Mindfulness practice has been linked to reduced depressive rumination and described as involving inhibition of information that has been relevant in the past and is no longer relevant in the present moment. Backward inhibition (BI) is considered to be one of the purest measures of task set inhibition, and impaired BI has been linked to depressive rumination. BI was contrasted with Competitor Rule Suppression (CRS), which is another phenomenon observed in task switching, yet one which involves episodic memory tagging of information that is currently conflicting rather than active inhibition. Although similar at baseline level, a randomly assigned group (n = 38) who underwent an eight session mindfulness training program exhibited improved BI but not CRS compared to a waiting list group (n = 38). Findings indicate that mindfulness improves the specific component of task set inhibition, which has previously been linked to reduced rumination. Implications regarding the potential role of task set inhibition in mediating between mindfulness and reduced rumination, as well as the role of mindfulness in "being in the present moment" are discussed.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3542708 | PMC |
| http://dx.doi.org/10.3389/fpsyg.2012.00618 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
AiGPro: a multi-tasks model for profiling of GPCRs for agonist and antagonist.
J Cheminform
January 2025
School of Systems Biomedical Science, Soongsil University, 369 Sangdo-ro, Dongjak-gu, 06978, Seoul, Republic of Korea.
G protein-coupled receptors (GPCRs) play vital roles in various physiological processes, making them attractive drug discovery targets. Meanwhile, deep learning techniques have revolutionized drug discovery by facilitating efficient tools for expediting the identification and optimization of ligands. However, existing models for the GPCRs often focus on single-target or a small subset of GPCRs or employ binary classification, constraining their applicability for high throughput virtual screening.
View Article and Find Full Text PDFA SNARC-like effect for visual speed.
Atten Percept Psychophys
January 2025
Department of Developmental and Social Psychology, University of Padova, via Venezia 8, 35131, Padova, Italy.
Numerical and nonnumerical magnitudes can be represented along a hypothetical left-to-right continuum, where smaller quantities are associated with the left side and larger quantities with the right side. However, these representations are flexible, as their intensity and direction can be modulated by various contextual cues and task demands. In four experiments, we investigated the spatial representation of visual speed.
View Article and Find Full Text PDFAn Open-Source 3D-Printable Platform for Testing Head-Fixed Cognitive Flexibility in Rodents.
eNeuro
January 2025
Department of Neuroscience, University of Connecticut School of Medicine, Farmington, Connecticut 06030
The study of the neural circuitry underlying complex mammalian decision-making, particularly cognitive flexibility, is critical for understanding psychiatric disorders. To test cognitive flexibility, as well as potentially other decision-making paradigms involving multimodal sensory perception, we developed FlexRig, an open-source, modular behavioral platform for use in head-fixed mice. FlexRig enables the administration of tasks relying upon olfactory, somatosensory, and/or auditory cues and employing left and right licking as a behavior readout and reward delivery mechanism.
View Article and Find Full Text PDFHeterogeneity analysis provides evidence for a genetically homogeneous subtype of bipolar-disorder.
PLoS One
January 2025
Department of Psychiatry, University of California San Diego, La Jolla, CA, United States of America.
Background: Bipolar Disorder (BD) is a complex disease. It is heterogeneous, both at the phenotypic and genetic level, although the extent and impact of this heterogeneity is not fully understood. One way to assess this heterogeneity is to look for patterns in the subphenotype data.
View Article and Find Full Text PDFGenerative artificial intelligence enables the generation of bone scintigraphy images and improves generalization of deep learning models in data-constrained environments.
Eur J Nucl Med Mol Imaging
January 2025
Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Spitalgasse 23, Vienna, 1090, Austria.
Purpose: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.
Methods: We trained a generative model on Tc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!