Molecular and spatial signatures of mouse brain aging at single-cell resolution.

The diversity and complex organization of cells in the brain have hindered systematic characterization of age-related changes in its cellular and molecular architecture, limiting our ability to understand the mechanisms underlying its functional decline during aging. Here, we generated a high-resolution cell atlas of brain aging within the frontal cortex and striatum using spatially resolved single-cell transcriptomics and quantified changes in gene expression and spatial organization of major cell types in these regions over the mouse lifespan. We observed substantially more pronounced changes in cell state, gene expression, and spatial organization of non-neuronal cells over neurons. Our data revealed molecular and spatial signatures of glial and immune cell activation during aging, particularly enriched in the subcortical white matter, and identified both similarities and notable differences in cell-activation patterns induced by aging and systemic inflammatory challenge. These results provide critical insights into age-related decline and inflammation in the brain.

The interaction effects of age, APOE and common environmental risk factors on human brain structure.

Mounting evidence suggests considerable diversity in brain aging trajectories, primarily arising from the complex interplay between age, genetic, and environmental risk factors, leading to distinct patterns of micro- and macro-cerebral aging. The underlying mechanisms of such effects still remain unclear. We conducted a comprehensive association analysis between cerebral structural measures and prevalent risk factors, using data from 36,969 UK Biobank subjects aged 44-81. Participants were assessed for brain volume, white matter diffusivity, Apolipoprotein E (APOE) genotypes, polygenic risk scores, lifestyles, and socioeconomic status. We examined genetic and environmental effects and their interactions with age and sex, and identified 726 signals, with education, alcohol, and smoking affecting most brain regions. Our analysis revealed negative age-APOE-ε4 and positive age-APOE-ε2 interaction effects, respectively, especially in females on the volume of amygdala, positive age-sex-APOE-ε4 interaction on the cerebellar volume, positive age-excessive-alcohol interaction effect on the mean diffusivity of the splenium of the corpus callosum, positive age-healthy-diet interaction effect on the paracentral volume, and negative APOE-ε4-moderate-alcohol interaction effects on the axial diffusivity of the superior fronto-occipital fasciculus. These findings highlight the need of considering age, sex, genetic, and environmental joint effects in elucidating normal or abnormal brain aging.

Age-related brain atrophy is not a homogenous process: Different functional brain networks associate differentially with aging and blood factors.

Aging is characterized by a progressive loss of brain volume at an estimated rate of 5% per decade after age 40. While these morphometric changes, especially those affecting gray matter and atrophy of the temporal lobe, are predictors of cognitive performance, the strong association with aging obscures the potential parallel, but more specific role, of individual subject physiology. Here, we studied a cohort of 554 human subjects who were monitored using structural MRI scans and blood immune protein concentrations. Using machine learning, we derived a cytokine clock (CyClo), which predicted age with good accuracy (Mean Absolute Error = 6 y) based on the expression of a subset of immune proteins. These proteins included, among others, Placenta Growth Factor (PLGF) and Vascular Endothelial Growth Factor (VEGF), both involved in angiogenesis, the chemoattractant vascular cell adhesion molecule 1 (VCAM-1), the canonical inflammatory proteins interleukin-6 (IL-6) and tumor necrosis factor alpha (TNFα), the chemoattractant IP-10 (CXCL10), and eotaxin-1 (CCL11), previously involved in brain disorders. Age, sex, and the CyClo were independently associated with different functionally defined cortical networks in the brain. While age was mostly correlated with changes in the somatomotor system, sex was associated with variability in the frontoparietal, ventral attention, and visual networks. Significant canonical correlation was observed for the CyClo and the default mode, limbic, and dorsal attention networks, indicating that immune circulating proteins preferentially affect brain processes such as focused attention, emotion, memory, response to social stress, internal evaluation, and access to consciousness. Thus, we identified immune biomarkers of brain aging which could be potential therapeutic targets for the prevention of age-related cognitive decline.

Beyond network connectivity: A classification approach to brain age prediction with resting-state fMRI.

