Predicting cognitive dysfunction and regional hubs using Braak staging amyloid-beta biomarkers and machine learning

Abstract

Mild cognitive impairment (MCI) is a transitional stage between normal aging and early Alzheimer's disease (AD). The presence of extracellular amyloid-beta (Aβ) in Braak regions suggests a connection with cognitive dysfunction in MCI/AD. Investigating the multivariate predictive relationships between regional Aβ biomarkers and cognitive function can aid in the early detection and prevention of AD. We introduced machine learning approaches to estimate cognitive dysfunction from regional Aβ biomarkers and identify the Aβ-related dominant brain regions involved with cognitive impairment. We employed Aβ biomarkers and cognitive measurements from the same individuals to train support vector regression (SVR) and artificial neural network (ANN) models and predict cognitive performance solely based on Aβ biomarkers on the test set. To identify Aβ-related dominant brain regions involved in cognitive prediction, we built the local interpretable model-agnostic explanations (LIME) model. We found elevated Aβ in MCI compared to controls and a stronger correlation between Aβ and cognition, particularly in Braak stages III-IV and V-VII (p < 0.05) biomarkers. Both SVR and ANN, especially ANN, showed strong predictive relationships between regional Aβ biomarkers and cognitive impairment (p < 0.05). LIME integrated with ANN showed that the parahippocampal gyrus, inferior temporal gyrus, and hippocampus were the most decisive Braak regions for predicting cognitive decline. Consistent with previous findings, this new approach suggests relationships between Aβ biomarkers and cognitive impairment. The proposed analytical framework can estimate cognitive impairment from Braak staging Aβ biomarkers and delineate the dominant brain regions collectively involved in AD pathophysiology.

Keywords: Amyloid-beta; Braak staging; Feature importance; Machine learning; Mild cognitive impairment; Neuroimaging.

Fig. 1

SVR model predicts MMSE with DVRs as feature variables. A tenfold CV was applied to the DVR and MMSE data sets. Grid Search CV is used for determining the optimum c, ϵ, and γ values in each fold. The SVR model used these values to fit train data and predict test set MMSE outcomes. The process is repeated for all tenfold data combinations

Fig. 2

ANN model predicts MMSE with DVRs as feature variables through a tenfold CV. Batch normalization and dropout are applied after each dense layer and ReLU activation, except for the output layer. Input dimension shape was used for the first dense layer with 40 units. The next three hidden layers contain 30, 20, and 10 units. The final output layer contains only one unit with a linear activation function for the regression problem

Fig. 3

Boxplots and scatterplots of Aβ DVR comparison between mild cognitive impairment (MCI) and controls in different Braak stages (*: FDR-p < 0.05)

Fig. 4

DVR of various Braak stages correlates with MMSE in MCI and controls. a Has a weak positive correlation for Braak I–II, while (b–d) have a negative correlation for III–IV, V–VI, and all stages combined (FDR-p < 0.05). Linear fit (solid line) and 95% confidence interval (shadowed area) are shown

Fig. 5

DVR of various Braak stages correlate with MMSE in MCI. Plot (a) has a weak positive correlation for Braak I–II, while (b–d) have a negative correlation for III–IV, V–VI, and all stages combined (FDR-p < 0.05). Linear fit (solid line) and 95% confidence interval (shadowed area) are shown

Fig. 6

Positive correlation between predicted and actual MMSE in MCI using SVR modeling. While the correlation in (a) was weak, there was statistical significance (FDR-p < 0.05) in (b–d), especially all stages combined (d) had a stronger correlation. The linear fit (solid line) and 95% confidence interval (shadowed area) are shown

Fig. 7

Positive correlation between predicted and actual MMSE in MCI using ANN modeling. While the correlation in (a) was weak, there was statistical significance (FDR-p < 0.05) in (b–d), especially all stages combined (d) had a stronger correlation. The linear fit (solid line) and 95% confidence interval (shadowed area) are shown

Fig. 8

Chart compares the feature importance of the ANN model using the LIME method for different Braak regions. The importance values for each region are displayed on the x-axis, with the labels of the respective brain regions on the y-axis