Clinical utility of FDG-PET for the clinical diagnosis in MCI

Cerebral glucose metabolism in Alzheimer's disease

18F-fluoro-deoxy-glucose positron emission tomography (FDG-PET) is a useful paraclinical exam for the diagnosis of Alzheimer's disease (AD). In this narrative review, we report seminal studies in clinically probable AD that have shown the importance of posterior brain metabolic decrease and the paradoxical variability of the hippocampal metabolism. The FDG-PET pattern was a sensitive indicator of AD in pathologically confirmed cases and it was used for differential diagnosis of dementia conditions. In prodromal AD, the AD FDG-PET pattern was observed in converters and predicted conversion. Automated data analysis techniques provided variable accuracy according to the reported indices and machine learning methods showed variable reliability of results. FDG-PET could confirm AD clinical heterogeneity and image data driven analyses identified hypometabolic subtypes with variable involvement of the hippocampus, reminiscent if the paradoxical FDG uptake. In studies dedicated to clinical and metabolic correlations, episodic memory was related to metabolism in the default mode network (and Papez's circuit) in prodromal and mild AD stages, and specific cognitive processes were associated to precisely distributed brain metabolism. Cerebral metabolic correlates of anosognosia could also be related to current neuropsychological models. AD FDG-PET pattern was reported in preclinical AD stages and related to cognition or to conversion to mild cognitive impairment (MCI). Using other biomarkers, the AD FDG-PET pattern was confirmed in AD participants with positive PET-amyloid. Intriguing observations reported increased metabolism related to brain amyloid and/or tau deposition. Preserved glucose metabolism sometimes appear as a compensation, but it was frequently detrimental and the nature of such a preservation of glucose metabolism remains an open question. Limbic metabolic involvement was frequently related to non-AD biomarkers profile and clinical stability, and it was reported in non-AD pathologies, such as the limbic predominant age-related encephalopathy (LATE). FDG-PET abnormalities observed in the absence of classical AD proteinopathies can be useful to search for pathological mechanisms and differential diagnosis of AD.

Exploring the Mechanisms and Therapeutic Approaches of Mitochondrial Dysfunction in Alzheimer's Disease: An Educational Literature Review

Alzheimer's disease (AD) presents a significant challenge to global health. It is characterized by progressive cognitive deterioration and increased rates of morbidity and mortality among older adults. Among the various pathophysiologies of AD, mitochondrial dysfunction, encompassing conditions such as increased reactive oxygen production, dysregulated calcium homeostasis, and impaired mitochondrial dynamics, plays a pivotal role. This review comprehensively investigates the mechanisms of mitochondrial dysfunction in AD, focusing on aspects such as glucose metabolism impairment, mitochondrial bioenergetics, calcium signaling, protein tau and amyloid-beta-associated synapse dysfunction, mitophagy, aging, inflammation, mitochondrial DNA, mitochondria-localized microRNAs, genetics, hormones, and the electron transport chain and Krebs cycle. While lecanemab is the only FDA-approved medication to treat AD, we explore various therapeutic modalities for mitigating mitochondrial dysfunction in AD, including antioxidant drugs, antidiabetic agents, acetylcholinesterase inhibitors (FDA-approved to manage symptoms), nutritional supplements, natural products, phenylpropanoids, vaccines, exercise, and other potential treatments.

Guideline for the cognitive assessment and follow-up in the Guangdong-Hong Kong-Macao Greater Bay Area (2024 edition)

This practice guideline focuses on the cognitive assessment for mild cognitive impairment in the Guangdong-Hong Kong-Macao Greater Bay Area. To achieve the standardization and normalization of its clinical practice and generate individualized intervention, the National Core Cognitive Center of the Second Affiliated Hospital of Guangzhou Medical University, the Cognitive Disorders Branch of Chinese Geriatic Society, the Dementia Group of Neurology Branch of Guangdong Medical Association and specialists from Hong Kong and Macao developed guidelines based on China's actual conditions and efficiency, economic cost and accuracy. The article addresses the significance, background, and the process of the assessment and follow-up to realize the promotion and dissemination of cognitive assessment.

Diagnostic performance of molecular imaging methods in predicting the progression from mild cognitive impairment to dementia: an updated systematic review

PURPOSE

Epidemiological and logistical reasons are slowing the clinical validation of the molecular imaging biomarkers in the initial stages of neurocognitive disorders. We provide an updated systematic review of the recent advances (2017-2022), highlighting methodological shortcomings.

METHODS

Studies reporting the diagnostic accuracy values of the molecular imaging techniques (i.e., amyloid-, tau-, [18F]FDG-PETs, DaT-SPECT, and cardiac [123I]-MIBG scintigraphy) in predicting progression from mild cognitive impairment (MCI) to dementia were selected according to the Preferred Reporting Items for a Systematic Review and Meta-Analysis (PRISMA) method and evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Main eligibility criteria were as follows: (1) ≥ 50 subjects with MCI, (2) follow-up ≥ 3 years, (3) gold standard: progression to dementia or diagnosis on pathology, and (4) measures of prospective accuracy.

RESULTS

Sensitivity (SE) and specificity (SP) in predicting progression to dementia, mainly to Alzheimer's dementia were 43-100% and 63-94% for [18F]FDG-PET and 64-94% and 48-93% for amyloid-PET. Longitudinal studies were lacking for less common disorders (Dementia with Lewy bodies-DLB and Frontotemporal lobe degeneration-FTLD) and for tau-PET, DaT-SPECT, and [123I]-MIBG scintigraphy. Therefore, the accuracy values from cross-sectional studies in a smaller sample of subjects (n > 20, also including mild dementia stage) were chosen as surrogate outcomes. DaT-SPECT showed 47-100% SE and 71-100% SP in differentiating Lewy body disease (LBD) from non-LBD conditions; tau-PET: 88% SE and 100% SP in differentiating DLB from Posterior Cortical Atrophy. [123I]-MIBG scintigraphy differentiated LBD from non-LBD conditions with 47-100% SE and 71-100% SP.

CONCLUSION

Molecular imaging has a moderate-to-good accuracy in predicting the progression of MCI to Alzheimer's dementia. Longitudinal studies are sparse in non-AD conditions, requiring additional efforts in these settings.

Mechanisms of sex differences in Alzheimer's disease

Alzheimer's disease (AD) and the mechanisms underlying its etiology and progression are complex and multifactorial. The higher AD risk in women may serve as a clue to better understand these complicated processes. In this review, we examine aspects of AD that demonstrate sex-dependent effects and delve into the potential biological mechanisms responsible, compiling findings from advanced technologies such as single-cell RNA sequencing, metabolomics, and multi-omics analyses. We review evidence that sex hormones and sex chromosomes interact with various disease mechanisms during aging, encompassing inflammation, metabolism, and autophagy, leading to unique characteristics in disease progression between men and women.

Differential diagnosis of MCI with Lewy bodies and MCI due to Alzheimer's disease by visual assessment of occipital hypoperfusion on SPECT images

PURPOSE

Predicting progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) or dementia with Lewy bodies (DLB) is important. We evaluated morphological and functional differences between MCI with Lewy bodies (MCI-LB) and MCI due to AD (MCI-AD), and a method for differentiating between these conditions using brain MRI and brain perfusion SPECT.

METHODS

A continuous series of 101 subjects, who had visited our memory clinic and met the definition of MCI, were enrolled retrospectively. They were consisted of 60 MCI-LB and 41 MCI-AD subjects. Relative cerebral blood flow (rCBF) on SPECT images and relative brain atrophy on MRI images were evaluated. We performed voxel-based analysis and visually inspected brain perfusion SPECT images for regional brain atrophy, occipital hypoperfusion and the cingulate island sign (CIS), for differential diagnosis of MCI-LB and MCI-AD.

RESULTS

MRI showed no significant differences in regional atrophy between the MCI-LB and MCI-AD groups. In MCI-LB subjects, occipital rCBF was significantly decreased compared with MCI-AD subjects (p < 0.01, family wise error [FWE]-corrected). Visual inspection of occipital hypoperfusion had sensitivity, specificity, and accuracy values of 100%, 73.2% and 89.1%, respectively, for differentiating MCI-LB and MCI-AD. Occipital hypoperfusion was offered higher diagnostic utility than the CIS.

CONCLUSIONS

The occipital lobe was the region with significantly decreased rCBF in MCI-LB compared with MCI-AD subjects. Occipital hypoperfusion on brain perfusion SPECT may be a more useful imaging biomarker than the CIS for visually differentiating MCI-LB and MCI-AD.

