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IntroductionBehavioral variant frontotemporal dementia (bvFTD) may present sporadically or due to an autosomal dominant mutation. Characterization of both forms will improve understanding of the generalizability of assessments and treatments.MethodsA total of 135 sporadic (s-bvFTD; mean age 63.3 years; 34% female) and 99 familial (f-bvFTD; mean age 59.9; 48% female) bvFTD participants were identified. f-bvFTD cases included 43 with known or presumed chromosome 9 open reading frame 72 (C9orf72) gene expansions, 28 with known or presumed microtubule-associated protein tau (MAPT) mutations, 14 with known progranulin (GRN) mutations, and 14 with a strong family history of FTD but no identified mutation.ResultsParticipants with f-bvFTD were younger and had earlier age at onset. s-bvFTD had higher total Neuropsychiatric Inventory Questionnaire (NPI-Q) scores due to more frequent endorsement of depression and irritability.Discussionf-bvFTD and s-bvFTD cases are clinically similar, suggesting the generalizability of novel biomarkers, therapies, and clinical tools developed in either form to the other.

Jonathan Graff-Radford,1 Kejal Kantarci21Department of Neurology, 2Department of Radiology, Mayo Clinic, Rochester, MN, USAAbstract: Aging is the primary risk factor for dementia. With increasing life expectancy and aging populations worldwide, dementia is becoming one of the significant public health problems of the century. The most common pathology underlying dementia in older adults is Alzheimer’s disease. Proton magnetic resonance spectroscopy (MRS) may provide a window into the biochemical changes associated with the loss of neuronal integrity and other neurodegenerative pathology that involve the brain before the manifestations of cognitive impairment in patients who are at risk for Alzheimer’s disease. This review focuses on proton MRS studies in normal aging, mild cognitive impairment, and dementia, and how proton MRS metabolite levels may be potential biomarkers for early diagnosis of dementia-related pathologic changes in the brain.Keywords: Alzheimer’s disease, magnetic resonance spectroscopy, mild cognitive impairment

Background: Othello's syndrome (OS) is a delusion of infidelity. We describe seven cases of OS in Parkinson's disease (iPD) patients using dopamine agonists.Methods: We searched the Mayo Clinic Medical Records System to identify all patients with OS. Clinical data abstracted include sex, age of onset of iPD, age of onset of OS, medications, effect of discontinuing the dopamine agonist, neuroimaging, and comorbidities.Results: Seven non-demented iPD patients with dopamine agonist implementation time locked to the development and resolution of OS are reported. The average age of iPD onset was 46.6 years (Standard deviation: 5.0 years), and OS onset was 53.7 years (7.1 years). All seven patients had significant marital conflict as a result of the delusions.Conclusions: OS can be associated with dopamine agonist use and can lead to serious consequences. Dopamine agonist cessation eliminates the delusion of infidelity and should be the first treatment option. [ABSTRACT FROM AUTHOR]

Background and Objectives: Isolated REM sleep behavior disorder (iRBD) is strongly associated with synucleinopathies. Previous iRBD cohort studies have primarily focused on older (>50 years), male-predominant cohorts. Risk of phenoconversion in women and younger adults remains unclear. The study aimed to determine clinical features associated with conversion to a defined neurodegenerative disorder in women and men with iRBD., Methods: One hundred eighty-six women and 186 men with iRBD were matched by polysomnography month. Baseline clinical variables and subsequent neurodegenerative outcomes were abstracted by chart review. Kaplan-Meier curves assessed conversion rates. Cox proportional hazards modeling evaluated factors associated with phenoconversion risk., Results: Age at iRBD diagnosis was younger in women compared with men (54.9 vs 62.5 years, p < 0.01). Forty-eight patients (12.9%), including 18 women (9.7%) and 30 men (16.1%), phenoconverted during a median follow-up of 6.0 years. Conversion rates were lower in antidepressant users and patients with chronic pain or psychiatric comorbidity while rates were higher in those with vascular comorbidity. Only age at diagnosis (HR 1.09, 95% CI 1.06-1.13) was associated with phenoconversion after adjusting for RBD symptom duration; sex; antidepressant use; and psychiatric, chronic pain, and vascular comorbidities., Discussion: Age at diagnosis was independently associated with phenoconversion risk in women and men with iRBD.

