Metabolic Brain Networks Research Articles

Objective.To determine whether pre-treatment brain metabolic network patterns measured with18F-FDG PET are associated with treatment response and survival in cancer patients.Approach.Exploratory retrospective study of two independent cohorts: stage III breast cancer patients treated with neoadjuvant chemotherapy and stage IV melanoma patients treated with anti-PD-1 immunotherapy. Metabolic brain network scores were derived from pre-treatment18F-FDG PET scans and evaluated for their ability to stratify good versus poor responders using ROC analysis (AUC). Longitudinal changes in network scores were assessed across follow-up, and progression-free survival (PFS) and overall survival (OS) analyses were performed in the melanoma cohort.Main results.Specific brain networks were associated with treatment outcome; the cognition/language network was the strongest predictor (AUC > 0.84 for distinguishing good vs. poor responders in both cohorts). Good responders showed lower cognition/language scores than poor responders and healthy controls. Longitudinally, cognition/language scores remained stable in good responders, while poor responders exhibited a gradual convergence toward the scores observed in good responders. In the melanoma cohort, lower cognition/language scores were significantly associated with longer PFS and OS.Significance.These findings indicate that metabolic brain network patterns, particularly the cognition/language network, may serve as noninvasive biomarkers linked to treatment efficacy and survival in oncology. The results support a possible complex interaction between brain metabolism, immune response, and clinical outcomes. Key limitations include the retrospective design and lack of direct immune-function and psychometric measures; prospective, multimodal studies are needed to validate these observations and elucidate underlying mechanisms.

Interregional communication within the human brain is essential for maintaining functional integrity. A promising approach for investigating how brain regions communicate relies on the assumption that the brain operates as a complex network. In this context, positron emission tomography (PET) images have been suggested as a valuable source for understanding brain networks. However, such networks are typically assembled through direct computation without accounting for outliers, impacting the reliability of group representative networks. In this study, we used brain [18F]fluoro-2-deoxyglucose PET data from 1,227 individuals in the Alzheimer’s disease (AD) continuum from the Alzheimer’s Disease Neuroimaging Initiative cohort to develop a novel method for constructing stable metabolic brain networks that are resilient to spurious data points. Our multiple sampling scheme generates brain networks with greater stability compared with conventional approaches. The proposed method is robust to imbalanced datasets and requires 50% fewer subjects to achieve stability than the conventional method. We further validated the approach in an independent AD cohort (n = 114) from São Paulo, Brazil (Faculdade de Medicina da Universidade de São Paulo). This innovative method is flexible and improves the robustness of metabolic brain network analyses, supporting better insights into brain connectivity and resilience to data variability across multiple radiotracers for both health and disease.

Cancer-related cognitive impairment (CRCI) is prevalent among patients with diffuse large B-cell lymphoma (DLBCL), often manifesting prior to treatment and frequently remaining unrecognized. This study aims to assess CRCI in DLBCL patients before treatment by utilizing 18F-FDG PET/CT metabolic networks to facilitate early detection and intervention. We conducted a cross-sectional study involving 77 participants from our hospital's PET-CT center, which included 15 healthy controls and 62 DLBCL patients (29 in stages I + II and 33 in stages III + IV). Using the automated anatomical atlas 3 (AAL3) template, we segmented the 18F-FDG PET brain images into 90 regions, extracted the standard uptake value (SUV) for each region, and calculated its ratio (SUVr) using the cerebellum as a reference. We analyzed connectivity within the brain's metabolic network across the three groups. DLBCL patients exhibited significantly lower mean degree, closeness centrality, degree of participation in connected components (DPCC), average clustering coefficient (ACC), and clustering coefficient compared to controls (P < 0.001). Patients in stages III + IV demonstrated even lower values. The average shortest path length (ASPL) was significantly higher in DLBCL patients, with stage III + IV patients showing an ASPL nearly three times that of controls and twice that of stage I + II patients. Significant differences in mean degree, closeness centrality, and clustering coefficient were observed among the groups (P < 0.001). CRCI is apparent in DLBCL patients at early stages and deteriorates with disease progression. It is crucial to integrate early detection and cognitive assessments into clinical practice for DLBCL patients.