A 3D convolutional neural network to classify subjects as Alzheimer's disease, frontotemporal dementia or healthy controls using brain 18F-FDG PET

With the arrival of disease-modifying drugs, neurodegenerative diseases will require an accurate diagnosis for optimal treatment. Convolutional neural networks are powerful deep learning techniques that can provide great help to physicians in image analysis. The purpose of this study is to introduce and validate a 3D neural network for classification of Alzheimer's disease (AD), frontotemporal dementia (FTD) or cognitively normal (CN) subjects based on brain glucose metabolism. Retrospective [18F]-FDG-PET scans of 199 CE, 192 FTD and 200 CN subjects were collected from our local database, Alzheimer's disease and frontotemporal lobar degeneration neuroimaging initiatives. Training and test sets were created using randomization on a 90 %-10 % basis, and training of a 3D VGG16-like neural network was performed using data augmentation and cross-validation. Performance was compared to clinical interpretation by three specialists in the independent test set. Regions determining classification were identified in an occlusion experiment and Gradient-weighted Class Activation Mapping. Test set subjects were age- and sex-matched across categories. The model achieved an overall 89.8 % accuracy in predicting the class of test scans. Areas under the ROC curves were 93.3 % for AD, 95.3 % for FTD, and 99.9 % for CN. The physicians' consensus showed a 69.5 % accuracy, and there was substantial agreement between them (kappa = 0.61, 95 % CI: 0.49-0.73). To our knowledge, this is the first study to introduce a deep learning model able to discriminate AD and FTD based on [18F]-FDG PET scans, and to isolate CN subjects with excellent accuracy. These initial results are promising and hint at the potential for generalization to data from other centers.

Upstream lipid and metabolic systems are potential causes of Alzheimer's disease, Parkinson's disease and dementias

Brain health requires circuits, cells and molecular pathways to adapt when challenged and to promptly reset once the challenge has resolved. Neurodegeneration occurs when adaptability becomes confined, causing challenges to overwhelm neural circuitry. Studies of rare and common neurodegenerative diseases suggest that the accumulation of lipids can compromise circuit adaptability. Using microglia as an example, we review data that suggest increased lipid concentrations cause dysfunctional inflammatory responses to immune challenges, leading to Alzheimer's disease, Parkinson's disease and dementia. We highlight current approaches to treat lipid metabolic and clearance pathways and identify knowledge gaps towards restoring adaptive homeostasis in individuals who are at-risk of losing cognition.

Combining pathological and cognitive tests scores: A novel data analytics process to improve dementia prediction models1

BACKGROUND

The term 'dementia' covers a range of progressive brain diseases from which many elderly people suffer. Traditional cognitive and pathological tests are currently used to detect dementia, however, applications using Artificial Intelligence (AI) methods have recently shown improved results from improved detection accuracy and efficiency.

OBJECTIVE

This research paper investigates the efficacy of one type of data analytics called supervised learning to detect Alzheimer's disease (AD) - a common dementia condition.

METHODS

The aim is to evaluate cognitive tests and common biological markers (biomarkers) such as cerebrospinal fluid (CSF) to develop predictive classification systems for dementia detection.

RESULTS

A data analytics process has been proposed, implemented, and tested against real data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) repository.

CONCLUSION

The models showed good power in predicting AD levels, notably from specified cognitive tests' scores and tauopathy related features.

Characteristics of amnestic patients with hypometabolism patterns suggestive of Lewy body pathology

A clinical diagnosis of Alzheimer's disease dementia (ADD) encompasses considerable pathological and clinical heterogeneity. While Alzheimer's disease patients typically show a characteristic temporo-parietal pattern of glucose hypometabolism on 18F-fluorodeoxyglucose (FDG)-PET imaging, previous studies have identified a subset of patients showing a distinct posterior-occipital hypometabolism pattern associated with Lewy body pathology. Here, we aimed to improve the understanding of the clinical relevance of these posterior-occipital FDG-PET patterns in patients with Alzheimer's disease-like amnestic presentations. Our study included 1214 patients with clinical diagnoses of ADD (n = 305) or amnestic mild cognitive impairment (aMCI, n = 909) from the Alzheimer's Disease Neuroimaging Initiative, who had FDG-PET scans available. Individual FDG-PET scans were classified as being suggestive of Alzheimer's (AD-like) or Lewy body (LB-like) pathology by using a logistic regression classifier trained on a separate set of patients with autopsy-confirmed Alzheimer's disease or Lewy body pathology. AD- and LB-like subgroups were compared on amyloid-β and tau-PET, domain-specific cognitive profiles (memory versus executive function performance), as well as the presence of hallucinations and their evolution over follow-up (≈6 years for aMCI, ≈3 years for ADD). Around 12% of the aMCI and ADD patients were classified as LB-like. For both aMCI and ADD patients, the LB-like group showed significantly lower regional tau-PET burden than the AD-like subgroup, but amyloid-β load was only significantly lower in the aMCI LB-like subgroup. LB- and AD-like subgroups did not significantly differ in global cognition (aMCI: d = 0.15, P = 0.16; ADD: d = 0.02, P = 0.90), but LB-like patients exhibited a more dysexecutive cognitive profile relative to the memory deficit (aMCI: d = 0.35, P = 0.01; ADD: d = 0.85 P < 0.001), and had a significantly higher risk of developing hallucinations over follow-up [aMCI: hazard ratio = 1.8, 95% confidence interval = (1.29, 3.04), P = 0.02; ADD: hazard ratio = 2.2, 95% confidence interval = (1.53, 4.06) P = 0.01]. In summary, a sizeable group of clinically diagnosed ADD and aMCI patients exhibit posterior-occipital FDG-PET patterns typically associated with Lewy body pathology, and these also show less abnormal Alzheimer's disease biomarkers as well as specific clinical features typically associated with dementia with Lewy bodies.

Application of Artificial Intelligence in Image Processing of Neurodegenerative Disorders: A Review Study

: Neurodegenerative diseases can make life difficult and lead to death in many cases. They also can be difficult, time-consuming, and costly to diagnose with enough accuracy/certainty. Artificial intelligence (AI) has shown promise in tackling some of the challenges present in medical imaging and is anticipated to become a crucial tool in health care applications in the near future. In particular, deep learning methods have displayed great performance in various subfields of image processing, including but not limited to image segmentation, image synthesis, and image reconstruction. In this paper, many state-of-the-art applications of deep learning models in image processing were reviewed.

Impact of normative brain volume reports on the diagnosis of neurodegenerative dementia disorders in neuroradiology: A real-world, clinical practice study

Background: Normative brain volume reports (NBVR) are becoming more available in the work-up of patients with suspected dementia disorders, potentially leveraging the value of structural MRI in clinical settings. The present study aims to investigate the impact of NBVRs on the diagnosis of neurodegenerative dementia disorders in real-world clinical practice.

Methods: We retrospectively analyzed data of 112 memory clinic patients, who were consecutively referred for MRI and 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) during a 12-month period. Structural MRI was assessed by two residents with 2 and 3 years of neuroimaging experience. Statements and diagnostic confidence regarding the presence of a neurodegenerative disorder in general (first level) and Alzheimer’s disease (AD) pattern in particular (second level) were recorded without and with NBVR information. FDG-PET served as the reference standard.

Results: Overall, despite a trend towards increased accuracy, the impact of NBVRs on diagnostic accuracy was low and non-significant. We found a significant drop of sensitivity (0.75–0.58; p < 0.001) and increase of specificity (0.62–0.85; p < 0.001) for rater 1 at identifying patients with neurodegenerative dementia disorders. Diagnostic confidence increased for rater 2 (p < 0.001).

Conclusions: Overall, NBVRs had a limited impact on diagnostic accuracy in real-world clinical practice. Potentially, NBVR might increase diagnostic specificity and confidence of neuroradiology residents. To this end, a well-defined framework for integration of NBVR in the diagnostic process and improved algorithms of NBVR generation are essential.

Differential Diagnosis of Alzheimer Disease vs. Mild Cognitive Impairment Based on Left Temporal Lateral Lobe Hypomethabolism on 18F-FDG PET/CT and Automated Classifiers

Purpose: We evaluate the ability of Artificial Intelligence with automatic classification methods applied to semi-quantitative data from brain ¹⁸F-FDG PET/CT to improve the differential diagnosis between Alzheimer Disease (AD) and Mild Cognitive Impairment (MCI). Procedures: We retrospectively analyzed a total of 150 consecutive patients who underwent diagnostic evaluation for suspected AD (n = 67) or MCI (n = 83). All patients received brain 18F-FDG PET/CT according to the international guidelines, and images were analyzed both Qualitatively (QL) and Quantitatively (QN), the latter by a fully automated post-processing software that produced a z score metabolic map of 25 anatomically different cortical regions. A subset of n = 122 cases with a confirmed diagnosis of AD (n = 53) or MDI (n = 69) by 18–24-month clinical follow-up was finally included in the study. Univariate analysis and three automated classification models (classification tree –ClT-, ridge classifier –RC- and linear Support Vector Machine –lSVM-) were considered to estimate the ability of the z scores to discriminate between AD and MCI cases in. Results: The univariate analysis returned 14 areas where the z scores were significantly different between AD and MCI groups, and the classification accuracy ranged between 74.59% and 76.23%, with ClT and RC providing the best results. The best classification strategy consisted of one single split with a cut-off value of ≈ −2.0 on the z score from temporal lateral left area: cases below this threshold were classified as AD and those above the threshold as MCI. Conclusions: Our findings confirm the usefulness of brain 18F-FDG PET/CT QL and QN analyses in differentiating AD from MCI. Moreover, the combined use of automated classifications models can improve the diagnostic process since its use allows identification of a specific hypometabolic area involved in AD cases in respect to MCI. This data improves the traditional 18F-FDG PET/CT image interpretation and the diagnostic assessment of cognitive disorders.