The brain is a complex, dynamic organ that shows differences in the same subject at various periods. Understanding how brain activity changes across age as a function of the brain networks has been greatly abetted by fMRI. Canonical analysis consists of determining how alterations in connectivity patterns (CPs) of certain regions are affected. An alternative approach is taken here by not considering connectivity but rather features computed from recordings at the regions of interest (ROIs). Using machine learning (ML) we assess how neural signals are altered by and prospectively predictive of age and sex via a methodology that is novel in drawing upon pairwise classification across six decades of subjects' chronological ages. ML is used to answer the equally important questions of what properties of the computed features are most predictive as well as which brain networks are most affected by aging. It was found that there is decreased differentiation among the neural signals of older subjects that are separated in age by the same number of years as younger subjects. Furthermore, the burstiness of the signals change at different rates between males and females. The findings provide insight into brain aging via an ROI-based analysis, the consideration of several feature groups, and a novel classification-based ML pipeline. There is also a contribution to understanding the effects of data aggregated from different recording centers on the conclusions of fMRI studies.

Brain extended and closed forms glutathione levels decrease with age and extended glutathione is associated with visuospatial memory.

During aging, the brain is subject to greater oxidative stress (OS), which is thought to play a critical role in cognitive impairment. Glutathione (GSH), as a major antioxidant in the brain, can be used to combat OS. However, how brain GSH levels vary with age and their associations with cognitive function is unclear. In this study, we combined point-resolved spectroscopy and edited spectroscopy sequences to investigate extended and closed forms GSH levels in the anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), and occipital cortex (OC) of 276 healthy participants (extended form, 166 females, age range 20-70 years) and 15 healthy participants (closed form, 7 females, age range 26-56 years), and examined their relationships with age and cognitive function. The results revealed decreased extended form GSH levels with age in the PCC among 276 participants. Notably, the timecourse of extended form GSH level changes in the PCC and ACC differed between males and females. Additionally, positive correlations were observed between extended form GSH levels in the PCC and OC and visuospatial memory. Additionally, a decreased trend of closed form GSH levels with age was also observed in the PCC among 15 participants. Taken together, these findings enhance our understanding of the brain both closed and extended form GSH time course during normal aging and associations with sex and memory, which is an essential first step for understanding the neurochemical underpinnings of healthy aging.

Harnessing myelin water fraction as an imaging biomarker of human cerebral aging, neurodegenerative diseases, and risk factors influencing myelination: A review.

Myelin water fraction (MWF) imaging has emerged as a promising magnetic resonance imaging (MRI) biomarker for investigating brain function and composition. This comprehensive review synthesizes the current state of knowledge on MWF as a biomarker of human cerebral aging, neurodegenerative diseases, and risk factors influencing myelination. The databases used include Web of Science, Scopus, Science Direct, and PubMed. We begin with a brief discussion of the theoretical foundations of MWF imaging, including its basis in MR physics and the mathematical modeling underlying its calculation, with an overview of the most adopted MRI methods of MWF imaging. Next, we delve into the clinical and research applications that have been explored to date, highlighting its advantages and limitations. Finally, we explore the potential of MWF to serve as a predictive biomarker for neurological disorders and identify future research directions for optimizing MWF imaging protocols and interpreting MWF in various contexts. By harnessing the power of MWF imaging, we may gain new insights into brain health and disease across the human lifespan, ultimately informing novel diagnostic and therapeutic strategies.

Brain-age prediction: Systematic evaluation of site effects, and sample age range and size.

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

Left ventricular hypertrophy as a risk factor for accelerated brain aging: Results from the Study of Health in Pomerania.

Previous studies provided evidence for the importance of cardiac structure abnormalities, in particular greater left ventricular (LV) mass, for brain aging, but longitudinal studies are lacking to date. We included 926 individuals (median age 48 years; 53% women) from the TREND cohort of the Study of Health in Pomerania (SHIP) without reduced ejection fraction or a history of myocardial infarction. LV mass index (LVMI) was determined by echocardiography at baseline. Brain morphometric measurements were derived from magnetic resonance images at baseline and 7-year follow-up. Direct effects of baseline LVMI on brain morphometry at follow-up were estimated using linear regression models with adjustment for baseline brain morphometry. At baseline, median LVMI was 40 g/m2.7 and 241 individuals (26%) met the criterion of LV hypertrophy. After correction for multiple testing, baseline LVMI was directly associated with reduced global cortical thickness and increased cortical brain age at follow-up independent from hypertension and blood pressure. Exposure-outcome relations were nonlinear and significantly stronger in the upper half of the exposure distribution. Specifically, an increase in baseline LVMI from the 50% quantile to the 95% quantile was associated additional 2.7 years (95% confidence interval = [1.5 years, 3.8 years]) of cortical brain age at follow-up. Additional regional analyses yielded bilateral effects on multiple frontal cortical regions. Our findings highlight the role of cardiac structure in brain aging. LVMI constitutes an easily measurable marker that might help to identify persons at risk for cognitive impairment and dementia.

