Validation of machine learning models to detect amyloid pathologies across institutions.

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

Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline.

Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and lack precise neuroanatomic localization. We report a proof-of-concept deep learning pipeline that identifies specific neuropathologies-amyloid plaques and cerebral amyloid angiopathy-in immunohistochemically-stained archival slides. Using automated segmentation of stained objects and a cloud-based interface, we annotate > 70,000 plaque candidates from 43 whole slide images (WSIs) to train and evaluate convolutional neural networks. Networks achieve strong plaque classification on a 10-WSI hold-out set (0.993 and 0.743 areas under the receiver operating characteristic and precision recall curve, respectively). Prediction confidence maps visualize morphology distributions at high resolution. Resulting network-derived amyloid beta (Aβ)-burden scores correlate well with established semi-quantitative scores on a 30-WSI blinded hold-out. Finally, saliency mapping demonstrates that networks learn patterns agreeing with accepted pathologic features. This scalable means to augment a neuropathologist's ability suggests a route to neuropathologic deep phenotyping

The Impact of Aging on Brain Pituitary Adenylate Cyclase Activating Polypeptide Pathology and Cognition in Mice and Rhesus Macaques

Pituitary adenylate cyclase activating polypeptide (PACAP) is associated with Alzheimer\u27s disease (AD), but its age-related effects are unknown. We chose the rhesus macaque due to its closeness to human anatomy and physiology. We examined four variables: aging, cognitive performance, amyloid plaques and PACAP. Delayed nonmatching-to-sample recognition memory scores declined with age and correlated with PACAP levels in the striatum, parietal and temporal lobes. Because amyloid plaques were the only AD pathology in the old rhesus macaque, we further studied human amyloid precursor protein (hAPP) transgenic mice. Aging was associated with decreased performance in the Morris Water Maze (MWM). In wild type (WT) C57BL/6 mice, the performance was decreased at age 24-26 month whereas in hAPP transgenic mice, it was decreased as early as 9-12 month. Neuritic plaques in adult hAPP mice clustered in hippocampus and adjacent cortical regions, but did not propagate further into the frontal cortex. Cerebral PACAP protein levels were reduced in hAPP mice compared to age-matched WT mice, but the genetic predisposition dominated cognitive decline. Taken together, these data suggest an association among PACAP levels, aging, cognitive function and amyloid load in nonhuman primates, with both similarities and differences from human AD brains. Our results suggest caution in choosing animal models and in extrapolating data to human AD studies

Frequency of LATE neuropathologic change across the spectrum of Alzheimer’s disease neuropathology: combined data from 13 community-based or population-based autopsy cohorts

Limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) and Alzheimer’s disease neuropathologic change (ADNC) are each associated with substantial cognitive impairment in aging populations. However, the prevalence of LATE-NC across the full range of ADNC remains uncertain. To address this knowledge gap, neuropathologic, genetic, and clinical data were compiled from 13 high-quality community- and population-based longitudinal studies. Participants were recruited from United States (8 cohorts, including one focusing on Japanese–American men), United Kingdom (2 cohorts), Brazil, Austria, and Finland. The total number of participants included was 6196, and the average age of death was 88.1 years. Not all data were available on each individual and there were differences between the cohorts in study designs and the amount of missing data. Among those with known cognitive status before death (n = 5665), 43.0% were cognitively normal, 14.9% had MCI, and 42.4% had dementia—broadly consistent with epidemiologic data in this age group. Approximately 99% of participants (n = 6125) had available CERAD neuritic amyloid plaque score data. In this subsample, 39.4% had autopsy-confirmed LATE-NC of any stage. Among brains with “frequent” neuritic amyloid plaques, 54.9% had comorbid LATE-NC, whereas in brains with no detected neuritic amyloid plaques, 27.0% had LATE-NC. Data on LATE-NC stages were available for 3803 participants, of which 25% had LATE-NC stage > 1 (associated with cognitive impairment). In the subset of individuals with Thal Aβ phase = 0 (lacking detectable Aβ plaques), the brains with LATE-NC had relatively more severe primary age-related tauopathy (PART). A total of 3267 participants had available clinical data relevant to frontotemporal dementia (FTD), and none were given the clinical diagnosis of definite FTD nor the pathological diagnosis of frontotemporal lobar degeneration with TDP-43 inclusions (FTLD-TDP). In the 10 cohorts with detailed neurocognitive assessments proximal to death, cognition tended to be worse with LATE-NC across the full spectrum of ADNC severity. This study provided a credible estimate of the current prevalence of LATE-NC in advanced age. LATE-NC was seen in almost 40% of participants and often, but not always, coexisted with Alzheimer’s disease neuropathology

