Investigating metabolic connectivity in patients with multiple myeloma receiving BCMA CAR T cell therapy.

Decision letter: Stage-dependent differential influence of metabolic and structural networks on memory across Alzheimer’s disease continuum

Background: Changes in the metabolic and structural brain networks in mild cognitive impairment (MCI) have been widely researched. However, few studies have compared the differences in the topological properties of the metabolic and structural brain networks in patients with MCI.Methods: We analyzedmagnetic resonance imaging (MRI) and fluoro-deoxyglucose positron emission tomography (FDG-PET) data of 137 patients with MCI and 80 healthy controls (HCs). The HC group data comes from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The permutation test was used to compare the network parameters (characteristic path length, clustering coefficient, local efficiency, and global efficiency) between the two groups. Partial Pearson’s correlation analysis was used to calculate the correlations of the changes in gray matter volume and glucose intake in the key brain regions in MCI with the Alzheimer’s Disease Assessment Scale-Cognitive (ADAS-cog) sub-item scores.Results: Significant changes in the brain network parameters (longer characteristic path length, larger clustering coefficient, and lower local efficiency and global efficiency) were greater in the structural network than in the metabolic network (longer characteristic path length) in MCI patients than in HCs. We obtained the key brain regions (left globus pallidus, right calcarine fissure and its surrounding cortex, left lingual gyrus) by scanning the hubs. The volume of gray matter atrophy in the left globus pallidus was significantly positively correlated with comprehension of spoken language (p = 0.024) and word-finding difficulty in spontaneous speech item scores (p = 0.007) in the ADAS-cog. Glucose intake in the three key brain regions was significantly negatively correlated with remembering test instructions items in ADAS-cog (p = 0.020, p = 0.014, and p = 0.008, respectively).Conclusion: Structural brain networks showed more changes than metabolic brain networks in patients with MCI. Some brain regions with significant changes in betweenness centrality in both structural and metabolic networks were associated with MCI.

Background: Changes in the metabolic and structural brain networks in mild cognitive impairment (MCI) have been widely researched. However, few studies have compared the differences in the topological properties of the two brain networks assessed using magnetic resonance imaging (MRI) and fluoro-deoxyglucose positron emission tomography (FDG-PET) in patients with MCI. Methods: This study included 137 patients with MCI and 80 healthy controls (HCs). Sequential interictal scans were performed using FDG-PET and MRI. The MCI metabolic and structural brain networks were constructed according to the standardized uptake value ratio (SUVR) obtained using FDG-PET and gray matter volume obtained using MRI. The permutation test was used to compare the network parameters (characteristic path length, clustering coefficient, local efficiency, and global efficiency) between the two groups. Partial Pearson’s correlation analysis was used to calculate the correlations of the changes in gray matter volume and glucose intake in the key brain regions in MCI with the Alzheimer's Disease Assessment Scale-Cognitive (ADAS-cog) sub-item scores. Results: Significant changes in the brain network parameters (longer characteristic path length, larger clustering coefficient, and lower local efficiency and global efficiency) were greater in the structural network than in the metabolic network (longer characteristic path length) in MCI patients than in HCs. We obtained the key brain regions by scanning the hubs and found that the betweenness centrality of the right calcarine fissure and its surrounding cortex (CAL.R), left lingual gyrus (LING.L), and left globus pallidus (PAL.L) differed significantly between HCs and patients with MCI in both structural and metabolic networks (all p<0.05). The volume of gray matter atrophy in the PAL.L was significantly positively correlated with comprehension of spoken language (p=0.024) and word-finding difficulty in spontaneous speech item scores (p=0.007) in the ADAS-cog. Glucose intake in the three key brain regions (CAL.R, LING.L, and PAL.L) was significantly negatively correlated with remembering test instructions items in ADAS-cog (p=0.020, p=0.014, and p=0.008, respectively). Conclusion: MRI brain networks showed more changes than FDG-PET brain networks in patients with MCI. Some brain regions with significant changes in betweenness centrality in both structural and metabolic networks were associated with MCI.

Modular architecture of metabolic brain network and its effects on the spread of perturbation impact

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.

