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

Alterations of Structural Network Efficiency in Early-Onset and Late-Onset Alzheimer's Disease

BACKGROUND AND PURPOSE

Early- and late-onset Alzheimer's disease (EOAD and LOAD, respectively) share the same neuropathological hallmarks of amyloid and neurofibrillary tangles but have distinct cognitive features. We compared structural brain connectivity between the EOAD and LOAD groups using structural network efficiency and evaluated the association of structural network efficiency with the cognitive profile and pathological markers of Alzheimer's disease (AD).

METHODS

The structural brain connectivity networks of 80 AD patients (47 with EOAD and 33 with LOAD) and 57 healthy controls were reconstructed using diffusion-tensor imaging. Graph-theoretic indices were calculated and intergroup differences were evaluated. Correlations between network parameters and neuropsychological test results were analyzed. The correlations of the amyloid and tau burdens with network parameters were evaluated for the patients and controls.

RESULTS

Compared with the age-matched control group, the EOAD patients had increased global path length and decreased global efficiency, averaged local efficiency, and averaged clustering coefficient. In contrast, no significant differences were found in the LOAD patients. Locally, the EOAD patients showed decreases in local efficiency and the clustering coefficient over a wide area compared with the control group, whereas LOAD patients showed such decreases only within a limited area. Changes in network parameters were significantly correlated with multiple cognitive domains in EOAD patients, but only with Clinical Dementia Rating Sum-of-Boxes scores in LOAD patients. Finally, the tau burden was correlated with changes in network parameters in AD signature areas in both patient groups, while there was no correlation with the amyloid burden.

CONCLUSIONS

The impairment of structural network efficiency and its effects on cognition may differ between EOAD and LOAD.

Long-term dietary folic acid supplementation attenuated aging-induced hippocampus atrophy and promoted glucose uptake in 25-month-old rats with cognitive decline

The brain has high energy demand making it sensitive to changes in energy fuel supply. Aging shrinks brain volume, decreases glucose uptake availability of the brain, and finally, causes cognitive dysfunction. Folic acid supplementation delayed cognitive decline and neurodegeneration. However, whether folic acid affects brain energy metabolism and structural changes is unclear. The study aimed to determine if long-term dietary folic acid supplementation could alleviate age-related cognitive decline by attenuating hippocampus atrophy and promoting brain glucose uptake in Sprague-Dawley (SD) rats. According to folic acid levels in diet, three-month-old male SD rats were randomly divided into four intervention groups for 22 months in equal numbers: folic acid-deficient diet (FA-D) group, folic acid-normal diet (FA-N) group, low folic acid-supplemented diet (FA-L) group, and high folic acid-supplemented diet (FA-H) group. The results showed that serum folate concentrations decreased and serum homocysteine (Hcy) concentrations increased with age, and dietary folic acid supplementation increased serum folate concentrations and decreased Hcy concentrations at 11, 18, and 22 months of intervention. Dietary folic acid supplementation attenuated aging-induced hippocampus atrophy, which was showed by higher fractional anisotropy and lower mean diffusivity in the hippocampus, increased brain ¹⁸F-Fluorodeoxyglucose (¹⁸F-FDG) uptake, then stimulated neuronal survival, and alleviated age-related cognitive decline in SD rats. In conclusion, long-term dietary folic acid supplementation alleviated age-related cognitive decline by attenuating hippocampus atrophy and promoting brain glucose uptake in SD rats.

Glucose metabolism patterns: A potential index to characterize brain ageing and predict high conversion risk into cognitive impairment

