Citations

AbstractIntroductionClinicopathological correlations differ by sex in Lewy body dementia (LBD). However, previous studies have focused on pathological staging systems that place less emphasis on regional pathologies.MethodsWe included 357 people (131 female, 226 male) with a high likelihood of LBD based on pathology from the Brain Bank for Neurodegenerative (Jacksonville, FL). Sex differences for regional Lewy body, senile plaque, and neurofibrillary tangle counts and their associations with clinical LBD diagnosis were assessed.ResultsFemales were less likely to have a clinical LBD diagnosis; they had more Lewy bodies, neurofibrillary tangles, and senile plaques in various regions than males (all p’s < 0.05). A higher likelihood of clinical LBD diagnosis was associated with more middle frontal, cingulate, and entorhinal Lewy body pathology, and more so for males than for females (all p’s < 0.045).DiscussionSex differences for clinicopathological correlations in LBD also occur at the regional pathology level. Females have a higher frequency of clinical misdiagnosis than males.Highlights Females have a higher risk of clinical underdiagnosis for Lewy body dementia (LBD) than males. Regional pathology counts differ by sex for people with a high likelihood of LBD. Regional pathology association with clinical LBD diagnosis differs by sex. Regional Lewy body counts have a stronger association with LBD phenotype for males.

AbstractBackgroundDirected by the enzyme pair PINK1 and PRKN, mitophagy is a crucial mitochondrial quality control mechanism that selectively decorates damaged mitochondria with phosphorylated ubiquitin (pS65‐Ub), facilitating their lysosomal degradation. The dynamic pS65‐Ub signal accumulates upon enhanced activation from increased mitochondrial damage or upon reduced autophagic‐lysosomal flux. Previous studies including ours demonstrated altered mitophagy and elevated pS65‐Ub levels in Parkinson’s and Alzheimer’s disease brains that also independently associated with α‐synuclein, tau, or amyloid pathology. However, their combined impact on mitophagy and organelle function remains unclear. Notably these three pathologies often coexist in susceptible brain areas of a neurodegenerative condition named dementia with Lewy bodies (DLB). We herein investigated the interactions of α‐synuclein, tau, and amyloid pathology to uncover their combined effects on mitophagy in a large DLB cohort.MethodThis study included 371 DLB autopsy brains with neurologically normal cases as controls. Immunohistochemistry for the mitophagy marker pS65‐Ub in the hippocampus and amygdala was performed and associations of pS65‐Ub levels with neuropathological measures from the same region were evaluated. Immunofluorescence co‐staining of pS65‐Ub, phospho‐α‐synuclein, and phospho‐tau was conducted to study their interactions at single‐cell level.ResultpS65‐Ub positive cells were significantly more abundant in the hippocampus and amygdala of DLB cases compared to controls. Significant associations were observed between pS65‐Ub levels with age, brain weight, Braak stage, Thal phase as well as counts of Lewy body (LB), neurofibrillary tangle (NFT), and senile plaque (SP) in both regions. Strong synergistic effects of LB and NFT pathologies on pS65‐Ub accumulation were found in the amygdala, while only additive effects of LB and SP pathologies were observed. Single‐cell analysis in the amygdala showed that most affected cells contained either LB or NFT inclusion rather than co‐residing two pathologies within the same cell. Notably, cells harboring LB pathology exhibited mainly smaller, granular pS65‐Ub deposits (type G), possibly associated with increased mitochondrial dysfunction. Conversely, those containing NFT showed larger, vacuolar pS65‐Ub deposits (type V), likely resulting from reduced lysosomal degradation.ConclusionOur study revealed complex interactions of α‐synuclein, tau, and amyloid pathology on mitophagy alteration in DLB brains, highlighting different molecular mechanisms underlying α‐synuclein‐ and tau‐associated mitophagy alterations.