[18F]FDG PET integrated with structural MRI for accurate brain age prediction.

PURPOSE: Brain aging is a complex and heterogeneous process characterized by both structural and functional decline. This study aimed to establish a novel deep learning (DL) method for predicting brain age by utilizing structural and metabolic imaging data. METHODS: The dataset comprised participants from both the Universal Medical Imaging Diagnostic Center (UMIDC) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). The former recruited 395 normal control (NC) subjects, while the latter included 438 NC subjects, 51 mild cognitive impairment (MCI) subjects, and 56 Alzheimer's disease (AD) subjects. We developed a novel dual-pathway, 3D simple fully convolutional network (Dual-SFCNeXt) to estimate brain age using [18F]fluorodeoxyglucose positron emission tomography ([18F]FDG PET) and structural magnetic resonance imaging (sMRI) images of NC subjects as input. Several prevailing DL models were trained and tested using either MRI or PET data for comparison. Model accuracies were evaluated using mean absolute error (MAE) and Pearson's correlation coefficient (r). Brain age gap (BAG), deviations of brain age from chronologic age, was correlated with cognitive assessments in MCI and AD subjects. RESULTS: Both PET- and MRI-based models achieved high prediction accuracy. The leading model was the SFCNeXt (the single-pathway version) for PET (MAE = 2.92, r = 0.96) and MRI (MAE = 3.23, r = 0.95) on all samples. By integrating both PET and MRI images, the Dual-SFCNeXt demonstrated significantly improved accuracy (MAE = 2.37, r = 0.97) compared to all single-modality models. Significantly higher BAG was observed in both the AD (P < 0.0001) and MCI (P < 0.0001) groups compared to the NC group. BAG correlated significantly with Mini-Mental State Examination (MMSE) scores (r=-0.390 for AD, r=-0.436 for MCI) and the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) scores (r = 0.333 for AD, r = 0.372 for MCI). CONCLUSION: The integration of [18F]FDG PET with structural MRI enhances the accuracy of brain age prediction, potentially introducing a new avenue for related multimodal brain age prediction studies.

Aerobic exercise mitigates hippocampal neuronal apoptosis by regulating DAPK1/CDKN2A/REDD1/FoXO1/FasL signaling pathway in D-galactose-induced aging mice.

Brain aging is the most important risk factor for neurodegenerative disorders, and abnormal apoptosis is linked to neuronal dysfunction. Specifically, studies have found that exercise effectively inhibits hippocampal neuronal apoptosis, while the molecular mechanism remains unclear. In the present study, we investigated the impact of aerobic exercise on hippocampal neuronal apoptosis in aging mice and the potential involvement of DAPK1 and its downstream pathways based on recent data that DAPK1 may be associated with neuronal death in neurodegenerative diseases. Senescent mice were subjected to 8 weeks of Aerobic training. Following behavioral testing, hippocampal samples were examined histologically and biochemically to detect pathological changes, neuronal apoptosis, and mRNA and protein levels. We found that the exercise intervention improved spatial memory and alleviated neuronal apoptosis in the brain. Notably, exercise down-regulated DAPK1 expression and inhibited Fas death receptor transactivation and the mitochondrial apoptotic pathway in the hippocampus. These results shed new light on the protective effect of regular exercise against brain aging though modulating the DAPK1 pathway.

Association of Triglyceride-Glucose Index With Cognitive Function and Brain Atrophy: A Population-Based Study.

