Brain Aging Research Articles

Clinical Predictors of Postmortem Amyloid and Nonamyloid Cerebral Small Vessel Disease in Middle-Aged to Older Adults.

Sporadic cerebral small vessel disease (CSVD) is a class of important pathologic processes known to affect the aging brain and to contribute to cognitive impairment. We aimed to identify clinical risk factors associated with postmortem CSVD in middle-aged to older adults. We developed and tested risk models for their predictive accuracy of a pathologic diagnosis of nonamyloid CSVD and cerebral amyloid angiopathy (CAA) in a retrospective sample of 160 autopsied cases from the Edinburgh Brain Bank. Individuals aged 40 years and older covering the spectrum of healthy aging and common forms of dementia (i.e., highly-prevalent etiologies such as Alzheimer disease (AD), vascular cognitive impairment (VCI), and mixed dementia) were included. We performed binomial logistic regression models using sample splitting and cross-validation methods. Demographics, lifestyle habits, traditional vascular risk factors, chronic medical conditions, APOE4, and cognitive status were assessed as potential predictors. Forty percent of our sample had a clinical diagnosis of dementia (AD = 33, VCI = 26 and mixed = 5) while others were cognitively healthy (n = 96). The mean age at death was 73.8 (SD 14.1) years, and 40% were female. The presence of none-to-mild vs moderate-to-severe nonamyloid CSVD was predicted by our model with good accuracy (area under the curve [AUC] = 0.84, sensitivity [SEN] = 72%, specificity [SPE] = 95%), with the most significant clinical predictors being age, history of cerebrovascular events, and cognitive impairment. The presence of CAA pathology was also predicted with high accuracy (AUC = 0.86, SEN = 93%, SPE = 79%). Significant predictors included alcohol intake, history of cerebrovascular events, and cognitive impairment. In a subset of atypical dementias (n = 24), our models provided poor predictive performance for both nonamyloid CSVD (AUC = 0.50) and CAA (AUC = 0.43). CSVD pathology can be predicted with high accuracy based on clinical factors in patients within the spectrum of AD, VCI, and normal aging. Whether this prediction can be enhanced by the addition of fluid and neuroimaging biomarkers warrants additional study. Improving our understanding of clinical determinants of vascular brain health may lead to novel strategies in the prevention and treatment of vascular etiologies contributing to cognitive decline. This study provides Class II evidence that selected clinical factors accurately distinguish between middle-aged to older adults with and without cerebrovascular small vessel disease (amyloid and nonamyloid) pathology.

MMANet: A Multi-Task Residual Network for Alzheimer's Disease Classification and Brain Age Prediction

Objective: Alzheimer's disease (AD) is an irreversible neurodegenerative disease, while mild cognitive impairment (MCI) is a clinical precursor of AD, thus differentiation of AD, MCI and normal control (NC) from noninvasive magnetic resonance imaging (MRI) has positive clinical implications. Material and method: We utilize a 3D residual network to classify AD, MCI, and NC, and add a multiscale module to the original network to enhance the feature representation capability of the network, as well as a cross-dimensional attentional mechanism to enhance the network's attention to important brain regions. We experimentally verified that the network is more inclined to overestimate the brain age of patients in AD and MCI subgroups, thus proving that there is a high correlation between the brain age prediction task and the AD classification task. Therefore, we adopted a multi-task learning approach, using brain age prediction as a supplementary task for AD classification to reduce the risk of overfitting of the network during the training process. Results: Our method achieved 96.02% accuracy, 93.40% precision, 91.48% recall, and 92.24% F1 value in AD/MCI/NC classification. Conclusions: Ablation experiments confirmed that our proposed cross-dimensional attention and multiscale modules can improve the diagnostic performance of AD and MCI, and that multi-task learning in conjunction with brain age prediction can further improve the performance.

Air quality and aging brain: Air pollution as a risk factor of developing dementia

Air quality and aging brain: Air pollution as a risk factor of developing dementia

Oral Administration of Nacre Extract from Pearl Oyster Shells Has Anti-Aging Effects on Skin and Muscle, and Extends the Lifespan in SAMP8 Mice.