Recently approved anti-amyloid immunotherapies for Alzheimer's disease (AD) require evidence of amyloid-β pathology from positron emission tomography (PET) or cerebrospinal fluid (CSF) before initiating treatment. Blood-based biomarkers promise to reduce the need for PET or CSF testing; however, their interpretation at the individual level and the circumstances requiring confirmatory testing are poorly understood. Individual-level interpretation of diagnostic test results requires knowledge of disease prevalence in relation to clinical presentation (clinical pretest probability). Here, in a study of 6,896 individuals evaluated from 11 cohort studies from six countries, we determined the positive and negative predictive value of five plasma biomarkers for amyloid-β pathology in cognitively impaired individuals in relation to clinical pretest probability. We observed that p-tau217 could rule in amyloid-β pathology in individuals with probable AD dementia (positive predictive value above 95%). In mild cognitive impairment, p-tau217 interpretation depended on patient age. Negative p-tau217 results could rule out amyloid-β pathology in individuals with non-AD dementia syndromes (negative predictive value between 90% and 99%). Our findings provide a framework for the individual-level interpretation of plasma biomarkers, suggesting that p-tau217 combined with clinical phenotyping can identify patients where amyloid-β pathology can be ruled in or out without the need for PET or CSF confirmatory testing., Competing Interests: Competing interests J.T. has served as a consultant for the Neurotorium educational platform, outside of the scope of the submitted work. H.Z. has served at scientific advisory boards and/or as a consultant for Abbvie, Acumen, Alector, Alzinova, ALZPath, Annexon, Apellis, Artery Therapeutics, AZTherapies, Cognito Therapeutics, CogRx, Denali, Eisai, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon Therapeutics, Prothena, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics and Wave; has given lectures in symposia sponsored by Cellectricon, Fujirebio, Alzecure, Biogen and Roche; and is a co-founder of Brain Biomarker Solutions in Gothenburg AB, which is a part of the GU Ventures Incubator Program (outside submitted work). O.H. has acquired research support (for the institution) from ADx, AVID Radiopharmaceuticals, Biogen, Eli Lilly, Eisai, Fujirebio, GE Healthcare, Pfizer and Roche. In the past 2 years, he has received consultancy/speaker fees from AC Immune, Amylyx, Alzpath, BioArctic, Biogen, Cerveau, Eisai, Eli Lilly, Fujirebio, Merck, Novartis, Novo Nordisk, Roche, Sanofi and Siemens. R.C.P. has consulted for Roche, Genentech, Eli Lilly, Eisai and Nestle, all outside the scope of the current work. M.S.-C. has served as a consultant and at advisory boards for Roche Diagnostics International Ltd and Grifols S.L.; has given lectures in symposia sponsored by Roche Diagnostics, S.L.U and Roche Farma, S.A.; and was granted with a project funded by Roche Diagnostics International Ltd; payments were made to the institution (BBRC). A.P.P. has served at advisory boards for Schwabe Farma Iberica. D.A. participated in advisory boards from Fujirebio-Europe, Roche Diagnostics, Grifols S.A. and Lilly, and received speaker honoraria from Fujirebio-Europe, Roche Diagnostics, Nutricia, Krka Farmacéutica S.L., Zambon S.A.U. and Esteve Pharmaceuticals S.A. D.A. declares a filed patent application (WO2019175379 A1 Markers of synaptopathy in neurodegenerative disease). S. Johnson has served at scientific advisory boards or as a consultant for ALZpath, Prothena, Roche Diagnostics, and Enigma. M.M.M. has served as a consultant and at advisory boards for Biogen, Eisai, Lilly, Merck, Roche, and Siemens Healthineers. P.R.-N. has served at scientific advisory boards and/or as a consultant for Roche, Novo Nordisk, Eisai, and Cerveau radiopharmaceuticals. A.A.-S. has participated in advisory boards for Roche Diagnostics, Fujirebio Diagnostics and Siemens Healthineers. The remaining authors declare no competing interests., (© 2024. The Author(s).)