OBJECTIVE: To investigate the associations of triglyceride-glucose (TyG) index, a reliable surrogate marker for insulin resistance, with the function of various cognitive domains and brain structures among older adults. DESIGN: A population-based cross-sectional study. SETTING: Older adults living in the rural communities in China. PARTICIPANTS: About 4,541 rural-dwelling dementia-free participants (age ≥65 years; 56.37% women) undertook examinations in March-September 2018 for MIND-China. MEASUREMENTS: TyG index was calculated as ln[fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. A neuropsychological test battery was used to assess memory, attention, verbal fluency, and executive function. Volumetric brain measures were assessed on magnetic resonance imaging (MRI) in a subsample (n = 1,019). Data were analyzed with restricted cubic spline and multivariable general linear models. RESULTS: An inverted J-shaped association was observed between TyG index and z-scores of multiple cognitive domains, such that among individuals with TyG index ≥8.57 (median), a higher TyG index was significantly associated with lower z-scores of memory, attention, verbal fluency, executive function, and global cognition (all p < 0.05); among people with TyG index <8.57, a higher TyG index was significantly associated with a higher executive function z-score (p < 0.05), but not with any of the other examined cognitive domains. In the MRI subsample, a higher TyG index was significantly associated with lower volumes of total brain tissue, gray matter, and white matter as well as greater cerebrospinal fluid volume (p < 0.05), but not with white matter hyperintensity volume. CONCLUSIONS: Insulin resistance, as indicated by a high TyG index, was associated with poor function in multiple cognitive domains and global brain atrophy.

Disrupted HSF1 regulation in normal and exceptional brain aging.

Brain aging is a major risk factor for cognitive diseases such as Alzheimer's disease (AD) and vascular dementia. The rate of aging and age-related pathology are modulated by stress responses and repair pathways that gradually decline with age. However, recent reports indicate that exceptional longevity sustains and may even enhance the stress response. Whether normal and exceptional aging result in either attenuated or enhanced stress responses across all organs is unknown. This question arises from our understanding that biological age differs from chronological age and evidence that the rate of aging varies between organs. Thus, stress responses may differ between organs and depend upon regenerative capacity and ability to manage damaged proteins and proteotoxicity. To answer these questions, we assessed age-dependent changes in brain stress responses with normally aged wild type and long-lived Dwarf mice. Results from this study show that normal aging unfavorably impacts activation of the brain heat shock (HS) axis with key changes noted in the transcription factor, HSF1, and its regulation. Exceptional aging appears to preserve and strengthen many elements of HSF1 activation in the brain. These results support the possibility that reconstitution of aging brain stress responses requires a multi-factorial approach that addresses HSF1 protein levels, its DNA binding, and regulatory elements such as phosphorylation and protein interactions.

Nonalcoholic fatty liver disease, liver fibrosis, and structural brain imaging: The Cross-Cohort Collaboration.

BACKGROUND AND PURPOSE: Prior studies reported conflicting findings regarding the association of nonalcoholic fatty liver disease (NAFLD) and liver fibrosis with measures of brain health. We examined whether NAFLD and liver fibrosis are associated with structural brain imaging measures in middle- and old-age adults. METHODS: In this cross-sectional study among dementia- and stroke-free individuals, data were pooled from the Offspring and Third Generation cohorts of the Framingham Heart Study (FHS), the Rotterdam Study (RS), and the Study of Health in Pomerania. NAFLD was assessed through abdominal imaging. Transient hepatic elastography (FibroScan) was used to assess liver fibrosis in FHS and RS. Linear regression models were used to explore the relation of NAFLD and liver fibrosis with brain volumes, including total brain, gray matter, hippocampus, and white matter hyperintensities, adjusting for potential confounders. Results were combined using fixed effects meta-analysis. RESULTS: In total, 5660 and 3022 individuals were included for NAFLD and liver fibrosis analyses, respectively. NAFLD was associated with smaller volumes of total brain (ß = -3.5, 95% confidence interval [CI] = -5.4 to -1.7), total gray matter (ß = -1.9, 95% CI = -3.4 to -0.3), and total cortical gray matter (ß = -1.9, 95% CI = -3.7 to -0.01). In addition, liver fibrosis (defined as liver stiffness measure ≥8.2 kPa) was related to smaller total brain volumes (ß = -7.3, 95% CI = -11.1 to -3.5). Heterogeneity between studies was low. CONCLUSIONS: NAFLD and liver fibrosis may be directly related to brain aging. Larger and prospective studies are warranted to validate these findings and identify liver-related preventive strategies for neurodegeneration.

