Understanding the Temporal Dynamics of Accelerated Brain Aging and Resilient Brain Aging: Insights from Discriminative Event-Based Analysis of UK Biobank Data.

The intricate dynamics of brain aging, especially the neurodegenerative mechanisms driving accelerated (ABA) and resilient brain aging (RBA), are pivotal in neuroscience. Understanding the temporal dynamics of these phenotypes is crucial for identifying vulnerabilities to cognitive decline and neurodegenerative diseases. Currently, there is a lack of comprehensive understanding of the temporal dynamics and neuroimaging biomarkers linked to ABA and RBA. This study addressed this gap by utilizing a large-scale UK Biobank (UKB) cohort, with the aim to elucidate brain aging heterogeneity and establish the foundation for targeted interventions. Employing Lasso regression on multimodal neuroimaging data, structural MRI (sMRI), diffusion MRI (dMRI), and resting-state functional MRI (rsfMRI), we predicted the brain age and classified individuals into ABA and RBA cohorts. Our findings identified 1949 subjects (6.2%) as representative of the ABA subpopulation and 3203 subjects (10.1%) as representative of the RBA subpopulation. Additionally, the Discriminative Event-Based Model (DEBM) was applied to estimate the sequence of biomarker changes across aging trajectories. Our analysis unveiled distinct central ordering patterns between the ABA and RBA cohorts, with profound implications for understanding cognitive decline and vulnerability to neurodegenerative disorders. Specifically, the ABA cohort exhibited early degeneration in four functional networks and two cognitive domains, with cortical thinning initially observed in the right hemisphere, followed by the temporal lobe. In contrast, the RBA cohort demonstrated initial degeneration in the three functional networks, with cortical thinning predominantly in the left hemisphere and white matter microstructural degeneration occurring at more advanced stages. The detailed aging progression timeline constructed through our DEBM analysis positioned subjects according to their estimated stage of aging, offering a nuanced view of the aging brain's alterations. This study holds promise for the development of targeted interventions aimed at mitigating age-related cognitive decline.

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.

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.

Lung function in relation to brain aging and cognitive transitions in older adults: A population-based cohort study.

BACKGROUND: We investigated the association of peak expiratory flow (PEF) with dementia; cognitive impairment, no dementia (CIND); and transition from CIND to dementia, and possible underlying neuropathological mechanisms. METHODS: A population-based cohort of adults aged 60+ was followed over 15 years to detect dementia (Diagnostic and Statistical Manual of Mental Disorders, 4th edition criteria), CIND (assessed through a cognitive battery), and progression from CIND to dementia, in relation to baseline PEF observations. A subsample (n = 462) had 6-year follow-up data on brain magnetic resonance imaging markers of neurodegeneration and small vessel disease. RESULTS: In fully adjusted models, poor PEF performance (< 10th vs. ≥ 80th percentile) was associated with increased hazards for dementia (hazard ratio [HR] = 1.89; 95% confidence interval [CI] = 1.23-2.92) and CIND (HR = 1.55; 95% CI = 1.01-2.38) and CIND progression to dementia, although not statistically significantly (HR = 2.44; 95% CI = 0.78-6.88). People with poor PEF also experienced the fastest ventricular enlargement (ß coefficient = 0.67 mL/year; 95% CI = 0.13-1.21) and had the highest likelihood of developing lacunes (odds ratio = 5.05; 95% CI = 1.01-25.23). DISCUSSION: Poor lung function contributes to cognitive deterioration possibly through accelerated brain atrophy and microvascular damage. HIGHLIGHTS: Poor lung function increased the risk of dementia and mild cognitive impairment (MCI). Poor lung function accelerated the progression from MCI to dementia. Poor lung function was linked to brain microvascular damage and global brain atrophy.

Relationship between frailty and executive function by age and sex in the Canadian Longitudinal Study on Aging.