Introduction: Understanding the relationship between amyloid beta (Aβ) positron emission tomography (PET) and Aβ cerebrospinal fluid (CSF) biomarkers will define their potential utility in Aβ treatment. Few population-based or neuropathologic comparisons have been reported., Methods: Participants 50+ years with Aβ PET and Aβ CSF biomarkers (phosphorylated tau [p-tau]181/Aβ42, n = 505, and Aβ42/40, n = 54) were included from the Mayo Clinic Study on Aging. From these participants, an autopsy subgroup was identified (n = 47). The relationships of Aβ PET and Aβ CSF biomarkers were assessed cross-sectionally in all participants and longitudinally in autopsy data., Results: Cross-sectionally, more participants were Aβ PET+ versus Aβ CSF- than Aβ PET- versus Aβ CSF+ with an incremental effect when using Aβ PET regions selected for early Aβ deposition. The sensitivity for the first detection of Thal phase ≥ 1 in longitudinal data was higher for Aβ PET (89%) than p-tau181/Aβ42 (64%)., Discussion: Aβ PET can detect earlier cortical Aβ deposition than Aβ CSF biomarkers. Aβ PET+ versus Aβ CSF- findings are several-fold greater using regional Aβ PET analyses and in peri-threshold-standardized uptake value ratio participants., Highlights: Amyloid beta (Aβ) positron emission tomography (PET) has greater sensitivity for Aβ deposition than Aβ cerebrospinal fluid (CSF) in early Aβ development. A population-based sample of participants (n = 505) with PET and CSF tests was used. Cortical regions showing early Aβ on Aβ PET were also used in these analyses. Neuropathology was used to validate detection of Aβ by Aβ PET and Aβ CSF biomarkers., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

There are recognized neuroimaging regions of interest in typical Alzheimer's disease which have been used to track disease progression and aid prognostication. However, there is a need for validated baseline imaging markers to predict clinical decline in atypical Alzheimer's Disease. We aimed to address this need by producing models from baseline imaging features using penalized regression and evaluating their predictive performance on various clinical measures. Baseline multimodal imaging data, in combination with clinical testing data at two time points from 46 atypical Alzheimer's Disease patients with a diagnosis of logopenic progressive aphasia (N = 24) or posterior cortical atrophy (N = 22), were used to generate our models. An additional 15 patients (logopenic progressive aphasia = 7, posterior cortical atrophy = 8), whose data were not used in our original analysis, were used to test our models. Patients underwent MRI, FDG-PET and Tau-PET imaging and a full neurologic battery at two time points. The Schaefer functional atlas was used to extract network-based and regional gray matter volume or PET SUVR values from baseline imaging. Penalized regression (Elastic Net) was used to create models to predict scores on testing at Time 2 while controlling for baseline performance, education, age, and sex. In addition, we created models using clinical or Meta Region of Interested (ROI) data to serve as comparisons. We found the degree of baseline involvement on neuroimaging was predictive of future performance on cognitive testing while controlling for the above measures on all three imaging modalities. In many cases, model predictability improved with the addition of network-based neuroimaging data to clinical data. We also found our network-based models performed superiorly to the comparison models comprised of only clinical or a Meta ROI score. Creating predictive models from imaging studies at a baseline time point that are agnostic to clinical diagnosis as we have described could prove invaluable in both the clinical and research setting, particularly in the development and implementation of future disease modifying therapies., Competing Interests: Declaration of competing interest None., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Obstructive sleep apnoea (OSA) is associated with an increased risk for cognitive impairment and dementia, which likely involves Alzheimer's disease pathology. Non-rapid eye movement slow-wave activity (SWA) has been implicated in amyloid clearance, but it has not been studied in the context of longitudinal amyloid accumulation in OSA. This longitudinal retrospective study aims to investigate the relationship between polysomnographic and electrophysiological SWA features and amyloid accumulation. From the Mayo Clinic Study of Aging cohort, we identified 71 participants ≥60 years old with OSA (mean baseline age = 72.9 ± 7.5 years, 60.6% male, 93% cognitively unimpaired) who had at least 2 consecutive Amyloid Pittsburgh Compound B (PiB)-PET scans and a polysomnographic study within 5 years of the baseline scan and before the second scan. Annualized PiB-PET accumulation [global ΔPiB(log)/year] was estimated by the difference between the second and first log-transformed global PiB-PET uptake estimations divided by the interval between scans (years). Sixty-four participants were included in SWA analysis. SWA was characterized by the mean relative spectral power density (%) in slow oscillation (SO: 0.5-0.9 Hz) and delta (1-3.9 Hz) frequency bands and by their downslopes (SO-slope and delta-slope, respectively) during the diagnostic portion of polysomnography. We fit linear regression models to test for associations among global ΔPiB(log)/year, SWA features (mean SO% and delta% or mean SO-slope and delta-slope), and OSA severity markers, after adjusting for age at baseline PiB-PET, APOE ɛ4 and baseline amyloid positivity. For 1 SD increase in SO% and SO-slope, global ΔPiB(log)/year increased by 0.0033 (95% CI: 0.0001; 0.0064, P = 0.042) and 0.0069 (95% CI: 0.0009; 0.0129, P = 0.026), which were comparable to 32% and 59% of the effect size associated with baseline amyloid positivity, respectively. Delta-slope was associated with a reduction in global ΔPiB(log)/year by -0.0082 (95% CI: -0.0143; -0.0021, P = 0.009). Sleep apnoea severity was not associated with amyloid accumulation. Regional associations were stronger in the pre-frontal region. Both slow-wave slopes had more significant and widespread regional associations. Annualized PiB-PET accumulation was positively associated with SO and SO-slope, which may reflect altered sleep homeostasis due to increased homeostatic pressure in the setting of unmet sleep needs, increased synaptic strength, and/or hyper-excitability in OSA. Delta-slope was inversely associated with PiB-PET accumulation, suggesting it may represent residual physiological activity. Further investigation of SWA dynamics in the presence of sleep disorders before and after treatment is necessary for understanding the relationship between amyloid accumulation and SWA physiology., Competing Interests: D.Z.C. is supported by the NIA/NIH. E.K.S. has received research support from the Mayo Clinic CCaTS, NIH, Michael J. Fox Foundation and Sunovion, Inc. M.M.M. is supported by the NIA/NIH. B.F.B. has served as an investigator for clinical trials sponsored by Alector, Biogen, Cognition Therapeutics, EIP Pharma and Transposon and served on the Scientific Advisory Board of the Tau Consortium. R.C.P. has served as a consultant for Roche, Inc., Merck, Inc., Genentech, Inc., Biogen, Inc. and GE Healthcare and received royalties from the Oxford University Press for the publication of Mild Cognitive Impairment. V.J.L. has served as a consultant for Bayer Schering Pharma, Piramal Life Sciences, Life Molecular Imaging, Eisai Inc., Eli Lilly, AVID Radiopharmaceuticals and Merck Research and received research support from GE Healthcare, Siemens Molecular Imaging, AVID Radiopharmaceuticals and the NIH. C.R.J. is supported by the NIA/NIH. J.G.-R. is supported by the NIH. He is on the drug safety medical board for NINDS and is the site PI for a clinical trial funded by Eisai. G.A.W. is supported by the NIH. He has licenced intellectual property to NeuroOne Inc. and Cadence Neuroscience Inc. He has served on the Scientific Advisory Boards for LivaNova Inc., NeuroPace Inc., UNEEG Inc and NeuroOne Inc. He has received royalties from NeuroOne Inc. V.K.S. has served as a consultant for ResMed, Zoll, Bayer, Lilly, Huxley, Apnimed, Jazz Pharmaceuticals and Axsome and is on the Scientific Advisory Board for Sleep Number Corporation. A.W.V. has served as a consultant for Jazz Pharmaceuticals. He is supported by R01 AG066870 and R01 AG080609. P.V. is supported by the NIA/NIH. The remaining authors report no competing interests., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Objective: Few normative data for unsupervised, remotely-administered computerized cognitive measures are available. We examined variables to include in normative models for Mayo Test Drive (a multi-device remote cognitive assessment platform) measures, developed normative data, and validated the norms., Method: 1240 Cognitively Unimpaired (CU) adults ages 32-100-years (96% white) from the Mayo Clinic Study of Aging and Mayo Alzheimer's Disease Research Center with Clinical Dementia Rating ® of 0 were included. We converted raw scores to normalized scaled scores and derived regression-based normative data adjusting for age, age 2 , sex and education (base model); alternative norms are also provided (age+age 2 +sex; age+age 2 ). We assessed additional terms using an a priori cut-off of 1% variance improvement above the base model. We examined low test performance rates (<-1 standard deviation) in independent validation samples (n=167 CU, n=64 mild cognitive impairment (MCI), n=14 dementia). Rates were significantly different when 95% confidence intervals (CI) did not include the expected 14.7% base rate., Results: No model terms met the a priori cut-off beyond the base model, including device type, response input source (e.g., mouse, etc.) or session interference. Norms showed expected low performance rates in CU and greater rates of low performance in MCI and dementia in independent validation samples., Conclusion: Typical normative models appear appropriate for remote self-administered MTD measures and are sensitive to cognitive impairment. Device type and response input source did not explain enough variance for inclusion in normative models but are important for individual-level interpretation. Future work will increase inclusion of individuals from under-represented groups.