Pearl oysters have been extensively utilized in pearl production; however, most pearl oyster shells are discarded as industrial waste. In a previous study, we demonstrated that the intraperitoneal administration of pearl oyster shell-derived nacre extract (NE) prevented d-galactose-induced brain and skin aging. In this study, we examined the anti-aging effects of orally administered NE in senescence-accelerated mice (SAMP8). Feeding SAMP8 mice NE prevented the development of aging-related characteristics, such as coarse and dull hair, which are commonly observed in aged mice. Additionally, the NE mitigated muscle aging in SAMP8 mice, such as a decline in grip strength. Histological analysis of skeletal muscle revealed that the NE suppressed the expression of aging markers, cyclin-dependent kinase inhibitor 2A (p16) and cyclin-dependent kinase inhibitor 1 (p21), and increased the expression of sirtuin1 and peroxisome proliferator-activated receptor gamma coactivator 1 (PGC1)- α, which are involved in muscle synthesis. These findings suggest that the oral administration of NE suppresses skeletal muscle aging. Moreover, NE administration suppressed skin aging, including a decline in water content. Interestingly, oral administration of NE significantly extended the lifespan of SAMP8 mice, suggesting that its effectiveness as an anti-aging agent of various tissues including skeletal muscle, skin, and adipose tissue.

Locus coeruleus integrity and left frontoparietal connectivity provide resilience against attentional decline in preclinical alzheimer’s disease

BackgroundAutopsy work reported that neuronal density in the locus coeruleus (LC) provides neural reserve against cognitive decline in dementia. Recent neuroimaging and pharmacological studies reported that left frontoparietal network functional connectivity (LFPN-FC) confers resilience against beta-amyloid (Aβ)-related cognitive decline in preclinical sporadic and autosomal dominant Alzheimer’s disease (AD), as well as against LC-related cognitive changes. Given that the LFPN and the LC play important roles in attention, and attention deficits have been observed early in the disease process, we examined whether LFPN-FC and LC structural health attenuate attentional decline in the context of AD pathology.Methods142 participants from the Harvard Aging Brain Study who underwent resting-state functional MRI, LC structural imaging, PiB(Aβ)-PET, and up to 5 years of cognitive follow-ups were included (mean age = 74.5 ± 9.9 years, 89 women). Cross-sectional robust linear regression associated LC integrity (measured as the average of five continuous voxels with the highest intensities in the structural LC images) or LFPN-FC with Digit Symbol Substitution Test (DSST) performance at baseline. Longitudinal robust mixed effect analyses examined associations between DSST decline and (i) two-way interactions of baseline LC integrity (or LFPN-FC) and PiB or (ii) the three-way interaction of baseline LC integrity, LFPN-FC, and PiB. Baseline age, sex, and years of education were included as covariates.ResultsAt baseline, lower LFPN-FC, but not LC integrity, was related to worse DSST performance. Longitudinally, lower baseline LC integrity was associated with a faster DSST decline, especially at PiB > 10.38 CL. Lower baseline LFPN-FC was associated with a steeper decline on the DSST but independent of PiB. At elevated PiB levels (> 46 CL), higher baseline LFPN-FC was associated with an attenuated decline on the DSST, despite the presence of lower LC integrity.ConclusionsOur findings demonstrate that the LC can provide resilience against Aβ-related attention decline. However, when Aβ accumulates and the LC’s resources may be depleted, the functioning of cortical target regions of the LC, such as the LFPN-FC, can provide additional resilience to sustain attentional performance in preclinical AD. These results provide critical insights into the neural correlates contributing to individual variability at risk versus resilience against Aβ-related cognitive decline.

Imbalance of thalamic metabolites in an experimental model of hypertension: role of bergamot polyphenols

