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.

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

Importance Plasma phosphorylated tau at threonine 181 (p-tau181) has been proposed as an easily accessible biomarker for the detection of Alzheimer disease (AD) pathology, but its ability to monitor disease progression in AD remains unclear. Objective To study the potential of longitudinal plasma p-tau181 measures for assessing neurodegeneration progression and cognitive decline in AD in comparison to plasma neurofilament light chain (NfL), a disease-nonspecific marker of neuronal injury. Design, Setting, and Participants This longitudinal cohort study included data from the Alzheimer’s Disease Neuroimaging Initiative from February 1, 2007, to June 6, 2016. Follow-up blood sampling was performed for up to 8 years. Plasma p-tau181 measurements were performed in 2020. This was a multicentric observational study of 1113 participants, including cognitively unimpaired participants as well as patients with cognitive impairment (mild cognitive impairment and AD dementia). Participants were eligible for inclusion if they had available plasma p-tau181 and NfL measurements and at least 1 fluorine-18–labeled fluorodeoxyglucose (FDG) positron emission tomography (PET) or structural magnetic resonance imaging scan performed at the same study visit. Exclusion criteria included any significant neurologic disorder other than suspected AD; presence of infection, infarction, or multiple lacunes as detected by magnetic resonance imaging; and any significant systemic condition that could lead to difficulty complying with the protocol. Exposures Plasma p-tau181 and NfL measured with single-molecule array technology. Main Outcomes and Measures Longitudinal imaging markers of neurodegeneration (FDG PET and structural magnetic resonance imaging) and cognitive test scores (Preclinical Alzheimer Cognitive Composite and Alzheimer Disease Assessment Scale–Cognitive Subscale with 13 tasks). Data were analyzed from June 20 to August 15, 2020. Results Of the 1113 participants (mean [SD] age, 74.0 [7.6] years; 600 men [53.9%]; 992 non-Hispanic White participants [89.1%]), a total of 378 individuals (34.0%) were cognitively unimpaired (CU) and 735 participants (66.0%) were cognitively impaired (CImp). Of the CImp group, 537 (73.1%) had mild cognitive impairment, and 198 (26.9%) had AD dementia. Longitudinal changes of plasma p-tau181 were associated with cognitive decline (CU:r = –0.24,P  Conclusions and Relevance Study findings suggest that plasma p-tau181 was an accessible and scalable marker for predicting and monitoring neurodegeneration and cognitive decline and was, unlike plasma NfL, AD specific. The study findings suggest implications for the use of plasma biomarkers as measures to monitor AD progression in clinical practice and treatment trials.

/pdf/longitudinal-associations-of-blood-phosphorylated-tau181-and-3c0hapg0x8.pdf

Longitudinal Associations of Blood Phosphorylated Tau181 and Neurofilament Light Chain With Neurodegeneration in Alzheimer Disease

Objective To determine the diagnostic and prognostic performance of serum neurofilament light chain (NFL) in amyotrophic lateral sclerosis (ALS). Methods This single-centre, prospective, longitudinal study included the following patients: 124 patients with ALS; 50 patients without neurodegenerative diseases; 44 patients with conditions included in the differential diagnosis of ALS (disease controls); 65 patients with other neurodegenerative diseases (20 with frontotemporal dementia, 20 with Alzheimer’s disease, 19 with Parkinson’s disease, 6 with Creutzfeldt-Jakob disease (CJD)). Serum NFL levels were measured using the ultrasensitive single molecule array (Simoa) technology. Results Serum NFL levels were higher in ALS in comparison to all other categories except for CJD. A cut-off level of 62 pg/mL discriminated between ALS and all other conditions with 85.5% sensitivity (95% CI 78% to 91.2%) and 81.8% specificity (95% CI 74.9% to 87.4%). Among patients with ALS, serum NFL correlated positively with disease progression rate (rs=0.336, 95% CI 0.14 to 0.506, p=0.0008), and higher levels were associated with shorter survival (p=0.0054). Serum NFL did not differ among patients in different ALS pathological stages as evaluated by diffusion-tensor imaging, and in single patients NFL levels were stable over time. Conclusions Serum NFL is increased in ALS in comparison to other conditions and can serve as diagnostic and prognostic biomarker. We established a cut-off level for the diagnosis of ALS.

https://jnnp.bmj.com/content/jnnp/90/2/157.full.pdf

Neurofilament light chain in serum for the diagnosis of amyotrophic lateral sclerosis