There is increasing interest in Alzheimer's disease related plasma biomarkers due to their accessibility and scalability. We hypothesized that integrating plasma biomarkers with other commonly used and available participant data (MRI, cardiovascular factors, lifestyle, genetics) using machine learning (ML) models can improve individual prediction of cognitive outcomes. Further, our goal was to evaluate the heterogeneity of these predictors across different age strata. This longitudinal study included 1185 participants from the Mayo Clinic Study of Aging who had complete plasma analyte work-up at baseline. We used the Quanterix Simoa immunoassay to measure neurofilament light, Aβ 1-42 and Aβ 1-40 (used as Aβ 42 /Aβ 40 ratio), glial fibrillary acidic protein, and phosphorylated tau 181 (p-tau181). Participants' brain health was evaluated through gray and white matter structural MRIs. The study also considered cardiovascular factors (hyperlipidemia, hypertension, stroke, diabetes, chronic kidney disease), lifestyle factors (area deprivation index, body mass index, cognitive and physical activities), and genetic factors ( APOE , single nucleotide polymorphisms, and polygenic risk scores). An ML model was developed to predict cognitive outcomes at baseline and decline (slope). Three models were created: a base model with groups of risk factors as predictors, an enhanced model included socio-demographics, and a final enhanced model by incorporating plasma and socio-demographics into the base models. Models were explained for three age strata: younger than 65 years, 65-80 years, and older than 80 years, and further divided based on amyloid positivity status. Regardless of amyloid status the plasma biomarkers showed comparable performance ( R ² = 0.15) to MRI ( R ² = 0.18) and cardiovascular measures ( R ² = 0.10) when predicting cognitive decline. Inclusion of cardiovascular or MRI measures with plasma in the presence of socio-demographic improved cognitive decline prediction ( R ² = 0.26 and 0.27). For amyloid positive individuals Aβ 42 /Aβ 40 , glial fibrillary acidic protein and p-tau181 were the top predictors of cognitive decline while Aβ 42 /Aβ 40 was prominent for amyloid negative participants across all age groups. Socio-demographics explained a large portion of the variance in the amyloid negative individuals while the plasma biomarkers predominantly explained the variance in amyloid positive individuals (21% to 37% from the younger to the older age group). Plasma biomarkers performed similarly to MRI and cardiovascular measures when predicting cognitive outcomes and combining them with either measure resulted in better performance. Top predictors were heterogeneous between cross-sectional and longitudinal cognition models, across age groups, and amyloid status. Multimodal approaches will enhance the usefulness of plasma biomarkers through careful considerations of a study population's socio-demographics, brain and cardiovascular health., Competing Interests: There are no relevant disclosures relevant to this publication., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Alzheimer disease (AD) exhibits spatially heterogeneous 3- or 4-repeat tau deposition across participants. Our overall goal was to develop an automated method to quantify the heterogeneous burden of tau deposition into a single number that would be clinically useful. Methods: We used tau PET scans from 3 independent cohorts: the Mayo Clinic Study of Aging and Alzheimer's Disease Research Center (Mayo, n = 1,290), the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 831), and the Open Access Series of Imaging Studies (OASIS-3, n = 430). A machine learning binary classification model was trained on Mayo data and validated on ADNI and OASIS-3 with the goal of predicting visual tau positivity (as determined by 3 raters following Food and Drug Administration criteria for 18 F-flortaucipir). The machine learning model used region-specific SUV ratios scaled to cerebellar crus uptake. We estimated feature contributions based on an artificial intelligence-explainable method (Shapley additive explanations) and formulated a global tau summary measure, Tau Heterogeneity Evaluation in Alzheimer's Disease (THETA) score, using SUV ratios and Shapley additive explanations for each participant. We compared the performance of THETA with that of commonly used meta-regions of interest (ROIs) using the Mini-Mental State Examination, the Clinical Dementia Rating-Sum of Boxes, clinical diagnosis, and histopathologic staging. Results: The model achieved a balanced accuracy of 95% on the Mayo test set and at least 87% on the validation sets. It classified tau-positive and -negative participants with an AUC of 1.00, 0.96, and 0.94 on the Mayo, ADNI, and OASIS-3 cohorts, respectively. Across all cohorts, THETA showed a better correlation with the Mini-Mental State Examination and the Clinical Dementia Rating-Sum of Boxes (ρ ≥ 0.45, P < 0.05) than did meta-ROIs (ρ < 0.44, P < 0.05) and discriminated between participants who were cognitively unimpaired and those who had mild cognitive impairment with an effect size of 10.09, compared with an effect size of 3.08 for meta-ROIs. Conclusion: Our proposed approach identifies positive tau PET scans and provides a quantitative summary measure, THETA, that effectively captures heterogeneous tau deposition observed in AD. The application of THETA for quantifying tau PET in AD exhibits great potential., (© 2024 by the Society of Nuclear Medicine and Molecular Imaging.)

