Multimodality neuroimaging brain-age in UK biobank: relationship to biomedical, lifestyle, and cognitive factors

Mega-analysis of the brain-age gap in substance use disorder: An ENIGMA Addiction working group study

BACKGROUND AND AIMS

The brain age gap (BAG), calculated as the difference between a machine learning model-based predicted brain age and chronological age, has been increasingly investigated in psychiatric disorders. Tobacco and alcohol use are associated with increased BAG; however, no studies have compared global and regional BAG across substances other than alcohol and tobacco. This study aimed to compare global and regional estimates of brain age in individuals with substance use disorders and healthy controls.

DESIGN

This was a cross-sectional study.

SETTING

This is an Enhancing Neuro Imaging through Meta-Analysis Consortium (ENIGMA) Addiction Working Group study including data from 38 global sites.

PARTICIPANTS

This study included 2606 participants, of whom 1725 were cases with a substance use disorder and 881 healthy controls.

MEASUREMENTS

This study used the Kaufmann brain age prediction algorithms to generate global and regional brain age estimates using T1 weighted magnetic resonance imaging (MRI) scans. We used linear mixed effects models to compare global and regional (FreeSurfer lobestrict output) BAG (i.e. predicted minus chronological age) between individuals with one of five primary substance use disorders as well as healthy controls.

FINDINGS

Alcohol use disorder (β = -5.49, t = -5.51, p < 0.001) was associated with higher global BAG, whereas amphetamine-type stimulant use disorder (β = 3.44, t = 2.42, p = 0.02) was associated with lower global BAG in the separate substance-specific models.

CONCLUSIONS

People with alcohol use disorder appear to have a higher brain-age gap than people without alcohol use disorder, which is consistent with other evidence of the negative impact of alcohol on the brain.

Relationship between MRI brain-age heterogeneity, cognition, genetics and Alzheimer's disease neuropathology

BACKGROUND

Brain ageing is highly heterogeneous, as it is driven by a variety of normal and neuropathological processes. These processes may differentially affect structural and functional brain ageing across individuals, with more pronounced ageing (older brain age) during midlife being indicative of later development of dementia. Here, we examined whether brain-ageing heterogeneity in unimpaired older adults related to neurodegeneration, different cognitive trajectories, genetic and amyloid-beta (Aβ) profiles, and to predicted progression to Alzheimer's disease (AD).

METHODS

Functional and structural brain age measures were obtained for resting-state functional MRI and structural MRI, respectively, in 3460 cognitively normal individuals across an age range spanning 42-85 years. Participants were categorised into four groups based on the difference between their chronological and predicted age in each modality: advanced age in both (n = 291), resilient in both (n = 260) or advanced in one/resilient in the other (n = 163/153). With the resilient group as the reference, brain-age groups were compared across neuroimaging features of neuropathology (white matter hyperintensity volume, neuronal loss measured with Neurite Orientation Dispersion and Density Imaging, AD-specific atrophy patterns measured with the Spatial Patterns of Abnormality for Recognition of Early Alzheimer's Disease index, amyloid burden using amyloid positron emission tomography (PET), progression to mild cognitive impairment and baseline and longitudinal cognitive measures (trail making task, mini mental state examination, digit symbol substitution task).

FINDINGS

Individuals with advanced structural and functional brain-ages had more features indicative of neurodegeneration and they had poor cognition. Individuals with a resilient brain-age in both modalities had a genetic variant that has been shown to be associated with age of onset of AD. Mixed brain-age was associated with selective cognitive deficits.

INTERPRETATION

The advanced group displayed evidence of increased atrophy across all neuroimaging features that was not found in either of the mixed groups. This is in line with biomarkers of preclinical AD and cerebrovascular disease. These findings suggest that the variation in structural and functional brain ageing across individuals reflects the degree of underlying neuropathological processes and may indicate the propensity to develop dementia in later life.

FUNDING

The National Institute on Aging, the National Institutes of Health, the Swiss National Science Foundation, the Kaiser Foundation Research Institute and the National Heart, Lung, and Blood Institute.

Brain predicted age in chronic pelvic pain: a study by the Multidisciplinary Approach to the Study of Chronic Pelvic Pain Research Network

ABSTRACT

The effect of chronic pain on brain-predicted age is unclear. We performed secondary analyses of a large cross-sectional and 3-year longitudinal data set from the Multidisciplinary Approach to the Study of Chronic Pelvic Pain Research Network to test the hypothesis that chronic pelvic pain accelerates brain aging and brain aging rate. Brain-predicted ages of 492 chronic pelvic pain patients and 72 controls were determined from T1-weighted MRI scans and used to calculate the brain-predicted age gap estimation (brainAGE; brain-predicted - chronological age). Separate regression models determined whether the presence of chronic pelvic pain could explain brainAGE and brain aging rate when accounting for covariates. We performed secondary analyses to understand whether brainAGE was associated with factors that subtype chronic pelvic pain patients (inflammation, widespread pain, and psychological comorbidities). We found a significant association between chronic pelvic pain and brainAGE that differed by sex. Women with chronic pelvic pain had higher brainAGE than female controls, whereas men with chronic pelvic pain exhibited lower brainAGE than male controls on average-however, the effect was not statistically significant in men or women when considered independently. Secondary analyses demonstrated preliminary evidence of an association between inflammatory load and brainAGE. Further studies of brainAGE and inflammatory load are warranted.

Exploring associations between psychotic experiences and structural brain age: a population-based study in late adolescence

Neuroimaging studies show advanced structural "brain age" in schizophrenia and related psychotic disorders, potentially reflecting aberrant brain ageing or maturation. The extent to which altered brain age is associated with subthreshold psychotic experiences (PE) in youth remains unclear. We investigated the association between PE and brain-predicted age difference (brain-PAD) in late adolescence using a population-based sample of 117 participants with PE and 115 without PE (aged 19-21 years) from the Avon Longitudinal Study of Parents and Children. Brain-PAD was estimated using a publicly available machine learning model previously trained on a combination of region-wise T1-weighted grey-matter measures. We found little evidence for an association between PEs and brain-PAD after adjusting for age and sex (Cohen's d = -0.21 [95% CI -0.47, 0.05], p = 0.11). While there was some evidence for lower brain-PAD in those with PEs relative to those without PEs after additionally adjusting for parental social class (Cohen's d = -0.31 [95% CI -0.58, -0.03], p = 0.031) or birth weight (Cohen's d = -0.29 [95% CI -0.55, -0.03], p = 0.038), adjusting for maternal education or childhood IQ did not alter the primary results. These findings do not support the notion of advanced brain age in older adolescents with PEs. However, they weakly suggest there might be a younger-looking brain in those individuals, indicative of subtle delays in structural brain maturation. Future studies with larger samples covering a wider age range and multimodal measures could further investigate brain age as a marker of psychotic experiences in youth.

Comparative analysis of brain age prediction using structural and diffusion MRIs in neonates

Using machine learning techniques to predict brain age from multimodal data has become a crucial biomarker for assessing brain development. Among various types of brain imaging data, structural magnetic resonance imaging (sMRI) and diffusion magnetic resonance imaging (dMRI) are the most commonly used modalities. sMRI focuses on depicting macrostructural features of the brain, while dMRI reveals the orientation of major white matter fibers and changes in tissue microstructure. However, their differential capabilities in reflecting newborn age and clinical implications have not been systematically studied. This study aims to explore the impact of sMRI and dMRI on brain age prediction. Comparing predictions based on T2-weighted(T2w) and fractional anisotropy (FA) images, we found their mean absolute errors (MAE) in predicting infant age to be similar. Exploratory analysis revealed for T2w images, areas such as the cerebral cortex and ventricles contribute most significantly to age prediction, whereas FA images highlight the cerebral cortex and regions of the main white matter tracts. Despite both modalities focusing on the cerebral cortex, they exhibit significant region-wise differences, reflecting developmental disparities in macro- and microstructural aspects of the cortex. Additionally, we examined the effects of prematurity, gender, and hemispherical asymmetry of the brain on age prediction for both modalities. Results showed significant differences (p<0.05) in age prediction biases based on FA images across gender and hemispherical asymmetry, whereas no significant differences were observed with T2w images. This study underscores the differences between T2w and FA images in predicting infant brain age, offering new perspectives for studying infant brain development and aiding more effective assessment and tracking of infant development.

Diabetes, Prediabetes, and Brain Aging: The Role of Healthy Lifestyle

OBJECTIVE

Diabetes is a well-known risk factor for dementia. We investigated the association between (pre)diabetes and older brain age and whether this can be attenuated by modifiable lifestyle behaviors.

