Episodic memory (EM) impairments in schizophrenia (SZ) are predictive of functional outcome and are a potential endophenotype of the disorder. The current study investigated the neuroanatomical correlates of EM encoding and retrieval in SZ with structural magnetic resonance and diffusion tensor imaging (DTI) measures in 22 patients with SZ and 22 age- and gender-matched healthy controls. Tract-based Spatial Statistics (TBSS) was used to investigate microstructural alterations in white matter (WM), while FreeSurfer surface-based analysis was used to determine abnormalities in grey matter (GM) and WM volumetrics and cortical thickness. Compared to controls, patients demonstrated GM deficits in temporal and parietal regions and lower fractional anisotropy (FA) of WM in diffuse brain regions. Patients also demonstrated reduced functioning in both encoding and retention of auditory-verbal EM. Among patients but not controls, EM encoding correlated with WM volume in the orbitofrontal cortex and increased radial diffusivity in the fornix, whereas EM retrieval correlated with WM volume in posterior parietal cortex. These findings suggest a differential role for frontal and parietal WM in EM encoding and retrieval processes, while myelin integrity of the fornix may play a specific role in mediating EM encoding processes in SZ.
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http://dx.doi.org/10.1016/j.pscychresns.2016.07.002 | DOI Listing |
Sensors (Basel)
December 2024
Department of Information Management, Tunghai University, Taichung 407224, Taiwan.
Today, huge amounts of time series data are sensed continuously by AIoT devices, transmitted to edge nodes, and to data centers. It costs a lot of energy to transmit these data, store them, and process them. Data compression technologies are commonly used to reduce the data size and thus save energy.
View Article and Find Full Text PDFInt J Mol Sci
December 2024
Department of Medicine, Division of Clinical Immunology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto 14049-900, SP, Brazil.
Citrullination, a post-translational modification (PTM), plays a critical role in rheumatoid arthritis (RA) by triggering immune responses to citrullinated self-antigens. Some HLA-DRB1 genes encode molecules with the shared epitope (QKRAA/QRRAA) sequence in the peptide-binding groove which preferentially presents citrulline-modified peptides, like vimentin, that intensifies the immune response in RA. In this study, we used computational approaches to evaluate intermolecular interactions between vimentin peptide-ligands (with/without PTM) and HLA-DRB1 alleles associated with a significantly increased risk for RA development.
View Article and Find Full Text PDFNat Photonics
October 2024
Institut national de la recherche scientifique, Centre Énergie Matériaux Télécommunications, Varennes, Quebec Canada.
Quantum walks on photonic platforms represent a physics-rich framework for quantum measurements, simulations and universal computing. Dynamic reconfigurability of photonic circuitry is key to controlling the walk and retrieving its full operation potential. Universal quantum processing schemes based on time-bin encoding in gated fibre loops have been proposed but not demonstrated yet, mainly due to gate inefficiencies.
View Article and Find Full Text PDFComput Med Imaging Graph
January 2025
College of Medicine and Biological Information Engineering, Northeastern University, 110819, China. Electronic address:
With the increasing popularity of medical imaging and its expanding applications, posing significant challenges for radiologists. Radiologists need to spend substantial time and effort to review images and manually writing reports every day. To address these challenges and speed up the process of patient care, researchers have employed deep learning methods to automatically generate medical reports.
View Article and Find Full Text PDFJ Am Med Inform Assoc
December 2024
Department of Radiology, Stanford University, Stanford, CA 94304, United States.
Objective: Brief hospital course (BHC) summaries are clinical documents that summarize a patient's hospital stay. While large language models (LLMs) depict remarkable capabilities in automating real-world tasks, their capabilities for healthcare applications such as synthesizing BHCs from clinical notes have not been shown. We introduce a novel preprocessed dataset, the MIMIC-IV-BHC, encapsulating clinical note and BHC pairs to adapt LLMs for BHC synthesis.
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