Citations

Abstract Background Immunohistochemically detected phosphorylated α‐synuclein (p‐α‐syn) in cutaneous nerves has been found in >95% of patients meeting consensus criteria for dementia with Lewy bodies (DLB); however, its performance in prodromal stages (mild cognitive impairment‐Lewy body [MCI‐LB]) and clinical practice is unknown. Objective We evaluated skin biopsy for p‐α‐syn in a convenience cohort of patients with suspected MCI‐LB or DLB in a tertiary care setting. Methods 53 patients underwent commercially available skin biopsy for skin nerve p‐α‐syn as part of a clinical evaluation for suspected MCI‐LB/DLB. Consensus MCI‐LB/DLB criteria blinded to skin biopsy result were applied through retrospective chart review; those not definitively meeting criteria were considered questionable synucleinopathy (QS). Results Median age at skin biopsy was 70 years (range, 43–87 years) with 81% men. 36 patients (68%) had positive skin biopsies. 32/43 (74%) patients meeting clinical consensus criteria for DLB/MCI‐LB had a positive skin biopsy, with higher frequency in MCI‐LB (87%) compared with DLB (60%). 4/10 (40%) QS had positive biopsies. One patient with a positive skin biopsy had no evidence of central nervous system (CNS) α‐syn on autopsy. Conclusions Positive skin biopsy for p‐α‐syn occurs across the spectrum of cognitive presentations of Lewy body disease including prodromal DLB, albeit at a lower frequency in this retrospective review of atypical cases compared with typical clinical presentations. A positive skin biopsy may not reflect CNS synucleinopathy as the etiology for a patient's symptoms in some cases. Longitudinal studies of skin biopsies for p‐α‐syn in suspected MCI‐LB/DLB with autopsy confirmation are needed. © 2025 International Parkinson and Movement Disorder Society.

Abstract Background Primary progressive apraxia of speech (PPAOS) is a frontotemporal lobar degeneration spectrum disorder characterized by progressive speech motor planning/programming decline. Patients often develop neurological symptoms, frequently evolving into progressive supranuclear palsy (PSP)/corticobasal syndrome (CBS) overlap syndromes. Requests for information about disease progression and causes of death are common amongst our clinical and research encounters; however, empiric evidence to guide these discussions is lacking. Understanding causes of death may enhance counseling and support targeted interventions to delay fatal outcomes. This study aimed to describe causes of death in PPAOS. Methods Data from 29 deceased PPAOS patients (16 males) seen in research studies (July 2010–May 2023) were reviewed. Causes of death were established from medical charts and family reports. Fifteen had prosodic and 12 had phonetic‐predominant PPAOS; two had no clear predominance. Median age at onset was 67.08 years, disease duration 9.78 years, and age at death 76.25 years. 28 patients were white and one was Asian Indian. Results Overall, fifteen (52%) patients died from overall deterioration (cachexia/dehydration), nine (31%) from aspiration pneumonia, and the remainder from falls (10%), asphyxiation (3%), or medical aid in dying (3%). There was no clear association with AOS subtype and cause of death. Conclusions Most deaths in PPAOS resulted from systemic failure or dysphagia‐related complications. Half of the patients died without a clear precipitating event, described by family members as “shutting down,” similar to other prior studies that have shown central nervous system deterioration associated with dementia as cause of death. Aspiration pneumonia caused 34% of deaths, highlighting the need for early swallowing management, including assessments, dietary adjustments, and caregiver education. Falls underscore the importance of addressing balance and motor impairments through proactive prevention strategies. Other factors, such as access to healthcare and medical and psychiatric comorbidities were not analyzed but may influence outcomes. Future research should validate these findings in larger, diverse cohorts and compare outcomes to motor‐onset presentations of PSP and CBS. A comprehensive, multidisciplinary approach addressing motor speech impairments, dysphagia, and fall risks is essential to improve care and potentially extend survival for patients with PPAOS.