RESEARCH DESIGN AND METHODS

The study included 31,229 dementia-free adults from the UK Biobank between the ages of 40 and 70 years. Glycemic status (normoglycemia, prediabetes, or diabetes) was ascertained based on medical history, medication use, and HbA1c measured at baseline. Information on cardiometabolic risk factors (obesity, hypertension, low HDL, and high triglycerides) and lifestyle behaviors (smoking, drinking, and physical activity) was also collected at baseline. Participants underwent up to two brain MRI scans over 11 years of follow-up. Brain age was estimated using a machine learning model based on 1,079 brain MRI phenotypes and used to calculate brain age gap (BAG; i.e., brain age minus chronological age).

RESULTS

At baseline, 13,518 participants (43.3%) had prediabetes and 1,149 (3.7%) had diabetes. Prediabetes (β = 0.22 [95% CI 0.10, 0.34]) and diabetes (2.01 [1.70, 2.32]) were both associated with significantly higher BAG, and diabetes was further associated with significant increase in BAG over time (0.27 [0.01, 0.53]). The association between (pre)diabetes and higher BAG was more pronounced in men and in people with two or more cardiometabolic risk factors. In joint exposure analysis, having a healthy lifestyle (i.e., no smoking, no heavy drinking, and high physical activity) significantly attenuated the diabetes-BAG association.

CONCLUSIONS

Diabetes and even prediabetes are associated with accelerated brain aging, especially among men and people with poor cardiometabolic health. However, a healthy lifestyle may counteract this.

A perspective on brain-age estimation and its clinical promise

Brain-age estimation has gained increased attention in the neuroscientific community owing to its potential use as a biomarker of brain health. The difference between estimated and chronological age based on neuroimaging data enables a unique perspective on brain development and aging, with multiple open questions still remaining in the brain-age research field. This Perspective presents an overview of current advancements in the field and envisions the future evolution of the brain-age framework before its potential deployment in hospital settings.

Transformer-based approaches for neuroimaging: an in-depth review of their role in classification and regression tasks

In the ever-evolving landscape of deep learning (DL), the transformer model emerges as a formidable neural network architecture, gaining significant traction in neuroimaging-based classification and regression tasks. This paper presents an extensive examination of transformer's application in neuroimaging, surveying recent literature to elucidate its current status and research advancement. Commencing with an exposition on the fundamental principles and structures of the transformer model and its variants, this review navigates through the methodologies and experimental findings pertaining to their utilization in neuroimage classification and regression tasks. We highlight the transformer model's prowess in neuroimaging, showcasing its exceptional performance in classification endeavors while also showcasing its burgeoning potential in regression tasks. Concluding with an assessment of prevailing challenges and future trajectories, this paper proffers insights into prospective research directions. By elucidating the current landscape and envisaging future trends, this review enhances comprehension of transformer's role in neuroimaging tasks, furnishing valuable guidance for further inquiry.

Nongenetic and Genetic Factors Associated with White Matter Brain Aging: Exposome-Wide and Genome-Wide Association Study

BACKGROUND/OBJECTIVES

Human brain aging is a complex process that affects various aspects of brain function and structure, increasing susceptibility to neurological and psychiatric disorders. A number of nongenetic (e.g., environmental and lifestyle) and genetic risk factors are found to contribute to the varying rates at which the brain ages among individuals.

METHODS

In this paper, we conducted both an exposome-wide association study (XWAS) and a genome-wide association study (GWAS) on white matter brain aging in the UK Biobank, revealing the multifactorial nature of brain aging. We applied a machine learning algorithm and leveraged fractional anisotropy tract measurements from diffusion tensor imaging data to predict the white matter brain age gap (BAG) and treated it as the marker of brain aging. For XWAS, we included 107 variables encompassing five major categories of modifiable exposures that potentially impact brain aging and performed both univariate and multivariate analysis to select the final set of nongenetic risk factors.

RESULTS

We found current tobacco smoking, dietary habits including oily fish, beef, lamb, cereal, and coffee intake, length of mobile phone use, use of UV protection, and frequency of solarium/sunlamp use were associated with the BAG. In genetic analysis, we identified several SNPs on chromosome 3 mapped to genes IP6K1, GMNC, OSTN, and SLC25A20 significantly associated with the BAG, showing the high heritability and polygenic architecture of human brain aging.

CONCLUSIONS

The critical nongenetic and genetic risk factors identified in our study provide insights into the causal relationship between white matter brain aging and neurodegenerative diseases.

Alcohol, smoking, and brain structure: common or substance specific associations

Alcohol use and smoking are common substance-use behaviors with well-established negative health effects, including decreased brain health. We examined whether alcohol use and smoking were associated with the same neuroimaging-derived brain measures. We further explored whether the effects of alcohol use and smoking on the brain were additive or interactive. We leveraged a cohort of 36,309 participants with neuroimaging data from the UK Biobank. We used linear regression to determine the association between 354 neuroimaging-derived brain measures and alcohol use defined as drinks per week, pack years of smoking, and drinks per week × pack years smoking interaction. To assess whether the brain associations with alcohol are broadly similar or different from the associations with smoking, we calculated the correlation between z-scores of association for drinks per week and pack years smoking. Results indicated overall moderate positive correlation in the associations across measures representing brain structure, magnetic susceptibility, and white matter tract microstructure, indicating greater similarity than difference in the brain measures associated with alcohol use and smoking. The only evidence of an interaction between drinks per week and pack years smoking was seen in measures representing magnetic susceptibility in subcortical structures. The effects of alcohol use and smoking on brain health appeared to be additive rather than multiplicative for all other brain measures studied. 97% (224/230) of associations with alcohol and 100% (167/167) of the associations with smoking that surpassed a p value threshold are in a direction that can be interpreted to reflect reduced brain health. Our results underscore the similarity of the adverse associations between use of these substances and neuroimaging derived brain measures.

Sex-Dependent Effects of Cardiometabolic Health and APOE4 on Brain Age: A Longitudinal Cohort Study

BACKGROUND AND OBJECTIVES

The aging population is growing faster than all other demographic strata. With older age comes a greater risk of health conditions such as obesity and high blood pressure (BP). These cardiometabolic risk factors (CMRs) exhibit prominent sex differences in midlife and aging, yet their influence on brain health in females vs males is largely unexplored. In this study, we investigated sex differences in relationships between BP, body mass index (BMI), and brain age over time and tested for interactions with APOE ε4 genotype (APOE4), a known genetic risk factor of Alzheimer disease.

METHODS

The sample included participants from 2 United Kingdom-based longitudinal birth cohorts, the Lothian Birth Cohort (1936) and Insight 46 (1946). Participants with MRI data from at least 1 time point were included to evaluate sex differences in associations between CMRs and brain age. The open-access software package brainageR 2.1 was used to estimate brain age for each participant. Linear mixed-effects models were used to assess the relationships between brain age, BMI, BP, and APOE4 status (i.e., carrier vs noncarrier) in males and females over time.

RESULTS

The combined sample comprised 1,120 participants (48% female) with a mean age (SD) of 73 (0.72) years in the Lothian Birth Cohort and 71 (0.68) years in Insight 46 at the time point 1 assessment. Approximately 30% of participants were APOE4 carriers. Higher systolic and diastolic BP was significantly associated with older brain age in females only (β = 0.43-0.56, p < 0.05). Among males, higher BMI was associated with older brain age across time points and APOE4 groups (β = 0.72-0.77, p < 0.05). In females, higher BMI was linked to older brain age among APOE4 noncarriers (β = 0.68-0.99, p < 0.05), whereas higher BMI was linked to younger brain age among carriers, particularly at the last time point (β = -1.75, p < 0.05).

DISCUSSION

This study indicates sex-dependent and time-dependent relationships between CMRs, APOE4 status, and brain age. Our findings highlight the necessity of sex-stratified analyses to elucidate the role of CMRs in individual aging trajectories, providing a basis for developing personalized preventive interventions.

Analysis of Brain Age Gap across Subject Cohorts and Prediction Model Architectures

Background: Brain age prediction from brain MRI scans and the resulting brain age gap (BAG)-the difference between predicted brain age and chronological age-is a general biomarker for a variety of neurological, psychiatric, and other diseases or disorders. Methods: This study examined the differences in BAG values derived from T1-weighted scans using five state-of-the-art deep learning model architectures previously used in the brain age literature: 2D/3D VGG, RelationNet, ResNet, and SFCN. The models were evaluated on healthy controls and cohorts with sleep apnea, diabetes, multiple sclerosis, Parkinson's disease, mild cognitive impairment, and Alzheimer's disease, employing rigorous statistical analysis, including repeated model training and linear mixed-effects models. Results: All five models consistently identified a statistically significant positive BAG for diabetes (ranging from 0.79 years with RelationNet to 2.13 years with SFCN), multiple sclerosis (2.67 years with 3D VGG to 4.24 years with 2D VGG), mild cognitive impairment (2.13 years with 2D VGG to 2.59 years with 3D VGG), and Alzheimer's dementia (5.54 years with ResNet to 6.48 years with SFCN). For Parkinson's disease, a statistically significant BAG increase was observed in all models except ResNet (1.30 years with 2D VGG to 2.59 years with 3D VGG). For sleep apnea, a statistically significant BAG increase was only detected with the SFCN model (1.59 years). Additionally, we observed a trend of decreasing BAG with increasing chronological age, which was more pronounced in diseased cohorts, particularly those with the largest BAG, such as multiple sclerosis (-0.34 to -0.2), mild cognitive impairment (-0.37 to -0.26), and Alzheimer's dementia (-0.66 to -0.47), compared to healthy controls (-0.18 to -0.1). Conclusions: Consistent with previous research, Alzheimer's dementia and multiple sclerosis exhibited the largest BAG across all models, with SFCN predicting the highest BAG overall. The negative BAG trend suggests a complex interplay of survival bias, disease progression, adaptation, and therapy that influences brain age prediction across the age spectrum.

