Journal: Schizophrenia Research

Loading...

Abbreviation

Schizophr Res

Publisher

Elsevier

Journal Volumes

ISSN

0920-9964
1573-2509

Description

Search Results

Publications 1 - 10 of 30
  • Peleg-Raibstein, Daria; Hauser, Jonas; Lopez, Luis L.; et al. (2010)
    Schizophrenia Research
  • Ebisch, Sjoerd J.H.; Gallese, Vittorio; Salone, Anatolia; et al. (2018)
    Schizophrenia Research
  • Rössler, Wulf; Hengartner, Michael P.; Ajdacic-Gross, Vladeta; et al. (2014)
    Schizophrenia Research
  • Bitanihirwe, Byron K.Y.; Peleg-Raibstein, Daria; Mouttet, Forouhar; et al. (2010)
    Schizophrenia Research
  • Hartmann, Matthias N.; Hager, Oliver M.; Reimann, Anna V.; et al. (2014)
    Schizophrenia Research
  • Palominos, Claudio; Kirdun, Maryia; Nikzad, Amir H.; et al. (2025)
    Schizophrenia Research
    Semantic variables automatically extracted from spontaneous speech characterize anomalous semantic associations generated by groups with schizophrenia spectrum disorders (SSD). However, with the use of different language models and numerous aspects of semantic associations that could be tracked, the semantic space has become very high-dimensional, challenging both theoretical understanding and practical applications. This study aimed to summarize this space into a single composite semantic index and to test whether it can track diagnosis and symptom profiles over time at an individual level. The index was derived from a principal component analysis (PCA) yielding a linear combination of 117 semantic variables. It was tested in discourse samples of English speakers performing a picture description task, involving a total of 103 individuals with SSD and 36 healthy controls (HC) compared across four time points. Results showed that the index distinguished between SSD and HC groups, identified transitions from acute psychosis to remission and stabilization, predicted the sum of scores of the Thought, Language and Communication (TLC) index as well as subscores, capturing 65 % of the variance in the sum of TLC scores. These findings show that a single indicator meaningfully summarizes a shift in semantic associations in psychosis and tracks symptoms over time, while also pointing to variance unexplained, which is likely covered by other semantic and non-semantic factors.
  • Nielsen, Philip R.; Meyer, Urs; Mortensen, Preben B. (2016)
    Schizophrenia Research
  • Xu, Ziyan; Müller, Mario; Heekeren, Karsten; et al. (2016)
    Schizophrenia Research
  • Erdmann, Tore; Mathys, Christoph (2022)
    Schizophrenia Research
    Despite the ubiquity of delusional information processing in psychopathology and everyday life, formal characterizations of such inferences are lacking. In this article, we propose a generative framework that entails a computational mechanism which, when implemented in a virtual agent and given new information, generates belief updates (i.e., inferences about the hidden causes of the information) that resemble those seen in individuals with delusions. We introduce a particular form of Dirichlet process mixture model with a sampling-based Bayesian inference algorithm. This procedure, depending on the setting of a single parameter, preferentially generates highly precise (i.e. over-fitting) explanations, which are compartmentalized and thus can co-exist despite being inconsistent with each other. Especially in ambiguous situations, this can provide the seed for delusional ideation. Further, we show by simulation how the excessive generation of such over-precise explanations leads to new information being integrated in a way that does not lead to a revision of established beliefs. In all configurations, whether delusional or not, the inference generated by our algorithm corresponds to Bayesian inference. Furthermore, the algorithm is fully compatible with hierarchical predictive coding. By virtue of these properties, the proposed model provides a basis for the empirical study and a step toward the characterization of the aberrant inferential processes underlying delusions.
  • Surbeck, Werner; Hänggi, Jürgen; Scholtes, Felix; et al. (2020)
    Schizophrenia Research
    The core symptoms of schizophrenia spectrum disorders (SSD) include abnormal semantic processing which may rely on the ventral language stream of the human brain. Thus, structural disruption of the ventral language stream may play an important role in semantic deficits observed in SSD patients. Therefore, we compared white matter tract integrity in SSD patients and healthy controls using diffusion tensor imaging combined with probabilistic fiber tractography. For the ventral language stream, we assessed the inferior fronto-occipital fasciculus [IFOF], inferior longitudinal fasciculus, and uncinate fasciculus. The arcuate fasciculus and corticospinal tract were used as control tracts. In SSD patients, the relationship between semantic processing impairments and tract integrity was analyzed separately. Three-dimensional tract reconstructions were performed in 45/44 SSD patients/controls (“Bern sample”) and replicated in an independent sample of 24/24 SSD patients/controls (“Basel sample”). Multivariate analyses of fractional anisotropy, mean, axial, and radial diffusivity of the left IFOF showed significant differences between SSD patients and controls (p(FDR-corr) < 0.001, ηp2 = 0.23) in the Bern sample. Axial diffusivity (AD) of the left UF was inversely correlated with semantic impairments (r = −0.454, p(FDR-corr) = 0.035). In the Basel sample, significant group differences for the left IFOF were replicated (p < .01, ηp2 = 0.29), while the correlation between AD of the left IFOF and semantic processing decline (r = −0.376, p = .09) showed a statistical trend. No significant effects were found for the dorsal language stream. This is direct evidence for the importance of the integrity of the ventral language stream, in particular the left IFOF, in semantic processing deficits in SSD. (© 2020 Elsevier).
Publications 1 - 10 of 30