Abstract Funding Acknowledgements None. Hypertension is emerging as a pathogenic factor in vascular-based cognitive impairment, altering the structure and function of cerebral vasculature, and representing the main risk factor for cerebral small vessel diseases and neuronal loss. Some studies correlated a reduction in regional cerebral blood flow in the thalamus of elderly hypertensive brains with the decrease in certain cerebral metabolites, because of hypoperfusion. Patients with severe hypertension show a reduced oxygen consumption in regions with reduced cerebral blood flow suggesting that, the vascular dysfunction and the reduced cerebral blood flow could be related to reduced metabolic demands due to brain dysfunction and damage. Chronic cerebral hypoperfusion is associated with increased inflammatory state and oxidative stress. Herein, we hypothesized to prevent or reduce the onset of oxidative stress using natural occurring antioxidant compounds could be of particular importance for the management of hypertension-induced cerebral complications. Hypertension was induced on Wistar male rats by left renal artery ligation (RAL). After complete recovery from surgery, the animals were treated with 20 mg/kg deoxycorticosterone acetate twice a week for 4 weeks, and 1% sodium chloride (NaCl) water. A subgroup of hypertensive rats received Bergamot Polyphenolic Fraction (BPF) by gavage. A control group was treated with sub-cutaneous injection of saline. At baseline (T0), after 2 weeks (T1) and at the end of the experimental period (T2), the thalamic neurochemical profile were evaluated using proton magnetic resonance spectroscopy (1H-MRS), performed with a Bruker Pharmascan 70/16 7T. VAPOR water-suppression module was used and optimized to reduce residual water, then thalamic spectra were acquired using a short echo time single voxel point-resolved spectroscopy technique (voxel size 3x3x3 mm). The absolute quantification of metabolite concentrations has been performed using TARQUIN (v.4.3.10). A reduction tending to significance of the tNAA/tCr ratio, emerged in the thalamus of RAL DOCA-Salt rats compared to control rats at T2. A significant reduction of the tNAA/tCr ratio was observed in the group of RAL DOCA-Salt rats at the T2 compared to the same animals at the T1. In addition, a significantly decrease of the tCho/tCr ratio was highlighted in the RAL DOCA-Salt animals compared to the CTRL rats starting from the T1. The administration of BPF ameliorates the tNAA/tCr ratios in the thalamus of RAL DOCA-Salt rats. Such evidence of the influence of hypertension on tNAA/tCr and tCho/tCr ratios could better explain the relationship between brain structure and modulation of some metabolites, providing new insights into hypertension-induced brain complications and the contribution that the use of natural compounds could give to their management.

Brain elastography in aging relates to fluid/solid trendlines

The relatively new tools of brain elastography have established a general trendline for healthy, aging adult humans, whereby the brain’s viscoelastic properties ‘soften’ over many decades. Earlier studies of the aging brain have demonstrated a wide spectrum of changes in morphology and composition towards the later decades of lifespan. This leads to a major question of causal mechanisms: of the many changes documented in structure and composition of the aging brain, which ones drive the long term trendline for viscoelastic properties of grey matter and white matter? The issue is important for illuminating which factors brain elastography is sensitive to, defining its unique role for study of the brain and clinical diagnoses of neurological disease and injury. We address these issues by examining trendlines in aging from our elastography data, also utilizing data from an earlier landmark study of brain composition, and from a biophysics model that captures the multiscale biphasic (fluid/solid) structure of the brain. Taken together, these imply that long term changes in extracellular water in the glymphatic system of the brain along with a decline in the extracellular matrix have a profound effect on the measured viscoelastic properties. Specifically, the trendlines indicate that water tends to replace solid fraction as a function of age, then grey matter stiffness decreases inversely as water fraction squared, whereas white matter stiffness declines inversely as water fraction to the 2/3 power, a behavior consistent with the cylindrical shape of the axons. These unique behaviors point to elastography of the brain as an important macroscopic measure of underlying microscopic structural change, with direct implications for clinical studies of aging, disease, and injury.

Novel Insights into Psychosis and Antipsychotic Interventions: From Managing Symptoms to Improving Outcomes.

For the past 70 years, the dopamine hypothesis has been the key working model in schizophrenia. This has contributed to the development of numerous inhibitors of dopaminergic signaling and antipsychotic drugs, which led to rapid symptom resolution but only marginal outcome improvement. Over the past decades, there has been limited research on the quantifiable pathological changes in schizophrenia, including premature cellular/neuronal senescence, brain volume loss, the attenuation of gamma oscillations in electroencephalograms, and the oxidation of lipids in the plasma and mitochondrial membranes. We surmise that the aberrant activation of the aryl hydrocarbon receptor by toxins derived from gut microbes or the environment drives premature cellular and neuronal senescence, a hallmark of schizophrenia. Early brain aging promotes secondary changes, including the impairment and loss of mitochondria, gray matter depletion, decreased gamma oscillations, and a compensatory metabolic shift to lactate and lactylation. The aim of this narrative review is twofold: (1) to summarize what is known about premature cellular/neuronal senescence in schizophrenia or schizophrenia-like disorders, and (2) to discuss novel strategies for improving long-term outcomes in severe mental illness with natural senotherapeutics, membrane lipid replacement, mitochondrial transplantation, microbial phenazines, novel antioxidant phenothiazines, inhibitors of glycogen synthase kinase-3 beta, and aryl hydrocarbon receptor antagonists.