Objective: To identify brain metabolic network features for temporal lobe epilepsy (TLE) subtype classification and surgical prognosis prediction using machine learning algorithms, thereby supporting clinical decision-making for TLE subtyping and outcome assessment. Methods: ¹⁸F-FDG PET images from 137 patients with drug-resistant TLE treated at Xiangya Hospital's Comprehensive Epilepsy Center from January 2016 to June 2021 were retrospectively analyzed as the training cohort. Network connectivity data were derived using Kullback-Leibler divergence similarity estimation (KLSE), yielding 6 902 network attributes alongside relevant demographic and clinical features. Eight machine learning models (including decision tree and random forest) were trained. The resulting models classified TLE subtypes and were validated using ¹⁸F-FDG PET metabolic network data from an independent cohort of 92 drug-resistant TLE patients (from July 2021 to August 2023). Decision curve analysis was used to select the most clinically practical model for predicting the surgical prognosis of 138 temporal lobe epilepsy patients, including 105 with mesial TLE (76 in the training group and 29 in the independent test group) and 33 with neocortical TLE (23 in the training group and 10 in the independent test group). Results: There were 84 males and 53 females in the training group, with an age of (22.0±8.0) years; in the independent test group, there were 45 males and 47 females, with an age of (24.2±12.8) years. The area under the receiver operating characteristic curve(AUC) of the 8 machine learning models in the training group ranged from 0.904 to 0.985; the AUC in the independent test group ranged from 0.859 to 0.946. According to the comparison of the performance of the above models, it was found that the prediction result of the random forest model was the most accurate and stable [AUC 0.985 (95%CI: 0.985-0.986), accuracy 0.998(95%CI: 0.995-1.000), sensitivity 0.950 (95%CI: 0.898-1.000), specificity 1.000 (95%CI: 1.000-1.000)]. For patients with mesial temporal lobe epilepsy who underwent surgery, the AUC for predicting surgical prognosis in the training group was 0.838 (95%: 0.753-0.923), and the accuracy was 0.838 (95%CI: 0.836-0.841); the AUC in the independent test group reached 0.783(95%CI: 0.549-1.000), with an accuracy of 0.793 (95%CI: 0.782-0.804). For patients with neocortical temporal lobe epilepsy who underwent surgery, the AUC for predicting surgical prognosis in the training group was 0.962(95%CI: 0.881-1.000), and the accuracy was 0.957 (95%CI: 0.953-0.960); while the AUC in the independent test group also reached 0.800 (95%CI: 0.408-1.000), with an accuracy of 0.900 (95%CI: 0.882-0.918). Conclusion: Machine learning models incorporating metabolic network features extracted from ¹⁸F-FDG PET data effectively support TLE subtype classification and surgical prognosis assessment.

Connectivity analyses of fluorodeoxyglucose positron emission tomography (FDG-PET) static images provide a valuable means of investigating brain network organization by capturing metabolic activity at rest. Graph theory is emergently applied to model these networks at individual level; however, the choice of graph construction method can significantly impact analytical outcomes. In this study, we systematically evaluate and compare methods for building individual graphs from FDG-PET images in healthy control subjects. Specifically, we assess five methods, categorized into mean-based graphs and probability density function (PDF)-based graphs, using two criteria: structural similarity between individual and group-level graphs, and their hub topology structure analysis. Our findings indicate that the Effect Size-based (ES) method best preserves group-level graph structure, achieving 98.9% similarity for the averaged graph while also maintaining around 84% similarity for individual graphs. Among PDF-based approaches, the Wasserstein (WA) method, with its adaptability in PDF-based settings, provides the highest similarity across both averaged (82.5%) and individual (79.1%) graphs, with its adaptive in PDF-settings, making it the most effective for multi-scale network analysis. Meanwhile, Dynamic Time Warping (DTW) captures the highest individual variability, as reflected by its largest variation among individual graphs (11.5%). This analysis highlights the unique strengths and limitations of each method, emphasizing the critical importance of careful method selection tailored to specific research objectives. Additionally, our study suggests a framework for selecting the appropriate methods, with implications for further both research and clinical applications.