Effects of exercise-targeted hippocampal PDE-4 methylation on synaptic plasticity and spatial learning/memory impairments in D-galactose-induced aging rats.

Physical exercise reduces the effects of aging and cognitive decline by improving synaptic plasticity and spatial learning. However, the underlying neurobiological mechanisms are unclear. A total of 45 Male SPF Sprague-Dawley rats were acclimatized and then allocated into three groups, 15 in each group: the saline control (DC) group, D-gal-induced aging (DA) group, and D-gal-induced aging + exercise (DE) group. Six weeks of intraperitoneal injections of D-gal at a concentration of 100 mg/kg body weight/d was injected to establish model of aging in the DA and DE groups. Morris water maze test was implemented to evaluate the hippocampus related cognition. SOD activity and MDA was tested to assess the aging in all groups. H&E and Nissl staining was used to observe the histopathological changes of hippocampal neurons in aging rats. Quantitative real-time polymerase chain reaction, western blotting and immunofluorescence staining techniques were used to investigate the expression of synaptic genes and proteins in the hippocampus. Massarray methylation system was employed to measure the PDE-4 gene methylation level in rat hippocampal tissues. Our results demonstrated that exercise intervention improves cognitive function in D-gal-induced aging rats. The methylation of CpG sites in PDE-4 in the hippocampus was significantly increased. The physical exercise significantly increased PDE-4 gene methylation and effectively decreased PDE-4 gene and protein expression. These beneficial behavioral and morphological effects were attributed to PDE-4 methylation, which was activated cAMP/PKA/CREB pathway and improved synaptic plasticity. Exercise induced PDE-4 methylation is key mechanism underpinning the amelioration of learning/memory impairment, suggesting the potential efficacy of physical exercise training in delaying brain aging.

Circular RNAs: emerging players in brain aging and neurodegenerative diseases.

Brain aging is closely related to neurodegenerative diseases. Circular RNAs (circRNAs) are a type of conserved RNAs with covalently closed continuous loops. Emerging evidence has shown that circRNAs are implicated in the biology of brain aging and the pathology of age-related neurodegenerative diseases. Here, we summarize current studies on circRNAs associated with brain aging and neurodegenerative diseases by discussing their expression features, pathophysiological roles, and mechanisms of action. We also discuss the potential challenges of circRNA-based therapy against brain aging and neurodegenerative diseases, as well as their potential as diagnostic biomarkers of neurodegenerative diseases. The review provides insights into current progress in the functions of circRNAs in the process of brain aging and neurodegenerative diseases. © 2022 The Pathological Society of Great Britain and Ireland.

Association Between Psychosocial Stress and Brain Aging: Results of the Population-Based Cohort Study of Health in Pomerania (SHIP).

OBJECTIVE: Recent studies suggest that psychosocial factors can have an impact on brain health. Yet, it is unclear whether psychosocial stress affects aging of the brain. The aim of the study was to investigate the association between psychosocial stress and brain aging. METHODS: Data from the German population-based cohort Study of Health in Pomerania (N=991; age range 20-78 years) were used to calculate a total psychosocial stress score by combining subscores from five domains: stress related to the living situation, the occupational situation, the social situation, danger experiences, and emotions. Associations with brain aging, indicated by an MRI-derived score quantifying age-related brain atrophy, were estimated by using regression models adjusted for age, gender, education, diabetes, problematic alcohol consumption, smoking, and hypertension. RESULTS: The relative risk ratio for advanced brain aging was 1.21 (95% CI=1.04-1.41) for stress related to emotions in fully adjusted models. The interactions between stress related to emotions and mental health symptoms were also significantly associated with advanced brain aging. The association between higher total psychosocial stress and brain aging was not statistically significant. CONCLUSIONS: These findings highlight that high stress related to emotions is associated with advanced brain aging. To protect brain health in older age, more research is needed to explore the role of emotional distress.

Resting-state functional connectivity correlates of brain structural aging in schizophrenia.