Identification of protein aggregates in the aging vertebrate brain with prion-like and phase-separation properties

Protein aggregation, which can sometimes spread in a prion-like manner, is a hallmark of neurodegenerative diseases. However, whether prion-like aggregates form during normal brain aging remains unknown. Here, we use quantitative proteomics in the African turquoise killifish to identify protein aggregates that accumulate in old vertebrate brains. These aggregates are enriched for prion-like RNA-binding proteins, notably the ATP-dependent RNA helicase DDX5. We validate that DDX5 forms aggregate-like puncta in the brains of old killifish and mice. Interestingly, DDX5's prion-like domain allows these aggregates to propagate across many generations in yeast. Invitro, DDX5 phase separates into condensates. Mutations that abolish DDX5 prion propagation also impair the protein's ability to phase separate. DDX5 condensates exhibit enhanced enzymatic activity, but they can mature into inactive, solid aggregates. Our findings suggest that protein aggregates with prion-like properties form during normal brain aging, which could have implications for the age-dependency of cognitive decline.

The effect of late-life environmental enrichment on stress and anxiety: The role of sex and age-related differences in coping with aversive stimuli

Anxiety significantly diminishes the quality of life in older adults, and the drugs used for its treatment often come with risky side effects. Non-pharmacological protocols could be valuable, but more research is needed in this area. Environmental enrichment induces positive effects on anxiety-like behavior in young and adult animals; whether the same happens in aged animals is still elusive. The aged brain undergoes changes that contribute to make it “fragile” and consequently even mild, potentially positive stimuli can trigger dyshomeostasis, worsening rather than ameliorating functioning. Here, by combining behavioral analysis and measurement of serum and brain corticosterone levels, we show that late-life environmental enrichment can induce eustress or distress, depending on sex and hypothalamic-pituitary-adrenal axis function. These findings pave the way for optimizing outcomes and minimizing undesired effects in the clinical setting, underscoring the need to overcome the limits of gender medicine and emphasizing the crucial role of individually tailored therapies.

Complexity measures reveal age-dependent changes in electroencephalogram during working memory task

The alterations in electroencephalogram (EEG) signals are the complex outputs of functional factors, such as normal physiological aging, pathological process, which results in further cognitive decline. It is not clear that when brain aging initiates, but elderly people are vulnerable to be incipient of neurodegenerative diseases such as Alzheimer’s disease. The EEG signals were recorded from 20 healthy middle age and 20 healthy elderly subjects while performing a working memory task. Higuchi’s fractal dimension (HFD), Katz’s fractal dimension (KFD), sample entropy and three Hjorth parameters were extracted to analyse the complexity of EEG signals. Four machine learning classifiers, multilayer perceptron (MLP), support vector machine (SVM), K-nearest neighbour (KNN), and logistic model tree (LMT) were employed to distinguish the EEG signals of middle age and elderly age groups. HFD, KFD and Hjorth complexity were found significantly correlated with age. MLP achieved the highest overall accuracy of 93.75%. For posterior region, the maximum accuracy of 92.50% was achieved using MLP. Since fractal dimension associated with the complexity of EEG signals, HFD, KFD and Hjorth complexity demonstrated the decreased complexity from middle age to elderly groups. The complexity features appear to be more appropriate indicators of monitoring EEG signal complexity in healthy aging.

Curcumin protects against aging-related stress and dysfunction through autophagy activation in rat brain.