The study aimed to investigate the consistency and diversity between metabolic and structural brain networks at individual level constructed with divergence-based method in healthy Chinese population. The 18F-FDG PET and T1-weighted images of brain were collected from 209 healthy participants. The Jensen-Shannon divergence (JSD) was used to calculate metabolic or structural connectivities between any pair of brain regions and then individual brain networks were constructed. The global and regional topological properties of both networks were analyzed with graph theoretical analysis. Regional properties including nodal efficiency, degree, and betweenness centrality were used to define hub regions of networks. Cross-modality similarity of brain connectivity was analyzed with differential power (DP) analysis. The default mode network (DMN) had the largest number of brain connectivities with high DP values. The small-worldness indexes of metabolic and structural networks in all participants were greater than 1. The structural network showed higher assortativity and local efficiency than metabolic network, while hierarchy and global efficiency were greater in the metabolic network (all P < 0.001). Most of hubs in both networks were symmetrically spatial distributed in the regions of the DMN and subcortical nuclei including thalamus and amygdala, etc. The human brain presented small-world architecture both in perspective of individual metabolic and structural networks. There was a structural substrate that supported the brain to globally and efficiently integrate and process metabolic interaction across brain regions. The cross-modality cooperation or specialization in both networks might imply mechanisms of achieving higher-order brain functions.

Metabolic Brain Network and Surgical Outcome in Temporal Lobe Epilepsy: A Graph Theoretical Study Based on 18F-fluorodeoxyglucose PET

Lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) are major subtypes of lung cancer (LC) with distinct clinical features and pathological mechanisms, necessitating tailored treatments. However, early noninvasive differentiation remains challenging. With the development of total-body PET/CT, this study attempts to provide new insights into this challenge by analyzing and comparing inter-organ and inter-system metabolic connections across the total body in ADC and SCC patients. Based on static and dynamic total-body 18F-fluorodeoxyglucose (18F-FDG) PET/CT data from 12 ADC patients, 12 SCC patients, and 20 healthy controls (HCs), abnormal metabolic connection networks of ADC and SCC were constructed relative to HCs for individual and group analysis. An external patient cohort consisting of 7 ADC patients and 7 SCC patients was introduced to evaluate the generalizability of the analysis results by topological correlation. The metabolic correlations among organs/systems were observed by Pearson correlation coefficient between standardized uptake values or Ki values of 36 regions of interest (ROIs) across the total body. ROIs were also classified into 18 organs and 8 major systems. Specifically, for the individual analysis, the alteration in metabolic connections between the lungs (respiratory system), and other systems were investigated, as well as among the seven nonrespiratory systems, relative to HCs. For the group analysis, the abnormal metabolic connections across the total body of the ADC group and the SCC group were analyzed at the organ and system levels to explore the disease abnormal patterns of different LC subtypes. At the individual level, ADC and SCC patients exhibited distinct patterns of abnormal metabolic connectivity between the lungs (respiratory system) and other systems, highlighting subtype-specific metabolic reorganization. Both subtypes consistently showed significant abnormalities in nervous-motor and nervous-digestive system connectivity among the seven nonrespiratory systems. At the group level, ADC patients showed significant metabolic connectivity abnormalities mainly between specific organs and systems, with most changes reflecting reduced connectivity as compared to HCs. In contrast, SCC patients demonstrated markedly enhanced connectivity across multiple organ and system pairs, consistent with their broader diffusion patterns in clinical practice. Notably, dynamic Ki-based analysis was more sensitive than static SUV-based analysis in capturing these subtype-specific systemic metabolic alterations. These findings were highly consistent across internal and external cohorts, as evidenced by the generally strong topological correlations (Pearson correlation coefficient>0.9) at both the organ and system levels. Moreover, the raw SUV and Ki analyses of organs among HC, ADC, and SCC groups provided complementary evidence to the metabolic network findings. The proposed abnormal metabolic networks are feasible to characterize the metabolic alterations between organs or systems in ADC and SCC patients based on static and dynamic total-body PET/CT relative to HCs. Dynamic Ki-based analysis sensitively captures the complex and heterogeneous metabolic network alterations in LC subtypes, with SCC showing more extensive systemic abnormalities than ADC. The findings have the potential to enhance understanding of LC's physiological mechanisms and inform precision medicine strategies for early diagnosis and tailored therapies.

