Differential Synaptic Loss in ß-Amyloid Positive Versus ß-Amyloid Negative Corticobasal Syndrome.

BACKGROUND/OBJECTIVE: The corticobasal syndrome (CBS) is a complex asymmetric movement disorder, with cognitive impairment. Although commonly associated with the primary 4-repeat-tauopathy of corticobasal degeneration, clinicopathological correlation is poor, and a significant proportion is due to Alzheimer's disease (AD). Synaptic loss is a pathological feature of many clinical and preclinical tauopathies. We therefore measured the degree of synaptic loss in patients with CBS and tested whether synaptic loss differed according to ß-amyloid status. METHODS: Twenty-five people with CBS, and 32 age-/sex-/education-matched healthy controls participated. Regional synaptic density was estimated by [11C]UCB-J non-displaceable binding potential (BPND), AD-tau pathology by [18F]AV-1451 BPND, and gray matter volume by T1-weighted magnetic resonance imaging. Participants with CBS had ß-amyloid imaging with 11C-labeled Pittsburgh Compound-B ([11C]PiB) positron emission tomography. Symptom severity was assessed with the progressive supranuclear palsy-rating-scale, the cortical basal ganglia functional scale, and the revised Addenbrooke's Cognitive Examination. Regional differences in BPND and gray matter volume between groups were assessed by ANOVA. RESULTS: Compared to controls, patients with CBS had higher [18F]AV-1451 uptake, gray matter volume loss, and reduced synaptic density. Synaptic loss was more severe and widespread in the ß-amyloid negative group. Asymmetry of synaptic loss was in line with the clinically most affected side. DISCUSSION: Distinct patterns of [11C]UCB-J and [18F]AV-1451 binding and gray matter volume loss, indicate differences in the pathogenic mechanisms of CBS according to whether it is associated with the presence of Alzheimer's disease or not. This highlights the potential for different therapeutic strategies in CBSs. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Estimating demand for potential disease-modifying therapies for Alzheimer's disease in the UK.

BACKGROUND: Phase three trials of the monoclonal antibodies lecanemab and donanemab, which target brain amyloid, have reported statistically significant differences in clinical end-points in early Alzheimer's disease. These drugs are already in use in some countries and are going through the regulatory approval process for use in the UK. Concerns have been raised about the ability of healthcare systems, including those in the UK, to deliver these treatments, considering the resources required for their administration and monitoring. AIMS: To estimate the scale of real-world demand for monoclonal antibodies for Alzheimer's disease in the UK. METHOD: We used anonymised patient record databases from two National Health Service trusts for the year 2019 to collect clinical, demographic, cognitive and neuroimaging data for these cohorts. Eligibility for treatment was assessed using the inclusion criteria from the clinical trials of donanemab and lecanemab, with consideration given to diagnosis, cognitive performance, cerebrovascular disease and willingness to receive treatment. RESULTS: We examined the records of 82 386 people referred to services covering around 2.2 million people. After applying the trial criteria, we estimate that a maximum of 906 people per year would start treatment with monoclonal antibodies in the two services, equating to 30 200 people if extrapolated nationally. CONCLUSIONS: Monoclonal antibody treatments for Alzheimer's disease are likely to present a significant challenge for healthcare services to deliver in terms of the neuroimaging and treatment delivery. The data provided here allows health services to understand the potential demand and plan accordingly.

Robustly uncovering the heterogeneity of neurodegenerative disease by using data-driven subtyping in neuroimaging: A review.

Neurodegenerative diseases are associated with heterogeneity in genetics, pathology, and clinical manifestation. Understanding this heterogeneity is particularly relevant for clinical prognosis and stratifying patients for disease modifying treatments. Recently, data-driven methods based on neuroimaging have been applied to investigate the subtyping of neurodegenerative disease, helping to disentangle this heterogeneity. We reviewed brain-based subtyping studies in aging and representative neurodegenerative diseases, including Alzheimer's disease, mild cognitive impairment, frontotemporal dementia, and Lewy body dementia, from January 2000 to November 2022. We summarized clustering methods, validation, robustness, reproducibility, and clinical relevance of 71 eligible studies in the present study. We found vast variations in approaches between studies, including ten neuroimaging modalities, 24 cluster algorithms, and 41 methods of cluster number determination. The clinical relevance of subtyping studies was evaluated by summarizing the analysis method of clinical measurements, showing a relatively low clinical utility in the current studies. Finally, we conclude that future studies of heterogeneity in neurodegenerative disease should focus on validation, comparison between subtyping approaches, and prioritise clinical utility.