Abstract Background To promote equitable access to neurologic diagnostics, our team developed MASTERMIND—the Mobile Assessment of Speech To Evaluate, Recognize, and Monitor Indicators of Neurologic health and Disease. One subtest, the MASTERMIND Assessment of Naming, includes ten pictures randomly selected from a list of 45 words varying in frequency. This study aimed to 1) evaluate the feasibility of a remotely self‐administered naming test for patients with cognitive, linguistic, and/or communication difficulties and 2) assess the sensitivity and specificity of performance measures (accuracy, error type, and response time) for detecting aphasia and cognitive‐communication difficulties. Method The task was remotely administered to 138 controls, 26 cognitively impaired individuals, 15 with motor speech disorders, 8 with cognitive concerns, and 6 with aphasia. Participants named pictures displayed on‐screen, advancing through the task by clicking “Next.” Responses were processed using natural language processing (NLP). Audio was transcribed with Whisper Large‐v3, and responses were matched to stimuli within a 0.4‐second threshold. Errors were categorized by type (semantic, phonemic, or unclear source) through consensus between two raters. Performance metrics (accuracy, error type, response time) were analyzed using Kruskal‐Wallis and Pearson's Chi‐Square tests, with AUROC analysis evaluating diagnostic performance. Results Differences in accuracy, response time, and error type were observed across groups (Table 1). Controls and cognitively impaired participants performed similarly, with longer response times observed in patients with aphasia and motor speech disorders. Accuracy alone was a poor predictor of all patients versus controls (AUROC = 0.468) but prediction was better in distinguishing aphasia patients from 1) controls (AUROC = 0.698), 2) cognitively impaired participants (AUROC = 0.679), and 3) those with MSDs (AUROC = 0.670). Conclusions The MASTERMIND Assessment of Naming is a feasible, scalable, self‐administered diagnostic tool enhanced by automated NLP. It provides critical insights through response time, accuracy, and error analysis. Limitations include potential bias in error classification and fixed processing thresholds. The study highlights MASTERMIND's potential for patient triaging and screening. Future research will evaluate the sensitivity of combined performance metrics to disease progression, expand sample sizes and diversity, and refine usability based on participant feedback.

Abstract Background Primary progressive apraxia of speech (PPAOS) is a neurodegenerative disorder affecting speech motor planning/programming. Current interventions for PPAOS are limited and often adapted from treatments for nondegenerative AOS, which rely on intensive practice to maintain speech accuracy for practiced items. Systematic interventions for PPAOS are scarce, and no studies have explored differential effects across AOS subtypes. PPAOS subtypes include those with predominant phonetic (articulatory distortions and substitutions) or prosodic (slow speech rate, segmentation) speech changes. These subtypes likely differ in their neural underpinnings and may respond variably to intervention. Delayed auditory feedback (DAF), which introduces a slight delay in hearing one's speech, has shown promise in altering speech rate and fluency for stuttering and hypokinetic dysarthria, though its effects on stroke‐related AOS have been mixed. Recent studies suggest DAF can increase speech rate in patients with AOS in the context of nonfluent/agrammatic primary progressive aphasia. We hypothesized DAF would improve speech clarity and fluency in PPAOS, with subtype‐specific effects: phonetic PPAOS may see slower, more accurate speech, while prosodic PPAOS may produce faster, less segmented speech. Method Four PPAOS patients (3 prosodic, 1 phonetic) completed a web‐based speech battery with and without DAF. Feedback delay was calibrated individually using the DAF Pro app. Speech metrics were analyzed acoustically, and blinded perceptual ratings of deviant speech features were conducted. Results Key data are summarized in Table 1. Three out of four patients found the DAF helpful and two re‐recorded with DAF to allow for quantitative analysis. Patient 1 produced words faster with DAF but had slower total sentence production times; he did not find it helpful. Patient 2 spoke slower with DAF. Both patients produced AMRs faster with DAF, while SMRs were faster for Patient 1 and slower for Patient 2. Blinded perceptual judgments of speech clarity and prosody revealed mixed effects of DAF across participants. Conclusions Preliminary results suggest DAF may differentially benefit phonetic and prosodic PPAOS, particularly in sentences compared to word production. Further research is needed to assess clinical significance, refine DAF calibration, and explore patient‐perceived improvements with guided practice in a larger, more diverse sample.

