Crary, John F.

Abstract Primary age-related tauopathy (PART) is a neurodegenerative entity defined as neurofibrillary degeneration generally restricted to the medial temporal region (Braak stage I–IV) with complete or near absence of diffuse and neuritic plaques. Symptoms range in severity but are generally milder and later in onset than in Alzheimer disease (AD). Recently, an early predilection for neurofibrillary degeneration in the hippocampal CA2 subregion has been demonstrated in PART, whereas AD neuropathologic change (ADNC) typically displays relative sparing of CA2 until later stages. In this study, we utilized a semiquantitative scoring system to evaluate asymmetry of neurofibrillary degeneration between left and right hippocampi in 67 PART cases and 17 ADNC cases. 49% of PART cases demonstrated asymmetric findings in at least one hippocampal subregion, and 79% of the asymmetric cases displayed some degree of CA2 asymmetry. Additionally, 19% of cases revealed a difference in Braak score between the right and left hippocampi. There was a significant difference in CA2 neurofibrillary degeneration (p = 0.0006) and CA2/CA1 ratio (p < 0.0001) when comparing the contralateral sides, but neither right nor left was more consistently affected. These data show the importance of analyzing bilateral hippocampi in the diagnostic evaluation of PART and potentially of other neurodegenerative diseases.

Abstract Chronic traumatic encephalopathy (CTE) is a neurodegenerative disorder associated with exposure to head trauma. In 2015, a panel of neuropathologists funded by the NINDS/NIBIB defined preliminary consensus neuropathological criteria for CTE, including the pathognomonic lesion of CTE as “an accumulation of abnormal hyperphosphorylated tau (p-tau) in neurons and astroglia distributed around small blood vessels at the depths of cortical sulci and in an irregular pattern,” based on review of 25 tauopathy cases. In 2016, the consensus panel met again to review and refine the preliminary criteria, with consideration around the minimum threshold for diagnosis and the reproducibility of a proposed pathological staging scheme. Eight neuropathologists evaluated 27 cases of tauopathies (17 CTE cases), blinded to clinical and demographic information. Generalized estimating equation analyses showed a statistically significant association between the raters and CTE diagnosis for both the blinded (OR = 72.11, 95% CI = 19.5–267.0) and unblinded rounds (OR = 256.91, 95% CI = 63.6–1558.6). Based on the challenges in assigning CTE stage, the panel proposed a working protocol including a minimum threshold for CTE diagnosis and an algorithm for the assessment of CTE severity as “Low CTE” or “High CTE” for use in future clinical, pathological, and molecular studies.

Abstract Primary age-related tauopathy (PART) is a neurodegenerative entity defined as Alzheimer-type neurofibrillary degeneration primarily affecting the medial temporal lobe with minimal to absent amyloid-β (Aβ) plaque deposition. The extent to which PART can be differentiated pathoanatomically from Alzheimer disease (AD) is unclear. Here, we examined the regional distribution of tau pathology in a large cohort of postmortem brains (n = 914). We found an early vulnerability of the CA2 subregion of the hippocampus to neurofibrillary degeneration in PART, and semiquantitative assessment of neurofibrillary degeneration in CA2 was significantly greater than in CA1 in PART. In contrast, subjects harboring intermediate-to-high AD neuropathologic change (ADNC) displayed relative sparing of CA2 until later stages of their disease course. In addition, the CA2/CA1 ratio of neurofibrillary degeneration in PART was significantly higher than in subjects with intermediate-to-high ADNC burden. Furthermore, the distribution of tau pathology in PART diverges from the Braak NFT staging system and Braak stage does not correlate with cognitive function in PART as it does in individuals with intermediate-to-high ADNC. These findings highlight the need for a better understanding of the contribution of PART to cognitive impairment and how neurofibrillary degeneration interacts with Aβ pathology in AD and PART.

AbstractBackgroundPost‐mortem assessment remains the only option and a gold standard for a definitive diagnosis of Parkinson’s disease (PD). The antemortem diagnosis is challenging due to a variable clinical presentation and the abundance of mimicking conditions, obscuring the clinical picture and delaying the treatment. It is well established that while PD pathologically manifests in central nervous system with aggregation of α‐synuclein as Lewy bodies and Lewy neurites, this pathology is also found in the peripheral nervous system. Peripheral Lewy‐type synucleinopathy (LTS) is present in early PD, suggesting its utility as a diagnostic and prognostic biomarker. We have previously confirmed that detection of LTS in submandibular gland (SMG) biopsies is sensitive (.75) and specific (.90) for early PD. However, the assessment by a neuropathologist of multiple levels of such biopsy is laborious and time‐consuming.MethodHere work we applied two Convolutional Neural Networks (CNN) for detection of LTS on 283 digital whole slide images (WSI) from 95 unique SMG biopsies. A total number of 8,386 LTS were annotated following the interrater reliability study with kappa=.7. We used transfer learning to train a CNN model to classify image patches (40‐by‐40 pixels at 20x) with and without presence of LTS objects.ResultThe trained CNNs showed the following performance on image patches: sensitivity: .982/.988, specificity: .998/.999, precision: .852/.950, accuracy: .995/.998, and F‐1 score .912/.968 for ResNet30/InceptionV4 respectively. On test WSI the sensitivity in detecting LTS was .963/.971 respectively.ConclusionThis work is first to demonstrate the practical utility of an CNN for screening for LTS, which can further translate into a practical screening tool facilitating the antemortem diagnosis of PD. We further plan to develop neural networks for accurate and precise detection and quantification of LTS in antemortem SMG biopsies. This, altogether, would offer a screening, confirmatory, prognostic, and quantitative tool for clinical assessment of early PD.

AbstractBackgroundDigital pathology and machine learning (ML) workflows have paradigm shifting potential especially in the field of Alzheimer’s disease and related disorders. However, many institutions/centers may not have access to these technologies. To provide benchmark data, a survey was developed and distributed to National Institutes of Health’s Alzheimer’s Disease Centers (ADCs) in the United States.MethodSurvey questions covered topics such as: infrastructure (i.e. type of digital slide scanners used), funding sources (i.e. how the scanner was funded), and data management/storage (i.e. size of digital files). After development, review, and approval by the ADC digital pathology working group, the survey was distributed via email to 35 past and current ADC directors and/or ADC neuropathology core leaders. Participation in the survey was completely voluntary and answers did not contain any personal data.ResultThe survey generated over a 90% response rate, and the majority of those who completed the survey were neuropathology core leaders; 81% stated their ADC had access to a digital slide scanner, most common brand being Aperio/Leica. One third of respondents stated there was a fee for service to utilize the scanner. For digital pathology and/or ML resources, 40% of respondents stated none are supported in any way by their ADC. To cover the purchase and operation of the scanner, 50% stated they had institutional support. Many were unsure of the approximate average scanned file size of digital images (37%) and total amount of storage space that files occupied (50%). Many (75%) were aware of other departments at their institution working with digital pathology and/or ML, but a similar percentage was unaware of multi‐university or industry partnerships.ConclusionThese results demonstrate many ADCs have access to a digital slide scanner and had institutional support to cover the purchase. However, further investigation is needed to understand hurdles and barriers for implementing both digital pathology and ML workflows aiding in standardized methods across ADCs.