7524 Background: BCMA-targeted CAR T cell therapy is a highly effective treatment for patients with relapsed/refractory multiple myeloma (MM) with side effects such as CRS, ICANS, and movement and neurocognitive toxicities (MNTs). Regional changes on [ 18 F]fluoro-deoxy-2-D-glucose PET (PET) can be used to derive metabolic connectivity, an emerging technique that models brain function from the uptake on a PET, allowing us to investigate alterations of regional metabolism (SUV) and changes in metabolic brain networks (connectivity) peri-CAR T. Methods: Patients were included in this retrospective study if they were treated with commercially available BCMA-directed CAR T cells and had pre-and post-CAR therapy PET imaging. We analyzed the connectivity of the whole brain and parcellated the brain to generate global and regional connectivity matrices and investigated the association of regional metabolic differences and differences in metabolic connectivity with clinical parameters. Results: Of the 108 consecutive patients (65 Cilta-cel, 43 Ide-cel), there were 61 men and 47 women (median age 65), with PET a median of 12 days prior to infusion 28 days post infusion. Toxicities included CRS alone (n=66), CRS + ICANS (n=8), CRS+facial palsy (n=3), and CRS + Parkinsonism + facial palsy (n=2). Within the entire cohort, a significantly higher SUV-mean was noted in putamen (p&lt;0.0004) post-CAR T compared to pre-CAR T, with other brain regions not showing a difference. These regional differences were significantly and inversely associated with the grade of ICANS (Post-Pre: Left: t=-1.76, p=0.08; Right t=-2.1 p=0.04). When comparing patients with (n = 79) and without (n=29) any post-CAR T cell CRS/ICANS/MNT, the post SUV-mean was significantly higher in the bilateral basal ganglia (BG) of patients who experienced toxicity (p&lt;0.05). The SUV-mean was significantly lower in the bilateral inferior frontal opercularis, triangularis, and bilateral Rolandic operculum of those who developed ICANS (Grade 1-2, all with CRS, n=8) vs with CRS alone (all grade 1, n=46) (p&lt;0.05). Globally, the metabolic connectivity network had less efficiency (post&lt;pre: 0.69&lt;0.75), and density (post&lt;pre: 63&lt;74). In local measurements, post-CAR T cell PET showed significantly lower local efficiency (p=10 -30 ), degree (p=10 -15 ), strengths (p=0.001), clustering coefficient (p=10 -12 ), and higher edge betweenness centrality (p=0.02) compared to the pre-CAR T timepoint. The decreases in network measurement were more severe in the frontal lobe and basal ganglia (p=10 -6 and p=0.004, respectively). Conclusions: Patients with neurotoxicity after BCMA CAR T had an increased SUV in the putamen, but decreased in the frontal regions and basal ganglia at Day 28. Metabolic networks were globally less efficient and less dense and have changes that signify injury or attempts at compensation.

Neuroimaging with positron emission tomography (PET) has been instrumental in elucidating neurobiological mechanisms behind therapeutical trials in Parkinson's disease (PD). A variety of medical and neurosurgical interventions have been evaluated using many radioligands that reveal molecular basis for target engagement and brain responses in relation to clinical outcome measures. This review article describes major applications of metabolic brain network analysis in therapeutical studies in non-demented PD to restore functional abnormality by drug therapy, ablative lesioning, deep brain stimulation, gene therapy, and cell transplantation alongside placebo effects. The findings with brain network biomarkers using multivariate analysis are supported by regionally specific metabolic changes and clinical correlations detected by complementary univariate analysis. The review demonstrates a powerful methodology of combining multimodal neuroimaging data and network modeling approaches followed by some perspectives on future directions in this specialty area of translational research. Different neuroimaging biomarkers have been compared in light of recent advances in biofluid biomarkers. These efforts not only bring more precise understanding on mechanisms of action associated with different therapies, but also provide a road map for conducting successful clinical trials of emerging disease-modifying therapies in PD and related disorders. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Metabolic brain networks in prodromal dementia with Lewy bodies and prodromal dementia due to Alzheimer disease

Brain-thyroid crosstalk: 18F-FDG-PET/MRI evidence in patients with follicular thyroid adenomas.

