Adjustment for the Age- and Gender-Related Metabolic Changes Improves the Differential Diagnosis of Parkinsonism

Recent progress in the applications of presynaptic dopaminergic positron emission tomography imaging in parkinsonism

Nowadays, presynaptic dopaminergic positron emission tomography, which assesses deficiencies in dopamine synthesis, storage, and transport, is widely utilized for early diagnosis and differential diagnosis of parkinsonism. This review provides a comprehensive summary of the latest developments in the application of presynaptic dopaminergic positron emission tomography imaging in disorders that manifest parkinsonism. We conducted a thorough literature search using reputable databases such as PubMed and Web of Science. Selection criteria involved identifying peer-reviewed articles published within the last 5 years, with emphasis on their relevance to clinical applications. The findings from these studies highlight that presynaptic dopaminergic positron emission tomography has demonstrated potential not only in diagnosing and differentiating various Parkinsonian conditions but also in assessing disease severity and predicting prognosis. Moreover, when employed in conjunction with other imaging modalities and advanced analytical methods, presynaptic dopaminergic positron emission tomography has been validated as a reliable in vivo biomarker. This validation extends to screening and exploring potential neuropathological mechanisms associated with dopaminergic depletion. In summary, the insights gained from interpreting these studies are crucial for enhancing the effectiveness of preclinical investigations and clinical trials, ultimately advancing toward the goals of neuroregeneration in parkinsonian disorders.

Prediction of Cognitive Progression Due to Alzheimer's Disease in Normal Participants Based on Individual Default Mode Network Metabolic Connectivity Strength

BACKGROUND

Predicting cognitive decline among individuals in the aging population who are already amyloid-β (Aβ) positive or tau positive poses clinical challenges. In Alzheimer's disease research, intra-default mode network (DMN) connections play a pivotal role in diagnosis. In this article, we propose metabolic connectivity within the DMN as a supplementary biomarker to the Aβ, pathological tau, and neurodegeneration framework.

METHODS

Extracting data from 1292 participants in the Alzheimer's Disease Neuroimaging Initiative, we collected paired T1-weighted structural magnetic resonance imaging and 18F-labeled-fluorodeoxyglucose positron emission computed tomography scans. Individual metabolic DMN networks were constructed, and metabolic connectivity (MC) strength in the DMN was assessed. In the cognitively unimpaired group, the Cox model identified cognitively unimpaired (MC+), high-risk participants, with Kaplan-Meier survival analyses and hazard ratios revealing the strength of MC's predictive performance. Spearman correlation analyses explored relationships between MC strength, and Aβ, pathological tau, neurodegeneration biomarkers, and clinical scales. DMN standard uptake value ratio (SUVR) provided comparative insights in the analyses.

RESULTS

Both MC strength and SUVR exhibited gradual declines with cognitive deterioration, displaying significant intergroup differences. Survival analyses indicated enhanced Aβ and tau prediction with both metrics, with MC strength outperforming SUVR. Combined MC strength and Aβ yielded optimal predictive performance (hazard ratio = 9.29), followed by MC strength and tau (hazard ratio = 8.92). Generally, the strength of MC's correlations with Aβ, pathological tau, and neurodegeneration biomarkers exceeded SUVR.

CONCLUSIONS

Individuals with normal cognition and disrupted DMN metabolic connectivity face an elevated risk of cognitive decline linked to Aβ that precedes metabolic issues.

