Brain-predicted Age Difference Research Articles

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.

White matter brain-age in diverse forms of epilepsy and interictal psychosis

Abnormal brain aging is suggested in epilepsy. Given the brain network dysfunction in epilepsy, the white matter tracts, which primarily interconnect brain regions, could be of special importance. We focused on white matter brain aging in diverse forms of epilepsy and comorbid psychosis. We obtained brain diffusion tensor imaging (DTI) data at 3 T-MRI in 257 patients with epilepsy and 429 healthy subjects. The tract-based fractional anisotropy values of the healthy subjects were used to build a brain-age prediction model, and we calculated the brain-predicted age difference (brain-PAD: predicted age—chronological age) of all subjects. As a result, almost all epilepsy categories showed significantly increased brain-PAD (p < 0.001), including temporal lobe epilepsy (TLE) with no MRI-lesion (+ 4.2 yr), TLE with hippocampal sclerosis (+ 9.1 yr), extratemporal focal epilepsy (+ 5.1 yr), epileptic encephalopathy or progressive myoclonus epilepsy (+ 18.4 yr), except for idiopathic generalized epilepsy (IGE). Patients with psychogenic non-epileptic seizures also presented increased brain-PAD. In TLE, interictal psychosis significantly raised brain-PAD by 8.7 years. In conclusion, we observed increased brain aging in most types of epilepsy, which was generally consistent with brain morphological aging results in previous studies. Psychosis may accelerate brain aging in TLE. These findings may suggest abnormal aging mechanisms in epilepsy and comorbid psychotic symptoms.

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

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). 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. 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]). 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.

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. 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. 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. 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. 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.

Accelerated biological brain aging in major depressive disorder.

Major depressive disorder (MDD) patients commonly encounter multiple types of functional disabilities, such as social, physical, and role functioning. MDD is related to an accreted risk of brain atrophy, aging-associated brain diseases, and mortality. Based on recently available studies, there are correlations between notable biological brain aging and MDD in adulthood. Despite several clinical and epidemiological studies that associate MDD with aging phenotypes, the underlying mechanisms in the brain remain unknown. The key areas in the study of biological brain aging in MDD are structural brain aging, impairment in functional connectivity, and the impact on cognitive function and age-related disorders. Various measurements have been used to determine the severity of brain aging, such as the brain age gap estimate (BrainAGE) or brain-predicted age difference (BrainPAD). This review summarized the current results of brain imaging data on the similarities between the manifestation of brain structural changes and the age-associated processes in MDD. This review also provided recent evidence of BrainPAD or BrainAGE scores in MDD, brain structural abnormalities, and functional connectivity, which are commonly observed between MDD and age-associated processes. It serves as a basis of current reference for future research on the potential areas of investigation for diagnostic, preventive, and potentially therapeutic purposes for brain aging in MDD.

Resting-state functional connectivity correlates of brain structural aging in schizophrenia.

A large body of research has shown that schizophrenia patients demonstrate increased brain structural aging. Although this process may be coupled with aberrant changes in intrinsic functional architecture of the brain, they remain understudied. We hypothesized that there are brain regions whose whole-brain functional connectivity at rest is differently associated with brain structural aging in schizophrenia patients compared to healthy controls. Eighty-four male schizophrenia patients and eighty-six male healthy controls underwent structural MRI and resting-state fMRI. The brain-predicted age difference (b-PAD) was a measure of brain structural aging. Resting-state fMRI was applied to obtain global correlation (GCOR) maps comprising voxelwise values of the strength and sign of functional connectivity of a given voxel with the rest of the brain. Schizophrenia patients had higher b-PAD compared to controls (mean between-group difference + 2.9 years). Greater b-PAD in schizophrenia patients, compared to controls, was associated with lower whole-brain functional connectivity of a region in frontal orbital cortex, inferior frontal gyrus, Heschl's Gyrus, plana temporale and polare, insula, and opercular cortices of the right hemisphere (rFTI). According to post hoc seed-based correlation analysis, decrease of functional connectivity with the posterior cingulate gyrus, left superior temporal cortices, as well as right angular gyrus/superior lateral occipital cortex has mainly driven the results. Lower functional connectivity of the rFTI was related to worse verbal working memory and language production. Our findings demonstrate that well-established frontotemporal functional abnormalities in schizophrenia are related to increased brain structural aging.