The delusions and hallucinations that characterize Alzheimer's disease psychosis (AD + P) are associated with violence towards caregivers and an accelerated cognitive and functional decline whose management relies on the utilization of medications developed for young people with schizophrenia. The development of novel therapies requires biomarkers that distinguish AD + P from non-psychotic Alzheimer's disease. We investigated whether there might exist a brain metabolic network that distinguishes AD + P from non-psychotic Alzheimer's disease that could be used as a biomarker to predict and track the course of AD + P for use in clinical trials. Utilizing F-18 fluorodeoxyglucose positron emission tomography scans from cohorts of cognitively healthy elderly (N = 174), those with Alzheimer's disease without psychosis (N = 174) and those with AD + P (N = 88) participating in the Alzheimer's Disease Neuroimaging Initiative study, we employed a convolutional neural network to identify and validate the Alzheimer's Psychosis Network. We analysed network progression, clinical correlations and psychosis prediction using expression scores and network organization using graph theory. The Alzheimer's Psychosis Network accurately distinguishes AD + P from controls (97%), with increasing scores correlating with cognitive decline. The Alzheimer's Psychosis Network-based approach predicts psychosis in Alzheimer's disease with 77% accuracy and identifies specific brain regions and connections associated with psychosis. Alzheimer's Psychosis Network expression was found to be associated with increased cognitive and functional decline that characterizes AD + P. The increased metabolic connectivity between motor and language/social cognition regions in AD + P may drive delusions and agitated behaviour. Alzheimer's Psychosis Network holds promise as a biomarker for AD + P, aiding in treatment development and patient stratification.

Investigating brain metabolic networks is crucial for understanding the pathogenesis and functional alterations in Creutzfeldt-Jakob disease (CJD). However, studies on presymptomatic individuals remain limited. This study aimed to examine metabolic network topology reconfiguration in asymptomatic carriers of the PRNP G114V mutation. Seven asymptomatic PRNP G114V mutation carriers from a familial genetic CJD (gCJD) cohort, 43 CJD patients, and 35 healthy controls were included. All participants underwent neuropsychological assessments, genetic testing, and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET)/MRI scans. Voxel-based gray matter volume and FDG PET standardized uptake value ratios (SUVRs) were analyzed between asymptomatic PRNP G114V mutation carriers and healthy controls and between CJD patients and controls. Graph theory and sparse inverse covariance estimation (SICE) were used to assess the whole-brain metabolic connectomes and topological properties. Spatial independent component analysis (ICA) was used to evaluate subnetworks, including the default mode network (DMN), salience network (SN), and central executive network (CEN). With respect to global properties, assortativity was significantly increased in asymptomatic carriers, which was consistent with the findings in CJD patients. We revealed lost hubs in the right anterior cingulate, left ventral prefrontal lobe, left parahippocampal gyrus, and left lingual gyrus and reconfigured hubs in prefrontal lobes, including right ventromedial prefrontal cortex, right anterior prefrontal cortex, and right middle frontal gyrus of the orbit in asymptomatic carriers compared with healthy controls, which overlapped with the comparisons between CJD patients and controls. Alterations in the local parameters and metabolic connectivity in the left parahippocampal gyrus were most pronounced. Among the subnetworks, asymptomatic carriers presented higher assortativity and lower hierarchy in the SN, whereas the global parameters of the DMN and CEN were not significantly altered. The DMN and SN showed partial hypoconnectivity and hyperconnectivity, whereas the CEN mainly showed significantly enhanced connectivity in asymptomatic PRNP carriers. This study revealed altered brain metabolic topology and connectomics in asymptomatic PRNP G114V mutation carriers, which could be detected before gray matter or regional metabolic changes, suggesting that metabolism topology reconfiguration may serve as a sensitive imaging biomarker for investigating early CJD pathological changes.

Subjective cognitive decline (SCD) is an early manifestation of the Alzheimer's disease (AD) continuum, and accurately diagnosing SCD to differentiate it from neurotypical aging in older adults is a common challenge for researchers. This review examines and summarizes relevant studies regarding the neuroimaging of the AD continuum, and comprehensively summarizes and outlines the SCD clinical features characterizing along with the corresponding neuroimaging changes involving structural, functional, and metabolic networks. The clinical characteristics of SCD include a subjective decline in self-perceived cognitive function, and there are significant imaging changes, such as reductions in gray matter volume in certain brain regions, abnormalities in the integrity of white matter tracts and diffusion metrics, alterations in functional connectivity between different sub-networks or within networks, as well as abnormalities in brain metabolic networks and cerebral blood flow perfusion. The 147 referenced studies in this paper indicate that exploring the structural, functional, and metabolic network changes in the brain related to SCD through neuroimaging aims to enhance the goals and mission of brain science development programs: "Understanding the Brain," "Protecting the Brain," and "Creating the Brain," thereby strengthening researchers' investigation into the mechanisms of brain function. Early diagnosis of SCD, along with prompt intervention, can reduce the incidence of AD spectrum while improving patients' quality of life, even integrating numerous scientific research achievements into unified and established standards and applying them in clinical practice by doctors, thus all encouraging researchers to further investigate SCD issues in older adults.

