Perrin, Richard J.

AbstractIntroductionMachine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease.MethodsLongitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non‐carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein ε4 (APOE ε4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status.ResultsThe Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R2 = 0.95), fluorodeoxyglucose (R2 = 0.93), and atrophy (R2 = 0.95) in mutation carriers compared to non‐carriers.DiscussionResults suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions.

AbstractThe contributors to persistent cognitive impairment and hippocampal atrophy in leucine‐rich glioma‐inactivated 1 antibody encephalitis (LGI1) patients are unknown. We evaluated whether tau neuropathology measured with [18F]flortaucipir PET neuroimaging associated with persistent cognitive impairment and hippocampal atrophy in four recovering LGI1 patients (3 men; median age, 67 [37–88] years). Imaging findings in cases were compared with those observed in age‐ and gender‐similar cognitively normal individuals (n = 124) and individuals with early‐symptomatic Alzheimer disease (n = 11). Elevated [18F]flortaucipir retention was observed in the two LGI1 patients with hippocampal atrophy and persistent cognitive impairment, including one with autopsy‐confirmed Alzheimer disease. Tau neuropathology may associate with cognitive complaints and hippocampal atrophy in recovering LGI1 patients.

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.

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.

Abstract Background The clinical diagnosis of Parkinson's disease (PD) requires the presence of parkinsonism and supportive criteria that include a clear and dramatic beneficial response to dopaminergic therapy. Our aim was to test the diagnostic criterion of dopaminergic response by evaluating its association with pathologically confirmed diagnoses in a large population of parkinsonian patients. Methods We reviewed clinical data maintained in an electronic medical record from all patients with autopsy data who had been seen in the Movement Disorders Center at Washington University, St. Louis, between 1996 and 2018. All patients with parkinsonism who underwent postmortem neuropathologic examination were included in this analysis. Results There were 257 unique parkinsonian patients with autopsy‐based diagnoses who had received dopaminergic therapy. Marked or moderate response to dopaminergic therapy occurred in 91.2% (166/182) of those with autopsy‐confirmed PD, 52.0% (13/25) of those with autopsy‐confirmed multiple systems atrophy, 44.4% (8/18) of those with autopsy‐confirmed progressive supranuclear palsy, and 1 (1/8) with autopsy‐confirmed corticobasal degeneration. Other diagnoses were responsible for the remaining 24 individuals, 9 of whom had a moderate response to dopaminergic therapy. Conclusion A substantial response to dopaminergic therapy is frequent but not universal in PD. An absent response does not exclude PD. In other neurodegenerative disorders associated with parkinsonism, a prominent response may also be evident, but this occurs less frequently than in PD. © 2020 International Parkinson and Movement Disorder Society

Abstract Structural grey matter covariance networks provide an individual quantification of morphological patterns in the brain. The network integrity is disrupted in sporadic Alzheimer’s disease, and network properties show associations with the level of amyloid pathology and cognitive decline. Therefore, these network properties might be disease progression markers. However, it remains unclear when and how grey matter network integrity changes with disease progression. We investigated these questions in autosomal dominant Alzheimer’s disease mutation carriers, whose conserved age at dementia onset allows individual staging based upon their estimated years to symptom onset. From the Dominantly Inherited Alzheimer Network observational cohort, we selected T1-weighted MRI scans from 269 mutation carriers and 170 non-carriers (mean age 38 ± 15 years, mean estimated years to symptom onset −9 ± 11), of whom 237 had longitudinal scans with a mean follow-up of 3.0 years. Single-subject grey matter networks were extracted, and we calculated for each individual the network properties which describe the network topology, including the size, clustering, path length and small worldness. We determined at which time point mutation carriers and non-carriers diverged for global and regional grey matter network metrics, both cross-sectionally and for rate of change over time. Based on cross-sectional data, the earliest difference was observed in normalized path length, which was decreased for mutation carriers in the precuneus area at 13 years and on a global level 12 years before estimated symptom onset. Based on longitudinal data, we found the earliest difference between groups on a global level 6 years before symptom onset, with a greater rate of decline of network size for mutation carriers. We further compared grey matter network small worldness with established biomarkers for Alzheimer disease (i.e. amyloid accumulation, cortical thickness, brain metabolism and cognitive function). We found that greater amyloid accumulation at baseline was associated with faster decline of small worldness over time, and decline in grey matter network measures over time was accompanied by decline in brain metabolism, cortical thinning and cognitive decline. In summary, network measures decline in autosomal dominant Alzheimer’s disease, which is alike sporadic Alzheimer’s disease, and the properties show decline over time prior to estimated symptom onset. These data suggest that single-subject networks properties obtained from structural MRI scans form an additional non-invasive tool for understanding the substrate of cognitive decline and measuring progression from preclinical to severe clinical stages of Alzheimer’s disease.

AbstractIntroductionAlthough some members of families with autosomal dominant Alzheimer's disease mutations learn their mutation status, most do not. How knowledge of mutation status affects clinical disease progression is unknown. This study quantifies the influence of mutation awareness on clinical symptoms, cognition, and biomarkers.MethodsMutation carriers and non‐carriers from the Dominantly Inherited Alzheimer Network (DIAN) were stratified based on knowledge of mutation status. Rates of change on standard clinical, cognitive, and neuroimaging outcomes were examined.ResultsMutation knowledge had no associations with cognitive decline, clinical progression, amyloid deposition, hippocampal volume, or depression in either carriers or non‐carriers. Carriers who learned their status mid‐study had slightly higher levels of depression and lower cognitive scores.DiscussionKnowledge of mutation status does not affect rates of change on any measured outcome. Learning of status mid‐study may confer short‐term changes in cognitive functioning, or changes in cognition may influence the determination of mutation status.