Dickson, Dennis W.

[ 18 F]-Flortaucipir positron emission tomography (PET) is considered a good biomarker of Alzheimer’s disease. However, it is unknown how flortaucipir is associated with the distribution of tau across brain regions and how these associations are influenced by amyloid-β. It is also unclear whether flortaucipir can detect tau in definite primary age–related tauopathy (PART). We identified 248 individuals at Mayo Clinic who had undergone [ 18 F]-flortaucipir PET during life, had died, and had undergone an autopsy, 239 cases of which also had amyloid-β PET. We assessed nonlinear relationships between flortaucipir uptake in nine medial temporal and cortical regions, Braak tau stage, and Thal amyloid-β phase using generalized additive models. We found that flortaucipir uptake was greater with increasing tau stage in all regions. Increased uptake at low tau stages in medial temporal regions was only observed in cases with a high amyloid-β phase. Flortaucipir uptake linearly increased with the amyloid-β phase in medial temporal and cortical regions. The highest flortaucipir uptake occurred with high Alzheimer’s disease neuropathologic change (ADNC) scores, followed by low-intermediate ADNC scores, then PART, with the entorhinal cortex providing the best differentiation between groups. Flortaucipir PET had limited ability to detect PART, and imaging-defined PART did not correspond with pathologically defined PART. In summary, spatial patterns of flortaucipir mirrored the histopathological tau distribution, were influenced by the amyloid-β phase, and were useful for distinguishing different ADNC scores and PART.

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 Lewy body dementia and Alzheimer's disease (AD) are leading causes of cognitive impairment, characterized by distinct but overlapping neuropathological hallmarks. Lewy body disease (LBD) is characterized by α-synuclein aggregates in the form of Lewy bodies as well as the deposition of extracellular amyloid plaques, with many cases also exhibiting neurofibrillary tangle (NFT) pathology. In contrast, AD is characterized by amyloid plaques and neurofibrillary tangles. Both conditions often co-occur with additional neuropathological changes, such as vascular disease and TDP-43 pathology. To elucidate shared and distinct molecular signatures underlying these mixed neuropathologies, we extensively analysed transcriptional changes in the anterior cingulate cortex, a brain region critically involved in cognitive processes. We performed bulk tissue RNA sequencing from the anterior cingulate cortex and determined differentially expressed genes (q-value <0.05) in control (n = 81), LBD (n = 436), AD (n = 53) and pathological amyloid cases consisting of amyloid pathology with minimal or no tau pathology (n = 39). We used gene set enrichment and weighted gene correlation network analysis to understand the pathways associated with each neuropathologically defined group. LBD cases had strong upregulation of inflammatory pathways and downregulation of metabolic pathways. The LBD cases were further subdivided into either high Thal amyloid, Braak NFT, or low pathological burden cohorts. Compared to the control cases, the LBD cohorts consistently showed upregulation for genes involved in protein folding and cytokine immune response, as well as downregulation of fatty acid metabolism. Surprisingly, concomitant tau pathology within the LBD cases resulted in no additional changes. Some core inflammatory pathways were shared between AD and LBD but with numerous disease-specific changes. Direct comparison of LBD cohorts versus AD cases revealed strong enrichment of synaptic signalling, behaviour and neuronal system pathways. Females had a stronger response overall in both LBD and AD, with several sex-specific changes. Overall, the results identify genes commonly and uniquely dysregulated in neuropathologically defined LBD and AD cases, shedding light on shared and distinct molecular pathways. Additionally, the study underscores the importance of considering sex-specific changes in understanding the complex transcriptional landscape of these neurodegenerative diseases.

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.

