Background: Mild cognitive impairment (MCI) represents a stage between cognitively normal and Alzheimer's disease. Despite much published research on MCI, there continues to be a knowledge gap of volumetric brain changes in MCI versus cognitively normal (CN) in racially diverse, community-based samples.
Objective: The study aimed to understand differences in volume of selected brain regions in individuals with MCI versus those who are cognitively normal.
Methods: This was a cross-sectional study with 1835 participants, which sampled all cognitively impaired participants (n = 667) and a subsample of cognitively normal participants from the ARIC neurocognitive study (ARIC-NCS). All individuals underwent a brain MRI. Two models (5 versus 22 regions of interest [ROI]) were built to analyze differences in brain volume between cognitively normal and MCI, and among 3 cognitive domains (memory, language, executive function). Using previous visits data, we estimated the standard deviations of 20-year cognitive decline equivalent to the difference in brain volume between MCI and CN.
Results: Every lobe was significantly smaller in individuals with MCI, with the largest difference observed in the temporal lobe. Moreover, there was a significant difference between MCI and CN in every subregion within the temporal lobe. The difference in volume between CN and MCI was equivalent to the total brain volume difference associated with a 1.24 standard deviation greater long-term cognitive decline.
Conclusions: Loss of volume in all cortical lobes, but particularly in the temporal lobe, was associated with MCI. Additionally, significant volume differences were observed in the temporal lobe in all three cognitive domains.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1177/13872877251313816 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
The Effects of Hospital Delivery Volume and Travel Time on Perinatal Mortality and Delivery in Transit: Causal Inference with Triangulation.
Epidemiology
January 2025
Norwegian University of Science and Technology, Department of Public Health and Nursing, Trondheim, Norway.
Background: Hospital regionalization involves balancing hospital volume and travel time. We investigated how hospital volume and travel time affect perinatal mortality and the risk of delivery in transit using three different study designs.
Methods: This nationwide cohort study used data from the Medical Birth Registry of Norway (1999-2016) and Statistics Norway.
Measurement of inferior colliculus volume based on MRI image stacks and its relationship with age and hearing status.
PLoS One
January 2025
Department of Otolaryngology, University Hospital Regensburg, Regensburg, Germany.
The inferior colliculus is a key nucleus in the central auditory pathway, integrating acoustic stimuli from both cochleae and playing a crucial role in sound localization. It undergoes functional and structural development in childhood and experiences age-related degeneration later in life, contributing to the progression of age-related hearing loss. This study aims at finding out, whether the volume of the human inferior colliculus can be determined by analysis of routinely performed MRIs and whether there is any age-related variation.
View Article and Find Full Text PDFMedial orbitofrontal cortex structure, function, and cognition associates with weight loss for laparoscopic sleeve gastrectomy.
Obesity (Silver Spring)
February 2025
Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Objective: The objective of this study was to investigate underlying mechanisms of long-term effective weight loss after laparoscopic sleeve gastrectomy (LSG) and effects on the medial orbitofrontal cortex (mOFC) and cognition.
Methods: A total of 18 individuals with obesity (BMI ≥ 30 kg/m) underwent LSG. Clinical data, cognitive scores, and brain magnetic resonance imaging scans were evaluated before LSG and 12 months after LSG.
Multiscale Analysis of Alzheimer's Disease Using Feature Fusion in Cognitive and Sensory Brain Regions.
Digit Biomark
December 2024
Electrical and Computer Engineering, Western Michigan University, Kalamazoo, MI, USA.
Introduction: This research is focused on early detection of Alzheimer's disease (AD) using a multiscale feature fusion framework, combining biomarkers from memory, vision, and speech regions extracted from magnetic resonance imaging and positron emission tomography images.
Methods: Using 2D gray level co-occurrence matrix (2D-GLCM) texture features, volume, standardized uptake value ratios (SUVR), and obesity from different neuroimaging modalities, the study applies various classifiers, demonstrating a feature importance analysis in each region of interest. The research employs four classifiers, namely linear support vector machine, linear discriminant analysis, logistic regression (LR), and logistic regression with stochastic gradient descent (LRSGD) classifiers, to determine feature importance, leading to subsequent validation using a probabilistic neural network classifier.
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!