Regional glucose metabolic decreases with ageing are associated with microstructural white matter changes: a simultaneous PET/MR study

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.

Effects of individual metabolic brain network changes co-affected by T2DM and aging on the probabilities of T2DM: protective and risk factors

Neuroimaging markers for risk and protective factors related to type 2 diabetes mellitus are critical for clinical prevention and intervention. In this work, the individual metabolic brain networks were constructed with Jensen-Shannon divergence for 4 groups (elderly type 2 diabetes mellitus and healthy controls, and middle-aged type 2 diabetes mellitus and healthy controls). Regional network properties were used to identify hub regions. Rich-club, feeder, and local connections were subsequently obtained, intergroup differences in connections and correlations between them and age (or fasting plasma glucose) were analyzed. Multinomial logistic regression was performed to explore effects of network changes on the probability of type 2 diabetes mellitus. The elderly had increased rich-club and feeder connections, and decreased local connection than the middle-aged among type 2 diabetes mellitus; type 2 diabetes mellitus had decreased rich-club and feeder connections than healthy controls. Protective factors including glucose metabolism in triangle part of inferior frontal gyrus, metabolic connectivity between triangle of the inferior frontal gyrus and anterior cingulate cortex, degree centrality of putamen, and risk factors including metabolic connectivities between triangle of the inferior frontal gyrus and Heschl's gyri were identified for the probability of type 2 diabetes mellitus. Metabolic interactions among critical brain regions increased in type 2 diabetes mellitus with aging. Individual metabolic network changes co-affected by type 2 diabetes mellitus and aging were identified as protective and risk factors for the likelihood of type 2 diabetes mellitus, providing guiding evidence for clinical interventions.

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

Age and gender are the important factors for brain metabolic declines in both normal aging and neurodegeneration, and the confounding effects may influence early and differential diagnosis of neurodegenerative diseases based on the [18F]fluorodeoxyglucose positron emission tomography ([18F]FDG PET). We aimed to explore the potential of the adjustment of age- and gender-related confounding factors on [18F]FDG PET images in differentiation of Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supra-nuclear palsy (PSP). Eight hundred and seventy-seven clinically definitely diagnosed Parkinsonian patients from a benchmark Huashan Parkinsonian PET imaging database were included. An age- and gender-adjusted Z (AGAZ) score was established based on the gender-specific longitudinal metabolic changes on healthy subjects. AGAZ scores and standardized uptake value ratio (SUVR) values were quantified at regional-level and support vector machine-based error-correcting output codes method was applied for classification. Additional references of the classifications based on metabolic pattern scores were included. The feature-based AGAZ score showed the best performance in classification (accuracy for PD, MSA, PSP: 93.1%, 96.3%, 94.8%). In both genders, the AGAZ score consistently achieved the best efficiency, and the improvements compared to the conventional SUVR value for PD, MSA, and PSP mainly laid in specificity (Male: 5.7%; Female: 11.1%), sensitivity (Male: 7.2%; Female: 7.3%), and sensitivity (Male: 7.3%; Female: 17.2%). Female patients benefited more from the adjustment on [18F]FDG PET in MSA and PSP groups (absolute net reclassification index, p < 0.001). Collectively, the adjustment of age- and gender-related confounding factors may improve the differential diagnosis of Parkinsonism. Particularly, the diagnosis of female Parkinsonian population has the best improvement from this correction.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-022-00079-6.

Simultaneous 18F-FDG PET/MR metabolic and structural changes in visual snow syndrome and diagnostic use

PURPOSE

Visual snow syndrome (VSS) is a recently recognized chronic neurologic condition characterized by the constant perceiving of tiny flickering dots throughout the entire visual field. Metabolic overactivity and grey matter volume increase in the lingual gyrus has been reported. We investigated this by ¹⁸F-FDG PET/MR in comparison to healthy controls. Aside from voxel-based characterization, the classification accuracy of volume-of-interest (VOI)-based multimodal assessment was evaluated, also in comparison with visual analysis.

