Classification of neurodegenerative diseases using brain effective connectivity and machine learning techniques: a systematic review

Abstract

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.

Keywords: Alzheimer’s disease; brain effective connectivity; classification model; deep learning; electroencephalogram; functional magnetic resonance imaging; machine learning; neurodegenerative diseases.

Figure 1

PRISMA flow diagram of literature search.

Figure 2

Brain FC network (A) and brain EC network (B). (A) The functional connectivity network is represented as an undirected graph, where nodes correspond to brain regions, and edges reflect the statistical correlation between regional time series. (B) The effective connectivity network is depicted as a directed graph, capturing the direction and strength of information flow between brain regions.

Figure 3

Number of studies for different EC extraction methods. CCM, Convergent Cross-Mapping; FDCCM, Frequency-Domain Convergent Cross-Mapping; GCA, Granger Causality Analysis; gKF, Group Constrained Kalman Filter; TE, Transfer Entropy; UG-LASSO, Ultra-Group LASSO; UOLS, Ultra-Orthogonal Least Squares; UOFR, Ultra-Orthogonal Forward Regression.