Brain Aging Research Articles

Compromised CD8+ T cell immunity in the aged brain increases severity of neurotropic coronavirus infection and postinfectious cognitive impairment.

Advanced age increases the risk of severe disease from SARS-CoV-2 infection, as well as incidence of long COVID and SARS-CoV-2 reinfection. We hypothesized that perturbations in the aged antiviral CD8+ T cell response predisposes elderly individuals to severe coronavirus infection, re-infection, and postinfectious cognitive sequelae. Using MHV-A59 as a murine model of respiratory coronavirus, we found that aging increased CNS infection and lethality to MHV infection. This was coupled with increased CD8+ T cells within the aged CNS but reduced antigen specificity. Aged animals also displayed a decreased proportion of CD103+ resident memory cells (TRM), which correlated with increased severity of secondary viral challenge. Using a reciprocal adoptive transfer paradigm, data show that not only were fewer aged CD8+ T cells retained within the adult brain post-infection, but also that adult CD8+ cells expressed lower levels of TRM marker CD103 when in the aged microenvironment. Furthermore, aged animals demonstrated spatial learning impairment following MHV infection, which worsened in both aged and adult animals following secondary viral challenge. Spatial learning impairment was accompanied by increased TUNEL positivity in hippocampal neurons, suggestive of neuronal apoptosis. Additionally, primary cell coculture showed that activated CD8+ T cells induced TUNEL positivity in neurons, independent of antigen-specificity. Altogether, these results show that non-antigen specific CD8+ T cells are recruited to the aged brain and cause broad neuronal death without establishing a TRM phenotype that confers lasting protection against a secondary infection.

Influence of individual's age on the characteristics of brain effective connectivity.

Given the increasing number of older adults in society, there is a growing need for studies on changes in the aging brain. The aim of this research is to investigate the effective connectivity of different age groups using resting-state functional magnetic resonance imaging (fMRI) and graph theory. By examining connectivity in different age groups, a better understanding of age-related changes can be achieved. Lifespan pilot data from the Human Connectome Project (HCP) were used to examine dynamic effective connectivity (dEC) changes across different age groups. The Granger causality method with time windowing was employed to calculate dEC. After extracting graph measures, statistical analyses were performed to compare the age groups. Support vector machine and decision tree classifiers were used to classify the different age groups based on the extracted graph measures. Based on the obtained results, it can be concluded that there are significant differences in the effective connectivity among the three age groups. Statistical analyses revealed disassortativity. The global efficiency exhibited a decreasing trend, and the transitivity measure showed an increasing trend with the advancing age. The decision tree classifier showed an accuracy of with Kruskal-Wallis selected features. This study demonstrates that changes in effective connectivity across different age brackets can serve as a tool for better understanding brain function during the aging process.

Peripheral CD4+ T cell phenotype and brain microglial activation associated with cognitive heterogeneity in aged rats.

Cognitive decline is a critical hallmark of brain aging. Although aging is a natural process, there is significant heterogeneity in cognition levels among individuals; however, the underlying mechanisms remain uncertain. In our study, we classified aged male Sprague‒Dawley rats into aged cognition-unimpaired (AU) group and aged cognition-impaired (AI) group by using an attentional set-shifting task. The transcriptome sequencing results of medial prefrontal cortex (mPFC) demonstrated significant differences in microglial activation and inflammatory response pathways between the two groups. Specifically, compared to AU rats, AI rats exhibited a greater presence of CD86-positive microglia and major histocompatibility complex class II (MHC-II)-positive microglia, along with elevated inflammatory molecules, in mPFC. Conversely, AI rats exhibited a reduction in the percentage of microglia expressing CD200R and the anti-inflammatory molecules Arg-1 and TGF-β. Additionally, peripheral blood analysis of AI rats demonstrated elevated levels of Th17 and Th1 cells, along with proinflammatory molecules; however, decreased levels of Treg cells, along with anti-inflammatory molecules, were observed in AI rats. Our research suggested that peripheral Th17/Treg cells and central microglial activation were associated with cognitive heterogeneity in aged rats. These findings may provide a new target for healthy aging.

