Advanced brain aging: relationship with epidemiologic and genetic risk factors, and overlap with Alzheimer disease atrophy patterns

Molecular mechanisms linking type 2 diabetes mellitus and late-onset Alzheimer's disease: A systematic review and qualitative meta-analysis

Research evidence indicating common metabolic mechanisms through which type 2 diabetes mellitus (T2DM) increases risk of late-onset Alzheimer's dementia (LOAD) has accumulated over recent decades. The aim of this systematic review is to provide a comprehensive review of common mechanisms, which have hitherto been discussed in separate perspectives, and to assemble and evaluate candidate loci and epigenetic modifications contributing to polygenic risk linkages between T2DM and LOAD. For the systematic review on pathophysiological mechanisms, both human and animal studies up to December 2023 are included. For the qualitative meta-analysis of genomic bases, human association studies were examined; for epigenetic mechanisms, data from human studies and animal models were accepted. Papers describing pathophysiological studies were identified in databases, and further literature gathered from cited work. For genomic and epigenomic studies, literature mining was conducted by formalised search codes using Boolean operators in search engines, and augmented by GeneRif citations in Entrez Gene, and other sources (WikiGenes, etc.). For the systematic review of pathophysiological mechanisms, 923 publications were evaluated, and 138 gene loci extracted for testing candidate risk linkages. 3 57 publications were evaluated for genomic association and descriptions of epigenomic modifications. Overall accumulated results highlight insulin signalling, inflammation and inflammasome pathways, proteolysis, gluconeogenesis and glycolysis, glycosylation, lipoprotein metabolism and oxidation, cell cycle regulation or survival, autophagic-lysosomal pathways, and energy. Documented findings suggest interplay between brain insulin resistance, neuroinflammation, insult compensatory mechanisms, and peripheral metabolic dysregulation in T2DM and LOAD linkage. The results allow for more streamlined longitudinal studies of T2DM-LOAD risk linkages.

Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals

Importance

Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases.

Objective

To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories.

Design, Setting, and Participants

Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points.

Exposures

Individuals WODCI at baseline scan.

Main Outcomes and Measures

Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid β (Aβ), and future cognitive decline were assessed.

Results

In a sample of 27 402 individuals (mean [SD] age, 63.0 [8.3] years; 15 146 female [55%]) WODCI, 3 subgroups were identified in contrast with the reference group: a typical aging subgroup, A1, with a specific pattern of modest atrophy and white matter hyperintensity (WMH) load, and 2 accelerated aging subgroups, A2 and A3, with characteristics that were more distinct at age 65 years and older. A2 was associated with hypertension, WMH, and vascular disease-related genetic variants and was enriched for Aβ positivity (ages ≥65 years) and apolipoprotein E (APOE) ε4 carriers. A3 showed severe, widespread atrophy, moderate presence of CVRFs, and greater cognitive decline. Genetic variants associated with A1 were protective for WMH (rs7209235: mean [SD] B = -0.07 [0.01]; P value = 2.31 × 10-9) and Alzheimer disease (rs72932727: mean [SD] B = 0.1 [0.02]; P value = 6.49 × 10-9), whereas the converse was observed for A2 (rs7209235: mean [SD] B = 0.1 [0.01]; P value = 1.73 × 10-15 and rs72932727: mean [SD] B = -0.09 [0.02]; P value = 4.05 × 10-7, respectively); variants in A3 were associated with regional atrophy (rs167684: mean [SD] B = 0.08 [0.01]; P value = 7.22 × 10-12) and white matter integrity measures (rs1636250: mean [SD] B = 0.06 [0.01]; P value = 4.90 × 10-7).

Conclusions and Relevance

The 3 subgroups showed distinct associations with CVRFs, genetics, and subsequent cognitive decline. These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care.

Trends in Intracranial and Cerebral Volumes of Framingham Heart Study Participants Born 1930 to 1970

Importance

Human brain development and maintenance is under both genetic and environmental influences that likely affect later-life dementia risk.

Objective

To examine environmental influences by testing whether time-dependent secular differences occurred in cranial and brain volumes and cortical thickness over birth decades spanning 1930 to 1970.

Design, Setting, and Participants

This cross-sectional study used data from the community-based Framingham Heart Study cohort for participants born in the decades 1930 to 1970. Participants did not have dementia or history of stroke and had magnetic resonance imaging (MRI) obtained from March 18, 1999, to November 15, 2019. The final analysis dataset was created in October 2023.

Exposure

Years of birth ranging from 1925 to 1968.

Main Measures

Cross-sectional analysis of intracranial, cortical gray matter, white matter, and hippocampal volumes as well as cortical surface area and cortical thickness. The secular measure was the decade in which the participant was born. Covariates included age at MRI and sex.

Results

The main study cohort consisted of 3226 participants with a mean (SD) age of 57.7 (7.8) years at the time of their MRI. A total of 1706 participants were female (53%) and 1520 (47%) were male. The birth decades ranged from the 1930s to 1970s. Significant trends for larger intracranial, hippocampal, and white matter volumes and cortical surface area were associated with progressive birth decades. Comparing the 1930s birth decade to the 1970s accounted for a 6.6% greater volume (1234 mL; 95% CI, 1220-1248, vs 1321 mL; 95% CI, 1301-1341) for ICV, 7.7% greater volume (441.9 mL; 95% CI, 435.2-448.5, vs 476.3 mL; 95% CI, 467.0-485.7) for white matter, 5.7% greater value (6.51 mL; 95% CI, 6.42-6.60, vs 6.89 mL; 95% CI, 6.77-7.02) for hippocampal volume, and a 14.9% greater value (1933 cm2; 95% CI, 1908-1959, vs 2222 cm2; 95% CI, 2186-2259) for cortical surface area. Repeat analysis applied to a subgroup of 1145 individuals of similar age range born in the 1940s (mean [SD] age, 60.0 [2.8] years) and 1950s (mean [SD] age, 59.0 [2.8] years) resulted in similar findings.

Conclusion and Relevance

In this study, secular trends for larger brain volumes suggested improved brain development among individuals born between 1930 and 1970. Early life environmental influences may explain these results and contribute to the declining dementia incidence previously reported in the Framingham Heart Study cohort.

Comparing the Efficacy of Two Cognitive Screening Tools in Identifying Gray and White Matter Brain Damage among Older Adults

Background

Ageing is associated with structural changes in brain regions and functional decline in cognitive domains. Noninvasive tools for identifying structural damage in the brains of older adults are relevant for early treatment.

Aims

This study aims to evaluate and compare the accuracy of the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA©) in identifying gray and white matter brain damage in older individuals with varying degrees of cognitive impairment.

Methods

Ninety older adults (62 women) with an average age of 69 ± 7 years were enrolled and categorized as having no cognitive impairment (NCI), mild cognitive impairment (MCI), or moderate cognitive impairment (MoCI). Magnetic resonance imaging (MRI) was utilized to assess the number, volume, and distribution of brain damage. The Fazekas and Scheltens scales were applied to the brain MRIs, and inferential statistics were employed to compare variables among the groups.

Results

Cognitive impairment was observed in 56.7% of the participants (95% confidence interval (CI): 46.4-66.4%), with thirty-six older adults (40%) classified as MCI and 15 (17%) as MoCI. Cognitive impairment and medial temporal lobe (MTL) atrophy were found to be associated (p=0.001), exhibiting higher mean volume scales of the MTL atrophied area in the MoCI group (p < 0.001). The MMSE accurately revealed MTL atrophy based on the Scheltens (p < 0.05) and Fazekas (p < 0.05) scales. At the same time, the MoCA accurately identified periventricular white matter (PWM) abnormalities according to the Fazekas scale (p < 0.05).

Conclusions

The MMSE and MoCA screening tools effectively identified gray and white matter brain damage in older adults with varying degrees of cognitive impairment. Lower MMSE scores are associated with MTL atrophy and lesions, and lower MoCA scores are related to PWM lesions. The concurrent use of MMSE and MoCA is recommended for assessing structural changes in distinct brain regions.

Associations of quantitative susceptibility mapping with cortical atrophy and brain connectome in Alzheimer's disease: A multi-parametric study

Aberrant susceptibility due to iron level abnormality and brain network disconnections are observed in Alzheimer's disease (AD), with disrupted iron homeostasis hypothesized to be linked to AD pathology and neuronal loss. However, whether associations exist between abnormal quantitative susceptibility mapping (QSM), brain atrophy, and altered brain connectome in AD remains unclear. Based on multi-parametric brain imaging data from 30 AD patients and 26 healthy controls enrolled at the China-Japan Friendship Hospital, we investigated the abnormality of the QSM signal and volumetric measure across 246 brain regions in AD patients. The structural and functional connectomes were constructed based on diffusion MRI tractography and functional connectivity, respectively. The network topology was quantified using graph theory analyses. We identified seven brain regions with both reduced cortical thickness and abnormal QSM (p < 0.05) in AD, including the right superior frontal gyrus, left superior temporal gyrus, right fusiform gyrus, left superior parietal lobule, right superior parietal lobule, left inferior parietal lobule, and left precuneus. Correlations between cortical thickness and network topology computed across patients in the AD group resulted in statistically significant correlations in five of these regions, with higher correlations in functional compared to structural topology. We computed the correlation between network topological metrics, QSM value and cortical thickness across regions at both individual and group-averaged levels, resulting in a measure we call spatial correlations. We found a decrease in the spatial correlation of QSM and the global efficiency of the structural network in AD patients at the individual level. These findings may provide insights into the complex relationships among QSM, brain atrophy, and brain connectome in AD.

