Sekiya, Hiroaki

AbstractBackgroundMixed pathology is common at autopsy for a number of age‐associated neurodegenerative disorders; however, the frequency of comorbid pathologies in multiple system atrophy (MSA) and their clinical correlations are poorly understood.ObjectiveWe determined the frequency of comorbid pathologic processes in autopsy‐confirmed MSA and assessed their clinical correlates.MethodsThis study included 160 neuropathologically established MSA from the Mayo Clinic brain bank. Clinical information, including age at onset or death, clinical subtype, initial symptoms, antemortem clinical diagnosis, and cognitive dysfunction was collected. We assessed comorbid pathologies including Alzheimer's disease neuropathologic change, Lewy‐related pathology, argyrophilic grain disease, age‐related τ astrogliopathy, transactive DNA‐binding protein 43 pathology, cerebral amyloid angiopathy, and cerebrovascular small vessel disease and examined their clinical impact.ResultsThe majority of MSA patients (62%) had no significant comorbid pathologies. There was a positive correlation between age at onset or death with the number of comorbid pathologies; however, even in the highest quartile group (average age at death 78 ± 6 years), the average number of comorbid pathologies was <2. Logistic regression analysis revealed that none of the assessed variables, including sex, age at onset, and the presence or absence of each comorbid pathology, were significantly associated with cognitive dysfunction.ConclusionsThe majority of MSA patients do not have comorbid pathologies, even in advanced age, indicating that MSA is unique among neurodegenerative disorders in this regard. There was minimal clinical impact of comorbid pathologies in MSA. These findings warrant focusing on α‐synuclein for the treatment strategy for MSA. © 2023 International Parkinson and Movement Disorder Society.

AbstractIntraneuronal accumulation of misfolded α-synuclein is the pathological hallmark of Parkinson’s disease and dementia with Lewy bodies, often co-occurring with variable degrees of Alzheimer’s disease related neuropathology. Genetic association studies have successfully identified common variants associated with disease risk and phenotypic traits in Lewy body disease, yet little is known about the genetic contribution to neuropathological heterogeneity.Using summary statistics from Parkinson’s disease and Alzheimer’s disease genome-wide association studies, we calculated polygenic risk scores and investigated the relationship with Lewy, amyloid-β and tau pathology. Associations were nominated in neuropathologically defined samples with Lewy body disease from the Netherlands Brain Bank (n = 217) and followed up in an independent sample series from the Mayo Clinic Brain Bank (n = 394). We also generated stratified polygenic risk scores based on single-nucleotide polymorphisms annotated to eight functional pathways or cell types previously implicated in Parkinson’s disease and assessed for association with Lewy pathology in subgroups with and without significant Alzheimer’s disease co-pathology.In an ordinal logistic regression model, the Alzheimer’s disease polygenic risk score was associated with concomitant amyloid-β and tau pathology in both cohorts. Moreover, both cohorts showed a significant association between lysosomal pathway polygenic risk and Lewy pathology, which was more consistent than the association with a general Parkinson’s disease risk score and specific to the subset of samples without significant concomitant Alzheimer’s disease related neuropathology.Our findings provide proof of principle that the specific risk alleles a patient carries for Parkinson’s and Alzheimer’s disease also influence key aspects of the underlying neuropathology in Lewy body disease. The interrelations between genetic architecture and neuropathology are complex, as our results implicate lysosomal risk loci specifically in the subset of samples without Alzheimer’s disease co-pathology. Our findings hold promise that genetic profiling may help predict the vulnerability to specific neuropathologies in Lewy body disease, with potential relevance for the further development of precision medicine in these disorders.