Plasma proteomics-based brain aging signature and incident dementia risk.

Investigating brain-enriched proteins with machine learning methods may enable a brain-specific understanding of brain aging and provide insights into the molecular mechanisms and pathological pathways of dementia. The study aims to analyze associations of brain-specific plasma proteomic aging signature with risks of incident dementia. In 45,429 dementia-free UK Biobank participants at baseline, we generated a brain-specific biological age using 63 brain-enriched plasma proteins with machine learning methods. The brain age gap was estimated, and Cox proportional hazards models were used to study the association with incident all-cause dementia, Alzheimer's disease (AD), and vascular dementia. Per-unit increment in the brain age gap z-score was associated with significantly higher risks of all-cause dementia (hazard ratio [95% confidence interval], 1.67 [1.56-1.79], P < 0.001), AD (1.85 [1.66-2.08], P < 0.001), and vascular dementia (1.86 [1.55-2.24], P < 0.001), respectively. Notably, 2.1% of the study population exhibited extreme old brain aging defined as brain age gap z-score > 2, correlating with over threefold increased risks of all-cause dementia and vascular dementia (3.42 [2.25-5.20], P < 0.001, and 3.41 [1.05-11.13], P = 0.042, respectively), and fourfold increased risk of AD (4.45 [2.32-8.54], P < 0.001). The associations were stronger among participants with healthier lifestyle factors (all P-interaction < 0.05). These findings were corroborated by magnetic resonance imaging assessments showing that a higher brain age gap aligns global pathophysiology of dementia, including global and regional atrophy in gray matter, and white matter lesions (P < 0.001). The brain-specific proteomic age gap is a powerful biomarker of brain aging, indicative of dementia risk and neurodegeneration.

Quantitative assessment of neurodevelopmental maturation: a comprehensive systematic literature review of artificial intelligence-based brain age prediction in pediatric populations

IntroductionOver the past few decades, numerous researchers have explored the application of machine learning for assessing children’s neurological development. Developmental changes in the brain could be utilized to gauge the alignment of its maturation status with the child’s chronological age. AI is trained to analyze changes in different modalities and estimate the brain age of subjects. Disparities between the predicted and chronological age can be viewed as a biomarker for a pathological condition. This literature review aims to illuminate research studies that have employed AI to predict children’s brain age.MethodsThe inclusion criteria for this study were predicting brain age via AI in healthy children up to 12 years. The search term was centered around the keywords “pediatric,” “artificial intelligence,” and “brain age” and was utilized in PubMed and IEEEXplore. The selected literature was then examined for information on data acquisition methods, the age range of the study population, pre-processing, methods and AI techniques utilized, the quality of the respective techniques, model explanation, and clinical applications.ResultsFifty one publications from 2012 to 2024 were included in the analysis. The primary modality of data acquisition was MRI, followed by EEG. Structural and functional MRI-based studies commonly used publicly available datasets, while EEG-based studies typically relied on self-recruitment. Many studies utilized pre-processing pipelines provided by toolkit suites, particularly in MRI-based research. The most frequently used model type was kernel-based learning algorithms, followed by convolutional neural networks. Overall, prediction accuracy may improve when multiple acquisition modalities are used, but comparing studies is challenging. In EEG, the prediction error decreases as the number of electrodes increases. Approximately one-third of the studies used explainable artificial intelligence methods to explain the model and chosen parameters. However, there is a significant clinical translation gap as no study has tested their model in a clinical routine setting.DiscussionFurther research should test on external datasets and include low-quality routine images for MRI. T2-weighted MRI was underrepresented. Furthermore, different kernel types should be compared on the same dataset. Implementing modern model architectures, such as convolutional neural networks, should be the next step in EEG-based research studies.

Age-related volume decrease in subcortical gray matter is a part of healthy brain aging in men.