Elucidating Multimodal Imaging Patterns in Accelerated Brain Aging: Heterogeneity through a Discriminant Analysis Approach Using the UK Biobank Dataset

Accelerated brain aging (ABA) intricately links with age-associated neurodegenerative and neuropsychiatric diseases, emphasizing the critical need for a nuanced exploration of heterogeneous ABA patterns. This investigation leveraged data from the UK Biobank (UKB) for a comprehensive analysis, utilizing structural magnetic resonance imaging (sMRI), diffusion magnetic resonance imaging (dMRI), and resting-state functional magnetic resonance imaging (rsfMRI) from 31,621 participants. Pre-processing employed tools from the FMRIB Software Library (FSL, version 5.0.10), FreeSurfer, DTIFIT, and MELODIC, seamlessly integrated into the UKB imaging processing pipeline. The Lasso algorithm was employed for brain-age prediction, utilizing derived phenotypes obtained from brain imaging data. Subpopulations of accelerated brain aging (ABA) and resilient brain aging (RBA) were delineated based on the error between actual age and predicted brain age. The ABA subgroup comprised 1949 subjects (experimental group), while the RBA subgroup comprised 3203 subjects (control group). Semi-supervised heterogeneity through discriminant analysis (HYDRA) refined and characterized the ABA subgroups based on distinctive neuroimaging features. HYDRA systematically stratified ABA subjects into three subtypes: SubGroup 2 exhibited extensive gray-matter atrophy, distinctive white-matter patterns, and unique connectivity features, displaying lower cognitive performance; SubGroup 3 demonstrated minimal atrophy, superior cognitive performance, and higher physical activity; and SubGroup 1 occupied an intermediate position. This investigation underscores pronounced structural and functional heterogeneity in ABA, revealing three subtypes and paving the way for personalized neuroprotective treatments for age-related neurological, neuropsychiatric, and neurodegenerative diseases.

The interaction effects of age, APOE and common environmental risk factors on human brain structure

Mounting evidence suggests considerable diversity in brain aging trajectories, primarily arising from the complex interplay between age, genetic, and environmental risk factors, leading to distinct patterns of micro- and macro-cerebral aging. The underlying mechanisms of such effects still remain unclear. We conducted a comprehensive association analysis between cerebral structural measures and prevalent risk factors, using data from 36,969 UK Biobank subjects aged 44-81. Participants were assessed for brain volume, white matter diffusivity, Apolipoprotein E (APOE) genotypes, polygenic risk scores, lifestyles, and socioeconomic status. We examined genetic and environmental effects and their interactions with age and sex, and identified 726 signals, with education, alcohol, and smoking affecting most brain regions. Our analysis revealed negative age-APOE-ε4 and positive age-APOE-ε2 interaction effects, respectively, especially in females on the volume of amygdala, positive age-sex-APOE-ε4 interaction on the cerebellar volume, positive age-excessive-alcohol interaction effect on the mean diffusivity of the splenium of the corpus callosum, positive age-healthy-diet interaction effect on the paracentral volume, and negative APOE-ε4-moderate-alcohol interaction effects on the axial diffusivity of the superior fronto-occipital fasciculus. These findings highlight the need of considering age, sex, genetic, and environmental joint effects in elucidating normal or abnormal brain aging.

Five dominant dimensions of brain aging are identified via deep learning: associations with clinical, lifestyle, and genetic measures

Brain aging is a complex process influenced by various lifestyle, environmental, and genetic factors, as well as by age-related and often co-existing pathologies. MRI and, more recently, AI methods have been instrumental in understanding the neuroanatomical changes that occur during aging in large and diverse populations. However, the multiplicity and mutual overlap of both pathologic processes and affected brain regions make it difficult to precisely characterize the underlying neurodegenerative profile of an individual from an MRI scan. Herein, we leverage a state-of-the art deep representation learning method, Surreal-GAN, and present both methodological advances and extensive experimental results that allow us to elucidate the heterogeneity of brain aging in a large and diverse cohort of 49,482 individuals from 11 studies. Five dominant patterns of neurodegeneration were identified and quantified for each individual by their respective (herein referred to as) R-indices. Significant associations between R-indices and distinct biomedical, lifestyle, and genetic factors provide insights into the etiology of observed variances. Furthermore, baseline R-indices showed predictive value for disease progression and mortality. These five R-indices contribute to MRI-based precision diagnostics, prognostication, and may inform stratification into clinical trials.

Brain-predicted age difference estimated using DeepBrainNet is significantly associated with pain and function-a multi-institutional and multiscanner study

ABSTRACT

Brain age predicted differences (brain-PAD: predicted brain age minus chronological age) have been reported to be significantly larger for individuals with chronic pain compared with those without. However, a debate remains after one article showed no significant differences. Using Gaussian Process Regression, an article provides evidence that these negative results might owe to the use of mixed samples by reporting a differential effect of chronic pain on brain-PAD across pain types. However, some remaining methodological issues regarding training sample size and sex-specific effects should be tackled before settling this controversy. Here, we explored differences in brain-PAD between musculoskeletal pain types and controls using a novel convolutional neural network for predicting brain-PADs, ie, DeepBrainNet. Based on a very large, multi-institutional, and heterogeneous training sample and requiring less magnetic resonance imaging preprocessing than other methods for brain age prediction, DeepBrainNet offers robust and reproducible brain-PADs, possibly highly sensitive to neuropathology. Controlling for scanner-related variability, we used a large sample (n = 660) with different scanners, ages (19-83 years), and musculoskeletal pain types (chronic low back [CBP] and osteoarthritis [OA] pain). Irrespective of sex, brain-PAD of OA pain participants was ∼3 to 4.7 years higher than that of CBP and controls, whereas brain-PAD did not significantly differ among controls and CBP. Moreover, brain-PAD was significantly related to multiple variables underlying the multidimensional pain experience. This comprehensive work adds evidence of pain type-specific effects of chronic pain on brain age. This could help in the clarification of the debate around possible relationships between brain aging mechanisms and pain.

Explainable Brain Age Prediction using coVariance Neural Networks

In computational neuroscience, there has been an increased interest in developing machine learning algorithms that leverage brain imaging data to provide estimates of "brain age" for an individual. Importantly, the discordance between brain age and chronological age (referred to as "brain age gap") can capture accelerated aging due to adverse health conditions and therefore, can reflect increased vulnerability towards neurological disease or cognitive impairments. However, widespread adoption of brain age for clinical decision support has been hindered due to lack of transparency and methodological justifications in most existing brain age prediction algorithms. In this paper, we leverage coVariance neural networks (VNN) to propose an explanation-driven and anatomically interpretable framework for brain age prediction using cortical thickness features. Specifically, our brain age prediction framework extends beyond the coarse metric of brain age gap in Alzheimer's disease (AD) and we make two important observations: (i) VNNs can assign anatomical interpretability to elevated brain age gap in AD by identifying contributing brain regions, (ii) the interpretability offered by VNNs is contingent on their ability to exploit specific eigenvectors of the anatomical covariance matrix. Together, these observations facilitate an explainable and anatomically interpretable perspective to the task of brain age prediction.

Association Between Psychosocial Stress and Brain Aging: Results of the Population-Based Cohort Study of Health in Pomerania (SHIP)

OBJECTIVE

Recent studies suggest that psychosocial factors can have an impact on brain health. Yet, it is unclear whether psychosocial stress affects aging of the brain. The aim of the study was to investigate the association between psychosocial stress and brain aging.

METHODS

Data from the German population-based cohort Study of Health in Pomerania (N=991; age range 20-78 years) were used to calculate a total psychosocial stress score by combining subscores from five domains: stress related to the living situation, the occupational situation, the social situation, danger experiences, and emotions. Associations with brain aging, indicated by an MRI-derived score quantifying age-related brain atrophy, were estimated by using regression models adjusted for age, gender, education, diabetes, problematic alcohol consumption, smoking, and hypertension.

RESULTS

The relative risk ratio for advanced brain aging was 1.21 (95% CI=1.04-1.41) for stress related to emotions in fully adjusted models. The interactions between stress related to emotions and mental health symptoms were also significantly associated with advanced brain aging. The association between higher total psychosocial stress and brain aging was not statistically significant.

CONCLUSIONS

These findings highlight that high stress related to emotions is associated with advanced brain aging. To protect brain health in older age, more research is needed to explore the role of emotional distress.

A systematic comparison of VBM pipelines and their application to age prediction

Voxel-based morphometry (VBM) analysis is commonly used for localized quantification of gray matter volume (GMV). Several alternatives exist to implement a VBM pipeline. However, how these alternatives compare and their utility in applications, such as the estimation of aging effects, remain largely unclear. This leaves researchers wondering which VBM pipeline they should use for their project. In this study, we took a user-centric perspective and systematically compared five VBM pipelines, together with registration to either a general or a study-specific template, utilizing three large datasets (n>500 each). Considering the known effect of aging on GMV, we first compared the pipelines in their ability of individual-level age prediction and found markedly varied results. To examine whether these results arise from systematic differences between the pipelines, we classified them based on their GMVs, resulting in near-perfect accuracy. To gain deeper insights, we examined the impact of different VBM steps using the region-wise similarity between pipelines. The results revealed marked differences, largely driven by segmentation and registration steps. We observed large variability in subject-identification accuracies, highlighting the interpipeline differences in individual-level quantification of GMV. As a biologically meaningful criterion we correlated regional GMV with age. The results were in line with the age-prediction analysis, and two pipelines, CAT and the combination of fMRIPrep for tissue characterization with FSL for registration, reflected age information better.

Disentangling accelerated cognitive decline from the normal aging process and unraveling its genetic components: A neuroimaging-based deep learning approach

Background

The progressive cognitive decline that is an integral component of AD unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and Alzheimer's disease between different chronological points.

Methods

We developed a deep-learning framework based on dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We then conducted genome-wide association studies (GWAS) and post-GWAS analyses to reveal the genetic basis of AD-related accelerated cognitive decline.