Characterization of tau propagation pattern and cascading hypometabolism from functional connectivity in Alzheimer's disease

Tau pathology and its spatial propagation in Alzheimer's disease (AD) play crucial roles in the neurodegenerative cascade leading to dementia. However, the underlying mechanisms linking tau spreading to glucose metabolism remain elusive. To address this, we aimed to examine the association between pathologic tau aggregation, functional connectivity, and cascading glucose metabolism and further explore the underlying interplay mechanisms. In this prospective cohort study, we enrolled 79 participants with 18F-Florzolotau positron emission tomography (PET), 18F-fluorodeoxyglucose PET, resting-state functional, and anatomical magnetic resonance imaging (MRI) images in the hospital-based Shanghai Memory Study. We employed generalized linear regression and correlation analyses to assess the associations between Florzolotau accumulation, functional connectivity, and glucose metabolism in whole-brain and network-specific manners. Causal mediation analysis was used to evaluate whether functional connectivity mediates the association between pathologic tau and cascading glucose metabolism. We examined 22 normal controls and 57 patients with AD. In the AD group, functional connectivity was associated with Florzolotau covariance (β = .837, r = 0.472, p < .001) and glucose covariance (β = 1.01, r = 0.499, p < .001). Brain regions with higher tau accumulation tend to be connected to other regions with high tau accumulation through functional connectivity or metabolic connectivity. Mediation analyses further suggest that functional connectivity partially modulates the influence of tau accumulation on downstream glucose metabolism (mediation proportion: 49.9%). Pathologic tau may affect functionally connected neurons directly, triggering downstream glucose metabolism changes. This study sheds light on the intricate relationship between tau pathology, functional connectivity, and downstream glucose metabolism, providing critical insights into AD pathophysiology and potential therapeutic targets.

Glucose metabolism in posterior cingulate cortex has supplementary value to predict the progression of cognitively unimpaired to dementia due to Alzheimer's disease: an exploratory study of 18F-FDG-PET

Amyloid-β (Aβ) and tau are important biomarkers to predict the progression of cognitively unimpaired (CU) to dementia due to Alzheimer's disease (AD), according to the diagnosis framework from the US National Institute on Aging and the Alzheimer's Association (NIA-AA). However, it is clinically difficult to predict those subjects who were already with Aβ positive (A +) or tau positive (T +). As a typical characteristic of neurodegeneration in the diagnosis framework, the hypometabolism of the posterior cingulate cortex (PCC) has significant clinical value in the early prediction and prevention of AD. In this paper, we proposed the glucose metabolism in the PCC as a biomarker supplement to Aβ and tau biomarkers. First, we calculated the standard uptake value ratio (SUVR) of PCC based on fluorodeoxyglucose positron emission computed tomography (FDG PET) imaging. Secondly, we performed Kaplan-Meier (KM) survival analyses to explore the predictive performance of PCC SUVR, and the hazard ratio (HR) was calculated. Finally, we performed Pearson correlation analyses to explore the physiological significance of PCC SUVR. As a result, the PCC SUVR showed a consistent downward trend along the AD continuum. KM analyses showed better predictive performance when we combined PCC SUVR with cerebro-spinal fluid (CSF) Aβ42 (from HR = 2.56 to 3.00 within 5 years; from HR = 2.76 to 4.20 within 10 years) and ptau-181 (from 2.83 to 3.91 within 5 years; from HR = 2.32 to 4.17 within 10 years). There was a slight correlation between Aβ42/Aβ40 and PCC SUVR (r = 0.14, p = 0.02). In addition, several cognition scales were also correlated to PCC SUVR (from r = -0.407 to 0.383, p < 0.05). Our results showed that glucose metabolism in PCC may be a potential biomarker supplement to the Aβ and tau biomarkers to predict the progression of CU to AD.

Diagnostic performance of artificial intelligence-assisted PET imaging for Parkinson's disease: a systematic review and meta-analysis

