Metabolic Hyperconnectivity in Underrepresented Individuals with long‐COVID

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<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.

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.

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

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 < 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 < 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 < 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 < 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.

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.

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

Long-term changes in metabolic brain network drive memory impairments in rats following neonatal hypoxia-ischemia.

Although the method of visualizing eye-tracking data as a time-series might enhance performance in the understanding of gaze behavior, it has not yet been thoroughly examined in the context of rapid automated naming (RAN). This study attempted, for the first time, to measure gaze behavior during RAN from the perspective of network-domain, which constructed a complex network [referred to as gaze-time-series-based complex network (GCN)] from gaze time-series. Hence, without designating regions of interest, the features of gaze behavior during RAN were extracted by computing topological parameters of GCN. A sample of 98 children (52 males, aged 11.50 ± 0.28 years) was studied. Nine topological parameters (i.e., average degree, network diameter, characteristic path length, clustering coefficient, global efficiency, assortativity coefficient, modularity, community number, and small-worldness) were computed. Findings showed that GCN in each RAN task was assortative and possessed "small-world" and community architecture. Additionally, observations regarding the influence of RAN task types included that: (i) five topological parameters (i.e., average degree, clustering coefficient, assortativity coefficient, modularity, and community number) could reflect the difference between tasks N-num (i.e., naming of numbers) and N-cha (i.e., naming of Chinese characters); (ii) there was only one topological parameter (i.e., network diameter) which could reflect the difference between tasks N-obj (i.e., naming of objects) and N-col (i.e., naming of colors); and (iii) when compared to GCN in alphanumeric RAN, GCN in non-alphanumeric RAN may have higher average degree, global efficiency, and small-worldness, but lower network diameter, characteristic path length, clustering coefficient, and modularity. Findings also illustrated that most of these topological parameters were largely independent of traditional eye-movement metrics. This article revealed the architecture and topological parameters of GCN as well as the influence of task types on them, and thus brought some new insights into the understanding of RAN from the perspective of complex network.

BackgroundGlutamate is the main excitatory neurotransmitter in the brain, acting through ionotropic and metabotropic receptors, such as the neuronal metabotropic glutamate receptor 5 (mGluR5). The radiotracer [11C]ABP688 binds allosterically to the mGluR5, providing a valuable tool to understand glutamatergic function. We have previously shown that neuronal [11C]ABP688 binding is influenced by astrocyte activation. Specifically, the activation of glutamate transport in astrocytes via glutamate‐transporter‐1 (GLT‐1) results in an elevated [11C]ABP688 binding within the ventral anterior thalamus, but no changes in other regions. However, whether changes at the network level occur remains elusive. We evaluated Glutamatergic Brain Networks (GBNs) before and after activating astrocytes.MethodMicro‐PET acquisitions were performed in adult Sprague‐Dawley rats (n = 5) at baseline and after a pharmacological challenge to activate astrocytes. Before the scan, rats received either saline (vehicle) or ceftriaxone (CEF, 200mg/kg), an antibiotic that induces GLT‐1 activation. Then, [11C]ABP688 was administered i.v., followed by a 60‐minute dynamic scan. The nondisplaceable binding potential (BPND) was estimated using the simplified reference tissue model, with the cerebellum as a reference region. [11C]ABP688 BPND was extracted for 12 volumes of interest. GBNs based on [11C]ABP688 regional BPND were assembled with a multiple sampling scheme in MATLAB (p‐value &lt; 0.05).ResultAstrocyte activation induced GBN hyperconnectivity after astrocyte activation, with the strength of association between the cerebral hemispheres, such as in the frontal and parietal cortices (Figure 1, A‐B). After GLT‐1 activation, the connections were diffused, including regions that were not part of the network at baseline, such as the cerebellum. Graph measures show a significant increase in global efficiency, average degree, density, and average clustering coefficient (Figure 1, C‐G). These measures indicate an increase in connections and network cohesiveness. The reduction in assortativity can be related to the new regions on the GBN after the challenge.ConclusionOur results show that GBNs assessed with [11C]ABP688 are sensitive to astrocyte activation. Alterations in glutamate levels, as observed in dysfunctions like glutamatergic excitotoxicity present in AD, could disrupt the cohesion of the glutamatergic network, leading to brain dysfunction.

BackgroundGlutamate is the main excitatory neurotransmitter in the brain, acting through ionotropic and metabotropic receptors, such as the neuronal metabotropic glutamate receptor 5 (mGluR5). The radiotracer [11C]ABP688 binds allosterically to the mGluR5, providing a valuable tool to understand glutamatergic function. We have previously shown that neuronal [11C]ABP688 binding is influenced by astrocyte activation. Specifically, the activation of glutamate transport in astrocytes via glutamate‐transporter‐1 (GLT‐1) results in an elevated [11C]ABP688 binding within the ventral anterior thalamus, but no changes in other regions. However, whether changes at the network level occur remains elusive. We evaluated Glutamatergic Brain Networks (GBNs) before and after activating astrocytes.MethodMicro‐PET acquisitions were performed in adult Sprague‐Dawley rats (n = 5) at baseline and after a pharmacological challenge to activate astrocytes. Before the scan, rats received either saline (vehicle) or ceftriaxone (CEF, 200mg/kg), an antibiotic that induces GLT‐1 activation. Then, [11C]ABP688 was administered i.v., followed by a 60‐minute dynamic scan. The nondisplaceable binding potential (BPND) was estimated using the simplified reference tissue model, with the cerebellum as a reference region. [11C]ABP688 BPND was extracted for 12 volumes of interest. GBNs based on [11C]ABP688 regional BPND were assembled with a multiple sampling scheme in MATLAB (p‐value &lt; 0.05).ResultAstrocyte activation induced GBN hyperconnectivity after astrocyte activation, with the strength of association between the cerebral hemispheres, such as in the frontal and parietal cortices (Figure 1, A‐B). After GLT‐1 activation, the connections were diffused, including regions that were not part of the network at baseline, such as the cerebellum. Graph measures show a significant increase in global efficiency, average degree, density, and average clustering coefficient (Figure 1, C‐G). These measures indicate an increase in connections and network cohesiveness. The reduction in assortativity can be related to the new regions on the GBN after the challenge.ConclusionOur results show that GBNs assessed with [11C]ABP688 are sensitive to astrocyte activation. Alterations in glutamate levels, as observed in dysfunctions like glutamatergic excitotoxicity present in AD, could disrupt the cohesion of the glutamatergic network, leading to brain dysfunction.

