BACKGROUND

Effective connectivity (EC) refers to the directional influences or causal relationships between brain regions. In the field of artificial intelligence, machine learning has demonstrated remarkable proficiency in image recognition and the complex dataset analysis. In recent years, machine learning models leveraging EC have been increasingly used to classify neurodegenerative diseases and differentiate them from healthy controls. This review aims to comprehensively examine research employing EC-derived from techniques such as functional magnetic resonance imaging, electroencephalography, and magnetoencephalography-in conjunction with machine learning methods to classify neurodegenerative diseases.

METHODS

We conducted a literature search in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, collecting articles published prior to June 13, 2024, from the PubMed and Embase databases.

RESULTS

We selected 16 relevant studies based on predefined inclusion criteria: six focused on Alzheimer's disease (AD), six on mild cognitive impairment (MCI), one on Parkinson's disease (PD), two on both AD and MCI, and one on both AD and PD. We summarized the methods for EC feature extraction and selection, the application of classifiers, validation techniques, and the accuracy of the classification models.

CONCLUSION

The integration of EC with machine learning techniques has demonstrated promising potential in the classification of neurodegenerative diseases. Studies have shown that combining EC with multimodal features such as functional connectivity offers novel approaches to enhancing the performance of classification models.