Identifying individual brain development using multimodality brain network

The cortical development of our brains is in a hierarchical manner and promotes the emergence of large-scale functional hierarchy. However, under interindividual heterogenicity, how the spatiotemporal features of brain networks reflect brain development and mental health remains unclear. Here we collect both resting-state electroencephalography and functional magnetic resonance imaging data from the Child Mind Institute Biobank to demonstrate that during brain growth, the global dynamic patterns of brain states become more active and the dominant networks shift from sensory to higher-level networks; the individual functional network patterns become more similar to that of adults and their spatial coupling tends to be invariable. Furthermore, the properties of multimodality brain networks are sufficiently robust to identify healthy brain age and mental disorders at specific ages. Therefore, multimodality brain networks provide new insights into the functional development of the brain and a more reliable and reasonable approach for age prediction and individual diagnosis.

Normative Neuroimaging Library: Designing a Comprehensive and Demographically Diverse Dataset of Healthy Controls to Support Traumatic Brain Injury Diagnostic and Therapeutic Development

The past decade has seen impressive advances in neuroimaging, moving from qualitative to quantitative outputs. Available techniques now allow for the inference of microscopic changes occurring in white and gray matter, along with alterations in physiology and function. These existing and emerging techniques hold the potential of providing unprecedented capabilities in achieving a diagnosis and predicting outcomes for traumatic brain injury (TBI) and a variety of other neurological diseases. To see this promise move from the research lab into clinical care, an understanding is needed of what normal data look like for all age ranges, sex, and other demographic and socioeconomic categories. Clinicians can only use the results of imaging scans to support their decision-making if they know how the results for their patient compare with a normative standard. This potential for utilizing magnetic resonance imaging (MRI) in TBI diagnosis motivated the American College of Radiology and Cohen Veterans Bioscience to create a reference database of healthy individuals with neuroimaging, demographic data, and characterization of psychological functioning and neurocognitive data that will serve as a normative resource for clinicians and researchers for development of diagnostics and therapeutics for TBI and other brain disorders. The goal of this article is to introduce the large, well-curated Normative Neuroimaging Library (NNL) to the research community. NNL consists of data collected from ∼1900 healthy participants. The highlights of NNL are (1) data are collected across a diverse population, including civilians, veterans, and active-duty service members with an age range (18-64 years) not well represented in existing datasets; (2) comprehensive structural and functional neuroimaging acquisition with state-of-the-art sequences (including structural, diffusion, and functional MRI; raw scanner data are preserved, allowing higher quality data to be derived in the future; standardized imaging acquisition protocols across sites reflect sequences and parameters often recommended for use with various neurological and psychiatric conditions, including TBI, post-traumatic stress disorder, stroke, neurodegenerative disorders, and neoplastic disease); and (3) the collection of comprehensive demographic details, medical history, and a broad structured clinical assessment, including cognition and psychological scales, relevant to multiple neurological conditions with functional sequelae. Thus, NNL provides a demographically diverse population of healthy individuals who can serve as a comparison group for brain injury study and clinical samples, providing a strong foundation for precision medicine. Use cases include the creation of imaging-derived phenotypes (IDPs), derivation of reference ranges of imaging measures, and use of IDPs as training samples for artificial intelligence-based biomarker development and for normative modeling to help identify injury-induced changes as outliers for precision diagnosis and targeted therapeutic development. On its release, NNL is poised to support the use of advanced imaging in clinician decision support tools, the validation of imaging biomarkers, and the investigation of brain-behavior anomalies, moving the field toward precision medicine.

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

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

Brain reserve in midlife is associated with executive function changes across 12 years

We examined how brain reserve in midlife, measured by brain-predicted age difference scores (Brain-PADs), predicted executive function concurrently and longitudinally into early old age, and whether these associations were moderated by young adult cognitive reserve or APOE genotype. 508 men in the Vietnam Era Twin Study of Aging (VETSA) completed neuroimaging assessments at mean age 56 and six executive function tasks at mean ages 56, 62, and 68 years. Results indicated that greater brain reserve at age 56 was associated with better concurrent executive function (r=.10, p=.040) and less decline in executive function over 12 years (r=.34, p=.001). These associations were not moderated by cognitive reserve or APOE genotype. Twin analysis suggested associations with executive function slopes were driven by genetic influences. Our findings suggest that greater brain reserve allowed for better cognitive maintenance from middle- to old age, driven by a genetic association. The results are consistent with differential preservation of executive function based on brain reserve that is independent of young adult cognitive reserve or APOE genotype.

Alcohol and brain structure across the lifespan: A systematic review of large-scale neuroimaging studies

Alcohol exposure affects brain structure, but the extent to which its effects differ across development remains unclear. Several countries are considering changes to recommended guidelines for alcohol consumption, so high-quality evidence is needed. Many studies have been conducted among small samples, but recent efforts have been made to acquire large samples to characterize alcohol's effects on the brain on a population level. Several large-scale consortia have acquired such samples, but this evidence has not been synthesized across the lifespan. We conducted a systematic review of large-scale neuroimaging studies examining effects of alcohol exposure on brain structure at multiple developmental stages. We included studies with an alcohol-exposed sample of at least N = 100 from the following consortia: ABCD, ENIGMA, NCANDA, IMAGEN, Framingham Offspring Study, HCP and UK BioBank. Twenty-seven studies were included, examining prenatal (N = 1), adolescent (N = 9), low-to-moderate-level adult (N = 11) and heavy adult (N = 7) exposure. Prenatal exposure was associated with greater brain volume at ages 9-10, but contemporaneous alcohol consumption during adolescence and adulthood was associated with smaller volume/thickness. Both low-to-moderate consumption and heavy consumption were characterized by smaller volume and thickness in frontal, temporal and parietal regions, and reductions in insula, cingulate and subcortical structures. Adolescent consumption had similar effects, with less consistent evidence for smaller cingulate, insula and subcortical volume. In sum, prenatal exposure was associated with larger volume, while adolescent and adult alcohol exposure was associated with smaller volume and thickness, suggesting that regional patterns of effects of alcohol are similar in adolescence and adulthood.

Brain Age Gap in Early Illness Schizophrenia and the Clinical High-Risk Syndrome: Associations With Experiential Negative Symptoms and Conversion to Psychosis

BACKGROUND AND HYPOTHESIS

Brain development/aging is not uniform across individuals, spawning efforts to characterize brain age from a biological perspective to model the effects of disease and maladaptive life processes on the brain. The brain age gap represents the discrepancy between estimated brain biological age and chronological age (in this case, based on structural magnetic resonance imaging, MRI). Structural MRI studies report an increased brain age gap (biological age > chronological age) in schizophrenia, with a greater brain age gap related to greater negative symptom severity. Less is known regarding the nature of this gap early in schizophrenia (ESZ), if this gap represents a psychosis conversion biomarker in clinical high-risk (CHR-P) individuals, and how altered brain development and/or aging map onto specific symptom facets.

STUDY DESIGN

Using structural MRI, we compared the brain age gap among CHR-P (n = 51), ESZ (n = 78), and unaffected comparison participants (UCP; n = 90), and examined associations with CHR-P psychosis conversion (CHR-P converters n = 10; CHR-P non-converters; n = 23) and positive and negative symptoms.

STUDY RESULTS

ESZ showed a greater brain age gap relative to UCP and CHR-P (Ps < .010). CHR-P individuals who converted to psychosis showed a greater brain age gap (P = .043) relative to CHR-P non-converters. A larger brain age gap in ESZ was associated with increased experiential (P = .008), but not expressive negative symptom severity.

CONCLUSIONS

Consistent with schizophrenia pathophysiological models positing abnormal brain maturation, results suggest abnormal brain development is present early in psychosis. An increased brain age gap may be especially relevant to motivational and functional deficits in schizophrenia.

