Classification of cognitive syndromes in a Southeast Asian population: Interpretable graph convolutional neural networks

Dementia is a debilitating disease that afflicts a large population worldwide. Early diagnosis of cognitive impairment can allow for preventative measures to be taken to slow down or prevent the progression to dementia. In this study, we devise an interpretable graph convolutional neural network approach, GCNEnsemble, using both non-clinical variables such as MRI preprocessed features including cortical thickness and gray matter volumes, and clinical features from a community-dwelling Southeast Asian population in Singapore aged between 30 and 95 years from the Biomarker and Cognition study (BIOCIS), to classify participants into cognitively normal, subjective cognitive decline, and mild cognitive impairment. We further conducted ablation studies and varied the quantities of labeled data to understand the contribution of the non-clinical features and the applicability of GCNEnsemble in low to high labeled data availability scenarios. GCNEnsemble was able to attain the highest accuracy and Matthew's correlation coefficient compared to existing state-of-the-art methods. Feature interpretability via Integrated Gradients identified features such as visual cognitive assessment test (VCAT), systolic and diastolic blood pressure, and cerebrospinal fluid volume as key features for the classification, with VCAT having the highest feature importance. There was higher median cerebrospinal fluid volume, right frontal pole thickness, left pallidum volume, and right hippocampal fissure volume but lower VCAT for the mild cognitive impairment group than the two other groups. In conclusion, GCNEnsemble can be used as a semisupervised interpretable classification tool for cognitive syndrome in a Southeast Asian population.