Discourse comprehension processes attempt to produce an elaborate and well-connected representation in the reader's mind. A common network of regions including the angular gyrus, posterior cingulate, and dorsal frontal cortex appears to be involved in constructing coherent representations in a variety of tasks including social cognition tasks, narrative comprehension, and expository text comprehension. Reading strategies that require the construction of explicit inferences are used in the present research to examine how this coherence network interacts with other brain regions. A psychophysiological interaction analysis was used to examine regions showing changed functional connectivity with this coherence network when participants were engaged in either a non-inferencing reading strategy, paraphrasing, or a strategy requiring coherence-building inferences, self-explanation. Results of the analysis show that the coherence network increases in functional connectivity with a cognitive control network that may be specialized for the manipulation of semantic representations and the construction of new relations among these representations.
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http://dx.doi.org/10.3389/fnhum.2015.00562 | DOI Listing |
Elife
January 2025
Department of Psychology, University of York, North Yorkshire, United Kingdom.
Processing pathways between sensory and default mode network (DMN) regions support recognition, navigation, and memory but their organisation is not well understood. We show that functional subdivisions of visual cortex and DMN sit at opposing ends of parallel streams of information processing that support visually mediated semantic and spatial cognition, providing convergent evidence from univariate and multivariate task responses, intrinsic functional and structural connectivity. Participants learned virtual environments consisting of buildings populated with objects, drawn from either a single semantic category or multiple categories.
View Article and Find Full Text PDFJ Biomed Opt
January 2025
Columbia University, Department of Electrical Engineering, New York, United States.
Significance: Radiofrequency ablation to treat atrial fibrillation (AF) involves isolating the pulmonary vein from the left atria to prevent AF from occurring. However, creating ablation lesions within the pulmonary veins can cause adverse complications.
Aim: We propose automated classification algorithms to classify optical coherence tomography (OCT) volumes of human venoatrial junctions.
Quant Imaging Med Surg
January 2025
Department of Ophthalmology, the Fourth Affiliated Hospital of China Medical University, Shenyang, China.
Background: Recently, deep learning has become a popular area of research, and has revolutionized the diagnosis and prediction of ocular diseases, especially fundus diseases. This study aimed to conduct a bibliometric analysis of deep learning in the field of ophthalmology to describe international research trends and examine the current research directions.
Methods: This cross-sectional bibliometric analysis examined the development of research on deep learning in the field of ophthalmology and its sub-topics from 2015 to 2024.
Qual Life Res
January 2025
Heart and Mind Wellbeing Center, Heart Institute and Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue (MLC 7039), Cincinnati, OH, 45229, USA.
Purpose: To examine global and health-related quality of life (QOL) among parents of individuals with Fontan physiology and determine associations with sociodemographic, parent and child-related health, psychological, and relational factors.
Methods: Parents participating in the Australian and New Zealand Fontan Registry (ANZFR) QOL Study (N = 151, Parent Mean age = 47.9 ± 10.
Commun Biol
January 2025
Dept. Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, FL, 33431, USA.
Predicting novel mutations has long-lasting impacts on life science research. Traditionally, this problem is addressed through wet-lab experiments, which are often expensive and time consuming. The recent advancement in neural language models has provided stunning results in modeling and deciphering sequences.
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