Results

We used our model to process data from 1,313 individuals, training it on 414 cognitively normal people and predicting cognitive assessment for all participants. In our analysis of accelerated cognitive decline GWAS, we identified two genome-wide significant loci: APOE locus (chromosome 19 p13.32) and rs144614292 (chromosome 11 p15.1). Variant rs144614292 (G>T) has not been reported in previous AD GWA studies. It is within the intronic region of NELL1, which is expressed in neuron and plays a role in controlling cell growth and differentiation. In addition, MUC7 and PROL1/OPRPNon chromosome 4 were significant at the gene level. The cell-type-specific enrichment analysis and functional enrichment of GWAS signals highlighted the microglia and immune-response pathways. Furthermore, we found that the cognitive decline slope GWAS was positively correlated with previous AD GWAS.

Conclusion

Our deep learning model was demonstrated effective on extracting relevant neuroimaging features and predicting individual cognitive decline. We reported a novel variant (rs144614292) within the NELL1 gene. Our approach has the potential to disentangle accelerated cognitive decline from the normal aging process and to determine its related genetic factors, leveraging opportunities for early intervention.

Patterns of Regional Brain Atrophy and Brain Aging in Middle- and Older-Aged Adults With Type 1 Diabetes

Importance

Little is known about structural brain changes in type 1 diabetes (T1D) and whether there are early manifestations of a neurodegenerative condition like Alzheimer disease (AD) or evidence of premature brain aging.

Objective

To evaluate neuroimaging markers of brain age and AD-like atrophy in participants with T1D in the Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) study, identify which brain regions are associated with the greatest changes in patients with T1D, and assess the association between cognition and brain aging indices.

Design, Setting, and Participants

This cohort study leveraged data collected during the combined DCCT (randomized clinical trial, 1983-1993) and EDIC (observational study, 1994 to present) studies at 27 clinical centers in the US and Canada. A total of 416 eligible EDIC participants and 99 demographically similar adults without diabetes were enrolled in the magnetic resonance imaging (MRI) ancillary study, which reports cross-sectional data collected in 2018 to 2019 and relates it to factors measured longitudinally in DCCT/EDIC. Data analyses were performed between July 2020 and April 2022.

Exposure

T1D diagnosis.

Main Outcomes and Measures

Psychomotor and mental efficiency were evaluated using verbal fluency, digit symbol substitution test, trail making part B, and the grooved pegboard. Immediate memory scores were derived from the logical memory subtest of the Wechsler memory scale and the Wechsler digit symbol substitution test. MRI and machine learning indices were calculated to predict brain age and quantify AD-like atrophy.

Results

This study included 416 EDIC participants with a median (range) age of 60 (44-74) years (87 of 416 [21%] were older than 65 years) and a median (range) diabetes duration of 37 (30-51) years. EDIC participants had consistently higher brain age values compared with controls without diabetes, indicative of approximately 6 additional years of brain aging (EDIC participants: β, 6.16; SE, 0.71; control participants: β, 1.04; SE, 0.04; P < .001). In contrast, AD regional atrophy was comparable between the 2 groups. Regions with atrophy in EDIC participants vs controls were observed mainly in the bilateral thalamus and putamen. Greater brain age was associated with lower psychomotor and mental efficiency among EDIC participants (β, -0.04; SE, 0.01; P < .001), but not among controls.

Conclusions and Relevance

The findings of this study suggest an increase in brain aging among individuals with T1D without any early signs of AD-related neurodegeneration. These increases were associated with reduced cognitive performance, but overall, the abnormal patterns seen in this sample were modest, even after a mean of 38 years with T1D.

Assessment of Risk Factors and Clinical Importance of Enlarged Perivascular Spaces by Whole-Brain Investigation in the Multi-Ethnic Study of Atherosclerosis

Importance

Enlarged perivascular spaces (ePVSs) have been associated with cerebral small-vessel disease (cSVD). Although their etiology may differ based on brain location, study of ePVSs has been limited to specific brain regions; therefore, their risk factors and significance remain uncertain.

Objective

Toperform a whole-brain investigation of ePVSs in a large community-based cohort.

Design, Setting, and Participants

This cross-sectional study analyzed data from the Atrial Fibrillation substudy of the population-based Multi-Ethnic Study of Atherosclerosis. Demographic, vascular risk, and cardiovascular disease data were collected from September 2016 to May 2018. Brain magnetic resonance imaging was performed from March 2018 to July 2019. The reported analysis was conducted between August and October 2022. A total of 1026 participants with available brain magnetic resonance imaging data and complete information on demographic characteristics and vascular risk factors were included.

Main Outcomes and Measures

Enlarged perivascular spaces were quantified using a fully automated deep learning algorithm. Quantified ePVS volumes were grouped into 6 anatomic locations: basal ganglia, thalamus, brainstem, frontoparietal, insular, and temporal regions, and were normalized for the respective regional volumes. The association of normalized regional ePVS volumes with demographic characteristics, vascular risk factors, neuroimaging indices, and prevalent cardiovascular disease was explored using generalized linear models.

Results

In the 1026 participants, mean (SD) age was 72 (8) years; 541 (53%) of the participants were women. Basal ganglia ePVS volume was positively associated with age (β = 3.59 × 10-3; 95% CI, 2.80 × 10-3 to 4.39 × 10-3), systolic blood pressure (β = 8.35 × 10-4; 95% CI, 5.19 × 10-4 to 1.15 × 10-3), use of antihypertensives (β = 3.29 × 10-2; 95% CI, 1.92 × 10-2 to 4.67 × 10-2), and negatively associated with Black race (β = -3.34 × 10-2; 95% CI, -5.08 × 10-2 to -1.59 × 10-2). Thalamic ePVS volume was positively associated with age (β = 5.57 × 10-4; 95% CI, 2.19 × 10-4 to 8.95 × 10-4) and use of antihypertensives (β = 1.19 × 10-2; 95% CI, 6.02 × 10-3 to 1.77 × 10-2). Insular region ePVS volume was positively associated with age (β = 1.18 × 10-3; 95% CI, 7.98 × 10-4 to 1.55 × 10-3). Brainstem ePVS volume was smaller in Black than in White participants (β = -5.34 × 10-3; 95% CI, -8.26 × 10-3 to -2.41 × 10-3). Frontoparietal ePVS volume was positively associated with systolic blood pressure (β = 1.14 × 10-4; 95% CI, 3.38 × 10-5 to 1.95 × 10-4) and negatively associated with age (β = -3.38 × 10-4; 95% CI, -5.40 × 10-4 to -1.36 × 10-4). Temporal region ePVS volume was negatively associated with age (β = -1.61 × 10-2; 95% CI, -2.14 × 10-2 to -1.09 × 10-2), as well as Chinese American (β = -2.35 × 10-1; 95% CI, -3.83 × 10-1 to -8.74 × 10-2) and Hispanic ethnicities (β = -1.73 × 10-1; 95% CI, -2.96 × 10-1 to -4.99 × 10-2).

Conclusions and Relevance

In this cross-sectional study of ePVSs in the whole brain, increased ePVS burden in the basal ganglia and thalamus was a surrogate marker for underlying cSVD, highlighting the clinical importance of ePVSs in these locations.

ADCoC: Adaptive Distribution Modeling Based Collaborative Clustering for Disentangling Disease Heterogeneity from Neuroimaging Data

Conventional clustering techniques for neuroimaging applications usually focus on capturing differences between given subjects, while neglecting arising differences between features and the potential bias caused by degraded data quality. In practice, collected neuroimaging data are often inevitably contaminated by noise, which may lead to errors in clustering and clinical interpretation. Additionally, most methods ignore the importance of feature grouping towards optimal clustering. In this paper, we exploit the underlying heterogeneous clusters of features to serve as weak supervision for improved clustering of subjects, which is achieved by simultaneously clustering subjects and features via nonnegative matrix tri-factorization. In order to suppress noise, we further introduce adaptive regularization based on coefficient distribution modeling. Particularly, unlike conventional sparsity regularization techniques that assume zero mean of the coefficients, we form the distributions using the data of interest so that they could better fit the non-negative coefficients. In this manner, the proposed approach is expected to be more effective and robust against noise. We compared the proposed method with standard techniques and recently published methods demonstrating superior clustering performance on synthetic data with known ground truth labels. Furthermore, when applying our proposed technique to magnetic resonance imaging (MRI) data from a cohort of patients with Parkinson's disease, we identified two stable and highly reproducible patient clusters characterized by frontal and posterior cortical/medial temporal atrophy patterns, respectively, which also showed corresponding differences in cognitive characteristics.

Deep neural network heatmaps capture Alzheimer's disease patterns reported in a large meta-analysis of neuroimaging studies

Deep neural networks currently provide the most advanced and accurate machine learning models to distinguish between structural MRI scans of subjects with Alzheimer's disease and healthy controls. Unfortunately, the subtle brain alterations captured by these models are difficult to interpret because of the complexity of these multi-layer and non-linear models. Several heatmap methods have been proposed to address this issue and analyze the imaging patterns extracted from the deep neural networks, but no quantitative comparison between these methods has been carried out so far. In this work, we explore these questions by deriving heatmaps from Convolutional Neural Networks (CNN) trained using T1 MRI scans of the ADNI data set and by comparing these heatmaps with brain maps corresponding to Support Vector Machine (SVM) activation patterns. Three prominent heatmap methods are studied: Layer-wise Relevance Propagation (LRP), Integrated Gradients (IG), and Guided Grad-CAM (GGC). Contrary to prior studies where the quality of heatmaps was visually or qualitatively assessed, we obtained precise quantitative measures by computing overlap with a ground-truth map from a large meta-analysis that combined 77 voxel-based morphometry (VBM) studies independently from ADNI. Our results indicate that all three heatmap methods were able to capture brain regions covering the meta-analysis map and achieved better results than SVM activation patterns. Among them, IG produced the heatmaps with the best overlap with the independent meta-analysis.