Abstract Background Some data suggest that neuroinflammation may be associated with pro‐inflammatory states in the body. Systemic, proinflammatory cytokines (TNFα, IL‐1, IL‐6, etc.) are produced by a variety of comorbidities. Neuroinflammation has been shown to be associated with the accumulation of amyloid in the brain (Becher, Heneka). We hypothesized that systemic cytokine production could be associated with neuroinflammation and thereby be related to brain amyloid accumulation. This study aims to evaluate the relationship between neuroinflammation, amyloid accumulation and systemic cytokines. Method 90 participants from the Mayo Clinic Study of Aging (MCSA) received a fasting cytokine panel over a 5‐year span where IL‐1, IL‐6, IL‐10, TNFa levels were measured. Participants were divided into four groups: cognitively unimpaired with normal levels of amyloid (CU A‐, n = 25), cognitively unimpaired with elevated amyloid (CU A+, n = 25), and mild cognitively impaired (MCI) with amyloid positive (MCI A+, n = 25), and Alzheimer's disease (AD) with elevated amyloid (AD A+, n = 15). To compare regional standardized uptake value ratio (SUVr) from neuroinflammation PET based on amyloid status (A+ vs A‐) and the four cytokines, Spearman's rank correlation and Fishers r‐to‐z transformations were used to assess pairwise group differences. Result Using Spearman's rank correlation and Fishers r‐to‐z transformations, no strong relationship was found between systemic inflammation and neuroinflammation. While comparing CUA‐ vs CUA+ in Figure 1, three regions (supplementary motor area, frontal superior medial lobe, and middle cingulate cortex) showed a significant difference ( p < 0.05). However, Figure 2 showed an overarching trend bias towards significance across most regions in IL10, IL6, and TNFa. Conclusion Although significant relationships between systemic inflammation, brain amyloid and neuroinflammation were limited, the data show an overarching trend bias in broad brain regions, suggesting that further analyses are needed, possibly with larger sample sizes, to assess the relationship of regional neuroinflammation and amyloid to systemic inflammation. Future analyses could also regress out factors like sex, age, and genotype to further proinflammatory cytokine research.

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 Understanding long-term sequelae of cancer care is critical. Cognitive impairment is a common and disabling survivorship issue. Because sex influences tumor biology, therapy exposure, and dementia risk, we examined whether cancer care for a prior malignancy alters late-life cognitive outcomes differently in males and females. METHODS We assessed adults ≥50 years, cognitively normal at study entry, from a longitudinal, population-based cohort (Mayo Clinic Study of Aging). Participants were linked to our institutional cancer registry (excluding non-melanoma skin cancers). Mild cognitive impairment (MCI) and dementia were adjudicated by consensus. We fitted multivariable Cox proportional-hazards models to estimate how prevalent cancer diagnosis influenced the risk of developing MCI/dementia. Sex-specific effects were determined by incorporating a cancer×sex interaction term and by generating separate hazard ratios. Analyses were controlled for age, APOE4 status, education, and presence of vascular/metabolic conditions. RESULTS 5793 MCSA participants (female: 49.4%, APOE4 positive: 27.5%) were included in the study, with a mean age of 73 years at study entry. Incident MCI was recorded in 866 participants, and 737 were diagnosed with incident dementia. About 20% (n=1176) of the cohort had a cancer diagnosis, most commonly prostate (n=423) and breast (n=277). There was no independent relationship between history of cancer and risk for MCI or dementia. We found that males with cancer had an increased risk for MCI (HR 1.28, 95% CI 1.04-1.57, p=0.003) and dementia (HR 1.29, 95% CI 1.04-1.6, p=0.053). Conversely, females with cancer had a lower risk for MCI (HR 0.79, 95% CI 0.61-1.01, p=0.063), although this was not statistically significant. CONCLUSIONS Our data suggests male cancer survivors may be more susceptible to cognitive impairment, independent of age, APOE4 status, education, and vascular/metabolic burden. Elucidating the hormonal, treatment-related, and systemic pathways driving this should be a priority for future mechanistic studies.