Many studies have demonstrated the small-worldness of the human brain, and have revealed a sexual dimorphism in brain network properties. However, little is known about the gender effects on the topological organization of the brain metabolic covariance networks. To investigate the small-worldness and the gender differences in the topological architectures of human brain metabolic networks. FDG-PET data of 400 healthy right-handed subjects (200 women and 200 age-matched men) were involved in the present study. Metabolic networks of each gender were constructed by calculating the covariance of regional cerebral glucose metabolism (rCMglc) across subjects on the basis of AAL parcellation. Gender differences of network and nodal properties were investigated by using the graph theoretical approaches. Moreover, the gender-related difference of rCMglc in each brain region was tested for investigating the relationships between the hub regions and the brain regions showing significant gender-related differences in rCMglc. We found prominent small-world properties in the domain of metabolic networks in each gender. No significant gender difference in the global characteristics was found. Gender differences of nodal characteristic were observed in a few brain regions. We also found bilateral and lateralized distributions of network hubs in the females and males. Furthermore, we first reported that some hubs of a gender located in the brain regions showing weaker rCMglc in this gender than the other gender. The present study demonstrated that small-worldness was existed in metabolic networks, and revealed gender differences of organizational patterns in metabolic network. These results maybe provided insights into the understanding of the metabolic substrates underlying individual differences in cognition and behaviors.

Finding environmentally critical transmission sectors, transactions, and paths in global supply chain networks

18 F-FDG PET captures the relationship between glucose metabolism and synaptic activity, allowing for modeling brain function through metabolic connectivity. We investigated tumor-induced modifications of brain metabolic connectivity. Forty-three patients with left hemispheric tumors and 18 F-FDG PET/MRI were retrospectively recruited. We included 37 healthy controls (HCs) from the database CERMEP-IDB-MRXFDG. We analyzed the whole brain and right versus left hemispheres connectivity in patients and HC, frontal versus temporal tumors, active tumors versus radiation necrosis, and patients with high Karnofsky performance score (KPS = 100) versus low KPS (KPS < 70). Results were compared with 2-sided t test ( P < 0.05). Twenty high-grade glioma, 4 low-grade glioma, and 19 metastases were included. The patients' whole-brain network displayed lower connectivity metrics compared with HC ( P < 0.001), except assortativity and betweenness centrality ( P = 0.001). The patients' left hemispheres showed decreased similarity, and lower connectivity metrics compared with the right ( P < 0.01), with the exception of betweenness centrality ( P = 0.002). HC did not show significant hemispheric differences. Frontal tumors showed higher connectivity metrics ( P < 0.001) than temporal tumors, but lower betweenness centrality ( P = 4.5 -7 ). Patients with high KPS showed higher distance local efficiency ( P = 0.01), rich club coefficient ( P = 0.0048), clustering coefficient ( P = 0.00032), betweenness centrality ( P = 0.008), and similarity ( P = 0.0027) compared with low KPS. Patients with active tumor(s) (14/43) demonstrated significantly lower connectivity metrics compared with necroses. Tumors cause reorganization of metabolic brain networks, characterized by formation of new connections and decreased centrality. Patients with frontal tumors retained a more efficient, centralized, and segregated network than patients with temporal tumors. Stronger metabolic connectivity was associated with higher KPS.

Among functional imaging methods, metabolic connectivity (MC) is increasingly used for investigation of regional network changes to examine the pathophysiology of neurodegenerative diseases such as Alzheimer's disease (AD) or movement disorders. Hitherto, MC was mostly used in clinical studies, but only a few studies demonstrated the usefulness of MC in the rodent brain. The goal of the current work was to analyze and validate metabolic regional network alterations in three different mouse models of neurodegenerative diseases (β-amyloid and tau) by use of 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography (FDG-PET) imaging. We compared the results of FDG-µPET MC with conventional VOI-based analysis and behavioral assessment in the Morris water maze (MWM). The impact of awake versus anesthesia conditions on MC read-outs was studied and the robustness of MC data deriving from different scanners was tested. MC proved to be an accurate and robust indicator of functional connectivity loss when sample sizes ≥12 were considered. MC readouts were robust across scanners and in awake/ anesthesia conditions. MC loss was observed throughout all brain regions in tauopathy mice, whereas β-amyloid indicated MC loss mainly in spatial learning areas and subcortical networks. This study established a methodological basis for the utilization of MC in different β-amyloid and tau mouse models. MC has the potential to serve as a read-out of pathological changes within neuronal networks in these models.

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.

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.

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.