Detecting order–disorder transitions in discourse: Implications for schizophrenia

Cabana, Álvaro; Valle-Lisboa, Juan C.; Elvevåg, Brita; Mizraji, Eduardo

doi:10.1016/j.schres.2011.04.026

Cited by 30 publications

(22 citation statements)

References 43 publications

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“…Computing the cosine between two vectors in this space generates a semantic similarity measure, which can be computed even if the two units of text share no words in common. This technique has been widely used in such fields as information retrieval (Deerwester et al, 1990), automated essay scoring (Foltz, Laham, & Landauer, 1999), analyzing prose recall (Dunn et al, 2002), and in the analysis of prose from patients with schizophrenia (Cabana, Valle-Lisboa, Elvevåg, & Mizraji, 2011; Elvevåg, Foltz, Weinberger, & Goldberg, 2007; Elvevåg, Foltz, Rosenstein, & DeLisi, 2010). …”

Section: Methodsmentioning

confidence: 99%

A computational language approach to modeling prose recall in schizophrenia

Rosenstein

Diaz-Asper

Foltz

et al. 2014

Cortex

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Many cortical disorders are associated with memory problems. In schizophrenia, verbal memory deficits are a hallmark feature. However, the exact nature of this deficit remains elusive. Modeling aspects of language features used in memory recall have the potential to provide means for measuring these verbal processes. We employ computational language approaches to assess time-varying semantic and sequential properties of prose recall at various retrieval intervals (immediate, 30 min and 24 h later) in patients with schizophrenia, unaffected siblings and healthy unrelated control participants. First, we model the recall data to quantify the degradation of performance with increasing retrieval interval and the effect of diagnosis (i.e., group membership) on performance. Next we model the human scoring of recall performance using an n-gram language sequence technique, and then with a semantic feature based on Latent Semantic Analysis. These models show that automated analyses of the recalls can produce scores that accurately mimic human scoring. The final analysis addresses the validity of this approach by ascertaining the ability to predict group membership from models built on the two classes of language features. Taken individually, the semantic feature is most predictive, while a model combining the features improves accuracy of group membership prediction slightly above the semantic feature alone as well as over the human rating approach. We discuss the implications for cognitive neuroscience of such a computational approach in exploring the mechanisms of prose recall.

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Section: Methodsmentioning

confidence: 99%

A computational language approach to modeling prose recall in schizophrenia

Rosenstein

Diaz-Asper

Foltz

et al. 2014

Cortex

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“…5–13 A particularly useful example of such computational phenotyping is the assessment of verbal reports by graph analysis, which provides a precise and automated quantification of speech features that are related with negative symptoms 9 and show potential to help the differential diagnosis of psychosis. 9, 10 By representing each word as a node and the temporal sequence of consecutive words as directed edges, it is possible to calculate attributes that characterize graph structure.…”

Section: Introductionmentioning

confidence: 99%

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

2017

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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.npj Schizophrenia (2017) 3:18 ; doi:10.1038/s41537-017-0019-3 INTRODUCTION Schizophrenia is associated with negative symptoms, major impacts on social behavior and poor prognosis. 1 In particular, elevated negative symptoms are associated with low rates of recovery. 1, 2 Formal thought disorder-which comprises poverty of speech, derailment, and incoherence-constitutes an important set of psychotic symptoms, and negative formal thought disorder is associated with the schizophrenia diagnosis even during first episode psychosis. 2,3 The early stages of the disease constitute a critical opportunity for prevention of major cognitive damage. 4 Improved behavioral measures subjected to novel mathematical analyses are emerging as part of a new field that uses computational tools to better characterize psychiatric phenomena. 5-13 A particularly useful example of such computational phenotyping is the assessment of verbal reports by graph analysis, which provides a precise and automated quantification of speech features that are related with negative symptoms 9 and show potential to he...

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“…The underlying assump-tion is that the graph structure represents the organization of the associations between elements, for instance, the organization of words in terms of its semantic relations in semantic knowledge investigations. These techniques have been used for analyzing a variety of psycholinguistic tasks in both healthy and clinical conditions (Cabana, Valle-Lisboa, Elvevåg, & Mizraji, 2011;Mota et al, 2012). In language studies, this organization can infl uence inter alia the speed of the cognitive activity, including memory processes (Coronges, Stacy, & Valente, 2007).…”

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confidence: 99%

Graph analysis of semantic word association among children, adults, and the elderly

Zortéa¹,

Menegola²,

Villavicencio³

et al. 2014

Psicol. Reflex. Crit.

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This study used graph analysis to investigate how age differences modify the structure of semantic word association networks of children and adults and if the networks present a small-world structure and a scale-free distribution which are typical of natural languages. Three age groups of Brazilian Portuguese speakers (children, adults and elderly people) participated in the experiment. Quantitative and qualitative measures suggested that adults and elderly speakers have similar network structures. Children's network showed fewer nodes, connections and clusters, and longer inter-node distances. All networks presented a small-world structure, but they did not show entirely scale-free distributions. These results suggest that from childhood to adulthood, there is an increase not only in the number of words semantically linked to a target but also an increase in the connectivity of the network. Keywords: Graph theory, word association, developmental age groups, semantic memory. ResumoEste estudo utilizou análise de grafos para investigar como a idade modifi ca a estrutura das redes de associações semânticas de palavras de crianças e adultos e se estas redes apresentam estrutura small-world e distribuição scale-free típicas de linguagens naturais. Participaram dos experimentos três grupos etários (crianças, adultos e idosos) falantes do Português Brasileiro. Medidas quantitativas e qualitativas sugeriram que adultos e idosos possuem redes semelhantes quanto a número de nós, conexões e agrupamentos. A rede das crianças expressou menor número de nós, conexões e agrupamentos e maiores distancias inter-nós. As redes apresentaram uma estrutura small-world, mas não uma distribuição scale-free completa. Os resultados sugerem que além do número de palavras semanticamente associadas aos alvos aumentar das crianças para os adultos, as redes se tornaram mais conectadas. Palavras-chave: Teoria dos grafos, associação de palavras, grupos etários, memória semântica.The structure and organization of human knowledge in terms of semantic relations have been the topic of much debate in areas such as cognitive sciences, psychology, and computer sciences. Nevertheless, the effects of age in semantic processing are not yet fully understood, mainly due to diverging research results. For example, Rönnlund, Nyberg, Bäckman, and Nilsson (2005), found a decline in semantic fl uency and vocabulary tasks, in participants from 60 to 85 years old. By contrast Park et al. (2002) showed an increase in the performance scores of vocabulary tests  Mailing address:

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Detecting order–disorder transitions in discourse: Implications for schizophrenia

Cited by 30 publications

References 43 publications

A computational language approach to modeling prose recall in schizophrenia

A computational language approach to modeling prose recall in schizophrenia

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

Graph analysis of semantic word association among children, adults, and the elderly

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