Synaptic density affects clinical severity via network dysfunction in syndromes associated with frontotemporal lobar degeneration.

There is extensive synaptic loss from frontotemporal lobar degeneration, in preclinical models and human in vivo and post mortem studies. Understanding the consequences of synaptic loss for network function is important to support translational models and guide future therapeutic strategies. To examine this relationship, we recruited 55 participants with syndromes associated with frontotemporal lobar degeneration and 24 healthy controls. We measured synaptic density with positron emission tomography using the radioligand [11C]UCB-J, which binds to the presynaptic vesicle glycoprotein SV2A, neurite dispersion with diffusion magnetic resonance imaging, and network function with task-free magnetic resonance imaging functional connectivity. Synaptic density and neurite dispersion in patients was associated with reduced connectivity beyond atrophy. Functional connectivity moderated the relationship between synaptic density and clinical severity. Our findings confirm the importance of synaptic loss in frontotemporal lobar degeneration syndromes, and the resulting effect on behaviour as a function of abnormal connectivity.

Accurate digital quantification of tau pathology in progressive supranuclear palsy.

The development of novel treatments for Progressive Supranuclear Palsy (PSP) is hindered by a knowledge gap of the impact of neurodegenerative neuropathology on brain structure and function. The current standard practice for measuring postmortem tau histology is semi-quantitative assessment, which is prone to inter-rater variability, time-consuming and difficult to scale. We developed and optimized a tau aggregate type-specific quantification pipeline for cortical and subcortical regions, in human brain donors with PSP. We quantified 4 tau objects ('neurofibrillary tangles', 'coiled bodies', 'tufted astrocytes', and 'tau fragments') using a probabilistic random forest machine learning classifier. The tau pipeline achieved high classification performance (F1-score > 0.90), comparable to neuropathologist inter-rater reliability in the held-out test set. Using 240 AT8 slides from 32 postmortem brains, the tau burden was correlated against the PSP pathology staging scheme using Spearman's rank correlation. We assessed whether clinical severity (PSP rating scale, PSPRS) score reflects neuropathological severity inferred from PSP stage and tau burden using Bayesian linear mixed regression. Tufted astrocyte density in cortical regions and coiled body density in subcortical regions showed the highest correlation to PSP stage (r = 0.62 and r = 0.38, respectively). Using traditional manual staging, only PSP patients in stage 6, not earlier stages, had significantly higher clinical severity than stage 2. Cortical tau density and neurofibrillary tangle density in subcortical regions correlated with clinical severity. Overall, our data indicate the potential for highly accurate digital tau aggregate type-specific quantification for neurodegenerative tauopathies; and the importance of studying tau aggregate type-specific burden in different brain regions as opposed to overall tau, to gain insights into the pathogenesis and progression of tauopathies.

Understanding ethnic diversity in open dementia neuroimaging data sets.

Ethnic differences in dementia are increasingly recognized in epidemiological measures and diagnostic biomarkers. Nonetheless, ethnic diversity remains limited in many study populations. Here, we provide insights into ethnic diversity in open-access neuroimaging dementia data sets. Data sets comprising dementia populations with available data on ethnicity were included. Statistical analyses of sample and effect sizes were based on the Cochrane Handbook. Nineteen databases were included, with 17 studies of healthy groups or a combination of diagnostic groups if breakdown was unavailable and 12 of mild cognitive impairment and dementia groups. Combining all studies on dementia patients, the largest ethnic group was Caucasian (20 547 participants), with the next most common being Afro-Caribbean (1958), followed by Asian (1211). The smallest effect size detectable within the Caucasian group was 0.03, compared to Afro-Caribbean (0.1) and Asian (0.13). Our findings quantify the lack of ethnic diversity in openly available dementia data sets. More representative data would facilitate the development and validation of biomarkers relevant across ethnicities.