Analyzing Wav2Vec 1.0 Embeddings for Cross-Database Parkinson's Disease Detection and Speech Features Extraction

Advancements in deep learning speech representations have facilitated the effective use of extensive unlabeled speech datasets for Parkinson's disease (PD) modeling with minimal annotated data. This study employs the non-fine-tuned wav2vec 1.0 architecture to develop machine learning models for PD speech diagnosis tasks, such as cross-database classification and regression to predict demographic and articulation characteristics. The primary aim is to analyze overlapping components within the embeddings on both classification and regression tasks, investigating whether latent speech representations in PD are shared across models, particularly for related tasks. Firstly, evaluation using three multi-language PD datasets showed that wav2vec accurately detected PD based on speech, outperforming feature extraction using mel-frequency cepstral coefficients in the proposed cross-database classification scenarios. In cross-database scenarios using Italian and English-read texts, wav2vec demonstrated performance comparable to intra-dataset evaluations. We also compared our cross-database findings against those of other related studies. Secondly, wav2vec proved effective in regression, modeling various quantitative speech characteristics related to articulation and aging. Ultimately, subsequent analysis of important features examined the presence of significant overlaps between classification and regression models. The feature importance experiments discovered shared features across trained models, with increased sharing for related tasks, further suggesting that wav2vec contributes to improved generalizability. The study proposes wav2vec embeddings as a next promising step toward a speech-based universal model to assist in the evaluation of PD.

Brain age prediction using interpretable multi-feature-based convolutional neural network in mild traumatic brain injury

BACKGROUND

Convolutional neural network (CNN) can capture the structural features changes of brain aging based on MRI, thus predict brain age in healthy individuals accurately. However, most studies use single feature to predict brain age in healthy individuals, ignoring adding information from multiple sources and the changes in brain aging patterns after mild traumatic brain injury (mTBI) were still unclear.

METHODS

Here, we leveraged the structural data from a large, heterogeneous dataset (N = 1464) to implement an interpretable 3D combined CNN model for brain-age prediction. In addition, we also built an atlas-based occlusion analysis scheme with a fine-grained human Brainnetome Atlas to reveal the age-sstratified contributed brain regions for brain-age prediction in healthy controls (HCs) and mTBI patients. The correlations between brain predicted age gaps (brain-PAG) following mTBI and individual's cognitive impairment, as well as the level of plasma neurofilament light were also examined.

RESULTS

Our model utilized multiple 3D features derived from T1w data as inputs, and reduced the mean absolute error (MAE) of age prediction to 3.08 years and improved Pearson's r to 0.97 on 154 HCs. The strong generalizability of our model was also validated across different centers. Regions contributing the most significantly to brain age prediction were the caudate and thalamus for HCs and patients with mTBI, and the contributive regions were mostly located in the subcortical areas throughout the adult lifespan. The left hemisphere was confirmed to contribute more in brain age prediction throughout the adult lifespan. Our research showed that brain-PAG in mTBI patients was significantly higher than that in HCs in both acute and chronic phases. The increased brain-PAG in mTBI patients was also highly correlated with cognitive impairment and a higher level of plasma neurofilament light, a marker of neurodegeneration. The higher brain-PAG and its correlation with severe cognitive impairment showed a longitudinal and persistent nature in patients with follow-up examinations.

CONCLUSION

We proposed an interpretable deep learning framework on a relatively large dataset to accurately predict brain age in both healthy individuals and mTBI patients. The interpretable analysis revealed that the caudate and thalamus became the most contributive role across the adult lifespan in both HCs and patients with mTBI. The left hemisphere contributed significantly to brain age prediction may enlighten us to be concerned about the lateralization of brain abnormality in neurological diseases in the future. The proposed interpretable deep learning framework might also provide hope for testing the performance of related drugs and treatments in the future.

Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review

The concept of 'brain age', derived from neuroimaging data, serves as a crucial biomarker reflecting cognitive vitality and neurodegenerative trajectories. In the past decade, machine learning (ML) and deep learning (DL) integration has transformed the field, providing advanced models for brain age estimation. However, achieving precise brain age prediction across all ages remains a significant analytical challenge. This comprehensive review scrutinizes advancements in ML- and DL-based brain age prediction, analyzing 52 peer-reviewed studies from 2020 to 2024. It assesses various model architectures, highlighting their effectiveness and nuances in lifespan brain age studies. By comparing ML and DL, strengths in forecasting and methodological limitations are revealed. Finally, key findings from the reviewed articles are summarized and a number of major issues related to ML/DL-based lifespan brain age prediction are discussed. Through this study, we aim at the synthesis of the current state of brain age prediction, emphasizing both advancements and persistent challenges, guiding future research, technological advancements, and improving early intervention strategies for neurodegenerative diseases.

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

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

Associations of plasma proteomics and age-related outcomes with brain age in a diverse cohort

Machine learning models are increasingly being used to estimate "brain age" from neuroimaging data. The gap between chronological age and the estimated brain age gap (BAG) is potentially a measure of accelerated and resilient brain aging. Brain age calculated in this fashion has been shown to be associated with mortality, measures of physical function, health, and disease. Here, we estimate the BAG using a voxel-based elastic net regression approach, and then, we investigate its associations with mortality, cognitive status, and measures of health and disease in participants from Atherosclerosis Risk in Communities (ARIC) study who had a brain MRI at visit 5 of the study. Finally, we used the SOMAscan assay containing 4877 proteins to examine the proteomic associations with the MRI-defined BAG. Among N = 1849 participants (age, 76.4 (SD 5.6)), we found that increased values of BAG were strongly associated with increased mortality and increased severity of the cognitive status. Strong associations with mortality persisted when the analyses were performed in cognitively normal participants. In addition, it was strongly associated with BMI, diabetes, measures of physical function, hypertension, prevalent heart disease, and stroke. Finally, we found 33 proteins associated with BAG after a correction for multiple comparisons. The top proteins with positive associations to brain age were growth/differentiation factor 15 (GDF-15), Sushi, von Willebrand factor type A, EGF, and pentraxin domain-containing protein 1 (SEVP 1), matrilysin (MMP7), ADAMTS-like protein 2 (ADAMTS), and heat shock 70 kDa protein 1B (HSPA1B) while EGF-receptor (EGFR), mast/stem-cell-growth-factor-receptor (KIT), coagulation-factor-VII, and cGMP-dependent-protein-kinase-1 (PRKG1) were negatively associated to brain age. Several of these proteins were previously associated with dementia in ARIC. These results suggest that circulating proteins implicated in biological aging, cellular senescence, angiogenesis, and coagulation are associated with a neuroimaging measure of brain aging.

Frontoparietal and salience network synchronizations during nonsymbolic magnitude processing predict brain age and mathematical performance in youth

The development and refinement of functional brain circuits crucial to human cognition is a continuous process that spans from childhood to adulthood. Research increasingly focuses on mapping these evolving configurations, with the aim to identify markers for functional impairments and atypical development. Among human cognitive systems, nonsymbolic magnitude representations serve as a foundational building block for future success in mathematical learning and achievement for individuals. Using task-based frontoparietal (FPN) and salience network (SN) features during nonsymbolic magnitude processing alongside machine learning algorithms, we developed a framework to construct brain age prediction models for participants aged 7-30. Our study revealed differential developmental profiles in the synchronization within and between FPN and SN networks. Specifically, we observed a linear increase in FPN connectivity, concomitant with a decline in SN connectivity across the age span. A nonlinear U-shaped trajectory in the connectivity between the FPN and SN was discerned, revealing reduced FPN-SN synchronization among adolescents compared to both pediatric and adult cohorts. Leveraging the Gradient Boosting machine learning algorithm and nested fivefold stratified cross-validation with independent training datasets, we demonstrated that functional connectivity measures of the FPN and SN nodes predict chronological age, with a correlation coefficient of .727 and a mean absolute error of 2.944 between actual and predicted ages. Notably, connectivity within the FPN emerged as the most contributing feature for age prediction. Critically, a more matured brain age estimate is associated with better arithmetic performance. Our findings shed light on the intricate developmental changes occurring in the neural networks supporting magnitude representations. We emphasize brain age estimation as a potent tool for understanding cognitive development and its relationship to mathematical abilities across the critical developmental period of youth. PRACTITIONER POINTS: This study investigated the prolonged changes in the brain's architecture across childhood, adolescence, and adulthood, with a focus on task-state frontoparietal and salience networks. Distinct developmental pathways were identified: frontoparietal synchronization strengthens consistently throughout development, while salience network connectivity diminishes with age. Furthermore, adolescents show a unique dip in connectivity between these networks. Leveraging advanced machine learning methods, we accurately predicted individuals' ages based on these brain circuits, with a more mature estimated brain age correlating with better math skills.