Progressive supranuclear palsy (PSP) is a primary tauopathy characterized by dopaminergic impairment and abnormal glucose metabolism. The glymphatic system can promote the elimination of tau protein. The association between glymphatic function and pathological hallmark in neuroimaging remains unknown. Diffusion tensor imaging (DTI) and positron emission tomography (PET) scanning with 18F-Florzolotau, 18F-FPCIT, and 18F-FDG were performed in PSP patients. DTI analysis along the perivascular space (ALPS) index was computed to assess glymphatic function, while the semi-quantitative value was employed to measure tau burden and dopaminergic impairment. The PSP-related pattern (PSPRP) served as an indicator of abnormal metabolic brain network activity. PSP patients exhibited changes in ALPS index and tau deposition. ALPS index, tau deposition, and PSPRP expression showed significant correlations with clinical scores. Additionally, ALPS index was correlated with tau deposition and PSPRP expression. However, neither ALPS index nor the clinical scores were correlated with striatum dysfunction. Finally, tau deposition in subcortical regions and PSPRP expression exhibited mediating effects between ALPS index and clinical scores. The glymphatic dysfunction is associated with tau deposition and abnormal metabolic brain network activity and is independent of dopaminergic impairment in PSP.

The neuronal correlate of tremor genesis and cognitive function in essential tremor (ET) and its modulation by deep brain stimulation (DBS) are poorly understood. To explore the underlying metabolic topography of motor and cognitive symptoms, sixteen ET patients (age 63.6 ± 49.1 years) and 18 healthy controls (HC) (61.1 ± 6.3 years) underwent tremor and cognitive assessments and18F-fluorodeoxyglucose PET of the brain. Multivariate spatial covariance analysis was applied for identifying ET related metabolic brain networks. For network validation and to explore DBS effects, 8 additional ET patients (68.1 ± 8.2 years) treated with DBS were assessed in both the ON and OFF state, respectively. The ET related metabolic spatial covariance pattern (ETRP) was characterized by relatively increased metabolism in the cerebellum, brainstem, and temporo-occipital cortices, accompanied by relative metabolic decreases mainly in fronto-temporal and motor cortices. Network expression showed inverse correlations with tremor severity and disease duration and positive correlations with cognitive dysfunction. DBS substantially alleviated tremor, but had only marginal effects on cognitive performance. There were no significant DBS effects on ETRP expression at the group level, but all but one subject showed higher scores in the ON state. Our findings suggest ET is characterized by an abnormal brain network associated with disease phenotype.

This investigation aimed to elucidate alterations in metabolic brain network connectivity in drug-resistant mesial temporal lobe epilepsy (DR-MTLE) patients, relating these changes to varying surgical outcomes. A retrospective cohort of 87 DR-MTLE patients who underwent selective amygdalohippocampectomy was analyzed. Patients were categorized based on Engel surgical outcome classification into seizure-free (SF) or non-seizure-free (NSF) groups. Additionally, 38 healthy individuals constituted a control group (HC). Employing effect size (ES) methodology, we constructed individualized metabolic brain networks and compared metabolic connectivity matrices across these groups using the DPABINet toolbox. Compared to HCs, both SF and NSF groups exhibited diminished metabolic connectivity, with the NSF group showing pronounced reductions across the whole brain. Notably, the NSF group demonstrated weaker metabolic links between key networks, including the default mode network (DMN), frontoparietal network (FPN), and visual network (VN), in comparison to the SF group. Individual metabolic brain networks, constructed via ES methodology, revealed significant disruptions in DR-MTLE patients, predominantly in the NSF group. These alterations, particularly between limbic structures and cognitive networks like the DMN, suggested impaired and inefficient information processing across the brain's networks. This study identified abnormal brain networks associated with DR-MTLE and, importantly, contributed novel insights into the mechanisms underlying poor postoperative seizure control, and offered potential implications for refining preoperative assessments.