Frailty reflects age-related damage to multiple physiological systems. Executive dysfunction is often a presenting symptom of diseases characterized by cognitive impairment. A decline in cardiovascular health is associated with worse executive function. We tested the hypothesis that higher frailty would be associated with executive dysfunction and that cardiovascular health would mediate this relationship. Middle- and older-aged adults at baseline (n = 29,591 [51% female]) and 3-year follow-up (n = 25,488 [49% females]) from the Canadian Longitudinal Study on Aging (comprehensive cohort) were included. Frailty was determined at baseline from a 61-item index, a cumulative cardiovascular health score was calculated from 30 variables at baseline, and participants completed a word-color Stroop task as an assessment of executive function. Multiple linear regressions and mediation analyses of cardiovascular health were conducted between frailty, Stroop interference-condition reaction time, and cardiovascular health in groups stratified by both age and sex (middle-aged males [MM], middle-aged females [MF], older-aged males [OM], older-aged females [OF]). Frailty (MM, 0.15 ± 0.05; MF, 0.16 ± 0.06; OM, 0.21 ± 0.06; OF, 0.23 ± 0.06) was negatively associated with cardiovascular health (MM, 0.12 ± 0.08; MF, 0.11 ± 0.07; OM, 0.20 ± 0.10; OF, 0.18 ± 0.09; ß > 0.037, p < 0.001), as well as the Stroop reaction time at 3-year follow-up (MM, 23.7 ± 7.9; MF, 23.1 ± 7.3; OM, 32.9 ± 13.1; OF, 30.9 ± 12.0; ß > 2.57, p < 0.001) across all groups when adjusted for covariates. Cardiovascular health was a partial (~ 10%) mediator between frailty and reaction time, aside from MFs. In conclusion, higher frailty levels are associated with executive dysfunction, which was partially mediated by cardiovascular health. Strategies to improve frailty and better cardiovascular health may be useful for combatting the age-related decline in executive function.

Amyloid-ß Pathology Is the Common Nominator Proteinopathy of the Primate Brain Aging.

Senile plaques, mainly diffuse, and cerebral amyloid-ß (Aß) angiopathy are prevalent in the aging brain of non-human primates, from lemurs to non-human Hominidae. Aß but not hyper-phosphorylated tau (HPtau) pathology is the common nominator proteinopathy of non-human primate brain aging. The abundance of Aß in the aging primate brain is well tolerated, and the impact on cognitive functions is usually limited to particular tasks. In contrast, human brain aging is characterized by the early appearance of HPtau pathology, mainly forming neurofibrillary tangles, dystrophic neurites of neuritic plaques, and neuropil threads, preceding Aß deposits by several decades and by its severity progressing from selected nuclei of the brain stem, entorhinal cortex, and hippocampus to the limbic system, neocortex, and other brain regions. Neurofibrillary tangles correlate with cognitive impairment and dementia in advanced cases. Aß pathology is linked in humans to altered membrane protein and lipid composition, particularly involving lipid rafts. Although similar membrane alterations are unknown in non-human primates, membrane senescence is postulated to cause the activated ß-amyloidogenic pathway, and Aß pathology is the prevailing signature of non-human and human primate brain aging.

Sex as a Determinant of Age-Related Changes in the Brain.

The notion of notable anatomical, biochemical, and behavioral distinctions within male and female brains has been a contentious topic of interest within the scientific community over several decades. Advancements in neuroimaging and molecular biological techniques have increasingly elucidated common mechanisms characterizing brain aging while also revealing disparities between sexes in these processes. Variations in cognitive functions; susceptibility to and progression of neurodegenerative conditions, notably Alzheimer's and Parkinson's diseases; and notable disparities in life expectancy between sexes, underscore the significance of evaluating aging within the framework of gender differences. This comprehensive review surveys contemporary literature on the restructuring of brain structures and fundamental processes unfolding in the aging brain at cellular and molecular levels, with a focus on gender distinctions. Additionally, the review delves into age-related cognitive alterations, exploring factors influencing the acceleration or deceleration of aging, with particular attention to estrogen's hormonal support of the central nervous system.

Sex-dependent nonlinear Granger connectivity patterns of brain aging in healthy population.

BACKGROUND: Brain aging is a complex process that involves functional alterations in multiple subnetworks and brain regions. However, most previous studies investigating aging-related functional connectivity (FC) changes using resting-state functional magnetic resonance images (rs-fMRIs) have primarily focused on the linear correlation between brain subnetworks, ignoring the nonlinear casual properties of fMRI signals. METHODS: We introduced the neural Granger causality technique to investigate the sex-dependent nonlinear Granger connectivity (NGC) during aging on a publicly available dataset of 227 healthy participants acquired cross-sectionally in Leipzig, Germany. RESULTS: Our findings indicate that brain aging may cause widespread declines in NGC at both regional and subnetwork scales. These findings exhibit high reproducibility across different network sparsities, demonstrating the efficacy of static and dynamic analysis strategies. Females exhibit greater heterogeneity and reduced stability in NGC compared to males during aging, especially the NGC between the visual network and other subnetworks. Besides, NGC strengths can well reflect the individual cognitive function, which may therefore work as a sensitive metric in cognition-related experiments for individual-scale or group-scale mechanism understanding. CONCLUSION: These findings indicate that NGC analysis is a potent tool for identifying sex-dependent brain aging patterns. Our results offer valuable perspectives that could substantially enhance the understanding of sex differences in neurological diseases in the future, especially in degenerative disorders.