Introduction: We aimed to evaluate clinical interpretation cutpoints for two plasma phosphorylated tau (p-tau)217 assays (ALZpath and Lumipulse) as predictors of amyloid status for implementation in clinical practice., Methods: Clinical performance of plasma p-tau217 against amyloid positron emission tomography status was evaluated in participants with mild cognitive impairment or mild dementia (n = 427)., Results: Using a one-cutpoint approach (negative/positive), neither assay achieved ≥ 90% in both sensitivity and specificity. A two-cutpoint approach yielding 92% sensitivity and 96% specificity provided the desired balance of false positives and false negatives, while categorizing 20% and 39% of results as indeterminate for the Lumipulse and ALZpath assays, respectively., Discussion: This study provides a systematic framework for selection of assay-specific cutpoints for clinical use of plasma p-tau217 for determination of amyloid status. Our findings suggest that a two-cutpoint approach may have advantages in optimizing diagnostic accuracy while minimizing potential harm from false positive results., Highlights: Phosphorylated tau (p-tau)217 cutpoints for detection of amyloid pathology were established. A two-cutpoint approach exhibited the best performance for clinical laboratory use. p-tau217 assays differed in the percentage of results categorized as intermediate., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Background: Primary progressive aphasia (PPA) defines a group of neurodegenerative disorders characterised by language decline. Three PPA variants correlate with distinct underlying pathologies: semantic variant PPA (svPPA) with transactive response DNA-binding protein of 43 kD (TDP-43) proteinopathy, agrammatic variant PPA (agPPA) with tau deposition and logopenic variant PPA (lvPPA) with Alzheimer's disease (AD). Our objectives were to differentiate PPA variants using clinical and neuroimaging features, assess progression and evaluate structural MRI and a novel 18-F fluorodeoxyglucose positron emission tomography (FDG-PET) image decomposition machine learning algorithm for neuropathology prediction., Methods: We analysed 82 autopsied patients diagnosed with PPA from 1998 to 2022. Clinical histories, language characteristics, neuropsychological results and brain imaging were reviewed. A machine learning framework using a k -nearest neighbours classifier assessed FDG-PET scans from 45 patients compared with a large reference database., Results: PPA variant distribution: 35 lvPPA (80% AD), 28 agPPA (89% tauopathy) and 18 svPPA (72% frontotemporal lobar degeneration-TAR DNA-binding protein (FTLD-TDP)). Apraxia of speech was associated with 4R-tauopathy in agPPA, while pure agrammatic PPA without apraxia was linked to 3R-tauopathy. Longitudinal data revealed language dysfunction remained the predominant deficit for patients with lvPPA, agPPA evolved to corticobasal or progressive supranuclear palsy syndrome (64%) and svPPA progressed to behavioural variant frontotemporal dementia (44%). agPPA-4R-tauopathy exhibited limited pre-supplementary motor area atrophy, lvPPA-AD displayed temporal atrophy extending to the superior temporal sulcus and svPPA-FTLD-TDP had severe temporal pole atrophy. The FDG-PET-based machine learning algorithm accurately predicted clinical diagnoses and underlying pathologies., Conclusions: Distinguishing 3R-taupathy and 4R-tauopathy in agPPA may rely on apraxia of speech presence. Additional linguistic and clinical features can aid neuropathology prediction. Our data-driven brain metabolism decomposition approach effectively predicts underlying neuropathology., Competing Interests: Competing interests: DSK serves on a Data Safety Monitoring Board for the DIAN study. He served on a Data Safety Monitoring Board for a tau therapeutic for Biogen but receives no personal compensation. He is a site investigator in the Biogen aducanumab trials. He is an investigator in a clinical trial sponsored by Lilly Pharmaceuticals and the University of Southern California. He serves as a consultant for Samus Therapeutics, Roche and Alzeca Biosciences but receives no personal compensation. RCP serves as a consultant for Roche, Genentech, Nestle, Eli Lilly and Co and Eisai, and receives publishing royalties from Mild Cognitive Impairment (Oxford University Press, 2003) and