Martin, Nicholas B.

AbstractBackgroundAlzheimer’s disease (AD) is neuropathologically heterogeneous and can be objectively classified along a spectrum of corticolimbic tangle distribution as hippocampal sparing (HpSp) AD, typical AD, and limbic predominant AD. The olfactory bulb is an early area of tau accumulation with a direct connection to the amygdala. Although tau pathology has been identified in the olfactory bulb, its association with AD subtypes remains unclear. To investigate these differences, tau markers were characterized within the olfactory bulb among AD subtypes.MethodThe cohort included 39 AD cases (HpSp AD: n = 15, typical AD: n = 12, limbic AD: n = 12). Immunofluorescence staining was conducted on 5 µm thick formalin‐fixed paraffin‐embedded olfactory bulb sections using phosphorylated tau (AT8 & PHF‐1) and C‐terminal tau (2E9) antibodies. Slides were digitally scanned on the PhenoImager™ HT slide scanner and analyzed using QuPath Digital Analysis software, quantifying tau burden as a percentage of positive staining.ResultIn whole olfactory bulbs, AT8 was observed as intrasomal tangle‐like immunoreactivity with some extrasomal neuritic staining. AT8 burden did not differ among subtypes (HpSp AD median = 0.28% [interquartile range 0.014‐1.7%], typical AD = 0.28% [0.027‐2.24%], limbic AD = 0.39% [interquartile range 0.39‐0.74%]). PHF‐1 was observed as intrasomal tangle‐like immunoreactivity with abundant extrasomal neuritic pathology. PHF‐1 was observed to be highest in limbic AD (HpSp AD = 3.5% [0.63‐11%)], typical AD = 3.2% [0.38‐13%], and limbic AD = 5.3% [1.97‐10%]) and differed from typical AD (p<0.05). 2E9 was observed as intrasomal tangle‐like immunoreactivity with minimal extrasomal staining and not found to differ among subtypes (HpSp AD = 0.18% [0.036‐2.2%], typical AD = 0.15% [0.021‐2.4%], and limbic AD = 0.29% [0.0030‐8.2%]).ConclusionAbundant tau accumulation was observed among all AD subtypes with some suggestion of higher tau burden in limbic predominant AD. Given the direct connection between olfactory bulb and amygdala, it is interesting to speculate of an environmental contribution to limbic predominant AD through olfactory bulb damage. Future studies will expand the cohort to include AD cases with Lewy body disease co‐pathology and primary tauopathies with a focus on the anterior olfactory nucleus as the primary site of tau pathology.

AbstractThis study explores the utility of the large language models (LLMs), specifically ChatGPT and Google Bard, in predicting neuropathologic diagnoses from clinical summaries. A total of 25 cases of neurodegenerative disorders presented at Mayo Clinic brain bank Clinico‐Pathological Conferences were analyzed. The LLMs provided multiple pathologic diagnoses and their rationales, which were compared with the final clinical diagnoses made by physicians. ChatGPT‐3.5, ChatGPT‐4, and Google Bard correctly made primary diagnoses in 32%, 52%, and 40% of cases, respectively, while correct diagnoses were included in 76%, 84%, and 76% of cases, respectively. These findings highlight the potential of artificial intelligence tools like ChatGPT in neuropathology, suggesting they may facilitate more comprehensive discussions in clinicopathological conferences.

Abstract This study explores the utility of the artificial intelligence model ChatGPT in predicting neuropathologic diagnoses from clinical summaries presented at clinicopathological conferences. Twenty-five cases of various neurodegenerative disorders were analyzed using ChatGPT versions 3.5 and 4. The primary neuropathological diagnoses predicted by the models aligned with the official neuropathologic diagnoses in 32% (ChatGPT-3.5) and 52% (ChatGPT-4) of cases. When considering all proposed diagnoses, accuracy increased to 76% and 84% for ChatGPT-3.5 and ChatGPT-4, respectively. These findings highlight the potential of artificial intelligence tools like ChatGPT in neuropathology, suggesting they may facilitate more comprehensive discussions in clinicopathological conferences.

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.