Background: Neurocognitive deficits in schizophrenia (SZ) are established and the Consortium on the Genetics of Schizophrenia (COGS) investigated such measures as endophenotypes in family-based (COGS-1) and case-control (COGS-2) studies. By requiring family participation, family-based sampling may result in samples that vary demographically and perform better on neurocognitive measures.
Methods: The Penn computerized neurocognitive battery (CNB) evaluates accuracy and speed of performance for several domains and was administered across sites in COGS-1 and COGS-2. Most tests were included in both studies. COGS-1 included 328 patients with SZ and 497 healthy comparison subjects (HCS) and COGS-2 included 1195 patients and 1009 HCS.
Results: Demographically, COGS-1 participants were younger, more educated, with more educated parents and higher estimated IQ compared to COGS-2 participants. After controlling for demographics, the two samples produced very similar performance profiles compared to their respective controls. As expected, performance was better and with smaller effect sizes compared to controls in COGS-1 relative to COGS-2. Better performance was most pronounced for spatial processing while emotion identification had large effect sizes for both accuracy and speed in both samples. Performance was positively correlated with functioning and negatively with negative and positive symptoms in both samples, but correlations were attenuated in COGS-2, especially with positive symptoms.
Conclusions: Patients ascertained through family-based design have more favorable demographics and better performance on some neurocognitive domains. Thus, studies that use case-control ascertainment may tap into populations with more severe forms of illness that are exposed to less favorable factors compared to those ascertained with family-based designs.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4441547 | PMC |
http://dx.doi.org/10.1016/j.schres.2014.10.049 | DOI Listing |
NMR Biomed
March 2025
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
In clinical practice, particularly in neurology assessments, imaging multiparametric MR images with a single-sequence scan is often limited by either insufficient imaging contrast or the constraints of accelerated imaging techniques. A novel single scan 3D imaging method, incorporating Wave-CAIPI and MULTIPLEX technologies and named WAMP, has been developed for rapid and comprehensive parametric imaging in clinical diagnostic applications. Featuring a hybrid design that includes wave encoding, the CAIPIRINHA sampling pattern, dual time of repetition (TR), dual flip angle (FA), multiecho, and optional flow modulation, the WAMP method captures information on RF B1t fields, proton density (PD), T1, susceptibility, and blood flow.
View Article and Find Full Text PDFHeliyon
January 2025
Department of Support and Information Technology, D. Serikbayev East Kazakhstan Technical University, Ust-Kamenogorsk, 070001, Kazakhstan.
The article examines the territory of East Kazakhstan, where a sharply continental climate prevails with hot summers, cold and snowy winters. The mountainous regions of East Kazakhstan are represented by the Kalba, Altai and Saur-Tarbagatay ranges, they are surrounded by rolling plains. The highest points are at 3000-4500 m.
View Article and Find Full Text PDFFront Plant Sci
January 2025
College of Big Data, Yunnan Agricultural University, Kunming, China.
Introduction: Weeds are a major factor affecting crop yield and quality. Accurate identification and localization of crops and weeds are essential for achieving automated weed management in precision agriculture, especially given the challenges in recognition accuracy and real-time processing in complex field environments. To address this issue, this paper proposes an efficient crop-weed segmentation model based on an improved UNet architecture and attention mechanisms to enhance both recognition accuracy and processing speed.
View Article and Find Full Text PDFFuture Microbiol
January 2025
Infectious Diseases Department, The First Hospital of China Medical University, Shenyang, Liaoning, China.
() is an opportunistic pathogenic fungus that often causes severe infections in immunosuppressed patients. Among species, is the most pathogenic and lethal species. Current research faces challenges related to unknown pathogenic mechanisms, complex resistance mechanisms, insufficiently rapid and accurate diagnostic methods, and insufficient research on susceptibility to infection.
View Article and Find Full Text PDFBehav Res Methods
January 2025
CogNosco Lab, Department of Psychology and Cognitive Sciences, University of Trento, Trento, Italy.
We introduce EmoAtlas, a computational library/framework extracting emotions and syntactic/semantic word associations from texts. EmoAtlas combines interpretable artificial intelligence (AI) for syntactic parsing in 18 languages and psychologically validated lexicons for detecting the eight emotions in Plutchik's theory. We show that EmoAtlas can match or surpass transformer-based natural language processing techniques, BERT or large language models like ChatGPT 3.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!