Abstract Predominant limbic degeneration has been associated with various underlying aetiologies and an older age, predominant impairment of episodic memory and slow clinical progression. However, the neurological syndrome associated with predominant limbic degeneration is not defined. This endeavour is critical to distinguish such a syndrome from those originating from neocortical degeneration, which may differ in underlying aetiology, disease course and therapeutic needs. We propose a set of clinical criteria for a limbic-predominant amnestic neurodegenerative syndrome that is highly associated with limbic-predominant age-related TDP-43 encephalopathy but also other pathologic entities. The criteria incorporate core, standard and advanced features, including older age at evaluation, mild clinical syndrome, disproportionate hippocampal atrophy, impaired semantic memory, limbic hypometabolism, absence of neocortical degeneration and low likelihood of neocortical tau, with degrees of certainty (highest, high, moderate and low). We operationalized this set of criteria using clinical, imaging and biomarker data to validate its associations with clinical and pathologic outcomes. We screened autopsied patients from Mayo Clinic and Alzheimer’s Disease Neuroimaging Initiative cohorts and applied the criteria to those with an antemortem predominant amnestic syndrome (Mayo, n = 165; Alzheimer’s Disease Neuroimaging Initiative, n = 53) and who had Alzheimer’s disease neuropathological change, limbic-predominant age-related TDP-43 encephalopathy or both pathologies at autopsy. These neuropathology-defined groups accounted for 35, 37 and 4% of cases in the Mayo cohort, respectively, and 30, 22 and 9% of cases in the Alzheimer’s Disease Neuroimaging Initiative cohort, respectively. The criteria effectively categorized these cases, with Alzheimer’s disease having the lowest likelihoods, limbic-predominant age-related TDP-43 encephalopathy patients having the highest likelihoods and patients with both pathologies having intermediate likelihoods. A logistic regression using the criteria features as predictors of TDP-43 achieved a balanced accuracy of 74.6% in the Mayo cohort, and out-of-sample predictions in an external cohort achieved a balanced accuracy of 73.3%. Patients with high likelihoods had a milder and slower clinical course and more severe temporo-limbic degeneration compared to those with low likelihoods. Stratifying patients with both Alzheimer’s disease neuropathological change and limbic-predominant age-related TDP-43 encephalopathy from the Mayo cohort according to their likelihoods revealed that those with higher likelihoods had more temporo-limbic degeneration and a slower rate of decline and those with lower likelihoods had more lateral temporo-parietal degeneration and a faster rate of decline. The implementation of criteria for a limbic-predominant amnestic neurodegenerative syndrome has implications to disambiguate the different aetiologies of progressive amnestic presentations in older age and guide diagnosis, prognosis, treatment and clinical trials.

Background: TAR DNA binding protein 43 (TDP-43) has been shown to be associated with whole hippocampal atrophy in primary age-related tauopathy (PART). It is currently unknown which subregions of the hippocampus are contributing to TDP-43 associated whole hippocampal atrophy in PART. Objective: To identify which specific hippocampal subfield regions are contributing to TDP-43-associated whole hippocampal atrophy in PART. Methods: A total of 115 autopsied cases from the Mayo Clinic Alzheimer Disease Research Center, Neurodegenerative Research Group, and the Mayo Clinic Study of Aging were analyzed. All cases underwent antemortem brain volumetric MRI, neuropathological assessment of the distribution of Aβ (Thal phase), and neurofibrillary tangle (Braak stage) to diagnose PART, as well as assessment of TDP-43 presence/absence in the amygdala, hippocampus and beyond. Hippocampal subfield segmentation was performed using FreeSurfer version 7.4.1. Statistical analyses using logistic regression were performed to assess for associations between TDP-43 and hippocampal subfield volumes, accounting for potential confounders. Results: TDP-43 positive patients ( n = 37, 32%), of which 15/15 were type-α, had significantly smaller whole hippocampal volumes, and smaller volumes of the body and tail of the hippocampus compared to TDP-43 negative patients. Subfield analyses revealed an association between TDP-43 and the molecular layer of hippocampal body and the body of cornu ammonis 1 (CA1), subiculum, and presubiculum regions. There was no association between TDP-43 stage and subfield volumes. Conclusions: Whole hippocampal volume loss linked to TDP-43 in PART is mainly due to volume loss occurring in the molecular layer, CA1, subiculum and presubiculum of the hippocampal body.