TY - GEN
T1 - Graph-Based Counterfactual Causal Inference Modeling for Neuroimaging Analysis
AU - Dai, Haixing
AU - Hu, Mengxuan
AU - Li, Qing
AU - Zhang, Lu
AU - Zhao, Lin
AU - Zhu, Dajiang
AU - Diez, Ibai
AU - Sepulcre, Jorge
AU - Zhang, Fan
AU - Gao, Xingyu
AU - Liu, Manhua
AU - Li, Quanzheng
AU - Li, Sheng
AU - Liu, Tianming
AU - Li, Xiang
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
PY - 2023
Y1 - 2023
N2 - Alzheimer’s disease (AD) is a neurodegenerative disorder that is beginning with amyloidosis, followed by neuronal loss and deterioration in structure, function, and cognition. The accumulation of amyloid-β in the brain, measured through 18F-florbetapir (AV45) positron emission tomography (PET) imaging, has been widely used for early diagnosis of AD. However, the relationship between amyloid-β accumulation and AD pathophysiology remains unclear, and causal inference approaches are needed to uncover how amyloid-β levels can impact AD development. In this paper, we propose a Graph-VCNet for estimating the individual treatment effect with continuous treatment levels using a graph convolutional neural network. We highlight the potential of causal inference approaches, including Graph-VCNet, for measuring the regional causal connections between amyloid-β accumulation and AD pathophysiology, which may serve as a robust tool for early diagnosis and tailored care.
AB - Alzheimer’s disease (AD) is a neurodegenerative disorder that is beginning with amyloidosis, followed by neuronal loss and deterioration in structure, function, and cognition. The accumulation of amyloid-β in the brain, measured through 18F-florbetapir (AV45) positron emission tomography (PET) imaging, has been widely used for early diagnosis of AD. However, the relationship between amyloid-β accumulation and AD pathophysiology remains unclear, and causal inference approaches are needed to uncover how amyloid-β levels can impact AD development. In this paper, we propose a Graph-VCNet for estimating the individual treatment effect with continuous treatment levels using a graph convolutional neural network. We highlight the potential of causal inference approaches, including Graph-VCNet, for measuring the regional causal connections between amyloid-β accumulation and AD pathophysiology, which may serve as a robust tool for early diagnosis and tailored care.
KW - Alzehimer’s disease
KW - Amyloid accumulation
KW - Causal inference
KW - Counterfactual inference
UR - https://www.scopus.com/pages/publications/85185723381
UR - https://www.scopus.com/pages/publications/85185723381#tab=citedBy
U2 - 10.1007/978-3-031-47425-5_19
DO - 10.1007/978-3-031-47425-5_19
M3 - Conference contribution
AN - SCOPUS:85185723381
SN - 9783031474248
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 205
EP - 213
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops - MTSAIL 2023, LEAF 2023, AI4Treat 2023, MMMI 2023, REMIA 2023, Held in Conjunction with MICCAI 2023, Proceedings
A2 - Woo, Jonghye
A2 - Hering, Alessa
A2 - Silva, Wilson
A2 - Li, Xiang
A2 - Fu, Huazhu
A2 - Liu, Xiaofeng
A2 - Xing, Fangxu
A2 - Purushotham, Sanjay
A2 - Mathai, T.S.
A2 - Mukherjee, Pritam
A2 - De Grauw, Max
A2 - Beets Tan, Regina
A2 - Corbetta, Valentina
A2 - Kotter, Elmar
A2 - Reyes, Mauricio
A2 - Baumgartner, C.F.
A2 - Li, Quanzheng
A2 - Leahy, Richard
A2 - Dong, Bin
A2 - Chen, Hao
A2 - Huo, Yuankai
A2 - Lv, Jinglei
A2 - Xu, Xinxing
A2 - Li, Xiaomeng
A2 - Mahapatra, Dwarikanath
A2 - Cheng, Li
A2 - Petitjean, Caroline
A2 - Presles, Benoît
PB - Springer Science and Business Media Deutschland GmbH
T2 - 26th International Conference on Medical Image Computing and Computer-Assisted Intervention , MICCAI 2023
Y2 - 8 October 2023 through 12 October 2023
ER -