The emerging role of PET imaging in dementia

In vivo imaging of synaptic density in neurodegenerative disorders with positron emission tomography: A systematic review

Positron emission tomography (PET) with radiotracers that bind to synaptic vesicle glycoprotein 2 A (SV2A) enables quantification of synaptic density in the living human brain. Assessing the regional distribution and severity of synaptic density loss will contribute to our understanding of the pathological processes that precede atrophy in neurodegeneration. In this systematic review, we provide a discussion of in vivo SV2A PET imaging research for quantitative assessment of synaptic density in various dementia conditions: amnestic Mild Cognitive Impairment and Alzheimer's disease, Frontotemporal dementia, Progressive supranuclear palsy and Corticobasal degeneration, Parkinson's disease and Dementia with Lewy bodies, Huntington's disease, and Spinocerebellar Ataxia. We discuss the main findings concerning group differences and clinical-cognitive correlations, and explore relations between SV2A PET and other markers of pathology. Additionally, we touch upon synaptic density in healthy ageing and outcomes of radiotracer validation studies. Studies were identified on PubMed and Embase between 2018 and 2023; last searched on the 3rd of July 2023. A total of 36 studies were included, comprising 5 on normal ageing, 21 clinical studies, and 10 validation studies. Extracted study characteristics were participant details, methodological aspects, and critical findings. In summary, the small but growing literature on in vivo SV2A PET has revealed different spatial patterns of synaptic density loss among various neurodegenerative disorders that correlate with cognitive functioning, supporting the potential role of SV2A PET imaging for differential diagnosis. SV2A PET imaging shows tremendous capability to provide novel insights into the aetiology of neurodegenerative disorders and great promise as a biomarker for synaptic density reduction. Novel directions for future synaptic density research are proposed, including (a) longitudinal imaging in larger patient cohorts of preclinical dementias, (b) multi-modal mapping of synaptic density loss onto other pathological processes, and (c) monitoring therapeutic responses and assessing drug efficacy in clinical trials.

High-Fat Diets in Animal Models of Alzheimer's Disease: How Can Eating Too Much Fat Increase Alzheimer's Disease Risk?

High dietary intake of saturated fatty acids is a suspected risk factor for neurodegenerative diseases, including Alzheimer's disease (AD). To decipher the causal link behind these associations, high-fat diets (HFD) have been repeatedly investigated in animal models. Preclinical studies allow full control over dietary composition, avoiding ethical concerns in clinical trials. The goal of the present article is to provide a narrative review of reports on HFD in animal models of AD. Eligibility criteria included mouse models of AD fed a HFD defined as > 35% of fat/weight and western diets containing > 1% cholesterol or > 15% sugar. MEDLINE and Embase databases were searched from 1946 to August 2022, and 32 preclinical studies were included in the review. HFD-induced obesity and metabolic disturbances such as insulin resistance and glucose intolerance have been replicated in most studies, but with methodological variability. Most studies have found an aggravating effect of HFD on brain Aβ pathology, whereas tau pathology has been much less studied, and results are more equivocal. While most reports show HFD-induced impairment on cognitive behavior, confounding factors may blur their interpretation. In summary, despite conflicting results, exposing rodents to diets highly enriched in saturated fat induces not only metabolic defects, but also cognitive impairment often accompanied by aggravated neuropathological markers, most notably Aβ burden. Although there are important variations between methods, particularly the lack of diet characterization, these studies collectively suggest that excessive intake of saturated fat should be avoided in order to lower the incidence of AD.

Effect of the identification group size and image resolution on the diagnostic performance of metabolic Alzheimer's disease-related pattern

BACKGROUND

Alzheimer's disease-related pattern (ADRP) is a metabolic brain biomarker of Alzheimer's disease (AD). While ADRP is being introduced into research, the effect of the size of the identification cohort and the effect of the resolution of identification and validation images on ADRP's performance need to be clarified.

METHODS

240 2-[¹⁸F]fluoro-2-deoxy-D-glucose positron emission tomography images [120 AD/120 cognitive normals (CN)] were selected from the Alzheimer's disease neuroimaging initiative database. A total of 200 images (100 AD/100 CN) were used to identify different versions of ADRP using a scaled subprofile model/principal component analysis. For this purpose, five identification groups were randomly selected 25 times. The identification groups differed in the number of images (20 AD/20 CN, 30 AD/30 CN, 40 AD/40 CN, 60 AD/60 CN, and 80 AD/80 CN) and image resolutions (6, 8, 10, 12, 15 and 20 mm). A total of 750 ADRPs were identified and validated through the area under the curve (AUC) values on the remaining 20 AD/20 CN with six different image resolutions.

RESULTS

ADRP's performance for the differentiation between AD patients and CN demonstrated only a marginal average AUC increase, when the number of subjects in the identification group increases (AUC increase for about 0.03 from 20 AD/20 CN to 80 AD/80 CN). However, the average of the lowest five AUC values increased with the increasing number of participants (AUC increase for about 0.07 from 20 AD/20 CN to 30 AD/30 CN and for an additional 0.02 from 30 AD/30 CN to 40 AD/40 CN). The resolution of the identification images affects ADRP's diagnostic performance only marginally in the range from 8 to 15 mm. ADRP's performance stayed optimal even when applied to validation images of resolution differing from the identification images.

CONCLUSIONS

While small (20 AD/20 CN images) identification cohorts may be adequate in a favorable selection of cases, larger cohorts (at least 30 AD/30 CN images) shall be preferred to overcome possible/random biological differences and improve ADRP's diagnostic performance. ADRP's performance stays stable even when applied to the validation images with a resolution different than the resolution of the identification ones.

