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.

Delayed development of metabolic brain network in ADHD-model rats with persistent symptoms

Recent research of persistent homology in algebraic topology has shown that the altered network organization of human brain provides a promising indicator of many neuropsychiatric disorders and neurodegenerative diseases. However, the current slope-based approach may not accurately characterize changes of persistent features over graph filtration because such curves are not strictly linear. Moreover, our previous integrated persistent feature (IPF) works well on an rs-fMRI cohort while it has not yet been studied on metabolic brain networks. To address these issues, we propose a novel univariate network measurement, kernel-based IPF (KBI), based on the prior IPF, to quantify the difference between IPF curves. In our experiments, we apply the KBI index to study fluorodeoxyglucose positron emission tomography (FDG-PET) imaging data from 140 subjects with Alzheimer’s disease (AD), 280 subjects with mild cognitive impairment (MCI), and 280 healthy normal controls (NC). The results show the disruption of network integration in the progress of AD. Compared to previous persistent homology-based measures, as well as other standard graph-based measures that characterize small-world organization and modular structure, our proposed network index KBI possesses more significant group difference and better classification performance, suggesting that it may be used as an effective preclinical AD imaging biomarker.

Multiple sclerosis (MS) is an immune-mediated neurodegenerative disease of the central nervous system (CNS). One of the most promising recent medications for MS is teriflunomide. Its primary mechanism of action is linked to effects on the peripheral immune system by inhibiting dihydroorotate dehydrogenase (DHODH)-catalyzed de novo pyrimidine synthesis and reducing the expansion of lymphocytes in the peripheral immune system. Some in vitro studies suggested, however, that it can also have a direct effect on the CNS compartment. This potential alternative mode of action depends on the drug's capacity to traverse the blood-brain barrier (BBB) and to exert an effect on the complex network of brain biochemical pathways. In this paper, we demonstrate the application of high-resolution/high-accuracy matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry for molecular imaging of the mouse brain coronal sections from animals treated with teriflunomide. Specifically, in order to assess the effect of teriflunomide on the mouse CNS compartment, we investigated the feasibility of teriflunomide to traverse the BBB. Secondly, we systematically evaluated the spatial and semi-quantitative brain metabolic profiles of 24 different endogenous compounds after 4-day teriflunomide administration. Even though the drug was not detected in the examined cerebral sections (despite the high detection sensitivity of the developed method), in-depth study of the endogenous metabolic compartment revealed noticeable alterations as a result of teriflunomide administration compared to the control animals. The observed differences, particularly for purine and pyrimidine nucleotides as well as for glutathione and carbohydrate metabolism intermediates, shed some light on the potential impact of teriflunomide on the mouse brain metabolic networks. Graphical Abstract.

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

The change in hypometabolism affects the regional links in the brain network. Here, to understand the underlying brain metabolic network deficits during the early stage and disease evolution of AD (Alzheimer disease), we applied correlation analysis to identify the metabolic connectivity patterns using 18F-FDG PET data for NC (normal control), sMCI (stable MCI), pMCI (progressive MCI) and AD, and explore the inter- and intra-hemispheric connectivity between anatomically-defined brain regions. Regions extracted from 90 anatomical structures were used to construct the matrix for measuring the inter- and intra-hemispheric connectivity. The brain connectivity patterns from the metabolic network show a decreasing trend of inter- and intra-hemispheric connections for NC, sMCI, pMCI and AD. Connection of temporal to the frontal or occipital regions is a characteristic pattern for conversion of NC to MCI, and the density of links in the parietal-occipital network is a differential pattern between sMCI and pMCI. The reduction pattern of inter and intra-hemispheric brain connectivity in the metabolic network depends on the disease stages, and is with a decreasing trend with respect to disease severity. Both frontal-occipital and parietal-occipital connectivity patterns in the metabolic network using 18F-FDG PET are the key feature for differentiating disease groups in AD.