Structural correlates of survival in progressive supranuclear palsy.

INTRODUCTION: Many studies of the Richardson's syndrome phenotype of progressive supranuclear palsy (PSP) have elucidated regions of progressive atrophy and neural correlates of clinical severity. However, the neural correlates of survival and how these differ according to variant phenotypes are poorly understood. We set out to identify structural changes that predict severity and survival from scanning date to death. METHODS: Structural magnetic resonance imaging data from 112 deceased people with clinically defined 'probable' or 'possible' PSP were analysed. Neuroanatomical regions of interest volumes, thickness and area were correlated with 'temporal stage', defined as the ratio of time from symptom onset to death, time from scan to death ('survival from scan'), and in a subset of patients, clinical severity, adjusting for age and total intracranial volume. Forty-nine participants had post mortem confirmation of the diagnosis. RESULTS: Using T1-weighted magnetic resonance imaging, we confirmed the midbrain, and bilateral cortical structural correlates of contemporary disease severity. Atrophy of the striatum, cerebellum and frontotemporal cortex correlate with temporal stage and survival from scan, even after adjusting for severity. Subcortical structure-survival relationships were stronger in Richardson's syndrome than variant phenotypes. CONCLUSIONS: Although the duration of PSP varies widely between people, an individual's progress from disease onset to death (their temporal stage) reflects atrophy in striatal, cerebellar and frontotemporal cortical regions. Our findings suggest magnetic resonance imaging may contribute to prognostication and stratification of patients with heterogenous clinical trajectories and clarify the processes that confer mortality risk in PSP.

Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia.

With the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice. HIGHLIGHTS: Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery. Remaining challenges include data set suitability due to size and bias in cohort selection. Multimodal data, diverse data sets, improved machine learning approaches, real-world validation, and interdisciplinary collaboration are required.

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review.

INTRODUCTION: Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. METHODS: We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. RESULTS: A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. DISCUSSION: The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. HIGHLIGHTS: There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias.

Identifying healthy individuals with Alzheimer's disease neuroimaging phenotypes in the UK Biobank.

BACKGROUND: Identifying prediagnostic neurodegenerative disease is a critical issue in neurodegenerative disease research, and Alzheimer's disease (AD) in particular, to identify populations suitable for preventive and early disease-modifying trials. Evidence from genetic and other studies suggests the neurodegeneration of Alzheimer's disease measured by brain atrophy starts many years before diagnosis, but it is unclear whether these changes can be used to reliably detect prediagnostic sporadic disease. METHODS: We trained a Bayesian machine learning neural network model to generate a neuroimaging phenotype and AD score representing the probability of AD using structural MRI data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) Cohort (cut-off 0.5, AUC 0.92, PPV 0.90, NPV 0.93). We go on to validate the model in an independent real-world dataset of the National Alzheimer's Coordinating Centre (AUC 0.74, PPV 0.65, NPV 0.80) and demonstrate the correlation of the AD-score with cognitive scores in those with an AD-score above 0.5. We then apply the model to a healthy population in the UK Biobank study to identify a cohort at risk for Alzheimer's disease. RESULTS: We show that the cohort with a neuroimaging Alzheimer's phenotype has a cognitive profile in keeping with Alzheimer's disease, with strong evidence for poorer fluid intelligence, and some evidence of poorer numeric memory, reaction time, working memory, and prospective memory. We found some evidence in the AD-score positive cohort for modifiable risk factors of hypertension and smoking. CONCLUSIONS: This approach demonstrates the feasibility of using AI methods to identify a potentially prediagnostic population at high risk for developing sporadic Alzheimer's disease.