Cognitive and Brain Metabolism Profiles of Mild Cognitive Impairment in Prodromal Alpha-Synucleinopathy

Background: Mild cognitive impairment (MCI) is a heterogeneous condition. Idiopathic REM sleep behavior disorder (iRBD) can be associated with MCI (MCI-RBD). Objective: To investigate neuropsychological and brain metabolism features of patients with MCI-RBD by comparison with matched MCI-AD patients. To explore their predictive value toward conversion to a full-blown neurodegenerative disease. Methods: Seventeen MCI-RBD patients (73.6±6.5 years) were enrolled. Thirty-four patients with MCI-AD were matched for age (74.8±4.4 years), Mini-Mental State Exam score and education with a case-control criterion. All patients underwent a neuropsychological assessment and brain 18F-FDG-PET. Images were compared between groups to identify hypometabolic volumes of interest (MCI-RBD-VOI and MCI-AD-VOI). The dependency of whole-brain scaled metabolism levels in MCI-RBD-VOI and MCI-AD-VOI on neuropsychological test scores was explored with linear regression analyses in both groups, adjusting for age and education. Survival analysis was performed to investigate VOIs phenoconversion prediction power. Results: MCI-RBD group scored lower in executive functions and higher in verbal memory compared to MCI-AD group. Also, compared with MCI-AD, MCI-RBD group showed relative hypometabolism in a posterior brain area including cuneus, precuneus, and occipital regions while the inverse comparison revealed relative hypometabolism in the hippocampus/parahippocampal areas in MCI-AD group. MCI-RBD-VOI metabolism directly correlated with executive functions in MCI-RBD (p = 0.04). MCI-AD-VOI metabolism directly correlated with verbal memory in MCI-AD (p = 0.001). MCI-RBD-VOI metabolism predicted (p = 0.03) phenoconversion to an alpha-synucleinopathy. MCI-AD-VOI metabolism showed a trend (p = 0.07) in predicting phenoconversion to dementia. Conclusion: MCI-RBD and MCI-AD showed distinct neuropsychological and brain metabolism profiles, that may be helpful for both diagnosis and prognosis purposes.

Development of a Machine Learning Model to Discriminate Mild Cognitive Impairment Subjects from Normal Controls in Community Screening

Background: Mild cognitive impairment (MCI) is a transitional stage between normal aging and probable Alzheimer's disease. It is of great value to screen for MCI in the community. A novel machine learning (ML) model is composed of electroencephalography (EEG), eye tracking (ET), and neuropsychological assessments. This study has been proposed to identify MCI subjects from normal controls (NC). Methods: Two cohorts were used in this study. Cohort 1 as the training and validation group, includes184 MCI patients and 152 NC subjects. Cohort 2 as an independent test group, includes 44 MCI and 48 NC individuals. EEG, ET, Neuropsychological Tests Battery (NTB), and clinical variables with age, gender, educational level, MoCA-B, and ACE-R were selected for all subjects. Receiver operating characteristic (ROC) curves were adopted to evaluate the capabilities of this tool to classify MCI from NC. The clinical model, the EEG and ET model, and the neuropsychological model were compared. Results: We found that the classification accuracy of the proposed model achieved 84.5 ± 4.43% and 88.8 ± 3.59% in Cohort 1 and Cohort 2, respectively. The area under curve (AUC) of the proposed tool achieved 0.941 (0.893-0.982) in Cohort 1 and 0.966 (0.921-0.988) in Cohort 2, respectively. Conclusions: The proposed model incorporation of EEG, ET, and neuropsychological assessments yielded excellent classification performances, suggesting its potential for future application in cognitive decline prediction.

Associations of digital neuro-signatures with molecular and neuroimaging measures of brain resilience: The altoida large cohort study

Background

Mixed results in the predictive ability of traditional biomarkers to determine cognitive functioning and changes in older adults have led to misdiagnosis and inappropriate treatment plans to address mild cognitive impairment and dementia among older adults. To address this critical gap, the primary goal of the current study is to investigate whether a digital neuro signature (DNS-br) biomarker predicted global cognitive functioning and change over time relative among cognitively impaired and cognitive healthy older adults. The secondary goal is to compare the effect size of the DNS-br biomarker on global cognitive functioning compared to traditional imaging and genomic biomarkers. The tertiary goal is to investigate which demographic and clinical factors predicted DNS-br in cognitively impaired and cognitively healthy older adults.

Methods

We conducted two experiments (Study A and Study B) to assess DNS for brain resilience (DNS-br) against the established FDG-PET brain imaging signature for brain resilience, based on a 10 min digital cognitive assessment tool. Study A was a semi-naturalistic observational study that included 29 participants, age 65+, with mild to moderate mild cognitive impairment and AD diagnosis. Study B was also a semi-naturalistic observational multicenter study which included 496 participants (213 mild cognitive impairment (MCI) and 283 cognitively healthy controls (HC), a total of 525 participants-cognitively healthy (n = 283) or diagnosed with MCI (n = 213) or AD (n = 29).

Results

DNS-br total score and majority of the 11 DNS-br neurocognitive subdomain scores were significantly associated with FDG-PET resilience signature, PIB ratio, cerebral gray matter and white matter volume after adjusting for multiple testing. DNS-br total score predicts cognitive impairment for the 80+ individuals in the Altoida large cohort study. We identified a significant interaction between the DNS-br total score and time, indicating that participants with higher DNS-br total score or FDG-PET in the resilience signature would show less cognitive decline over time.

Conclusion

Our findings highlight that a digital biomarker predicted cognitive functioning and change, which established biomarkers are unable to reliably do. Our findings also offer possible etiologies of MCI and AD, where education did not protect against cognitive decline.

Positron Emission Tomography-Based Assessment of Cognitive Impairment and Dementias, Critical Role of Fluorodeoxyglucose in such Settings

Positron emission tomography (PET) has been a key component in the diagnostic armamentarium for assessing neurodegenerative diseases such as Alzheimer or Parkinson disease. PET imaging has been useful for diagnosing these disorders, identifying their pathophysiology, and following their treatment. Further, PET imaging has been extensively used for both clinical and research purposes, particularly for helping with potential therapeutic approaches for managing neurodegenerative diseases. This article will review the current literature regarding PET imaging in patients with neurodegenerative disorders. This includes an evaluation of the most commonly used tracer fluorodeoxyglucose that measures cerebral glucose metabolism, tracers that assess neurotransmitter systems, and tracers designed to reveal disease-specific pathophysiological processes. With the continuing development of an expanding variety of radiopharmaceuticals, PET imaging will likely play a prominent role in future research and clinical applications for neurodegenerative diseases.

Novel App knock-in mouse model shows key features of amyloid pathology and reveals profound metabolic dysregulation of microglia

BACKGROUND

Genetic mutations underlying familial Alzheimer's disease (AD) were identified decades ago, but the field is still in search of transformative therapies for patients. While mouse models based on overexpression of mutated transgenes have yielded key insights in mechanisms of disease, those models are subject to artifacts, including random genetic integration of the transgene, ectopic expression and non-physiological protein levels. The genetic engineering of novel mouse models using knock-in approaches addresses some of those limitations. With mounting evidence of the role played by microglia in AD, high-dimensional approaches to phenotype microglia in those models are critical to refine our understanding of the immune response in the brain.

METHODS

We engineered a novel App knock-in mouse model (AppSAA) using homologous recombination to introduce three disease-causing coding mutations (Swedish, Arctic and Austrian) to the mouse App gene. Amyloid-β pathology, neurodegeneration, glial responses, brain metabolism and behavioral phenotypes were characterized in heterozygous and homozygous AppSAA mice at different ages in brain and/ or biofluids. Wild type littermate mice were used as experimental controls. We used in situ imaging technologies to define the whole-brain distribution of amyloid plaques and compare it to other AD mouse models and human brain pathology. To further explore the microglial response to AD relevant pathology, we isolated microglia with fibrillar Aβ content from the brain and performed transcriptomics and metabolomics analyses and in vivo brain imaging to measure energy metabolism and microglial response. Finally, we also characterized the mice in various behavioral assays.