With the global population aging, the number of individuals over 60 is expected to double by 2050. Brain volume increases until age 13, stabilizes between 18 and 35, then declines by 0.2% annually. Magnetic resonance imaging (MRI) studies highlight significant gray matter atrophy, necessitating differentiation between normal aging and neurodegeneration. This study assessed the impact of aging on subcortical gray matter in healthy males to identify biomarkers of physiological aging. A retrospective study of 106 healthy males who underwent brain MRI from 2012 to 2016, divided into two age groups: younger and older than 35years. MRI scans were performed using a 3T machine, and volumetric analysis was conducted with VolBrain software. Subcortical gray matter volumes were compared between groups. The Shapiro-Wilk test evaluated normality. Student's t-test and Mann-Whitney U test were used for statistical analysis, with significance defined as p < 0.05. Total intracranial volume was comparable between age groups (p = 0.527). Significant volume reductions (p < 0.05) were observed in subcortical gray matter structures, including the nucleus accumbens, caudate nucleus, globus pallidus, putamen, thalamus, and ventral diencephalon, particularly on the right side in the elderly group. Subcortical gray matter volume in healthy males shows significant differences between older and younger individuals (p < 0.05), with asymmetrical reduction and certain structures on the right aging more rapidly. These findings are significant for distinguishing healthy aging from neurodegeneration.

TMIC-05. AGING-SPECIFIC MICRORNA EXPRESSION SIGNATURE IN ELDERLY GLIOBLASTOMA

Abstract PURPOSE The rise in aging population worldwide is increasing death from cancer, including glioblastoma. MicroRNAs (miRNAs/miRs) are small non-coding RNAs that mediate the posttranscriptional silencing of specific target mRNAs and that are currently recognized as important regulators of tumorigenesis and development. Here, we explore the impact of brain aging and aging-specific miRNAs on elderly glioblastomas. METHODS We comprehensively analyzed miRNAs expression using the TCGA dataset and identified miRNAs that are specifically expressed in elderly glioblastomas. To create the Ehime Glioma Genome Atlas (EGGA), RNA sequencing was performed using tumor tissues from 8 glioblastoma patients (4 elderly and 4 non-elderly). First, we evaluated the impact of the identified miRNAs on prognosis using TCGA and CGGA datasets and searched for target genes of specific miRNAs using TargetScan. The expression patterns of the identified target genes were verified using EGGA, TCGA, and CGGA datasets. RESULTS We divided the TCGA dataset into elderly and non-elderly groups based on three ages (75 years-old, 70 years-old, and 65 years-old) and analyzed the expression of miRNAs in tumor tissues. There were 3 miRNAs that showed abnormalities (1 increased miRNA, 2 decreased miRNAs), and among these, miR-210 (increased expression) and miR-507 (increased expression) were identified as miRNAs related to prognosis. Next, we analyzed the expression of EGGA and identified 410 mRNAs (368 increased mRNAs, 42 decreased mRNAs) that showed significantly abnormal expression in the elderly group. The target genes searched using TargetScan were 4,048 genes for miR-210 and 4,704 genes for miR-507. When compared with the 410 genes identified using EGGA, PLA2G3, a target gene to miR-507, was identified. The expression of PLA2G3 was also specifically increased in elderly glioblastoma in the TCGA dataset, and it had a strong influence on prognosis. CONCLUSION Our results found an aging-specific miRNA “miR-507” and its target gene “PLA2G3” that suggested as potential new therapeutic target molecules for improving the prognosis of elderly glioblastomas.

Performance of the Healthy Aging Brain Care Monitor Self Report in Monitoring Post-Intensive Care Syndrome Among Acute Respiratory Failure Survivors.