The human brain develops rapidly from infant to adolescent. Establishment of the brain developmental trajectory is important to understand cognition, behavior, and emotions, as well to evaluate the risk of neuropsychiatric disorders. 18F-FDG PET has been widely used to study brain glucose metabolism, but functional brain segregation and integration based on 18F-FDG PET remains largely unknown. Two hundred one Chinese child patients with extracranial malignancy were retrospectively enrolled as surrogates to healthy children. All images were spatially normalized into MNI space using pediatric brain template, and the 18F-FDG uptakes were calculated for 90 regions using AAL atlas. The group-level metabolic brain network was constructed by measuring Pearson correlation coefficients between each pair of brain regions in an inter-subject manner for infant (1 to 4years), childhood (5 to 8years), early adolescent (9 to 12years), and adolescent (13 to 18years) group, respectively. Global efficiency of each group was calculated, and the modular architectures were detected by a greedy algorithm. Both metabolic brain network connectivity and global efficiency increased with aging. Brain network was grouped into 4, 6, 4, and 4 modules from infant to adolescent, respectively. The modular architecture dramatically reorganized from childhood to early adolescent. The hubs spatiotemporally rewired. The ratio of the connector hub to the provincial hub increased from infant to early adolescent, but decreased during the adolescent period. The topological properties and modular reorganization of human brain network dramatically changed with age, especially from childhood to early adolescence. These findings would help understand the Chinese developmental trajectory of human brain functional integration and segregation.

Background: Modules in brain network represent groups of brain regions that are collectively involved in one or more cognitive domains. Exploring aging-related reorganization of the brain modular architecture using metabolic brain network could further our understanding about aging-related neuromechanism and neurodegenerations. Materials and Methods: In this study, 432 subjects who performed 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) were enrolled and divided into young and old adult groups, as well as female and male groups. The modular architecture was detected, and the connector and hub nodes were identified to explore the topological role of the brain regions based on the metabolic brain network. Results: This study revealed that human metabolic brain network was modular and could be clustered into three modules. The modular architecture was reorganized from young to old ages with regions related to sensorimotor function clustered into the same module; and the number of connector nodes was reduced and most connector nodes were localized in temporo-occipital areas related to visual and auditory functions in old ages. The major gender difference is that the metabolic brain network was delineated into four modules in old female group with the nodes related to sensorimotor function split into two modules. Discussion: Those findings suggest aging is associated with reorganized brain modular architecture. Clinical Trial Registration number: ChiCTR2000036842. Impact statement Distinguishing the basic biology underlying aging from that underlying disease is critical for the prevention, diagnosis, and treatment of the aging-related brain disorders. In this study, we tried to uncover aging-related brain modular reorganization by using metabolic brain network. We found the modular architecture was slightly reorganized from young to old ages with regions related to sensorimotor function more converged. The number of connector nodes was reduced and most connector nodes were localized into the temporo-occipital regions. The major gender difference was that metabolic brain network was delineated into four modules in the old female group with the sensorimotor functions split into two modules.

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

Metabolic brain networks can provide insight into the network processes underlying progression from healthy aging to Alzheimer's disease. We explore the effect of two Alzheimer's disease risk factors, amyloid-β and ApoE ε4 genotype, on metabolic brain networks in cognitively normal older adults (N = 64, ages 69–89) compared to young adults (N = 17, ages 20–30) and patients with Alzheimer's disease (N = 22, ages 69–89). Subjects underwent MRI and PET imaging of metabolism (FDG) and amyloid-β (PIB). Normal older adults were divided into four subgroups based on amyloid-β and ApoE genotype. Metabolic brain networks were constructed cross-sectionally by computing pairwise correlations of metabolism across subjects within each group for 80 regions of interest. We found widespread elevated metabolic correlations and desegregation of metabolic brain networks in normal aging compared to youth and Alzheimer's disease, suggesting that normal aging leads to widespread loss of independent metabolic function across the brain. Amyloid-β and the combination of ApoE ε4 led to less extensive elevated metabolic correlations compared to other normal older adults, as well as a metabolic brain network more similar to youth and Alzheimer's disease. This could reflect early progression towards Alzheimer's disease in these individuals. Altered metabolic brain networks of older adults and those at the highest risk for progression to Alzheimer's disease open up novel lines of inquiry into the metabolic and network processes that underlie normal aging and Alzheimer's disease.