Spotting people with dementia early is challenging, but important to identify people for trials of treatment and prevention. We used brain scans of people with Alzheimer's disease, the commonest type of dementia, and applied an artificial intelligence method to spot people with Alzheimer's disease. We used this to find people in the Healthy UK Biobank study who might have early Alzheimer's disease. The people we found had subtle changes in their memory and thinking to suggest they may have early disease, and we also found they had high blood pressure and smoked for longer. We have demonstrated an approach that could be used to select people at high risk of future dementia for clinical trials.

Network connectivity and structural correlates of survival in progressive supranuclear palsy and corticobasal syndrome.

There is a pressing need to understand the factors that predict prognosis in progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS), with high heterogeneity over the poor average survival. We test the hypothesis that the magnitude and distribution of connectivity changes in PSP and CBS predict the rate of progression and survival time, using datasets from the Cambridge Centre for Parkinson-plus and the UK National PSP Research Network (PROSPECT-MR). Resting-state functional MRI images were available from 146 participants with PSP, 82 participants with CBS, and 90 healthy controls. Large-scale networks were identified through independent component analyses, with correlations taken between component time series. Independent component analysis was also used to select between-network connectivity components to compare with baseline clinical severity, longitudinal rate of change in severity, and survival. Transdiagnostic survival predictors were identified using partial least squares regression for Cox models, with connectivity compared to patients' demographics, structural imaging, and clinical scores using five-fold cross-validation. In PSP and CBS, between-network connectivity components were identified that differed from controls, were associated with disease severity, and were related to survival and rate of change in clinical severity. A transdiagnostic component predicted survival beyond demographic and motion metrics but with lower accuracy than an optimal model that included the clinical and structural imaging measures. Cortical atrophy enhanced the connectivity changes that were most predictive of survival. Between-network connectivity is associated with variability in prognosis in PSP and CBS but does not improve predictive accuracy beyond clinical and structural imaging metrics.

Longitudinal Synaptic Loss in Primary Tauopathies: An In Vivo [11 C]UCB-J Positron Emission Tomography Study.

BACKGROUND: Synaptic loss is characteristic of many neurodegenerative diseases; it occurs early and is strongly related to functional deficits. OBJECTIVE: In this longitudinal observational study, we determine the rate at which synaptic density is reduced in the primary tauopathies of progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD), and we test the relationship with disease progression. METHODS: Our cross-sectional cohort included 32 participants with probable PSP and 16 with probable CBD (all amyloid-negative corticobasal syndrome), recruited from tertiary care centers in the United Kingdom, and 33 sex- and age-matched healthy control subjects. Synaptic density was estimated by positron emission tomography imaging with the radioligand [11 C]UCB-J that binds synaptic vesicle 2A. Clinical severity and cognition were assessed by the PSP Rating Scale and the Addenbrooke's cognitive examination. Regional [11 C]UCB-J nondisplaceable binding potential was estimated in Hammersmith Atlas regions of interest. Twenty-two participants with PSP/CBD had a follow-up [11 C]UCB-J positron emission tomography scan after 1 year. We calculated the annualized change in [11 C]UCB-J nondisplaceable binding potential and correlated this with the change in clinical severity. RESULTS: We found significant annual synaptic loss within the frontal lobe (-3.5%, P = 0.03) and the right caudate (-3.9%, P = 0.046). The degree of longitudinal synaptic loss within the frontal lobe correlated with the rate of change in the PSP Rating Scale (R = 0.47, P = 0.03) and cognition (Addenbrooke's Cognitive Examination-Revised, R = -0.62, P = 0.003). CONCLUSIONS: We provide in vivo evidence for rapid progressive synaptic loss, correlating with clinical progression in primary tauopathies. Synaptic loss may be an important therapeutic target and outcome variable for early-phase clinical trials of disease-modifying treatments. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Microglial activation in the frontal cortex predicts cognitive decline in frontotemporal dementia.