Deep Learning-based Brain Age Prediction in Patients With Schizophrenia Spectrum Disorders

BACKGROUND AND HYPOTHESIS

The brain-predicted age difference (brain-PAD) may serve as a biomarker for neurodegeneration. We investigated the brain-PAD in patients with schizophrenia (SCZ), first-episode schizophrenia spectrum disorders (FE-SSDs), and treatment-resistant schizophrenia (TRS) using structural magnetic resonance imaging (sMRI).

STUDY DESIGN

We employed a convolutional network-based regression (SFCNR), and compared its performance with models based on three machine learning (ML) algorithms. We pretrained the SFCNR with sMRI data of 7590 healthy controls (HCs) selected from the UK Biobank. The parameters of the pretrained model were transferred to the next training phase with a new set of HCs (n = 541). The brain-PAD was analyzed in independent HCs (n = 209) and patients (n = 233). Correlations between the brain-PAD and clinical measures were investigated.

STUDY RESULTS

The SFCNR model outperformed three commonly used ML models. Advanced brain aging was observed in patients with SCZ, FE-SSDs, and TRS compared to HCs. A significant difference in brain-PAD was observed between FE-SSDs and TRS with ridge regression but not with the SFCNR model. Chlorpromazine equivalent dose and cognitive function were correlated with the brain-PAD in SCZ and FE-SSDs.

CONCLUSIONS

Our findings indicate that there is advanced brain aging in patients with SCZ and higher brain-PAD in SCZ can be used as a surrogate marker for cognitive dysfunction. These findings warrant further investigations on the causes of advanced brain age in SCZ. In addition, possible psychosocial and pharmacological interventions targeting brain health should be considered in early-stage SCZ patients with advanced brain age.

Association of Cardiovascular Health With Brain Age Estimated Using Machine Learning Methods in Middle-Aged and Older Adults

BACKGROUND AND OBJECTIVES

Cardiovascular health (CVH) has been associated with cognitive decline and dementia, but the extent to which CVH affects brain health remains unclear. We investigated the association of CVH, assessed using Life's Essential 8 (LE8), with neuroimaging-based brain age and brain-predicted age difference (brain-PAD).

METHODS

This longitudinal community-based study was based on UK Biobank participants aged 40-69 years who were free from dementia and other neurologic diseases at baseline. LE8 score at baseline was assessed with 8 measures and tertiled as low, moderate, and high CVH. Structural and functional brain MRI scans were performed approximately 9 years after baseline, and 1,079 measures from 6 neuroimaging modalities were used to model brain age. A Least Absolute Shrinkage and Selection Operator regression model was trained in 4,355 healthy participants and then used to calculate brain age and brain-PAD in the whole population. Data were analyzed using linear regression models.

RESULTS

The study included 32,646 participants (mean age at baseline 54.74 years; 53.44% female; mean LE8 score: 71.90). In multivariable-adjusted linear regression, higher LE8 score was associated with younger brain age (β [95% CI] -0.037 [-0.043 to -0.031]) and more negative brain-PAD (β [95% CI] -0.043 [-0.048 to -0.038]) (brain looks younger for chronological age). Compared with high CVH, low/moderate CVH was associated with older brain age (β [95% CI] 1.030 [0.852-1.208]/0.475 [0.303-0.647]) and increased brain-PAD (β [95% CI] 1.193 [1.029-1.357]/0.528 [0.370-0.686]). The associations between low CVH and older brain age/brain-PAD remained similar and significant in both middle-aged (β [95% CI] 1.199 [0.992-1.405]/1.351 [1.159-1.542]) and older adults (β [95% CI] 0.764 [0.417-1.110]/0.948 [0.632-1.263]).

DISCUSSION

Low CVH is associated with older brain age and greater brain-PAD, even among middle-aged adults. Our findings suggest that optimizing CVH could support brain health. The main limitation of our study is that the study sample was healthier than the general population, thus caution is required when generalizing our findings to other populations.

MRI-informed machine learning-driven brain age models for classifying mild cognitive impairment converters

BACKGROUND

Brain age model, including estimated brain age and brain-predicted age difference (brain-PAD), has shown great potentials for serving as imaging markers for monitoring normal ageing, as well as for identifying the individuals in the pre-diagnostic phase of neurodegenerative diseases.

PURPOSE

This study aimed to investigate the brain age models in normal ageing and mild cognitive impairments (MCI) converters and their values in classifying MCI conversion.

METHODS

Pre-trained brain age model was constructed using the structural magnetic resonance imaging (MRI) data from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) project (N = 609). The tested brain age model was built using the baseline, 1-year and 3-year follow-up MRI data from normal ageing (NA) adults (n = 32) and MCI converters (n = 22) drew from the Open Access Series of Imaging Studies (OASIS-2). The quantitative measures of morphometry included total intracranial volume (TIV), gray matter volume (GMV) and cortical thickness. Brain age models were calculated based on the individual's morphometric features using the support vector machine (SVM) algorithm.

RESULTS

With comparable chronological age, MCI converters showed significant increased TIV-based (Baseline: P = 0.021; 1-year follow-up: P = 0.037; 3-year follow-up: P = 0.001) and left GMV-based brain age than NA adults at all time points. Higher brain-PAD scores were associated with worse global cognition. Acceptable classification performance of TIV-based (AUC = 0.698) and left GMV-based brain age (AUC = 0.703) was found, which could differentiate the MCI converters from NA adults at the baseline.

CONCLUSIONS

This is the first demonstration that MRI-informed brain age models exhibit feature-specific patterns. The greater GMV-based brain age observed in MCI converters may provide new evidence for identifying the individuals at the early stage of neurodegeneration. Our findings added value to existing quantitative imaging markers and might help to improve disease monitoring and accelerate personalized treatments in clinical practice.

Brain-age prediction: Systematic evaluation of site effects, and sample age range and size

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

Predictive values of pre-treatment brain age models to rTMS effects in neurocognitive disorder with depression: Secondary analysis of a randomised sham-controlled clinical trial

INTRODUCTION

One major challenge in developing personalised repetitive transcranial magnetic stimulation (rTMS) is that the treatment responses exhibited high inter-individual variations. Brain morphometry might contribute to these variations. This study sought to determine whether individual's brain morphometry could predict the rTMS responders and remitters.

METHODS

This was a secondary analysis of data from a randomised clinical trial that included fifty-five patients over the age of 60 with both comorbid depression and neurocognitive disorder. Based on magnetic resonance imaging scans, estimated brain age was calculated with morphometric features using a support vector machine. Brain-predicted age difference (brain-PAD) was computed as the difference between brain age and chronological age.

RESULTS

The rTMS responders and remitters had younger brain age. Every additional year of brain-PAD decreased the odds of relieving depressive symptoms by ∼25.7% in responders (Odd ratio [OR] = 0.743, p = .045) and by ∼39.5% in remitters (OR = 0.605, p = .022) in active rTMS group. Using brain-PAD score as a feature, responder-nonresponder classification accuracies of 85% (3rd week) and 84% (12th week), respectively were achieved.

CONCLUSION

In elderly patients, younger brain age appears to be associated with better treatment responses to active rTMS. Pre-treatment brain age models informed by morphometry might be used as an indicator to stratify suitable patients for rTMS treatment.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: ChiCTR-IOR-16008191.

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

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

Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease

The electrocardiogram (ECG) can capture obesity-related cardiac changes. Artificial intelligence-enhanced ECG (AI-ECG) can identify subclinical disease. We trained an AI-ECG model to predict body mass index (BMI) from the ECG alone. Developed from 512,950 12-lead ECGs from the Beth Israel Deaconess Medical Center (BIDMC), a secondary care cohort, and validated on UK Biobank (UKB) (n = 42,386), the model achieved a Pearson correlation coefficient (r) of 0.65 and 0.62, and an R2 of 0.43 and 0.39 in the BIDMC cohort and UK Biobank, respectively for AI-ECG BMI vs. measured BMI. We found delta-BMI, the difference between measured BMI and AI-ECG-predicted BMI (AI-ECG-BMI), to be a biomarker of cardiometabolic health. The top tertile of delta-BMI showed increased risk of future cardiometabolic disease (BIDMC: HR 1.15, p < 0.001; UKB: HR 1.58, p < 0.001) and diabetes mellitus (BIDMC: HR 1.25, p < 0.001; UKB: HR 2.28, p < 0.001) after adjusting for covariates including measured BMI. Significant enhancements in model fit, reclassification and improvements in discriminatory power were observed with the inclusion of delta-BMI in both cohorts. Phenotypic profiling highlighted associations between delta-BMI and cardiometabolic diseases, anthropometric measures of truncal obesity, and pericardial fat mass. Metabolic and proteomic profiling associates delta-BMI positively with valine, lipids in small HDL, syntaxin-3, and carnosine dipeptidase 1, and inversely with glutamine, glycine, colipase, and adiponectin. A genome-wide association study revealed associations with regulators of cardiovascular/metabolic traits, including SCN10A, SCN5A, EXOG and RXRG. In summary, our AI-ECG-BMI model accurately predicts BMI and introduces delta-BMI as a non-invasive biomarker for cardiometabolic risk stratification.