Objective Lewy body disease (LBD) is a complex neurodegenerative disorder characterized by the accumulation of misfolded α‐synuclein in the brain. Neuroinflammation has long been implicated in LBD pathogenesis, and recent genetic studies in Parkinson's disease (a clinical manifestation of LBD) have shown consistent association with the human leukocyte antigen (HLA) gene complex. Here, we assessed whether variation in HLA alleles influences neuropathological burden in a neuropathologically‐defined series of LBD cases. Methods We conducted a comprehensive analysis of HLA allelic variants in a cohort of 539 LBD cases of European descent from the Mayo Clinic brain bank. High‐resolution whole‐genome sequencing was used, and the HLA alleles of each sample were called using the HLA*LA tool and assessed for association with neuropathological outcomes. Results Our analysis identified 1 significant ( P < 3.43 × 10 −5 ) and 4 suggestive ( P < 0.001) associations between certain HLA alleles and specific neuropathological outcomes in LBD, suggesting a potential role for HLA‐mediated immune mechanisms in disease progression and subtype differentiation. Specifically, HLA‐DPB1*06:01 was significantly associated with lower Lewy body counts in the parahippocampal gyrus ( P = 3.30 × 10 −5 ), with weaker and suggestive associations observed in the middle frontal ( P = 1.80 × 10 −4 ) and inferior parietal gyrus ( P = 6.33 × 10 −4 ). Additionally, although only suggestive, HLA‐DRB1*11:01 correlated with a lower Thal amyloid phase ( P = 1.56 x 10 −4 ), and HLA‐B*15:01 correlated with an increased risk of diffuse LBD ( P = 7.58 x 10 −4 ). Interpretation This study provides a detailed evaluation of the relationship between HLA alleles and LBD pathology, highlighting the importance of immune‐related genetic factors in the etiology of LBD. ANN NEUROL 2026;99:492–501

Abstract Importance Voice-based health technologies are growing rapidly, but they lack standardized terminology, which hinders interdisciplinary collaboration, research quality, and clinical translation. Objective The objective of this work is to develop universally accepted definitions in the rapidly evolving field of vocal biomarkers, as part of the VOCAL ( V ocal Biomarker Guidelines for O ntology, C lassification, A pplication, and L ogistics) initiative, a structured, international consensus-based framework that aims to provide standards, and guidelines. Design VOCAL is a rigorous, international, multi-stage consensus-building study conducted in 2024-2025. Setting Multi-institutional collaboration between representatives from the Bridge2AI-Voice Consortium (North America) and the eVoiceNet Network (European Union), culminating in an in-person workshop at the 2025 Bridge2AI Voice Symposium. Participants A group of 24 international experts in medicine, clinical research, speech and language, audio signal processing, statistics, methodology, regulation, ethics. Methods VOCAL’s iterative process involved five rounds of review, feedback, and an in-person workshop at the international 2025 Bridge2AI Voice Symposium, ensuring the incorporation of diverse perspectives and achieving a robust agreement on the proposed definitions. Main outcomes and measures Consensus-based definitions for vocal biomarkers, spanning from broad concepts (biomarker, digital biomarker, vocal biomarker) to domain-specific measures (cardio-respiratory acoustic, voice, speech/articulatory, cognitive/language). Results A hierarchical continuum model of vocal biomarkers was established. We first distinguished between the concepts of vocal measures and vocal biomarkers. We then defined terms from broad, overarching concepts (Level 0: Biomarker, Digital Biomarker, Vocal Biomarker) to more specific physiological and cognitive domains (Level 1: Cardio-Respiratory Acoustic; Level 2: Voice; Level 3: Speech/Articulatory; Level 4: Cognitive/Language, including linguistic and paralinguistic subtypes). Conclusions and Relevance This work provides a shared vocabulary that is essential for fostering communication through interdisciplinary collaboration, improving the quality and efficiency of research and development, and ensuring the ethical, reliable, and scalable deployment of future voice-based health technologies. It lays foundational groundwork for upcoming guidelines and standards, which are crucial for advancing the field of vocal biomarkers into widespread clinical utility. Key points Question Can we establish an international consensus on the definitions related to vocal biomarkers? Findings Through a multi-stage international consensus process involving expert representatives from the Bridge2AI-Voice Consortium and eVoiceNet Network, a hierarchical model of vocal biomarkers was developed, defining terms from broad concepts (biomarker, digital biomarker, vocal biomarker) to specific physiological and cognitive domains (cardio-respiratory acoustic, voice, speech/articulatory, cognitive/language). Meaning Standardized definitions for vocal biomarkers provide an essential shared vocabulary for interdisciplinary collaboration and lay the foundational groundwork for future guidelines needed to advance the implementation of voice-based health technologies into clinical practice and clinical research.