Clear-headed into old age: Resilience and resistance against brain aging-A PET imaging perspective

With the advances in modern medicine and the adaptation towards healthier lifestyles, the average life expectancy has doubled since the 1930s, with individuals born in the millennium years now carrying an estimated life expectancy of around 100 years. And even though many individuals around the globe manage to age successfully, the prevalence of aging-associated neurodegenerative diseases such as sporadic Alzheimer's disease has never been as high as nowadays. The prevalence of Alzheimer's disease is anticipated to triple by 2050, increasing the societal and economic burden tremendously. Despite all efforts, there is still no available treatment defeating the accelerated aging process as seen in this disease. Yet, given the advances in neuroimaging techniques that are discussed in the current Review article, such as in positron emission tomography (PET) or magnetic resonance imaging (MRI), pivotal insights into the heterogenous effects of aging-associated processes and the contribution of distinct lifestyle and risk factors already have and are still being gathered. In particular, the concepts of resilience (i.e. coping with brain pathology) and resistance (i.e. avoiding brain pathology) have more recently been discussed as they relate to mechanisms that are associated with the prolongation and/or even stop of the progressive brain aging process. Better understanding of the underlying mechanisms of resilience and resistance may one day, hopefully, support the identification of defeating mechanism against accelerating aging.

Learning Cognitive-Test-Based Interpretable Rules for Prediction and Early Diagnosis of Dementia Using Neural Networks

Background: Accurate, cheap, and easy to promote methods for dementia prediction and early diagnosis are urgently needed in low- and middle-income countries. Integrating various cognitive tests using machine learning provides promising solutions. However, most effective machine learning models are black-box models that are hard to understand for doctors and could hide potential biases and risks. Objective: To apply cognitive-test-based machine learning models in practical dementia prediction and diagnosis by ensuring both interpretability and accuracy. Methods: We design a framework adopting Rule-based Representation Learner (RRL) to build interpretable diagnostic rules based on the cognitive tests selected by doctors. According to the visualization and test results, doctors can easily select the final rules after analysis and trade-off. Our framework is verified on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset (n = 606) and Peking Union Medical College Hospital (PUMCH) dataset (n = 375). Results: The predictive or diagnostic rules learned by RRL offer a better trade-off between accuracy and model interpretability than other representative machine learning models. For mild cognitive impairment (MCI) conversion prediction, the cognitive-test-based rules achieve an average area under the curve (AUC) of 0.904 on ADNI. For dementia diagnosis on subjects with a normal Mini-Mental State Exam (MMSE) score, the learned rules achieve an AUC of 0.863 on PUMCH. The visualization analyses also verify the good interpretability of the learned rules. Conclusion: With the help of doctors and RRL, we can obtain predictive and diagnostic rules for dementia with high accuracy and good interpretability even if only cognitive tests are used.

Clozapine induces astrocyte-dependent FDG-PET hypometabolism

PURPOSE

Advances in functional imaging allowed us to visualize brain glucose metabolism in vivo and non-invasively with [¹⁸F]fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) imaging. In the past decades, FDG-PET has been instrumental in the understanding of brain function in health and disease. The source of the FDG-PET signal has been attributed to neuronal uptake, with hypometabolism being considered as a direct index of neuronal dysfunction or death. However, other brain cells are also metabolically active, including astrocytes. Based on the astrocyte-neuron lactate shuttle hypothesis, the activation of the glutamate transporter 1 (GLT-1) acts as a trigger for glucose uptake by astrocytes. With this in mind, we investigated glucose utilization changes after pharmacologically downregulating GLT-1 with clozapine (CLO), an anti-psychotic drug.

METHODS

Adult male Wistar rats (control, n = 14; CLO, n = 12) received CLO (25/35 mg kg^-1) for 6 weeks. CLO effects were evaluated in vivo with FDG-PET and cortical tissue was used to evaluate glutamate uptake and GLT-1 and GLAST levels. CLO treatment effects were also assessed in cortical astrocyte cultures (glucose and glutamate uptake, GLT-1 and GLAST levels) and in cortical neuronal cultures (glucose uptake).

RESULTS

CLO markedly reduced in vivo brain glucose metabolism in several brain areas, especially in the cortex. Ex vivo analyses demonstrated decreased cortical glutamate transport along with GLT-1 mRNA and protein downregulation. In astrocyte cultures, CLO decreased GLT-1 density as well as glutamate and glucose uptake. By contrast, in cortical neuronal cultures, CLO did not affect glucose uptake.

CONCLUSION

This work provides in vivo demonstration that GLT-1 downregulation induces astrocyte-dependent cortical FDG-PET hypometabolism-mimicking the hypometabolic signature seen in people developing dementia-and adds further evidence that astrocytes are key contributors of the FDG-PET signal.

Distinct brain dysfunctions underlying visuo-constructive deficit in DLB and AD

Visuo-constructive abilities are a multicomponential process that can be impaired in several neurodegenerative dementias. Among visuo-constructive tasks, the Rey-Osterrieth Complex Figure-copy (ROCF-c) is the most commonly used and it seems influenced by different skills mediated by specific brain regions. This task complexity allows exploring differential mechanisms of impairment in different neurodegenerative disorders. In this study we examined the neuroanatomical substrates of ROCF-c performance in patients with Dementia with Lewy Bodies (DLB) and patients with Alzheimer's disease (AD). We included forty-five patients with probable DLB, and thirty-four patients with probable typical-AD. To identify the ROCF-c scores neural correlates we performed a regression analysis with brain hypometabolism using the ROCF-c scores as independent variable. Then we evaluated the correlation between regional hypometabolism and ROCF-c scores in each group separately, throughout offline Pearson correlation analysis. The DLB and AD groups differed only in visuo-constructive and memory performances. DLB patients performed worse at the visuo-constructive test, while typical-AD patients performed worse at the verbal memory task. In DLB, worse performance at ROCF-c scores (more severe visuo-constructive impairment) correlated with occipital and temporo-parietal hypometabolism. In AD, worse performance at ROCF-c score was associated with brain hypometabolism in the temporo-parietal regions. The inability to correctly perform the ROCF-c derives from distinct brain dysfunctions in DLB and AD. The present results suggest alterations in visuoperceptual processes due to the severe occipital hypometabolism in DLB, and in visuospatial processes related to temporo-parietal hypometabolism in AD.