Brain age has limited utility as a biomarker for capturing fluid cognition in older individuals

One well-known biomarker candidate that supposedly helps capture fluid cognition is Brain Age, or a predicted value based on machine-learning models built to predict chronological age from brain MRI. To formally evaluate the utility of Brain Age for capturing fluid cognition, we built 26 age-prediction models for Brain Age based on different combinations of MRI modalities, using the Human Connectome Project in Aging (n=504, 36-100 years old). First, based on commonality analyses, we found a large overlap between Brain Age and chronological age: Brain Age could uniquely add only around 1.6% in explaining variation in fluid cognition over and above chronological age. Second, the age-prediction models that performed better at predicting chronological age did NOT necessarily create better Brain Age for capturing fluid cognition over and above chronological age. Instead, better-performing age-prediction models created Brain Age that overlapped larger with chronological age, up to around 29% out of 32%, in explaining fluid cognition. Third, Brain Age missed around 11% of the total variation in fluid cognition that could have been explained by the brain variation. That is, directly predicting fluid cognition from brain MRI data (instead of relying on Brain Age and chronological age) could lead to around a 1/3-time improvement of the total variation explained. Accordingly, we demonstrated the limited utility of Brain Age as a biomarker for fluid cognition and made some suggestions to ensure the utility of Brain Age in explaining fluid cognition and other phenotypes of interest.

Heterogeneous associations of multiplexed environmental factors and multidimensional aging metrics

Complicated associations between multiplexed environmental factors and aging are poorly understood. We manipulated aging using multidimensional metrics such as phenotypic age, brain age, and brain volumes in the UK Biobank. Weighted quantile sum regression was used to examine the relative individual contributions of multiplexed environmental factors to aging, and self-organizing maps (SOMs) were used to examine joint effects. Air pollution presented a relatively large contribution in most cases. We also found fair heterogeneities in which the same environmental factor contributed inconsistently to different aging metrics. Particulate matter contributed the most to variance in aging, while noise and green space showed considerable contribution to brain volumes. SOM identified five subpopulations with distinct environmental exposure patterns and the air pollution subpopulation had the worst aging status. This study reveals the heterogeneous associations of multiplexed environmental factors with multidimensional aging metrics and serves as a proof of concept when analyzing multifactors and multiple outcomes.

Brain age gap estimation using attention-based ResNet method for Alzheimer's disease detection

This study investigates the correlation between brain age and chronological age in healthy individuals using brain MRI images, aiming to identify potential biomarkers for neurodegenerative diseases like Alzheimer's. To achieve this, a novel attention-based ResNet method, 3D-Attention-Resent-SVR, is proposed to accurately estimate brain age and distinguish between Cognitively Normal (CN) and Alzheimer's disease (AD) individuals by computing the brain age gap (BAG). Unlike conventional methods, which often rely on single datasets, our approach addresses potential biases by employing four datasets for training and testing. The results, based on a combined dataset from four public sources comprising 3844 data points, demonstrate the model's efficacy with a mean absolute error (MAE) of 2.05 for brain age gap estimation. Moreover, the model's generalizability is showcased by training on three datasets and testing on a separate one, yielding a remarkable MAE of 2.4. Furthermore, leveraging BAG as the sole biomarker, our method achieves an accuracy of 92% and an AUC of 0.87 in Alzheimer's disease detection on the ADNI dataset. These findings underscore the potential of our approach in assisting with early detection and disease monitoring, emphasizing the strong correlation between BAG and AD.

Multi-organ imaging-derived polygenic indexes for brain and body health

The UK Biobank (UKB) imaging project is a crucial resource for biomedical research, but is limited to 100,000 participants due to cost and accessibility barriers. Here we used genetic data to predict heritable imaging-derived phenotypes (IDPs) for a larger cohort. We developed and evaluated 4,375 IDP genetic scores (IGS) derived from UKB brain and body images. When applied to UKB participants who were not imaged, IGS revealed links to numerous phenotypes and stratified participants at increased risk for both brain and somatic diseases. For example, IGS identified individuals at higher risk for Alzheimer's disease and multiple sclerosis, offering additional insights beyond traditional polygenic risk scores of these diseases. When applied to independent external cohorts, IGS also stratified those at high disease risk in the All of Us Research Program and the Alzheimer's Disease Neuroimaging Initiative study. Our results demonstrate that, while the UKB imaging cohort is largely healthy and may not be the most enriched for disease risk management, it holds immense potential for stratifying the risk of various brain and body diseases in broader external genetic cohorts.

Targeting the epigenetically older individuals for geroprotective trials: the use of DNA methylation clocks

Chronological age is the most important risk factor for the incidence of age-related diseases. The pace of ageing determines the magnitude of that risk and can be expressed as biological age. Targeting fundamental pathways of human aging with geroprotectors has the potential to lower the biological age and therewith prolong the healthspan, the period of life one spends in good health. Target populations for geroprotective interventions should be chosen based on the ageing mechanisms being addressed and the expected effect of the geroprotector on the primary outcome. Biomarkers of ageing, such as DNA methylation age, can be used to select populations for geroprotective interventions and as a surrogate outcome. Here, the use of DNA methylation clocks for selecting target populations for geroprotective intervention is explored.

Classifying Routine Clinical Electroencephalograms With Multivariate Iterative Filtering and Convolutional Neural Networks

Electroencephalogram (EEG) is widely used in basic and clinical neuroscience to explore neural states in various populations, and classifying these EEG recordings is a fundamental challenge. While machine learning shows promising results in classifying long multivariate time series, optimal prediction models and feature extraction methods for EEG classification remain elusive. Our study addressed the problem of EEG classification under the framework of brain age prediction, applying a deep learning model on EEG time series. We hypothesized that decomposing EEG signals into oscillatory modes would yield more accurate age predictions than using raw or canonically frequency-filtered EEG. Specifically, we employed multivariate intrinsic mode functions (MIMFs), an empirical mode decomposition (EMD) variant based on multivariate iterative filtering (MIF), with a convolutional neural network (CNN) model. Testing a large dataset of routine clinical EEG scans (n = 6540) from patients aged 1 to 103 years, we found that an ad-hoc CNN model without fine-tuning could reasonably predict brain age from EEGs. Crucially, MIMF decomposition significantly improved performance compared to canonical brain rhythms (from delta to lower gamma oscillations). Our approach achieved a mean absolute error (MAE) of 13.76 ± 0.33 and a correlation coefficient of 0.64 ± 0.01 in brain age prediction over the entire lifespan. Our findings indicate that CNN models applied to EEGs, preserving their original temporal structure, remains a promising framework for EEG classification, wherein the adaptive signal decompositions such as the MIF can enhance CNN models' performance in this task.

Brain age monotonicity and functional connectivity differences of healthy subjects

Alterations in the brain's connectivity or the interactions among brain regions have been studied with the aid of resting state (rs)fMRI data attained from large numbers of healthy subjects of various demographics. This has been instrumental in providing insight into how a phenotype as fundamental as age affects the brain. Although machine learning (ML) techniques have already been deployed in such studies, novel questions are investigated in this work. We study whether young brains develop properties that progressively resemble those of aged brains, and if the aging dynamics of older brains provide information about the aging trajectory in young subjects. The degree of a prospective monotonic relationship will be quantified, and hypotheses of brain aging trajectories will be tested via ML. Furthermore, the degree of functional connectivity across the age spectrum of three datasets will be compared at a population level and across sexes. The findings scrutinize similarities and differences among the male and female subjects at greater detail than previously performed.

Computational limits to the legibility of the imaged human brain

Our knowledge of the organisation of the human brain at the population-level is yet to translate into power to predict functional differences at the individual-level, limiting clinical applications and casting doubt on the generalisability of inferred mechanisms. It remains unknown whether the difficulty arises from the absence of individuating biological patterns within the brain, or from limited power to access them with the models and compute at our disposal. Here we comprehensively investigate the resolvability of such patterns with data and compute at unprecedented scale. Across 23 810 unique participants from UK Biobank, we systematically evaluate the predictability of 25 individual biological characteristics, from all available combinations of structural and functional neuroimaging data. Over 4526 GPU*hours of computation, we train, optimize, and evaluate out-of-sample 700 individual predictive models, including fully-connected feed-forward neural networks of demographic, psychological, serological, chronic disease, and functional connectivity characteristics, and both uni- and multi-modal 3D convolutional neural network models of macro- and micro-structural brain imaging. We find a marked discrepancy between the high predictability of sex (balanced accuracy 99.7%), age (mean absolute error 2.048 years, R2 0.859), and weight (mean absolute error 2.609Kg, R2 0.625), for which we set new state-of-the-art performance, and the surprisingly low predictability of other characteristics. Neither structural nor functional imaging predicted an individual's psychology better than the coincidence of common chronic disease (p < 0.05). Serology predicted chronic disease (p < 0.05) and was best predicted by it (p < 0.001), followed by structural neuroimaging (p < 0.05). Our findings suggest either more informative imaging or more powerful models will be needed to decipher individual level characteristics from the human brain. We make our models and code openly available.

