Synaptic Loss in Frontotemporal Dementia Revealed by [11 C]UCB-J Positron Emission Tomography.

OBJECTIVE: Synaptic loss is an early feature of neurodegenerative disease models, and is severe in post mortem clinical studies, including frontotemporal dementia. Positron emission tomography (PET) with radiotracers that bind to synaptic vesicle glycoprotein 2A enables quantification of synaptic density in vivo. This study used [11 C]UCB-J PET in participants with behavioral variant frontotemporal dementia (bvFTD), testing the hypothesis that synaptic loss is severe and related to clinical severity. METHODS: Eleven participants with clinically probable bvFTD and 25 age- and sex-matched healthy controls were included. Participants underwent dynamic [11 C]UCB-J PET, structural magnetic resonance imaging, and a neuropsychological battery, including the revised Addenbrooke Cognitive Examination, and INECO frontal screening. General linear models compared [11 C]UCB-J binding potential maps and gray matter volume between groups, and assessed associations between synaptic density and clinical severity in patients. Analyses were also performed using partial volume corrected [11 C]UCB-J binding potential from regions of interest (ROIs). RESULTS: Patients with bvFTD showed severe synaptic loss compared to controls. [11 C]UCB-J binding was reduced bilaterally in medial and dorsolateral frontal regions, inferior frontal gyri, anterior and posterior cingulate gyrus, insular cortex, and medial temporal lobe. Synaptic loss in the frontal and cingulate regions correlated significantly with cognitive impairments. Synaptic loss was more severe than atrophy. Results from ROI-based analyses mirrored the voxelwise results. INTERPRETATION: In accordance with preclinical models, and human postmortem evidence, there is widespread frontotemporal loss of synapses in symptomatic bvFTD, in proportion to severity. [11 C]UCB-J PET could support translational studies and experimental medicine strategies for new disease-modifying treatments for neurodegeneration. ANN NEUROL 2023;93:142-154.

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.

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.

Pre-diagnostic cognitive and functional impairment in multiple sporadic neurodegenerative diseases.

INTRODUCTION: The pathophysiological processes of neurodegenerative diseases begin years before diagnosis. However, pre-diagnostic changes in cognition and physical function are poorly understood, especially in sporadic neurodegenerative disease. METHODS: UK Biobank data were extracted. Cognitive and functional measures in individuals who subsequently developed Alzheimer's disease (AD), Parkinson disease, frontotemporal dementia, progressive supranuclear palsy, dementia with Lewy bodies, or multiple system atrophy were compared against individuals without neurodegenerative diagnoses. The same measures were regressed against time to diagnosis, after adjusting for the effects of age. RESULTS: There was evidence for pre-diagnostic cognitive impairment and decline with time, particularly in AD. Pre-diagnostic functional impairment and decline were observed in multiple diseases. DISCUSSION: The scale and longitudinal follow-up of UK Biobank participants provides evidence for cognitive and functional decline years before symptoms become obvious in multiple neurodegenerative diseases. Identifying pre-diagnostic functional and cognitive changes could improve selection for preventive and early disease-modifying treatment 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.

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.

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.

Altered network stability in progressive supranuclear palsy.

The clinical syndromes of Progressive Supranuclear Palsy (PSP) may be mediated by abnormal temporal dynamics of brain networks, due to the impact of atrophy, synapse loss and neurotransmitter deficits. We tested the hypothesis that alterations in signal complexity in neural networks influence short-latency state transitions. Ninety-four participants with PSP and 64 healthy controls were recruited from two independent cohorts. All participants underwent clinical and neuropsychological testing and resting-state functional MRI. Network dynamics were assessed using hidden Markov models and neural signal complexity measured in terms of multiscale entropy. In both cohorts, PSP increased the proportion of time in networks associated with higher cognitive functions. This effect correlated with clinical severity as measured by the PSP-rating-scale, and with reduced neural signal complexity. Regional atrophy influenced abnormal brain-state occupancy, but abnormal network topology and dynamics were not restricted to areas of atrophy. Our findings show that the pathology of PSP causes clinically relevant changes in neural temporal dynamics, leading to a greater proportion of time in inefficient brain-states.