Related Experiment Video
Updated: Jan 8, 2026

Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451
Published on: April 18, 2025
Alzheimer's Imaging Consortium
Sindhuja Tirumalai Govindarajan1, Elizabeth Mamourian2, Dhivya Srinivasan3
1Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Background:
Smoking is a well-established risk factor for cardiovascular disease, and its association with neurodegeneration and cognitive decline is an area of ongoing research. Critically, the interplay between smoking, Alzheimer's disease (AD) pathology, and cognitive impairment remains incompletely understood. This study investigated the relationship between smoking, AD pathology as indexed by amyloid-beta (Aβ) deposition, and cognitive performance using SPARE-SM, a novel machine learning-based marker that quantifies smoking-related spatial patterns of abnormalities on individual structural magnetic resonance images (sMRI).
Methods:
SPARE-Smoking, derived from N = 37,098 cognitively unimpaired individuals from diverse cohorts, was evaluated in N = 222 individuals who had amyloid (Aβ) status available within +/- 1 year of the MRI scan in a subset of the training cohort. Amyloid deposition was determined using study-specific cut-offs for CSF and PET SUVR measures, categorizing participants as Aβ-/Aβ+. Multivariable regression models were used to assess interactions between Aβ status, smoking history, and age on SPARE-SM scores. Multivariable linear regression models, adjusted for age, sex, and years of education, examined associations between SPARE-SM and cognitive performance.
Results:
While the proportion of smokers was similar between Aβ+ and Aβ- participants (Table 1), SPARE-SM showed a nuanced relationship with both Aβ and smoking status (Figure 1A). Specifically, SPARE-SM was higher than SM+Aβ- individuals in SM+ Aβ+ individuals (p <0.05) but lower in SM- Aβ+ individuals (p <0.05). Importantly, higher SPARE-SM was associated with worse cognitive performance, whereas simply classifying individuals as smokers or non-smokers showed no associations with cognitive outcomes (Figure 1B).
Conclusion:
These findings suggest a complex relationship between smoking, amyloid pathology, and cognition. The observation that SPARE-SM differed by Aβ in smoking individuals highlights their potential synergistic effects on neurodegeneration. SPARE-SM demonstrated associations with cognitive decline, even when clinical smoking status did not, emphasizing its potential for early risk identification. Further research is needed to disentangle the mechanisms linking smoking, brain changes, amyloid, and dementia.
More Related Videos
09:31Visualization of Amyloid β Deposits in the Human Brain with Matrix-assisted Laser Desorption/Ionization Imaging Mass Spectrometry
Published on: March 7, 2019
12:50Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
Published on: April 14, 2014
Related Concept Videos
Alzheimer's Disease: Overview
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...
Imaging Studies III: Computed Tomography
Imaging Studies IV: Magnetic Resonance Imaging
Brain Imaging
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
Imaging Studies II: Positron Emission Tomography and Scintigraphy
Fundamental Principles of PET
Imaging Studies I: CT and MRI
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...