A causal counterfactual graph neural network for arising-from-chair abnormality detection in parkinsonians

The arising-from-chair task assessment is a key aspect of the evaluation of movement disorders in Parkinson's disease (PD). However, common scale-based clinical assessment methods are highly subjective and dependent on the neurologist's expertise. Alternate automated methods for arising-from-chair assessment can be established based on quantitative susceptibility mapping (QSM) images with multiple-instance learning. However, performance stability for such methods can be typically undermined by the presence of irrelevant or spuriously-relevant features that mask the intrinsic causal features. Therefore, we propose a QSM-based arising-from-chair assessment method using a causal graph-neural-network framework, where counterfactual and debiasing strategies are developed and integrated into this framework for capturing causal features. Specifically, the counterfactual strategy is proposed to suppress irrelevant features caused by background noise, by producing incorrect predictions when dropping causal parts. The debiasing strategy is proposed to suppress spuriously relevant features caused by the sampling bias and it comprises a resampling guidance scheme for selecting stable instances and a causal invariance constraint for improving stability under various interferences. The results of extensive experiments demonstrated the superiority of the proposed method in detecting arising-from-chair abnormalities. Its clinical feasibility was further confirmed by the coincidence between the selected causal features and those reported in earlier medical studies. Additionally, the proposed method was extensible for another motion task of leg agility. Overall, this study provides a potential tool for automated arising-from-chair assessment in PD patients, and also introduces causal counterfactual thinking in medical image analysis. Our source code is publicly available at https://github.com/SJTUBME-QianLab/CFGNN-PDarising.