People with mild cognitive impairment (MCI) are at an elevated risk of developing Alzheimer's disease or other forms of dementia. Although the neural correlates of successful memory performance in MCI have been widely investigated, the neural mechanisms involved in unsuccessful memory performance remain unknown. The current study examines the differences between patients suffering from stable amnestic MCI with multiple deficit syndromes and healthy elderly controls in relation to the neural correlates of both successful and unsuccessful encoding and recognition. Forty-six subjects (27 controls, 19 MCI) from the HelMA (Helmholtz Alliance for Mental Health in an Aging Society) completed a comprehensive neuropsychological test battery and participated in an fMRI experiment for associative face-name memory. In patients, the areas of frontal, parietal, and temporal cortices were less involved during unsuccessful encoding and recognition. A temporary dysfunction of the top-down control of frontal or parietal (or both) areas is likely to result in a non-selective propagation of task-related information to memory.
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http://dx.doi.org/10.3389/fnagi.2014.00201 | DOI Listing |
Brain Res Bull
January 2025
Department of Geriatrics, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China. Electronic address:
Purpose: To investigate the differences in brain spontaneous neural activity between limb-onset and bulbar-onset amyotrophic lateral sclerosis (ALS-L and ALS-B, respectively) patients using resting-state functional MRI (rs-fMRI) with amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo).
Materials And Methods: The rs-fMRI data were collected from 41 ALS patients (11 ALS-B and 30 ALS-L) and 25 healthy controls (HC). ALFF and ReHo values were calculated, and group differences were assessed using one-way ANCOVA and two-sample t-tests.
Am J Hum Genet
January 2025
Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA. Electronic address:
Dysregulation of genes encoding the homologous to E6AP C-terminus (HECT) E3 ubiquitin ligases has been linked to cancer and structural birth defects. One member of this family, the HECT-domain-containing protein 1 (HECTD1), mediates developmental pathways, including cell signaling, gene expression, and embryogenesis. Through GeneMatcher, we identified 14 unrelated individuals with 15 different variants in HECTD1 (10 missense, 3 frameshift, 1 nonsense, and 1 splicing variant) with neurodevelopmental disorders (NDDs), including autism, attention-deficit/hyperactivity disorder, and epilepsy.
View Article and Find Full Text PDFNeural Netw
January 2025
Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy.
Shadow removal remains a challenging visual task aimed at restoring the original brightness of shadow regions in images. Many existing methods overlook the implicit clues within non-shadow regions, leading to inconsistencies in the color, texture, and illumination of the reconstructed shadow-free images. To address these issues, we propose an efficient hybrid model of Transformer and Generative Adversarial Network (GAN), named ShadowGAN-Former, which utilizes information from non-shadow regions to assist in shadow removal.
View Article and Find Full Text PDFSci Rep
January 2025
School of Architecture and Urban Planning, Beijing University of Civil Engineering and Architecture, Beijing, 100055, China.
Air pollution is a critical global environmental issue, further exacerbated by rapid industrialization and urbanization. Accurate prediction of air pollutant concentrations is essential for effective pollution prevention and control measures. The complex nature of pollutant data is influenced by fluctuating meteorological conditions, diverse pollution sources, and propagation processes, underscores the crucial importance of the spatial and temporal feature extraction for accurately predicting air pollutant concentrations.
View Article and Find Full Text PDFPhlebology
January 2025
Department of Vascular Surgery, University Hospital Leipzig, Leipzig, Germany.
Aim: This study aimed to develop a web-based machine learning (ML) model to predict the lifetime likelihood of developing varicose veins using global disease prevalence data.
Methods: We utilized data from a systematic review, registered under PROSPERO (CRD42021279513), which included 81 studies on varicose vein prevalence across various geographic regions. The data used to build the ML model included disease prevalence as the outcome (%), along with the following predictors: mean age, gender distribution (%), mean body mass index (BMI) of the study cohort, and the mean gravity field of the study region (mGal), representing variations in Earth's underground mass distribution that influence blood and fluid redistribution in the human body, affecting disease prevalence.
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