To describe the performance of the Healthy Aging Brain Care Monitor Self Report (HABC-M SR) in assessment of post-intensive care syndrome (PICS) among Acute Respiratory Failure ICU survivors. Secondary data analysis of a randomized controlled trial. Patients evaluated by a nurse care coordinator in an out-of-hospital setting. English-speaking adults 18 years old or older who were admitted to the ICU with acute respiratory failure requiring invasive or noninvasive mechanical ventilation for greater than or equal to 24 hours. Patients randomized to the intervention arm of the mobile critical care recovery program, a negative trial testing multidisciplinary care to improve quality of life. HABC-M SR scale was used to assess PICS in the intervention group at ICU discharge, 3, and 6 months post-discharge. Hospital Anxiety and Depression Scale; Pain, Enjoyment of Life, and General Activity Scale; Timed Up and Go; and Patient-Reported Outcomes Measurement Information System sleep scores were obtained at the same time. Mini-Mental State Examination (MMSE) was administered at baseline and 6 months. ICU survivors reported mild PICS symptoms, which improved over 6 months (mean HABC-M SR scores: baseline [8.5, sd 7.6], 3 mo [5.3 mo, sd 6.6 mo], and 6 mo [5.2 mo, sd 6.9 mo; p < 0.001]). HABC-M SR total score had moderate internal consistency that improved over time (Cronbach's alpha = 0.78 at baseline and 0.84 at 6 mo). The psychological subscale of HABC-M SR was moderately correlated with standardized scales for mood, pain, and sleep. The cognitive subscale was not significantly correlated with MMSE. While HABC-M SR correlated with mood, physical, and sleep symptoms, the cognitive subscale was less sensitive compared with standardized scales.

Senolysis potentiates endothelial progenitor cell adhesion to and integration into the brain vasculature.

One of the most severe consequences of ageing is cognitive decline, which is associated with dysfunction of the brain microvasculature. Thus, repairing the brain vasculature could result in healthier brain function. To better understand the potential beneficial effect of endothelial progenitor cells (EPCs) in vascular repair, we studied the adhesion and integration of EPCs using the early embryonic mouse aorta-gonad-mesonephros - MAgEC 10.5 endothelial cell line. The EPC interaction with brain microvasculature was monitored ex vivo and in vivo using epifluorescence, laser confocal and two-photon microscopy in healthy young and old animals. The effects of senolysis, EPC activation and ischaemia (two-vessel occlusion model) were analysed in BALB/c and FVB/Ant: TgCAG-yfp_sb #27 mice. MAgEC 10.5 cells rapidly adhered to brain microvasculature and some differentiated into mature endothelial cells (ECs). MAgEC 10.5-derived endothelial cells integrated into microvessels, established tight junctions and co-formed vessel lumens with pre-existing ECs within five days. Adhesion and integration were much weaker in aged mice, but were increased by depleting senescent cells using abt-263 or dasatinib plus quercetin. Furthermore, MAgEC 10.5 cell adhesion to and integration into brain vessels were increased by ischaemia and by pre-activating EPCs with TNFα. Combining progenitor cell therapy with senolytic therapy and the prior activation of EPCs are promising for improving EPC adhesion to and integration into the cerebral vasculature and could help rejuvenate the ageing brain.

The influence of nutrition on brain aging: A Comprehensive Review

Purpose: Aging is characterized by a progressive decline in physiological functions, including those of the brain. As individuals age, cognitive and emotional functionalities are subject to change, Consequently, it is essential to consider both cognitive abilities and emotional functioning when examining the effects of aging on the brain. Objectives: The objective is to synthesize current scientific evidence on the relationship between nutrition and brain aging, with a specific focus on cognitive abilities as a measure of brain function. Methodology: A systematic review was conducted on nine studies. The PRISMA statement standards were adhered to in this systematic review. Findings: The findings showed how important it is for people to change their diets to prevent the onset and progression of various comorbidities and to encourage healthy aging. The findings of the study included in this Comprehensive review indicate that good aging and a higher quality of life may be achieved by abstaining from bad behaviors, particularly those related to food. Conclusions: The current body of study indicates a relationship between nutrition and brain aging, but additional studies are necessary to expand our understanding of these complex interactions and to develop clear dietary recommendations to support cognitive health across the lifespan.