RESULTS

Leveraging multi-omics approaches, we discovered profound alteration of diverse lipids and metabolites as well as an exacerbated disease-associated transcriptomic response in microglia with high intracellular Aβ content. The AppSAA knock-in mouse model recapitulates key pathological features of AD such as a progressive accumulation of parenchymal amyloid plaques and vascular amyloid deposits, altered astroglial and microglial responses and elevation of CSF markers of neurodegeneration. Those observations were associated with increased TSPO and FDG-PET brain signals and a hyperactivity phenotype as the animals aged.

DISCUSSION

Our findings demonstrate that fibrillar Aβ in microglia is associated with lipid dyshomeostasis consistent with lysosomal dysfunction and foam cell phenotypes as well as profound immuno-metabolic perturbations, opening new avenues to further investigate metabolic pathways at play in microglia responding to AD-relevant pathogenesis. The in-depth characterization of pathological hallmarks of AD in this novel and open-access mouse model should serve as a resource for the scientific community to investigate disease-relevant biology.

18F-FDG PET Imaging in Neurodegenerative Dementing Disorders: Insights into Subtype Classification, Emerging Disease Categories, and Mixed Dementia with Copathologies

Since the invention of ¹⁸F-FDG as a neurochemical tracer in the 1970s, ¹⁸F-FDG PET has been used extensively for dementia research and clinical applications. FDG, a glucose analog, is transported into the brain via glucose transporters and metabolized in a concerted process involving astrocytes and neurons. Although the exact cellular mechanisms of glucose consumption are still under investigation, ¹⁸F-FDG PET can sensitively detect altered neuronal activity due to neurodegeneration. Various neurodegenerative disorders affect different areas of the brain, which can be depicted as altered ¹⁸F-FDG uptake by PET. The spatial patterns and severity of such changes can be reproducibly visualized by statistical mapping technology, which has become widely available in the clinic. The differentiation of 3 major neurodegenerative disorders by ¹⁸F-FDG PET, Alzheimer disease (AD), frontotemporal dementia (FTD), and dementia with Lewy bodies (DLB), has become standard practice. As the nosology of FTD evolves, frontotemporal lobar degeneration, the umbrella term for pathology affecting the frontal and temporal lobes, has been subclassified clinically into behavioral variant FTD; primary progressive aphasia with 3 subtypes, semantic, nonfluent, and logopenic variants; and movement disorders including progressive supranuclear palsy and corticobasal degeneration. Each of these subtypes is associated with differential ¹⁸F-FDG PET findings. The discovery of new pathologic markers and clinicopathologic correlations via larger autopsy series have led to newly recognized or redefined disease categories, such as limbic-predominant age-related TDP-43 encephalopathy, hippocampus sclerosis, primary age-related tauopathy, and argyrophilic grain disease, which have become a focus of investigations by molecular imaging. These findings need to be integrated into the modern interpretation of ¹⁸F-FDG PET. Recent pathologic investigations also have revealed a high prevalence, particularly in the elderly, of mixed dementia with overlapping and coexisting pathologies. The interpretation of ¹⁸F-FDG PET is evolving from a traditional dichotomous diagnosis of AD versus FTD (or DLB) to a determination of the most predominant underlying pathology that would best explain the patient's symptoms, for the purpose of care guidance. ¹⁸F-FDG PET is a relatively low cost and widely available imaging modality that can help assess various neurodegenerative disorders in a single test and remains the workhorse in clinical dementia evaluation.

Functional Imaging for Neurodegenerative Diseases

Diagnosis and monitoring of neurodegenerative diseases has changed profoundly over the past twenty years. Biomarkers are now included in most diagnostic procedures as well as in clinical trials. Neuroimaging biomarkers provide access to brain structure and function over the course of neurodegenerative diseases. They have brought new insights into a wide range of neurodegenerative diseases and have made it possible to describe some of the imaging challenges in clinical populations. MRI mainly explores brain structure while molecular imaging, functional MRI and electro- and magnetoencephalography examine brain function. In this paper, we describe and analyse the current and potential contribution of MRI and molecular imaging in the field of neurodegenerative diseases.

Diagnostic Performance of Generative Adversarial Network-Based Deep Learning Methods for Alzheimer’s Disease: A Systematic Review and Meta-Analysis

Alzheimer’s disease (AD) is the most common form of dementia. Currently, only symptomatic management is available, and early diagnosis and intervention are crucial for AD treatment. As a recent deep learning strategy, generative adversarial networks (GANs) are expected to benefit AD diagnosis, but their performance remains to be verified. This study provided a systematic review on the application of the GAN-based deep learning method in the diagnosis of AD and conducted a meta-analysis to evaluate its diagnostic performance. A search of the following electronic databases was performed by two researchers independently in August 2021: MEDLINE (PubMed), Cochrane Library, EMBASE, and Web of Science. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was applied to assess the quality of the included studies. The accuracy of the model applied in the diagnosis of AD was determined by calculating odds ratios (ORs) with 95% confidence intervals (CIs). A bivariate random-effects model was used to calculate the pooled sensitivity and specificity with their 95% CIs. Fourteen studies were included, 11 of which were included in the meta-analysis. The overall quality of the included studies was high according to the QUADAS-2 assessment. For the AD vs. cognitively normal (CN) classification, the GAN-based deep learning method exhibited better performance than the non-GAN method, with significantly higher accuracy (OR 1.425, 95% CI: 1.150–1.766, P = 0.001), pooled sensitivity (0.88 vs. 0.83), pooled specificity (0.93 vs. 0.89), and area under the curve (AUC) of the summary receiver operating characteristic curve (SROC) (0.96 vs. 0.93). For the progressing MCI (pMCI) vs. stable MCI (sMCI) classification, the GAN method exhibited no significant increase in the accuracy (OR 1.149, 95% CI: 0.878–1.505, P = 0.310) or the pooled sensitivity (0.66 vs. 0.66). The pooled specificity and AUC of the SROC in the GAN group were slightly higher than those in the non-GAN group (0.81 vs. 0.78 and 0.81 vs. 0.80, respectively). The present results suggested that the GAN-based deep learning method performed well in the task of AD vs. CN classification. However, the diagnostic performance of GAN in the task of pMCI vs. sMCI classification needs to be improved.

Systematic Review Registration: [PROSPERO], Identifier: [CRD42021275294].

Activation of Adenosine Monophosphate-Activated Protein Kinase Drives the Aerobic Glycolysis in Hippocampus for Delaying Cognitive Decline Following Electroacupuncture Treatment in APP/PS1 Mice

Aerobic glycolysis (AG), an important pathway of glucose metabolism, is dramatically declined in Alzheimer’s disease (AD). AMP-activated protein kinase (AMPK) is a key regulator to maintain the stability of energy metabolism by promoting the process of AG and regulating glucose metabolism. Interestingly, it has been previously reported that electroacupuncture (EA) treatment can improve cognitive function in AD through the enhancement of glucose metabolism. In this study, we generated AMPK-knockdown mice to confirm the EA effect on AMPK activation and further clarify the mechanism of EA in regulating energy metabolism and improving cognitive function in APP/PS1 mice. The behavioral results showed that EA treatment can improve the learning and memory abilities in APP/PS1 mice. At the same time, the glucose metabolism in the hippocampus was increased detected by MRI-chemical exchange saturation transfer (MRI-CEST). The expression of proteins associated with AG in the hippocampus was increased simultaneously, including hexokinase II (HK2), 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3), and pyruvate kinase M2 (PKM2). Moreover, the knockdown of AMPK attenuated AG activated by EA treatment. In conclusion, this study proves that EA can activate AMPK to enhance the process of AG in the early stage of AD.

How an increase in the copy number of HSV-1 during latency can cause Alzheimer's disease: the viral and cellular dynamics according to the microcompetition model

Numerous studies observed a link between the herpes smplex virus-1 (HSV-1) and Alzheimer's disease. However, the exact viral and cellular dynamics that lead from an HSV-1 infection to Alzheimer's disease are unknown. In this paper, we use the microcompetition model to formulate these dynamics by connecting seemingly unconnected observations reported in the literature. We concentrate on four pathologies characteristic of Alzheimer's disease. First, we explain how an increase in the copy number of HSV-1 during latency can decrease the expression of BECN1/Beclin1, the degradative trafficking protein, which, in turn, can cause a dysregulation of autophagy and Alzheimer's disease. Second, we show how an increase in the copy number of the latent HSV-1 can decrease the expression of many genes important for mitochondrial genome metabolism, respiratory chain, and homeostasis, which can lead to oxidative stress and neuronal damage, resulting in Alzheimer's disease. Third, we describe how an increase in this copy number can reduce the concentration of the NMDA receptor subunits NR1 and NR2b (Grin1 and Grin2b genes), and brain derived neurotrophic factor (BDNF), which can cause an impaired synaptic plasticity, Aβ accumulation and eventually Alzheimer's disease. Finally, we show how an increase in the copy number of HSV-1 in neural stem/progenitor cells in the hippocampus during the latent phase can lead to an abnormal quantity and quality of neurogenesis, and the clinical presentation of Alzheimer's disease. Since the current understanding of the dynamics and homeostasis of the HSV-1 reservoir during latency is limited, the proposed model represents only a first step towards a complete understanding of the relationship between the copy number of HSV-1 during latency and Alzheimer's disease.

