Background: Compensation and adaptation therapies have been developed to improve community functioning via improving neurocognitive abilities in people with schizophrenia. Various modes of delivering compensation and adaptation therapies have been found to be effective. The aim of this trial is to compare two different cognitive interventions, Compensatory Cognitive Training (CCT) and Computerised Interactive Remediation of Cognition-Training for Schizophrenia (CIRCuiTS). The trial also aims to identify if mismatch negativity (MMN) can predict an individual's response to the compensation and adaptation programmes.
Methods: This study will use a randomised, controlled trial of two cognitive interventions to compare the impact of these programmes on measures of neurocognition and function. One hundred clinically stable patients aged between 18 and 65 years with a diagnosis of a schizophrenia spectrum disorder will be recruited. Participants will be randomised to either the CCT or the CIRCuiTS therapy groups. The outcome measures are neurocognition (BACS), subjective sense of cognitive impairment (SSTICS), social functioning (SFS), and MMN (measured by EEG) in people with schizophrenia spectrum disorders.
Discussion: This trial will determine whether different approaches to addressing the cognitive deficits found in schizophrenia spectrum disorders are of comparable benefit using the outcome measures chosen. This has implications for services where cost and lack of computer technology limit the implementation and dissemination of interventions to address cognitive impairment in routine practice. The trial will contribute to the emerging evidence of MMN as a predictor of response to cognitive interventions.
Trial Registration: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12618000161224 . Registered on 2 February 2018. Protocol version: 4.0, 18 June 2018.
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http://dx.doi.org/10.1186/s13063-020-04743-y | DOI Listing |
Front Neurosci
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Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, China.
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Neuroradiology Group, Vall d'Hebron Research Institute (VHIR), Barcelona, Spain.
Multiple sclerosis (MS) is a neurodegenerative disease that affects the central nervous system. Structures affected in MS include the corpus callosum, connecting the hemispheres. Studies have shown that in mammalian brains, structural connectivity is organized according to a conservation principle, an inverse relationship between intra- and interhemispheric connectivity.
View Article and Find Full Text PDFISA Trans
December 2024
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China. Electronic address:
Denial-of-service (DoS) attacks and antagonistic interactions may exist in complex networks, which will destroy cooperative communication between agents and thus cannot realize collaborative tasks. Therefore, this paper studies time-varying formation tracking (TVFT) of heterogeneous multi-agent systems (HMASs) with DoS attacks and cooperative-antagonistic interactions. It aims to ensure system communication connectivity and allow followers to achieve distributed secure bipartite TVFT.
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December 2024
University of Sassari, Department of Chemical, Physical, Mathematical and Natural Sciences, Sassari, Italy; National Biodiversity Future Centre, Palermo, Italy.
Recovering seagrass ecosystems through restoration has become impellent to re-establish their functionality and services. Although the use of seedlings may represent an appropriate solution, little information is provided on the seedling-based restoration effectiveness with influence of biotic and abiotic interactions. Survival, morphological development and leaf total phenol content of transplanted Posidonia oceanica seedlings were evaluated under different origin, thermal regimes and herbivore pressure through a five-months field experiment in two MPAs, located on the west (cold) and east (warm) Sardinia coast to explore the effectiveness of seedling-based restoration.
View Article and Find Full Text PDFSci Rep
December 2024
Guangzhou University, School of Computer Science and Cyber Engineering, Guangzhou, 510006, China.
Underwater image enhancement (UIE) is challenging since image degradation in aquatic environments is complicated and changing over time. Existing mainstream methods rely on either physical-model or data-driven, suffering from performance bottlenecks due to changes in imaging conditions or training instability. In this article, we attempt to adapt the diffusion model to the UIE task and propose a Content-Preserving Diffusion Model (CPDM) to address the above challenges.
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