Brain age as a biomarker for pathological versus healthy ageing – a REMEMBER study

ObjectivesThis study aimed to evaluate the potential clinical value of a new brain age prediction model as a single interpretable variable representing the condition of our brain. Among many clinical use cases, brain age could be a novel outcome measure to assess the preventive effect of life-style interventions.MethodsThe REMEMBER study population (N = 742) consisted of cognitively healthy (HC,N = 91), subjective cognitive decline (SCD,N = 65), mild cognitive impairment (MCI,N = 319) and AD dementia (ADD,N = 267) subjects. Automated brain volumetry of global, cortical, and subcortical brain structures computed by the CE-labeled and FDA-cleared software icobrain dm (dementia) was retrospectively extracted from T1-weighted MRI sequences that were acquired during clinical routine at participating memory clinics from the Belgian Dementia Council. The volumetric features, along with sex, were combined into a weighted sum using a linear model, and were used to predict ‘brain age’ and ‘brain predicted age difference’ (BPAD = brain age–chronological age) for every subject.ResultsMCI and ADD patients showed an increased brain age compared to their chronological age. Overall, brain age outperformed BPAD and chronological age in terms of classification accuracy across the AD spectrum. There was a weak-to-moderate correlation between total MMSE score and both brain age (r = -0.38,p < .001) and BPAD (r = -0.26,p < .001). Noticeable trends, but no significant correlations, were found between BPAD and incidence of conversion from MCI to ADD, nor between BPAD and conversion time from MCI to ADD. BPAD was increased in heavy alcohol drinkers compared to non-/sporadic (p = .014) and moderate (p = .040) drinkers.ConclusionsBrain age and associated BPAD have the potential to serve as indicators for, and to evaluate the impact of lifestyle modifications or interventions on, brain health.

Enlarged perivascular spaces are associated with brain microangiopathy and aging in multiple sclerosis

Background: Growing evidence links brain-MRI enlarged perivascular spaces (EPVS) and multiple sclerosis (MS), but their role remains unclear. Objective: This study aimed to investigate the cross-sectional associations of EPVS with several neuroinflammatory and neurodegenerative features in a large multicentric-MS cohort. Methods: In total, 207 patients underwent 3T axial-T2-weighted brain-MRI for EPVS assessment (EPVS dichotomized into high/low according to ⩾ 2/< 2 rating categories). MRI biomarkers included brain-predicted age and brain-predicted age difference (brain-PAD), central vein sign (CVS)-positive lesion percentage (CVS%), paramagnetic rim and cortical lesions, T2-lesion load, and brain volumetry. The variable relative importance for EPVS-category prediction was explored using a classification random forest approach. Results: High EPVS patients were older (49 vs 44 years, p = 0.003), had ⩾ 1 vascular risk factors (VRFs; p = 0.005), lower CVS% (67% vs 78%, p < 0.001), reduced brain volumes (whole brain: 0.63 vs 0.73, p = 0.01; gray matter: 0.36 vs 0.40; p = 0.002), and older brain-predicted age (58 vs 50 years, p < 0.001). No differences were found for neuroinflammatory markers. After adjusting for age and VFRs (multivariate analyses), the high EPVS category correlated with lower CVS% (odds ratio (OR) = 0.98, 95% confidence interval (CI) = 0.96–0.99; p = 0.02), lower whole brain (OR = 0.01, 95% CI = 0.0003–0.5; p = 0.02), gray matter (OR = 0.0004, 95% CI = 0.0000004–0.4; p = 0.03) volumes, and higher brain-PAD (OR = 1.05, 95% CI = 1.01–1.09; p = 0.02). Random forest identified brain-PAD as the most important predictor of high EPVS. Conclusion: EPVS in MS likely reflect microangiopathic disease rather than neuroinflammation, potentially contributing to accelerated neurodegeneration.

