Association between metabolic syndrome and resting-state functional brain connectivity

Bridging metabolic syndrome and cognitive dysfunction: role of astrocytes

Metabolic syndrome (MetS) and cognitive dysfunction pose significant challenges to global health and the economy. Systemic inflammation, endocrine disruption, and autoregulatory impairment drive neurodegeneration and microcirculatory damage in MetS. Due to their unique anatomy and function, astrocytes sense and integrate multiple metabolic signals, including peripheral endocrine hormones and nutrients. Astrocytes and synapses engage in a complex dialogue of energetic and immunological interactions. Astrocytes act as a bridge between MetS and cognitive dysfunction, undergoing diverse activation in response to metabolic dysfunction. This article summarizes the alterations in astrocyte phenotypic characteristics across multiple pathological factors in MetS. It also discusses the clinical value of astrocytes as a critical pathologic diagnostic marker and potential therapeutic target for MetS-associated cognitive dysfunction.

Functional MRI for Acute Covert Consciousness: Emerging Data and Implementation Case Series

Although research studies have begun to demonstrate relationships between disorders of consciousness and brain network biomarkers, there are limited data on the practical aspects of obtaining such network biomarkers to potentially guide care. As the state of knowledge continues to evolve, guidelines from professional societies such as the American and European Academies of Neurology and many experts have advocated that the risk-benefit ratio for the assessment of network biomarkers has begun to favor their application toward potentially detecting covert consciousness. Given the lack of detailed operationalization guidance and the context of the ethical implications, herein we offer a roadmap based on local institutional experience with the implementation of functional MRI in the neonatal, pediatric, and adult intensive care units of our local government-supported health system. We provide a case-based demonstrative approach intended to review the current literature and to assist with the initiation of such services at other facilities.

Neuroprotection in metabolic syndrome by environmental enrichment. A lifespan perspective

Metabolic syndrome (MetS) is defined by the concurrence of different metabolic conditions: obesity, hypertension, dyslipidemia, and hyperglycemia. Its incidence has been increasingly rising over the past decades and has become a global health problem. MetS has deleterious consequences on the central nervous system (CNS) and neurological development. MetS can last several years or be lifelong, affecting the CNS in different ways and treatments can help manage condition, though there is no known cure. The early childhood years are extremely important in neurodevelopment, which extends beyond, encompassing a lifetime. Neuroplastic changes take place all life through - childhood, adolescence, adulthood, and old age - are highly sensitive to environmental input. Environmental factors have an important role in the etiopathogenesis and treatment of MetS, so environmental enrichment (EE) stands as a promising non-invasive therapeutic approach. While the EE paradigm has been designed for animal housing, its principles can be and actually are applied in cognitive, sensory, social, and physical stimulation programs for humans. Here, we briefly review the central milestones in neurodevelopment at each life stage, along with the research studies carried out on how MetS affects neurodevelopment at each life stage and the contributions that EE models can provide to improve health over the lifespan.

Cardiovascular and metabolic health is associated with functional brain connectivity in middle-aged and older adults: Results from the Human Connectome Project-Aging study

Several cardiovascular and metabolic indicators, such as cholesterol and blood pressure have been associated with altered neural and cognitive health as well as increased risk of dementia and Alzheimer's disease in later life. In this cross-sectional study, we examined how an aggregate index of cardiovascular and metabolic risk factor measures was associated with correlation-based estimates of resting-state functional connectivity (FC) across a broad adult age-span (36-90+ years) from 930 volunteers in the Human Connectome Project Aging (HCP-A). Increased (i.e., worse) aggregate cardiometabolic scores were associated with reduced FC globally, with especially strong effects in insular, medial frontal, medial parietal, and superior temporal regions. Additionally, at the network-level, FC between core brain networks, such as default-mode and cingulo-opercular, as well as dorsal attention networks, showed strong effects of cardiometabolic risk. These findings highlight the lifespan impact of cardiovascular and metabolic health on whole-brain functional integrity and how these conditions may disrupt higher-order network integrity.