Extensive T1-weighted MRI preprocessing improves generalizability of deep brain age prediction models

Brain age is an estimate of chronological age obtained from T1-weighted magnetic resonance images (T1w MRI), representing a straightforward diagnostic biomarker of brain aging and associated diseases. While the current best accuracy of brain age predictions on T1w MRIs of healthy subjects ranges from two to three years, comparing results across studies is challenging due to differences in the datasets, T1w preprocessing pipelines, and evaluation protocols used. This paper investigates the impact of T1w image preprocessing on the performance of four deep learning brain age models from recent literature. Four preprocessing pipelines, which differed in terms of registration transform, grayscale correction, and software implementation, were evaluated. The results showed that the choice of software or preprocessing steps could significantly affect the prediction error, with a maximum increase of 0.75 years in mean absolute error (MAE) for the same model and dataset. While grayscale correction had no significant impact on MAE, using affine rather than rigid registration to brain atlas statistically significantly improved MAE. Models trained on 3D images with isotropic 1mm3 resolution exhibited less sensitivity to the T1w preprocessing variations compared to 2D models or those trained on downsampled 3D images. Our findings indicate that extensive T1w preprocessing improves MAE, especially when predicting on a new dataset. This runs counter to prevailing research literature, which suggests that models trained on minimally preprocessed T1w scans are better suited for age predictions on MRIs from unseen scanners. We demonstrate that, irrespective of the model or T1w preprocessing used during training, applying some form of offset correction is essential to enable the model's performance to generalize effectively on datasets from unseen sites, regardless of whether they have undergone the same or different T1w preprocessing as the training set.

Hearing function moderates age-related changes in brain morphometry in the HCP Aging cohort

Introduction

There are well-established relationships between aging and neurodegenerative changes, and between aging and hearing loss. The goal of this study was to determine how structural brain aging is influenced by hearing loss.

Methods

Human Connectome Project Aging (HCP-A) data were analyzed, including T1-weighted MRI and Words in Noise (WIN) thresholds (n=623). Freesurfer extracted gray and white matter volume, and cortical thickness, area, and curvature. Linear regression models targeted (1) interactions between age and WIN threshold and (2) correlations with WIN threshold adjusted for age, both corrected for false discovery rate (pFDR<0.05).

Results

WIN threshold moderated age-related increase in volume in bilateral inferior lateral ventricles, with higher threshold associated with increased age-related ventricle expansion. Age-related deterioration in occipital cortex was also increased with higher WIN thresholds. When controlling for age, high WIN threshold was correlated with reduced cortical thickness in Heschl's gyrus, calcarine sulcus, and other sensory regions, and reduced temporal lobe white matter. Older volunteers with poorer hearing and cognitive scores had the lowest volume in left parahippocampal white matter.

Conclusions

Preserved hearing abilities in aging associated with a reduction of age-related changes to medial temporal lobe, and preserved hearing at any age associated with preserved cortical tissue in auditory and other sensory regions. Future longitudinal studies are needed to assess the causal nature of these relationships, but these results indicate interventions which preserve hearing function may combat some neurodegenerative changes in aging.

Beyond network connectivity: A classification approach to brain age prediction with resting-state fMRI

The brain is a complex, dynamic organ that shows differences in the same subject at various periods. Understanding how brain activity changes across age as a function of the brain networks has been greatly abetted by fMRI. Canonical analysis consists of determining how alterations in connectivity patterns (CPs) of certain regions are affected. An alternative approach is taken here by not considering connectivity but rather features computed from recordings at the regions of interest (ROIs). Using machine learning (ML) we assess how neural signals are altered by and prospectively predictive of age and sex via a methodology that is novel in drawing upon pairwise classification across six decades of subjects' chronological ages. ML is used to answer the equally important questions of what properties of the computed features are most predictive as well as which brain networks are most affected by aging. It was found that there is decreased differentiation among the neural signals of older subjects that are separated in age by the same number of years as younger subjects. Furthermore, the burstiness of the signals change at different rates between males and females. The findings provide insight into brain aging via an ROI-based analysis, the consideration of several feature groups, and a novel classification-based ML pipeline. There is also a contribution to understanding the effects of data aggregated from different recording centers on the conclusions of fMRI studies.

Using a deep generation network reveals neuroanatomical specificity in hemispheres

Asymmetry is an important property of brain organization, but its nature is still poorly understood. Capturing the neuroanatomical components specific to each hemisphere facilitates the understanding of the establishment of brain asymmetry. Since deep generative networks (DGNs) have powerful inference and recovery capabilities, we use one hemisphere to predict the opposite hemisphere by training the DGNs, which automatically fit the built-in dependencies between the left and right hemispheres. After training, the reconstructed images approximate the homologous components in the hemisphere. We use the difference between the actual and reconstructed hemispheres to measure hemisphere-specific components due to asymmetric expression of environmental and genetic factors. The results show that our model is biologically plausible and that our proposed metric of hemispheric specialization is reliable, representing a wide range of individual variation. Together, this work provides promising tools for exploring brain asymmetry and new insights into self-supervised DGNs for representing the brain.

The mediating role of neuroimaging-derived biological brain age in the association between risk factors for dementia and cognitive decline in middle-aged and older individuals without cognitive impairment: a cohort study

BACKGROUND

Neuroimaging-based brain-age delta has been shown to be a mediator linking cardiovascular risk factors to cognitive function. We aimed to assess the mediating role of brain-age delta in the association between modifiable risk factors of dementia and longitudinal cognitive decline in middle-aged and older individuals who are asymptomatic, stratified by Alzheimer's disease pathology. We also explored whether the mediation effect is specific to cognitive domain.

METHODS

In this cohort study, we included participants from the ALFA+ cohort aged between 45 years and 65 years who were cognitively unimpaired and who had available structural MRI, cerebrospinal fluid β-amyloid (Aβ)42 and Aβ40 measurements obtained within 1 year of each other, modifiable risk factors assessment, and cognitive evaluation over 3 years. Participants were recruited from the Barcelonaβeta Brain Research Center (Barcelona, Spain). Included individuals underwent a first assessment between Oct 25, 2016, and Jan 28, 2020, and a follow-up cognitive assessment 3·28 (SD 0·27) years later. We computed brain-age delta and composites of different cognitive function domains (preclinical Alzheimer's cognitive composite [PACC], attention, executive function, episodic memory, visual processing, and language). We used partial least squares path modelling to explore mediation effects in the associations between modifiable risk factors (including cardiovascular, mental health, mood, metabolic or endocrine history, and alcohol use) and changes in cognitive composites. To assess the role of Alzheimer's disease pathology, we computed separate models for Aβ-negative and Aβ-positive individuals.

FINDINGS

Of the 419 participants enrolled in ALFA+, 302 met our inclusion criteria, of which 108 participants were classified as Aβ-positive and 194 as Aβ-negative. In Aβ-positive individuals, brain-age delta partially mediated (percent mediation proportion 15·73% [95% CI 14·22-16·66]) the association between modifiable risk factors and decline in overall cognition (across cognitive domains). Brain-age delta fully mediated (mediation proportion 28·03% [26·25-29·21]) the effect of modifiable risk factors on the PACC, wherein increased values for risk factors correlated with an older brain-age delta, and, consequently, an older brain-age delta was linked to greater PACC decline. This effect appears to be primarily driven by memory decline. Mediation was not significant in Aβ-negative individuals (3·52% [0·072-4·17]) on PACC, although path coefficients were not significantly different from those in the Aβ-positive group.

INTERPRETATION

Our findings suggest that brain-age delta captures the association between modifiable risk factors and longitudinal cognitive decline in middle-aged and older people. In asymptomatic middle-aged and older individuals who are Aβ-positive, the pathology might be the strongest driver of cognitive decline, whereas the effect of risk factors is smaller. Our results highlight the potential of brain-age delta as an objective outcome measure for preventive lifestyle interventions targeting cognitive decline.

FUNDING

La Caixa Foundation, the TriBEKa Imaging Platform, and the Universities and Research Secretariat of the Catalan Government.

TRANSLATION

For the Spanish translation of the abstract see Supplementary Materials section.