Brain Age Gap as a Predictor of Early Treatment Response and Functional Outcomes in First-Episode Schizophrenia: A Longitudinal Study: L'écart d'âge cérébral comme prédicteur de la réponse en début de traitement et des résultats fonctionnels dans un premier épisode de schizophrénie : une étude longitudinale.

Accelerated brain aging, i.e., the age-related structural changes in the brain appearing earlier than expected from one's chronological age, is a feature that is now well established in schizophrenia. Often interpreted as a feature of a progressive pathophysiological process that typifies schizophrenia, its prognostic relevance is still unclear. We investigate its role in response to antipsychotic treatment in first-episode schizophrenia. We recruited 49 drug-naive patients with schizophrenia who were then treated with risperidone at a standard dose range of 2-6 mg/day. We followed them up for 3 months to categorize their response status. We acquired T1-weighted anatomical images and used the XGboost method to evaluate individual brain age. The brain age gap (BAG) is the difference between the predicted brain age and chronological age. Patients with FES had more pronounced BAG compared to healthy subjects, and this difference was primarily driven by those who did not respond adequately after 12 weeks of treatment. BAG did not worsen significantly over the 12-week period, indicating a lack of prominent brain-ageing effect induced by the early antipsychotic exposure per se. However, highly symptomatic patients had a more prominent increase in BAG, while patients with higher BAG when initiating treatment later showed lower gains in global functioning upon treatment, highlighting the prognostic value of BAG measures in FES. Accelerated brain aging is a feature of first-episode schizophrenia that is more likely to be seen among those who will not respond adequately to first-line antipsychotic use. Given that early poor response indicates later treatment resistance, measuring BAG using structural MRI in the first 12 weeks of treatment initiation may provide useful prognostic information when considering second-line treatments in schizophrenia.

Connectome-based predictive modelling of ageing, overall cognitive functioning and memory performance.

Resting-state functional magnetic resonance imaging (rs-fMRI) and brain functional connectome (we use 'brain connectome' hereafter for simplicity) have advanced our understanding of the ageing brain and age-related changes in cognitive function. Previous studies have investigated the association among brain connectome and age, global cognition, and memory function separately. However, very few have predicted age, overall cognitive functioning and memory performance in a single study to better understand their complex relationship. In this cross-sectional study, we applied an exploratory, data-driven method to investigate the brain connectome markers that could predict ageing, overall cognitive functioning assessed as intelligence quotient (IQ, measured by Wechsler Memory Scale) and memory performance assessed as memory quotient (MQ, measured by Wechsler Memory Scale) in a carefully designed, multicentre, normal ageing cohort (n = 313). Our results showed that brain connectome could predict ageing and IQ, but the association with MQ was weak. We found that the connectivity with orbital frontal cortex was associated with both ageing and IQ. Mediation analysis further showed that the brain connectome mediated the relationship between age and overall cognitive functioning, suggesting a protective brain connectomic mechanism for maintaining normal cognitive functions during healthy ageing. This work may shed light on the potential neural correlates of healthy ageing, overall cognitive functioning and memory performance.

Caffeic Acid Attenuates Neuronal Apoptosis, Oxidative Stress, and Memory Deficits via Antioxidant Properties in Aging Rats Induced by D-Galactose.

