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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

Schematic illustration of STAGIN

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