Topology assisted clustering of temporal fMRI

Pipelines

TDA pipeline:
(nonTDA) Direct time-series clustering pipeline:
(nonTDA) PCA-based dimensionality reduction and clustering pipeline:
(nonTDA) Traditional functional connectivity network analysis pipeline:

Dataset

Data dimensions:
- DFC 2500: 316 x 86 x 113 x 113,
- DFC 1400: 316 x 336 x 113 x 113
- DFC 645: 316 x 754 x 113 x 113
Normalize data should be in three folders:

../dfc_645_normal
../dfc_1400_normal
../dfc_2500_normal

Sample name for normalize data: normalize_dfc_2500_subject_1_time_1.txt
Generated distance matrix folders for TDA pipeline:

../dfc_645_subjects_distance_matrix_ws
../dfc_1400_subjects_distance_matrix_ws
../dfc_2500_subjects_distance_matrix_ws

Generated distance matrix folders for nonTDA pipeline:

../dfc_645_non_tda_subjects_distance_matrix_eu
../dfc_1400_non_tda_subjects_distance_matrix_eu
../dfc_2500_non_tda_subjects_distance_matrix_eu

Generated mds folders:

../dfc_645_subjects_mds_ws
../dfc_1400_subjects_mds_ws
../dfc_2500_subjects_mds_ws

Generated cluster folders:

../clusters_kmeans_ws
../clusters_kmeans_non_tda

Run Instructions

Set up environment

Create, activate, and install required packages using Python 3:

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

# Distance calculation
python distance_calculation.py --data 645 --method ws --start 1 --end 316 --distance y --mds y
python distance_calculation.py --data 1400 --method ws --start 1 --end 316 --distance y --mds y
python distance_calculation.py --data 2500 --method ws --start 1 --end 316 --distance y --mds y
python non_tda_distance_calculation.py --data 2500 --method eu --start 1 --end 316 --distance y --mds y
python non_tda_distance_calculation.py --data 1400 --method eu --start 1 --end 316 --distance y --mds y
python non_tda_distance_calculation.py --data 645 --method eu --start 1 --end 316 --distance y --mds y
python cluster_calculation.py
python cluster_calculation_direct_clustering.py
python cluster_calculation_pca.py
python cluster_calculation_nontda.py
python statistical_calculation.py -d output/clusters_kmeans/clusters.json
python statistical_calculation.py -d output/clusters_kmeans_non_tda/clusters.json

Results

TDA pipeline and nonTDA pipelines cohort wide cluster distance comparison (less distance is better):
TDA pipeline and nonTDA pipelines pairwise cluster distance comparison (less distance is better):

Clustering result for a single subject (subject 8) using TDA and nonTDA pipelines:

Cohort wide cluster distance comparison

TDA (Wasserstein distance):

Clustering Method: KMeans
Best cluster selection using Silhouette Score in 2-15 range
Total subjects: 316
Match percentages:
Distance:   0, number of subjects: 118, percentage: 37.34%
Distance:   1, number of subjects:  70, percentage: 22.15%
Distance:   2, number of subjects:  18, percentage: 5.70%
Distance:   3, number of subjects:  17, percentage: 5.38%
Distance:   4, number of subjects:  22, percentage: 6.96%
Distance:   5, number of subjects:  13, percentage: 4.11%
Distance:   6, number of subjects:   8, percentage: 2.53%
Distance:   7, number of subjects:  11, percentage: 3.48%
Distance:   8, number of subjects:   8, percentage: 2.53%
Distance:   9, number of subjects:   8, percentage: 2.53%
Distance:  10, number of subjects:   6, percentage: 1.90%
Distance:  11, number of subjects:   5, percentage: 1.58%
Distance:  12, number of subjects:   6, percentage: 1.90%
Distance:  13, number of subjects:   6, percentage: 1.90%

nonTDA direct clustering:

Clustering Method: KMeans
Best cluster selection using Silhouette Score in 2-15 range
Total subjects: 316
Match percentages:
Distance:   0, number of subjects:   7, percentage: 2.22%
Distance:   1, number of subjects:  12, percentage: 3.80%
Distance:   2, number of subjects:  23, percentage: 7.28%
Distance:   3, number of subjects:  23, percentage: 7.28%
Distance:   4, number of subjects:  25, percentage: 7.91%
Distance:   5, number of subjects:  24, percentage: 7.59%
Distance:   6, number of subjects:  32, percentage: 10.13%
Distance:   7, number of subjects:  34, percentage: 10.76%
Distance:   8, number of subjects:  50, percentage: 15.82%
Distance:   9, number of subjects:  34, percentage: 10.76%
Distance:  10, number of subjects:  22, percentage: 6.96%
Distance:  11, number of subjects:  11, percentage: 3.48%
Distance:  12, number of subjects:  13, percentage: 4.11%
Distance:  13, number of subjects:   6, percentage: 1.90%
Method main((), {}) executed in 0.0094 seconds

nonTDA PCA and clustering:

Clustering Method: KMeans
Best cluster selection using Silhouette Score in 2-15 range
Total subjects: 316
Match percentages:
Distance:   0, number of subjects:  19, percentage: 6.01%
Distance:   1, number of subjects:  41, percentage: 12.97%
Distance:   2, number of subjects:  41, percentage: 12.97%
Distance:   3, number of subjects:  46, percentage: 14.56%
Distance:   4, number of subjects:  36, percentage: 11.39%
Distance:   5, number of subjects:  30, percentage: 9.49%
Distance:   6, number of subjects:  21, percentage: 6.65%
Distance:   7, number of subjects:  16, percentage: 5.06%
Distance:   8, number of subjects:  21, percentage: 6.65%
Distance:   9, number of subjects:  17, percentage: 5.38%
Distance:  10, number of subjects:  12, percentage: 3.80%
Distance:  11, number of subjects:   8, percentage: 2.53%
Distance:  12, number of subjects:   8, percentage: 2.53%

nonTDA traditional FCN analysis (Euclidean distance):

Clustering Method: KMeans
Best cluster selection using Silhouette Score in 2-15 range
Total subjects: 316
Match percentages:
Distance:   0, number of subjects:   1, percentage: 0.32%
Distance:   1, number of subjects:   4, percentage: 1.27%
Distance:   2, number of subjects:   9, percentage: 2.85%
Distance:   3, number of subjects:  15, percentage: 4.75%
Distance:   4, number of subjects:  23, percentage: 7.28%
Distance:   5, number of subjects:  35, percentage: 11.08%
Distance:   6, number of subjects:  31, percentage: 9.81%
Distance:   7, number of subjects:  28, percentage: 8.86%
Distance:   8, number of subjects:  34, percentage: 10.76%
Distance:   9, number of subjects:  32, percentage: 10.13%
Distance:  10, number of subjects:  33, percentage: 10.44%
Distance:  11, number of subjects:  32, percentage: 10.13%
Distance:  12, number of subjects:  29, percentage: 9.18%
Distance:  13, number of subjects:  10, percentage: 3.16%

Pairwise cluster distance comparison

Pairwise analysis for TDA pipeline:

Similarity between DFC2500 and DFC1400
Distance:   0, number of subjects: 172, percentage: 54.43%
Distance:   1, number of subjects:  47, percentage: 14.87%
Distance:   2, number of subjects:  18, percentage: 5.70%
Distance:   3, number of subjects:  17, percentage: 5.38%
Distance:   4, number of subjects:  20, percentage: 6.33%
Distance:   5, number of subjects:   8, percentage: 2.53%
Distance:   6, number of subjects:   3, percentage: 0.95%
Distance:   7, number of subjects:   8, percentage: 2.53%
Distance:   8, number of subjects:   6, percentage: 1.90%
Distance:   9, number of subjects:   5, percentage: 1.58%
Distance:  10, number of subjects:   4, percentage: 1.27%
Distance:  11, number of subjects:   1, percentage: 0.32%
Distance:  12, number of subjects:   3, percentage: 0.95%
Distance:  13, number of subjects:   4, percentage: 1.27%


Similarity between DFC1400 and DFC645
Distance:   0, number of subjects: 176, percentage: 55.70%
Distance:   1, number of subjects:  61, percentage: 19.30%
Distance:   2, number of subjects:  12, percentage: 3.80%
Distance:   3, number of subjects:  11, percentage: 3.48%
Distance:   4, number of subjects:   9, percentage: 2.85%
Distance:   5, number of subjects:   9, percentage: 2.85%
Distance:   6, number of subjects:   8, percentage: 2.53%
Distance:   7, number of subjects:   6, percentage: 1.90%
Distance:   8, number of subjects:   3, percentage: 0.95%
Distance:   9, number of subjects:   7, percentage: 2.22%
Distance:  10, number of subjects:   3, percentage: 0.95%
Distance:  11, number of subjects:   5, percentage: 1.58%
Distance:  12, number of subjects:   3, percentage: 0.95%
Distance:  13, number of subjects:   3, percentage: 0.95%


Similarity between DFC645 and DFC2500
Distance:   0, number of subjects: 160, percentage: 50.63%
Distance:   1, number of subjects:  65, percentage: 20.57%
Distance:   2, number of subjects:  19, percentage: 6.01%
Distance:   3, number of subjects:   8, percentage: 2.53%
Distance:   4, number of subjects:  17, percentage: 5.38%
Distance:   5, number of subjects:  10, percentage: 3.16%
Distance:   6, number of subjects:   5, percentage: 1.58%
Distance:   7, number of subjects:   9, percentage: 2.85%
Distance:   8, number of subjects:   5, percentage: 1.58%
Distance:   9, number of subjects:   3, percentage: 0.95%
Distance:  10, number of subjects:   4, percentage: 1.27%
Distance:  11, number of subjects:   3, percentage: 0.95%
Distance:  12, number of subjects:   6, percentage: 1.90%
Distance:  13, number of subjects:   2, percentage: 0.63%