Examining the influence of self-care practices on brain activity in healthy older adults.

Introduction: Studies on the aging brain often occur in active settings, but comparatively few investigate brain activity in resting states. However, exploring brain activity in a resting state offers valuable insights into spontaneous neural processes unaffected by task-specific influences. Objective: To investigate the relationship between self-care practices, cognitive function, and patterns of brain activity in healthy older adults, taking into account predictions from aging brain models. Methodology: 77 older adults aged 61 to 87 completing a self-care practices questionnaire, neuropsychological tests, and resting-state electroencephalogram (EEG) recordings. Participants were classified into two groups according to their self-care practices: traditional self-care (T-SC) and developmental self-care (D-SC). Results: Although neuropsychological tests did not yield significant differences between the D-SC and T-SC groups, patterns of brain activity revealed distinct behaviors. The T-SC group demonstrated patterns more consistent with established aging brain models, contrasting with the D-SC group, which exhibited brain activity akin to that observed in younger adults. Specifically, the T-SC group displayed hyperactivation related to memory and executive function performance, alongside heightened alpha power in posterior regions. Furthermore, bilateral frontal activation in the beta band was evident. Conclusions: The findings suggest a nuanced relationship between self-care practices and brain activity in older adults. While the T-SC group demonstrated brain activity patterns consistent with conservative aging, indicating the preservation of typical aging characteristics, the D-SC group displayed activity suggestive of a potential protective effect. This effect may be linked to self-care strategies that foster development and resilience in cognitive aging.

Pivotal role of AGE-RAGE axis in brain aging with current interventions.

Brain aging is characterized by several structural, biochemical and molecular changes which can vary among different individuals and can be influenced by genetic, environmental and lifestyle factors. Accumulation of protein aggregates, altered neurotransmitter composition, low-grade chronic inflammation and prolonged oxidative stress have been shown to contribute to brain tissue damage. Among key metabolic byproducts, advanced glycation end products (AGEs), formed endogenously through non-enzymatic reactions or acquired directly from the diet or other exogenous sources, have been detected to accumulate in brain tissue, exerting detrimental effects on cellular structure and function, contributing to neurodegeneration and cognitive decline. Upon binding to signal transduction receptor RAGE, AGEs can initiate pro-inflammatory pathways, exacerbate oxidative stress and neuroinflammation, thus impairing neuronal function and cognition. AGE-RAGE signaling induces programmed cell death, disrupts the blood-brain barrier and promotes protein aggregation, further compromising brain health. In this review, we investigate the intricate relationship between the AGE-RAGE pathway and brain aging in order to detect affected molecules and potential targets for intervention. Reduction of AGE deposition in brain tissue either through novel pharmacological therapeutics, dietary modifications, and lifestyle changes, shows a great promise in mitigating cognitive decline associated with brain aging.

[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.

Brain responses to intermittent fasting and the healthy living diet in older adults.

Diet may promote brain health in metabolically impaired older individuals. In an 8-week randomized clinical trial involving 40 cognitively intact older adults with insulin resistance, we examined the effects of 5:2 intermittent fasting and the healthy living diet on brain health. Although intermittent fasting induced greater weight loss, the two diets had comparable effects in improving insulin signaling biomarkers in neuron-derived extracellular vesicles, decreasing the brain-age-gap estimate (reflecting the pace of biological aging of the brain) on magnetic resonance imaging, reducing brain glucose on magnetic resonance spectroscopy, and improving blood biomarkers of carbohydrate and lipid metabolism, with minimal changes in cerebrospinal fluid biomarkers for Alzheimer's disease. Intermittent fasting and healthy living improved executive function and memory, with intermittent fasting benefiting more certain cognitive measures. In exploratory analyses, sex, body mass index, and apolipoprotein E and SLC16A7 genotypes modulated diet effects. The study provides a blueprint for assessing brain effects of dietary interventions and motivates further research on intermittent fasting and continuous diets for brain health optimization. For further information, please see ClinicalTrials.gov registration: NCT02460783.