METHODS

Simultaneous ¹⁸F-FDG PET and MR imaging was performed in 7 patients with VSS (24.6 ± 5.7 years; 5 M/2F) and 15 age-matched healthy controls (CON) (28.0 ± 5.3 years; 8 M/7F). SPM12 and voxel-based morphometric analysis was performed. A VOI-based discriminant analysis was performed with relative ¹⁸F-FDG uptake, MR grey matter (GM) volumes and their combination. A visual analysis was done by two blinded experienced readers.

RESULTS

Relative increased hypermetabolism was found in VSS patients in the lingual gyrus and cuneus (p_FWE < 0.05, peak change + 24%), and hypometabolism in the mesiotemporal cortex (p_{height,uncorr} < 0.001, peak change - 14%). VSS patients also had increased GM volume in the limbic system and frontotemporal cortex bilaterally (p_FWE < 0.05), and in the left secondary and associative visual cortex and in the left lingual gyrus (p_{height,uncorr} < 0.001). Discriminant analysis resulted in 100% correct classification accuracy for ¹⁸F-FDG with lingual gyrus, cuneus and lateral occipital lobe (BA 17 and BA 18) as main discriminators. Unimodal MR- and combined ¹⁸F-FDG + MR classification resulted in an accuracy of 91% and 95%, respectively. Visual analysis of ¹⁸F-FDG was highly observer dependent.

CONCLUSION

Patients with VSS have highly significant structural and metabolic abnormalities in the visual and limbic system. VOI-based discriminant analysis of ¹⁸F-FDG PET allows reliable individual classification versus controls, whereas visual analysis of experienced observers was highly variable. Further investigation in larger series, also in comparison to VSS mimicking disorders such as migraine, is warranted.

TRAIL REGISTRATION

Retrospectively registered at clinicaltrials.gov under NCT05569733 on Oct 5, 2022.

Differences in Striatal Metabolism in [18F]FDG PET in Parkinson's Disease and Atypical Parkinsonism

Neurodegenerative parkinsonisms affect mainly cognitive and motor functions and are syndromes of overlapping symptoms and clinical manifestations such as tremor, rigidness, and bradykinesia. These include idiopathic Parkinson's disease (PD) and the atypical parkinsonisms, namely progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), multiple system atrophy (MSA) and dementia with Lewy body (DLB). Differences in the striatal metabolism among these syndromes are evaluated using [¹⁸F]FDG PET, caused by alterations to the dopaminergic activity and neuronal loss. A study cohort of three patients with PD, 29 with atypical parkinsonism (10 PSP, 6 CBD, 2 MSA, 7 DLB, and 4 non-classifiable), and a control group of 25 patients with normal striatal metabolism is available. Standardized uptake value ratios (SUVR) are extracted from the striatum, and the caudate and the putamen separately. SUVRs are compared among the study groups. In addition, hemispherical and caudate-putamen differences are evaluated in atypical parkinsonisms. Striatal hypermetabolism is detected in patients with PD, while atypical parkinsonisms show hypometabolism, compared to the control group. Hemispherical differences are observed in CBD, MSA and DLB, with the latter also showing statistically significant caudate-putamen asymmetry (p = 0.018). These results indicate disease-specific metabolic uptake patterns in the striatum that can support the differential diagnosis.

Healthy brain aging assessed with [18F]FDG and [11C]UCB-J PET

BACKGROUND

The average human lifespan has increased dramatically over the past century. However, molecular and physiological alterations of the healthy brain during aging remain incompletely understood. Generalized synaptic restructuring may contribute to healthy aging and the reduced metabolism observed in the aged brain. The aim of this study was to assess healthy brain aging using [¹⁸F]FDG as a measure of cerebral glucose consumption and [¹¹C]UCB-J PET as an indicator of synaptic density.

METHOD

Using in vivo PET imaging and the novel synaptic-vesicle-glycoprotein 2A (SV2A) radioligand [¹¹C]UCB-J alongside with the fluorodeoxyglucose radioligand [¹⁸F]FDG, we obtained SUVR-1 values for 14 pre-defined volume-of-interest brain regions defined on MRI T1 scans. Regional differences in relative [¹⁸F]FDG and [¹¹C]UCB-J uptake were investigated using a voxel-wise approach. Finally, correlations between [¹¹C]UCB-J, [¹⁸F]FDG PET, and age were examined.