Alzheimer's Disease Genetic Risk, Cognition, and Brain Aging in Midlife

We examined associations of an Alzheimer's disease (AD) Genetic Risk Score (AD-GRS) and midlife cognitive and neuroimaging outcomes in 1,252 middle-aged participants (311 with brain MRI). A higher AD-GRS based on 25 previously identified loci (excluding apolipoprotein E [APOE]) was associated with worse Montreal Cognitive Assessment (-0.14 standard deviation [SD] [95% confidence interval {CI}: -0.26, -0.02]), older machine learning predicted brain age (2.35 years[95%CI: 0.01, 4.69]), and white matter hyperintensity volume (0.35 SD [95% CI: 0.00, 0.71]), but not with a composite cognitive outcome, total brain, or hippocampal volume. APOE ε4 allele was not associated with any outcomes. AD risk genes beyond APOE may contribute to subclinical differences in cognition and brain health in midlife. ANN NEUROL 2023;93:629-634.

Brain predicted age difference mediates pain impact on physical performance in community dwelling middle to older aged adults

The purpose of the study was to examine associations between physical performance and brain aging in individuals with knee pain and whether the association between pain and physical performance is mediated by brain aging. Participants (n=202) with low impact knee pain (n=111), high impact knee pain (n=60) and pain-free controls (n=31) completed self-reported pain, magnetic resonance imaging (MRI), and a Short Physical Performance Battery (SPPB) that included balance, walking, and sit to stand tasks. Brain predicted age difference, calculated using machine learning from MRI images, significantly mediated the relationships between walking and knee pain impact (CI: -0.124; -0.013), walking and pain-severity (CI: -0.008; -0.001), total SPPB score and knee pain impact (CI: -0.232; -0.025), and total SPPB scores and pain-severity (CI: -0.019; -0.001). Brain-aging begins to explain the association between pain and physical performance, especially walking. This study supports the idea that a brain aging prediction can be calculated from shorter duration MRI sequences and possibly implemented in a clinical setting to be used to identify individuals with pain who are at risk for accelerated brain atrophy and increased likelihood of disability.

Deep Learning Based Detection of Enlarged Perivascular Spaces on Brain MRI

Deep learning has been demonstrated effective in many neuroimaging applications. However, in many scenarios, the number of imaging sequences capturing information related to small vessel disease lesions is insufficient to support data-driven techniques. Additionally, cohort-based studies may not always have the optimal or essential imaging sequences for accurate lesion detection. Therefore, it is necessary to determine which imaging sequences are crucial for precise detection. This study introduces a deep learning framework to detect enlarged perivascular spaces (ePVS) and aims to find the optimal combination of MRI sequences for deep learning-based quantification. We implemented an effective lightweight U-Net adapted for ePVS detection and comprehensively investigated different combinations of information from SWI, FLAIR, T1-weighted (T1w), and T2-weighted (T2w) MRI sequences. The experimental results showed that T2w MRI is the most important for accurate ePVS detection, and the incorporation of SWI, FLAIR and T1w MRI in the deep neural network had minor improvements in accuracy and resulted in the highest sensitivity and precision (sensitivity =0.82, precision =0.83). The proposed method achieved comparable accuracy at a minimal time cost compared to manual reading. The proposed automated pipeline enables robust and time-efficient readings of ePVS from MR scans and demonstrates the importance of T2w MRI for ePVS detection and the potential benefits of using multimodal images. Furthermore, the model provides whole-brain maps of ePVS, enabling a better understanding of their clinical correlates compared to the clinical rating methods within only a couple of brain regions.

Is an MRI-derived anatomical measure of dementia risk also a measure of brain aging?

Machine learning methods have been applied to estimate measures of brain aging from neuroimages. However, only rarely have these measures been examined in the context of biologic age. Here, we investigated associations of an MRI-based measure of dementia risk, the Alzheimer's disease pattern similarity (AD-PS) scores, with measures used to calculate biological age. Participants were those from visit 5 of the Atherosclerosis Risk in Communities Study with cognitive status adjudication, proteomic data, and AD-PS scores available. The AD-PS score estimation is based on previously reported machine learning methods. We evaluated associations of the AD-PS score with all-cause mortality. Sensitivity analyses using only cognitively normal (CN) individuals were performed treating CNS-related causes of death as competing risk. AD-PS score was examined in association with 32 proteins measured, using a Somalogic platform, previously reported to be associated with age. Finally, associations with a deficit accumulation index (DAI) based on a count of 38 health conditions were investigated. All analyses were adjusted for age, race, sex, education, smoking, hypertension, and diabetes. The AD-PS score was significantly associated with all-cause mortality and with levels of 9 of the 32 proteins. Growth/differentiation factor 15 (GDF-15) and pleiotrophin remained significant after accounting for multiple-testing and when restricting the analysis to CN participants. A linear regression model showed a significant association between DAI and AD-PS scores overall. While the AD-PS scores were created as a measure of dementia risk, our analyses suggest that they could also be capturing brain aging.

Secular Trends in Head Size and Cerebral Volumes In the Framingham Heart Study for Birth Years 1902-1985

Background Recent data suggest that dementia incidence is declining. We investigated whether similar secular trends consisting of increasing size of brain structures and improving memory performance could be simultaneously occurring as a possible explanation. Method The Framingham Heart Study is a 3 generation, longitudinal study that includes cognitive assessment and medical surveillance. This study cohort consisted of 4,506 unique, non-demented, stroke free, individuals with brain MRI, cognitive assessment, and demographic information spanning dates of birth from 1902 to 1985. Outcomes consisted of height, MRI, and memory measures. Covariates included age at MRI, sex, decade of birth, and all interactions. Models with neuropsychological outcomes also included educational achievement as a covariate. Results Height and intracranial (TCV), hippocampus and cortical gray matter volumes were significantly larger, and memory performance significantly better, with advancing decades of birth after adjusting for age, sex, and interactions. Sensitivity analysis using progressively restricted age-ranges to reduce the association between age and decade of birth, confirmed the findings. Mediation analysis showed that hippocampal volume mediated approximately 5-7% of the effect of decade of birth on logical memory performance. Discussion These findings indicate improvement in brain health and memory performance with advancing decades of birth. Although brain structures are under substantial genetic influence, we conclude that improved early life environmental influences over ensuing decades likely explain these results. We hypothesize that these secular improvements are consistent with declining dementia incidence in this cohort potentially through a mechanism of increased brain reserve.

Long-term depressive symptoms and midlife brain age

BACKGROUND

Evidence suggests that depression may be a risk factor for dementia in older adults, but the link between depressive symptoms and brain health earlier in life is less understood. Our aim was to investigate the association between long-term depressive symptoms in young to mid-adulthood and a measure of brain age derived from structural MRI.

METHODS

From the Coronary Artery Risk Development in Young Adults study, we identified 649 participants (age 23-36 at baseline) with brain MRI and cognitive testing. Long-term depressive symptoms were measured with the Center for Epidemiological Studies Depression scale (CESD) six times across 25 years and analyzed as time-weighted averages (TWA). Brain age was derived using previously validated high dimensional neuroimaging pattern analysis, quantifying individual differences in age-related atrophy. Elevated depressive symptoms were defined as CES-D ≥16. Linear regression was used to test the association between TWA depressive symptoms, brain aging, and cognition.

RESULTS

Each standard deviation (5-points) increment in TWA depression symptoms over 25 years was associated with one-year greater brain age (β: 1.14, 95 % confidence interval [CI]: 0.57 to 1.71). Participants with elevated TWA depressive symptoms had on average a 3-year greater brain age (β: 2.75, 95 % CI: 0.43 to 5.08). Moreover, elevated depressive symptoms were associated with higher odds of poor cognitive function in midlife (OR: 3.30, 95 % CI: 1.37 to 7.97).

LIMITATIONS

Brain age was assessed at one time, limiting our ability to evaluate the temporality of depressive symptoms and brain aging.

CONCLUSIONS

Elevated depressive symptoms in early adulthood may have implications for brain health as early as in midlife.

Postmortem Brain Imaging in Alzheimer's Disease and Related Dementias: The South Texas Alzheimer's Disease Research Center Repository

BACKGROUND

Neuroimaging bears the promise of providing new biomarkers that could refine the diagnosis of dementia. Still, obtaining the pathology data required to validate the relationship between neuroimaging markers and neurological changes is challenging. Existing data repositories are focused on a single pathology, are too small, or do not precisely match neuroimaging and pathology findings.

OBJECTIVE

The new data repository introduced in this work, the South Texas Alzheimer's Disease research center repository, was designed to address these limitations. Our repository covers a broad diversity of dementias, spans a wide age range, and was specifically designed to draw exact correspondences between neuroimaging and pathology data.

METHODS

Using four different MRI sequences, we are reaching a sample size that allows for validating multimodal neuroimaging biomarkers and studying comorbid conditions. Our imaging protocol was designed to capture markers of cerebrovascular disease and related lesions. Quantification of these lesions is currently underway with MRI-guided histopathological examination.

RESULTS

A total of 139 postmortem brains (70 females) with mean age of 77.9 years were collected, with 71 brains fully analyzed. Of these, only 3% showed evidence of AD-only pathology and 76% had high prevalence of multiple pathologies contributing to clinical diagnosis.

CONCLUSION

This repository has a significant (and increasing) sample size consisting of a wide range of neurodegenerative disorders and employs advanced imaging protocols and MRI-guided histopathological analysis to help disentangle the effects of comorbid disorders to refine diagnosis, prognosis and better understand neurodegenerative disorders.

The Spectrum of Alzheimer-Type Pathology in Cognitively Normal Individuals

BACKGROUND

The strongest risk factor for the development of Alzheimer's disease (AD) is age. The progression of Braak stage and Thal phase with age has been demonstrated. However, prior studies did not include cognitive status.

OBJECTIVE

We set out to define normative values for Alzheimer-type pathologic changes in individuals without cognitive decline, and then define levels that would qualify them to be resistant to or resilient against these changes.

METHODS

Utilizing neuropathology data obtained from the National Alzheimer's Coordinating Center (NACC), we demonstrate the age-related progression of Alzheimer-type pathologic changes in cognitively normal individuals (CDR = 0, n = 542). With plots generated from these data, we establish standard lines that may be utilized to measure the extent to which an individual's Alzheimer-type pathology varies from the estimated normal range of pathology.

