Citations

Abstract Background Neuropathologic evaluation is essential for diagnosing and treating dementia, but definitive diagnoses is only available after post‐mortem brain autopsy. Positron emission tomography (PET) offers a potential in vivo proxy for predicting neuropathology. This study proposes a semi‐supervised learning (SSL) model using a single FDG‐PET image to predict underlying neuropathology. Method The SSL model leverages a self‐organized graph structure based on a patient similarity network (PSN) created from 1,495 FDG‐PET images across the aging and neurodegenerative spectrum. Each patient is represented by a node, with edges indicating image similarity derived from principal component analysis features and cosine similarity weights. The PSN structure connects 20 nearest neighbors. Neuropathologic labels were available for 204 autopsied patients. The Poisson Learning Method was employed to propagate labels from these annotated nodes to unlabeled nodes, guided by the PSN structure. FDG‐PET images were collected from clinical and research participants at the Mayo Clinic and processed using an MRI‐free pipeline. Neuropathologic labels were assigned based on widely accepted diagnostic criteria. Leave‐one‐out cross‐validation (LOOCV) was used to assess model performance. Results The confusion matrix demonstrated high accuracy in predicting Alzheimer's disease, frontotemporal lobar degeneration with tau inclusions, and Lewy body disease pathology. A Sankey diagram and label distribution analysis revealed strong correlations between clinical and predicted neuropathologic labels. Conclusion This study highlights the potential of a graph‐based SSL pipeline to predict neuropathological changes using FDG‐PET imaging data, despite limited pathology labels. The findings underscore the value of SSL in bridging the gap between abundant neuroimaging data and scarce neuropathologic labels, contributing to improved dementia diagnosis and understanding.

Abstract Background Dementia with Lewy Bodies (DLB) has been overlooked or misdiagnosed as Alzheimer’s disease (AD) dementia due to its frequent concomitant AD pathology with Lewy Body Disease (LBD) pathology. Although autopsy confirmation has been the gold standard for the precise diagnosis of DLB, time‐course 123 I‐FP‐CIT SPECT (DaT‐SPECT) findings in autopsy‐confirmed DLB are not well characterized. We previously reported that the DaTQUANT z‐score of the more affected side of the putamen has the best discriminatory power in detecting LBD pathology and nigrostriatal degeneration in dementia patients and demonstrated z‐score of −1 as the cutoff value. We sought to examine how DaT‐SPECT findings change over time in autopsy confirmed LBD patients in preparation for its use in LBD targeted clinical trials. Method Eight autopsy confirmed neocortical or limbic LBD patients who underwent two or more DaT‐SPECT were included. Nigrostriatal dopamine transporter (DaT) binding and age‐adjusted z‐scores were analyzed using the DaTQUANT 2.0 software (GE Healthcare). Result Six patients (75%) were male. The patients had 2.5±0.7 longitudinal DaT‐SPECT scans. Clinical diagnosis at initial scan was: DLB (75%), mild cognitive impairment (MCI) with REM sleep behavior disorder (RBD) (13%), and isolated RBD (iRBD) (13%). Clinical diagnosis at last scan was: DLB (88%) and iRBD (12%). All patients developed dementia during life. Mean age at initial scan/last scan/autopsy were 71.1±7.0/73.4±6.9/75.1±7.3 years old. Time from initial and last scan to autopsy were 4.0±1.9 and 1.8±1.9 years respectively. Seven patients (88%) had neocortical LBD and one (12%) had limbic LBD. Concomitant AD pathology was observed in four patients (two males (33%), two females (100%)). All patients had DaTQUANT putamen z‐score <‐1.0 at their initial scan including in MCI+RBD (‐1.76) and iRBD (‐1.53) patients. In the DLB patients at their initial scans, five (83%) had DaTQUANT putamen z‐score <‐2.0 and one (17%) had z‐score of ‐1.55. All patients (100%) showed DaTQUANT putamen z‐score <‐2.0 at their last scans. Conclusion DaTQUANT using cut‐off putamen z‐score of ‐1 was associated with underlying LBD pathology including at their iRBD and MCI stages. Nigrostriatal DaT binding tends to decline over time with DaTQUANT z‐score <‐2 at their last scans.

Abstract Two recent studies suggested that the APOE ε4 haplotype was associated with increased α-synuclein pathology in cell and mouse models. Genetic variants in the SNCA region have strong association with Parkinson’s disease (PD), dementia with Lewy bodies (DLB) and idiopathic REM sleep behaviour disorder (iRBD), while APOE is a genetic risk determinant for only DLB. To determine if genetic-level interactions between SNCA and APOE exists that can explain the protein-level association, we investigated the genotypic interaction of APOE and SNCA in cohorts of PD, DLB and iRBD. We analysed genome-wide association study (GWAS) data from 5229 PD patients and 5480 controls, 2610 DLB patients and 1920 controls, and 1055 iRBD patients and 3667 controls. We used logistic regression interaction models across all three cohorts independently between the (i) top GWAS signals of SNCA single nucleotide polymorphisms (SNPs) and APOE haplotypes and (ii) SNP×SNP and three-way SNP interaction across the entire coding region plus 200 kb flanking each gene. No significant interactions were found to be associated with any of the synucleinopathies after correction for multiple testing. Our results do not support a role for genetic interactions between APOE and SNCA across PD, DLB and iRBD. Since the tested genetic variants affect the expression and function of these proteins, it is likely that any interactions between them do not affect the risk of PD, DLB and iRBD.