Exploring the Effect of Aliphatic Substituents on Aryl Cyano Amides on Enhancement of Fluorescence upon Binding to Amyloid-β Aggregates

The self-assembly of amyloid-β (Aβ) peptides into amyloid aggregates is a pathological hallmark of Alzheimer's Disease. We previously reported a fluorescent Aryl Cyano Amide (ARCAM) probe that exhibits an increase in fluorescence emission upon binding to Aβ aggregates in solution and in neuronal tissue. Here, we investigate the effect of introducing small aliphatic substituents on the spectroscopic properties of ARCAM both free in solution and when bound to aggregated Aβ. We found that introducing substituents designed to hinder the rotation of bonds between the electron donor and acceptor on these fluorophores can affect the overall brightness of fluorescence emission of the probes in amyloid-free solutions, but the relative fluorescence enhancement of these probes in amyloid-containing solutions is dependent on the location of the substituents on the ARCAM scaffold. We also observed the capability to tune the excitation or emission wavelength of these probes by introducing electron-donating or -withdrawing substituents that putatively affect either the energy required for photoexcitation or the stability of the photoexcited state. These studies reveal new design principles for developing ARCAM-based fluorescent Aβ-binding probes with an enhanced fluorescence signal compared to background and tunable spectroscopic properties, which may lead to improved chemical tools for aiding in the diagnosis of amyloid-associated neurodegenerative diseases.

In vivo MRI Structural and PET Metabolic Connectivity Study of Dopamine Pathways in Alzheimer's Disease

BACKGROUND

Alzheimer's disease (AD) is characterized by an involvement of brain dopamine (DA) circuitry, the presence of which has been associated with emergence of both neuropsychiatric symptoms and cognitive deficits.

OBJECTIVE

In order to investigate whether and how the DA pathways are involved in the pathophysiology of AD, we assessed by in vivo neuroimaging the structural and metabolic alterations of subcortical and cortical DA pathways and targets.

METHODS

We included 54 healthy control participants, 53 amyloid-positive subjects with mild cognitive impairment due to AD (MCI-AD), and 60 amyloid-positive patients with probable dementia due to AD (ADD), all with structural 3T MRI and 18F-FDG-PET scans. We assessed MRI-based gray matter reductions in the MCI-AD and ADD groups within an anatomical a priori-defined Nigrostriatal and Mesocorticolimbic DA pathways, followed by 18F-FDG-PET metabolic connectivity analyses to evaluate network-level metabolic connectivity changes.

RESULTS

We found significant tissue loss in the Mesocorticolimbic over the Nigrostriatal pathway. Atrophy was evident in the ventral striatum, orbitofrontal cortex, and medial temporal lobe structures, and already plateaued in the MCI-AD stage. Degree of atrophy in Mesocorticolimbic regions positively correlated with the severity of depression, anxiety, and apathy in MCI-AD and ADD subgroups. Additionally, we observed significant alterations of metabolic connectivity between the ventral striatum and fronto-cingulate regions in ADD, but not in MCI-AD. There were no metabolic connectivity changes within the Nigrostriatal pathway.

CONCLUSION

Our cross-sectional data support a clinically-meaningful, yet stage-dependent, involvement of the Mesocorticolimbic system in AD. Longitudinal and clinical correlation studies are needed to further establish the relevance of DA system involvement in AD.

Individual Brain Metabolic Signatures in Corticobasal Syndrome

BACKGROUND

Corticobasal syndrome (CBS) is the usual clinical presentation of patients with corticobasal degeneration pathology. Nevertheless, there are CBS individuals with postmortem neuropathology typical of Alzheimer's disease (AD).

OBJECTIVE

In this study, we aim to detect FDG-PET metabolic signatures at the single-subject level in a CBS sample, also evaluated with cerebrospinal fluid (CSF) markers for AD pathology.

METHODS

21 patients (68.9±6.4 years; MMSE score = 21.7±6.3) fulfilling current criteria for CBS were enrolled. All underwent a clinical-neuropsychological assessment and an instrumental evaluation for biomarkers of neurodegeneration, amyloid and tau pathology (i.e., FDG-PET imaging and CSF Aβ42 and tau levels) at close intervals. CBS subjects were classified according to the presence or absence of CSF markers of AD pathology (i.e., low Aβ42 and high phosphorylated tau levels). Optimized voxel-based SPM procedures provided FDG-PET metabolic patterns at the single-subject and group levels.

RESULTS

Eight CBS had an AD-like CSF profile (CBS-AD), while thirteen were negative (CBS-noAD). The two subgroups did not differ in demographic characteristics or global cognitive impairment. FDG-PET SPM t-maps identified different metabolic signatures. Namely, all CBS-AD patients showed the typical AD-like hypometabolic pattern involving posterior cingulate cortex, precuneus and temporo-parietal cortex, whereas CBS-noAD cases showed bilateral hypometabolism in fronto-insular cortex and basal ganglia that is typical of the frontotemporal lobar degeneration spectrum.

DISCUSSION

These results strongly suggest the inclusion of FDG-PET imaging in the diagnostic algorithm of individuals with CBS clinical phenotype in order to early identify functional metabolic signatures due to different neuropathological substrates, thus improving the diagnostic accuracy.

Brain Molecular Connectivity in Neurodegenerative Conditions

Positron emission tomography (PET) allows for the in vivo assessment of early brain functional and molecular changes in neurodegenerative conditions, representing a unique tool in the diagnostic workup. The increased use of multivariate PET imaging analysis approaches has provided the chance to investigate regional molecular processes and long-distance brain circuit functional interactions in the last decade. PET metabolic and neurotransmission connectome can reveal brain region interactions. This review is an overview of concepts and methods for PET molecular and metabolic covariance assessment with evidence in neurodegenerative conditions, including Alzheimer’s disease and Lewy bodies disease spectrum. We highlight the effects of environmental and biological factors on brain network organization. All of the above might contribute to innovative diagnostic tools and potential disease-modifying interventions.

Brain Metabolism and Microglia Activation in Mild Cognitive Impairment: A Combined [18F]FDG and [11C]-(R)-PK11195 PET Study

BACKGROUND

Mild cognitive impairment (MCI) is a transitional condition between normal cognition and dementia. [18F]FDG-PET reveals brain hypometabolism patterns reflecting neuronal/synaptic dysfunction, already in the prodromal MCI phase. Activated microglia is part of the pathogenetic processes leading to neurodegeneration.

OBJECTIVE

Using [11C]-(R)-PK11195 and [18F]FDG-PET, we aimed to in vivo investigate the presence of microglial activation, and the relationship with brain glucose metabolism, in single MCI subjects.