Abstract BackgroundThe default‐mode network (DMN) consists of brain regions with higher resting activity levels. Amyloid‐β (Aβ) deposition in Alzheimer’s disease (AD) occurs predominantly throughout the DMN, suggesting that activity within the network may facilitate disease processes. Indeed, increased neural activity is positively associated with Aβ production. In this context, variations in DMN activity and associated metabolic networks may be linked to the risk of developing AD. However, how patterns of metabolic disruption relate to the progression of AD pathology remains unknown. Here, we investigated whether the metabolic brain networks (MBNs) architecture predicts clinical conversion in cognitively unimpaired (CU) individuals.MethodWe selected CU individuals negative to amyloid and tau (A‐T‐) from the ADNI cohort with [18F]FDG‐PET imaging data at baseline. These patients were divided in stable (non‐converters, n = 18) and clinical progressors (converters, n = 22). Individuals were age‐ and APOEε4‐matched (Table 1). The mean [18F]FDG standard uptake value ratio (SUVR, pons as reference) of brain regions of interest (ROIs) was extracted based on the DKT atlas. MBNs were assembled with a multiple sampling bootstrap scheme and corrected for group imbalance with the Adaptive Synthetic Sampling Approach for Imbalance (ADASYN) and for multiple comparisons using FDR (p &lt; 0.05).Result[18F]FDG regional SUVRs presented no differences between groups (Figure 1). However, converters had a prominent brain PET metabolic hyperconnectivity compared to non‐converters, with a 1.5 fold‐change in connection density (p &lt; 0.001, Figure 2A). Notably, this hyperactivation was not limited to the ROIs comprising the DMN; MBNs constructed with all brain regions reveal that the brains of converters typically display metabolic hyperactivity before the onset of CI (Figure 2B).ConclusionOur findings suggest the existence of early metabolic alterations at the network level in amyloid negative converters. This corroborates the notion that early soluble forms of amyloid, considered synaptoxins, may trigger brain metabolic hyperconnectivity. MBNs hold promise as biomarkers for detecting individuals at risk of clinical progression, even before amyloid positivity status.

Abstract BackgroundThe default‐mode network (DMN) consists of brain regions with higher resting activity levels. Amyloid‐ß (Aß) deposition in Alzheimer’s disease (AD) occurs predominantly throughout the DMN, suggesting that activity within the network may facilitate disease processes. Indeed, increased neural activity is positively associated with Aß production. In this context, variations in DMN activity and associated metabolic networks may be linked to the risk of developing AD. However, how patterns of metabolic disruption relate to the progression of AD pathology remains unknown. Here, we investigated whether the metabolic brain networks (MBNs) architecture predicts clinical conversion in cognitively unimpaired (CU) individuals.MethodWe selected CU individuals negative to amyloid and tau (A‐T‐) from the ADNI cohort with [18F]FDG‐PET imaging data at baseline. These patients were divided in stable (non‐converters, n = 18) and clinical progressors (converters, n = 22). Individuals were age‐ and APOEe4‐matched (Table 1). The mean [18F]FDG standard uptake value ratio (SUVR, pons as reference) of brain regions of interest (ROIs) was extracted based on the DKT atlas. MBNs were assembled with a multiple sampling bootstrap scheme and corrected for group imbalance with the Adaptive Synthetic Sampling Approach for Imbalance (ADASYN) and for multiple comparisons using FDR (p &lt; 0.05).Result[18F]FDG regional SUVRs presented no differences between groups (Figure 1). However, converters had a prominent brain PET metabolic hyperconnectivity compared to non‐converters, with a 1.5 fold‐change in connection density (p &lt; 0.001, Figure 2A). Notably, this hyperactivation was not limited to the ROIs comprising the DMN; MBNs constructed with all brain regions reveal that the brains of converters typically display metabolic hyperactivity before the onset of CI (Figure 2B).ConclusionOur findings suggest the existence of early metabolic alterations at the network level in amyloid negative converters. This corroborates the notion that early soluble forms of amyloid, considered synaptoxins, may trigger brain metabolic hyperconnectivity. MBNs hold promise as biomarkers for detecting individuals at risk of clinical progression, even before amyloid positivity status.

