Alzheimer’s disease (AD) is a neurodegenerative disorder prevalent in older adults, leading to loss in memory, cognitive, and executive function. A characteristic feature of AD is the accumulation of amyloid-beta (Aβ) plaques, which are extracellular deposits of Aβ protein primarily found in grey matter. These Aβ deposits can appear in different forms. The six primary Aβ deposits covered in this work are: diffuse plaques, cored plaques, compact plaques, coarse grained plaques, cerebral amyloid angiopathy (CAA), and subpial deposits. It is speculated that some of the plaques types may be more harmful than others. Given that AD primarily affects an older population, the question arises of how individuals with AD differentiate from cognitively healthy centenarians (people over 100 years old). Consequently, this work attempts to classify and analyse different Aβ types present in donated brain tissue of cognitively healthy centenarians who escaped dementia and individuals diagnosed with AD. The main goal is to identify differences between these two cohorts. However, a challenge in this process is that the brain tissues contain many Aβ plaques, making manual identification difficult in terms of time and labor. To address this, a fine-tuned ResNet50 Aβ plaque classifier was developed in this research that was integrated into an Aβ detection pipeline capable of localising plaques. The model was initially pre-trained on the ImageNet dataset through contrastive learning, and subsequently fine-tuned using few-shot learning with a small number of annotated samples (315). The annotations included the six primary Aβ types whose structure are known and well-defined, and three other anomaly Aβ types that
served to filter out irregular plaques in the unlabeled Aβ data. After performing 5-fold cross-validation, the fine-tuned models demonstrated an average accuracy of 85.71% and precision of 89.47% on the primary types. The final classifier used on the unlabeled Aβ dataset was an ensemble model that incorporated majority voting, combining the predictions of the five models trained during cross-validation. Aβ loads were calculated for each primary Aβ type based on the classifier’s predictions. It was observed that across all considered primary Aβ types, the centenarians’ Aβ loads were statistically significantly lower compared to the AD cohort. The lower Aβ load in centenarians also held true across the frontal, temporal, parietal, and occipital cerebral regions for each primary Aβ type. To partially validate the
model’s performance, correlations were computed between the predicted Aβ loads of the primary Aβ types and neuropathological assessments collected from 75 centenarians. These assessments are common in related works and serve as a benchmark for the ensemble model. They include the Thal Aβ phase, which categorizes the distribution of general Aβ in the brain; the Thal CAA stage, which measures the severity of CAA; and CERAD NP scores, which evaluates the spread of neuritic plaques in the brain, which are a subset of cored plaques. Consequently, statistically significant positive correlations were revealed between: the Thal Aβ phase and the Aβ load of all primary types (r ranging 0.59-0.73); the Thal CAA stage and Aβ load of predicted CAA deposits (r = 0.66); and the CERAD NP scores and Aβ load of cored plaques (r = 0.68). Since the correlated types coincide with what the benchmark staging schemes measure, the model’s predictions seems to align with existing literature.

Alzheimer's disease (AD) is becoming more prevalent as the world population gets older. The formation of Amyloid-beta (\AB) plaques is one of the pathologies related to AD. Recent work has shown that the \ab load in brain tissue has a negative correlation with cognitive performance in cognitively healthy centenarians.
This work aims to expand this research by investigating whether the types of \ab plaque present are linked to cognition and by comparing the types of plaques in the centenarian cohort with an AD cohort.
For this task, a system is developed that can identify \ab plaques in images of brain tissue. It first automatically segments the grey matter using a fine-tuned U-net. Then the plaques are located using traditional image processing techniques. Lastly, shape and size features are extracted from the plaque in addition to a feature vector made by a pre-trained AlexNet. K-means clustering is used on AlexNet features to find categories for the plaques.
The clustering approach failed to yield good results. However, the area and roundness are differently distributed between the AD and centenarian cohorts, but the differences are small. Correlations have been found between the area and roundness of plaques in the occipital pole and cognitive performance in centenarians. They indicate that cognitively stronger individuals have smaller and less round \ab plaques in their brains. More research is necessary to reveal the true extent of the impact of plaque types on cognition.