Abstract A workgroup assembled by the Alzheimer’s Association recently described a conceptual framework for Alzheimer’s disease biological staging based on amyloid and tau PET imaging. However, specific tau PET cut points were left to be determined, a step necessary prior to clinical application. We sought to operationalize and evaluate Alzheimer’s disease biological staging by identifying meaningful tau PET cut points to define the four biological stages in a well-characterized participant cohort and describe the features of individuals placed into the different biological stages. The primary analysis included 896 participants in the Mayo Clinic Study of Aging or the Mayo Clinic Alzheimer’s Disease Research Center longitudinal cohorts. A validation cohort consisted of 328 participants in the Alzheimer’s Disease Neuroimaging Initiative. Both cognitively normal and impaired individuals with positive amyloid PET and evaluable tau PET imaging were included. Tau PET cut points were identified with Gaussian mixture models to characterize Alzheimer’s disease biological stage in study participants with different clinical diagnoses and objective degrees of cognitive impairment as measured by Mini-Mental State Examination. A tau PET cut point in the medial temporal region and two cut points in the temporoparietal region were identified to produce, collectively, the four Alzheimer’s disease biological stages described in the revised criteria. Increasing stage was associated with greater likelihood of mild cognitive impairment and dementia diagnosis and worsening cognitive performance on Mini-Mental State Examination and Clinical Dementia Rating Sum of Boxes, a result that was reproduced in the independent Alzheimer’s Disease Neuroimaging Initiative cohort. This study provided empirical validation for the concept of using amyloid PET and tau PET to separate subjects with biomarker-proven Alzheimer’s disease into four biological stages with distinct characteristics.

Background Dementia with Lewy bodies (DLB) is a common degenerative dementia, but no studies investigate bereaved caregiver experiences. Objective To investigate the experiences of caregivers three months after the death of persons with DLB using a mixed-methods approach. Methods Dyads of individuals with moderate-advanced DLB and their primary informal caregivers were followed prospectively every 6 months until the person with DLB died. Caregivers completed a study visit with questionnaires and a semi-structured interview ∼3 months later. Spearman correlation coefficients and Wilcoxon rank-sum tests evaluated the relationships of post-death measures with pre-death patient and caregiver variables. Thematic analysis was used to analyze the interviews. Results Seventy-three caregivers completed visits (mean 3.5 months post-death). Most of the caregivers were women (82.2%) and spouses (76.7%) or adult children (17.8%). Over 40% had scores indicating risk for clinical depression. Post-death caregiver experiences (depression, quality of life, grief, resilience) correlated with pre-death caregiver experiences. Post-death experiences did not associate with patient characteristics, disease-related symptoms, or healthcare services used in the last 6 months of life. Trajectories for caregiver measures from pre- to post-death visits varied widely. Interview themes included grief and sadness, anger, guilt and regret, relief, appreciation/gratitude, and adjusting to a new normal. Conclusions The finding that pre-death caregiving experiences have the strongest association with post-death experiences emphasizes the critical importance of accessible and evidence-based caregiver support before and after the death of a person with DLB. Research is needed to develop interventions for current and bereaved caregivers of individuals with DLB. Trial registration information NCT04829656 (submitted 2021-03-22).

Abstract INTRODUCTION Robust plasma-based biomarkers to distinguish Lewy body disease (LBD) and Alzheimer’s disease (AD) are currently lacking. We applied track-etch magnetic nanopore (TENPO) sorting for enrichment of brain-derived extracellular vesicle (EV) signatures as potential biomarkers to address this gap. METHODS We analyzed plasma from 137 autopsy-confirmed patients [30 LBD, 31 AD, 30 AD/LBD, 19 AD with amygdala Lewy bodies (AD/ALB), and 27 controls], sequencing miRNAs from TENPO-isolated GluR2-positive (neuron-enriched) and GLAST-positive (astrocyte-enriched) EVs, and measuring plasma proteins (Aβ40, Aβ42, tau, p-Tau181, p-Tau231) via SIMOA. RESULTS We identified 16 GluR2+, 8 GLAST+, and 4 protein biomarkers with differential expression (false discovery rate-corrected P value < .1) between LBD and AD. A multimodal 15-feature panel classified LBD versus AD with 10-fold cross-validated accuracy = 0.95 and area under the curve (AUC) = 0.96. DISCUSSION Brain-derived EVs offer accurate and accessible miRNA biomarkers for the differential diagnosis of LBD and AD.