AbstractBackgroundThe aggregation of α‐synuclein protein, encoded by the SNCA gene, forms Lewy bodies (LBs) in neurons and is a key pathological feature of Lewy body dementia (LBD). Interestingly, the apolipoprotein E gene (APOE), primarily expressed in glial cells, is the strongest genetic modifier for LBD. The ε4 allele of this gene (APOE4) notably increases the risk of LBD. However, the specific mechanisms through which APOE‐regulated glial cells affect α‐synuclein aggregation in neurons are not yet fully understood.MethodTo explore this, we conducted spatial transcriptomics using the 10X Genomics Visium CytAssist platform on Formalin‐Fixed Paraffin‐Embedded tissue slides from the temporal cortex of human LBD brains (N = 4 brains in controls and N = 6 brains in LBD group, with an equal ratio of APOE3/3 and APOE3/4 genotypes). We defined the neuronal layers of the gray matter (Layers 1 to 6, L1‐6) and white matter in each brain based on the gene signature of each spot. We also identified LBs from hematoxylin and eosin (H&E) staining on the same slides, and annotated LB‐positive (LB+), LB‐surrounding and LB‐negative (LB‐) spots in each brain using Loupe Browser. We evaluated SNCA and APOE gene expression in each layer and analyzed cell‐cell communication using the CellChat tool across the layers.ResultWe observed that the SNCA gene was highly expressed in L5, which corresponded to the areas where most LBs were found. Notably, within LB+ spots across all layers, both SNCA and APOE gene expression levels were elevated in APOE3/4 cases compared to APOE3/3 cases. Furthermore, we identified cell‐cell communication networks between LB+ and LB‐surrounding spots, particularly in L4 and L5. These networks were enriched by microglia‐related signaling pathways, including APOE signaling, migration inhibitory factor (MIF) signaling, and colony stimulating factor 1 (CSF1) signaling. Interestingly, APOE3/4 brains exhibited significantly reduced interactions and lower interaction strength in these networks compared to APOE3/3 samples.ConclusionOur findings indicate that impaired cell‐cell communication, such as microglia‐mediated immune responses around LB regions, may be associated with the increased risk presented by the APOE4 allele in LBD brains.

Abstract Purpose This study examined occupational histories in neuropathologically confirmed cases of multiple system atrophy to identify environmental associations of potential relevance to disease causation. Methods 270 neuropathologically confirmed cases of multiple system atrophy obtained from the Mayo Clinic brain bank for neurodegenerative disorders in Jacksonville, Florida, were included in this case-control study. Demographic and disease information was collected from medical records. Information regarding occupational history was collected retrospectively from medical records and published obituaries. Proportions of employment by occupational sector were compared to United States census data. Results The most common prior occupations in patients were healthcare (15.2%), education (15.2%), administration (14.8%), clerical (10.7%), petroleum industry (8.9%), business ownership (8.5%), and metal industry (7.8%). When comparing patients to United States census data, significant differences were identified for electrical (5.6% vs. 0.4%, P < 0.001), education (15.2% vs. 2.3%, P < 0.001), real estate (4.4% vs. 0.7%, P < 0.001), metal industry (7.8% vs. 3.0%, P < 0.001), information technology (4.1% vs. 1.8%, P = 0.011), clerical (10.7% vs. 5.5%, P = 0.001), and petroleum industry (8.9% vs. 5.6%, P = 0.024). Conclusions Electrical engineers and electricians, educators, real estate agents, workers in the metal industry, information technologists, clerical staff, and workers in the petroleum industry were significantly overrepresented in our series of multiple system atrophy patients as compared to population data. We hypothesize that these occupational associations may signify environmental exposures increasing disease risk in genetically susceptible individuals. We cannot exclude a potential selection bias in patients willing to donate their brains to an academic center in order to contribute to scientific knowledge.

Aggregated α-synuclein (α-SYN) proteins, encoded by the SNCA gene, are hallmarks of Lewy body disease (LBD), affecting multiple brain regions. However, the specific mechanisms underlying α-SYN pathology in cortical neurons, crucial for LBD-associated dementia, remain unclear. Here, we recapitulated α-SYN pathologies in human induced pluripotent stem cells (iPSCs)–derived cortical organoids generated from patients with LBD with SNCA gene triplication. Single-cell RNA sequencing, combined with functional and molecular validation, identified synaptic and mitochondrial dysfunction in excitatory neurons exhibiting high expression of the SNCA gene, aligning with observations in the cortex of autopsy-confirmed LBD human brains. Furthermore, we screened 1280 Food and Drug Administration–approved drugs and identified four candidates (entacapone, tolcapone, phenazopyridine hydrochloride, and zalcitabine) that inhibited α-SYN seeding activity in real-time quaking-induced conversion assays with human brains, reduced α-SYN aggregation, and alleviated mitochondrial dysfunction in SNCA triplication organoids and excitatory neurons. Our findings establish human cortical LBD models and suggest potential therapeutic drugs targeting α-SYN aggregation for LBD.