Abstract BackgroundLong‐COVID is characterized by persistent symptoms post‐infection with SARS‐CoV‐2. This condition includes neurological manifestations and has been proposed as a potential risk factor for the development of dementia. Individuals presenting with dementia due to Alzheimer's disease have dysfunctional brain metabolism, including metabolic brain network (MBN) hypoconnectivity. However, whether long‐COVID alters brain metabolic architecture remains elusive. Here, we aimed to evaluate the brain metabolic connectivity in a Brazilian cohort of individuals presenting with long‐COVID.Method[18F]FDG‐PET images were acquired from 52 community‐dwelling Brazilians above 50 year old. Standardized uptake value ratio (SUVr) parametric maps were processed to a common 8 mm FWHM and generated using the pons as the reference region (Figure 1). We extracted the mean values of regions of interest using the ICBM152 atlas. [18F]FDG‐PET MBNs were constructed using a novel multiple sampling scheme, which assembles a stable group representative MBN based on bootstrap (n = 2000). Adaptive Synthetic Sampling Approach for Imbalance (ADASYN) was used to account for group imbalance and generated the ADA‐MBNs. Graph measures, including density, global efficiency, average degree, and assortativity coefficient were computed. Data were corrected for multiple comparisons using the False Discovery Rate (FDR) method (p&lt;0.05).Result41 individuals with long‐COVID and 11 healthy controls (HC) were included (Table 1). We observed that long‐COVID individuals present PET hyperconnectivity in both MBN and ADA‐MBN. (Figure 2a‐b). The long‐COVID group presented increased density, global efficiency and average degree whereas assortativity coefficient were reduced in both MBN and ADA‐MBN.ConclusionOur findings showed that individuals with long‐COVID presented a brain metabolic hyperconnectivity, which is supported by increased density and average degree and may indicate a potential compensatory mechanism within the brain. In addition, the increase in global efficiency indicates that the brain of long‐COVID individuals exchanges metabolic information more efficiently, but the decreased assortativity coefficient suggests vertices with different properties connect to each other. Further longitudinal studies should follow these individuals for assessing microstructural and cognitive changes.

Abstract BackgroundLong‐COVID is characterized by persistent symptoms post‐infection with SARS‐CoV‐2. This condition includes neurological manifestations and has been proposed as a potential risk factor for the development of dementia. Individuals presenting with dementia due to Alzheimer's disease have dysfunctional brain metabolism, including metabolic brain network (MBN) hypoconnectivity. However, whether long‐COVID alters brain metabolic architecture remains elusive. Here, we aimed to evaluate the brain metabolic connectivity in a Brazilian cohort of individuals presenting with long‐COVID.Method[18F]FDG‐PET images were acquired from 52 community‐dwelling Brazilians above 50 year old. Standardized uptake value ratio (SUVr) parametric maps were processed to a common 8 mm FWHM and generated using the pons as the reference region (Figure 1). We extracted the mean values of regions of interest using the ICBM152 atlas. [18F]FDG‐PET MBNs were constructed using a novel multiple sampling scheme, which assembles a stable group representative MBN based on bootstrap (n = 2000). Adaptive Synthetic Sampling Approach for Imbalance (ADASYN) was used to account for group imbalance and generated the ADA‐MBNs. Graph measures, including density, global efficiency, average degree, and assortativity coefficient were computed. Data were corrected for multiple comparisons using the False Discovery Rate (FDR) method (p&lt;0.05).Result41 individuals with long‐COVID and 11 healthy controls (HC) were included (Table 1). We observed that long‐COVID individuals present PET hyperconnectivity in both MBN and ADA‐MBN. (Figure 2a‐b). The long‐COVID group presented increased density, global efficiency and average degree whereas assortativity coefficient were reduced in both MBN and ADA‐MBN.ConclusionOur findings showed that individuals with long‐COVID presented a brain metabolic hyperconnectivity, which is supported by increased density and average degree and may indicate a potential compensatory mechanism within the brain. In addition, the increase in global efficiency indicates that the brain of long‐COVID individuals exchanges metabolic information more efficiently, but the decreased assortativity coefficient suggests vertices with different properties connect to each other. Further longitudinal studies should follow these individuals for assessing microstructural and cognitive changes.

The establishment of brain metabolic network is based on 18fluoro-deoxyglucose positron emission computed tomography ( 18F-FDG PET) analysis, which reflect the brain functional network connectivity in normal physiological state or disease state. It is now applied to basic and clinical brain functional network research. In this paper, we constructed a metabolic network for the cerebral cortex firstly according to 18F-FDG PET image data from patients with temporal lobe epilepsy (TLE).Then, a statistical analysis to the network properties of patients with left or right TLE and controls was performed. It is shown that the connectivity of the brain metabolic network is weakened in patients with TLE, the topology of the network is changed and the transmission efficiency of the network is reduced, which means the brain metabolic network connectivity is extensively impaired in patients with TLE. It is confirmed that the brain metabolic network analysis based on 18F-FDG PET can provide a new perspective for the diagnose and therapy of epilepsy by utilizing PET images.