Contrastive Functional Connectivity Graph Learning for Population-based fMRI Classification

Authors: XueSong Wang, Lina Yao, Islem Rekik, Yu Zhang

This repo hosts the official implementation of our MICCAI 2022 paper.

Usage

Setup

pip

See the requirements.txt for environment configuration.

pip install -r requirements.txt

PYG

To install pyg library, please refer to the document

Dataset

ABIDE / ADHD200

We treat each fMRI as a brain graph. To download and construct the graphs:

python preprocessing/abide_01-fetch_data.py
python preprocessing/abide_02-process_data.py

Note:

1. you can download "subject_IDs.txt" from Sofia's project or generate your own using this sample code:

# data_folder: folder where you save .h5 files of the subjects, for instance: 50003, 50004, ... 
subject_IDs = os.listdir(data_folder) 
with open(os.path.join(data_folder, 'subject_IDs.txt'), "w") as output:
	for row in subject_IDs:
		if os.path.isdir(os.path.join(data_folder,row)):
			output.write(row + '\n')

2. You will need to change the root_folder and data_folder to your own path in both abide_01-fetch_data.py and abide_02-process_data.py

root_folder = '/share/scratch/xuesongwang/nilearn_data/'
data_folder = os.path.join(root_folder, 'ABIDE_pcp/cpac/filt_noglobal/')

3. run main_multiview() in abide_02-process_data.py first to generate .h5 files, then comment the function and run read_multiview_data() to save the dataset as .pt

To train the classification model:

Training and testing are integrated in file main_ABIDE.py. To run

python main_ABIDE.py

Note:

1. You will need to replace the dataroot in main_ABIDE.py with your own path where the dataset.pt is located.

parser.add_argument('--dataroot', type=str, default='/share/scratch/xuesongwang/nilearn_data/ADHD200/AAL90', help='root directory of the dataset')

2. If you are trying with your own dataset, indim and nroi should be modified.

parser.add_argument('--indim', type=int, default=97, help='feature dim, with PCD information, such as gender, handenss')
parser.add_argument('--nroi', type=int, default=90, help='num of ROIs')

3. To generate 'multiviewdata_miccai.pt', you will need to check preprocessing/abide_02-process_data.py and run read_multiview_data()

Citation

If you find the code and dataset useful, please cite our paper.

@article{wang2022contrastive,
  title={Contrastive Graph Learning for Population-based fMRI Classification},
  author={Wang, Xuesong and Yao, Lina and Rekik, Islem and Zhang, Yu},
  journal={arXiv preprint arXiv:2203.14044},
  year={2022}
}

Parameter setting

ContraGraphLearning(
  (conv1): ChebConv(206, 64, K=3, normalization=sym)
  (pool1): TopKPooling(64, ratio=0.5, multiplier=1)
  (conv2): ChebConv(64, 64, K=3, normalization=sym)
  (pool2): TopKPooling(64, ratio=0.5, multiplier=1)
  (fc_encoder): Linear(in_features=256, out_features=256, bias=True)
  (bn1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (contra_encoder): Sequential(
    (0): Linear(in_features=256, out_features=64, bias=False)
    (1): ReLU()
    (2): Linear(in_features=64, out_features=512, bias=True)
  )
)
DynamicGraphClassification(
  (nn1): Sequential(
    (0): Linear(in_features=1024, out_features=64, bias=False)
    (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): LeakyReLU(negative_slope=0.2)
  )
  (edge_conv1): DynamicEdgeConv(nn=Sequential(
    (0): Linear(in_features=1024, out_features=64, bias=False)
    (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): LeakyReLU(negative_slope=0.2)
  ), k=20)
  (nn2): Sequential(
    (0): Linear(in_features=128, out_features=64, bias=False)
    (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): LeakyReLU(negative_slope=0.2)
  )
  (edge_conv2): DynamicEdgeConv(nn=Sequential(
    (0): Linear(in_features=128, out_features=64, bias=False)
    (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): LeakyReLU(negative_slope=0.2)
  ), k=20)
  (nn3): Sequential(
    (0): Linear(in_features=128, out_features=64, bias=False)
    (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): LeakyReLU(negative_slope=0.2)
  )
  (edge_conv3): DynamicEdgeConv(nn=Sequential(
    (0): Linear(in_features=128, out_features=64, bias=False)
    (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): LeakyReLU(negative_slope=0.2)
  ), k=20)
  (concat_fc): Linear(in_features=192, out_features=512, bias=False)
  (mlp1): Sequential(
    (0): Linear(in_features=512, out_features=256, bias=True)
    (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): LeakyReLU(negative_slope=0.2)
  )
  (mlp2): Sequential(
    (0): Linear(in_features=256, out_features=128, bias=True)
    (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): LeakyReLU(negative_slope=0.2)
  )
  (clf): Linear(in_features=128, out_features=2, bias=True)
)

The best results we have achieved are shown as follows.

ADHD200 (You can obtain the result by loading the 'CGL_best_encoder.pth' and 'DGC_best_classifier.pth' from the folder saved_model/)

         accuracy  sensitivity  specificity       auc
PK       0.792453     0.608696     0.933333  0.844928
KKI      0.666667     0.555556     0.714286  0.613757
NYU      0.614035     0.742857     0.409091  0.661039
Overall  0.692857     0.671642     0.712329  0.740339

ABIDE

          accuracy  sensitivity  specificity       auc
CALTECH   0.333333     1.000000     0.000000  0.500000
CMU       1.000000     1.000000     1.000000  1.000000
KKI       0.625000     0.000000     1.000000  0.533333
LEUVEN_1  0.500000     0.333333     0.666667  0.555556
LEUVEN_2  0.714286     0.666667     0.750000  0.583333
MAX_MUN   0.600000     0.250000     0.833333  0.583333
NYU       0.600000     0.466667     0.700000  0.553333
OHSU      0.833333     0.666667     1.000000  0.888889
OLIN      0.500000     0.000000     1.000000  1.000000
PITT      0.545455     0.400000     0.666667  0.533333
SBL       1.000000     1.000000     1.000000  1.000000
SDSU      0.333333     0.000000     0.500000  0.500000
STANFORD  0.666667     0.666667     0.666667  0.888889
TRINITY   0.333333     0.000000     0.600000  0.350000
UCLA_1    0.714286     0.750000     0.666667  0.729167
UCLA_2    0.600000     1.000000     0.000000  1.000000
UM_1      0.722222     0.285714     1.000000  0.818182
UM_2      0.750000     0.666667     0.800000  0.866667
USM       0.857143     0.777778     1.000000  0.822222
YALE      0.777778     0.800000     0.750000  0.850000
Overall   0.650794     0.522727     0.762376  0.678555
Number of nodes: 871
Number of edges: 3396
Average node degree: 3.90
Number of training nodes: 590
Training node label rate: 0.68

Many thanks to Dr Xiaoxiao Li for addressing my concerns and sharing their project BrainGNN
Also thanks Dr Sofia Ira Ktena for providing critical preprocessing steps on FC graphs.