METHODS

Eight MCI subjects underwent both [18F]FDG-PET and [11C]-(R)-PK11195 PET. We used validated quantification methods to obtain brain hypometabolism maps and microglia activation peaks in single subjects. We investigated both the spatial overlap and the relationship between brain glucose hypometabolism and microglia activation, by means of Dice similarity coefficient and using Pearson's correlation at single subject level.

RESULTS

Each MCI showed a specific brain hypometabolism pattern indicative of different possible etiologies, as expected in MCI population (i.e., Alzheimer's disease-like, frontotemporal dementia-like, hippocampal-type, normal aging type). [11C]-(R)-PK11195 PET analysis revealed a spatial concordance with regional hypometabolism in all subjects with several clusters of significant microglia activation showing an inverse correlation with the regional metabolism. This was proportional to the strength of between-signals correlation coefficient (β  =  -0.804; p = 0.016).

CONCLUSION

Microglia activation is present in the prodromal MCI phase of different underlying etiologies, showing spatial concordance and inverse correlation with brain glucose metabolism at single-subject level. These findings suggest a possible contribution of activated microglia to neurodegeneration, showing important implications for local immune activity in the early neurodegenerative processes.

Validation of FDG-PET datasets of normal controls for the extraction of SPM-based brain metabolism maps

PURPOSE

An appropriate healthy control dataset is mandatory to achieve good performance in voxel-wise analyses. We aimed at evaluating [18F]FDG PET brain datasets of healthy controls (HC), based on publicly available data, for the extraction of voxel-based brain metabolism maps at the single-subject level.

METHODS

Selection of HC images was based on visual rating, after Cook's distance and jack-knife analyses, to exclude artefacts and/or outliers. The performance of these HC datasets (ADNI-HC and AIMN-HC) to extract hypometabolism patterns in single patients was tested in comparison with the standard reference HC dataset (HSR-HC) by means of Dice score analysis. We evaluated the performance and comparability of the different HC datasets in the assessment of single-subject SPM-based hypometabolism in three independent cohorts of patients, namely, ADD, bvFTD and DLB.

RESULTS

Two-step Cook's distance analysis and the subsequent jack-knife analysis resulted in the selection of n = 125 subjects from the AIMN-HC dataset and n = 75 subjects from the ADNI-HC dataset. The average concordance between SPM hypometabolism t-maps in the three patient cohorts, as obtained with the new datasets and compared to the HSR-HC standard reference dataset, was 0.87 for the AIMN-HC dataset and 0.83 for the ADNI-HC dataset. Pattern expression analysis revealed high overall accuracy (> 80%) of the SPM t-map classification according to different statistical thresholds and sample sizes.

CONCLUSIONS

The applied procedures ensure validity of these HC datasets for the single-subject estimation of brain metabolism using voxel-wise comparisons. These well-selected HC datasets are ready-to-use in research and clinical settings.

The molecular tweezer CLR01 improves behavioral deficits and reduces tau pathology in P301S-tau transgenic mice

BACKGROUND

Molecular tweezers (MTs) are broad-spectrum inhibitors of abnormal protein aggregation. A lead MT, called CLR01, has been demonstrated to inhibit the aggregation and toxicity of multiple amyloidogenic proteins in vitro and in vivo. Previously, we evaluated the effect of CLR01 in the 3 × Tg mouse model of Alzheimer's disease, which overexpresses mutant human presenilin 1, amyloid β-protein precursor, and tau and found that subcutaneous administration of the compound for 1 month led to a robust reduction of amyloid plaques, neurofibrillary tangles, and microgliosis. CLR01 also has been demonstrated to inhibit tau aggregation in vitro and tau seeding in cell culture, yet because in Alzheimer's disease (AD) and in the 3 × Tg model, tau hyperphosphorylation and aggregation are thought to be downstream of Aβ insults, the study in this model left open the question whether CLR01 affected tau in vivo directly or indirectly.

METHODS

To determine if CLR01 could ameliorate tau pathology directly in vivo, we tested the compound similarly using the P301S-tau (line PS19) mouse model. Mice were administered 0.3 or 1.0 mg/kg per day CLR01 and tested for muscle strength and behavioral deficits, including anxiety- and disinhibition-like behavior. Their brains then were analyzed by immunohistochemical and biochemical assays for pathological forms of tau, neurodegeneration, and glial pathology.

RESULTS

CLR01 treatment ameliorated muscle-strength deterioration, anxiety-, and disinhibition-like behavior. Improved phenotype was associated with decreased levels of pathologic tau forms, suggesting that CLR01 exerts a direct effect on tau in vivo. Limitations of the study included a relatively short treatment period of the mice at an age in which full pathology is not yet developed. In addition, high variability in this model lowered the statistical significance of the findings of some outcome measures.

CONCLUSIONS

The findings suggest that CLR01 is a particularly attractive candidate for the treatment of AD because it targets simultaneously the two major pathogenic proteins instigating and propagating the disease, amyloid β-protein (Aβ), and tau, respectively. In addition, our study suggests that CLR01 can be used for the treatment of other tauopathies in the absence of amyloid pathology.

Gender-Related Vulnerability of Dopaminergic Neural Networks in Parkinson's Disease

Background: In Parkinson's disease (PD), neurodegeneration of dopaminergic systems leads to motor and non-motor abnormalities. Sex might influence the clinical PD phenotypes and progression. Previous molecular imaging data focused only on the nigro-striato-cortical dopamine system that appeared more preserved in women. There is still a lack of evidence on gender/sex differences in the mesolimbic dopaminergic system. We aimed at assessing PD gender differences in both the dopaminergic pathways, by using a brain metabolic connectivity approach. This is based on the evidence of a significant coupling between the neurotransmission and metabolic impairments. Methods: We included 34 idiopathic PD patients (Female/Male: 16/18) and 34 healthy controls for comparison. The molecular architecture of both the dopaminergic networks was estimated throughout partial correlation analyses using brain metabolism data obtained by fluorine-18-fluorodeoxyglucose positron emission tomography (threshold set at p < 0.01, corrected for Bonferroni multiple comparisons). Results: Male patients were characterized by a widespread altered connectivity in the nigro-striato-cortical network and a sparing of the mesolimbic pathway. On the contrary, PD females showed a severe altered connectivity in the mesolimbic network and only a partial reconfiguration of the nigro-striato-cortical network. Discussion: Our findings add remarkable knowledge on the neurobiology of gender differences in PD, with the identification of specific neural vulnerabilities. The gender differences here revealed might be due to the combination of both biological and sociodemographic life factors. Gender differences in PD should be considered also for treatments and the targeting of modifiable risk factors.