Dementia, a chronic and progressive cognitive declination of brain function caused by disease or impairment, is becoming more prevalent due to the aging population. A major challenge in dementia is achieving accurate and timely diagnosis. In recent years, neuroimaging with computer-aided algorithms have made remarkable advances in addressing this challenge. The success of these approaches is mostly attributed to the application of machine learning techniques for neuroimaging. In this review paper, we present a comprehensive survey of automated diagnostic approaches for dementia using medical image analysis and machine learning algorithms published in the recent years. Based on the rigorous review of the existing works, we have found that, while most of the studies focused on Alzheimer's disease, recent research has demonstrated reasonable performance in the identification of other types of dementia remains a major challenge. Multimodal imaging analysis deep learning approaches have shown promising results in the diagnosis of these other types of dementia. The main contributions of this review paper are as follows. 1) Based on the detailed analysis of the existing literature, this paper discusses neuroimaging procedures for dementia diagnosis. 2) It systematically explains the most recent machine learning techniques and, in particular, deep learning approaches for early detection of dementia.

https://ieeexplore.ieee.org/ielaam/4664312/8643115/8572804-aam.pdf

Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects

Abstract Amyloid-β (Aβ) pathology is one of the earliest detectable brain changes in Alzheimer’s disease (AD) pathogenesis. The overall load and spatial distribution of brain Aβ can be determined in vivo using positron emission tomography (PET), for which three fluorine-18 labelled radiotracers have been approved for clinical use. In clinical practice, trained readers will categorise scans as either Aβ positive or negative, based on visual inspection. Diagnostic decisions are often based on these reads and patient selection for clinical trials is increasingly guided by amyloid status. However, tracer deposition in the grey matter as a function of amyloid load is an inherently continuous process, which is not sufficiently appreciated through binary cut-offs alone. State-of-the-art methods for amyloid PET quantification can generate tracer-independent measures of Aβ burden. Recent research has shown the ability of these quantitative measures to highlight pathological changes at the earliest stages of the AD continuum and generate more sensitive thresholds, as well as improving diagnostic confidence around established binary cut-offs. With the recent FDA approval of aducanumab and more candidate drugs on the horizon, early identification of amyloid burden using quantitative measures is critical for enrolling appropriate subjects to help establish the optimal window for therapeutic intervention and secondary prevention. In addition, quantitative amyloid measurements are used for treatment response monitoring in clinical trials. In clinical settings, large multi-centre studies have shown that amyloid PET results change both diagnosis and patient management and that quantification can accurately predict rates of cognitive decline. Whether these changes in management reflect an improvement in clinical outcomes is yet to be determined and further validation work is required to establish the utility of quantification for supporting treatment endpoint decisions. In this state-of-the-art review, several tools and measures available for amyloid PET quantification are summarised and discussed. Use of these methods is growing both clinically and in the research domain. Concurrently, there is a duty of care to the wider dementia community to increase visibility and understanding of these methods. 

/pdf/quantification-of-amyloid-pet-for-future-clinical-use-a-i55mvidu.pdf

Quantification of amyloid PET for future clinical use: a state-of-the-art review

Introduction Although some studies have previously addressed the clinical impact of amyloid positron emission tomography (PET), none has specifically addressed its selective and hierarchical implementation in relation to cerebrospinal fluid analysis in a naturalistic setting. Methods This multicenter study was performed at French tertiary memory clinics in patients presenting with most complex clinical situations (i.e., early-onset, atypical clinical profiles, suspected mixed etiological conditions, unexpected rate of progression), for whom cerebrospinal fluid analysis was indicated but either not feasible or considered as noncontributory (ClinicalTrials.gov: NCT02681172). Results Two hundred five patients were enrolled with evaluable florbetaben PET scans; 64.4% of scans were amyloid positive. PET results led to changed diagnosis and improved confidence in 66.8% and 81.5% of patients, respectively, and altered management in 80.0% of cases. Discussion High-level improvement of diagnostic certainty and management is provided by selective and hierarchical implementation of florbetaben PET into current standard practices for the most complex dementia cases.

https://hal.univ-lorraine.fr/hal-03266901/document

Added value of 18F-florbetaben amyloid PET in the diagnostic workup of most complex patients with dementia in France: A naturalistic study.

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Added value of 18F-florbetaben amyloid PET in the diagnostic workup of most complex patients with dementia in France: A naturalistic study.