UpToDate. BFB receives honoraria for SAB activities for the Tau Consortium; is a site investigator for clinical trials sponsored by Alector, Biogen and Transposon; and receives research support from NIH. GSD serves as a consultant for Parabon NanoLabs, as a topic editor (Dementia) for DynaMed (EBSCO) and as the clinical director of the Anti-NMDA Receptor Encephalitis Foundation (Canada, uncompensated). He is the co-Project PI for a clinical trial in Anti-NMDA Receptor Encephalitis, which receives support from Horizon Pharmaceuticals. He has developed educational materials for PeerView Media and Continuing Education. He owns stock in ANI Pharmaceuticals. GSD’s institution has received support from Eli Lilly for GSD’s development and participation in an educational event promoting early diagnosis of symptomatic Alzheimer's disease. WKK was supported in part by NIH funding. DWD, DTJ, KAJ and JLW received research funding from the NIH and declared no competing financial interests. MEM is a consultant for AVID Radiopharmaceuticals. She receives support from the NIH/NIA and Eli Lilly. NRG-R receives royalties from UpToDate, has participated in multicentre therapy studies sponsored by Biogen, TauRx, AbbVie, Novartis and Lilly, and he receives research support from NIH. JAF is on the OSMB for the SWAN-Aging Study, serves as a consultant for Medtronic and received NIH funding. JG-R serves on the DSMB for STROKENET, is a site investigator for a trial sponsored by Eisai and the NIH, and he receives research support from the NIH., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Alzheimer disease (AD) and other dementias are associated with vascular changes and amyloid deposition, which may be reflected as density changes in the retinal capillaries. These changes may can be directly visualized and quantified with optical coherence tomography angiography (OCTA), making OCTA a potential noninvasive preclinical biomarker of small vessel disease and amyloid positivity. Our objective was to investigate the feasibility of retinal imaging metrics as noninvasive biomarkers of small vessel disease and amyloid positivity in the brain., Methods: We investigated associations between OCTA and neuroimaging and cognitive metrics in 41 participants without dementia from the Mayo Clinic Study of Aging and Alzheimer's Disease Research Center. OCTA metrics included superficial, deep, and full retina capillary density of the fovea, parafovea, and macula as well as the area of the foveal avascular zone (FAZ). Neuroimaging metrics included a high burden of white matter hyperintensity (WMH), presence of cerebral microbleeds (CMB), lacunar infarcts, and amyloid positivity as evidenced on positron emission tomography (PET), whereas cognitive metrics included mini-mental status examination (MMSE) score. We performed generalized estimating equations to account for measurements in each eye while controlling for age and sex to estimate associations between OCTA metrics and neuroimaging and cognitive scores., Results: Associations between OCTA and neuroimaging metrics were restricted to the fovea. OCTA showed decreased capillary density with high burden of WMH in both the superficial (P = 0.003), deep (P = 0.004), and full retina (P = 0.01) in the fovea but not the parafovea or whole macula. Similarly, participants with amyloid PET positivity had significantly decreased capillary density in the superficial fovea (P = 0.027) and deep fovea (P = 0.03) but higher density in the superficial parafovea (P = 0.038). Participants with amyloid PET positivity also had a significantly larger FAZ (P = 0.031), whereas in those with high WMH burden the difference did not reach statistical significance (P = 0.075). There was also a positive association between MMSE and capillary density of the full retina within the fovea (P = 0.037) and in the superficial parafovea (P = 0.046). No associations were found between OCTA metrics and presence of CMB or presence of lacunar infarcts., Conclusion: The associations of lower foveal capillary density with cerebral WMH and amyloid positivity suggest that further research is warranted to evaluate for shared mechanisms of disease between small vessel disease and AD pathologies., Competing Interests: The authors report no conflicts of interest., (Copyright © 2024 by North American Neuro-Ophthalmology Society.)