RESULTS

Although Braak stage and Thal phase progressively increase with age in cognitively normal individuals, the Consortium to Establish a Registry for Alzheimer's Disease neuritic plaque score and Alzheimer's disease neuropathologic change remain at low levels.

CONCLUSION

These findings suggest that an increasing burden of neuritic plaques is a strong predictor of cognitive decline, whereas, neurofibrillary degeneration and amyloid-β (diffuse) plaque deposition, both to some degree, are normal pathologic changes of aging that occur in almost all individuals regardless of cognitive status. Furthermore, we have defined the amount of neuropathologic change in cognitively normal individuals that would qualify them to be "resilient" against the pathology (significantly above the normative values for age, but still cognitively normal) or "resistant" to the development of pathology (significantly below the normative values for age).

Epigenetic Aging Mediates the Association between Pain Impact and Brain Aging in Middle to Older Age Individuals with Knee Pain

Chronic musculoskeletal pain is a health burden that may accelerate the aging process. Accelerated brain aging and epigenetic aging have separately been observed in those with chronic pain. However, it is unknown whether these biological markers of aging are associated with each other in those with chronic pain. We aimed to explore the association of epigenetic aging and brain aging in middle-to-older age individuals with varying degrees of knee pain. Participants (57.91 ± 8.04 y) with low impact knee pain (n = 95), high impact knee pain (n = 53), and pain-free controls (n = 26) completed self-reported pain, a blood draw, and an MRI scan. We used an epigenetic clock previously associated with knee pain (DNAmGrimAge), the subsequent difference of predicted epigenetic and brain age from chronological age (DNAmGrimAge-Difference and Brain-PAD, respectively). There was a significant main effect for pain impact group (F (2,167) = 3.847, P = 0.023, r o t a t i o n a l   e n e r g y   =   1 / 2 I ω 2 = 0.038, ANCOVA) on Brain-PAD and DNAmGrimAge-difference (F (2,167) = 6.800, P = 0.001, I   =   m k 2 = 0.075, ANCOVA) after controlling for covariates. DNAmGrimAge-Difference and Brain-PAD were modestly correlated (r =0.198; P =0.010). Exploratory analysis revealed that DNAmGrimAge-difference mediated GCPS pain impact, GCPS pain severity, and pain-related disability scores on Brain-PAD. Based upon the current study findings, we suggest that pain could be a driver for accelerated brain aging via epigenome interactions.

SPARE-Tau: A flortaucipir machine-learning derived early predictor of cognitive decline

BACKGROUND

Recently, tau PET tracers have shown strong associations with clinical outcomes in individuals with cognitive impairment and cognitively unremarkable elderly individuals. flortaucipir PET scans to measure tau deposition in multiple brain areas as the disease progresses. This information needs to be summarized to evaluate disease severity and predict disease progression. We, therefore, sought to develop a machine learning-derived index, SPARE-Tau, which successfully detects pathology in the earliest disease stages and accurately predicts progression compared to a priori-based region of interest approaches (ROI).

METHODS

587 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort had flortaucipir scans, structural MRI scans, and an Aβ biomarker test (CSF or florbetapir PET) performed on the same visit. We derived the SPARE-Tau index in a subset of 367 participants. We evaluated associations with clinical measures for CSF p-tau, SPARE-MRI, and flortaucipir PET indices (SPARE-Tau, meta-temporal, and average Braak ROIs). Bootstrapped multivariate adaptive regression splines linear regression analyzed the association between the biomarkers and baseline ADAS-Cog13 scores. Bootstrapped multivariate linear regression models evaluated associations with clinical diagnosis. Cox-hazards and mixed-effects models investigated clinical progression and longitudinal ADAS-Cog13 changes. The Aβ positive cognitively unremarkable participants, not included in the SPARE-Tau training, served as an independent validation group.

RESULTS

Compared to CSF p-tau, meta-temporal, and averaged Braak tau PET ROIs, SPARE-Tau showed the strongest association with baseline ADAS-cog13 scores and diagnosis. SPARE-Tau also presented the strongest association with clinical progression in cognitively unremarkable participants and longitudinal ADAS-Cog13 changes. Results were confirmed in the Aβ+ cognitively unremarkable hold-out sample participants. CSF p-tau showed the weakest cross-sectional associations and longitudinal prediction.

DISCUSSION

Flortaucipir indices showed the strongest clinical association among the studied biomarkers (flortaucipir, florbetapir, structural MRI, and CSF p-tau) and were predictive in the preclinical disease stages. Among the flortaucipir indices, the machine-learning derived SPARE-Tau index was the most sensitive clinical progression biomarker. The combination of different biomarker modalities better predicted cognitive performance.

Regional rather than global brain age mediates cognitive function in cerebral small vessel disease

The factors and mechanisms underlying the heterogeneous cognitive outcomes of cerebral small vessel disease are largely unknown. Brain biological age can be estimated by machine learning algorithms that use large brain MRI data sets to integrate and compute neuroimaging-derived age-related features. Predicted and chronological ages difference (brain-age gap) reflects advanced or delayed brain aging in an individual. The present study firstly reports the brain aging status of cerebral small vessel disease. In addition, we investigated whether global or certain regional brain age could mediate the cognitive functions in cerebral small vessel disease. Global and regional (400 cortical, 14 subcortical and 28 cerebellum regions of interest) brain-age prediction models were constructed using grey matter features from MRI of 1482 healthy individuals (age: 18–92 years). Predicted and chronological ages differences were obtained and then applied to non-stroke, non-demented individuals, aged ≥50 years, from another community-dwelling population (I-Lan Longitudinal Aging Study cohort). Among the 734 participants from the I-Lan Longitudinal Aging Study cohort, 124 were classified into the cerebral small vessel disease group. The cerebral small vessel disease group demonstrated significantly poorer performances in global cognitive, verbal memory and executive functions than that of non-cerebral small vessel disease group. Global brain-age gap was significantly higher in the cerebral small vessel disease (3.71 ± 7.60 years) than that in non-cerebral small vessel disease (−0.43 ± 9.47 years) group (P = 0.003, η² = 0.012). There were 82 cerebral cortical, 3 subcortical and 4 cerebellar regions showing significantly different brain-age gap between the cerebral small vessel disease and non-cerebral small vessel disease groups. Global brain-age gap failed to mediate the relationship between cerebral small vessel disease and any of the cognitive domains. In 89 regions with increased brain-age gap in the cerebral small vessel disease group, seven regional brain-age gaps were able to show significant mediation effects in cerebral small vessel disease-related cognitive impairment (we set the statistical significance P < 0.05 uncorrected in 89 mediation models). Of these, the left thalamus and left hippocampus brain-age gap explained poorer global cognitive performance in cerebral small vessel disease. We demonstrated the interconnections between cerebral small vessel disease and brain age. Strategic brain aging, i.e. advanced brain aging in critical regions, may be involved in the pathophysiology of cerebral small vessel disease-related cognitive impairment. Regional rather than global brain-age gap could potentially serve as a biomarker for predicting heterogeneous cognitive outcomes in patients with cerebral small vessel disease.

Elevated blood pressure is associated with advanced brain aging in mid-life: A 30-year follow-up of The CARDIA Study

BACKGROUND

High blood pressure (BP) is a risk factor for late-life brain health; however, the association of elevated BP with brain health in mid-life is unclear.

METHODS

We identified 661 participants from the Coronary Artery Risk Development in Young Adults Study (age 18-30 at baseline) with 30 years of follow-up and brain magnetic resonance imaging at year 30. Cumulative exposure of BP was estimated by time-weighted averages (TWA). Ideal cardiovascular health was defined as systolic BP < 120 mm Hg, diastolic BP < 80 mm Hg. Brain age was calculated using previously validated high dimensional machine learning pattern analyses.

RESULTS

Every 5 mmHg increment in TWA systolic BP was associated with approximately 1-year greater brain age (95% confidence interval [CI]: 0.50-1.36) Participants with TWA systolic or diastolic BP over the recommended guidelines for ideal cardiovascular health, had on average 3-year greater brain age (95% CI: 1.00-4.67; 95% CI: 1.45-5.13, respectively).

CONCLUSION

Elevated BP from early to mid adulthood, even below clinical cut-offs, is associated with advanced brain aging in mid-life.

Biological aging processes underlying cognitive decline and neurodegenerative disease

Alzheimer's disease and related dementias (ADRD) are among the top contributors to disability and mortality in later life. As with many chronic conditions, aging is the single most influential factor in the development of ADRD. Even among older adults who remain free of dementia throughout their lives, cognitive decline and neurodegenerative changes are appreciable with advancing age, suggesting shared pathophysiological mechanisms. In this Review, we provide an overview of changes in cognition, brain morphology, and neuropathological protein accumulation across the lifespan in humans, with complementary and mechanistic evidence from animal models. Next, we highlight selected aging processes that are differentially regulated in neurodegenerative disease, including aberrant autophagy, mitochondrial dysfunction, cellular senescence, epigenetic changes, cerebrovascular dysfunction, inflammation, and lipid dysregulation. We summarize research across clinical and translational studies to link biological aging processes to underlying ADRD pathogenesis. Targeting fundamental processes underlying biological aging may represent a yet relatively unexplored avenue to attenuate both age-related cognitive decline and ADRD. Collaboration across the fields of geroscience and neuroscience, coupled with the development of new translational animal models that more closely align with human disease processes, is necessary to advance novel therapeutic discovery in this realm.

