Botha, Hugo

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.

Abstract Objective: We investigated differences in cognition between variants of progressive supranuclear palsy (PSP) including PSP-Richardson (PSP-RS) and subcortical and cortical variants using updated diagnostic criteria and comprehensive neuropsychological assessment. Method: We recruited 140 participants with PSP (age = 71.3 ± 6.9 years; education = 15.0 ± 2.8 years; 49.3% female) who completed neurological and neuropsychological assessment. Participants received diagnoses of PSP clinical variants at their evaluation (or retrospectively if evaluated before 2017) according to the Movement Disorder Society PSP criteria. We grouped variants as PSP-RS (62 participants), PSP-Cortical (25 with PSP-speech/language and 9 with PSP-corticobasal syndrome), and PSP-Subcortical (27 with PSP-parkinsonism, 11 with PSP-progressive gait freezing, and 6 with PSP-postural instability). Analysis of covariance adjusted for age assessed for differences in neuropsychological performance between variants across cognitive domains. Results: PSP-Cortical participants performed worst on measures of visual attention/working memory (Spatial Span Forward/Backward/Total), executive function (Frontal Assessment Battery), and language (Letter Fluency). PSP-RS participants performed worst on verbal memory (Camden Words). There were no significant group differences for the MoCA or indices of visuospatial function. There were no sex or education differences between PSP groups; however, there were differences in age at visit and disease duration. Conclusions: In a large sample of participants with PSP, there were differences in cognition across PSP-RS, PSP-Subcortical, and PSP-Cortical variants, with PSP-Cortical and, to a lesser extent, PSP-RS, performing worse on tests of attention and executive function. These findings suggest cognitive distinctions among PSP clinical variants and highlight the value of neuropsychological assessment in differential diagnosis of PSP subtypes for more accurate and timely clinical classification.

Dysarthric speech poses significant challenges for clinical assessment and diagnosis due to wide variability in the nature and severity of speech impairments associated with it. To address this, this study investigates the phonetic variations in different types of dysarthric speech using the deep learning model Phonet. Recordings of the word “Catastrophe” from 54 patients diagnosed with dysarthria were analyzed to extract posterior probabilities of consonants and vowels. The analysis revealed distinct posterior probability patterns for consonants and vowels across three types of dysarthria, indicating disorder-specific articulatory-based speech characteristics. Moreover, within each dysarthric group, variations in posterior probabilities aligned with the severity of rated articulation difficulty, reflecting nuanced intra-disease phonetic differences. These findings demonstrate Phonet’s ability to capture subtle speech variations and link them to both dysarthria type and severity. By quantifying these phonetic alterations, this study offers a novel and robust framework for objectively evaluating articulation difficulties in dysarthric speech. The results emphasize the clinical potential of Phonet as an advanced tool for improving the diagnosis and monitoring of dysarthria, paving the way for innovative applications in speech-based clinical assessments.

Abstract INTRODUCTION When cerebrospinal fluid (CSF) and positron emission tomography (PET) measurements for amyloid‐beta‐peptide (Aβ) related pathology are discordant, therapeutic decision‐making becomes uncertain. METHODS Using data from patients with mild cognitive impairment ( n = 541) from the Alzheimer's Disease Neuroimaging Initiative, we examined baseline characteristics and longitudinal clinical outcomes in persons grouped according to normal/abnormal Aβ via concurrent CSF and PET determinations using standard cutpoints. RESULTS Discordant groups for brain Aβ status (CSF+/PET− and CSF−/PET+) each represented about 5% of the mild cognitive impairment (MCI) population. Longitudinally, neither discordant group declined more than the CSF−/PET− group on either a memory measure or the Clinical Dementia Rating Sum of Boxes scores over a median 4 years of observation, while the CSF+/PET+ group exhibited worsening on both measures. DISCUSSION In contrast to the clinical decline observed in the CSF+/PET+ group, persons with MCI and CSF+/PET− or CSF−/PET+ brain amyloid patterns did not exhibit incipient decline. Highlights Discrepant abnormal cerebrospinal fluid (CSF) and positron emission tomography (PET) brain amyloid indicators are uncommon in mild cognitive impairment (MCI). CSF‐PET discrepant persons with MCI tend to have less abnormal values initially. CSF‐PET discrepant persons with MCI have a benign prognosis at 4 years.

OBJECTIVE Idiopathic normal pressure hydrocephalus (iNPH) is characterized by gait deterioration, cognitive disturbances, and urinary incontinence. Gait deficits pose safety hazards due to frequent falls, yet objective investigations of gait improvement following ventriculoperitoneal shunt (VPS) placement are limited. Therefore, the aim of this study was to assess objective improvement in gait parameters for patients with iNPH after VPS placement. METHODS All patients treated with a VPS for iNPH by the senior author at a single tertiary care center from December 2017 to January 2023 were retrospectively reviewed. Patients who underwent detailed motion analysis testing before surgery with > 1 follow-up assessment were included. Analyzed parameters included gait velocity, gait cadence, stride length, stride width, gait stability ratio, total support, and single support. Time-dependent linear mixed-effects models and generalized additive mixed-effects models (GAMMs) were used to model short-term and longitudinal gait parameter changes, respectively. RESULTS A total of 212 patients (137 male, median age was 75.2 years) who underwent VPS placement for iNPH were included, with a total of 747 gait analyses performed at a median last-gait analysis follow-up duration of 12.0 months. Statistically significant changes in all gait parameter values were seen between the preoperative time point and first postoperative follow-up (within 3 months) time point (paired Wilcoxon p < 0.001), with a statistically significant majority of patients experiencing improvement (binominal p < 0.0001). Longitudinal analyses using a GAMM showed increased gait velocity, single support, and stride length, with decreased stability ratio, step width, and total support at early time points, while a plateau was achieved around 6 months. CONCLUSIONS A detailed gait analysis demonstrated significant improvements in objective ambulation metrics, particularly in the gait velocity, stride length, and gait stability ratio, following VPS placement in patients with iNPH. Most patients maintained marked improvement from baseline to the last follow-up assessment, although a plateau of the benefit was experienced over longer follow-up.