Pairwise analysis for nonTDA direct clustering pipeline:

Distance & $2500ms$ and $1400ms$ & $1400ms$ and $645ms$ & $645ms$ and $2500ms$ \\ \hline 
0 & 20 & 6.329\% & 104 & 32.911\% & 17 & 5.380\% \\ \hline 
1 & 26 & 8.228\% & 81 & 25.633\% & 26 & 8.228\% \\ \hline 
2 & 27 & 8.544\% & 53 & 16.772\% & 25 & 7.911\% \\ \hline 
3 & 26 & 8.228\% & 24 & 7.595\% & 25 & 7.911\% \\ \hline 
4 & 33 & 10.443\% & 23 & 7.278\% & 22 & 6.962\% \\ \hline 
5 & 28 & 8.861\% & 15 & 4.747\% & 20 & 6.329\% \\ \hline 
6 & 33 & 10.443\% & 3 & 0.949\% & 36 & 11.392\% \\ \hline 
7 & 25 & 7.911\% & 6 & 1.899\% & 29 & 9.177\% \\ \hline 
8 & 40 & 12.658\% & 6 & 1.899\% & 45 & 14.241\% \\ \hline 
9 & 19 & 6.013\% & 1 & 0.316\% & 26 & 8.228\% \\ \hline 
10 & 19 & 6.013\% & 0 & 0.000\% & 16 & 5.063\% \\ \hline 
11 & 10 & 3.165\% & 0 & 0.000\% & 11 & 3.481\% \\ \hline 
12 & 6 & 1.899\% & 0 & 0.000\% & 12 & 3.797\% \\ \hline 
13 & 4 & 1.266\% & 0 & 0.000\% & 6 & 1.899\% \\ \hline 
Method main((), {}) executed in 0.0007 seconds

Pairwise analysis for nonTDA PCA pipeline:

Distance & $2500ms$ and $1400ms$ & $1400ms$ and $645ms$ & $645ms$ and $2500ms$ \\ \hline 
0 & 64 & 20.253\% & 70 & 22.152\% & 56 & 17.722\% \\ \hline 
1 & 73 & 23.101\% & 46 & 14.557\% & 70 & 22.152\% \\ \hline 
2 & 53 & 16.772\% & 43 & 13.608\% & 50 & 15.823\% \\ \hline 
3 & 36 & 11.392\% & 41 & 12.975\% & 40 & 12.658\% \\ \hline 
4 & 29 & 9.177\% & 23 & 7.278\% & 25 & 7.911\% \\ \hline 
5 & 20 & 6.329\% & 17 & 5.380\% & 17 & 5.380\% \\ \hline 
6 & 9 & 2.848\% & 15 & 4.747\% & 17 & 5.380\% \\ \hline 
7 & 4 & 1.266\% & 17 & 5.380\% & 8 & 2.532\% \\ \hline 
8 & 13 & 4.114\% & 14 & 4.430\% & 13 & 4.114\% \\ \hline 
9 & 5 & 1.582\% & 12 & 3.797\% & 9 & 2.848\% \\ \hline 
10 & 4 & 1.266\% & 8 & 2.532\% & 4 & 1.266\% \\ \hline 
11 & 3 & 0.949\% & 8 & 2.532\% & 2 & 0.633\% \\ \hline 
12 & 3 & 0.949\% & 2 & 0.633\% & 5 & 1.582\% \\ \hline

Pairwise analysis for nonTDA direct clustering pipeline:

Distance & $2500ms$ and $1400ms$ & $1400ms$ and $645ms$ & $645ms$ and $2500ms$ \\ \hline 
0 & 10 & 3.165\% & 39 & 12.342\% & 13 & 4.114\% \\ \hline 
1 & 31 & 9.810\% & 47 & 14.873\% & 23 & 7.278\% \\ \hline 
2 & 34 & 10.759\% & 41 & 12.975\% & 34 & 10.759\% \\ \hline 
3 & 31 & 9.810\% & 26 & 8.228\% & 32 & 10.127\% \\ \hline 
4 & 38 & 12.025\% & 35 & 11.076\% & 27 & 8.544\% \\ \hline 
5 & 37 & 11.709\% & 31 & 9.810\% & 18 & 5.696\% \\ \hline 
6 & 32 & 10.127\% & 24 & 7.595\% & 29 & 9.177\% \\ \hline 
7 & 18 & 5.696\% & 17 & 5.380\% & 22 & 6.962\% \\ \hline 
8 & 24 & 7.595\% & 25 & 7.911\% & 22 & 6.962\% \\ \hline 
9 & 16 & 5.063\% & 12 & 3.797\% & 20 & 6.329\% \\ \hline 
10 & 16 & 5.063\% & 7 & 2.215\% & 27 & 8.544\% \\ \hline 
11 & 18 & 5.696\% & 5 & 1.582\% & 22 & 6.962\% \\ \hline 
12 & 9 & 2.848\% & 4 & 1.266\% & 20 & 6.329\% \\ \hline 
13 & 2 & 0.633\% & 3 & 0.949\% & 7 & 2.215\% \\ \hline