Frontotemporal dementia is clinically and neuropathologically heterogeneous, but neuroinflammation, atrophy and cognitive impairment occur in all of its principal syndromes. Across the clinical spectrum of frontotemporal dementia, we assess the predictive value of in vivo neuroimaging measures of microglial activation and grey-matter volume on the rate of future cognitive decline. We hypothesized that inflammation is detrimental to cognitive performance, in addition to the effect of atrophy. Thirty patients with a clinical diagnosis of frontotemporal dementia underwent a baseline multimodal imaging assessment, including [11C]PK11195 PET to index microglial activation and structural MRI to quantify grey-matter volume. Ten people had behavioural variant frontotemporal dementia, 10 had the semantic variant of primary progressive aphasia and 10 had the non-fluent agrammatic variant of primary progressive aphasia. Cognition was assessed at baseline and longitudinally with the revised Addenbrooke's Cognitive Examination, at an average of 7-month intervals (for an average of ∼2 years, up to ∼5 years). Regional [11C]PK11195 binding potential and grey-matter volume were determined, and these were averaged within four hypothesis-driven regions of interest: bilateral frontal and temporal lobes. Linear mixed-effect models were applied to the longitudinal cognitive test scores, with [11C]PK11195 binding potentials and grey-matter volumes as predictors of cognitive performance, with age, education and baseline cognitive performance as covariates. Faster cognitive decline was associated with reduced baseline grey-matter volume and increased microglial activation in frontal regions, bilaterally. In frontal regions, microglial activation and grey-matter volume were negatively correlated, but provided independent information, with inflammation the stronger predictor of the rate of cognitive decline. When clinical diagnosis was included as a factor in the models, a significant predictive effect was found for [11C]PK11195 BPND in the left frontal lobe (-0.70, P = 0.01), but not for grey-matter volumes (P > 0.05), suggesting that inflammation severity in this region relates to cognitive decline regardless of clinical variant. The main results were validated by two-step prediction frequentist and Bayesian estimation of correlations, showing significant associations between the estimated rate of cognitive change (slope) and baseline microglial activation in the frontal lobe. These findings support preclinical models in which neuroinflammation (by microglial activation) accelerates the neurodegenerative disease trajectory. We highlight the potential for immunomodulatory treatment strategies in frontotemporal dementia, in which measures of microglial activation may also improve stratification for clinical trials.

Progression of atypical parkinsonian syndromes: PROSPECT-M-UK study implications for clinical trials.

The advent of clinical trials of disease-modifying agents for neurodegenerative disease highlights the need for evidence-based end point selection. Here we report the longitudinal PROSPECT-M-UK study of progressive supranuclear palsy (PSP), corticobasal syndrome (CBS), multiple system atrophy (MSA) and related disorders, to compare candidate clinical trial end points. In this multicentre UK study, participants were assessed with serial questionnaires, motor examination, neuropsychiatric and MRI assessments at baseline, 6 and 12 months. Participants were classified by diagnosis at baseline and study end, into Richardson syndrome, PSP-subcortical (PSP-parkinsonism and progressive gait freezing subtypes), PSP-cortical (PSP-frontal, PSP-speech and language and PSP-CBS subtypes), MSA-parkinsonism, MSA-cerebellar, CBS with and without evidence of Alzheimer's disease pathology and indeterminate syndromes. We calculated annual rate of change, with linear mixed modelling and sample sizes for clinical trials of disease-modifying agents, according to group and assessment type. Two hundred forty-three people were recruited [117 PSP, 68 CBS, 42 MSA and 16 indeterminate; 138 (56.8%) male; age at recruitment 68.7 ± 8.61 years]. One hundred and fifty-nine completed the 6-month assessment (82 PSP, 27 CBS, 40 MSA and 10 indeterminate) and 153 completed the 12-month assessment (80 PSP, 29 CBS, 35 MSA and nine indeterminate). Questionnaire, motor examination, neuropsychiatric and neuroimaging measures declined in all groups, with differences in longitudinal change between groups. Neuroimaging metrics would enable lower sample sizes to achieve equivalent power for clinical trials than cognitive and functional measures, often achieving N < 100 required for 1-year two-arm trials (with 80% power to detect 50% slowing). However, optimal outcome measures were disease-specific. In conclusion, phenotypic variance within PSP, CBS and MSA is a major challenge to clinical trial design. Our findings provide an evidence base for selection of clinical trial end points, from potential functional, cognitive, clinical or neuroimaging measures of disease progression.