Aging is a main factor related to cognitive deficits. D-Galactose (D-gal), a monosaccharide, increases oxidative stress leading to cellular senescence, memory deficits, and neuronal apoptosis. Caffeic acid (CA) is an antioxidant that can interrupt free radicals and reduce oxidative stress. The present study purposely evaluated the benefits of CA in attenuating loss of neuronal apoptosis, oxidative stress, and memory in D-gal-activated rat brain aging. Male Sprague-Dawley rats were arbitrarily allocated into 6 groups (9 rats per group). The D-gal group was intraperitoneal (i.p.) injected with D-gal (50mg/kg). The CA groups were orally given 20 or 40mg/kg CA for 8weeks. During that time, the co-treatment groups were given 50mg/kg of D-gal and 20 or 40mg/kg of CA. The results reveal that animals receiving only D-gal showed memory deficit in both the novel object location (NOL) and novel object recognition (NOR) tests. Reduction in scavenging enzyme activities and levels of B-cell lymphoma 2 (Bcl-2) protein expression were detected in the D-gal group. Furthermore, D-gal treatment significantly enhanced in the number of p21 positive cells in the subgranular zone (SGZ) of the hippocampal dentate gyrus, Bcl-2 associated X protein (Bax) and caspase3 protein expression, and malondialdehyde (MDA) levels. By contrast, both 20 and 40mg/kg CA treatment alleviated these effects. These consequences confirmed that D-gal-activated brain aging led to enhancing apoptotic protein expression including Bcl-2, Bax, and caspase3 and memory impairments. Nevertheless, CA attenuated these effects in brain aging induced by D-gal via antioxidant properties.

Energy metabolism dysregulation, cerebrovascular aging, and time-restricted eating: Current evidence and proof-of-concept findings

Abstract Dysregulated energy metabolism is a hallmark of aging, including brain aging; thus, strategies to restore normal metabolic regulation are at the forefront of aging research. Intermittent fasting, particularly time-restricted eating (TRE), is one of these strategies. Despite its well-established effectiveness in improving metabolic outcomes in older adults, the effect of TRE on preserving or improving cerebrovascular health during aging remains underexplored. We explored how aging itself affects energy metabolism and contextualized these age-related changes to cerebrovascular health. We also conducted a literature search on PubMed and Scopus to identify and summarize current studies on TRE in older adults. Finally, we provided preliminary data from our proof-of-concept pilot trial on the effect of 6-month TRE on cerebrovascular health in older adults. Current evidence shows the potential of TRE to improve energy metabolism and physiological outcomes in older adults. TRE may improve cerebrovascular function indirectly due to its effect on glucose homeostasis. However, to date, direct evidence of the effect of TRE on cerebrovascular parameters is lacking. TRE is a well-tolerated and promising dietary intervention for promoting and maintaining cerebrovascular health in older adults. Further studies on TRE in older adults must be better controlled for energy balance to elucidate its independent effects from those of caloric restriction.

Genome-wide transcriptome analysis of Aβ deposition on PET in a Korean cohort.

Despite the recognized importance of including ethnic diversity in Alzheimer's disease (AD) research, substantial knowledge gaps remain, particularly in Asian populations. RNA sequencing was performed on blood samples from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease (KBASE) to perform differential gene expression (DGE), gene co-expression network, gene-set enrichment, and machine learning analyses for amyloid beta (Aβ) deposition on positron emission tomography. DGE analysis identified 265 dysregulated genes associated with Aβ deposition and replicated three AD-associated genes in an independent Korean cohort. Network analysis identified two modules related to pathways including a natural killer (NK) cell-mediated immunity. Machine learning analysis showed the classification of Aβ positivity improved with the inclusion of gene expression data. Our results in a Korean population suggest Aβ deposition-associated genes are enriched in NK cell-mediated immunity, providing a better understanding of AD molecular mechanisms and yielding potential diagnostic and therapeutic strategies. Dysregulated genes were associated with amyloid beta (Aβ) deposition on positron emission tomography in a Korean cohort. Dysregulated genes in Alzheimer's disease were replicated in an independent Korean cohort. Gene network moduleswere associated with Aβ deposition. Natural killer (NK) cell proportion in blood was associated with Aβdeposition. Dysregulated genes were related to a NK cell-mediated immunity.

Association of amyloid and cardiovascular risk with cognition: Findings from KBASE.

