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.

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.

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.

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.

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.

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.

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.

[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.

Overlooked cases of mild cognitive impairment: Implications to early Alzheimer's disease.

Mild cognitive impairment (MCI) marks the initial phase of memory decline or other cognitive functions like language or spatial perception, while individuals typically retain the capacity to carry out everyday tasks independently. Our comprehensive article investigates the intricate landscape of cognitive disorders, focusing on MCI and Alzheimer's disease (AD) and Alzheimer's disease-related dementias (ADRD). The study aims to understand the signs of MCI, early Alzheimer's disease, and healthy brain aging while assessing factors influencing disease progression, pathology development and susceptibility. A systematic literature review of over 100 articles was conducted, emphasizing MCI, AD and ADRD within the elderly populations. The synthesis of results reveals significant findings regarding ethnicity, gender, lifestyle, comorbidities, and diagnostic tools. Ethnicity was found to influence MCI prevalence, with disparities observed across diverse populations. Gender differences were evident in cognitive performance and decline, highlighting the need for personalized management strategies. Lifestyle factors and comorbidities were identified as crucial influencers of cognitive health. Regarding diagnostic tools, the Montreal Cognitive Assessment (MoCA) emerged as superior to the Mini-Mental State Examination (MMSE) in early MCI detection. Overall, our article provides insights into the multifaceted nature of cognitive disorders, emphasizing the importance of tailored interventions and comprehensive assessment strategies for effective cognitive health management.

Cellular senescence, DNA damage, and neuroinflammation in the aging brain.

Aging may lead to low-level chronic inflammation that increases the susceptibility to age-related conditions, including memory impairment and progressive loss of brain volume. As brain health is essential to promoting healthspan and lifespan, it is vital to understand age-related changes in the immune system and central nervous system (CNS) that drive normal brain aging. However, the relative importance, mechanistic interrelationships, and hierarchical order of such changes and their impact on normal brain aging remain to be clarified. Here, we synthesize accumulating evidence that age-related DNA damage and cellular senescence in the immune system and CNS contribute to the escalation of neuroinflammation and cognitive decline during normal brain aging. Targeting cellular senescence and immune modulation may provide a logical rationale for developing new treatment options to restore immune homeostasis and counteract age-related brain dysfunction and diseases.

Association of cognitive reserve with transitions across cognitive states and death in older adults: A 15-year follow-up study.

INTRODUCTION: We investigated the association of cognitive reserve (CR) with transitions across cognitive states and death. METHODS: This population-based cohort study included 2631 participants (age ≥60 years) who were dementia-free at baseline and regularly examined up to 15 years. Data were analyzed using the Markov multistate models. RESULTS: Each 1-point increase in the composite CR score (range: -4.25 to 3.46) was significantly associated with lower risks of transition from normal cognition to cognitive impairment, no dementia (CIND) (multivariable-adjusted hazards ratio = 0.78; 95% confidence interval = 0.72-0.85) and death (0.85; 0.79-0.93), and from CIND to death (0.82; 0.73-0.91), but not from CIND to normal cognition or dementia. A greater composite CR score was associated with a lower risk of transition from CIND to death in people aged 60-72 but not in those aged ≥ 78 years. DISCUSSION: CR contributes to cognitive health by delaying cognitive deterioration in the prodromal phase of dementia. HIGHLIGHTS: We use Markov multistate model to examine the association between cognitive reserve and transitions across cognitive states and death. A great cognitive reserve contributes to cognitive health by delaying cognitive deterioration in the prodromal phase of dementia. A great cognitive reserve is associated with a lower risk of transition from cognitive impairment, no dementia to death in people at the early stage of old age, but not in those at the late stage of old age.

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.

Cardiovascular disease risk exacerbates brain aging among Hispanic/Latino adults in the SOL-INCA-MRI Study.

Background: Cardiovascular disease (CVD) risk factors are highly prevalent among Hispanic/Latino adults, while the prevalence of MRI infarcts is not well-documented. We, therefore, sought to examine the relationships between CVD risk factors and infarcts with brain structure among Hispanic/Latino individuals. Methods: Participants included 1,886 Hispanic/Latino adults (50-85 years) who underwent magnetic resonance imaging (MRI) as part of the Study of Latinos-Investigation of Neurocognitive Aging-MRI (SOL-INCA-MRI) study. CVD risk was measured approximately 10.5 years before MRI using the Framingham cardiovascular risk score, a measure of 10-year CVD risk (low (<10%), medium (10- < 20%), and high (≥20%)). MR infarcts were determined as present or absent. Outcomes included total brain, cerebral and lobar cortical gray matter, hippocampal, lateral ventricle, and total white matter hyperintensity (WMH) volumes. Linear regression models tested associations between CVD risk and infarct with MRI outcomes and for modifications by age and sex. Results: Sixty percent of participants were at medium or high CVD risk. Medium and high CVD risk were associated with lower total brain and frontal gray matter and higher WMH volumes compared to those with low CVD risk. High CVD risk was additionally associated with lower total cortical gray matter and parietal volumes and larger lateral ventricle volumes. Men tended to have greater CVDRF-related differences in total brain volumes than women. The association of CVD risk factors on total brain volumes increased with age, equal to an approximate 7-year increase in total brain aging among the high-CVD-risk group compared to the low-risk group. The presence of infarct(s) was associated with lower total brain volumes, which was equal to an approximate 5-year increase in brain aging compared to individuals without infarcts. Infarcts were also associated with smaller total cortical gray matter, frontal and parietal volumes, and larger lateral ventricle and WMH volumes. Conclusion: The high prevalence of CVD risk among Hispanic/Latino adults may be associated with accelerated brain aging.

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.

Multiscale brain age prediction reveals region-specific accelerated brain aging in Parkinson's disease.

Brain biological age, which measures the aging process in the brain using neuroimaging data, has been used to assess advanced brain aging in neurodegenerative diseases, including Parkinson disease (PD). However, assuming that whole brain degeneration is uniform may not be sufficient for assessing the complex neurodegenerative processes in PD. In this study we constructed a multiscale brain age prediction models based on structural MRI of 1240 healthy participants. To assess the brain aging patterns using the brain age prediction model, 93 PD patients and 91 healthy controls matching for sex and age were included. We found increased global and regional brain age in PD patients. The advanced aging regions were predominantly noted in the frontal and temporal cortices, limbic system, basal ganglia, thalamus, and cerebellum. Furthermore, region-level rather than global brain age in PD patients was associated with disease severity. Our multiscale brain age prediction model could aid in the development of objective image-based biomarkers to detect advanced brain aging in neurodegenerative diseases.

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.