Artificial intelligence (AI)-assisted PET imaging is emerging as a promising tool for the diagnosis of Parkinson's disease (PD). We aim to systematically review the diagnostic accuracy of AI-assisted PET in detecting PD. The Ovid MEDLINE, Ovid Embase, Web of Science, and IEEE Xplore databases were systematically searched for related studies that developed an AI algorithm in PET imaging for diagnostic performance from PD and were published by August 17, 2023. Binary diagnostic accuracy data were extracted for meta-analysis to derive outcomes of interest: area under the curve (AUC). 23 eligible studies provided sufficient data to construct contingency tables that allowed the calculation of diagnostic accuracy. Specifically, 11 studies were identified that distinguished PD from normal control, with a pooled AUC of 0.96 (95% CI: 0.94-0.97) for presynaptic dopamine (DA) and 0.90 (95% CI: 0.87-0.93) for glucose metabolism (18F-FDG). 13 studies were identified that distinguished PD from the atypical parkinsonism (AP), with a pooled AUC of 0.93 (95% CI: 0.91 - 0.95) for presynaptic DA, 0.79 (95% CI: 0.75-0.82) for postsynaptic DA, and 0.97 (95% CI: 0.96-0.99) for 18F-FDG. Acceptable diagnostic performance of PD with AI algorithms-assisted PET imaging was highlighted across the subgroups. More rigorous reporting standards that take into account the unique challenges of AI research could improve future studies.

International consensus on clinical use of presynaptic dopaminergic positron emission tomography imaging in parkinsonism

PURPOSE

Presynaptic dopaminergic positron emission tomography (PET) imaging serves as an essential tool in diagnosing and differentiating patients with suspected parkinsonism, including idiopathic Parkinson's disease (PD) and other neurodegenerative and non-neurodegenerative diseases. The PET tracers most commonly used at the present time mainly target dopamine transporters (DAT), aromatic amino acid decarboxylase (AADC), and vesicular monoamine type 2 (VMAT2). However, established standards for the imaging procedure and interpretation of presynaptic dopaminergic PET imaging are still lacking. The goal of this international consensus is to help nuclear medicine practitioners procedurally perform presynaptic dopaminergic PET imaging.

METHOD

A multidisciplinary task group formed by experts from various countries discussed and approved the consensus for presynaptic dopaminergic PET imaging in parkinsonism, focusing on standardized recommendations, procedures, interpretation, and reporting.

CONCLUSION

This international consensus and practice guideline will help to promote the standardized use of presynaptic dopaminergic PET imaging in parkinsonism. It will become an international standard for this purpose in clinical practice.

Improved interpretation of 18F-florzolotau PET in progressive supranuclear palsy using a normalization-free deep-learning classifier

While 18F-florzolotau tau PET is an emerging biomarker for progressive supranuclear palsy (PSP), its interpretation has been hindered by a lack of consensus on visual reading and potential biases in conventional semi-quantitative analysis. As clinical manifestations and regions of elevated 18F-florzolotau binding are highly overlapping in PSP and the Parkinsonian type of multiple system atrophy (MSA-P), developing a reliable discriminative classifier for 18F-florzolotau PET is urgently needed. Herein, we developed a normalization-free deep-learning (NFDL) model for 18F-florzolotau PET, which achieved significantly higher accuracy for both PSP and MSA-P compared to semi-quantitative classifiers. Regions driving the NFDL classifier's decision were consistent with disease-specific topographies. NFDL-guided radiomic features correlated with clinical severity of PSP. This suggests that the NFDL model has the potential for early and accurate differentiation of atypical parkinsonism and that it can be applied in various scenarios due to not requiring subjective interpretation, MR-dependent, and reference-based preprocessing.

Bidirectional Mapping Perception-enhanced Cycle-consistent Generative Adversarial Network for Super-resolution of Brain MRI images