BackgroundParkinson’s disease is a widespread neurodegenerative disorder which affects brain metabolism. Although changes in gene expression during disease are often measured, it is difficult to predict metabolic fluxes from gene expression data. Here we explore the hypothesis that changes in gene expression for enzymes tend to parallel flux changes in biochemical reaction pathways in the brain metabolic network. This hypothesis is the basis of a computational method to predict metabolic flux changes from post-mortem gene expression measurements in Parkinson’s disease (PD) brain.ResultsWe use a network model of central metabolism and optimize the correspondence between relative changes in fluxes and in gene expression. To this end we apply the Least-squares with Equalities and Inequalities algorithm integrated with Flux Balance Analysis (Lsei-FBA). We predict for PD (1) decreases in glycolytic rate and oxygen consumption and an increase in lactate production in brain cortex that correspond with measurements (2) relative flux decreases in ATP synthesis, in the malate-aspartate shuttle and midway in the TCA cycle that are substantially larger than relative changes in glucose uptake in the substantia nigra, dopaminergic neurons and most other brain regions (3) shifts in redox shuttles between cytosol and mitochondria (4) in contrast to Alzheimer’s disease: little activation of the gamma-aminobutyric acid shunt pathway in compensation for decreased alpha-ketoglutarate dehydrogenase activity (5) in the globus pallidus internus, metabolic fluxes are increased, reflecting increased functional activity.ConclusionOur method predicts metabolic changes from gene expression data that correspond in direction and order of magnitude with presently available experimental observations during Parkinson’s disease, indicating that the hypothesis may be useful for some biochemical pathways. Lsei-FBA generates predictions of flux distributions in neurons and small brain regions for which accurate metabolic flux measurements are not yet possible.

Stroke at the acute stage is a major cause of disability in adults, and is associated with dysfunction of brain networks. However, the mechanisms underlying changes in brain connectivity in stroke are far from fully elucidated. In the present study, we investigated brain metabolism and metabolic connectivity in a rat ischemic stroke model of middle cerebral artery occlusion (MCAO) at the acute stage using 18F-fluorodeoxyglucose positron emission tomography. Voxel-wise analysis showed decreased metabolism mainly in the ipsilesional hemisphere, and increased metabolism mainly in the contralesional cerebellum. We used further metabolic connectivity analysis to explore the brain metabolic network in MCAO. Compared to sham controls, rats with MCAO showed most significantly reduced nodal and local efficiency in the ipsilesional striatum. In addition, the MCAO group showed decreased metabolic central connection of the ipsilesional striatum with the ipsilesional cerebellum, ipsilesional hippocampus, and bilateral hypothalamus. Taken together, the present study demonstrated abnormal metabolic connectivity in rats at the acute stage of ischemic stroke, which might provide insight into clinical research.

The effects of image reconstruction algorithms on topographic characteristics, diagnostic performance and clinical correlation of metabolic brain networks in Parkinson’s disease

Dementia has become a serious disease in elderly population. Graph theory based brain network analysis method is considered as a popular and universal technique in the field of the neurosciences. It can help neuroscientists to investigate the neuropathology of dementia, especially to understand dementia subtypes. However, brain network analysis for various dementia subtypes at the same time is still limited. The aim at this study is to investigate the similarities and differences in brain network modular parameters of three dementia subtypes: Alzheimer's disease (AD), Parkinson's disease dementia (PDD), Lewy body dementia (DLB). Health control has also been compared. All subjects of health control(HC), AD, PDD and DLB groups were from Huashan Hospital. Using topological analysis algorithms, we found that HC brain was divided into 4 modules; AD brain was divided into 4 modules; PDD brain was divided into 6 modules; and DLB brain was divided into 10 modules, which demonstrated differences in these dementia subtypes. We also found that right thalamus area was scattered in all dementia subtypes. After using the seed point correlation analysis, it can be seen that the connections between right thalamus and subcortical, frontal gyrus were enhanced and the connections with the occipital gyrus was attenuated in dementia subtypes. As a result, findings in this paper are expected to be useful for neuroscientists to further understand the pathology of AD, PDD and DLB.

Correction to: Metabolic Brain Network Analysis of Hypothyroidism Symptom Based on [18F]FDG-PET of Rats

Temporal lobe epilepsy (TLE) is one of the most common types of epilepsy syndromes in the world. Depression is an important comorbidity of epilepsy, which has been reported in patients with TLE and in different experimental models of epilepsy. However, there is no established consensus on which brain regions are associated with the manifestation of depression in epilepsy. Here, we investigated the alterations in cerebral glucose metabolism and the metabolic network in the pilocarpine-induced rat model of epilepsy and correlated it with depressive behavior during the chronic phase of epilepsy. Fluorodeoxyglucose (18 F-FDG) was used to investigate the cerebral metabolism, and a cross-correlation matrix was used to examine the metabolic network in chronically epileptic rats using micro-positron emission tomography (microPET) imaging. An experimental model of epilepsy was induced by pilocarpine injection (320 mg/kg, ip). Forced swim test (FST), sucrose preference test (SPT), and eating-related depression test (ERDT) were used to evaluate depression-like behavior. Our results show an association between epilepsy and depression comorbidity based on changes in both cerebral glucose metabolism and the functional metabolic network. In addition, we have identified a significant correlation between brain glucose hypometabolism and depressive-like behavior in chronically epileptic rats. Furthermore, we found that the epileptic depressed group presents a hypersynchronous brain metabolic network in relation to the epileptic nondepressed group. This study revealed relevant alterations in glucose metabolism and the metabolic network among the brain regions of interest for both epilepsy and depression pathologies. Thus it seems that depression in epileptic animals is associated with a more diffuse hypometabolism and altered metabolic network architecture and plays an important role in chronic epilepsy.

