Background: Schizophrenia (SZ) is associated with devastating emotional, cognitive and language impairments. Understanding the deficits in each domain and their interactions is important for developing novel, targeted psychotherapies. This study tested whether negative-threat word processing is altered in individuals with SZ compared to healthy controls (HC), in relation to SZ symptom severity across domains.
Methods: Thirty-one SZ and seventeen HC subjects were scanned with functional magnetic resonance imaging while silently reading negative-threat and neutral words. Post-scan, subjects rated the valence of each word. The effects of group (SZ, HC), word type (negative, neutral), task period (early, late), and severity of clinical symptoms (positive, negative, excitement/hostility, cognitive, depression/anxiety), on word valence ratings and brain activation, were analyzed.
Results: SZ and HC subjects rated negative versus neutral words as more negative. The SZ subgroup with severe versus mild excitement/hostility symptoms rated the negative words as more negative. SZ versus HC subjects hyperactivated left language areas (angular gyrus, middle/inferior temporal gyrus (early period)) and the amygdala (early period) to negative words, and the amygdala (late period) to neutral words. In SZ, activation to negative versus neutral words in left dorsal temporal pole and dorsal anterior cingulate was positively correlated with excitement/hostility scores.
Conclusions: A negatively-biased behavioral response to negative-threat words was seen in SZ with severe versus mild excitement/hostility symptoms. The biased behavioral response was mediated by hyperactivation of brain networks associated with semantic processing of emotion concepts. Thus, word-level semantic processing may be a relevant psychotherapeutic target in SZ.
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http://dx.doi.org/10.1016/j.schres.2020.12.022 | DOI Listing |
Open Mind (Camb)
January 2025
Department of Computer Science, University of Toronto, Toronto, Canada.
The lexicon is an evolving symbolic system that expresses an unbounded set of emerging meanings with a limited vocabulary. As a result, words often extend to new meanings. Decades of research have suggested that word meaning extension is non-arbitrary, and recent work formalizes this process as cognitive models of semantic chaining whereby emerging meanings link to existing ones that are semantically close.
View Article and Find Full Text PDFSci Rep
January 2025
School of Computer Science, Hunan First Normal University, Changsha, 410205, China.
Retinal blood vessels are the only blood vessels in the human body that can be observed non-invasively. Changes in vessel morphology are closely associated with hypertension, diabetes, cardiovascular disease and other systemic diseases, and computers can help doctors identify these changes by automatically segmenting blood vessels in fundus images. If we train a highly accurate segmentation model on one dataset (source domain) and apply it to another dataset (target domain) with a different data distribution, the segmentation accuracy will drop sharply, which is called the domain shift problem.
View Article and Find Full Text PDFOphthalmologie
January 2025
Augenklinik Sulzbach, Knappschaftsklinikum Saar, An der Klinik 10, 66280, Sulzbach/Saar, Deutschland.
Background: The increasing bureaucratic burden in everyday clinical practice impairs doctor-patient communication (DPC). Effective use of digital technologies, such as automated semantic speech recognition (ASR) with automated extraction of diagnostically relevant information can provide a solution.
Objective: The aim was to determine the extent to which ASR in conjunction with semantic information extraction for automated documentation of the doctor-patient dialogue (ADAPI) can be integrated into everyday clinical practice using the IVI routine as an example and whether patient care can be improved through process optimization.
J Chem Inf Model
January 2025
School of Computer Science and Technology, Soochow University, Jiangsu 215006, China.
Accurate prediction of drug-target interactions (DTIs) is pivotal for accelerating the processes of drug discovery and drug repurposing. MVCL-DTI, a novel model leveraging heterogeneous graphs for predicting DTIs, tackles the challenge of synthesizing information from varied biological subnetworks. It integrates neighbor view, meta-path view, and diffusion view to capture semantic features and employs an attention-based contrastive learning approach, along with a multiview attention-weighted fusion module, to effectively integrate and adaptively weight the information from the different views.
View Article and Find Full Text PDFMethodsX
June 2025
Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, 76100 Melaka, Malaysia.
This study explores the possibility of integrating and retrieving heterogenous data across platforms by using ontology graph databases to enhance educational insights and enabling advanced data-driven decision-making. Motivated by some of the well-known universities and other Higher Education Institutions ontology, this study improvises the existing entities and introduces new entities in order to tackle a new topic identified from the preliminary interview conducted in the study to cover the study objective. The paper also proposes an innovative ontology, referred to as Student Performance and Course, to enhance resource management and evaluation mechanisms on course, students, and MOOC performance by the faculty.
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