Added value of semiquantitative analysis of brain FDG-PET for the differentiation between MCI-Lewy bodies and MCI due to Alzheimer's disease

PURPOSE

FDG-PET is an established supportive biomarker in dementia with Lewy bodies (DLB), but its diagnostic accuracy is unknown at the mild cognitive impairment (MCI-LB) stage when the typical metabolic pattern may be difficultly recognized at the individual level. Semiquantitative analysis of scans could enhance accuracy especially in less skilled readers, but its added role with respect to visual assessment in MCI-LB is still unknown.

METHODS

We assessed the diagnostic accuracy of visual assessment of FDG-PET by six expert readers, blind to diagnosis, in discriminating two matched groups of patients (40 with prodromal AD (MCI-AD) and 39 with MCI-LB), both confirmed by in vivo biomarkers. Readers were provided in a stepwise fashion with (i) maps obtained by the univariate single-subject voxel-based analysis (VBA) with respect to a control group of 40 age- and sex-matched healthy subjects, and (ii) individual odds ratio (OR) plots obtained by the volumetric regions of interest (VROI) semiquantitative analysis of the two main hypometabolic clusters deriving from the comparison of MCI-AD and MCI-LB groups in the two directions, respectively.

RESULTS

Mean diagnostic accuracy of visual assessment was 76.8 ± 5.0% and did not significantly benefit from adding the univariate VBA map reading (77.4 ± 8.3%) whereas VROI-derived OR plot reading significantly increased both accuracy (89.7 ± 2.3%) and inter-rater reliability (ICC 0.97 [0.96-0.98]), regardless of the readers' expertise.

CONCLUSION

Conventional visual reading of FDG-PET is moderately accurate in distinguishing between MCI-LB and MCI-AD, and is not significantly improved by univariate single-subject VBA but by a VROI analysis built on macro-regions, allowing for high accuracy independent of reader skills.

Comparison of [11C]UCB-J and [18F]FDG PET in Alzheimer's disease: A tracer kinetic modeling study

[11C]UCB-J PET for synaptic vesicle glycoprotein 2 A (SV2A) has been proposed as a suitable marker for synaptic density in Alzheimer's disease (AD). We compared [11C]UCB-J binding for synaptic density and [18F]FDG uptake for metabolism (correlated with neuronal activity) in 14 AD and 11 cognitively normal (CN) participants. We assessed both absolute and relative outcome measures in brain regions of interest, i.e., K1 or R1 for [11C]UCB-J perfusion, VT (volume of distribution) or DVR to cerebellum for [11C]UCB-J binding to SV2A; and Ki or KiR to cerebellum for [18F]FDG metabolism. [11C]UCB-J binding and [18F]FDG metabolism showed a similar magnitude of reduction in the medial temporal lobe of AD -compared to CN participants. However, the magnitude of reduction of [11C]UCB-J binding in neocortical regions was less than that observed with [18F]FDG metabolism. Inter-tracer correlations were also higher in the medial temporal regions between synaptic density and metabolism, with lower correlations in neocortical regions. [11C]UCB-J perfusion showed a similar pattern to [18F]FDG metabolism, with high inter-tracer regional correlations. In summary, we conducted the first in vivo PET imaging of synaptic density and metabolism in the same AD participants and reported a concordant reduction in medial temporal regions but a discordant reduction in neocortical regions.

Validation technique and improvements introduced in a new dedicated brain positron emission tomograph (CareMiBrain)

The goal of developing a PET dedicated to the brain (CareMiBrain) has evolved from its initial approach to diagnosis and monitoring of dementias, to the more ambitious of creating a revolutionary clinical pathway for the knowledge and personalized treatment of multiple neurological diseases. The main innovative feature of CareMiBrain is the use of detectors with continuous crystals, which allow a high resolution determination of the depth of annihilation photons interaction within the thickness of the scintillation crystal. The technical validation phase of the equipment consisted of a pilot, prospective and observational study whose objective was to obtain the first images (40 patients), analyze them and make adjustments in the acquisition, reconstruction and correction parameters, comparing the image quality of the CareMiBrain equipment with that of the whole-body PET-CT. Thanks to the team meetings and the joint analysis of the images, it was possible to detect its weak points and some of its causes. The calibration, acquisition and processing processes, as well as the reconstruction, were optimized, the number of iterations was set to achieve the best signal-to-noise ratio, the random correction was optimized and a post-processing algorithm was included in the reconstruction algorithm. The main technical improvements implemented in this phase of technical validation carried out through collaboration of the Services of Nuclear Medicine and Neurology of the Hospital Clínico San Carlos with the Spanish company Oncovision will be exposed in a project financed with funds from the European Union (Horizon 2020 innovation program, 713323).

Validation technique and improvements introduced in a new dedicated brain positron emission tomograph (CareMiBrain)

The goal of developing a PET dedicated to the brain (CareMiBrain) has evolved from its initial approach to diagnosis and monitoring of dementias, to the more ambitious of creating a revolutionary clinical pathway for the knowledge and personalized treatment of multiple neurological diseases. The main innovative feature of CareMiBrain is the use of detectors with continuous crystals, which allow a high resolution determination of the depth of annihilation photons interaction within the thickness of the scintillation crystal. The technical validation phase of the equipment consisted of a pilot, prospective and observational study whose objective was to obtain the first images (40 patients), analyze them and make adjustments in the acquisition, reconstruction and correction parameters, comparing the image quality of the CareMiBrain equipment with that of the whole-body PET/CT. Thanks to the team meetings and the joint analysis of the images, it was possible to detect its weak points and some of its causes. The calibration, acquisition and processing processes, as well as the reconstruction, were optimized, the number of iterations was set to achieve the best signal-to-noise ratio, the random correction was optimized and a post-processing algorithm was included in the reconstruction algorithm. The main technical improvements implemented in this phase of technical validation carried out through collaboration of the Services of Nuclear Medicine and Neurology of the Hospital Clínico San Carlos with the Spanish company Oncovision will be exposed in a project financed with funds from the European Union (Horizon 2020 innovation program, 713323).

PET Neuroimaging of Alzheimer's Disease: Radiotracers and Their Utility in Clinical Research

Alzheimer's Disease (AD), the leading cause of senile dementia, is a progressive neurodegenerative disorder affecting millions of people worldwide and exerting tremendous socioeconomic burden on all societies. Although definitive diagnosis of AD is often made in the presence of clinical manifestations in late stages, it is now universally believed that AD is a continuum of disease commencing from the preclinical stage with typical neuropathological alterations appearing decades prior to its first symptom, to the prodromal stage with slight symptoms of amnesia (amnestic mild cognitive impairment, aMCI), and then to the terminal stage with extensive loss of basic cognitive functions, i.e., AD-dementia. Positron emission tomography (PET) radiotracers have been developed in a search to meet the increasing clinical need of early detection and treatment monitoring for AD, with reference to the pathophysiological targets in Alzheimer's brain. These include the pathological aggregations of misfolded proteins such as β-amyloid (Aβ) plagues and neurofibrillary tangles (NFTs), impaired neurotransmitter system, neuroinflammation, as well as deficient synaptic vesicles and glucose utilization. In this article we survey the various PET radiotracers available for AD imaging and discuss their clinical applications especially in terms of early detection and cognitive relevance.

Alzheimer's Dementia: The Emerging Role of Positron Emission Tomography

Alzheimer's disease (AD) is the most common cause of dementia and accounts for approximately 50% to 80% of all cases of dementia. The diagnosis of probable AD is based on clinical criteria and overlapping clinical features pose a challenge to accurate diagnosis. However, neuroimaging has been included as a biomarker in various published criteria for the diagnosis of probable AD, in the absence of a confirmatory diagnostic test during life. Advances in neuroimaging techniques and their inclusion in diagnostic and research criteria for the diagnosis of AD includes the use of positron emission tomography (PET) imaging as a biomarker in various therapeutic and prognostic studies in AD. The development and application of a range of PET tracers will allow more detailed assessment of people with AD and will improve diagnostic specificity and targeted therapy of AD. The aim of this review is to summarize current evidence on PET imaging using the non-specific tracer [18F]fluorodeoxyglucose and specific tracers that target amyloid and tau pathology in people with AD.

A Role of PET/MR Imaging in Dementia?