Curcumin (Curcuma longa) is a well-known medicinal plant that induces autophagy in various model species, helping maintain cellular homeostasis. Its role as a caloric restriction mimetic (CRM) is being investigated. This study explores the potential of curcumin (CUR), as a CRM, to provide neuroprotection in D galactose induced accelerated senescence model of rats through modulation of autophagy. For six weeks, male rats received simultaneous supplementation of D-gal (300mg/kg b.w., subcutaneously) and CUR (200mg/kg b.w., oral). The oxidative stress indices, antioxidants, and electron transport chain complexes in brain tissues were measured using standard methods. Reverse transcriptase-polymerase chain reaction (RT-PCR) gene expression analysis was used to evaluate the expression of autophagy, neuroprotection, and aging marker genes. Our results show that curcumin significantly (p ≤ 0.05) enhanced the level of antioxidants and considerably lowered the level of oxidative stress markers. Supplementing with CUR also increased the activity of electron transport chain complexes in the mitochondria of aged brain tissue, demonstrating the antioxidant potential of CUR at the mitochondrial level. CUR was found to upregulate the expression of the aging marker gene (SIRT-1) and the genes associated with autophagy (Beclin-1 and ULK-1), as well as neuroprotection (NSE) in the brain. The expression of IL-6 and TNF-α was downregulated. Our findings demonstrate that CUR suppresses oxidative damage brought on by aging by modulating autophagy. These findings imply that curcumin might be beneficial for neuroprotection in aging and age-related disorders.

Predicted Brain Age in First-Episode Psychosis: Association with Inexpressivity.

Accelerated brain aging is a possible mechanism of pathology in schizophrenia. Advances in MRI-based brain development algorithms allow for the calculation of predicted brain age (PBA) for individuals. Here, we assessed PBA in 70 first-episode schizophrenia-spectrum individuals (FESz) and 76 matched healthy neurotypical comparison individuals (HC) to determine if FESz showed advanced aging proximal to psychosis onset and whether PBA was associated with neurocognitive, social functioning, or symptom severity measures. PBA was calculated with BrainAgeR (v2.1) from T1-weighted MR scans. There were no differences in the PBAs between groups. After controlling for actual age, a "younger" PBA was associated with higher vocabulary scores among all individuals, while an "older" PBA was associated with more severe negative symptom "Inexpressivity" component scores among FESz. Female participants in both groups had an elevated PBA relative to male participants. These results suggest that a relatively younger brain age is associated with a better semantic memory performance. There is no evidence for accelerated aging in FESz with a late adolescent/early adult onset. Despite a normative PBA, FESz with a greater residual PBA showed impairments in a cluster of negative symptoms, which may indicate some underlying age-related pathology proximal to psychosis onset. Although a period of accelerated aging cannot be ruled out with disease course, it does not occur at the time of the first episode.

NBEST: Deep-learning-based non-human primates Brain Extraction and Segmentation Toolbox across ages, sites and species

Accurate processing and analysis of non-human primate (NHP) brain magnetic resonance imaging (MRI) serves an indispensable role in understanding brain evolution, development, aging, and diseases. Despite the accumulation of diverse NHP brain MRI datasets at various developmental stages and from various imaging sites/scanners, existing computational tools designed for human MRI typically perform poor on NHP data, due to huge differences in brain sizes, morphologies, and imaging appearances across species, sites, and ages, highlighting the imperative for NHP-specialized MRI processing tools. To address this issue, in this paper, we present a robust, generic, and fully automated computational pipeline, called non-human primates Brain Extraction and Segmentation Toolbox (nBEST), whose main functionality includes brain extraction, non-cerebrum removal, and tissue segmentation. Building on cutting-edge deep learning techniques by employing lifelong learning to flexibly integrate data from diverse NHP populations and innovatively constructing 3D U-NeXt architecture, nBEST can well handle structural NHP brain MR images from multi-species, multi-site, and multi-developmental-stage (from neonates to the elderly). We extensively validated nBEST based on, to our knowledge, the largest assemblage dataset in NHP brain studies, encompassing 1,469 scans with 11 species (e.g., rhesus macaques, cynomolgus macaques, chimpanzees, marmosets, squirrel monkeys, etc.) from 23 independent datasets. Compared to alternative tools, nBEST outperforms in precision, applicability, robustness, comprehensiveness, and generalizability, greatly benefiting downstream longitudinal, cross-sectional, and cross-species quantitative analyses. We have made nBEST an open-source toolbox (https://github.com/TaoZhong11/nBEST) and we are committed to its continual refinement through lifelong learning with incoming data to greatly contribute to the research field.