Name	Name	Last commit message	Last commit date
Latest commit arsho updated adhd cluster figures Mar 27, 2025 9612b39 · Mar 27, 2025 History 66 Commits
adhd	adhd	updated adhd cluster figures	Mar 27, 2025
drawing	drawing	adhd dataset	Mar 23, 2025
matfiles	matfiles	clean repo	Feb 18, 2024
output	output	clean repo	Feb 18, 2024
supplimentary_experiments	supplimentary_experiments	clean repo	Feb 18, 2024
.gitignore	.gitignore	pushed adhd results and code	Feb 16, 2025
Graph_Generation.md	Graph_Generation.md	added graph barcode	Sep 15, 2022
README.md	README.md	clean repo	Feb 18, 2024
Run_on_Cheaha.md	Run_on_Cheaha.md	debugged direct clustering	Dec 3, 2023
Supplimentary_results.md	Supplimentary_results.md	clean repo	Feb 18, 2024
adhd_dataset.py	adhd_dataset.py	updated adhd cluster figures	Mar 27, 2025
cluster_calculation.py	cluster_calculation.py	adhd dataset	Mar 23, 2025
cluster_calculation_direct_clustering.py	cluster_calculation_direct_clustering.py	adhd dataset	Mar 23, 2025
cluster_calculation_nontda.py	cluster_calculation_nontda.py	adhd dataset	Mar 23, 2025
cluster_calculation_pca.py	cluster_calculation_pca.py	adhd dataset	Mar 23, 2025
clusteringresult.job	clusteringresult.job	changed project path to new git repo	Oct 14, 2022
clusteringresult_nontda.job	clusteringresult_nontda.job	modified non tda clustering	Oct 19, 2022
clusteringresult_pca.job	clusteringresult_pca.job	updated pca to generate all	Feb 17, 2024
dfc1400.job	dfc1400.job	updated job scripts for ws	Feb 21, 2023
dfc1400_1.job	dfc1400_1.job	added bn scripts	Feb 23, 2023
dfc1400_2.job	dfc1400_2.job	added bn scripts	Feb 23, 2023
dfc1400_nontda.job	dfc1400_nontda.job	changed project path to new git repo	Oct 14, 2022
dfc1400zip.job	dfc1400zip.job	clean repo	Feb 18, 2024
dfc2500.job	dfc2500.job	added bn scripts	Feb 23, 2023
dfc2500_nontda.job	dfc2500_nontda.job	changed project path to new git repo	Oct 14, 2022
dfc645.job	dfc645.job	updated job scripts for ws	Feb 21, 2023
dfc6452.job	dfc6452.job	changed project path to new git repo	Oct 14, 2022
dfc645_1.job	dfc645_1.job	added bn scripts	Feb 23, 2023
dfc645_mds.job	dfc645_mds.job	added bn scripts	Feb 23, 2023
dfc645_nontda.job	dfc645_nontda.job	changed project path to new git repo	Oct 14, 2022
dfc645zip.job	dfc645zip.job	clean repo	Feb 18, 2024
distance_calculation.py	distance_calculation.py	adhd dataset	Mar 23, 2025
graph_generation.py	graph_generation.py	adhd dataset	Mar 23, 2025
mds_calculation.py	mds_calculation.py	adhd dataset	Mar 23, 2025
non_tda_distance_calculation.py	non_tda_distance_calculation.py	updated nontda script	Mar 1, 2023
requirements.txt	requirements.txt	adhd dataset	Mar 23, 2025
requirements_312.txt	requirements_312.txt	working on adhd	Mar 19, 2025
statistical_calculation.py	statistical_calculation.py	adhd dataset	Mar 23, 2025
utils.py	utils.py	adhd dataset	Mar 23, 2025
ws_clustering.py	ws_clustering.py	adhd dataset	Mar 23, 2025

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Topology assisted clustering of temporal fMRI

Pipelines

Dataset

Run Instructions

Set up environment

Results

Cohort wide cluster distance comparison

Pairwise cluster distance comparison

References

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Topology assisted clustering of temporal fMRI

Pipelines

Dataset

Run Instructions

Set up environment

Results

Cohort wide cluster distance comparison

Pairwise cluster distance comparison

References

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