Research has shown that the left and right hemispheres display different neural atrophy patterns in the progression of Alzheimer’s Disease (AD). Yet, in current research practices, asymmetrical...Show moreResearch has shown that the left and right hemispheres display different neural atrophy patterns in the progression of Alzheimer’s Disease (AD). Yet, in current research practices, asymmetrical patterns are not usually considered during classification analyses. Therefore, in this study we analysed the difference in classification accuracy between the left and right hemisphere for different stages of the disease. To further analyse the impact of asymmetric neural atrophy, we calculated an asymmetry score to include in the classification analyses. The sample consisted out of 173 healthy controls and 75 AD patients divided in two groups: 38 mild AD patients and 37 with severe AD (based on MMSE-scores). The classification analyses were performed using a logistic regression analysis with a LASSO penalty including a nested cross-validation procedure to improve the reliability of our analyses. We found that in the case of both mild and severe AD, the combined model (left + right hemisphere) showed that the left hemisphere was better in classifying AD. However, in severe AD the model included more predictors from the right hemisphere, indicating that the right hemisphere offers valuable information in classifying severe AD. We found that asymmetry scores did improve classification accuracy for mild AD. In contrast, this was not the case for severe AD, since asymmetric atrophy diminishes as AD progresses. Furthermore, asymmetry on its own was consistently shown to be a poor predictor of AD. This could possibly be attributed to the calculation method of asymmetry in our study. Overall, our findings contribute to the idea of asymmetric neural atrophy in AD and the emphasize the need for further investigation into the calculation of asymmetry scores.Show less
Alzheimer’s dementia (AD) is a neurodegenerative brain disorder that impacts females substantially more than men. However, not much research has been conducted on the classification possibilities...Show moreAlzheimer’s dementia (AD) is a neurodegenerative brain disorder that impacts females substantially more than men. However, not much research has been conducted on the classification possibilities of functional MRI (fMRI) on the basis of sex, although fMRI may be a better classifier of AD in women as functional connectivity (FC) research has shown that women with mild cognitive impairment (MCI) show weaker connectivity in the hippocampal area as compared to males. This research investigated if 1) functional connectivity is a better classifier of AD for females compared to males and 2) the addition of functional connectivity to grey matter density for AD classification improves accuracy more for females than for males. The measures grey matter density (GMD), functional connectivity between brain regions (FC) and a combined model of the two measures predicted the classification of AD or control, analyzed separately for males (AD = 30, controls = 74) and females (AD = 46, controls = 99). This was done with a logistic regression analysis with LASSO penalty. The results found that, unlike expected, males increased their AUC value (AUC = 0.941) when the FC measure was combined with the GMD measure (AUC = 0.959). The females however showed a decrease in their AUC value (AUC = 0.876) when the FC measure was combined with the GMD measure (AUC = 0.860). The FC measure also had a higher AUC value on its own in males (AUC = 0.723) versus females (AUC = 0.626). This shows that the FC measure used in this research increases accuracy of AD classification in males and implies that fMRI is not a better classifier of Alzheimer in females and does not improve diagnosis when compared to solely using structural MRI (sMRI).Show less