Exploring individual hallmarks of brain ageing is important. Here, we propose the age-related glucose metabolism pattern (ARGMP) as a potential index to characterize brain ageing in cognitively normal (CN) elderly people. We collected 18F-fluorodeoxyglucose (18F-FDG) PET brain images from two independent cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI, N = 127) and the Xuanwu Hospital of Capital Medical University, Beijing, China (N = 84). During follow-up (mean 80.60 months), 23 participants in the ADNI cohort converted to cognitive impairment. ARGMPs were identified using the scaled subprofile model/principal component analysis method, and cross-validations were conducted in both independent cohorts. A survival analysis was further conducted to calculate the predictive effect of conversion risk by using ARGMPs. The results showed that ARGMPs were characterized by hypometabolism with increasing age primarily in the bilateral medial superior frontal gyrus, anterior cingulate and paracingulate gyri, caudate nucleus, and left supplementary motor area and hypermetabolism in part of the left inferior cerebellum. The expression network scores of ARGMPs were significantly associated with chronological age (R = 0.808, p < 0.001), which was validated in both the ADNI and Xuanwu cohorts. Individuals with higher network scores exhibited a better predictive effect (HR: 0.30, 95% CI: 0.1340 ~ 0.6904, p = 0.0068). These findings indicate that ARGMPs derived from CN participants may represent a novel index for characterizing brain ageing and predicting high conversion risk into cognitive impairment.

GLP-1 Receptor Agonists in Neurodegeneration: Neurovascular Unit in the Spotlight

Defects in brain energy metabolism and proteopathic stress are implicated in age-related degenerative neuronopathies, exemplified by Alzheimer’s disease (AD) and Parkinson’s disease (PD). As the currently available drug regimens largely aim to mitigate cognitive decline and/or motor symptoms, there is a dire need for mechanism-based therapies that can be used to improve neuronal function and potentially slow down the underlying disease processes. In this context, a new class of pharmacological agents that achieve improved glycaemic control via the glucagon-like peptide 1 (GLP-1) receptor has attracted significant attention as putative neuroprotective agents. The experimental evidence supporting their potential therapeutic value, mainly derived from cellular and animal models of AD and PD, has been discussed in several research reports and review opinions recently. In this review article, we discuss the pathological relevance of derangements in the neurovascular unit and the significance of neuron–glia metabolic coupling in AD and PD. With this context, we also discuss some unresolved questions with regard to the potential benefits of GLP-1 agonists on the neurovascular unit (NVU), and provide examples of novel experimental paradigms that could be useful in improving our understanding regarding the neuroprotective mode of action associated with these agents.

Structural brain splitting is a hallmark of Granulin-related frontotemporal dementia

Frontotemporal dementia associated with granulin (GRN) mutations presents asymmetric brain atrophy. We applied a Minimum Spanning Tree plus an Efficiency Cost Optimization approach to cortical thickness data in order to test whether graph theory measures could identify global or local impairment of connectivity in the presymptomatic phase of pathology, where other techniques failed in demonstrating changes. We included 52 symptomatic GRN mutation carriers (SC), 161 presymptomatic GRN mutation carriers (PSC) and 341 non-carriers relatives from the Genetic Frontotemporal dementia research Initiative cohort. Group differences of global, nodal and edge connectivity in (Minimum Spanning Tree plus an Efficiency Cost Optimization) graph were tested via Structural Equation Models. Global graph perturbation was selectively impaired in SC compared to non-carriers, with no changes in PSC. At the local level, only SC exhibited perturbation of frontotemporal nodes, but edge connectivity revealed a characteristic pattern of interhemispheric disconnection, involving homologous parietal regions, in PSC. Our results suggest that GRN-related frontotemporal dementia resembles a disconnection syndrome, with interhemispheric disconnection between parietal regions in presymptomatic phases that progresses to frontotemporal areas as symptoms emerge.

Age-Related Changes in Topological Properties of Individual Brain Metabolic Networks in Rats

Normal aging causes profound changes of structural degeneration and glucose hypometabolism in the human brain, even in the absence of disease. In recent years, with the extensive exploration of the topological characteristics of the human brain, related studies in rats have begun to investigate. However, age-related alterations of topological properties in individual brain metabolic network of rats remain unknown. In this study, a total of 48 healthy female Sprague–Dawley (SD) rats were used, including 24 young rats and 24 aged rats. We used Jensen-Shannon Divergence Similarity Estimation (JSSE) method for constructing individual metabolic networks to explore age-related topological properties and rich-club organization changes. Compared with the young rats, the aged rats showed significantly decreased clustering coefficient (Cp) and local efficiency (Eloc) across the whole-brain metabolic network. In terms of changes in local network measures, degree (D) and nodal efficiency (Enod) of left posterior dorsal hippocampus, and Enod of left olfactory tubercle were higher in the aged rats than in the young rats. About the rich-club analysis, the existence of rich-club organization in individual brain metabolic networks of rats was demonstrated. In addition, our findings further confirmed that rich-club connections were susceptible to aging. Relative to the young rats, the overall strength of rich-club connections was significantly reduced in the aged rats, while the overall strength of feeder and local connections was significantly increased. These findings demonstrated the age-related reorganization principle of the brain structure and improved our understanding of brain alternations during aging.