Using diffusion tensor imaging to detect cortical changes in fronto-temporal dementia subtypes

Fronto-temporal dementia (FTD) is a common type of presenile dementia, characterized by a heterogeneous clinical presentation that includes three main subtypes: behavioural-variant FTD, non-fluent/agrammatic variant primary progressive aphasia and semantic variant PPA. To better understand the FTD subtypes and develop more specific treatments, correct diagnosis is essential. This study aimed to test the discrimination power of a novel set of cortical Diffusion Tensor Imaging measures (DTI), on FTD subtypes. A total of 96 subjects with FTD and 84 healthy subjects (HS) were included in the study. A “selection cohort” was used to determine the set of features (measurements) and to use them to select the “best” machine learning classifier from a range of seven main models. The selected classifier was trained on a “training cohort” and tested on a third cohort (“test cohort”). The classifier was used to assess the classification power for binary (HS vs. FTD), and multiclass (HS and FTD subtypes) classification problems. In the binary classification, one of the new DTI features obtained the highest accuracy (85%) as a single feature, and when it was combined with other DTI features and two other common clinical measures (grey matter fraction and MMSE), obtained an accuracy of 88%. The new DTI features can distinguish between HS and FTD subgroups with an accuracy of 76%. These results suggest that DTI measures could support differential diagnosis in a clinical setting, potentially improve efficacy of new innovative drug treatments through effective patient selection, stratification and measurement of outcomes.

Cancer and Alzheimer's disease: intracellular pH scales the metabolic disorders

Alzheimer's disease (AD) and cancer have much in common than previously recognized. These pathologies share common risk factors (inflammation and aging), with similar epidemiological and biochemical features such as impaired mitochondria. Metabolic reprogramming occurs during aging and inflammation. We assume that inflammation is directly responsible of the Warburg effect in cancer cells, with a decreased oxidative phosphorylation and a compensatory highthroughput glycolysis (HTG). Similarly, the Warburg effect in cancer is thought to support an alkaline intracellular pH (pHi), a key component of unrelenting cell growth. In the brain, inflammation results in increased secretion of lactate by astrocytes. The increased uptake of lactic acid by neurons results in the inverse Warburg effect, such as seen in AD. The neuronal activity is dampened by a fall of pHi. Pronounced cytosol acidification results in decreased mitochondrial energy yield as well as apoptotic cell death. The link between AD and cancer is reinforced by the fact that treatment aiming at restoring the mitochondrial activity have been experimentally shown to be effective in both diseases. Low carb diet, lipoic acid, and/or methylene blue could then appear promising in both sets of these clinically diverse diseases.

Method to discriminate amyloids using fluorescent probes

The aggregation of misfolded proteins into amyloids is a common characteristic of many neurodegenerative and non-neurologic diseases. Fluorescent amyloid-targeting probes that discriminate amyloids based on differences in protein composition can provide rapid information to aid in disease diagnosis. In this chapter, we present protocols for the synthesis and use of ANCA-11 as an environmentally-sensitive amyloid-targeting probe that can fluorescently discriminate between amyloids with different disease origin. We also present a protocol for preparing amyloid samples of synthetic Amyloid-β(1-42), as problems with amyloid preparations can be a large driver of time and cost for research. The methods presented here can be generalized for evaluation of other amyloid-targeting fluorescent probes with different aggregates of amyloidogenic proteins in solution or in tissue.

Alzheimer's Disease, a Lipid Story: Involvement of Peroxisome Proliferator-Activated Receptor α

Alzheimer's disease (AD) is the leading cause of dementia in the elderly. Mutations in genes encoding proteins involved in amyloid-β peptide (Aβ) production are responsible for inherited AD cases. The amyloid cascade hypothesis was proposed to explain the pathogeny. Despite the fact that Aβ is considered as the main culprit of the pathology, most clinical trials focusing on Aβ failed and suggested that earlier interventions are needed to influence the course of AD. Therefore, identifying risk factors that predispose to AD is crucial. Among them, the epsilon 4 allele of the apolipoprotein E gene that encodes the major brain lipid carrier and metabolic disorders such as obesity and type 2 diabetes were identified as AD risk factors, suggesting that abnormal lipid metabolism could influence the progression of the disease. Among lipids, fatty acids (FAs) play a fundamental role in proper brain function, including memory. Peroxisome proliferator-activated receptor α (PPARα) is a master metabolic regulator that regulates the catabolism of FA. Several studies report an essential role of PPARα in neuronal function governing synaptic plasticity and cognition. In this review, we explore the implication of lipid metabolism in AD, with a special focus on PPARα and its potential role in AD therapy.

Imaging biomarkers in neurodegeneration: current and future practices

There is an increasing role for biological markers (biomarkers) in the understanding and diagnosis of neurodegenerative disorders. The application of imaging biomarkers specifically for the in vivo investigation of neurodegenerative disorders has increased substantially over the past decades and continues to provide further benefits both to the diagnosis and understanding of these diseases. This review forms part of a series of articles which stem from the University College London/University of Gothenburg course "Biomarkers in neurodegenerative diseases". In this review, we focus on neuroimaging, specifically positron emission tomography (PET) and magnetic resonance imaging (MRI), giving an overview of the current established practices clinically and in research as well as new techniques being developed. We will also discuss the use of machine learning (ML) techniques within these fields to provide additional insights to early diagnosis and multimodal analysis.

Brain metabolic signatures across the Alzheimer's disease spectrum

PURPOSE

Given the challenges posed by the clinical diagnosis of atypical Alzheimer's disease (AD) variants and the limited imaging evidence available in the prodromal phases of atypical AD, we assessed brain hypometabolism patterns at the single-subject level in the AD variants spectrum. Specifically, we tested the accuracy of [¹⁸F]FDG-PET brain hypometabolism, as a biomarker of neurodegeneration, in supporting the differential diagnosis of atypical AD variants in individuals with dementia and mild cognitive impairment (MCI).

