fNIRS-based graph frequency analysis to identify mild cognitive impairment in Parkinson's disease

Early identification of mild cognitive impairment (MCI) is essential for its treatment and the prevention of dementia in Parkinson's disease (PD). Existing approaches are mostly based on neuropsychological assessments, while brain activation and connection have not been well considered.

This paper presents a neuroimaging-based graph frequency analysis method and the generated features to quantify the brain functional neurodegeneration and distinguish between PD-MCI patients and healthy controls. The Stroop color-word experiment was conducted with 20 PD-MCI patients and 34 healthy controls, and the brain activation was recorded with functional near-infrared spectroscopy (fNIRS). Then, the functional brain network was constructed based on Pearson's correlation coefficient calculation between every two fNIRS channels. Next, the functional brain network was represented as a graph and decomposed in the graph frequency domain through the graph Fourier transform (GFT) to obtain the eigenvector matrix. Total variation and weighted zero crossings of eigenvectors were defined and integrated to quantify functional interaction between brain regions and the spatial variability of the brain network in specific graph frequency ranges, respectively. After that, the features were employed in training a support vector machine (SVM) classifier.

The presented method achieved a classification accuracy of 0.833 and an F1 score of 0.877, significantly outperforming existing methods and features.

Our method provided improved classification performance in the identification of PD-MCI.

The results suggest that the presented graph frequency analysis method well identify PD-MCI patients and the generated features promise functional brain biomarkers for PD-MCI diagnosis.