Objective: To evaluate the performance of Alzheimer disease (AD) cerebrospinal fluid (CSF) biomarkers in a tertiary neurology clinic setting with high frequency of non-AD cases, including normal pressure hydrocephalus (NPH)., Methods: There were 534 patients who underwent AD CSF biomarkers (Roche Elecsys Aβ42, p-Tau181, total-Tau) from April 1, 2020, through April 23, 2021. A behavioral neurologist blinded to CSF results assigned a clinical diagnosis retrospectively on the basis of consensus criteria, and a neuroradiologist blinded to the diagnosis and CSF studies graded brain magnetic resonance images for indicators of CSF dynamics disorders. Associations between biomarkers, diagnoses, and imaging were assessed by χ 2 , analysis of covariance, and linear regression methods., Results: Median age at time of testing was 67 years (range, 19 to 96 years), median symptom duration was 2 years (range, 0.4 to 28 years), and median Short Test of Mental Status score was 30 (range, 0 to 38). Clinical diagnoses significantly correlated with different CSF biomarker values (χ 2 =208.3; P=10e-4). p-Tau181/Aβ42 ratios above 0.023 positively correlated with Alzheimer dementia (more than individual measures). This ratio also had the best performance for differentiating Alzheimer dementia from NPH (area under the curve, 0.869). Imaging markers supportive of CSF dynamics disorders correlated with low Aβ42, p-Tau181, and total-Tau., Conclusion: In a heterogeneous clinical population, abnormal p-Tau181/Aβ42 ratios (>0.023) have the strongest association with Alzheimer dementia and probably represent a comorbid AD pathologic component in persons clearly matching non-AD neurodegenerative syndromes. Altered CSF dynamics were associated with lower concentrations of AD CSF biomarkers regardless of clinical diagnosis, but the ratio compensates for these changes. In the appropriate clinical setting, an isolated abnormal Aβ42 should prompt consideration of NPH., (Copyright © 2024 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.)

Introduction: Age-related and Alzheimer's disease (AD) dementia-related neurodegeneration impact brain health. While morphometric measures from T1-weighted scans are established biomarkers, they may be less sensitive to earlier changes. Neurite orientation dispersion and density imaging (NODDI), offering biologically meaningful interpretation of tissue microstructure, may be an advanced brain health biomarker., Methods: We contrasted regional gray matter NODDI and morphometric evaluations concerning their correlation with (1) age, (2) clinical diagnosis stage, and (3) tau pathology as assessed by AV1451 positron emission tomography., Results: Our study hypothesizes that NODDI measures are more sensitive to aging and early AD changes than morphometric measures. One NODDI output, free water fraction (FWF), showed higher sensitivity to age-related changes, generally better effect sizes in separating mild cognitively impaired from cognitively unimpaired participants, and stronger associations with regional tau deposition than morphometric measures., Discussion: These findings underscore NODDI's utility in capturing early neurodegenerative changes and enhancing our understanding of aging and AD., Highlights: Neurite orientation dispersion and density imaging can serve as an effective brain health biomarker for aging and early Alzheimer's disease (AD).Free water fraction has higher sensitivity to normal brain aging.Free water fraction has stronger associations with early AD and regional tau deposition., Competing Interests: The authors declare no conflicts of interest. Author disclosures are available in the supporting information., (© 2024 The Author(s). Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Digital quantification of gait can be used to measure aging- and disease-related decline in mobility. Gait performance also predicts prognosis, disease progression, and response to therapies. Most gait analysis systems require large amounts of space, resources, and expertise to implement and are not widely accessible. Thus, there is a need for a portable system that accurately characterizes gait. Here, depth video from two portable cameras accurately reconstructed gait metrics comparable to those reported by a pressure-sensitive walkway. 392 research participants walked across a four-meter pressure-sensitive walkway while depth video was recorded. Gait speed, cadence, and step and stride durations and lengths strongly correlated (r > 0.9) between modalities, with root-mean-squared-errors (RMSE) of 0.04 m/s, 2.3 steps/min, 0.03 s, and 0.05-0.08 m for speed, cadence, step/stride duration, and step/stride length, respectively. Step, stance, and double support durations (gait cycle percentage) significantly correlated (r > 0.6) between modalities, with 5% RMSE for step and stance and 10% RMSE for double support. In an exploratory analysis, gait speed from both modalities significantly related to healthy, mild, moderate, or severe categorizations of Charleson Comorbidity Indices (ANOVA, Tukey's HSD, p < 0.0125). These findings demonstrate the viability of using depth video to expand access to quantitative gait assessments., (© 2024. The Author(s).)

[ 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.