METHODS

We retrospectively collected N = 67 patients with a diagnosis of typical AD and AD variants according to the IWG-2 criteria (22 typical-AD, 15 frontal variant-AD, 14 logopenic variant-AD and 16 posterior variant-AD). Further, we included N = 11 MCI subjects, who subsequently received a clinical diagnosis of atypical AD dementia at follow-up (21 ± 11 months). We assessed brain hypometabolism patterns at group- and single-subject level, using W-score maps, measuring their accuracy in supporting differential diagnosis. In addition, the regional prevalence of cerebral hypometabolism was computed to identify the most vulnerable core regions.

RESULTS

W-score maps pointed at distinct, specific patterns of hypometabolism in typical and atypical AD variants, confirmed by the assessment of core hypometabolism regions, showing that each variant was characterized by specific regional vulnerabilities, namely in occipital, left-sided, or frontal brain regions. ROC curves allowed discrimination among AD variants and also non-AD dementia (i.e., dementia with Lewy bodies and behavioral variant of frontotemporal dementia), with high sensitivity and specificity. Notably, we provide preliminary evidence that, even in AD prodromal phases, these specific [¹⁸F]FDG-PET patterns are already detectable and predictive of clinical progression to atypical AD variants at follow-up.

CONCLUSIONS

The AD variant-specific patterns of brain hypometabolism, highly consistent at single-subject level and already evident in the prodromal stages, represent relevant markers of disease neurodegeneration, with highly supportive diagnostic and prognostic role.

Correlations of Neuropsychological and Metabolic Brain Changes in Parkinson's Disease and Other α-Synucleinopathies

Cognitive impairment is a common feature in Parkinson's disease (PD) and other α-synucleinopathies as 80% of PD patients develop dementia within 20 years. Early cognitive changes in PD patients present as a dysexecutive syndrome, broadly characterized as a disruption of the fronto-striatal dopamine network. Cognitive deficits in other domains (recognition memory, attention processes and visuospatial abilities) become apparent with the progression of PD and development of dementia. In dementia with Lewy bodies (DLB) the cognitive impairment develops early or even precedes parkinsonism and it is more pronounced in visuospatial skills and memory. Cognitive impairment in the rarer α-synucleinopathies (multiple system atrophy and pure autonomic failure) is less well studied. Metabolic brain imaging with positron emission tomography and [¹⁸F]-fluorodeoxyglucose (FDG-PET) is a well-established diagnostic method in neurodegenerative diseases, including dementias. Changes in glucose metabolism precede those seen on structural magnetic resonance imaging (MRI). Reduction in glucose metabolism and atrophy have been suggested to represent consecutive changes of neurodegeneration and are linked to specific cognitive disorders (e.g., dysexecutive syndrome, memory impairment, visuospatial deficits etc.). Advances in the statistical analysis of FDG-PET images enabling a network analysis broadened our understanding of neurodegenerative brain processes. A specific cognitive pattern related to PD was identified by applying voxel-based network modeling approach. The magnitude of this pattern correlated significantly with patients' cognitive skills. Specific metabolic brain changes were observed also in patients with DLB as well as in a prodromal phase of α-synucleinopathy: REM sleep behavior disorder. Metabolic brain imaging with FDG-PET is a reliable biomarker of neurodegenerative brain diseases throughout their course, precisely reflecting their topographic distribution, stage and functional impact.

The current status of blood epigenetic biomarkers for dementia

Dementia is an overarching term which describes a group of symptoms that result in long-term decline in cognitive functioning that is significant enough to affect daily function. It is caused by a number of different diseases, the most common of which is Alzheimer's disease. Currently, there are no definitive biomarkers for preclinical or diagnostic use, or which differentiate between underlying disease types. The purpose of this review is to highlight several important areas of research on blood-based biomarkers of dementia, with a specific focus on epigenetic biomarkers. A systematic search of the literature identified 77 studies that compared blood DNA methylation between individuals with dementia and controls and 45 studies that measured microRNA. Very few studies were identified that focused on histone modifications. There were many promising findings from studies in the field of blood-based epigenetic biomarkers of dementia, however, a lack of consistency in study design, technologies, and platforms used for the biomarker measurement, as well as statistical analysis methods, have hampered progress. To date, there are very few findings that have been independently replicated across more than one study, indicating a preponderance of false-positive findings and the field has likely been plagued by positive publication bias. Here, we highlight and discuss several of the limitations of existing studies and provide recommendations for how these could be overcome in future research. A robust framework should be followed to enable development of the most valid and reproducible biomarkers with the strongest clinical utility. Defining a series of biomarkers that may be complimentary to each other could permit a stronger multifactorial biomarker to be developed that would allow for not only accurate dementia diagnosis but preclinical detection.

Brain Molecular Connectivity in Neurodegenerative Diseases: Recent Advances and New Perspectives Using Positron Emission Tomography

Positron emission tomography (PET) represents a unique molecular tool to get in vivo access to a wide spectrum of biological and neuropathological processes, of crucial relevance for neurodegenerative conditions. Although most PET findings are based on massive univariate approaches, in the last decade the increasing interest in multivariate methods has paved the way to the assessment of unexplored cerebral features, spanning from resting state brain networks to whole-brain connectome properties. Currently, the combination of molecular neuroimaging techniques with multivariate connectivity methods represents one of the most powerful, yet still emerging, approach to achieve novel insights into the pathophysiology of neurodegenerative diseases. In this review, we will summarize the available evidence in the field of PET molecular connectivity, with the aim to provide an overview of how these studies may increase the understanding of the pathogenesis of neurodegenerative diseases, over and above "traditional" structural/functional connectivity studies. Considering the available evidence, a major focus will be represented by molecular connectivity studies using [18F]FDG-PET, today applied in the major neuropathological spectra, from amyloidopathies and tauopathies to synucleinopathies and beyond. Pioneering studies using PET tracers targeting brain neuropathology and neurotransmission systems for connectivity studies will be discussed, their strengths and limitations highlighted with reference to both applied methodology and results interpretation. The most common methods for molecular connectivity assessment will be reviewed, with particular emphasis on the available strategies to investigate molecular connectivity at the single-subject level, of potential relevance for not only research but also diagnostic purposes. Finally, we will highlight possible future perspectives in the field, with reference in particular to newly available PET tracers, which will expand the application of molecular connectivity to new, exciting, unforeseen possibilities.