AbstractAimsRecent advances in artificial intelligence, particularly with large language models like GPT‐4Vision (GPT‐4V)—a derivative feature of ChatGPT—have expanded the potential for medical image interpretation. This study evaluates the accuracy of GPT‐4V in image classification tasks of histopathological images and compares its performance with a traditional convolutional neural network (CNN).MethodsWe utilised 1520 images, including haematoxylin and eosin staining and tau immunohistochemistry, from patients with various neurodegenerative diseases, such as Alzheimer's disease (AD), progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD). We assessed GPT‐4V's performance using multi‐step prompts to determine how textual context influences image interpretation. We also employed few‐shot learning to enhance improvements in GPT‐4V's diagnostic performance in classifying three specific tau lesions—astrocytic plaques, neuritic plaques and tufted astrocytes—and compared the outcomes with the CNN model YOLOv8.ResultsGPT‐4V accurately recognised staining techniques and tissue origin but struggled with specific lesion identification. The interpretation of images was notably influenced by the provided textual context, which sometimes led to diagnostic inaccuracies. For instance, when presented with images of the motor cortex, the diagnosis shifted inappropriately from AD to CBD or PSP. However, few‐shot learning markedly improved GPT‐4V's diagnostic capabilities, enhancing accuracy from 40% in zero‐shot learning to 90% with 20‐shot learning, matching the performance of YOLOv8, which required 100‐shot learning to achieve the same accuracy.ConclusionsAlthough GPT‐4V faces challenges in independently interpreting histopathological images, few‐shot learning significantly improves its performance. This approach is especially promising for neuropathology, where acquiring extensive labelled datasets is often challenging.

AbstractAmyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder characterized by upper and lower motor neuron signs. There are, however, cases where upper motor neurons (UMNs) are predominantly affected, leading to clinical presentations of UMN‐dominant ALS or primary lateral sclerosis. Furthermore, cases exhibiting an UMN‐predominant pattern of motor neuron disease (MND) presenting with corticobasal syndrome (CBS) have been sparsely reported. This study aims to clarify the clinicopathological features of patients with UMN‐predominant MND. We reviewed 24 patients with UMN‐predominant MND with TDP‐43 pathology in the presence or absence of frontotemporal lobar degeneration. Additionally, we reviewed the medical records of patients with pathologically‐confirmed corticobasal degeneration (CBD) who received a final clinical diagnosis of CBS (n = 10) and patients with pathologically‐confirmed progressive supranuclear palsy (PSP) who received a final clinical diagnosis of PSP syndrome (n = 10). Of 24 UMN‐predominant MND patients, 20 had a clinical diagnosis of an atypical parkinsonian disorder, including CBS (n = 11) and PSP syndrome (n = 8). Only two patients had antemortem diagnoses of motor neuron disease. UMN‐predominant MND patients with CBS less frequently exhibited apraxia than those with CBD, and they were less likely to meet clinical criteria for possible or probable CBS. Similarly, UMN‐predominant MND patients with PSP syndrome less often met clinical criteria for probable PSP than PSP patients with PSP syndrome. Our findings suggest that UMN‐predominant MND can mimic atypical parkinsonism, and should be considered in the differential diagnosis of CBS and PSP syndrome, in particular when criteria are not met.

Abstract Introduction: Recent advancements in artificial intelligence, particularly with large language models like GPT-4Vision (GPT-4V)—a derivative feature of ChatGPT—have expanded the potential for medical image interpretation. This study evaluates the accuracy of GPT-4V in image classification tasks of histopathological images and compares its performance with traditional convolutional neural network (CNN). Methods: We utilized 1515 images, including hematoxylin and eosin staining and tau immunohistochemistry, from patients with various neurodegenerative diseases, such as Alzheimer’s disease (AD), progressive supranuclear palsy (PSP), and corticobasal degeneration (CBD). We assessed GPT-4V’s performance using multi-step prompts to determine how textual context influences image interpretation. We also employed few-shot learning to enhance improvements in GPT-4V’s diagnostic accuracy in classifying three specific tau lesions—astrocytic plaques, neuritic plaques, and tufted astrocytes—and compared the outcomes with the CNN model YOLOv8. Results: GPT-4V accurately recognized staining techniques and tissue origin but struggled with specific lesion identification. The interpretation of images was notably influenced by the provided textual context, which sometimes led to diagnostic inaccuracies. For instance, when presented with images of the motor cortex, the diagnosis shifted inappropriately from AD to CBD or PSP. However, few-shot learning markedly improved GPT-4V’s diagnostic capabilities, enhancing accuracy from 40% in zero-shot learning to 90% with 20-shot learning, matching the performance of YOLOv8, which required 100-shot learning to achieve the same accuracy. Conclusions: Although GPT-4V faces challenges in independently interpreting histopathological images, few-shot learning significantly improves its accuracy. This approach is especially promising for neuropathology, where acquiring extensive labeled datasets is often challenging.