Progressive supranuclear palsy (PSP) is a rare movement disorder and often difficult to distinguish clinically from Parkinson's disease (PD) and multiple system atrophy (MSA) in early phases. In this study, we report reproducible disease-related topographies of brain network and regional glucose metabolism associated with PSP in clinically-confirmed independent cohorts of PSP, MSA, and PD patients and healthy controls in the USA and China. Using 18 F-FDG PET images from PSP and healthy subjects, we applied spatial covariance analysis with bootstrapping to identify a PSP-related pattern (PSPRP) and estimate its reliability, and evaluated the ability of network scores for differential diagnosis. We also detected regional metabolic differences using statistical parametric mapping analysis. We produced a highly reliable PSPRP characterized by relative metabolic decreases in the middle prefrontal cortex/cingulate, ventrolateral prefrontal cortex, striatum, thalamus and midbrain, covarying with relative metabolic increases in the hippocampus, insula and parieto-temporal regions. PSPRP network scores correlated positively with PSP duration and accurately discriminated between healthy, PSP, MSA and PD groups in two separate cohorts of parkinsonian patients at both early and advanced stages. Moreover, PSP patients shared many overlapping areas with abnormal metabolism in the same cortical and subcortical regions as in the PSPRP. With rigorous cross-validation, this study demonstrated highly comparable and reproducible PSP-related metabolic topographies at network and regional levels across different patient populations and PET scanners. Metabolic brain network activity may serve as a reliable and objective marker of PSP, although cross-validation applying recent diagnostic criteria and classification is warranted.

Systems-based investigation of diseases requires integrated analysis of cellular networks and high-throughput data of gene products. The use of genome-scale metabolic networks for such integration has led to the elucidation of cellular mechanisms for several cell types from microorganisms to plants. It has become easier and cheaper to generate high-throughput data over years in the form of transcriptome, proteome and metabolome. This has tremendously improved the quality and quantity of information extracted from such data enabling the documentation of active pathways and reactions in cell metabolism. A number of omics-based datasets for several neurodegenerative diseases are now available in public repositories. This increases the potential of using genome-scale brain metabolic networks as a scaffold for this type of data to map metabolic alterations for the purpose of elucidating disease mechanisms and for the diagnosis and treatment of such disorders. This chapter first reviews omics data collected for neurodegenerative diseases to map their effect on metabolism. Later, the potential for genome-scale metabolic modeling of such data is reviewed and discussed in light of recently reconstructed brain metabolic networks at genome-scale.

Dementia with Lewy bodies (DLB) is the second most common degenerative dementia of the central nervous system. The technique 18F-fluorodeoxyglucose positron emission tomography (18F FDG PET) was used to investigate brain metabolism patterns in DLB patients. Conventional statistical methods did not consider intern metabolism transforming connections between various brain regions; therefore, most physicians do not understand the underlying neuropathology of DLB patients. In this study, 18F FDG-PET images and graph-theoretical methods were used to investigate alterations in whole-brain intrinsic functional connectivity in a Chinese DLB group and healthy control (HC) group. This experimental study was performed on 22 DLB patients and 22 HC subjects in Huashan Hospital, Shanghai, China. Experimental results indicate that compared with the HC group, the DLB group has severely impaired small-world network. Compared to those of the HC group, the clustering coefficients of the DLB group were higher and characteristic path lengths were longer, and in terms of global efficiencies, those of the DLB group was also lower. Moreover, four significantly altered regions were observed in the DLB group: Inferior frontal gyrus, opercular part (IFG.R), olfactory cortex (OLF.R), hippocampus (HIP.R), and fusiform gyrus (FFG.L). Amongst them, in the DLB group, betweenness centrality became strong in OLF.R, HIP.R, and FFG.L, whereas betweenness centrality became weaker in IFG.R. Finally, IFGoperc.R was selected as a seed and a voxel-wise correlation analysis was performed. Compared to the HC group, the DLB group showed several regions of strengthened connection with IFGoperc.R; these regions were located in the prefrontal cortex and regions of weakened connection were located in the occipital cortex. The results of this paper may help physicians to better understand and characterize DLB patients.