LATE-NC staging in routine neuropathologic diagnosis : an update

An international consensus report in 2019 recommended a classification system for limbic-predominant age-related TDP-43 encephalopathy neuropathologic changes (LATE-NC). The suggested neuropathologic staging system and nomenclature have proven useful for autopsy practice and dementia research. However, some issues remain unresolved, such as cases with unusual features that do not fit with current diagnostic categories. The goal of this report is to update the neuropathologic criteria for the diagnosis and staging of LATE-NC, based primarily on published data. We provide practical suggestions about how to integrate available genetic information and comorbid pathologies [e.g., Alzheimer's disease neuropathologic changes (ADNC) and Lewy body disease]. We also describe recent research findings that have enabled more precise guidance on how to differentiate LATE-NC from other subtypes of TDP-43 pathology [e.g., frontotemporal lobar degeneration (FTLD) and amyotrophic lateral sclerosis (ALS)], and how to render diagnoses in unusual situations in which TDP-43 pathology does not follow the staging scheme proposed in 2019. Specific recommendations are also made on when not to apply this diagnostic term based on current knowledge. Neuroanatomical regions of interest in LATE-NC are described in detail and the implications for TDP-43 immunohistochemical results are specified more precisely. We also highlight questions that remain unresolved and areas needing additional study. In summary, the current work lays out a number of recommendations to improve the precision of LATE-NC staging based on published reports and diagnostic experience.Peer reviewe

Aging-related tau astrogliopathy (ARTAG):harmonized evaluation strategy

Pathological accumulation of abnormally phosphorylated tau protein in astrocytes is a frequent, but poorly characterized feature of the aging brain. Its etiology is uncertain, but its presence is sufficiently ubiquitous to merit further characterization and classification, which may stimulate clinicopathological studies and research into its pathobiology. This paper aims to harmonize evaluation and nomenclature of aging-related tau astrogliopathy (ARTAG), a term that refers to a morphological spectrum of astroglial pathology detected by tau immunohistochemistry, especially with phosphorylation-dependent and 4R isoform-specific antibodies. ARTAG occurs mainly, but not exclusively, in individuals over 60 years of age. Tau-immunoreactive astrocytes in ARTAG include thorn-shaped astrocytes at the glia limitans and in white matter, as well as solitary or clustered astrocytes with perinuclear cytoplasmic tau immunoreactivity that extends into the astroglial processes as fine fibrillar or granular immunopositivity, typically in gray matter. Various forms of ARTAG may coexist in the same brain and might reflect different pathogenic processes. Based on morphology and anatomical distribution, ARTAG can be distinguished from primary tauopathies, but may be concurrent with primary tauopathies or other disorders. We recommend four steps for evaluation of ARTAG: (1) identification of five types based on the location of either morphologies of tau astrogliopathy: subpial, subependymal, perivascular, white matter, gray matter; (2) documentation of the regional involvement: medial temporal lobe, lobar (frontal, parietal, occipital, lateral temporal), subcortical, brainstem; (3) documentation of the severity of tau astrogliopathy; and (4) description of subregional involvement. Some types of ARTAG may underlie neurological symptoms; however, the clinical significance of ARTAG is currently uncertain and awaits further studies. The goal of this proposal is to raise awareness of astroglial tau pathology in the aged brain, facilitating communication among neuropathologists and researchers, and informing interpretation of clinical biomarkers and imaging studies that focus on tau-related indicators