A large body of research has shown that schizophrenia patients demonstrate increased brain structural aging. Although this process may be coupled with aberrant changes in intrinsic functional architecture of the brain, they remain understudied. We hypothesized that there are brain regions whose whole-brain functional connectivity at rest is differently associated with brain structural aging in schizophrenia patients compared to healthy controls. Eighty-four male schizophrenia patients and eighty-six male healthy controls underwent structural MRI and resting-state fMRI. The brain-predicted age difference (b-PAD) was a measure of brain structural aging. Resting-state fMRI was applied to obtain global correlation (GCOR) maps comprising voxelwise values of the strength and sign of functional connectivity of a given voxel with the rest of the brain. Schizophrenia patients had higher b-PAD compared to controls (mean between-group difference + 2.9 years). Greater b-PAD in schizophrenia patients, compared to controls, was associated with lower whole-brain functional connectivity of a region in frontal orbital cortex, inferior frontal gyrus, Heschl's Gyrus, plana temporale and polare, insula, and opercular cortices of the right hemisphere (rFTI). According to post hoc seed-based correlation analysis, decrease of functional connectivity with the posterior cingulate gyrus, left superior temporal cortices, as well as right angular gyrus/superior lateral occipital cortex has mainly driven the results. Lower functional connectivity of the rFTI was related to worse verbal working memory and language production. Our findings demonstrate that well-established frontotemporal functional abnormalities in schizophrenia are related to increased brain structural aging.

Specific association between retinal neural layer thinning and neurological soft signs in schizophrenia.

There is growing evidence of disproportionate retinal thinning in schizophrenia, but doubts are still raised regarding its significance in the context of the neurobiology of the disease. Therefore, we examined whether these abnormalities are significantly associated with neurological soft signs (NSS) which are closely related to the risk of schizophrenia. This cross-sectional study analyzing linear correlations between variables involved 56 schizophrenia inpatients and 60 controls. The results confirmed such relationships, and only in the patient sample. In addition, retinal abnormalities and NSS were significantly correlated with duration of illness. These findings provide further evidence for linked neurodevelopmental and neurodegenerative aspects of schizophrenia.

White-matter degradation and dynamical compensation support age-related functional alterations in human brain.

Structural connectivity of the brain at different ages is analyzed using diffusion-weighted magnetic resonance imaging (MRI) data. The largest decrease of streamlines is found in frontal regions and for long inter-hemispheric links. The average length of the tracts also decreases, but the clustering is unaffected. From functional MRI we identify age-related changes of dynamic functional connectivity (dFC) and spatial covariation features of functional connectivity (FC) links captured by metaconnectivity. They indicate more stable dFC, but wider range and variance of MC, whereas static features of FC did not show any significant differences with age. We implement individual connectivity in whole-brain models and test several hypotheses for the mechanisms of operation among underlying neural system. We demonstrate that age-related functional fingerprints are only supported if the model accounts for: (i) compensation of the individual brains for the overall loss of structural connectivity and (ii) decrease of propagation velocity due to the loss of myelination. We also show that with these 2 conditions, it is sufficient to decompose the time-delays as bimodal distribution that only distinguishes between intra- and inter-hemispheric delays, and that the same working point also captures the static FC the best, and produces the largest variability at slow time-scales.

The characteristics of glucose metabolism and functional connectivity in posterior default network during nondemented aging: relationship with executive function performance.

BACKGROUND: Understanding the characteristics of intrinsic connectivity networks (ICNs) in terms of both glucose metabolism and functional connectivity (FC) is important for revealing cognitive aging and neurodegeneration, but the relationships between these two aspects during aging has not been well established in older adults. OBJECTIVE: This study is to assess the relationship between age-related glucose metabolism and FC in key ICNs, and their direct or indirect effects on cognitive deficits in older adults. METHODS: We estimated the individual-level standard uptake value ratio (SUVr) and FC of eleven ICNs in 59 cognitively unimpaired older adults, then analyzed the associations of SUVr and FC of each ICN and their relationships with cognitive performance. RESULTS: The results showed both the SUVr and FC in the posterior default mode network (pDMN) had a significant decline with age, and the association between them was also significant. Moreover, both decline of metabolism and FC in the pDMN were significantly correlated with executive function decline. Finally, mediation analysis revealed the glucose metabolism mediated the FC decline with age and FC mediated the executive function deficits. CONCLUSIONS: Our findings indicated that covariance between glucose metabolism and FC in the pDMN is one of the main routes that contributes to age-related executive function decline.