Neurodegenerative disorders are characterized by progressive loss of selectively vulnerable populations of neurons, which contrasts with select static neuronal loss because of metabolic or toxic disorders. Neurodegenerative diseases can be classified according to primary clinical features (e.g., dementia, parkinsonism, or motor neuron disease), anatomic distribution of neurodegeneration (e.g., frontotemporal degenerations, extrapyramidal disorders, or spinocerebellar degenerations), or principal molecular abnormality. The most common neurodegenerative disorders are amyloidoses, tauopathies, α-synucleinopathies, and TDP-43 proteinopathies. The protein abnormalities in these disorders have abnormal conformational properties. Growing experimental evidence suggests that abnormal protein conformers may spread from cell to cell along anatomically connected pathways, which may in part explain the specific anatomical patterns observed at autopsy. In this review, we detail the human pathology of select neurodegenerative disorders, focusing on their main protein aggregates.

Alzheimer disease (AD) is a neurodegenerative disorder characterized pathologically by the presence of neurofibrillary tangles and amyloid beta (Aβ) plaques in the brain. The disease was first described in 1906 by Alois Alzheimer, and since then, there have been many advancements in technologies that have aided in unlocking the secrets of this devastating disease. Such advancements include improving microscopy and staining techniques, refining diagnostic criteria for the disease, and increased appreciation for disease heterogeneity both in neuroanatomic location of abnormalities as well as overlap with other brain diseases; for example, Lewy body disease and vascular dementia. Despite numerous advancements, there is still much to achieve as there is not a cure for AD and postmortem histological analyses is still the gold standard for appreciating AD neuropathologic changes. Recent technological advances such as in-vivo biomarkers and machine learning algorithms permit great strides in disease understanding, and pave the way for potential new therapies and precision medicine approaches. Here, we review the history of human AD neuropathology research to include the notable advancements in understanding common co-pathologies in the setting of AD, and microscopy and staining methods. We also discuss future approaches with a specific focus on deep phenotyping using machine learning.

Background

Studies have shown select associations between cardiovascular risk factors and dementia, but mostly focused on Alzheimer's Disease (AD).

Objective

We enhance these works by evaluating the relationship between the presence of cardiovascular risk factors and the rate of cognitive decline, measured using the Mini-Mental State Examination (MMSE) and Clinical Dementia Rating Sum of Boxes (CDR-SUM) on four common dementia subtypes (AD, dementia with Lewy bodies (DLB), frontotemporal dementia (FTD), and vascular dementia (VaD), as well as non-demented elderly individuals (normal)).

Method

We used generalized linear mixed models with random intercepts to account for correlation at the patient and center levels for each dementia subtype adjusting for time since initial visit, baseline cognitive score, age, and demographic factors. The cardiovascular risk factors evaluated included body mass index, diabetes, years of smoking, atrial fibrillation, hypertension, and hypercholesterolemia.

Results

Patients diagnosed with AD (n=1899), DLB (n=65), FTD (n=168), or VaD (n=13); or lacked cognitive impairment (normal) (n=3583) were evaluated using data from the National Alzheimer's Coordinating Centers. Cardiovascular risk factors were associated with select dementia subtypes including AD and FTD. Using MMSE and CDR-SUM, recent or active hypertension and hypercholesterolemia were associated with a slower cognitive decline for AD patients, while higher body mass index and years of smoking were associated with a slower cognitive decline for FTD patients. However, several cardiovascular factors demonstrated associations with more rapid cognitive decline.

Conclusion

These results demonstrate disease specific associations and can provide clinicians guidance on predicted cognitive changes at the group level using information about cardiovascular risk factors.

Although the discovery of the prion protein (PrP) resulted from its co-purification with scrapie infectivity in Syrian hamsters, work with genetically defined and genetically modified mice proved crucial for understanding the fundamental processes involved not only in prion diseases caused by PrP misfolding, aggregation, and spread but also in other, much more common, neurodegenerative brain diseases. In this review, we focus on methodological and conceptual approaches used to study scrapie and related PrP misfolding diseases in mice and how these approaches have advanced our understanding of related disorders including Alzheimer's and Parkinson's disease.

In 1984, the National Institute on Aging developed the Alzheimer's disease centers program. The main goal of these centers is to advance the understanding of Alzheimer's disease and related dementias (ADRD) through comprehensive patient evaluations and cutting-edge research in pathology, laboratory medicine, education, and scientific discovery. The neuropathology core of the Alzheimer's Disease Research Centers (ADRCs) collects postmortem brain tissue from consented donors ranging from cognitively normal individuals to those with late-stage dementia, whose samples and data can be shared around the world to further advance knowledge, diagnosis, and to eventually find cures for ADRD. Although recommended guidelines for biorepositories exist, we aimed to understand the current practices within neuropathology cores across the ADRCs. A survey was developed that focused on information related to sample processing methods, biospecimen requests, financial costs related to the repository, and data management. This survey was distributed to 28 current and former ADRC neuropathology cores. The survey obtained a response rate of 82% (23/28). Although most centers were consistent in responses related to sample processing and storage, they varied widely in processes by which neuropathological samples are shared and cost recovery mechanisms. The results of this survey provide benchmark data on practices within neuropathology cores across ADRCs and the overlap with biorepository best practices. Future studies focused on understanding factors that may influence current practices (such as available funds and personnel) are need to aid in minimizing barriers to optimally follow best practices. Sharing these data among ADRCs will allow for improvement in workflows and working toward cures for ADRD.

