Increased Global Amyloid Burden Enhances Network Efficiency of Tau Propogation in the Brain

In this repository, you will find all the code necessary to:

1. Clean and harmonize regional tau PET SUVr values and Centiloid values for ADNI and A4 cohorts.
2. Run graphical machine learning model to learn the strongest conditional dependencies between tau accumulation in different brain regions and prune weaker spurious correlations.
3. Analyze graph metrics to determine differences in efficiency and organization of tau deposition at varying global amyloid burdens.

I. Data Cleaning

A. Researchers can request the data used in this project from the ADNI and A4 websites.

1. A list of de-identified subject IDs of the ADNI participants used in this project are located in jad2024/data_paths_and_cleaning/data/demographic_csvs/ADNI/ADNI_patient_ids.csv
   

2. A list of de-identified subject IDs for the A4 participants used in this project are located in jad2024/data_paths_and_cleaning/data/demographic_csvs/A4/A4_patient_ids.csv
   

   

B. Data cleaning scripts are located in jad2024/data_paths_and_cleaning/data_cleaning_scripts

1. merging_cent_tau_csvs.ipynb which merges the centiloid and tau SUVR raw csvs into a master csv used for analysis and also uses a centiloid cut off value of >=21 to create a new csv with only amyloid positive patients with naming style merged_adni/adni_at_amy_pos.csv where adni/a4 is whichever dataset that csv belongs to
   
2. adni_a4_data_harmonization.ipynb which narrows down a list of 44 brain region shared across the ADNI and A4 data and saves them to jad2024/data_paths_and_cleaning/data/intermediate_data/a4/merged_a4_at_amy_pos_bi_harm.csv where a4/adni is the parent folder name for the csv depending ion whichever cohort that data belongs to.
   

II. Run Graphical Model, Visualize Graphs, and Analyze Metrics of Tau Efficiency

Graphical Modeling Scripts

In jad2024/analyze_graphs you will find scripts for hyperparamter selection and running the graphical models on the data that has been divided into 3 centiloid quantile groups

1. jad2024/analyze_graphs/hyperparamter_tuning/bic.ipynb is a script to show how different hyperparameter (alpha) values affect the sparsity of the precision and covariance matrices and BIC of the graphical model used to determine the optimal strength of the L1 regularization (alpha) that should be applied. A very high alpha results in a sparse precision matrix where almost all connections in the graph would be dropped and a very low alpha would result in no connections being dropped. Choosing an optimal alpha value ensures that the model is learning the most important relationships by dropping weak or spurious relationshiops, while still retaining vital connections in the data.
   

2. jad2024/analyze_graphs/construct_and_analyze_graphs/streamlined_build_graphs.py is a script that creates 1000 bootstrap samples of the data and fits a probabilistic graphical model to each bootstrapped sample, produces graph visualizations of the model's learned tau graph structure, and calculates metrics like weighted clustering coefficient, average shortest path length, and weighted small world coefficient to analyze how tau efficiency increases at higher amyloid burdens.

3. jad2024/anaqlyze_graphs/construct_and_analyze_graphs/sig_testing.ipynbis a script that preforms significicance testing between mean graph metrics between amyloid groups. It preforms an ANOVA test for clustering coefficient and average shortest path length and a Kruskal-Wallis test on small world coefficient (significance tests were chosen by running jad2024/anaqlyze_graphs/hyperparamter_tuning/metrics_dis_checker.ipynb to plot the distribution of each graph metric to determine the most approriate statistical test to apply to analyze differences across centiloid groups.)