Disentangling Alzheimer's disease neurodegeneration from typical brain ageing using machine learning

Neuroimaging biomarkers that distinguish between changes due to typical brain ageing and Alzheimer's disease are valuable for determining how much each contributes to cognitive decline. Supervised machine learning models can derive multivariate patterns of brain change related to the two processes, including the Spatial Patterns of Atrophy for Recognition of Alzheimer's Disease (SPARE-AD) and of Brain Aging (SPARE-BA) scores investigated herein. However, the substantial overlap between brain regions affected in the two processes confounds measuring them independently. We present a methodology, and associated results, towards disentangling the two. T1-weighted MRI scans of 4054 participants (48-95 years) with Alzheimer's disease, mild cognitive impairment (MCI), or cognitively normal (CN) diagnoses from the Imaging-based coordinate SysTem for AGIng and NeurodeGenerative diseases (iSTAGING) consortium were analysed. Multiple sets of SPARE scores were investigated, in order to probe imaging signatures of certain clinically or molecularly defined sub-cohorts. First, a subset of clinical Alzheimer's disease patients (n = 718) and age- and sex-matched CN adults (n = 718) were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using CN individuals) and SPARE-AD1 (classification of CN versus Alzheimer's disease) models. Second, analogous groups were selected based on clinical and molecular markers to train SPARE-BA2 and SPARE-AD2 models: amyloid-positive Alzheimer's disease continuum group (n = 718; consisting of amyloid-positive Alzheimer's disease, amyloid-positive MCI, amyloid- and tau-positive CN individuals) and amyloid-negative CN group (n = 718). Finally, the combined group of the Alzheimer's disease continuum and amyloid-negative CN individuals was used to train SPARE-BA3 model, with the intention to estimate brain age regardless of Alzheimer's disease-related brain changes. The disentangled SPARE models, SPARE-AD2 and SPARE-BA3, derived brain patterns that were more specific to the two types of brain changes. The correlation between the SPARE-BA Gap (SPARE-BA minus chronological age) and SPARE-AD was significantly reduced after the decoupling (r = 0.56-0.06). The correlation of disentangled SPARE-AD was non-inferior to amyloid- and tau-related measurements and to the number of APOE ε4 alleles but was lower to Alzheimer's disease-related psychometric test scores, suggesting the contribution of advanced brain ageing to the latter. The disentangled SPARE-BA was consistently less correlated with Alzheimer's disease-related clinical, molecular and genetic variables. By employing conservative molecular diagnoses and introducing Alzheimer's disease continuum cases to the SPARE-BA model training, we achieved more dissociable neuroanatomical biomarkers of typical brain ageing and Alzheimer's disease.

Long-term associations of cigarette smoking in early mid-life with predicted brain aging from mid- to late life

BACKGROUND AND AIMS

Smoking is associated with increased risk for brain aging/atrophy and dementia. Few studies have examined early associations with brain aging. This study aimed to measure whether adult men with a history of heavier smoking in early mid-life would have older than predicted brain age 16-28 years later.

DESIGN

Prospective cohort observational study, utilizing smoking pack years data from average age 40 (early mid-life) predicting predicted brain age difference scores (PBAD) at average ages 56, 62 (later mid-life) and 68 years (early old age). Early mid-life alcohol use was also evaluated.

SETTING

Population-based United States sample.

PARTICIPANTS/CASES

Participants were male twins of predominantly European ancestry who served in the United States military between 1965 and 1975. Structural magnetic resonance imaging (MRI) began at average age 56. Subsequent study waves included most baseline participants; attrition replacement subjects were added at later waves.

MEASUREMENTS

Self-reported smoking information was used to calculate pack years smoked at ages 40, 56, 62, and 68. MRIs were processed with the Brain-Age Regression Analysis and Computation Utility software (BARACUS) program to create PBAD scores (chronological age-predicted brain age) acquired at average ages 56 (n = 493; 2002-08), 62 (n = 408; 2009-14) and 68 (n = 499; 2016-19).

FINDINGS

In structural equation modeling, age 40 pack years predicted more advanced age 56 PBAD [β = -0.144, P = 0.012, 95% confidence interval (CI) = -0.257, -0.032]. Age 40 pack years did not additionally predict PBAD at later ages. Age 40 alcohol consumption, but not a smoking × alcohol interaction, predicted more advanced PBAD at age 56 (β = -0.166, P = 0.001, 95% CI = -0.261, -0.070) with additional influences at age 62 (β = -0.115, P = 0.005, 95% CI = -0.195, -0.036). Age 40 alcohol did not predict age 68 PBAD. Within-twin-pair analyses suggested some genetic mechanism partially underlying effects of alcohol, but not smoking, on PBAD.

CONCLUSIONS

Heavier smoking and alcohol consumption by age 40 appears to predict advanced brain aging by age 56 in men.

Mid-life epigenetic age, neuroimaging brain age, and cognitive function: coronary artery risk development in young adults (CARDIA) study

The proportion of aging populations affected by dementia is increasing. There is an urgent need to identify biological aging markers in mid-life before symptoms of age-related dementia present for early intervention to delay the cognitive decline and the onset of dementia. In this cohort study involving 1,676 healthy participants (mean age 40) with up to 15 years of follow up, we evaluated the associations between cognitive function and two classes of novel biological aging markers: blood-based epigenetic aging and neuroimaging-based brain aging. Both accelerated epigenetic aging and brain aging were prospectively associated with worse cognitive outcomes. Specifically, every year faster epigenetic or brain aging was on average associated with 0.19-0.28 higher (worse) Stroop score, 0.04-0.05 lower (worse) RAVLT score, and 0.23-0.45 lower (worse) DSST (all false-discovery-rate-adjusted p <0.05). While epigenetic aging is a more stable biomarker with strong long-term predictive performance for cognitive function, brain aging biomarker may change more dynamically in temporal association with cognitive decline. The combined model using epigenetic and brain aging markers achieved the highest accuracy (AUC: 0.68, p<0.001) in predicting global cognitive function status. Accelerated epigenetic age and brain age at midlife may aid timely identification of individuals at risk for accelerated cognitive decline and promote the development of interventions to preserve optimal functioning across the lifespan.

Disentangling tau and brain atrophy cluster heterogeneity across the Alzheimer's disease continuum

Introduction

Neuroimaging heterogeneity in dementia has been examined using single modalities. We evaluated the associations of magnetic resonance imaging (MRI) atrophy and flortaucipir positron emission tomography (PET) clusters across the Alzheimer's disease (AD) spectrum.

Methods

We included 496 Alzheimer's Disease Neuroimaging Initiative participants with brain MRI, flortaucipir PET scan, and amyloid beta biomarker measures obtained. We applied a novel robust collaborative clustering (RCC) approach on the MRI and flortaucipir PET scans. We derived indices for AD‐like (SPARE‐AD index) and brain age (SPARE‐BA) atrophy.

Results

We identified four tau (I–IV) and three atrophy clusters. Tau clusters were associated with the apolipoprotein E genotype. Atrophy clusters were associated with white matter hyperintensity volumes. Only the hippocampal sparing atrophy cluster showed a specific association with brain aging imaging index. Tau clusters presented stronger clinical associations than atrophy clusters. Tau and atrophy clusters were partially associated.

Conclusions

Each neuroimaging modality captured different aspects of brain aging, genetics, vascular changes, and neurodegeneration leading to individual multimodal phenotyping.

Self- and Partner-Reported Subjective Memory Complaints: Association with Objective Cognitive Impairment and Risk of Decline

Background

Episodic memory decline is a hallmark of Alzheimer's disease (AD). Subjective memory complaints (SMCs) may represent one of the earliest signs of impending cognitive decline. The degree to which self- or partner-reported SMCs predict cognitive change remains unclear.

Objective

We aimed to evaluate the relationship between self- and partner-reported SMCs, objective cognitive performance, AD biomarkers, and risk of future decline in a well-characterized longitudinal memory center cohort. We also evaluated whether study partner characteristics influence reports of SMCs.

Methods

758 participants and 690 study partners were recruited from the Penn Alzheimer's Disease Research Center Clinical Core. Participants included those with Normal Cognition, Mild Cognitive Impairment, and AD. SMCs were measured using the Prospective and Retrospective Memory Questionnaire (PRMQ), and were evaluated for their association with cognition, genetic, plasma, and neuroimaging biomarkers of AD, cognitive and functional decline, and diagnostic progression over an average of four years.

Results

We found that partner-reported SMCs were more consistent with cognitive test performance and increasing symptom severity than self-reported SMCs. Partner-reported SMCs showed stronger correlations with AD-associated brain atrophy, plasma biomarkers of neurodegeneration, and longitudinal cognitive and functional decline. A 10-point increase on baseline PRMQ increased the annual risk of diagnostic progression by approximately 70%. Study partner demographics and relationship to participants influenced reports of SMCs in AD participants only.

Conclusion

Partner-reported SMCs, using the PRMQ, have a stronger relationship with the neuroanatomic and cognitive changes associated with AD than patient-reported SMCs. Further work is needed to evaluate whether SMCs could be used to screen for future decline.

Multi-scale semi-supervised clustering of brain images: Deriving disease subtypes

Disease heterogeneity is a significant obstacle to understanding pathological processes and delivering precision diagnostics and treatment. Clustering methods have gained popularity for stratifying patients into subpopulations (i.e., subtypes) of brain diseases using imaging data. However, unsupervised clustering approaches are often confounded by anatomical and functional variations not related to a disease or pathology of interest. Semi-supervised clustering techniques have been proposed to overcome this and, therefore, capture disease-specific patterns more effectively. An additional limitation of both unsupervised and semi-supervised conventional machine learning methods is that they typically model, learn and infer from data using a basis of feature sets pre-defined at a fixed anatomical or functional scale (e.g., atlas-based regions of interest). Herein we propose a novel method, "Multi-scAle heteroGeneity analysIs and Clustering" (MAGIC), to depict the multi-scale presentation of disease heterogeneity, which builds on a previously proposed semi-supervised clustering method, HYDRA. It derives multi-scale and clinically interpretable feature representations and exploits a double-cyclic optimization procedure to effectively drive identification of inter-scale-consistent disease subtypes. More importantly, to understand the conditions under which the clustering model can estimate true heterogeneity related to diseases, we conducted extensive and systematic semi-simulated experiments to evaluate the proposed method on a sizeable healthy control sample from the UK Biobank (N = 4403). We then applied MAGIC to imaging data from Alzheimer's disease (ADNI, N = 1728) and schizophrenia (PHENOM, N = 1166) patients to demonstrate its potential and challenges in dissecting the neuroanatomical heterogeneity of common brain diseases. Taken together, we aim to provide guidance regarding when such analyses can succeed or should be taken with caution. The code of the proposed method is publicly available at https://github.com/anbai106/MAGIC.