Automated synthesis of [11C]L-glutamine on Synthra HCN plus synthesis module

Background

L-Glutamine (L-Gln) is the most abundant amino acid present in the human body and is involved in numerous metabolic pathways. Glutaminolysis is the metabolic process deployed by many aggressive cancers such as triple negative breast cancer (TNBC). Imaging the metabolic pathways of L-glutamine could provide more insights into tumor biology. Reliable and reproducible automated synthesis of [¹¹C]L-glutamine PET (Positron Emission Tomography) radiotracer is critical for these studies.

Results

[¹¹C]L-Glutamine ([¹¹C]L-Gln) was reliably and reproducibly synthesized. The automated process involves cleaning and drying of the synthesis module, azeotropic drying of crown ether and cesium bicarbonate, conversion of [¹¹C]CO₂ to [¹¹C] CsCN, incorporation of [¹¹C] CN into the starting material, and hydrolysis and deprotection of the corresponding [¹¹C] nitrile to yield [¹¹C]L-glutamine. Starting with approximately 1 Ci of [¹¹C] cesium cyanide ([¹¹C]CsCN), 47–77 mCi (n = 4) of the final product, [¹¹C]L-Gln, was obtained after sterile filtration. The radiochemical purity of the final product was > 90% with almost exclusively L-glutamine isomer. The yield of [¹¹C]L-Gln was 43–52% (n = 4), decay corrected to end of [¹¹C] CsCN trapping in the reaction vessel.

Conclusions

All the steps including drying of the mixture of base and crown ether, preparation of [¹¹C] cyanide, radiochemical synthesis and formulation were accomplished on a single synthesis unit. [¹¹C]L-Gln has been successfully adapted and optimized on an automated synthesis module, Synthra HCN Plus. This process can be readily adapted for clinical research use.

Screening for Dementia Caused by Modifiable Lifestyle Choices Using Hybrid PET/MRI

Significant advances in positron emission tomography (PET) and magnetic resonance imaging (MRI) brain imaging in the early detection of dementia indicate that hybrid PET/MRI would be an effective tool to screen for dementia in the population living with lifestyle risk factors. Here we investigate the associated costs and benefits along with the needed imaging infrastructure. A demographic analysis determined the prevalence of dementia and its incidence. The expected value of the screening program was calculated assuming a sensitivity and specificity of 0.9, a prevalence of 0.1, a QALY factor of 0.348, a willingness to pay $114,000 CAD and the cost per PET/MRI scan of $2,000 CAD. It was assumed that each head PET/MRI could screen 3,000 individuals per year. The prevalence of dementia is increasing by almost two-fold every 20 years due to the increased population at ages where dementia is more prevalent. It has been shown that a five-year delay in the incidence of dementia would decrease the prevalence by some 45%. In Canada, a five-year delay corresponds to a health care savings of $27,000 CAD per subject per year. The expected value for screening was estimated at $23,745 CAD. The number of subjects to be screened per year in Canada, USA, and China between 60 and 79 was 11,405,000. The corresponding number of head-only hybrid PET/MRI systems needed is 3,800. A brain PET/MRI screening program is financially justifiable with respect to health care costs and justifies the continuing development of MRI compatible brain PET technology.

Testing the diagnostic accuracy of [18F]FDG-PET in discriminating spinal- and bulbar-onset amyotrophic lateral sclerosis

PURPOSE

The role for [18F]FDG-PET in supporting amyotrophic lateral sclerosis (ALS) diagnosis is not fully established. In this study, we aim at evaluating [18F]FDG-PET hypo- and hyper-metabolism patterns in spinal- and bulbar-onset ALS cases, at the single-subject level, testing the diagnostic value in discriminating the two conditions, and the correlations with core clinical symptoms severity.

METHODS

We included 95 probable-ALS patients with [18F]FDG-PET scan and clinical follow-up. [18F]FDG-PET images were analyzed with an optimized voxel-based-SPM method. The resulting single-subject SPM-t maps were used to: (a) assess brain regional hypo- and hyper-metabolism; (b) evaluate the accuracy of regional hypo- and hyper metabolism in discriminating spinal vs. bulbar-onset ALS; (c) perform correlation analysis with motor symptoms severity, as measured by ALS-FRS-R.

RESULTS

Primary motor cortex showed the most frequent hypo-metabolism in both spinal-onset (∼57%) and bulbar-onset (∼64%) ALS; hyper-metabolism was prevalent in the cerebellum in both spinal-onset (∼56.5%) and bulbar-onset (∼55.7%) ALS, and in the occipital cortex in bulbar-onset (∼62.5%) ALS. Regional hypo- and hyper-metabolism yielded a very low accuracy (AUC < 0.63) in discriminating spinal- vs. bulbar-onset ALS, as obtained from single-subject SPM-t-maps. Severity of motor symptoms correlated with hypo-metabolism in sensorimotor cortex in spinal-onset ALS, and with cerebellar hyper-metabolism in bulbar-onset ALS.

CONCLUSIONS

The high variability in regional hypo- and hyper-metabolism patterns, likely reflecting the heterogeneous pathology and clinical phenotypes, limits the diagnostic potential of [18F]FDG-PET in discriminating spinal and bulbar onset patients.

Heterogeneous brain FDG-PET metabolic patterns in patients with C9orf72 mutation

OBJECTIVE

The hexanucleotide repeat expansion in C9orf72 is an associated genetic cause in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). In the "ALS/FTD" spectrum prevails clinical heterogeneity and an in vivo knowledge of the underling brain dysfunction in patients carrying C9orf72 mutation remain limited and only described at group level. The study aimed to assess the brain metabolic alterations characterizing patients with C9orf72 mutation using FDG-PET in single individuals.

