Insights into cognitive aging and Alzheimer’s disease using amyloid PET and structural MRI scans

A Gaussian-based model for early detection of mild cognitive impairment using multimodal neuroimaging

BACKGROUND

Diagnosis of early mild cognitive impairment (EMCI) as a prodromal stage of Alzheimer's disease (AD) with its delineation from the cognitively normal (CN) group remains a challenging but essential step for the planning of early treatment. Although several studies have focused on the MCI diagnosis, this study introduces the early stage of MCI to assess more thoroughly the earliest signs of disease manifestation and progression.

NEW METHOD

We used random forest feature selection model with a Gaussian-based algorithm to perform method evaluation. This integrated method serves to define multivariate normal distributions in order to classify different stages of AD, with the focus placed on detecting EMCI subjects in the most challenging classification of CN vs. EMCI.

RESULTS

Using 896 participants classified into the four categories of CN, EMCI, late mild cognitive impairment (LMCI) and AD, the results show that the EMCI group can be delineated from the CN group with a relatively high accuracy of 78.8% and sensitivity of 81.3%.

COMPARISON WITH EXISTING METHOD(S)

The feature selection model and classifier are compared with some other prominent algorithms. Although higher accuracy has been achieved using the Gaussian process (GP) model (78.8%) over the SVM classifier (75.6%) for CN vs. EMCI classification, with 0.05 being the cutoff for significance, and based on student's t-test, it was determined that the differences for accuracy, sensitivity, specificity between the GP method and support vector machine (SVM) are not statistically significant.

CONCLUSION

Addressing the challenging classification of CN vs. EMCI provides useful information to help clinicians and researchers determine essential measures that can help in the early detection of AD.

Network Disruption in the Preclinical Stages of Alzheimer’s Disease: From Subjective Cognitive Decline to Mild Cognitive Impairment

Introduction: Subjective Cognitive Decline (SCD) is a largely unknown state thought to represent a preclinical stage of Alzheimer’s Disease (AD) previous to mild cognitive impairment (MCI). However, the course of network disruption in these stages is scarcely characterized. Methods: We employed resting state magnetoencephalography in the source space to calculate network smallworldness, clustering, modularity and transitivity. Nodal measures (clustering and node degree) as well as modular partitions were compared between groups. Results: The MCI group exhibited decreased smallworldness, clustering and transitivity and increased modularity in theta and beta bands. SCD showed similar but smaller changes in clustering and transitivity, while exhibiting alterations in the alpha band in opposite direction to those showed by MCI for modularity and transitivity. At the node level, MCI disrupted both clustering and nodal degree while SCD showed minor changes in the latter. Additionally, we observed an increase in modular partition variability in both SCD and MCI in theta and beta bands. Conclusion: SCD elders exhibit a significant network disruption, showing intermediate values between HC and MCI groups in multiple parameters. These results highlight the relevance of cognitive concerns in the clinical setting and suggest that network disorganization in AD could start in the preclinical stages before the onset of cognitive symptoms.

Entorhinal and transentorhinal atrophy in mild cognitive impairment using longitudinal diffeomorphometry

INTRODUCTION

Autopsy findings have shown the entorhinal cortex and transentorhinal cortex are among the earliest sites of accumulation of pathology in patients developing Alzheimer's disease.

METHODS

Here, we study this region in subjects with mild cognitive impairment (n = 36) and in control subjects (n = 16). The cortical areas are manually segmented, and local volume and shape changes are quantified using diffeomorphometry, including a novel mapping procedure that reduces variability in anatomic definitions over time.

RESULTS

We find significant thickness and volume changes localized to the transentorhinal cortex through high field strength atlasing.

DISCUSSION

This demonstrates that in vivo neuroimaging biomarkers can detect these early changes among subjects with mild cognitive impairment.

The phospholipid composition of the human entorhinal cortex remains relatively stable over 80 years of adult aging

Membrane lipid composition is altered in the brain during the pathogenesis of several age-related neurodegenerative diseases, including Alzheimer's disease. The entorhinal cortex is one of the first regions of the brain to display the neuropathology typical of Alzheimer's disease, yet little is known about the changes that occur in membrane lipids within this brain region during normal aging (i.e., in the absence of dementia). In the present study, the phospholipid composition of mitochondrial and microsomal membranes from human entorhinal cortex was examined for any changes over the adult lifespan (18-98 years). Overall, changes in several molecular phospholipids were seen with age in the entorhinal cortex across both membranes. The proportion of total phosphatidylcholine within the mitochondrial fraction increased within the entorhinal cortex with age, while total mitochondrial phosphatidylethanolamine decreased. Many mitochondrial phosphatidylethanolamines containing docosahexaenoic acid increased with age; however, this did not translate into an overall age-related increase in total mitochondrial docosahexaenoic acid. The most abundant phospholipid present within the human brain, PC 16:0_18:1, also increased with age within the mitochondrial membranes of the entorhinal cortex. When compared to other regions of the brain, the phospholipid composition of the entorhinal cortex remains relatively stable in adults over the lifespan in the absence of dementia.

Inclusion of Neuropsychological Scores in Atrophy Models Improves Diagnostic Classification of Alzheimer's Disease and Mild Cognitive Impairment

Brain atrophy in mild cognitive impairment (MCI) and Alzheimer's disease (AD) are difficult to demarcate to assess the progression of AD. This study presents a statistical framework on the basis of MRI volumes and neuropsychological scores. A feature selection technique using backward stepwise linear regression together with linear discriminant analysis is designed to classify cognitive normal (CN) subjects, early MCI (EMCI), late MCI (LMCI), and AD subjects in an exhaustive two-group classification process. Results show a dominance of the neuropsychological parameters like MMSE and RAVLT. Cortical volumetric measures of the temporal, parietal, and cingulate regions are found to be significant classification factors. Moreover, an asymmetrical distribution of the volumetric measures across hemispheres is seen for CN versus EMCI and EMCI versus AD, showing dominance of the right hemisphere; whereas CN versus LMCI and EMCI versus LMCI show dominance of the left hemisphere. A 2-fold cross-validation showed an average accuracy of 93.9%, 90.8%, and 94.5%, for the CN versus AD, CN versus LMCI, and EMCI versus AD, respectively. The accuracy for groups that are difficult to differentiate like EMCI versus LMCI was 73.6%. With the inclusion of the neuropsychological scores, a significant improvement (24.59%) was obtained over using MRI measures alone.