Limited research has explored the effect of cardiovascular risk and amyloid interplay on cognitive decline in East Asians. Vascular burden was quantified using Framingham's General Cardiovascular Risk Score (FRS) in 526 Korean Brain Aging Study (KBASE) participants. Cognitive differences in groups stratified by FRS and amyloid positivity were assessed at baseline and longitudinally. Baseline analyses revealed that amyloid-negative (Aβ-) cognitively normal (CN) individuals with high FRS had lower cognition compared to Aβ- CN individuals with low FRS (p<0.0001). Longitudinally, amyloid pathology predominantly drove cognitive decline, while FRS alone had negligible effects on cognition in CN and mild cognitive impairment (MCI) groups. Our findings indicate that managing vascular risk may be crucial in preserving cognition in Aβ- individuals early on and before the clinical manifestation of dementia. Within the CN and MCI groups, irrespective of FRS status, amyloid-positive individuals had worse cognitive performance than Aβ- individuals. Vascular risk significantly affects cognition in amyloid-negative older Koreans. Amyloid-negative CN older adults with high vascular risk had lower baseline cognition. Amyloid pathology drives cognitive decline in CN and MCI, regardless of vascular risk. The study underscores the impact of vascular health on the AD disease spectrum.

Dementia risk reduction in the African context: Multi-national implementation of multimodal strategies to promote healthy brain aging in Africa (the Africa-FINGERS project).

Dementia prevention in Africa is critically underexplored, despite the continent's high prevalence of modifiable risk factors. With a predominantly young and middle-aged population, Africa presents a prime opportunity to implement evidence-based strategies that could significantly reduce future dementia cases and mitigate its economic impact. The multinational Africa-FINGERS program offers an innovative solution, pioneering culturally sensitive, multidomain interventions tailored to the unique challenges of the region. Leveraging insights from landmark global studies such as Worldwide-FINGERS and Alzheimer's Disease Neuroimaging Initiative, the program employs a multideterminant precision prevention framework, grounded in community based systems dynamics. Africa-FINGERS further integrates cutting-edge state-of-the-art multimodal biomarker evaluations tailored to regional contexts, with the goal of advancing brain health and establishing a global standard for dementia prevention. This groundbreaking initiative highlights the potential for scalableand sustainable interventions, thus is poised to transform dementia risk reduction efforts across the continent. HIGHLIGHTS: Dementia rates are escalating in Africa, largely due to longer life spans and increased prevalence of modifiable risk factors. Yet, few regional interventions have directly targeted lifestyle factors to reduce dementia risk. The multinational Africa-FINGERS study will address this gap by adapting the successful FINGERS lifestyle intervention to African populations. Africa-FINGERS will pioneer a culturally informed, multidomain dementia risk reduction intervention in the African region through feasibility dementia prevention trials in rural and urban sites across Kenya and Nigeria in the first instance, enrolling 600 at-risk adults (≥ 50 years). The program adoptsparticipatory research methods to develop culturally appropriate interventions and build infrastructure to evaluate dementia biomarkers from ante and post mortem samples. A cost-effectiveness analysis will be conducted to guide the strategic implementation of Africa-FINGERS into regional health systems. The Africa-FINGERS strategy aligns with the Worldwide-FINGERS framework and integrates the global Alzheimer's Disease Neuroimaging Initiative approach, emphasizing multimodal analysis.

Anatomic Interpretability in Neuroimage Deep Learning: Saliency Approaches for Typical Aging and Traumatic Brain Injury.