Since many years, magnetic resonance imaging (MRI) and positron emission tomography (PET) have a prominent role in neurodegenerative disorders and dementia, not only in a research setting but also in a clinical setting. For several decades, information from both modalities is combined ranging from individual visual assessments to fully integrating all images. Several tools are available to coregister images from MRI and PET and to covisualize these images. When studying neurodegenerative disorders with PET it is important to perform a partial volume correction and this can be done using the structural information obtained by MRI. With the advent of PET/MR, the question arises in how far this hybrid imaging modality is an added value compared to combining PET and MRI data from two separate modalities. One issue in PET/MR is still not yet completely settled, that is, the attenuation correction. This is of less importance for visual assessments but it can become an issue when combining data from PET/CT and PET/MR scanners in multicenter studies or when using cut-off values to classify patients. Simultaneous imaging has clearly some advantages: for the patient it is beneficial to have only one scan session instead of two but also in cases in which PET data are related to functional of physiological data acquired with MRI (such as functional MRI or arterial spin labeling). However, the most important benefit is currently the more integrated use of PET and MRI. This is also possible with separate measurements but requires more streamlining of the whole process. In that case coregistration of images is mandatory. It needs to be determined in which cases simultaneous PET/MRI leads to new insights or improved diagnosis compared to multimodal imaging using dedicated scanners.

Neuroimaging in Frontotemporal Lobar Degeneration: Research and Clinical Utility

Frontotemporal lobar dementia (FTLD) is a clinically and pathologically complex disease. Advances in neuroimaging techniques have provided a specialized set of tools to investigate underlying pathophysiology and identify clinical biomarkers that aid in diagnosis, prognostication, monitoring, and identification of appropriate endpoints in clinical trials. In this chapter, we review data discussing the utility of neuroimaging biomarkers in sporadic FTLD, with an emphasis on current and future clinical applications. Among those modalities readily utilized in clinical settings, T1-weighted structural magnetic resonance imaging (MRI) and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) are best supported in differential diagnosis and as targets for clinical trial endpoints. However, a number of nonclinical neuroimaging modalities, including diffusion tensor imaging and resting-state functional connectivity MRI, show promise as biomarkers to predict progression and as clinical trial endpoints. Other neuroimaging modalities, including amyloid PET, Tau PET, and arterial spin labeling MRI, are also discussed, though more work is required to establish their utility in FTLD in clinical settings.

Reciprocal Incremental Value of 18F-FDG-PET and Cerebrospinal Fluid Biomarkers in Mild Cognitive Impairment Patients Suspected for Alzheimer's Disease and Inconclusive First Biomarker

BACKGROUND

In Alzheimer's disease (AD) diagnosis, both cerebrospinal fluid (CSF) biomarkers and FDG-PET sometimes give inconclusive results.

OBJECTIVE

To evaluate the incremental diagnostic value of FDG-PET over CSF biomarkers, and vice versa, in patients with mild cognitive impairment (MCI) and suspected AD, in which the first biomarker resulted inconclusive.

METHODS

A consecutive series of MCI patients was retrospectively selected from two Memory Clinics where, as per clinical routine, either the first biomarker choice is FDG-PET and CSF biomarkers are only used in patients with uninformative FDG-PET, or vice versa. We defined criteria of uncertainty in interpretation of FDG-PET and CSF biomarkers, according to current evidence. The final diagnosis was established according to clinical-neuropsychological follow-up of at least one year (mean 4.4±2.2).

RESULTS

When CSF was used as second biomarker after FDG-PET, 14 out of 36 (39%) received informative results. Among these 14 patients, 11 (79%) were correctly classified with respect to final diagnosis, thus with a relative incremental value of CSF over FDG-PET of 30.6%. When FDG-PET was used as second biomarker, 26 out of 39 (67%) received informative results. Among these 26 patients, 15 (58%) were correctly classified by FDG-PET with respect to final diagnosis, thus with a relative incremental value over CSF of 38.5%.

CONCLUSION

Our real-world data confirm the added values of FDG-PET (or CSF) in a diagnostic pathway where CSF (or FDG-PET) was used as first biomarkers in suspected AD. These findings should be replicated in larger studies with prospective enrolment according to a Phase III design.

Clinical impact of digital and conventional PET control databases for semi-quantitative analysis of brain 18F-FDG digital PET scans

PURPOSE

Digital PET cameras markedly improve sensitivity and spatial resolution of brain ¹⁸F-FDG PET images compared to conventional cameras. Our study aimed to assess whether specific control databases are required to improve the diagnostic performance of these recent advances.

METHODS

We retrospectively selected two groups of subjects, twenty-seven Alzheimer's Disease (AD) patients and twenty-two healthy control (HC) subjects. All subjects underwent a brain ¹⁸F-FDG PET on a digital camera (Vereos, Philips®). These two group (AD and HC) are compared, using a Semi-Quantitative Analysis (SQA), to two age and sex matched controls acquired with a digital PET/CT (Vereos, Philips®) or a conventional PET/CT (Biograph 6, Siemens®) camera, at group and individual levels. Moreover, individual visual interpretation of SPM T-maps was provided for the positive diagnosis of AD by 3 experienced raters.

RESULTS

At group level, SQA using digital controls detected more marked hypometabolic areas in AD (+ 116 cm³ at p < 0.001 uncorrected for the voxel, corrected for the cluster) than SQA using conventional controls. At the individual level, the accuracy of SQA for discriminating AD using digital controls was higher than SQA using conventional controls (86% vs. 80%, p < 0.01, at p < 0.005 uncorrected for the voxel, corrected for the cluster), with higher sensitivity (89% vs. 78%) and similar specificity (82% vs. 82%). These results were confirmed by visual analysis (accuracies of 84% and 82% for digital and conventional controls respectively, p = 0.01).

CONCLUSION

There is an urgent need to establish specific digital PET control databases for SQA of brain ¹⁸F-FDG PET images as such databases improve the accuracy of AD diagnosis.

Amyloid-PET and 18F-FDG-PET in the diagnostic investigation of Alzheimer's disease and other dementias

Various biomarkers are available to support the diagnosis of neurodegenerative diseases in clinical and research settings. Among the molecular imaging biomarkers, amyloid-PET, which assesses brain amyloid deposition, and ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET, which assesses glucose metabolism, provide valuable and complementary information. However, uncertainty remains regarding the optimal timepoint, combination, and an order in which these PET biomarkers should be used in diagnostic evaluations because conclusive evidence is missing. Following an expert panel discussion, we reached an agreement on the specific use of the individual biomarkers, based on available evidence and clinical expertise. We propose a diagnostic algorithm with optimal timepoints for these PET biomarkers, also taking into account evidence from other biomarkers, for early and differential diagnosis of neurodegenerative diseases that can lead to dementia. We propose three main diagnostic pathways with distinct biomarker sequences, in which amyloid-PET and ¹⁸F-FDG-PET are placed at different positions in the order of diagnostic evaluations, depending on clinical presentation. We hope that this algorithm can support diagnostic decision making in specialist clinical settings with access to these biomarkers and might stimulate further research towards optimal diagnostic strategies.

Timely Detection of Mild Cognitive Impairment in Italy: An Expert Opinion

Mild cognitive impairment (MCI) generally evolves in a gradually progressive decline in memory and non-memory cognitive domains that may eventually decay to dementia. This process might be preventable by improving early detection of the MCI syndrome followed by proper and timely interventions. The aim of this work was providing helpful indications for a standardized early diagnosis of MCI, mainly focusing on the Italian elderly population. We reviewed here MCI epidemiology and classification, as well as the most recent advancements in early detection of the patient with MCI in the Italian scenario. Specialist centers in connection with general practitioners (GPs) have been established across the country and designated as Centers for Cognitive Disorders and Dementia (CDCD). CDCDs are dedicated to the diagnosis and management of patients for all forms of dementia across all the complex staging spectrum. New tools were made available by the advancements of imaging techniques and of the research on biomarkers, leading to novel approaches based on the combination of imaging and biomarker detection, to improve accuracy and effectiveness in the early diagnosis of MCI. Moreover, patient genotyping, alone or in combination with other techniques, was also revealed as a promising method in evaluating and preventing MCI progression. We recommend the introduction of all these novel tools in the diagnostic practice of the specialist centers and that further efforts and resources are spent into the research of the most effective techniques and biomarkers to be introduced as first-level tests into the practice of early diagnosis of MCI.

Clinical impact of 18F-FDG-PET among memory clinic patients with uncertain diagnosis

Purpose

To assess the clinical impact and incremental diagnostic value of ¹⁸F-fluorodeoxyglucose (FDG-PET) among memory clinic patients with uncertain diagnosis.

Methods

The study population consisted of 277 patients who, despite extensive baseline cognitive assessment, MRI, and CSF analyses, had an uncertain diagnosis of mild cognitive impairment (MCI) (n = 177) or dementia (n = 100). After baseline diagnosis, each patient underwent an FDG-PET, followed by a post-FDG-PET diagnosis formulation. We evaluated (i) the change in diagnosis (baseline vs. post-FDG-PET), (ii) the change in diagnostic accuracy when comparing each baseline and post-FDG-PET diagnosis to a long-term follow-up (3.6 ± 1.8 years) diagnosis used as reference, and (iii) comparative FDG-PET performance testing in MCI and dementia conditions.