Multivariate resting-state functional connectomes predict and characterize obesity phenotypes

The univariate obesity-brain associations have been extensively explored, while little is known about the multivariate associations between obesity and resting-state functional connectivity. We therefore utilized machine learning and resting-state functional connectivity to develop and validate predictive models of 4 obesity phenotypes (i.e. body fat percentage, body mass index, waist circumference, and waist-height ratio) in 3 large neuroimaging datasets (n = 2,992). Preliminary evidence suggested that the resting-state functional connectomes effectively predicted obesity/weight status defined by each obesity phenotype with good generalizability to longitudinal and independent datasets. However, the differences between resting-state functional connectivity patterns characterizing different obesity phenotypes indicated that the obesity-brain associations varied according to the type of measure of obesity. The shared structure among resting-state functional connectivity patterns revealed reproducible neuroimaging biomarkers of obesity, primarily comprising the connectomes within the visual cortex and between the visual cortex and inferior parietal lobule, visual cortex and orbital gyrus, and amygdala and orbital gyrus, which further suggested that the dysfunctions in the perception, attention and value encoding of visual information (e.g. visual food cues) and abnormalities in the reward circuit may act as crucial neurobiological bases of obesity. The recruitment of multiple obesity phenotypes is indispensable in future studies seeking reproducible obesity-brain associations.

Tracking functional network connectivity dynamics in the elderly

Introduction

Functional magnetic resonance imaging (fMRI) has shown that aging disturbs healthy brain organization and functional connectivity. However, how this age-induced alteration impacts dynamic brain function interaction has not yet been fully investigated. Dynamic function network connectivity (DFNC) analysis can produce a brain representation based on the time-varying network connectivity changes, which can be further used to study the brain aging mechanism for people at different age stages.

Method

This presented investigation examined the dynamic functional connectivity representation and its relationship with brain age for people at an elderly stage as well as in early adulthood. Specifically, the resting-state fMRI data from the University of North Carolina cohort of 34 young adults and 28 elderly participants were fed into a DFNC analysis pipeline. This DFNC pipeline forms an integrated dynamic functional connectivity (FC) analysis framework, which consists of brain functional network parcellation, dynamic FC feature extraction, and FC dynamics examination.

Results

The statistical analysis demonstrates that extensive dynamic connection changes in the elderly concerning the transient brain state and the method of functional interaction in the brain. In addition, various machine learning algorithms have been developed to verify the ability of dynamic FC features to distinguish the age stage. The fraction time of DFNC states has the highest performance, which can achieve a classification accuracy of over 88% by a decision tree.

Discussion

The results proved there are dynamic FC alterations in the elderly, and the alteration was found to be correlated with mnemonic discrimination ability and could have an impact on the balance of functional integration and segregation.

Pre-diabetes is associated with altered functional connectivity density in cortical regions of the default-mode network

Insulin resistance and glucose dysregulation are associated with patterns of regional brain hypometabolism characteristic of Alzheimer's disease (AD). As predicted by evidence linking brain glucose metabolism to brain functional connectivity, type 2 diabetes is accompanied by altered functional connectivity density (FCD) in regions highly vulnerable to AD, but whether these alterations start at earlier stages such as pre-diabetes remain to be elucidated. Here, in addition to assessing whether pre-diabetes leads to a functional reorganization of densely connected cortical areas (hubs), we will assess whether such reorganization is conditioned by sex and/or insulin resistance, and contributes to improved cognition. One hundred and forty-four cognitively unimpaired middle-aged and older adults (55-78 years, 79 females), 73 with normoglycemia and 71 with pre-diabetes, underwent resting-state fMRI scanning. We first computed FCD mapping on cortical surfaces to determine the number of short- and long-range functional connections of every vertex in the cortex, and next used hubs showing aberrant FCD as seeds for the resting-state functional connectivity (rs-FC) calculation. ANCOVAs and linear multiple regression analyses adjusted by demographic and cardiometabolic confounders using frequentist and Bayesian approaches were applied. Analyses revealed higher long-range FCD in the right precuneus of pre-diabetic females and lower short-range FCD in the left medial orbitofrontal cortex (mOFC) of pre-diabetic individuals with higher insulin resistance. Although the mOFC also showed altered rs-FC patterns with other regions of the default mode network in pre-diabetic individuals, it was FCD of the precuneus and mOFC, and not the magnitude of their rs-FC, that was associated with better planning abilities and Mini-Mental State Examination (MMSE) scores. Results suggest that being female and/or having high insulin resistance exacerbate pre-diabetes-induced alterations in the FCD of hubs of the default-mode network that are particularly vulnerable to AD pathology. These changes in brain network organization appear to be compensatory for pre-diabetic females, likely assisting them to maintain cognitive functioning at early stages of glucose dysregulation.