RESULTS

We found widespread cortical reduction of synaptic density in a cohort of older HC subjects (N = 15) compared with young HC subjects (N = 11). However, no reduction persisted after partial volume correction and corrections for multiple comparison. Our study confirms previously reported synaptic stability during aging. Regional differences in relative [¹⁸F]FDG and [¹¹C]UCB-J uptake were observed with up to 20 % higher [¹¹C]UCB-J uptake in the amygdala and temporal lobe and up to 34 % higher glucose metabolism in thalamus, striatum, occipital, parietal and frontal cortex.

CONCLUSION

In vivo PET using [¹¹C]UCB-J does not support declining synaptic density levels during aging. Thus, loss of synaptic density may be unrelated to aging and does not seem to be a sufficient explanation for the recognized reduction in brain metabolism during aging. Our study also demonstrates that the relationship between glucose consumption and synaptic density is not uniform throughout the human brain with implications for our understanding of neuroenergetics.

Alteration of the Individual Metabolic Network of the Brain Based on Jensen-Shannon Divergence Similarity Estimation in Elderly Patients With Type 2 Diabetes Mellitus

The aim of this study was to investigate the interactive effect between aging and type 2 diabetes mellitus (T2DM) on brain glucose metabolism, individual metabolic connectivity, and network properties. Using a 2 × 2 factorial design, 83 patients with T2DM (40 elderly and 43 middle-aged) and 69 sex-matched healthy control subjects (HCs) (34 elderly and 35 middle-aged) underwent 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance scanning. Jensen-Shannon divergence was applied to construct individual metabolic connectivity and networks. The topological properties of the networks were quantified using graph theoretical analysis. The general linear model was used to mainly estimate the interaction effect between aging and T2DM on glucose metabolism, metabolic connectivity, and network. There was an interaction effect between aging and T2DM on glucose metabolism, metabolic connectivity, and regional metabolic network properties (all P < 0.05). The post hoc analyses showed that compared with elderly HCs and middle-aged patients with T2DM, elderly patients with T2DM had decreased glucose metabolism, increased metabolic connectivity, and regional metabolic network properties in cognition-related brain regions (all P < 0.05). Age and fasting plasma glucose had negative correlations with glucose metabolism and positive correlations with metabolic connectivity. Elderly patients with T2DM had glucose hypometabolism, strengthened functional integration, and increased efficiency of information communication mainly located in cognition-related brain regions. Metabolic connectivity pattern changes might be compensatory changes for glucose hypometabolism.

The Effect of Aging on Brain Glucose Metabolic Connectivity Revealed by [18F]FDG PET-MR and Individual Brain Networks

Contrary to group-based brain connectivity analyses, the aim of this study was to construct individual brain metabolic networks to determine age-related effects on brain metabolic connectivity. Static 40–60 min [¹⁸F]FDG positron emission tomography (PET) images of 67 healthy subjects between 20 and 82 years were acquired with an integrated PET-MR system. Network nodes were defined by brain parcellation using the Schaefer atlas, while connectivity strength between two nodes was determined by comparing the distribution of PET uptake values within each node using a Kullback–Leibler divergence similarity estimation (KLSE). After constructing individual brain networks, a linear and quadratic regression analysis of metabolic connectivity strengths within- and between-networks was performed to model age-dependency. In addition, the age dependency of metrics for network integration (characteristic path length), segregation (clustering coefficient and local efficiency), and centrality (number of hubs) was assessed within the whole brain and within predefined functional subnetworks. Overall, a decrease of metabolic connectivity strength with healthy aging was found within the whole-brain network and several subnetworks except within the somatomotor, limbic, and visual network. The same decrease of metabolic connectivity was found between several networks across the whole-brain network and the functional subnetworks. In terms of network topology, a less integrated and less segregated network was observed with aging, while the distribution and the number of hubs did not change with aging, suggesting that brain metabolic networks are not reorganized during the adult lifespan. In conclusion, using an individual brain metabolic network approach, a decrease in metabolic connectivity strength was observed with healthy aging, both within the whole brain and within several predefined networks. These findings can be used in a diagnostic setting to differentiate between age-related changes in brain metabolic connectivity strength and changes caused by early development of neurodegeneration.