Utility of Machine Learning Approach with Neuropsychological Tests in Predicting Functional Impairment of Alzheimer's Disease

Background:

In assessing the levels of clinical impairment in dementia, a summary index of neuropsychological batteries has been widely used in describing the overall functional status.

Objective:

It remains unexamined how complex patterns of the test performances can be utilized to have specific predictive meaning when the machine learning approach is applied.

Methods:

In this study, the neuropsychological battery (CERAD-K) and assessment of functioning level (Clinical Dementia Rating scale and Instrumental Activities of Daily Living) were administered to 2,642 older adults with no impairment (n = 285), mild cognitive impairment (n = 1,057), and Alzheimer’s disease (n = 1,300). Predictive accuracy on functional impairment level with the linear models of the single total score or multiple subtest scores (Model 1, 2) and support vector regression with low or high complexity (Model 3, 4) were compared across different sample sizes.

Results:

The linear models (Model 1, 2) showed superior performance with relatively smaller sample size, while nonlinear models with low and high complexity (Model 3, 4) showed an improved accuracy with a larger dataset. Unlike linear models, the nonlinear models showed a gradual increase in the predictive accuracy with a larger sample size (n > 500), especially when the model training is allowed to exploit complex patterns of the dataset.

Conclusion:

Our finding suggests that nonlinear models can predict levels of functional impairment with a sufficient dataset. The summary index of the neuropsychological battery can be augmented for specific purposes, especially in estimating the functional status of dementia.

The Vital Role of Central Executive Network in Brain Age: Evidence From Machine Learning and Transcriptional Signatures

Recent studies combining neuroimaging with machine learning methods successfully infer an individual’s brain age, and its discrepancy with the chronological age is used to identify age-related diseases. However, which brain networks play decisive roles in brain age prediction and the underlying biological basis of brain age remain unknown. To answer these questions, we estimated an individual’s brain age in the Southwest University Adult Lifespan Dataset (N = 492) from the gray matter volumes (GMV) derived from T1-weighted MRI scans by means of Gaussian process regression. Computational lesion analysis was performed to determine the importance of each brain network in brain age prediction. Then, we identified brain age-related genes by using prior brain-wide gene expression data, followed by gene enrichment analysis using Metascape. As a result, the prediction model successfully inferred an individual’s brain age and the computational lesion prediction results identified the central executive network as a vital network in brain age prediction (Steiger’s Z = 2.114, p = 0.035). In addition, the brain age-related genes were enriched in Gene Ontology (GO) processes/Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways grouped into numbers of clusters, such as regulation of iron transmembrane transport, synaptic signaling, synapse organization, retrograde endocannabinoid signaling (e.g., dopaminergic synapse), behavior (e.g., memory and associative learning), neurotransmitter secretion, and dendrite development. In all, these results reveal that the GMV of the central executive network played a vital role in predicting brain age and bridged the gap between transcriptome and neuroimaging promoting an integrative understanding of the pathophysiology of brain age.

Lifestyle and the aging brain: interactive effects of modifiable lifestyle behaviors and cognitive ability in men from midlife to old age

We examined the influence of lifestyle on brain aging after nearly 30 years, and tested the hypothesis that young adult general cognitive ability (GCA) would moderate these effects. In the community-dwelling Vietnam Era Twin Study of Aging (VETSA), 431 largely non-Hispanic white men completed a test of GCA at mean age 20. We created a modifiable lifestyle behavior composite from data collected at mean age 40. During VETSA, MRI-based measures at mean age 68 included predicted brain age difference (PBAD), Alzheimer's disease (AD) brain signature, and abnormal white matter scores. There were significant main effects of young adult GCA and lifestyle on PBAD and the AD signature (ps ≤ 0.012), and a GCA-by-lifestyle interaction on both (ps ≤ 0.006). Regardless of GCA level, having more favorable lifestyle behaviors predicted less advanced brain age and less AD-like brain aging. Unfavorable lifestyles predicted advanced brain aging in those with lower age 20 GCA, but did not affect brain aging in those with higher age 20 GCA. Targeting early lifestyle modification may promote dementia risk reduction, especially among lower reserve individuals.

Measuring Resilience and Resistance in Aging and Alzheimer Disease Using Residual Methods: A Systematic Review and Meta-analysis

BACKGROUND AND OBJECTIVE

There is a lack of consensus on how to optimally define and measure resistance and resilience in brain and cognitive aging. Residual methods use residuals from regression analysis to quantify the capacity to avoid (resistance) or cope (resilience) "better or worse than expected" given a certain level of risk or cerebral damage. We reviewed the rapidly growing literature on residual methods in the context of aging and Alzheimer disease (AD) and performed meta-analyses to investigate associations of residual method-based resilience and resistance measures with longitudinal cognitive and clinical outcomes.

METHODS

A systematic literature search of PubMed and Web of Science databases (consulted until March 2020) and subsequent screening led to 54 studies fulfilling eligibility criteria, including 10 studies suitable for the meta-analyses.

RESULTS

We identified articles using residual methods aimed at quantifying resistance (n = 33), cognitive resilience (n = 23), and brain resilience (n = 2). Critical examination of the literature revealed that there is considerable methodologic variability in how the residual measures were derived and validated. Despite methodologic differences across studies, meta-analytic assessments showed significant associations of levels of resistance (hazard ratio [HR] [95% confidence interval (CI)] 1.12 [1.07-1.17]; p < 0.0001) and levels of resilience (HR [95% CI] 0.46 [0.32-0.68]; p < 0.001) with risk of progression to dementia/AD. Resilience was also associated with rate of cognitive decline (β [95% CI] 0.05 [0.01-0.08]; p < 0.01).

DISCUSSION

This review and meta-analysis supports the usefulness of residual methods as appropriate measures of resilience and resistance, as they capture clinically meaningful information in aging and AD. More rigorous methodologic standardization is needed to increase comparability across studies and, ultimately, application in clinical practice.

Brain age and Alzheimer's-like atrophy are domain-specific predictors of cognitive impairment in Parkinson's disease

Recently, it was shown that patients with Parkinson's disease (PD) who exhibit an "Alzheimer's disease (AD)-like" pattern of brain atrophy are at greater risk for future cognitive decline. This study aimed to investigate whether this association is domain-specific and whether atrophy associated with brain aging also relates to cognitive impairment in PD. SPARE-AD, an MRI index capturing AD-like atrophy, and atrophy-based estimates of brain age were computed from longitudinal structural imaging data of 178 PD patients and 84 healthy subjects from the LANDSCAPE cohort. All patients underwent an extensive neuropsychological test battery. Patients diagnosed with mild cognitive impairment or dementia were found to have higher SPARE-AD scores as compared to patients with normal cognition and healthy controls. All patient groups showed increased brain age. SPARE-AD predicted impairment in memory, language and executive functions, whereas advanced brain age was associated with deficits in attention and working memory. Data suggest that SPARE-AD and brain age are differentially related to domain-specific cognitive decline in PD. The underlying pathomechanisms remain to be determined.

A single mode of population covariation associates brain networks structure and behavior and predicts individual subjects' age

Multiple human behaviors improve early in life, peaking in young adulthood, and declining thereafter. Several properties of brain structure and function progress similarly across the lifespan. Cognitive and neuroscience research has approached aging primarily using associations between a few behaviors, brain functions, and structures. Because of this, the multivariate, global factors relating brain and behavior across the lifespan are not well understood. We investigated the global patterns of associations between 334 behavioral and clinical measures and 376 brain structural connections in 594 individuals across the lifespan. A single-axis associated changes in multiple behavioral domains and brain structural connections (r = 0.5808). Individual variability within the single association axis well predicted the age of the subject (r = 0.6275). Representational similarity analysis evidenced global patterns of interactions across multiple brain network systems and behavioral domains. Results show that global processes of human aging can be well captured by a multivariate data fusion approach.

Distributed functional connectivity predicts neuropsychological test performance among older adults

Neuropsychological test is an essential tool in assessing cognitive and functional changes associated with late-life neurocognitive disorders. Despite the utility of the neuropsychological test, the brain-wide neural basis of the test performance remains unclear. Using the predictive modeling approach, we aimed to identify the optimal combination of functional connectivities that predicts neuropsychological test scores of novel individuals. Resting-state functional connectivity and neuropsychological tests included in the OASIS-3 dataset (n = 428) were used to train the predictive models, and the identified models were iteratively applied to the holdout internal test set (n = 216) and external test set (KSHAP, n = 151). We found that the connectivity-based predicted score tracked the actual behavioral test scores (r = 0.08-0.44). The predictive models utilizing most of the connectivity features showed better accuracy than those composed of focal connectivity features, suggesting that its neural basis is largely distributed across multiple brain systems. The discriminant and clinical validity of the predictive models were further assessed. Our results suggest that late-life neuropsychological test performance can be formally characterized with distributed connectome-based predictive models, and further translational evidence is needed when developing theoretically valid and clinically incremental predictive models.

In vivo biomarkers of structural and functional brain development and aging in humans

Brain aging is a major determinant of aging. Along with the aging population, prevalence of neurodegenerative diseases is increasing, therewith placing economic and social burden on individuals and society. Individual rates of brain aging are shaped by genetics, epigenetics, and prenatal environmental. Biomarkers of biological brain aging are needed to predict individual trajectories of aging and the risk for age-associated neurological impairments for developing early preventive and interventional measures. We review current advances of in vivo biomarkers predicting individual brain age. Telomere length and epigenetic clock, two important biomarkers that are closely related to the mechanistic aging process, have only poor deterministic and predictive accuracy regarding individual brain aging due to their high intra- and interindividual variability. Phenotype-related biomarkers of global cognitive function and brain structure provide a much closer correlation to age at the individual level. During fetal and perinatal life, autonomic activity is a unique functional marker of brain development. The cognitive and structural biomarkers also boast high diagnostic specificity for determining individual risks for neurodegenerative diseases.