BrainAGE, brain health, and mental disorders: A systematic review

The imaging-based method of brainAGE aims to characterize an individual's vulnerability to age-related brain changes. The present study systematically reviewed brainAGE findings in neuropsychiatric conditions and discussed the potential of brainAGE as a marker for biological age. A systematic PubMed search (from inception to March 6th, 2023) identified 273 articles. The 30 included studies compared brainAGE between neuropsychiatric and healthy groups (n≥50). We presented results qualitatively and adapted a bias risk assessment questionnaire. The imaging modalities, design, and input features varied considerably between studies. While the studies found higher brainAGE in neuropsychiatric conditions (11 mild cognitive impairment/ dementia, 11 schizophrenia spectrum/ other psychotic and bipolar disorder, six depression/ anxiety, two multiple groups), the associations with clinical characteristics were mixed. While brainAGE is sensitive to group differences, limitations include the lack of diverse training samples, multi-modal studies, and external validation. Only a few studies obtained longitudinal data, and all have used algorithms built solely to predict chronological age. These limitations impede the validity of brainAGE as a biological age marker.

Morphological changes of the limbic system associated with acute and chronic low-back pain: A UK biobank imaging study

BACKGROUND

Low back pain (LBP) is a major public health issue that influences physical and emotional factors integral to the limbic system. This study aims to investigate the association between LBP and brain morphometry alterations as the duration of LBP increases (acute vs. chronic).

METHODS

We used the UK Biobank data to investigate the morphological features of the limbic system in acute LBP (N = 115), chronic LBP (N = 243) and controls (N = 358), and tried to replicate our findings with an independent dataset composed of 45 acute LBP participants evaluated at different timepoints throughout 1 year from the OpenPain database.

RESULTS

We found that in comparison with chronic LBP and pain-free controls, acute LBP was associated with increased volumes of the nucleus accumbens, amygdala, hippocampus, and thalamus, and increased grey matter volumes in the hippocampus and posterior cingulate gyrus. In the replication cohort, we found non-significantly larger hippocampus and thalamus volumes in the 3-month visit (acute LBP) compared to the 1-year visit (chronic LBP), with similar effect sizes as the UK Biobank dataset.

CONCLUSIONS

Our results suggest that acute LBP is associated with dramatic morphometric increases in the limbic system and mesolimbic pathway, which may reflect an active brain response and self-regulation in the early stage of LBP.

SIGNIFICANCE

Our study suggests that LBP in the acute phase is associated with the brain morphometric changes (increase) in some limbic areas, indicating that the acute phase of LBP may represent a crucial stage of self-regulation and active response to the disease's onset.

Combining Clinical With Cognitive or Magnetic Resonance Imaging Data for Predicting Transition to Psychosis in Ultra High-Risk Patients: Data From the PACE 400 Cohort

BACKGROUND

Multimodal modeling that combines biological and clinical data shows promise in predicting transition to psychosis in individuals who are at ultra-high risk. Individuals who transition to psychosis are known to have deficits at baseline in cognitive function and reductions in gray matter volume in multiple brain regions identified by magnetic resonance imaging.

METHODS

In this study, we used Cox proportional hazards regression models to assess the additive predictive value of each modality-cognition, cortical structure information, and the neuroanatomical measure of brain age gap-to a previously developed clinical model using functioning and duration of symptoms prior to service entry as predictors in the Personal Assessment and Crisis Evaluation (PACE) 400 cohort. The PACE 400 study is a well-characterized cohort of Australian youths who were identified as ultra-high risk of transitioning to psychosis using the Comprehensive Assessment of At Risk Mental States (CAARMS) and followed for up to 18 years; it contains clinical data (from N = 416 participants), cognitive data (n = 213), and magnetic resonance imaging cortical parameters extracted using FreeSurfer (n = 231).

RESULTS

The results showed that neuroimaging, brain age gap, and cognition added marginal predictive information to the previously developed clinical model (fraction of new information: neuroimaging 0%-12%, brain age gap 7%, cognition 0%-16%).

CONCLUSIONS

In summary, adding a second modality to a clinical risk model predicting the onset of a psychotic disorder in the PACE 400 cohort showed little improvement in the fit of the model for long-term prediction of transition to psychosis.

Prediction of brain age in individuals with and at risk for alcohol use disorder using brain morphological features

Brain age measures predicted from structural and functional brain features are increasingly being used to understand brain integrity, disorders, and health. While there is a vast literature showing aberrations in both structural and functional brain measures in individuals with and at risk for alcohol use disorder (AUD), few studies have investigated brain age in these groups. The current study examines brain age measures predicted using brain morphological features, such as cortical thickness and brain volume, in individuals with a lifetime diagnosis of AUD as well as in those at higher risk to develop AUD from families with multiple members affected with AUD (i.e., higher family history density (FHD) scores). The AUD dataset included a group of 30 adult males (mean age = 41.25 years) with a lifetime diagnosis of AUD and currently abstinent and a group of 30 male controls (mean age = 27.24 years) without any history of AUD. A second dataset of young adults who were categorized based on their FHD scores comprised a group of 40 individuals (20 males) with high FHD of AUD (mean age = 25.33 years) and a group of 31 individuals (18 males) with low FHD (mean age = 25.47 years). Brain age was predicted using 187 brain morphological features of cortical thickness and brain volume in an XGBoost regression model; a bias-correction procedure was applied to the predicted brain age. Results showed that both AUD and high FHD individuals showed an increase of 1.70 and 0.09 years (1.08 months), respectively, in their brain age relative to their chronological age, suggesting accelerated brain aging in AUD and risk for AUD. Increased brain age was associated with poor performance on neurocognitive tests of executive functioning in both AUD and high FHD individuals, indicating that brain age can also serve as a proxy for cognitive functioning and brain health. These findings on brain aging in these groups may have important implications for the prevention and treatment of AUD and ensuing cognitive decline.

Assessing the association between global structural brain age and polygenic risk for schizophrenia in early adulthood: A recall-by-genotype study

Neuroimaging studies consistently show advanced brain age in schizophrenia, suggesting that brain structure is often 'older' than expected at a given chronological age. Whether advanced brain age is linked to genetic liability for schizophrenia remains unclear. In this pre-registered secondary data analysis, we utilised a recall-by-genotype approach applied to a population-based subsample from the Avon Longitudinal Study of Parents and Children to assess brain age differences between young adults aged 21-24 years with relatively high (n = 96) and low (n = 93) polygenic risk for schizophrenia (SCZ-PRS). A global index of brain age (or brain-predicted age) was estimated using a publicly available machine learning model previously trained on a combination of region-wise gray-matter measures, including cortical thickness, surface area and subcortical volumes derived from T1-weighted magnetic resonance imaging (MRI) scans. We found no difference in mean brain-PAD (the difference between brain-predicted age and chronological age) between the high- and low-SCZ-PRS groups, controlling for the effects of sex and age at time of scanning (b = -.21; 95% CI -2.00, 1.58; p = .82; Cohen's d = -.034; partial R2 = .00029). These findings do not support an association between SCZ-PRS and brain-PAD based on global age-related structural brain patterns, suggesting that brain age may not be a vulnerability marker of common genetic risk for SCZ. Future studies with larger samples and multimodal brain age measures could further investigate global or localised effects of SCZ-PRS.

Slower speed of blood pressure recovery after standing is associated with accelerated brain aging: Evidence from The Irish Longitudinal Study on Ageing (TILDA)

Background

Impaired recovery of blood pressure (BP) in response to standing up is a prevalent condition in older individuals. We evaluated the relationship between the early recovery of hemodynamic responses to standing and brain health in adults over 50.

Methods

Participants from The Irish Longitudinal Study on Ageing (TILDA) (n=411; age 67.6 ± 7.3 years; 53.4 % women) performed an active stand challenge while blood pressure and heart rate were continuously monitored. The recovery of these parameters was determined as the slope of the BP and HR response, following the initial drop/rise after standing. We have previously reported a novel and validated measure of brain ageing using MRI data, which measures the difference between biological brain age and chronological age, providing a brain-predicted age difference (brainPAD) score.

Results

Slower recovery of systolic and diastolic BP was found to be significantly associated with higher brainPAD scores (i.e., biologically older brains), where a one-year increase in brainPAD was associated with a decrease of 0.02 mmHg/s and 0.01 mmHg/s in systolic and diastolic BP recovery, respectively, after standing. Heart rate (HR) recovery was not significantly associated with brainPAD score.

Conclusion

These results demonstrate that slower systolic and diastolic BP recovery in the early phase after standing is associated with accelerated brain aging in older individuals. This suggests that the BP response to standing, measured using beat-to-beat monitoring, has the potential to be used as a marker of accelerated brain aging, relying on a simple procedure and devices that are easily accessible.