Semi-quantitative scoring schemes like the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) are the most commonly used method in Alzheimer's disease (AD) neuropathology practice. Computational approaches based on machine learning have recently generated quantitative scores for whole slide images (WSIs) that are highly correlated with human derived semi-quantitative scores, such as those of CERAD, for Alzheimer's disease pathology. However, the robustness of such models have yet to be tested in different cohorts. To validate previously published machine learning algorithms using convolutional neural networks (CNNs) and determine if pathological heterogeneity may alter algorithm derived measures, 40 cases from the Goizueta Emory Alzheimer's Disease Center brain bank displaying an array of pathological diagnoses (including AD with and without Lewy body disease (LBD), and / or TDP-43-positive inclusions) and levels of Aβ pathologies were evaluated. Furthermore, to provide deeper phenotyping, amyloid burden in gray matter vs whole tissue were compared, and quantitative CNN scores for both correlated significantly to CERAD-like scores. Quantitative scores also show clear stratification based on AD pathologies with or without additional diagnoses (including LBD and TDP-43 inclusions) vs cases with no significant neurodegeneration (control cases) as well as NIA Reagan scoring criteria. Specifically, the concomitant diagnosis group of AD + TDP-43 showed significantly greater CNN-score for cored plaques than the AD group. Finally, we report that whole tissue computational scores correlate better with CERAD-like categories than focusing on computational scores from a field of view with densest pathology, which is the standard of practice in neuropathological assessment per CERAD guidelines. Together these findings validate and expand CNN models to be robust to cohort variations and provide additional proof-of-concept for future studies to incorporate machine learning algorithms into neuropathological practice.

This scientific commentary refers to ‘Tau protein quantification in skin biopsies differentiates tauopathies from alpha-synucleinopathies’ by Vacchi et al. (https://doi.org/10.1093/brain/awac161).

Alzheimer's disease (AD) and vascular dementia are two of the most prevalent dementias that afflict the aging population in the United States (US). Studies have made great strides in understanding the neuropathology of these diseases; however, many studies are conducted in the context of non-Hispanic whites (NHWs), and few include the rapidly growing underrepresented populations that reside in the US. We sought to characterize current knowledge of the neuropathologic landscape of AD and vascular dementia of the largest growing US minority groups, namely Latinos/Hispanics, Black Americans, and Asian Americans, compared with NHWs being the majority group. It is vital to note these historic categories are social constructs and cultural and social associations may underlie differences.  We conducted a literature search utilizing specific criteria to yield neuropathology papers that addressed the demographics and neuropathologies of relevance, then collated the findings into this review. We reveal that while there has been much progress in neuropathological research involving Latinos/Hispanics and Black Americans in the past decade, no cohesive conclusions could be extrapolated from the existing data due to the dearth of minority participants and even smaller amount of information related to the heterogeneity within each minority group, especially Latinos/Hispanics. Furthermore, we reveal an even greater scarcity in neuropathological studies involving Asian Americans, also a very heterogeneous group. We hope the presented findings will illuminate the paucity of minority representation in not just neuropathological research but the field of clinical research overall and serve to inspire clinicians and researchers to help reduce the health disparities underrepresented groups in the US face.

The collection of post-mortem brain tissue has been a core function of the Alzheimer Disease Research Center's (ADRCs) network located within the United States since its inception. Individual brain banks and centers follow detailed protocols to record, store, and manage complex datasets that include clinical data, demographics, and when post-mortem tissue is available, a detailed neuropathological assessment. Since each institution often has specific research foci, there can be variability in tissue collection and processing workflows. While published guidelines exist for select diseases, such as those put forth by the National Institute on Aging and Alzheimer Association (NIA-AA), it is of importance to denote the current practices across institutions. To this end a survey was developed and sent to United States based brain bank leaders, collecting data on brain region sampling, including anatomic landmarks used, staining (including antibodies used), as well as whole-slide-image scanning hardware. We distributed this survey to 40 brain banks and obtained a response rate of 95% (38 / 40). Most brain banks followed guidelines defined by the NIA-AA, having H&E staining in all recommended regions and targeted region-based amyloid beta, tau, and alpha-synuclein immunohistochemical staining. However, sampling consistency varied related to key anatomic landmarks/locations in select regions, such as the striatum, periventricular white matter, and parietal cortex. This study highlights the diversity and similarities amongst brain banks and discusses considerations when amalgamating data/samples across multiple centers. This survey aids in establishing benchmarks to enhance dialogues on divergent workflows in a feasible way.

The efficacy of supervised deep learning in medical image analyses, particularly in pathology, is hindered by the necessity for extensive manual annotations. Annotating images at the gigapixel level manually proves to be a highly labor-intensive and time-consuming task. Semi-supervised learning (SSL) has emerged as a promising approach that leverages unlabeled data to reduce labeling efforts. In this work, we introduce Semi-Path, a practical SSL framework enhanced with active learning (AL) for gigapixel pathology tasks. Unlike existing methods that treat SSL and AL as independent components where AL incurs significant computational complexity to SSL, we propose a deep fusion of SSL and AL into a unified framework. Our framework introduces Informative Active Annotation (IAA) that employs a SSL-AL iterative structure to effectively extract knowledge from unlabeled pathology data. This structure significantly minimizes labeling efforts and computational complexity. Then, we propose Adaptive Pseudo-Labeling (APL) to address heterogeneity in class distribution, and prediction difficulty that are often observed in real-world pathology tasks. We evaluate Semi-Path on pathology image classification and segmentation tasks over three datasets that include WSIs from breast, colorectal, and brain tissues. The experimental results demonstrate the consistent superiority of Semi-Path over state-of-the-art methods.