alzheimers_multimodal_research.pptx

1. INTEGRATING MRI AND MOLECULAR
DATA FOR ALZHEIMER'S RESEARCH
A Multimodal Approach for
Early Detection and
Validation

2. LITERATURE REVIEW
Through AI-based clustering, researchers found three subgroups:
typical aging, vascular-associated accelerated aging, and
neurodegenerative accelerated aging. Each group showed unique
brain atrophy patterns, genetic markers, and associations with
cardiovascular risk factors and amyloid-beta (A ) positivity. (2024)
β
Using non-invasive measures such as MRI, demographics, APOE
genotype, and neuropsychological tests, separate predictive models
were built for A positivity defined
β by cerebrospinal fluid (CSF) and
PET imaging. (2024)
MRI data and genomic profiles from over 25,000 participants—3,556
of whom exhibited microbleeds—across 11 population-based cohorts
and three stroke-focused cohorts, they performed comprehensive
GWAS on different BMB subtypes (lobar, deep, and mixed). (2020)

3. METHODOLOGY/CUSTOM AI SOLUTION
FOR EARLY DETECTION OF ALZHEIMERS
Step 1: Data Collection
Step 2: MRI Preprocessing
Step 3: Feature Extraction from MRIs
Step 4: AI Model Development
Step 5: Model Evaluation
Step 6: Correlation with Molecular/Genetic Data
Statistical analysis (correlation, regression) between model output and biomarkers
Use feature importance analysis to identify MRI features linked to molecular
markers
Train a multimodal model combining imaging and genetic features (e.g., MRI +
APOE 4) to assess added predictive value
ε

5. REFERENCES
Skampardoni, I., Nasrallah, I. M., Abdulkadir, A., Wen, J., Melhem, R.,
Mamourian, E., ... & Davatzikos, C. (2024). Genetic and clinical
correlates of AI-based brain aging patterns in cognitively unimpaired
individuals. JAMA psychiatry, 81(5), 456-467.
Moradi, E., Prakash, M., Hall, A., Solomon, A., Strange, B., Tohka, J., &
Alzheimer’s Disease Neuroimaging Initiative. (2024). Machine learning
prediction of future amyloid beta positivity in amyloid-negative
individuals. Alzheimer's Research & Therapy, 16(1), 46.
Knol, M. J., Lu, D., Traylor, M., Adams, H. H., Romero, J. R. J., Smith, A.
V., ... & Alzheimer's Disease Neuroimaging Initiative. (2020).
Association of common genetic variants with brain microbleeds: a
genome-wide association study. Neurology, 95(24), e3331-e3343.