Relationship between the disrupted topological efficiency of the structural brain connectome and glucose hypometabolism in normal aging

Bi, Qiuhui; Wang, Wenxiao; Niu, Na; Li, He; Wang, Yezhou; Huang, Wanyi; Chen, Kewei; Xu, Kai; Zhang, Junying; Chen, Yaojing; Wei, Dongfeng; Cui, Ruixue; Shu, Ni; Zhang, Zhanjun

doi:10.1016/j.neuroimage.2020.117591

Cited by 18 publications

(15 citation statements)

References 73 publications

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“…With age, aerobic glycolysis and consumption of glucose were found to be severely decreased in the brain, particularly in the temporal, parietal and frontal lobes, and motor cortex ( Goyal et al, 2017 ). This is accompanied in normal aging by the degeneration of brain structures, leading to loss in brain weight and volume ( Dekaban, 1978 ; Fox and Schott, 2004 ), cortical thickness in the prefrontal cortex, medial temporal lobe and hippocampus ( Sowell et al, 2003 ; Salat et al, 2004 ), gray matter atrophy, disruptions of white matter integrity ( Bi et al, 2021 ), and synaptic density ( Masliah et al, 1993 ). These non-pathological changes contribute to age-related cognitive decline in elderly subjects ( Resnick et al, 2003 ; Yang et al, 2015 ; Bender et al, 2016 ).…”

Section: Brain Energy Metabolic Dysfunctions In Aging and Neurodegenerative Diseasesmentioning

confidence: 99%

“…Advances in neuroimaging techniques, such as MRI and PET allow to investigate the dynamic brain changes with aging in vivo . For instance, brain network connectome, which is assessed through diffusion MRI tractography efficiency, was found to decline with age in specific brain regions such as the hippocampus, thalamus, and frontal and parietal cortices ( Bi et al, 2021 ). Glucose hypometabolism was observed with aging in the anterior cingulate cortex, several parts of the orbital and frontal gyrus and in the thalamus ( Bi et al, 2021 ).…”

Section: Brain Energy Metabolic Dysfunctions In Aging and Neurodegenerative Diseasesmentioning

confidence: 99%

“…For instance, brain network connectome, which is assessed through diffusion MRI tractography efficiency, was found to decline with age in specific brain regions such as the hippocampus, thalamus, and frontal and parietal cortices ( Bi et al, 2021 ). Glucose hypometabolism was observed with aging in the anterior cingulate cortex, several parts of the orbital and frontal gyrus and in the thalamus ( Bi et al, 2021 ). By combining these two measurements, the study revealed a close coupling between age-dependent decreased brain network connectome and hypometabolism in specific brain regions that include frontal and temporal lobes, cingulate gyrus, hippocampus and hypothalamus ( Gong et al, 2009 ; Bi et al, 2021 ).…”

Section: Brain Energy Metabolic Dysfunctions In Aging and Neurodegenerative Diseasesmentioning

confidence: 99%

“…Glucose hypometabolism was observed with aging in the anterior cingulate cortex, several parts of the orbital and frontal gyrus and in the thalamus ( Bi et al, 2021 ). By combining these two measurements, the study revealed a close coupling between age-dependent decreased brain network connectome and hypometabolism in specific brain regions that include frontal and temporal lobes, cingulate gyrus, hippocampus and hypothalamus ( Gong et al, 2009 ; Bi et al, 2021 ). Importantly, several studies showed that glucose hypometabolism is due to a decrease of brain aerobic glycolysis as measured by the difference between glucose and oxygen consumptions ( Goyal et al, 2017 ; Hipkiss, 2019 ; Tang, 2020 ; Yan et al, 2020 ).…”

Section: Brain Energy Metabolic Dysfunctions In Aging and Neurodegenerative Diseasesmentioning

confidence: 99%

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Astrocytes as Key Regulators of Brain Energy Metabolism: New Therapeutic Perspectives

et al. 2022

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Astrocytes play key roles in the regulation of brain energy metabolism, which has a major impact on brain functions, including memory, neuroprotection, resistance to oxidative stress and homeostatic tone. Energy demands of the brain are very large, as they continuously account for 20–25% of the whole body’s energy consumption. Energy supply of the brain is tightly linked to neuronal activity, providing the origin of the signals detected by the widely used functional brain imaging techniques such as functional magnetic resonance imaging and positron emission tomography. In particular, neuroenergetic coupling is regulated by astrocytes through glutamate uptake that triggers astrocytic aerobic glycolysis and leads to glucose uptake and lactate release, a mechanism known as the Astrocyte Neuron Lactate Shuttle. Other neurotransmitters such as noradrenaline and Vasoactive Intestinal Peptide mobilize glycogen, the reserve for glucose exclusively localized in astrocytes, also resulting in lactate release. Lactate is then transferred to neurons where it is used, after conversion to pyruvate, as a rapid energy substrate, and also as a signal that modulates neuronal excitability, homeostasis, and the expression of survival and plasticity genes. Importantly, glycolysis in astrocytes and more generally cerebral glucose metabolism progressively deteriorate in aging and age-associated neurodegenerative diseases such as Alzheimer’s disease. This decreased glycolysis actually represents a common feature of several neurological pathologies. Here, we review the critical role of astrocytes in the regulation of brain energy metabolism, and how dysregulation of astrocyte-mediated metabolic pathways is involved in brain hypometabolism. Further, we summarize recent efforts at preclinical and clinical stages to target brain hypometabolism for the development of new therapeutic interventions in age-related neurodegenerative diseases.

