Classification and Interpretability of Mild Cognitive Impairment Based on Resting-State Functional Magnetic Resonance and Ensemble Learning

Abstract

The combination and integration of multimodal imaging and clinical markers have introduced numerous classifiers to improve diagnostic accuracy in detecting and predicting AD; however, many studies cannot ensure the homogeneity of data sets and consistency of results. In our study, the XGBoost algorithm was used to classify mild cognitive impairment (MCI) and normal control (NC) populations through five rs-fMRI analysis datasets. Shapley Additive exPlanations (SHAP) is used to analyze the interpretability of the model. The highest accuracy for diagnosing MCI was 65.14% (using the mPerAF dataset). The characteristics of the left insula, right middle frontal gyrus, and right cuneus correlated positively with the output value using DC datasets. The characteristics of left cerebellum 6, right inferior frontal gyrus, opercular part, and vermis 6 correlated positively with the output value using fALFF datasets. The characteristics of the right middle temporal gyrus, left middle temporal gyrus, left temporal pole, and middle temporal gyrus correlated positively with the output value using mPerAF datasets. The characteristics of the right middle temporal gyrus, left middle temporal gyrus, and left hippocampus correlated positively with the output value using PerAF datasets. The characteristics of left cerebellum 9, vermis 9, and right precentral gyrus, right amygdala, and left middle occipital gyrus correlated positively with the output value using Wavelet-ALFF datasets. We found that the XGBoost algorithm constructed from rs-fMRI data is effective for the diagnosis and classification of MCI. The accuracy rates obtained by different rs-fMRI data analysis methods are similar, but the important features are different and involve multiple brain regions, which suggests that MCI may have a negative impact on brain function.

Role of Fluid Dynamics in Infectious Disease Transmission: Insights from COVID-19 and Other Pathogens

“…By transitioning from the concept of isolated droplet emissions to the turbulent multiphase puff cloud model, we can gain a new perspective on how pathogens spread in indoor environments over time and space [125]. This shift can help assess the effectiveness of interventions related to indoor space management and occupancy by focusing on the air and surface contamination levels [126]. All exhalations, such as breathing, sneezing, coughing, talking, and singing, can be described as pointsource emissions of a turbulent multiphase gas cloud containing respiratory liquid droplets [127].…”

Prediction Model for Cognitive Impairment among Disabled Older Adults: A Development and Validation Study

“…ML can identify complex relationships between variables from real-world data [31] and perform greater flexibility in handling large datasets. Several prediction models have been constructed for MCI or Alzheimer's disease by random forest (RF) [3], support vector machines (SVM) [32,33], and extreme gradient boosting (XGBoost) [34] with good performance. However, few studies focused on developing prediction models of CI among disabled older populations and assessing the predictive ability of ML in this group.…”