MousiPLIER: A Mouse Pathway-Level Information Extractor Model.

High-throughput gene expression profiling measures individual gene expression across conditions. However, genes are regulated in complex networks, not as individual entities, limiting the interpretability of gene expression data. Machine learning models that incorporate prior biological knowledge are a powerful tool to extract meaningful biology from gene expression data. Pathway-level information extractor (PLIER) is an unsupervised machine learning method that defines biological pathways by leveraging the vast amount of published transcriptomic data. PLIER converts gene expression data into known pathway gene sets, termed latent variables (LVs), to substantially reduce data dimensionality and improve interpretability. In the current study, we trained the first mouse PLIER model on 190,111 mouse brain RNA-sequencing samples, the greatest amount of training data ever used by PLIER. We then validated the mousiPLIER approach in a study of microglia and astrocyte gene expression across mouse brain aging. mousiPLIER identified biological pathways that are significantly associated with aging, including one latent variable (LV41) corresponding to striatal signal. To gain further insight into the genes contained in LV41, we performed k-means clustering on the training data to identify studies that respond strongly to LV41. We found that the variable was relevant to striatum and aging across the scientific literature. Finally, we built a Web server (http://mousiplier.greenelab.com/) for users to easily explore the learned latent variables. Taken together, this study defines mousiPLIER as a method to uncover meaningful biological processes in mouse brain transcriptomic studies.

The role of cortical structural variance in deep learning-based prediction of fetal brain age.

Deep-learning-based brain age estimation using magnetic resonance imaging data has been proposed to identify abnormalities in brain development and the risk of adverse developmental outcomes in the fetal brain. Although saliency and attention activation maps have been used to understand the contribution of different brain regions in determining brain age, there has been no attempt to explain the influence of shape-related cortical structural features on the variance of predicted fetal brain age. We examined the association between the predicted brain age difference (PAD: predicted brain age-chronological age) from our convolution neural networks-based model and global and regional cortical structural measures, such as cortical volume, surface area, curvature, gyrification index, and folding depth, using regression analysis. Our results showed that global brain volume and surface area were positively correlated with PAD. Additionally, higher cortical surface curvature and folding depth led to a significant increase in PAD in specific regions, including the perisylvian areas, where dramatic agerelated changes in folding structures were observed in the late second trimester. Furthermore, PAD decreased with disorganized sulcal area patterns, suggesting that the interrelated arrangement and areal patterning of the sulcal folds also significantly affected the prediction of fetal brain age. These results allow us to better understand the variance in deep learning-based fetal brain age and provide insight into the mechanism of the fetal brain age prediction model.

Menthol-Modified Quercetin Liposomes with Brain-Targeting Function for the Treatment of Senescent Alzheimer's Disease.

Oxidative stress and neuroinflammation in the aging brain are correlated with the development of neurodegenerative diseases, such as Alzheimer's disease (AD). The blood-brain barrier (BBB) poses a significant challenge to the effective delivery of therapeutics for AD. Prior research has demonstrated that menthol (Men) can augment the permeability of the BBB. Consequently, in the current study, we modified Men on the surface of liposomes to construct menthol-modified quercetin liposomes (Men-Qu-Lips), designed to cross the BBB and enhance quercetin (Qu) concentration in the brain for improved therapeutic efficacy. The experimental findings indicate that Men-Qu-Lips exhibited good encapsulation efficiency and stability, successfully crossed the BBB, improved oxidative stress and neuroinflammation in the brains of aged mice, protected neurons, and enhanced their learning and memory abilities.

Peripheral vascular dysfunction and the aging brain.