Pathway-Specific Genetic Risk for Alzheimer's Disease Differentiates Regional Patterns of Cortical Atrophy in Older Adults

Brain aging is highly variable and represents a challenge to delimit aging from disease processes. Moreover, genetic factors may influence both aging and disease. Here we focused on this issue and investigated effects of multiple genetic loci previously identified to be associated with late-onset Alzheimer's disease (AD) on brain structure of older adults from a population sample. We calculated a genetic risk score (GRS) using genome-wide significant single-nucleotide polymorphisms from genome-wide association studies of AD and tested its effect on cortical thickness (CT). We observed a common pattern of cortical thinning (right inferior frontal, left posterior temporal, medial occipital cortex). To identify CT changes by specific biological processes, we subdivided the GRS effect according to AD-associated pathways and performed follow-up analyses. The common pattern from the main analysis was further differentiated by pathway-specific effects yielding a more bilateral pattern. Further findings were located in the superior parietal and mid/anterior cingulate regions representing 2 unique pathway-specific patterns. All patterns, except the superior parietal pattern, were influenced by apolipoprotein E. Our step-wise approach revealed atrophy patterns that partially resembled imaging findings in early stages of AD. Our study provides evidence that genetic burden for AD contributes to structural brain variability in normal aging.

The stage-specifically accelerated brain aging in never-treated first-episode patients with depression

Depression associated with structural brain abnormalities is hypothesized to be related with accelerated brain aging. However, there is far from a unified conclusion because of clinical variations such as medication status, cumulative illness burden. To explore whether brain age is accelerated in never‐treated first‐episode patients with depression and its association with clinical characteristics, we constructed a prediction model where gray matter volumes measured by voxel‐based morphometry derived from T1‐weighted MRI scans were treated as features. The prediction model was first validated using healthy controls (HCs) in two Chinese Han datasets (Dataset 1, N = 130 for HCs and N = 195 for patients with depression; Dataset 2, N = 270 for HCs) separately or jointly, then the trained prediction model using HCs (N = 400) was applied to never‐treated first‐episode patients with depression (N = 195). The brain‐predicted age difference (brain‐PAD) scores defined as the difference between predicted brain age and chronological age, were calculated for all participants and compared between patients with age‐, gender‐, educational level‐matched HCs in Dataset 1. Overall, patients presented higher brain‐PAD scores suggesting patients with depression having an “older” brain than expected. More specially, this difference occurred at illness onset (illness duration <3 months) and following 2 years then disappeared as the illness further advanced (>2 years) in patients. This phenomenon was verified by another data‐driven method and significant correlation between brain‐PAD scores and illness duration in patients. Our results reveal that accelerated brain aging occurs at illness onset and suggest it is a stage‐dependent phenomenon in depression.

Inflammatory markers and imaging patterns of advanced brain aging in the general population

Inflammaging describes the complexity between low-grade chronic inflammation with the pathogenesis of brain aging and Alzheimer´s disease (AD). We aimed to find associations of inflammatory markers: i) white blood cell count (WBC), ii) high-sensitivity C-reactive protein (hs-CRP), and iii) fibrinogen with brain structures, sensitive neuroimaging markers of advanced brain aging and AD-like atrophy, and cognitive aging scores. We analyzed magnetic resonance imaging (MRI) scans of 2204 participants from the Study of Health in Pomerania-2 (SHIP-2) and SHIP-Trend (55.6% women, mean age 52.4±13.7 years). Associations of the inflammatory markers with specific brain signatures of brain aging (SPARE-BA), AD-like brain atrophy (SPARE-AD) and white matter disease (white matter hyperintensities volume (WMHV)) were investigated. Furthermore we explored their association with general brain structures including total brain volume (TBV), gray matter volume (GMV), and white matter volume (WMV), as well as cognitive scores (Nurnberger Age Inventory (NAI); Verbal Learning and Memory Test (VLMT). We adjusted for multiple vascular risk factors (VRF; e.g. smoking and blood pressure) and corresponding medication use to take their brain aging effects into account and corrected for false-discovery rate (FDR). Results:WBC was inversely associated with SPARE-BA (FDR-adjusted p=0.003), TBV (FDR-adjusted p=0.019) and GMV (FDR-adjusted p= 0.017). GMV was also inversely associated with hs-CRP (FDR-adjusted p=0.039) and fibrinogen (FDR-adjusted p=0.039). None of the inflammatory markers was associated with WMHV. Regression analysis also revealed a trend-level interaction between intake of antiinflammatory medication and hs-CRP with brain aging (SPARE-BA; FDR-adjusted p=0.062). Inflammatatory markers are associated with neuroimaging markers, with elevated WBC leading to significant acceleration in brain aging patterns but not with AD-like imaging structural changes. Given the overlap between accelerated brain aging and AD-like atrophy, increased WBC might be associated with global dementia symptoms due to this overlap in atrophy patterns. Elevated WBC may be not causal to preclinical AD dementia, but an accessory symptom of inflammaging. At population level, our results support the relevant roles of inflammatory markers on brain aging related atrophy.

Associations between sleep apnea and advanced brain aging in a large-scale population study

Advanced brain aging is commonly regarded as a risk factor for neurodegenerative diseases, for example, Alzheimer's dementia, and it was suggested that sleep disorders such as obstructive sleep apnea (OSA) are significantly contributing factors to these neurodegenerative processes. To determine the association between OSA and advanced brain aging, we investigated the specific effect of two indices quantifying OSA, namely the apnea-hypopnea index (AHI) and the oxygen desaturation index (ODI), on brain age, a score quantifying age-related brain patterns in 169 brain regions, using magnetic resonance imaging and overnight polysomnography data from 690 participants (48.8% women, mean age 52.5 ± 13.4 years) of the Study of Health in Pomerania. We additionally investigated the mediating effect of subclinical inflammation parameters on these associations via a causal mediation analysis. AHI and ODI were both positively associated with brain age (AHI std. effect [95% CI]: 0.07 [0.03; 0.12], p-value: 0.002; ODI std. effect [95% CI]: 0.09 [0.04; 0.13], p-value: < 0.0003). The effects remained stable in the presence of various confounders such as diabetes and were partially mediated by the white blood cell count, indicating a subclinical inflammation process. Our results reveal an association between OSA and brain age, indicating subtle but widespread age-related changes in regional brain structures, in one of the largest general population studies to date, warranting further examination of OSA in the prevention of neurodegenerative diseases.

Synergistic Effect of Serum Homocysteine and Diabetes Mellitus on Brain Alterations

BACKGROUND

Both elevated blood homocysteine and diabetes mellitus (DM) are related to cognitive impairments or dementia. A previous study also demonstrated that the association between homocysteine and cognitive decline was much stronger in individuals with DM than in those without DM.

OBJECTIVE

This study aimed to examine the interactive effect of blood homocysteine and DM on brain pathological changes including brain atrophy, amyloid-β and tau deposition, and small vessel disease (SVD) related to cognitive impairments.

METHODS

A total of 430 non-demented older adults underwent comprehensive clinical assessment, measurement of serum homocysteine level, [11C] Pittsburgh Compound B (PiB) PET, [18F] AV-1451 PET, and brain MRI.

RESULTS

The interactive effect of homocysteine with the presence of DM on brain atrophy, especially in aging-related brain regions, was significant. Higher homocysteine concentration was associated with more prominent brain atrophy in individuals with DM, but not in those without DM. In contrast, interaction effect of homocysteine and DM was found neither on Alzheimer's disease (AD) pathologies, including amyloid-β and tau deposition, nor white matter hyperintensity volume as a measure of SVD.

CONCLUSION

The present findings suggest that high blood homocysteine level and DM synergistically aggravate brain damage independently of AD and cerebrovascular disease. With regard to preventing dementia or cognitive decline in older adults, these results support the importance of strictly controlling blood glucose in individuals with hyperhomocysteinemia and lowering blood homocysteine level in those with DM.

Different posterior hippocampus and default mode network modulation in young APOE ε4 carriers: a functional connectome-informed phenotype longitudinal study

To determine the functional connectome change pattern based on subregions of the hippocampus in young APOEε4 carriers during a 3-year follow-up. All the participants (n = 213) were tested for resting-state functional MRI, neuropsychological scales, and APOE genotype. The age- and sex-matched APOE ε4/ε3 (23.9 ± 3.2 years old, 6 female/7 male) carriers and APOE ε3/ε3 (22.9 ± 1.6 years old, 7 female/12 male) carriers were finally followed up. The hippocampus and its anterior/middle/posterior subregion-based functional connectivity (FC) patterns were compared between APOEε4 and APOEε3 groups by a two-sample t-test at baseline and mixed-effect analysis at follow-up. The effective connectivity (EC) patterns among the altered regions of interaction effect were examined in the APOEε4 groups. APOEε4 carries displayed saliently enhanced FC in the right anterior/middle hippocampus and core regions of the default mode network (DMN) (P < 0.05 by Gaussian Random Fields (GRF) correction). However, the APOEε4-by-time interaction was evident in the middle/posterior hippocampus with connection to the lateral temporal lobe and anterior cingulate cortex (ACC) (P < 0.05 by GRF correction). Moreover, the APOEε4 group at follow-up showed increased EC separately from both the left middle hippocampus and lateral temporal lobe to the left posterior hippocampus, and its changes of FC/EC significantly correlated with altered memory function. The posterior hippocampus might be especially vulnerable to early modulation in young APOEε4 carriers. Its connection with the lateral temporal lobe, rather than with DMN, might be the early compensative mechanism of memory function regulation influenced by APOE ε4 in the young adults.