Harnessing the potential of machine learning and artificial intelligence for dementia research.

Progress in dementia research has been limited, with substantial gaps in our knowledge of targets for prevention, mechanisms for disease progression, and disease-modifying treatments. The growing availability of multimodal data sets opens possibilities for the application of machine learning and artificial intelligence (AI) to help answer key questions in the field. We provide an overview of the state of the science, highlighting current challenges and opportunities for utilisation of AI approaches to move the field forward in the areas of genetics, experimental medicine, drug discovery and trials optimisation, imaging, and prevention. Machine learning methods can enhance results of genetic studies, help determine biological effects and facilitate the identification of drug targets based on genetic and transcriptomic information. The use of unsupervised learning for understanding disease mechanisms for drug discovery is promising, while analysis of multimodal data sets to characterise and quantify disease severity and subtype are also beginning to contribute to optimisation of clinical trial recruitment. Data-driven experimental medicine is needed to analyse data across modalities and develop novel algorithms to translate insights from animal models to human disease biology. AI methods in neuroimaging outperform traditional approaches for diagnostic classification, and although challenges around validation and translation remain, there is optimism for their meaningful integration to clinical practice in the near future. AI-based models can also clarify our understanding of the causality and commonality of dementia risk factors, informing and improving risk prediction models along with the development of preventative interventions. The complexity and heterogeneity of dementia requires an alternative approach beyond traditional design and analytical approaches. Although not yet widely used in dementia research, machine learning and AI have the potential to unlock current challenges and advance precision dementia medicine.

Incidence of Syndromes Associated With Frontotemporal Lobar Degeneration in 9 European Countries.

Importance: Diagnostic incidence data for syndromes associated with frontotemporal lobar degeneration (FTLD) in multinational studies are urgent in light of upcoming therapeutic approaches. Objective: To assess the incidence of FTLD across Europe. Design, Setting, and Participants: The Frontotemporal Dementia Incidence European Research Study (FRONTIERS) was a retrospective cohort study conducted from June 1, 2018, to May 31, 2019, using a population-based registry from 13 tertiary FTLD research clinics from the UK, the Netherlands, Finland, Sweden, Spain, Bulgaria, Serbia, Germany, and Italy and including all new FTLD-associated cases during the study period, with a combined catchment population of 11 023 643 person-years. Included patients fulfilled criteria for the behavioral variant of frontotemporal dementia (BVFTD), the nonfluent variant or semantic variant of primary progressive aphasia (PPA), unspecified PPA, progressive supranuclear palsy, corticobasal syndrome, or frontotemporal dementia with amyotrophic lateral sclerosis (FTD-ALS). Data were analyzed from July 19 to December 7, 2021. Main Outcomes and Measures: Random-intercept Poisson models were used to obtain estimates of the European FTLD incidence rate accounting for geographic heterogeneity. Results: Based on 267 identified cases (mean [SD] patient age, 66.70 [9.02] years; 156 males [58.43%]), the estimated annual incidence rate for FTLD in Europe was 2.36 cases per 100 000 person-years (95% CI, 1.59-3.51 cases per 100 000 person-years). There was a progressive increase in FTLD incidence across age, reaching its peak at the age of 71 years, with 13.09 cases per 100 000 person-years (95% CI, 8.46-18.93 cases per 100 000 person-years) among men and 7.88 cases per 100 000 person-years (95% CI, 5.39-11.60 cases per 100 000 person-years) among women. Overall, the incidence was higher among men (2.84 cases per 100 000 person-years; 95% CI, 1.88-4.27 cases per 100 000 person-years) than among women (1.91 cases per 100 000 person-years; 95% CI, 1.26-2.91 cases per 100 000 person-years). BVFTD was the most common phenotype (107 cases [40.07%]), followed by PPA (76 [28.46%]) and extrapyramidal phenotypes (69 [25.84%]). FTD-ALS was the rarest phenotype (15 cases [5.62%]). A total of 95 patients with FTLD (35.58%) had a family history of dementia. The estimated number of new FTLD cases per year in Europe was 12 057. Conclusions and Relevance: The findings suggest that FTLD-associated syndromes are more common than previously recognized, and diagnosis should be considered at any age. Improved knowledge of FTLD incidence may contribute to appropriate health and social care planning and in the design of future clinical trials.

