Koga, Shunsuke

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.

Transactive response DNA‐binding protein 43 (TDP‐43) pathological inclusions are found in frontotemporal lobar degeneration (FTLD‐TDP) and Alzheimer's disease (AD‐TDP). While clinically different, TDP‐43 inclusions in FTLD‐TDP and AD can have similar morphological characteristics. However, TDP‐43 colocalizing with tau and forming “apple‐bite” or “flame‐shaped” neuronal cytoplasmic inclusions (NCI) are only found in AD‐TDP. Here, we describe a case with AD and neuritic plaque‐associated TDP‐43. The patient was a 96‐year‐old right‐handed Caucasian woman who had developed a slowly progressive amnestic syndrome compatible with typical AD at age 80. Genetic testing revealed APOE ε3/ε4, GRN r5848 CT, and MAPT H1/H2 genotype. Consistent with the old age at onset and long disease duration, limbic‐predominant AD was found at autopsy, with high hippocampal yet low cortical neurofibrillary tangle (NFT) counts. Hippocampal and amygdala sclerosis were present. Immunohistochemistry for phospho‐TDP‐43 showed NCIs, dystrophic neurites, and rare neuronal intranuclear inclusions consistent with FTLD‐TDP type A, as well as tau NFT‐associated TDP‐43 inclusions. These were frequent in the amygdala, entorhinal cortex, hippocampus, occipitotemporal gyrus, and inferior temporal gyrus but sparse in the mid‐frontal cortex. Additionally, there were TDP‐43‐immunoreactive inclusions forming plaque‐like structures in the molecular layer of the dentate fascia of the hippocampus. The presence of neuritic plaques in the same region was confirmed using thioflavin‐S fluorescent microscopy and immunohistochemistry for phospho‐tau. Double labeling immunofluorescence showed colocalization of TDP‐43 and tau within neuritic plaques. Other pathologies included mild Lewy body pathology predominantly affecting the amygdala and olfactory bulb, aging‐related tau astrogliopathy, and mixed small vessel disease (arteriolosclerosis and amyloid angiopathy) with several cortical microinfarcts. In conclusion, we have identified TDP‐43 colocalizing with tau in neuritic plaques in AD, which expands the association of TDP‐43 and tau in AD beyond NFTs. The clinical correlate of this plaque‐associated TDP‐43 appears to be a slowly progressive amnestic syndrome.

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.

Abstract Alpha‐synuclein (αsyn) aggregates are pathological features of several neurodegenerative conditions including Parkinson disease (PD), dementia with Lewy bodies, and multiple system atrophy (MSA). Accumulating evidence suggests that mitochondrial dysfunction and impairments of the autophagic‐lysosomal system can contribute to the deposition of αsyn, which in turn may interfere with health and function of these organelles in a potentially vicious cycle. Here we investigated a potential convergence of αsyn with the PINK1‐PRKN‐mediated mitochondrial autophagy pathway in cell models, αsyn transgenic mice, and human autopsy brain. PINK1 and PRKN identify and selectively label damaged mitochondria with phosphorylated ubiquitin (pS65‐Ub) to mark them for degradation (mitophagy). We found that disease‐causing multiplications of αsyn resulted in accumulation of the ubiquitin ligase PRKN in cells. This effect could be normalized by starvation‐induced autophagy activation and by CRISPR/Cas9‐mediated αsyn knockout. Upon acute mitochondrial damage, the increased levels of PRKN protein contributed to an enhanced pS65‐Ub response. We further confirmed increased pS65‐Ub‐immunopositive signals in mouse brain with αsyn overexpression and in postmortem human disease brain. Of note, increased pS65‐Ub was associated with neuronal Lewy body‐type αsyn pathology, but not glial cytoplasmic inclusions of αsyn as seen in MSA. While our results add another layer of complexity to the crosstalk between αsyn and the PINK1‐PRKN pathway, distinct mechanisms may underlie in cells and brain tissue despite similar outcomes. Notwithstanding, our finding suggests that pS65‐Ub may be useful as a biomarker to discriminate different synucleinopathies and may serve as a potential therapeutic target for Lewy body disease.

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.