Results

FDG-PET led to a change in diagnosis in 86 of 277 (31%) patients, in particular in 57 of 177 (32%) MCI and in 29 of 100 (29%) dementia patients. Diagnostic change was greater than two-fold in the sub-sample of cases with dementia “of unclear etiology” (change in diagnosis in 20 of 32 (63%) patients). In the dementia group, after results of FDG-PET, diagnostic accuracy improved from 77 to 90% in Alzheimer’s disease (AD) and from 85 to 94% in frontotemporal lobar degeneration (FTLD) patients (p < 0.01). FDG-PET performed better in dementia than in MCI (positive likelihood ratios >5 and < 5, respectively).

Conclusion

Within a selected clinical population, FDG-PET has a significant clinical impact, both in early and differential diagnosis of uncertain dementia. FDG-PET provides significant incremental value to detect AD and FTLD over a clinical diagnosis of uncertain dementia.

Electronic supplementary material

The online version of this article (10.1007/s00259-020-04969-7) contains supplementary material, which is available to authorized users.

Mitochondria dysfunction in the pathogenesis of Alzheimer's disease: recent advances

Alzheimer's disease (AD) is one of the most prevalent neurodegenerative diseases, characterized by impaired cognitive function due to progressive loss of neurons in the brain. Under the microscope, neuronal accumulation of abnormal tau proteins and amyloid plaques are two pathological hallmarks in affected brain regions. Although the detailed mechanism of the pathogenesis of AD is still elusive, a large body of evidence suggests that damaged mitochondria likely play fundamental roles in the pathogenesis of AD. It is believed that a healthy pool of mitochondria not only supports neuronal activity by providing enough energy supply and other related mitochondrial functions to neurons, but also guards neurons by minimizing mitochondrial related oxidative damage. In this regard, exploration of the multitude of mitochondrial mechanisms altered in the pathogenesis of AD constitutes novel promising therapeutic targets for the disease. In this review, we will summarize recent progress that underscores the essential role of mitochondria dysfunction in the pathogenesis of AD and discuss mechanisms underlying mitochondrial dysfunction with a focus on the loss of mitochondrial structural and functional integrity in AD including mitochondrial biogenesis and dynamics, axonal transport, ER-mitochondria interaction, mitophagy and mitochondrial proteostasis.

The path to biomarker-based diagnostic criteria for the spectrum of neurodegenerative diseases

Introduction: The postmortem examination still represents the reference standard for detecting the pathological nature of chronic neurodegenerative diseases (NDD). This approach displays intrinsic conceptual limitations since NDD represent a dynamic spectrum of partially overlapping phenotypes, shared pathomechanistic alterations that often give rise to mixed pathologies.Areas covered: We scrutinized the international clinical diagnostic criteria of NDD and the literature to provide a roadmap toward a biomarker-based classification of the NDD spectrum. A few pathophysiological biomarkers have been established for NDD. These are time-consuming, invasive, and not suitable for preclinical detection. Candidate screening biomarkers are gaining momentum. Blood neurofilament light-chain represents a robust first-line tool to detect neurodegeneration tout court and serum progranulin helps detect genetic frontotemporal dementia. Ultrasensitive assays and retinal scans may identify Aβ pathology early, in blood and the eye, respectively. Ultrasound also represents a minimally invasive option to investigate the substantia nigra. Protein misfolding amplification assays may accurately detect α-synuclein in biofluids.Expert opinion: Data-driven strategies using quantitative rather than categorical variables may be more reliable for quantification of contributions from pathophysiological mechanisms and their spatial-temporal evolution. A systems biology approach is suitable to untangle the dynamics triggering loss of proteostasis, driving neurodegeneration and clinical evolution.

Imaging Biomarkers for Neurodegeneration in Presymptomatic Familial Frontotemporal Lobar Degeneration

Frontotemporal lobar degeneration (FTLD) is a neurodegenerative disorder characterized by behavioral changes, language abnormality, as well as executive function deficits and motor impairment. In about 30-50% of FTLD patients, an autosomal dominant pattern of inheritance was found with major mutations in the MAPT, GRN, and the C9orf72 repeat expansion. These mutations could lead to neurodegenerative pathology years before clinical symptoms onset. With potential disease-modifying treatments that are under development, non-invasive biomarkers that help determine the early brain changes in presymptomatic FTLD patients will be critical for tracking disease progression and enrolling the right participants into the clinical trials at the right time during the disease course. In recent years, there is increasing evidence that a number of imaging biomarkers show the abnormalities during the presymptomatic stage. Imaging biomarkers of presymptomatic familial FTLD may provide insight into the underlying neurodegenerative process years before symptom onset. Structural magnetic resonance imaging (MRI) has demonstrated cortical degeneration with a mutation-specific neurodegeneration pattern years before onset of clinical symptoms in presymptomatic familial FTLD mutation carriers. In addition, diffusion tensor imaging (DTI) has shown the loss of white matter microstructural integrity in the presymptomatic stage of familial FTLD. Furthermore, proton magnetic resonance spectroscopy (¹H MRS), which provides a non-invasive measurement of brain biochemistry, has identified early neurochemical abnormalities in presymptomatic MAPT mutation carriers. Positron emission tomography (PET) imaging with [¹⁸F]-fluorodeoxyglucose (FDG) has demonstrated the glucose hypometabolism in the presymptomatic stage of familial FTLD. Also, a novel PET ligand, ¹⁸F-AV-1451, has been used in this group to evaluate tau deposition in the brain. Promising imaging biomarkers for presymptomatic familial FTLD have been identified and assessed for specificity and sensitivity for accurate prediction of symptom onset and tracking disease progression during the presymptomatic stage when clinical measures are not useful. Furthermore, identifying imaging biomarkers for the presymptomatic stage is important for the design of disease-modifying trials. We review the recent progress in imaging biomarkers of the presymptomatic phase of familial FTLD and discuss the imaging techniques and analysis methods, with a focus on the potential implication of these imaging techniques and their utility in specific mutation types.

Italian consensus recommendations for a biomarker-based aetiological diagnosis in mild cognitive impairment patients

BACKGROUND AND PURPOSE

Biomarkers support the aetiological diagnosis of neurocognitive disorders in vivo. Incomplete evidence is available to drive clinical decisions; available diagnostic algorithms are generic and not very helpful in clinical practice. The aim was to develop a biomarker-based diagnostic algorithm for mild cognitive impairment patients, leveraging on knowledge from recognized national experts.

METHODS

With a Delphi procedure, experienced clinicians making variable use of biomarkers in clinical practice and representing five Italian scientific societies (neurology - Società Italiana di Neurologia per le Demenze; neuroradiology - Associazione Italiana di Neuroradiologia; biochemistry - Società Italiana di Biochimica Clinica; psychogeriatrics - Associazione Italiana di Psicogeriatria; nuclear medicine - Associazione Italiana di Medicina Nucleare) defined the theoretical framework, relevant literature, the diagnostic issues to be addressed and the diagnostic algorithm. An N-1 majority defined consensus achievement.

RESULTS

The panellists chose the 2011 National Institute on Aging and Alzheimer's Association diagnostic criteria as the reference theoretical framework and defined the algorithm in seven Delphi rounds. The algorithm includes baseline clinical and cognitive assessment, blood examination, and magnetic resonance imaging with exclusionary and inclusionary roles; dopamine transporter single-photon emission computed tomography (if no/unclear parkinsonism) or metaiodobenzylguanidine cardiac scintigraphy for suspected dementia with Lewy bodies with clear parkinsonism (round VII, votes (yes-no-abstained): 3-1-1); ¹⁸ F-fluorodeoxyglucose positron emission tomography for suspected frontotemporal lobar degeneration and low diagnostic confidence of Alzheimer's disease (round VII, 4-0-1); cerebrospinal fluid for suspected Alzheimer's disease (round IV, 4-1-0); and amyloid positron emission tomography if cerebrospinal fluid was not possible/accepted (round V, 4-1-0) or inconclusive (round VI, 5-0-0).

CONCLUSIONS

These consensus recommendations can guide clinicians in the biomarker-based aetiological diagnosis of mild cognitive impairment, whilst guidelines cannot be defined with evidence-to-decision procedures due to incomplete evidence.