Temporal dynamics predict symptom onset and cognitive decline in familial frontotemporal dementia.

INTRODUCTION: We tested whether changes in functional networks predict cognitive decline and conversion from the presymptomatic prodrome to symptomatic disease in familial frontotemporal dementia (FTD). METHODS: For hypothesis generation, 36 participants with behavioral variant FTD (bvFTD) and 34 controls were recruited from one site. For hypothesis testing, we studied 198 symptomatic FTD mutation carriers, 341 presymptomatic mutation carriers, and 329 family members without mutations. We compared functional network dynamics between groups, with clinical severity and with longitudinal clinical progression. RESULTS: We identified a characteristic pattern of dynamic network changes in FTD, which correlated with neuropsychological impairment. Among presymptomatic mutation carriers, this pattern of network dynamics was found to a greater extent in those who subsequently converted to the symptomatic phase. Baseline network dynamic changes predicted future cognitive decline in symptomatic participants and older presymptomatic participants. DISCUSSION: Dynamic network abnormalities in FTD predict cognitive decline and symptomatic conversion. HIGHLIGHTS: We investigated brain network predictors of dementia symptom onset Frontotemporal dementia results in characteristic dynamic network patterns Alterations in network dynamics are associated with neuropsychological impairment Network dynamic changes predict symptomatic conversion in presymptomatic carriers Network dynamic changes are associated with longitudinal cognitive decline.

Ocular Biomarkers for Alzheimer Disease Dementia: An Umbrella Review of Systematic Reviews and Meta-analyses.

Importance: Several ocular biomarkers have been proposed for the early detection of Alzheimer disease (AD) and mild cognitive impairment (MCI), particularly fundus photography, optical coherence tomography (OCT), and OCT angiography (OCTA). Objective: To perform an umbrella review of systematic reviews to assess the diagnostic accuracy of ocular biomarkers for early diagnosis of Alzheimer disease. Data Sources: MEDLINE, Embase, and PsycINFO were searched from January 2000 to November 2021. The references of included reviews were also searched. Study Selection: Systematic reviews investigating the diagnostic accuracy of ocular biomarkers to detect AD and MCI, in secondary care or memory clinics, against established clinical criteria or clinical judgment. Data Extraction and Synthesis: The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline checklist was followed and the Risk Of Bias in Systematic reviews tool was used to assess review quality. Main Outcomes and Measures: The prespecified outcome was the accuracy of ocular biomarkers for diagnosing AD and MCI. The area under the curve (AUC) was derived from standardized mean difference. Results: From the 591 titles, 14 systematic reviews were included (median [range] number of studies in each review, 14 [5-126]). Only 4 reviews were at low risk of bias on all Risk of Bias in Systematic Reviews domains. The imaging-derived parameters with the most evidence for detecting AD compared with healthy controls were OCT peripapillary retinal nerve fiber layer thickness (38 studies including 1883 patients with AD and 2510 controls; AUC = 0.70; 95% CI, 0.53-0.79); OCTA foveal avascular zone (5 studies including 177 patients with AD and 371 controls; AUC = 0.73; 95% CI, 0.50-0.89); and saccadic eye movements prosaccade latency (30 studies including 651 patients with AD/MCI and 771 controls; AUC = 0.64; 95% CI, 0.58-0.69). Antisaccade error was investigated in fewer studies (12 studies including 424 patients with AD/MCI and 382 controls) and yielded the best accuracy (AUC = 0.79; 95% CI, 0.70-0.88). Conclusions and Relevance: This umbrella review has highlighted limitations in design and reporting of the existing research on ocular biomarkers for diagnosing AD. Parameters with the best evidence showed poor to moderate diagnostic accuracy in cross-sectional studies. Future longitudinal studies should investigate whether changes in OCT and OCTA measurements over time can yield accurate predictions of AD onset.