TY - JOUR
T1 - Multicenter and Multichannel Pooling GCN for Early AD Diagnosis Based on Dual-Modality Fused Brain Network
AU - Song, Xuegang
AU - Zhou, Feng
AU - Frangi, Alejandro F.
AU - Cao, Jiuwen
AU - Xiao, Xiaohua
AU - Lei, Yi
AU - Wang, Tianfu
AU - Lei, Baiying
N1 - Funding Information:
This work was supported in part by the Chinese National Science and Technology Pillar Program under Grant 2022YFC2009900 and Grant 2022YFC2009903; in part by the National Natural Science Foundation of China under Grant 62101338, Grant 61871274, and Grant U1909209; in part by the National Natural Science Foundation of Guangdong Province under Grant 2019A1515111205; in part by the Shenzhen Key Basic Research Project under Grant KCXFZ20201221173213036, Grant SGDX202011030958 02007, Grant JCYJ20180507184647636, Grant JCYJ20190808155618806, Grant GJHZ20190822 095414576, and Grant JCYJ20190808145011259; in part by the Royal Academy of Engineering Chair in Emerging Technologies Scheme under Grant CiET1819/19, and in part by the Pengcheng Visiting Scholars Program from the Shenzhen Government.
Publisher Copyright:
© 1982-2012 IEEE.
PY - 2023/2/1
Y1 - 2023/2/1
N2 - For significant memory concern (SMC) and mild cognitive impairment (MCI), their classification performance is limited by confounding features, diverse imaging protocols, and limited sample size. To address the above limitations, we introduce a dual-modality fused brain connectivity network combining resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), and propose three mechanisms in the current graph convolutional network (GCN) to improve classifier performance. First, we introduce a DTI-strength penalty term for constructing functional connectivity networks. Stronger structural connectivity and bigger structural strength diversity between groups provide a higher opportunity for retaining connectivity information. Second, a multi-center attention graph with each node representing a subject is proposed to consider the influence of data source, gender, acquisition equipment, and disease status of those training samples in GCN. The attention mechanism captures their different impacts on edge weights. Third, we propose a multi-channel mechanism to improve filter performance, assigning different filters to features based on feature statistics. Applying those nodes with low-quality features to perform convolution would also deteriorate filter performance. Therefore, we further propose a pooling mechanism, which introduces the disease status information of those training samples to evaluate the quality of nodes. Finally, we obtain the final classification results by inputting the multi-center attention graph into the multi-channel pooling GCN. The proposed method is tested on three datasets (i.e., an ADNI 2 dataset, an ADNI 3 dataset, and an in-house dataset). Experimental results indicate that the proposed method is effective and superior to other related algorithms, with a mean classification accuracy of 93.05% in our binary classification tasks. Our code is available at: https://github.com/Xuegang-S.
AB - For significant memory concern (SMC) and mild cognitive impairment (MCI), their classification performance is limited by confounding features, diverse imaging protocols, and limited sample size. To address the above limitations, we introduce a dual-modality fused brain connectivity network combining resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), and propose three mechanisms in the current graph convolutional network (GCN) to improve classifier performance. First, we introduce a DTI-strength penalty term for constructing functional connectivity networks. Stronger structural connectivity and bigger structural strength diversity between groups provide a higher opportunity for retaining connectivity information. Second, a multi-center attention graph with each node representing a subject is proposed to consider the influence of data source, gender, acquisition equipment, and disease status of those training samples in GCN. The attention mechanism captures their different impacts on edge weights. Third, we propose a multi-channel mechanism to improve filter performance, assigning different filters to features based on feature statistics. Applying those nodes with low-quality features to perform convolution would also deteriorate filter performance. Therefore, we further propose a pooling mechanism, which introduces the disease status information of those training samples to evaluate the quality of nodes. Finally, we obtain the final classification results by inputting the multi-center attention graph into the multi-channel pooling GCN. The proposed method is tested on three datasets (i.e., an ADNI 2 dataset, an ADNI 3 dataset, and an in-house dataset). Experimental results indicate that the proposed method is effective and superior to other related algorithms, with a mean classification accuracy of 93.05% in our binary classification tasks. Our code is available at: https://github.com/Xuegang-S.
KW - dual-modality fusion
KW - early Alzheimer's disease
KW - graph convolutional network
KW - Multi-center
KW - multi-channel pooling
UR - http://www.scopus.com/inward/record.url?scp=85133579692&partnerID=8YFLogxK
U2 - 10.1109/TMI.2022.3187141
DO - 10.1109/TMI.2022.3187141
M3 - Article
C2 - 35767511
AN - SCOPUS:85133579692
SN - 0278-0062
VL - 42
SP - 354
EP - 367
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
IS - 2
ER -