As an effective tool for visualizing neurodegeneration, high-resolution structural magnetism facilitates quantitative image analysis and clinical applications. Super-resolution reconstruction technology allows to improve the resolution of images without upgrading the scanning hardware. However, existing super-resolution techniques relied on paired image data sets and lacked further quantitative analysis of the generated images. In this study, we proposed a semi-supervised generative adversarial network (GAN) model for super-resolution of brain MRI, and the synthetic images were evaluated using various quantitative measures. This model adopted the cycle-consistency structure to allow for a mixture of unpaired data for training. Perceptual loss was further introduced into the model to preserve detailed texture features at high frequencies. 363 subjects with both high-resolution (HR) and low-resolution (LR) scans and 217 subjects with HR scans only were used for model derivation, training, and validation. We extracted multiple voxel-based and surface-based morphological features of the synthetic and real 3D HR images for comparison. We further evaluated the synthetic images in the differential diagnosis of diseases. Our model achieved superior mean absolute error (0.049±0.021), mean squared error (0.0059±0.0043), peak signal-to-noise ratio (29.41±3.71), structural similarity index measure (0.914±0.048). Eight morphological metrics, both voxel-based and surface-based, showed significant agreement (P<0.0001). The gap of accuracy in disease diagnosis between synthetic and real HR images was within 5% and significantly outperformed the LR images. Our proposed model enables the reconstruction of HR MRI and could be used accurately for image quantification.Clinical relevance- Quantitative evaluation of the synthetic high-resolution images was used to determine whether the synthetic images have sufficient realism and diversity.

Brain Connectome Imaging Markers Research of Glucose Metabolism in the Early Stage of Alzheimer's Disease

In recent years, increasing evidence had suggested that subjective cognitive decline (SCD) in unimpaired individuals may be the first symptom of Alzheimer's disease (AD). This study investigated the differences in the glucose metabolism network and the influence of the Apolipoprotein E (ApoE) gene between the SCD and normal control (NC) group by using graph theory. In this study, we included 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) scans from Xuanwu Hospital in Beijing, China. 85 SCD subjects and 74 NC subjects were included. First, we calculated and compared network parameters between the two groups. We then identified the bilateral insula and bilateral parahippocampal gyrus as seed sites and studied the connections to the whole brain. The results showed that both the SCD and the NC showed small-world nature, but the metabolic network of SCD tended to be more regular. The clustering coefficient and local efficiency of SCD were significantly higher than those of NC (P<0.05). In addition, we found that carrying APOE resulted in enhanced metabolic connectivity, but with weaker aggregation and local information exchangeability. Our results suggested that there are differences in the glucose metabolic brain network between SCD and NC, suggesting that the graph-theoretic analysis method may provide evidence for the early pathological mechanism of AD.Clinical relevance- This study suggests that the graph-theoretic analysis method may provide evidence for the early pathological mechanism of AD.

Head-to-head comparison of plasma and PET imaging ATN markers in subjects with cognitive complaints

BACKGROUND

Gaining more information about the reciprocal associations between different biomarkers within the ATN (Amyloid/Tau/Neurodegeneration) framework across the Alzheimer's disease (AD) spectrum is clinically relevant. We aimed to conduct a comprehensive head-to-head comparison of plasma and positron emission tomography (PET) ATN biomarkers in subjects with cognitive complaints.

METHODS

A hospital-based cohort of subjects with cognitive complaints with a concurrent blood draw and ATN PET imaging (18F-florbetapir for A, 18F-Florzolotau for T, and 18F-fluorodeoxyglucose [18F-FDG] for N) was enrolled (n = 137). The β-amyloid (Aβ) status (positive versus negative) and the severity of cognitive impairment served as the main outcome measures for assessing biomarker performances.

RESULTS

Plasma phosphorylated tau 181 (p-tau181) level was found to be associated with PET imaging of ATN biomarkers in the entire cohort. Plasma p-tau181 level and PET standardized uptake value ratios of AT biomarkers showed a similarly excellent diagnostic performance for distinguishing between Aβ+ and Aβ- subjects. An increased tau burden and glucose hypometabolism were significantly associated with the severity of cognitive impairment in Aβ+ subjects. Additionally, glucose hypometabolism - along with elevated plasma neurofilament light chain level - was related to more severe cognitive impairment in Aβ- subjects.

CONCLUSION

Plasma p-tau181, as well as 18F-florbetapir and 18F-Florzolotau PET imaging can be considered as interchangeable biomarkers in the assessment of Aβ status in symptomatic stages of AD. 18F-Florzolotau and 18F-FDG PET imaging could serve as biomarkers for the severity of cognitive impairment. Our findings have implications for establishing a roadmap to identifying the most suitable ATN biomarkers for clinical use.