The black box nature of deep neural networks (DNNs) makes researchers and clinicians hesitant to rely on their findings. Saliency maps can enhance DNN explainability by suggesting the anatomic localization of relevant brain features. This study compares seven popular attribution-based saliency approaches to assign neuroanatomic interpretability to DNNs that estimate biological brain age (BA) from magnetic resonance imaging (MRI). Cognitively normal (CN) adults (N = 13,394, 5,900 males; mean age: 65.82 ± 8.89years) are included for DNN training, testing, validation, and saliency map generation to estimate BA. To study saliency robustness to the presence of anatomic deviations from normality, saliency maps are also generated for adults with mild traumatic brain injury (mTBI, = 214, 135 males; mean age: 55.3 ± 9.9years). We assess saliency methods' capacities to capture known anatomic features of brain aging and compare them to a surrogate ground truth whose anatomic saliency is known a priori. Anatomic aging features are identified most reliably by the integrated gradients method, which outperforms all others through its ability to localize relevant anatomic features. Gradient Shapley additive explanations, input × gradient, and masked gradient perform less consistently but still highlight ubiquitous neuroanatomic features of aging (ventricle dilation, hippocampal atrophy, sulcal widening). Saliency methods involving gradient saliency, guided backpropagation, and guided gradient-weight class attribution mapping localize saliency outside the brain, which is undesirable. Our research suggests the relative tradeoffs of saliency methods to interpret DNN findings during BA estimation in typical aging and after mTBI.

Multiomic profiling reveals timing of menopause predicts prefrontal cortex aging and cognitive function.

A new case of dementia is diagnosed every 3 s. Beyond age, risk prediction of dementia is challenging. There is growing evidence of underlying processes that connect aging across organ systems and may provide insight for early detection, and there is a need to identify early biomarkers at an age when action can be taken to mitigate cognitive decline. We hypothesized that timing of menopause, a marker of ovarian aging, predicts brain age decades later. We used 2086 subjects with multiple "omics" measurements from post-mortem brain samples. Age at menopause (AAM) is positively correlated with cognitive function and negatively correlated with pre-frontal cortex aging acceleration (calculated as estimated biological age from DNA methylation minus chronological age). Genetic correlations showed that at least part of these associations is derived from shared heritability. To dissect the mechanism linking AAM to cognitive decline, we turned to transcriptomic data which confirmed that later AAM was associated with gene expression in pre-frontal cortex consistent with better cognition, and among those who reached menopause naturally, decreased gene expression of pathways implicated in aging. Those with surgical menopause displayed different molecular changes, including perturbed nicotinamide adenine dinucleotide (NAD+) activity, validated by metabolomics. Bile acid metabolism was perturbed in both groups, although different bile acid ratios were associated with AAM in each. Together, our data suggest that AAM is predictive of brain aging and cognition, with potential mediation by the gut, although through different mechanisms depending on the type of menopause.

Modifiable dementia risk associated with smaller white matter volume and altered 1/f aperiodic brain activity: cross-sectional insights from the LEISURE study.

The rising prevalence of dementia necessitates identifying early neurobiological markers of dementia risk. Reduced cerebral white matter volume and flattening of the slope of the electrophysiological 1/f spectral power distribution provide neurobiological markers of brain ageing alongside cognitive decline. However, their association with modifiable dementia risk remains to be understood. A cross-sectional sample of 98 healthy older adults (79 females, mean age = 65.44) underwent structural magnetic resonance imaging (sMRI), resting-state electroencephalography (EEG), cognitive assessments and dementia risk scoring using the CogDrisk framework. Univariate and multivariate linear regression models were conducted to investigate the relationships between modifiable dementia risk and sMRI brain volumes, the exponent of EEG 1/f spectral power, and cognition, whilst controlling for non-modifiable factors. Smaller global white matter volume (F(1,87) = 6.884, R2 = 0.073, P = .010), and not grey (F(1,87) = 0.540, R2 = 0.006, P = .468) or ventricle volume (F(1,87) = 0.087, R2 = 0.001, P = .769), was associated with higher modifiable dementia risk. A lower exponent, reflecting a flatter 1/f spectral power distribution, was associated with higher dementia risk at frontal (F(1,92) = 4.096, R2 = 0.043, P = .046) but not temporal regions. No significant associations were found between cognitive performance and dementia risk. In multivariate analyses, both white matter volume and the exponent of the 1/f spectral power distribution independently associated with dementia risk. Structural and functional neurobiological markers of early brain ageing, but not cognitive function, are independently associated with modifiable dementia risk in healthy older adults.