patients. In this context, verbal fluency (VF) tests could contribute to obtaining this type of information. VF is defined as an executive function that involves the access and evocation of clusters of linguistic information, and that is related to attention and working memory processes, since it is initiated by the activation of information groups through specific search strategies Moreover, it involves various linguistic functions, such as the naming, and executive functions of planning, working memory and initiation of behavior. Specifically, this type of task consists of requesting the subject under evaluation to nominate the largest number of elements of a lexical category according to a specific rule for a specific time, such as names of countries or word containing the phoneme / f /. From there, it has been observed which associated language areas are activated: ventral-anterior lower frontal gyrus in categorical tasks, dorsal-posterior lower frontal gyrus in phonological tasks and parietal areas of the right hemisphere that are associated with executive functions and attentional processes in tasks of greater complexity. Within this framework, the aim of the present study was to describe the performance in tasks of verbal fluency of a phonological, morph syntactic, semantic type and their combinations in participants diagnosed with Parkinson's disease (PD). For this, a cross-sectional study was conducted with 42 subjects, grouped as healthy older adults (AMS; n = 23) and older adults diagnosed with PD without dementia (EP; n = 19). The subjects of the control group had a mean age of 66 years (SD = 6.9) and 11.5 years of schooling (SD = 3.1); on the other hand, subjects with PD had a mean age of 71 years (SD = 8.03) and 13.2 years of schooling (SD = 3.9). Each subject performed a total of 15 verbal fluency tasks, in which he had to evoke the greatest number of lexical units in 60 seconds, which were also measured in intervals of 15 seconds. The results shown statistically significant differences in the sub-tasks of the phonemic type of excluded phoneme (FVFLE1: p = .012 and FVFLE2: p = .047), morph syntactic adjective categories (FVM2: p = .005), in the synonymy relationships (FVS3: p = .028) and lexical field and phonology (FVSF: p = .004). These data seem to indicate that subjects with Parkinson's disease show significantly lower performance in tasks that require high inhibitory control, since subtasks that combine tongue levels involve evocation and inhibition at the same time. These results are consistent with the pathophysiology of the condition.

Using Black Hole Algorithm to Improve EEG-Based Emotion Recognition.

Emotions are a critical aspect of human behavior. One widely used technique for research in emotion measurement is based on the use of EEG signals. In general terms, the first step of signal processing is the elimination of noise, which can be done in manual or automatic terms. The next step is determining the feature vector using, for example, entropy calculation and its variations to generate a classification model. It is possible to use this approach to classify theoretical models such as the Circumplex model. This model proposes that emotions are distributed in a two-dimensional circular space. However, methods to determine the feature vector are highly susceptible to noise that may exist in the signal. In this article, a new method to adjust the classifier is proposed using metaheuristics based on the black hole algorithm. The method is aimed at obtaining results similar to those obtained with manual noise elimination methods. In order to evaluate the proposed method, the MAHNOB HCI Tagging Database was used. Results show that using the black hole algorithm to optimize the feature vector of the Support Vector Machine we obtained an accuracy of 92.56% over 30 executions.