STAGIN

Spatio-Temporal Attention Graph Isomorphism Network

Paper

Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention \ Byung-Hoon Kim, Jong Chul Ye, Jae-Jin Kim \ presented at NeurIPS 2021 \ _{^{arXiv, OpenReview, proceeding}}

Concept

Dataset

The fMRI data used for the experiments of the paper should be downloaded from the Human Connectome Project.

Example structure of the directory tree

data (specified by option --sourcedir)
├─── behavioral
│    ├─── hcp.csv
│    ├─── hcp_taskrest_EMOTION.csv
│    ├─── hcp_taskrest_GAMBLING.csv
│    ├─── ...
│    └─── hcp_taskrest_WM.csv
├─── img
│    ├─── REST
│    │    ├─── 123456.nii.gz
│    │    ├─── 234567.nii.gz
│    │    ├─── ...
│    │    └─── 999999.nii.gz
│    └─── TASK
│         ├─── EMOTION
│         │    ├─── 123456.nii.gz
│         │    ├─── 234567.nii.gz
│         │    ├─── ...
│         │    └─── 999999.nii.gz
│         ├─── GAMBLING
│         │    ├─── ...
│         │    └─── 999999.nii.gz
│         ├─── ...
│         └─── WM
│              ├─── ...
│              └─── 999999.nii.gz
└───roi
     └─── 7_400_coord.csv

Example content of the csv files

data/behavioral/hcp.csv

| Subject | Gender |
|---------|--------|
| 123456  |   F    |
| 234567  |   M    |
| ......  | ...... |
| 999999  |   F    |

data/behavioral/hcp_taskrest_WM.csv

| Task | Rest |
|------|------|
|  0   |  1   |
|  0   |  1   |
| ...  | ...  |
|  1   |  0   |

data/roi/7_400_coord.csv

| ROI Index | Label Name                 | R | A | S |
|-----------|----------------------------|---|---|---|
|         0 | NONE                       | NA| NA| NA|
|         1 | 7Networks_LH_Vis_1         |-32|-42|-20|
|         2 | 7Networks_LH_Vis_2         |-30|-32|-18|
|       ... | .........                  | . | . | . |
|       400 | 7Networks_RH_Default_PCC_9 | 8 |-50| 44|

Commands

Run the main script to perform experiments

  python main.py

Command-line options can be listed with -h flag.

  python main.py -h

Requirements

• python 3.8.5
• numpy == 1.20.2
• torch == 1.7.0
• torchvision == 0.8.1
• einops == 0.3.0
• sklearn == 0.24.2
• nilearn == 0.7.1
• nipy == 0.5.0
• pingouin == 0.3.11
• tensorboard == 2.5.0
• tqdm == 4.60.0

For brainplot:

• MRIcroGL >= 1.2
• opencv-python == 4.5.2

Updates

• 2022-04-29 5c262d8d: Top k-percentile values from the adjacency matrix is now calculated without the need for calling .detach().cpu().numpy() which improves computation speed.
• 2023-04-11 2aa53b9-40e2bc6: Added dataset classes for ukb-rest, abide, and fmriprep; Implemented regression experiments.

Contact

egyptdj@yonsei.ac.kr