An interpretable machine learning-based cerebrospinal fluid proteomics clock for predicting age reveals novel insights into brain aging.

Machine learning can be used to create "biologic clocks" that predict age. However, organs, tissues, and biofluids may age at different rates from the organism as a whole. We sought to understand how cerebrospinal fluid (CSF) changes with age to inform the development of brain aging-related disease mechanisms and identify potential anti-aging therapeutic targets. Several epigenetic clocks exist based on plasma and neuronal tissues; however, plasma may not reflect brain aging specifically and tissue-based clocks require samples that are difficult to obtain from living participants. To address these problems, we developed a machine learning clock that uses CSF proteomics to predict the chronological age of individuals with a 0.79 Pearson correlation and mean estimated error (MAE) of 4.30 years in our validation cohort. Additionally, we analyzed proteins highly weighted by the algorithm to gain insights into changes in CSF and uncover novel insights into brain aging. We also demonstrate a novel method to create a minimal protein clock that uses just 109 protein features from the original clock to achieve a similar accuracy (0.75 correlation, MAE 5.41). Finally, we demonstrate that our clock identifies novel proteins that are highly predictive of age in interactions with other proteins, but do not directly correlate with chronological age themselves. In conclusion, we propose that our CSF protein aging clock can identify novel proteins that influence the rate of aging of the central nervous system (CNS), in a manner that would not be identifiable by examining their individual relationships with age.

Non-transgenic guinea pig strains exhibit divergent age-related changes in hippocampal mitochondrial respiration.

AIM: Alzheimer's disease (AD) is the most common form of dementia. However, while 150+ animal models of AD exist, drug translation from preclinical models to humans for treatment usually fails. One factor contributing to low translation is likely the absence of neurodegenerative models that also encompass the multi-morbidities of human aging. We previously demonstrated that, in comparison to the PigmEnTed (PET) guinea pig strain which models "typical" brain aging, the Hartley strain develops hallmarks of AD like aging humans. Hartleys also exhibit age-related impairments in cartilage and skeletal muscle. Impaired mitochondrial respiration is one driver of both cellular aging and AD. In humans with cognitive decline, diminished skeletal muscle and brain respiratory control occurs in parallel. We previously reported age-related declines in skeletal muscle mitochondrial respiration in Hartleys. It is unknown if there is concomitant mitochondrial dysfunction in the brain. METHODS: Therefore, we assessed hippocampal mitochondrial respiration in 5- and 12-month Hartley and PET guinea pigs using high-resolution respirometry. RESULTS: At 12 months, PETs had higher complex I supported mitochondrial respiration paralleling their increase in body mass compared to 5 months PETs. Hartleys were also heavier at 12 months compared to 5 months but did not have higher complex I respiration. Compared to 5 months Hartleys, 12 months Hartleys had lower complex I mitochondrial efficiency and compensatory increases in mitochondrial proteins collectively suggesting mitochondrial dysfunction with age. CONCLUSIONS: Therefore, Hartleys might be a relevant model to test promising therapies targeting mitochondria to slow brain aging and AD progression.

Prenatal exposure to air pollution and maternal depression: Combined effects on brain aging and mental health in young adulthood.