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

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.

Age-Related Changes in Micro Brain Characteristics Based on Relaxed Mean-Field Model

Brain health is an important research direction of neuroscience. In addition to the effects of diseases, we cannot ignore the negative effect of aging on brain health. There have been many studies on brain aging, but only a few have used dynamic models to analyze differences in micro brain characteristics in healthy people. In this article, we use the relaxed mean-field model (rMFM) to study the effects of normal aging. Two main parameters of this model are the recurrent connection strength and subcortical input strength. The sensitivity of the rMFM to the initial values of the parameters has not been fully discussed in previous research. We examine this issue through repeated numerical experiments and obtain a reasonable initial parameter range for this model. Differences in recurrent connection strength and subcortical input strength due to aging have also not been studied previously. We use statistical methods to find the regions of interest (ROIs) exhibiting significant differences between young and old groups. Further, we carry out a difference analysis on the process of change of these ROIs on a more detailed timescale. We find that even with the same final results, the trends of change in these ROIs are different. This shows that to develop possible methods to prevent or delay brain damage due to aging, more attention needs to be paid to the trends of change of different ROIs, not just the final results.

Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review

Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.

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

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.

Analysis of circulating metabolites to differentiate Parkinson's disease and essential tremor

Parkinson disease (PD) and essential tremor (ET) are two common adult-onset tremor disorders in which prevalence increases with age. PD is a neurodegenerative condition with progressive disability. In ET, neurodegeneration is not an established etiology. We sought to determine whether an underlying metabolic pattern may differentiate ET from PD. Circulating metabolites in plasma and cerebrospinal fluid were analyzed using gas chromatography-mass spectroscopy. There were several disrupted pathways in PD compared to ET plasma including glycolysis, tyrosine, phenylalanine, tyrosine biosynthesis, purine and glutathione metabolism. Elevated α-synuclein levels in plasma and CSF distinguished PD from ET. The perturbed metabolic state in PD was associated with imbalance in the pentose phosphate pathway, deficits in energy production, and change in NADPH, NADH and nicotinamide phosphoribosyltransferase levels. This work demonstrates significant metabolic differences in plasma and CSF of PD and ET patients.

Topological Alterations in White Matter Structural Networks in Blepharospasm

BACKGROUND

Accumulating evidence indicates regional structural changes in the white matter (WM) of brains in patients with blepharospasm (BSP); however, whether large-scale WM structural networks undergo widespread reorganization in these patients remains unclear.

OBJECTIVE

We investigated topology changes and global and local features of large-scale WM structural networks in BSP patients compared with hemifacial spasm (HFS) patients or healthy controls (HCs).

METHODS

This cross-sectional study applied graph theoretical analysis to assess deterministic diffusion tensor tractography findings in 41 BSP patients, 41 HFS patients, and 41 HCs. WM structural connectivity in 246 cortical and subcortical regions was assessed, and topological parameters of the resulting graphs were calculated. Networks were compared among BSP, HFS, and HCs groups.

RESULTS

Compared to HCs, both BSP and HFS patients showed alterations in network integration and segregation characterized by increased global efficiency and modularity and reduced shortest path length. Moreover, increased nodal efficiency in multiple cortical and subcortical regions was found in BSP and HFS patients compared with HCs. However, these differences were not found between BSP and HFS patients. Whereas all participants showed highly similar hub distribution patterns, BSP patients had additional hub regions not present in either HFS patients or HCs, which were located in the primary head and face motor cortex and basal ganglia.

CONCLUSIONS

Our findings suggest that the large-scale WM structural network undergoes an extensive reorganization in BSP, probably due to both dystonia-specific abnormalities and facial hyperkinetic movements. © 2021 International Parkinson and Movement Disorder Society.