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.

Type 2 diabetes (T2D) is associated with deterioration of brain structure and function. Here, we tested the hypothesis that T2D induces a reorganization of the brain metabolic networks that support brain function. For that, alterations of neuronal and glial energy metabolism were investigated in a T2D model, the Goto-Kakizaki (GK) rat. 13C magnetic resonance spectroscopy in vivo at 14.1T was used to detect 13C labeling incorporation into carbons of glutamate, glutamine, and aspartate in the brain of GK (n=7) and Wistar (n=13) rats during intravenous [1,6-13C]glucose administration. Labeling of brain glucose and amino acids over time was analyzed with a two-compartment mathematical model of brain energy metabolism to determine the rates of metabolic pathways in neurons and glia. Compared to controls, GK rats displayed lower rates of brain glutamine synthesis (-32%, P<0.001) and glutamate-glutamine cycle (-40%, P<0.001), and mitochondrial tricarboxylic acid (TCA) cycle rate in neurons (-7%, P=0.036). In contrast, the TCA cycle rate of astrocytes was larger in GK rats than controls (+21%, P=0.042). We conclude that T2D alters brain energy metabolism and impairs the glutamate-glutamine cycle between neurons and astrocytes, in line with diabetes-induced neurodegeneration and astrogliosis underlying brain dysfunction.

Abnormal metabolic brain networks in progressive supranuclear palsy and corticobasal syndrome: diagnostic performance using perfusion spect scans in patients with movement disorders

This study attempted to better understand the properties associated with the metabolic brain network in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Graph theory was employed to investigate the topological organization of metabolic brain network among 86 patients with MCI, 89 patients with AD, and 97 normal controls (NCs) using 18F fluoro-deoxy-glucose positron emission tomography (FDG-PET) data. The whole brain was divided into 82 areas by Brodmann atlas to construct networks. We found that MCI and AD showed a loss of small-world properties and topological aberrations, and MCI showed an intermediate measurement between NC and AD. The networks of MCI and AD were vulnerable to attacks resulting from the altered topological pattern. Furthermore, individual contributions were correlated with Mini-Mental State Examination and Clinical Dementia Rating. The present study indicated that the topological patterns of the metabolic networks were aberrant in patients with MCI and AD, which may be particularly helpful in uncovering the pathophysiology underlying the cognitive dysfunction in MCI and AD.

The purpose of this study was to identify the specific metabolic brain pattern characteristic for Parkinson's disease (PD): Parkinson's disease-related pattern (PDRP), using network analysis of [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) brain images in a cohort of Slovenian PD patients. Twenty PD patients (age 70.1±7.8years, Movement Disorder Society Unified Parkinson's Disease Motor Rating Scale (MDS-UPDRS-III) 38.3±12.2; disease duration 4.3±4.1years) and 20 age-matched normal controls (NCs) underwent FDG-PET brain imaging. An automatic voxel-based scaled subprofile model/principal component analysis (SSM/PCA) was applied to these scans for PDRP-Slovenia identification. The pattern was characterized by relative hypermetabolism in pallidum, putamen, thalamus, brain stem, and cerebellum associated with hypometabolism in sensorimotor cortex, posterior parietal, occipital, and frontal cortices. The expression of PDRP-Slovenia discriminated PD patients from NCs (p<0.0001) and correlated positively with patients' clinical score (MDS-UPDRS-III, p=0.03). Additionally, its topography agrees well with the original PDRP (p<0.001) identified in American cohort of PD patients. We validated the PDRP-Slovenia expression on additional FDG-PET scans of 20 PD patients, 20 NCs, and 25 patients with atypical parkinsonism (AP). We confirmed that the expression of PDRP-Slovenia manifests good diagnostic accuracy with specificity and sensitivity of 85-90% at optimal pattern expression cutoff for discrimination of PD patients and NCs and is not expressed in AP. PDRP-Slovenia proves to be a robust and reproducible functional imaging biomarker independent of patient population. It accurately differentiates PD patients from NCs and AP and correlates well with the clinical measure of PD progression.