Aging is the greatest non-modifiable risk factor for most diseases, including cardiovascular diseases (CVD), which remain the leading cause of mortality worldwide. Robust evidence indicates that CVD are a strong determinant for reduced brain health and all-cause dementia with advancing age. CVD are also closely linked with peripheral and cerebral vascular dysfunction, common contributors to the development and progression of all types of dementia, that are largely driven by excessive levels of oxidative stress (e.g., reactive oxygen species [ROS]). Emerging evidence suggests that several fundamental aging mechanisms (e.g., "hallmarks" of aging), including chronic low-grade inflammation, mitochondrial dysfunction, cellular senescence and deregulated nutrient sensing contribute to excessive ROS production and are common to both peripheral and cerebral vascular dysfunction. Therefore, targeting these mechanisms to reduce ROS-related oxidative stress and improve peripheral and/or cerebral vascular function may be a promising strategy to reduce dementia risk with aging. Investigating how certain lifestyle strategies (e.g., aerobic exercise and diet modulation) and/or select pharmacological agents (natural and synthetic) intersect with aging "hallmarks" to promote peripheral and/or cerebral vascular health represent a viable option for reducing dementia risk with aging. Therefore, the primary purpose of this review is to explore mechanistic links among peripheral vascular dysfunction, cerebral vascular dysfunction, and reduced brain health with aging. Such insight and assessments of non-invasive measures of peripheral and cerebral vascular health with aging might provide a new approach for assessing dementia risk in older adults.

Three-dimensional EPI with shot-selective CAIPIRIHANA for rapid high-resolution quantitative susceptibility mapping at 3 T.

QSM provides insight into healthy brain aging and neuropathologies such as multiple sclerosis (MS), traumatic brain injuries, brain tumors, and neurodegenerative diseases. Phase data for QSM are usually acquired from 3D gradient-echo (3D GRE) scans with long acquisition times that are detrimental to patient comfort and susceptible to patient motion. This is particularly true for scans requiring whole-brain coverage and submillimeter resolutions. In this work, we use a multishot 3D echo plannar imaging(3D EPI) sequence with shot-selective 2D CAIPIRIHANA to acquire high-resolution, whole-brain data for QSM with minimal distortion and blurring. To test clinical viability, the 3D EPI sequence was used to image a cohort of MS patients at 1-mm isotropic resolution at 3 T. Additionally, 3D EPI data of healthy subjects were acquired at 1-mm, 0.78-mm, and 0.65-mm isotropic resolution with varying echo train lengths (ETLs) and compared with a reference 3D GRE acquisition. The appearance of the susceptibility maps and the susceptibility values for segmented regions of interest were comparable between 3D EPI and 3D GRE acquisitions for both healthy and MS participants. Additionally, all lesions visible in the MS patients on the 3D GRE susceptibility maps were also visible on the 3D EPI susceptibility maps. The interplay among acquisition time, resolution, echo train length, and the effect of distortion on the calculated susceptibility maps was investigated. We demonstrate that the 3D EPI sequence is capable of rapidly acquiring submillimeter resolutions and providing high-quality, clinically relevant susceptibility maps.

Do try this at home: Age prediction from sleep and meditation with large-scale low-cost mobile EEG

Abstract EEG is an established method for quantifying large-scale neuronal dynamics which enables diverse real-world biomedical applications including brain-computer interfaces, epilepsy monitoring and sleep staging. Advances in sensor technology have freed EEG from traditional laboratory settings, making low-cost ambulatory or at-home assessments of brain function possible. While ecologically valid brain assessments are becoming more practical, the impact of their reduced spatial resolution and susceptibility to noise remain to be investigated. This study set out to explore the potential of at-home EEG assessments for biomarker discovery using the brain age framework and four-channel consumer EEG data. We analyzed recordings from more than 5200 human subjects (18-81 years) during meditation and sleep, to predict age at the time of recording. With cross-validated R2 scores between 0.3 - 0.5, prediction performance was within the range of results obtained by recent benchmarks focused on laboratory-grade EEG. While age prediction was successful from both meditation and sleep recordings, the latter led to higher performance. Analysis by sleep stage uncovered that N2-N3 stages contained most of the signal. When combined, EEG features extracted from all sleep stages gave the best performance, suggesting that the entire night of sleep contains valuable age-related information. Furthermore, model comparisons suggested that information was spread out across electrodes and frequencies, supporting the use of multivariate modeling approaches. Thanks to our unique dataset of longitudinal repeat sessions spanning 153 to 529 days from eight subjects, we finally evaluated the variability of EEG-based age predictions, showing that they reflect both trait- and state-like information. Overall, our results demonstrate that state-of-the-art machine learning approaches based on age prediction can be readily applied to real-world EEG recordings obtained during at-home sleep and meditation practice.