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Section: Brain Energy Metabolic Dysfunctions In Aging and Neurodegenerative Diseasesmentioning

confidence: 99%

Section: Brain Energy Metabolic Dysfunctions In Aging and Neurodegenerative Diseasesmentioning

confidence: 99%

Section: Brain Energy Metabolic Dysfunctions In Aging and Neurodegenerative Diseasesmentioning

confidence: 99%

Section: Brain Energy Metabolic Dysfunctions In Aging and Neurodegenerative Diseasesmentioning

confidence: 99%

See 2 more Smart Citations

Astrocytes as Key Regulators of Brain Energy Metabolism: New Therapeutic Perspectives

et al. 2022

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“…To our knowledge, the relationship between [ 18 F]FDG metabolism and FC temporal variability has never been tested before. While it is established that regions with stronger static FC tend to have higher cerebral blood flow 41 and higher energy metabolism 25 , possibly reflecting the fact that they are also more strongly connected anatomically 42 (with recent work highlighting that structural connectivity graph properties are positively associated with [ 18 F]FDG SUVR 43,44 ), the tvFC coupling with glucose metabolism is not established.…”

Section: Relationship Between Suvr and Rs-fmri Through Bivariate Correlationmentioning

confidence: 99%

The complexity of the relationship between spontaneous brain activity and glucose metabolism

Volpi

Silvestri

Aiello

et al. 2021

Preprint

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Brain glucose metabolism as assessed by [18F]FDG positron emission tomography (PET) is expected to be significantly related to resting-state functional MRI (rs-fMRI) activity and functional connectivity (FC), but the underlying coupling model is still incompletely understood. Employing simultaneous acquisitions, we related [18F]FDG standard uptake value ratio (SUVR) to 50 features pertaining to rs-fMRI 1) signal, 2) hemodynamic response, 3) static and 4) time-varying FC, and 5) phase synchronization. To assess which rs-fMRI variables better describe SUVR across regions, we employed a hierarchical approach, identifying the model at population level, and then estimating it on individual data. Multilevel modelling explained around 40% of the SUVR variance, with signal-related features as the most relevant fMRI variables. When the model was used to characterize between-network variability of the SUVR-fMRI coupling, the ranking changed. We demonstrate that local activity and synchronization are the most important predictors of glucose metabolism, while large-scale FC properties gain importance within specific networks.

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Methodological evaluation of individual cognitive prediction based on the brain white matter structural connectome

Feng

Wang

Huang

et al. 2022

Human Brain Mapping

Self Cite

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An emerging trend is to use regression‐based machine learning approaches to predict cognitive functions at the individual level from neuroimaging data. However, individual prediction models are inherently influenced by the vast options for network construction and model selection in machine learning pipelines. In particular, the brain white matter (WM) structural connectome lacks a systematic evaluation of the effects of different options in the pipeline on predictive performance. Here, we focused on the methodological evaluation of brain structural connectome‐based predictions. For network construction, we considered two parcellation schemes for defining nodes and seven strategies for defining edges. For the regression algorithms, we used eight regression models. Four cognitive domains and brain age were targeted as predictive tasks based on two independent datasets (Beijing Aging Brain Rejuvenation Initiative [BABRI]: 633 healthy older adults; Human Connectome Projects in Aging [HCP‐A]: 560 healthy older adults). Based on the results, the WM structural connectome provided a satisfying predictive ability for individual age and cognitive functions, especially for executive function and attention. Second, different parcellation schemes induce a significant difference in predictive performance. Third, prediction results from different data sets showed that dMRI with distinct acquisition parameters may plausibly result in a preference for proper fiber reconstruction algorithms and different weighting options. Finally, deep learning and Elastic‐Net models are more accurate and robust in connectome‐based predictions. Together, significant effects of different options in WM network construction and regression algorithms on the predictive performances are identified in this study, which may provide important references and guidelines to select suitable options for future studies in this field.

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Relationship between the disrupted topological efficiency of the structural brain connectome and glucose hypometabolism in normal aging

Cited by 18 publications

References 73 publications

Astrocytes as Key Regulators of Brain Energy Metabolism: New Therapeutic Perspectives

Astrocytes as Key Regulators of Brain Energy Metabolism: New Therapeutic Perspectives

The complexity of the relationship between spontaneous brain activity and glucose metabolism

Methodological evaluation of individual cognitive prediction based on the brain white matter structural connectome

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