ABSTRACTBackgroundAs part of the CurePSP brain donation program, a questionnaire was developed to gather basic clinical information on donors; however, its usefulness has not been evaluated.ObjectiveTo assess the value of information obtained from the questionnaire in differentiating between parkinsonian disorders.MethodsWe reviewed 150 questionnaires, including 50 patients, each with a neuropathologic diagnosis of Lewy body disease (LBD), multiple system atrophy (MSA), or progressive supranuclear palsy. The frequency of clinical features recorded in the questionnaires was compared for the three disorders, and a machine learning algorithm was used to identify features predicting neuropathologic diagnosis.ResultsThe information from the questionnaires correlated with core clinical features for each disorder, such as hallucinations for LBD and autonomic dysfunction for MSA. Hallucinations and disorientations were identified as the key variables that contributed most to the prediction of neuropathology.ConclusionThe questionnaire provides useful clinical information for clinicopathological correlative studies.

Abstract Background: As part of the CurePSP brain donation program, a questionnaire was developed to capture basic information from the next-of-kin or someone familiar with the brain donor with respect to symptoms and clinical course. The usefulness of information from the questionnaire has not been assessed. Objective: To assess the value of information from the brain donation questionnaire as it relates to differential diagnosis of parkinsonian disorders. Methods: We reviewed 150 questionnaires, including 50 patients, each with a neuropathologic diagnosis of Lewy body disease (LBD), multiple system atrophy (MSA), or progressive supranuclear palsy (PSP). The frequency of clinical features recorded in the questionnaires was compared for the three disorders, and machine learning algorithm, XGBoost, was applied to tabulated information from the questionnaires to identify features that had predictive value with respect to neuropathologic diagnosis. Results: The information from the questionnaires correlated with core clinical features for each disorder - hallucinations for LBD, autonomic dysfunction for MSA, and early falls for PSP. Age at onset and early falls were identified as the key variables that contributed most to prediction of neuropathology. Conclusion: While less optimal than prospectively collected information, a questionnaire can provide useful clinical information for clinicopathological correlative studies.

Abstract Neuropathological assessment at autopsy is the gold standard for diagnosing neurodegenerative disorders. We aimed to develop a pipeline for diagnosing Alzheimer's disease and other tauopathies, including corticobasal degeneration, globular glial tauopathy, Pick’s disease, and progressive supranuclear palsy. We used deep learning (DL)-based approach called clustering-constrained-attention multiple instance learning (CLAM) on whole slide images (WSIs) of tau immunohistochemistry in three brain regions from 120 patients. We also augmented gradient-weighted class activation mapping (Grad-CAM) to the model for visualizing cellular-level evidence in the model’s decisions. The model using the sections of cingulate and superior frontal gyri achieved the highest area under the curve (0.970±0.037) and diagnostic accuracy (0.873±0.087). Grad-CAM showed the highest attention in known pathognomonic tau lesions for each disease (e.g., Pick bodies for Pick’s disease). Our findings supported the feasibility of the DL-based approach for the classification task on WSIs, which encouraged further investigation, especially focusing on clinicopathological correlation studies.

Abstract Neuropathological assessment at autopsy is the gold standard for diagnosing neurodegenerative disorders. We aimed to develop a pipeline for diagnosing Alzheimer's disease and other tauopathies, including corticobasal degeneration, globular glial tauopathy, Pick’s disease, and progressive supranuclear palsy. We used deep learning (DL)-based approach called clustering-constrained-attention multiple instance learning (CLAM) on whole slide images (WSIs) of tau immunohistochemistry in three brain regions from 120 patients. We also augmented gradient-weighted class activation mapping (Grad-CAM) to the model for visualizing cellular-level evidence in the model’s decisions. The model using the sections of cingulate and superior frontal gyri achieved the highest area under the curve (0.970±0.037) and diagnostic accuracy (0.873±0.087). Grad-CAM showed the highest attention in known pathognomonic tau lesions for each disease (e.g., Pick bodies for Pick’s disease). Our findings supported the feasibility of the DL-based approach for the classification task on WSIs, which encouraged further investigation, especially focusing on clinicopathological correlation studies.