INTRODUCTION: Both maternal depression problems during pregnancy and prenatal exposure to air pollution have been associated with changes in the brain as well as worse mood and anxiety in the offspring in adulthood. However, it is not clear whether these effects are independent or whether and how they might interact and impact the brain age and mental health of the young adult offspring. METHODS: A total of 202 mother-child dyads from a prenatal birth cohort were assessed for maternal depression during pregnancy through self-report questionnaires administered in the early 90s, exposure to air pollutants (Sulfur dioxide [SO2], nitrogen oxides [NOx], and suspended particle matter [SPM]) during each trimester based on maternal address and air quality data, mental health of the young adult offspring (28-30 years of age; 52% men, all of European ancestry) using self-report questionnaires for depression (Beck Depression Inventory), mood dysregulation (Profile of Mood States), anxiety (State-Trait Anxiety Inventory), and psychotic symptoms (Schizotypal Personality Questionnaire), and brain age, estimated from structural magnetic resonance imaging (MRI) and previously published neuroanatomical age prediction model using cortical thickness maps. The brain age gap estimate (BrainAGE) was computed by subtracting structural brain age from chronological age. Trajectories of exposure to air pollution during pregnancy were assessed using Growth Mixture Modeling. The interactions of prenatal depression and prenatal exposure to air pollutants on adult mental health and BrainAGE were assessed using hierarchical linear regression. RESULTS: We revealed two distinct trajectories of exposure to air pollution during pregnancy: "early exposure," characterized by high exposure during the first trimester, followed by a steady decrease, and "late exposure," characterized by low exposure during the first trimester, followed by a steady increase in the exposure during the subsequent trimesters. Maternal depression during the first half of pregnancy interacted with NOX exposure trajectory, predicting mood dysregulation and schizotypal symptoms in young adults. In addition, maternal depression during the second half of pregnancy interacted with both NOx and SO2 exposure trajectories, respectively, and predicted BrainAGE in young adults. In those with early exposure to NOx, maternal depression during pregnancy was associated with worse mental health and accelerated brain aging in young adulthood. In contrast, in those with early exposure to SO2, maternal depression during pregnancy was associated with slower brain aging in young adulthood. CONCLUSIONS: Our findings provide the first evidence of the combined effects of prenatal exposure to air pollution and maternal depression on mental health outcomes and brain age in young adult offspring. Moreover, they point out the importance of the timing and trajectory of the exposure during prenatal development.

ChatGPT's Inconsistency in the Diagnosis of Alzheimer's Disease.

 A recent article by El Haj et al. provided evidence that ChatGPT could be a potential tool that complements the clinical diagnosis of various stages of Alzheimer's Disease (AD) as well as mild cognitive impairment (MCI). To reassess the accuracy and reproducibility of ChatGPT in the diagnosis of AD and MCI, we used the same prompt used by the authors. Surprisingly, we found that some of the responses of ChatGPT in the diagnoses of various stages of AD and MCI were different. In this commentary we discuss the possible reasons for these different results and propose strategies for future studies.

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.

Creating Cultural and Lifestyle Awareness About Dementia and Co-morbidities.

Dementia is a major health concern in society, particularly in the aging population. It is alarmingly increasing in ethnic minorities such as Native Americans, African Americans, Hispanics/Latinos, and to some extent Asians. With increasing comorbidities of dementia such as diabetes, obesity, and hypertension, dementia rates are expected to increase in the next decade and beyond. Understanding and treating dementia, as well as determining how to prevent it, has become a healthcare priority across the globe for all races and genders. Awareness about dementia and its consequences such as healthcare costs, and caregiver burden are immediate needs to be addressed. Therefore, it is high time for all of us to create awareness about dementia in society, particularly among Hispanics/Latinos, Native Americans, and African Americans. In the current article, we discuss the status of dementia, cultural, and racial impacts on dementia diagnosis and care, particularly in Hispanic populations, and possible steps to increase dementia awareness. We also discussed factors that need to be paid attention to, including, cultural & language barriers, low socioeconomic status, limited knowledge/education, religious/spiritual beliefs and not accepting modern medicine/healthcare facilities. Our article also covers both mental & physical health issues of caregivers who are living with patients with dementia, Alzheimer's disease, and Alzheimer's disease-related dementias. Most importantly, we discussed possible measures to create awareness about dementia, including empowering community advocacy, promoting healthy lifestyle choices, education on the impact of nutrition, encouraging community participation, and continued collaboration and evaluation of the success of dementia awareness.

Aging Brain from a Lifespan Perspective.

Research during the last two decades has shown that the brain undergoes continuous changes throughout life, with substantial heterogeneity in age trajectories between regions. Especially, temporal and prefrontal cortices show large changes, and these correlate modestly with changes in the corresponding cognitive abilities such as episodic memory and executive function. Changes seen in normal aging overlap with changes seen in neurodegenerative conditions such as Alzheimer's disease; differences between what reflects normal aging vs. a disease-related change are often blurry. This calls for a dimensional view on cognitive decline in aging, where clear-cut distinctions between normality and pathology cannot be always drawn. Although much progress has been made in describing typical patterns of age-related changes in the brain, identifying risk and protective factors, and mapping cognitive correlates, there are still limits to our knowledge that should be addressed by future research. We need more longitudinal studies following the same participants over longer time intervals with cognitive testing and brain imaging, and an increased focus on the representativeness vs. selection bias in neuroimaging research of aging.