Nodal Global Efficiency in Front-Parietal Lobe Mediated Periventricular White Matter Hyperintensity (PWMH)-Related Cognitive Impairment

White matter hyperintensity (WMH) is widely observed in the elderly population and serves as a key indicator of cognitive impairment (CI). However, the underlying mechanism remains to be elucidated. Herein, we investigated the topological patterns of resting state functional networks in WMH subjects and the relationship between the topological measures and CI. A graph theory-based analysis was employed in the resting-state functional magnetic resonance scans of 112 subjects (38 WMH subjects with cognitive impairment without dementia (CIND), 36 WMH subjects with normal cognition and 38 healthy controls (HCs), and we found that WMH-CIND subjects displayed decreased global efficiency at the levels of the whole brain, specific subnetworks [fronto-parietal network (FPN) and cingulo-opercular network (CON)] and certain nodes located in the FPN and CON, as well as decreased local efficiency in subnetworks. Our results demonstrated that nodal global efficiency in frontal and parietal regions mediated the impairment of information processing speed related to periventricular WMH (PWMH). Additionally, we performed support vector machine (SVM) analysis and found that altered functional efficiency can identify WMH-CIND subjects with high accuracy, sensitivity and specificity. These findings suggest impaired functional networks in WMH-CIND individuals and that decreased functional efficiency may be a feature of CI in WMH subjects.

Prodromal and Early bvFTD: Evaluating Clinical Features and Current Biomarkers

Despite the current diagnostic criteria, early diagnostics of behavioral variant of frontotemporal dementia (bvFTD) has remained challenging. Patients with bvFTD often present with misleading psychiatric phenotype, and, on the other hand, impairment in memory functions have increasingly been reported. However, impaired episodic memory is currently considered as an exclusion criterion for bvFTD. Single biofluid-based or imaging biomarkers do not currently provide sufficient sensitivity or specificity for early bvFTD diagnosis at single-subject level, although studies have suggested improved accuracy with different biomarker combinations. In this mini review, we evaluate the core clinical features of early bvFTD and summarize the most potential imaging and fluid biomarkers for bvFTD diagnostics.

Neuroimaging and Neurolaw: Drawing the Future of Aging

Human brain-aging is a complex, multidimensional phenomenon. Knowledge of the numerous aspects that revolve around it is therefore essential if not only the medical issues, but also the social, psychological, and legal issues related to this phenomenon are to be managed correctly. In the coming decades, it will be necessary to find solutions to the management of the progressive aging of the population so as to increase the number of individuals that achieve successful aging. The aim of this article is to provide a current overview of the physiopathology of brain aging and of the role and perspectives of neuroimaging in this context. The progressive development of neuroimaging has opened new perspectives in clinical and basic research and it has modified the concept of brain aging. Neuroimaging will play an increasingly important role in the definition of the individual's brain aging in every phase of the physiological and pathological process. However, when the process involved in age-related brain cognitive diseases is being investigated, factors that might affect this process on a clinical and behavioral level (genetic susceptibility, risks factors, endocrine changes) cannot be ignored but must, on the contrary, be integrated into a neuroimaging evaluation to ensure a correct and global management, and they are therefore discussed in this article. Neuroimaging appears important to the correct management of age-related brain cognitive diseases not only within a medical perspective, but also legal, according to a wider approach based on development of relationship between neuroscience and law. The term neurolaw, the neologism born from the relationship between these two disciplines, is an emerging field of study, that deals with various issues in the impact of neurosciences on individual rights. Neuroimaging, enhancing the detection of physiological and pathological brain aging, could give an important contribution to the field of neurolaw in elderly where the full control of cognitive and volitional functions is necessary to maintain a whole series of rights linked to legal capacity. For this reason, in order to provide the clinician and researcher with a broad view of the brain-aging process, the role of neurolaw will be introduced into the brain-aging context.

Secondary prevention of Alzheimer's dementia: neuroimaging contributions

BACKGROUND

In Alzheimer's disease (AD), pathological changes may arise up to 20 years before the onset of dementia. This pre-dementia window provides a unique opportunity for secondary prevention. However, exposing non-demented subjects to putative therapies requires reliable biomarkers for subject selection, stratification, and monitoring of treatment. Neuroimaging allows the detection of early pathological changes, and longitudinal imaging can assess the effect of interventions on markers of molecular pathology and rates of neurodegeneration. This is of particular importance in pre-dementia AD trials, where clinical outcomes have a limited ability to detect treatment effects within the typical time frame of a clinical trial. We review available evidence for the use of neuroimaging in clinical trials in pre-dementia AD. We appraise currently available imaging markers for subject selection, stratification, outcome measures, and safety in the context of such populations.

MAIN BODY

Amyloid positron emission tomography (PET) is a validated in-vivo marker of fibrillar amyloid plaques. It is appropriate for inclusion in trials targeting the amyloid pathway, as well as to monitor treatment target engagement. Amyloid PET, however, has limited ability to stage the disease and does not perform well as a prognostic marker within the time frame of a pre-dementia AD trial. Structural magnetic resonance imaging (MRI), providing markers of neurodegeneration, can improve the identification of subjects at risk of imminent decline and hence play a role in subject inclusion. Atrophy rates (either hippocampal or whole brain), which can be reliably derived from structural MRI, are useful in tracking disease progression and have the potential to serve as outcome measures. MRI can also be used to assess comorbid vascular pathology and define homogeneous groups for inclusion or for subject stratification. Finally, MRI also plays an important role in trial safety monitoring, particularly the identification of amyloid-related imaging abnormalities (ARIA). Tau PET to measure neurofibrillary tangle burden is currently under development. Evidence to support the use of advanced MRI markers such as resting-state functional MRI, arterial spin labelling, and diffusion tensor imaging in pre-dementia AD is preliminary and requires further validation.

CONCLUSION

We propose a strategy for longitudinal imaging to track early signs of AD including quantitative amyloid PET and yearly multiparametric MRI.

European Association of Nuclear Medicine and European Academy of Neurology recommendations for the use of brain 18 F-fluorodeoxyglucose positron emission tomography in neurodegenerative cognitive impairment and dementia: Delphi consensus

BACKGROUND AND PURPOSE

Recommendations for using fluorodeoxyglucose positron emission tomography (FDG-PET) to support the diagnosis of dementing neurodegenerative disorders are sparse and poorly structured.

METHODS

Twenty-one questions on diagnostic issues and on semi-automated analysis to assist visual reading were defined. Literature was reviewed to assess study design, risk of bias, inconsistency, imprecision, indirectness and effect size. Critical outcomes were sensitivity, specificity, accuracy, positive/negative predictive value, area under the receiver operating characteristic curve, and positive/negative likelihood ratio of FDG-PET in detecting the target conditions. Using the Delphi method, an expert panel voted for/against the use of FDG-PET based on published evidence and expert opinion.

RESULTS

Of the 1435 papers, 58 papers provided proper quantitative assessment of test performance. The panel agreed on recommending FDG-PET for 14 questions: diagnosing mild cognitive impairment due to Alzheimer's disease (AD), frontotemporal lobar degeneration (FTLD) or dementia with Lewy bodies (DLB); diagnosing atypical AD and pseudo-dementia; differentiating between AD and DLB, FTLD or vascular dementia, between DLB and FTLD, and between Parkinson's disease and progressive supranuclear palsy; suggesting underlying pathophysiology in corticobasal degeneration and progressive primary aphasia, and cortical dysfunction in Parkinson's disease; using semi-automated assessment to assist visual reading. Panellists did not support FDG-PET use for pre-clinical stages of neurodegenerative disorders, for amyotrophic lateral sclerosis and Huntington disease diagnoses, and for amyotrophic lateral sclerosis or Huntington-disease-related cognitive decline.

CONCLUSIONS

Despite limited formal evidence, panellists deemed FDG-PET useful in the early and differential diagnosis of the main neurodegenerative disorders, and semi-automated assessment helpful to assist visual reading. These decisions are proposed as interim recommendations.

Diagnostic utility of FDG-PET in the differential diagnosis between different forms of primary progressive aphasia

Purpose

A joint effort of the European Association of Nuclear Medicine (EANM) and the European Academy of Neurology (EAN) aims at clinical guidance for the use of FDG-PET in neurodegenerative diseases. This paper addresses the diagnostic utility of FDG-PET over clinical/neuropsychological assessment in the differentiation of the three forms of primary progressive aphasia (PPA).

Methods

Seven panelists were appointed by the EANM and EAN and a literature search was performed by using harmonized PICO (Population, Intervention, Comparison, Outcome) question keywords. The studies were screened for eligibility, and data extracted to assess their methodological quality. Critical outcomes were accuracy indices in differentiating different PPA clinical forms. Subsequently Delphi rounds were held with the extracted data and quality assessment to reach a consensus based on both literature and expert opinion.

Results

Critical outcomes for this PICO were available in four of the examined papers. The level of formal evidence supporting clinical utility of FDG-PET in differentiating among PPA variants was considered as poor. However, the consensual recommendation was defined on Delphi round I, with six out of seven panelists supporting clinical use.

Conclusions

Quantitative evidence demonstrating utility or lack thereof is still missing. Panelists decided consistently to provide interim support for clinical use based on the fact that a typical atrophy or metabolic pattern is needed for PPA according to the diagnostic criteria, and the synaptic failure detected by FDG-PET is an earlier phenomenon than atrophy. Also, a normal FDG-PET points to a non-neurodegenerative cause.