METHODS

We applied a validated statistical parametric mapping (SPM) voxel-based procedure for FDG-PET data to obtain maps of brain relative hypometabolism and hypermetabolism at single-subject level in six FTD/ALS patients carrying the C9orf72 mutation.

RESULTS

Clinical diagnoses classified the patients as right semantic variant of frontotemporal dementia (one case, C9svFTD), behavioral variant of frontotemporal dementia (two cases, C9bvFTD), and bulbar amyotrophic lateral sclerosis (three cases, C9bALS). The FDG-PET SPM revealed a prevalent frontal hypometabolism in C9bvFTD cases, and right temporal polar and lateral involvement in C9svFTD, consistent with the clinical diagnosis. There was a quite comparable occipital and cerebellar hypermetabolism in these cases. The three C9bALS patients showed variable patterns of hypo- and hypermetabolism.

CONCLUSIONS

The present work is the first in vivo FDG-PET study showing the heterogeneous patterns of brain regional hypo- and hypermetabolism in single patients sharing C9orf72 mutation. Brain hypometabolism was consistent with the clinical phenotypes, supporting the diagnostic importance of neuroimaging functional biomarkers to capture at single-subject level specific brain dysfunction.

Unfavourable gender effect of high body mass index on brain metabolism and connectivity

The influence of Body Mass Index (BMI) on neurodegeneration in dementia has yet to be elucidated. We aimed at exploring the effects of BMI levels on cerebral resting-state metabolism and brain connectivity, as crucial measures of synaptic function and activity, in a large group of patients with Alzheimer's Dementia (AD) (n = 206), considering gender. We tested the correlation between BMI levels and brain metabolism, as assessed by 18F-FDG-PET, and the modulation of the resting-state functional networks by BMI. At comparable dementia severity, females with high BMI can withstand a lower degree of brain metabolism dysfunction, as shown by a significant BMI-brain metabolism correlation in the temporal-parietal regions, which are typically vulnerable to AD pathology (R = 0.269, p = 0.009). Of note, high BMI was also associated with reduced connectivity in frontal and limbic brain networks, again only in AD females (p < 0.05 FDR-corrected, k = 100 voxels). This suggests a major vulnerability of neural systems known to be selectively involved in brain compensatory mechanisms in AD females. These findings indicate a strong gender effect of high BMI and obesity in AD, namely reducing the available reserve mechanisms in female patients. This brings to considerations for medical practice and health policy.

Low-dose CT for the spatial normalization of PET images: A validation procedure for amyloid-PET semi-quantification

The reference standard for spatial normalization of brain positron emission tomography (PET) images involves structural Magnetic Resonance Imaging (MRI) data. However, the lack of such structural information is fairly common in clinical settings. This might lead to lack of proper image quantification and to evaluation based only on visual ratings, which does not allow research studies or clinical trials based on quantification.

PET/CT systems are widely available and CT normalization procedures need to be explored. Here we describe and validate a procedure for the spatial normalization of PET images based on the low-dose Computed Tomography (CT) images contextually acquired for attenuation correction in PET/CT systems. We included N = 34 subjects, spanning from cognitively normal to mild cognitive impairment and dementia, who underwent amyloid-PET/CT (¹⁸F-Florbetaben) and structural MRI scans. The proposed pipeline is based on the SPM12 unified segmentation algorithm applied to low-dose CT images. The validation of the normalization pipeline focused on 1) statistical comparisons between regional and global ¹⁸F-Florbetaben-PET/CT standardized uptake value ratios (SUVrs) estimated from both CT-based and MRI-based normalized PET images (SUVr_CT, SUVr_MRI) and 2) estimation of the degrees of overlap between warped gray matter (GM) segmented maps derived from CT- and MRI-based spatial transformations.

We found negligible deviations between regional and global SUVrs in the two CT and MRI-based methods. SUVr_CT and SUVr_MRI global uptake scores showed negligible differences (mean ± sd 0.01 ± 0.03). Notably, the CT- and MRI-based warped GM maps showed excellent overlap (90% within 1 mm).

The proposed analysis pipeline, based on low-dose CT images, allows accurate spatial normalization and subsequent PET image quantification. A CT-based analytical pipeline could benefit both research and clinical practice, allowing the recruitment of larger samples and favoring clinical routine analysis.

Qualitative Analysis of Mini Mental State Examination Pentagon in Vascular Dementia and Alzheimer's Disease: A Longitudinal Explorative Study

BACKGROUND

Vascular dementia and Alzheimer's disease are the most diffuse forms of dementia. Sometimes, they are difficult to distinguish due to overlaps in symptomatology, pathophysiology, and comorbidity. Visual constructive apraxia is very common in dementia and impairment in these abilities can provide clinical information for differential diagnosis.

MATERIALS AND METHODS

All patients underwent Mini Mental State Examination (MMSE) at basal visit (T0) and after 1 year (T1). We analyzed differences in Qualitative Scoring Method for the Pentagon Copying Test and we explored the visual constructive apraxia evolution in these 2 types of dementia.

RESULTS

In intragroup analysis, we found a significant difference in each group between T0 and T1 in MMSE score (P < .001) and total qualitative scores (P < .001). In intergroup analysis, at T0, we found significance difference in total qualitative scores (P < .001), in numbers of angles (P = .005), in distance/intersection (P < .001), in closure/opening (P = .01), in rotation (P < .001), and in closing-in (P < .001). At T1, we found significance difference in total qualitative scores (P < .001), in particular, in numbers of angles (P < .001), in distance/intersection (P < .001), in closure/opening (P < .001), in rotation (P < .001), and in closing-in (P < .001). The total score showed the highest classification accuracy (.90, 95%CI = .81-0.96) in differentiating patients with Alzheimer's disease from patients with vascular dementia. The optimal threshold value was k = 5. with .84 (95%CI = .69-0.93) sensitivity and .81 (95%CI = .64-0.93) specificity.

CONCLUSION

Patients with vascular dementia showed more accuracy errors and graphic difficulties than patients with Alzheimer's disease. Qualitative analysis of copy provided a sensitive measure of visual constructive abilities in differentiating dementias, underlining a particularly vulnerability of visuoconstructive functions in vascular dementia compared with Alzheimer's disease.