Multimodal imaging analyses in neuromyelitis optica spectrum disorder with or without visual disturbance

Background and purposeNeuromyelitis optica spectrum disorder is a demyelinating and inflammatory affliction that often leads to visual disturbance. Various imaging techniques, including free-water imaging, have been used to determine neuroinflammation and degeneration. Therefore, this study aimed at determining multimodal imaging differences between patients with neuromyelitis optica spectrum disorder, especially those with visual disturbance, and healthy controls. Materials and methodsEighty-five neuromyelitis optica spectrum disorder patients and 89 age- and sex-matched healthy controls underwent 3-T magnetic resonance imaging (MRI). We analyzed adjusted brain-predicted age difference, voxel-based morphometry, and free-water–corrected diffusion tensor imaging (DTI) by tract-based spatial statistics in each patient group (MRI-positive/negative neuromyelitis optica spectrum disorder patients with or without a history of visual disturbance) compared with the healthy control group. ResultsMRI-positive neuromyelitis optica spectrum disorder patients exhibited reduced volumes of the bilateral thalamus. Tract-based spatial statistics showed diffuse white matter abnormalities in all DTI metrics in MRI-positive neuromyelitis optica spectrum disorder patients with a history of visual disturbance. In MRI-negative neuromyelitis optica spectrum disorder patients with a history of visual disturbance, voxel-based morphometry showed volume reduction of bilateral thalami and optic radiations, and tract-based spatial statistics revealed significantly lower free-water–corrected fractional anisotropy and higher mean diffusivity in the posterior dominant distributions, including the optic nerve radiation. ConclusionFree-water–corrected DTI and voxel-based morphometry analyses may reflect symptoms of visual disturbance in neuromyelitis optica spectrum disorder.

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.

The effect of head motion on brain age prediction using deep convolutional neural networks

Deep learning can be used effectively to predict participants' age from brain magnetic resonance imaging (MRI) data, and a growing body of evidence suggests that the difference between predicted and chronological age—referred to as brain-predicted age difference (brain-PAD)—is related to various neurological and neuropsychiatric disease states. A crucial aspect of the applicability of brain-PAD as a biomarker of individual brain health is whether and how brain-predicted age is affected by MR image artifacts commonly encountered in clinical settings. To investigate this issue, we trained and validated two different 3D convolutional neural network architectures (CNNs) from scratch and tested the models on a separate dataset consisting of motion-free and motion-corrupted T1-weighted MRI scans from the same participants, the quality of which were rated by neuroradiologists from a clinical diagnostic point of view. Our results revealed a systematic increase in brain-PAD with worsening image quality for both models. This effect was also observed for images that were deemed usable from a clinical perspective, with brains appearing older in medium than in good quality images. These findings were also supported by significant associations found between the brain-PAD and standard image quality metrics indicating larger brain-PAD for lower-quality images. Our results demonstrate a spurious effect of advanced brain aging as a result of head motion and underline the importance of controlling for image quality when using brain-predicted age based on structural neuroimaging data as a proxy measure for brain health.

Assessing brain involvement in Fabry disease with deep learning and the brain-age paradigm.

While neurological manifestations are core features of Fabry disease (FD), quantitative neuroimaging biomarkers allowing to measure brain involvement are lacking. We used deep learning and the brain-age paradigm to assess whether FD patients' brains appear older than normal and to validate brain-predicted age difference (brain-PAD) as a possible disease severity biomarker. MRI scans of FD patients and healthy controls (HCs) from a single Institution were, retrospectively, studied. The Fabry stabilization index (FASTEX) was recorded as a measure of disease severity. Using minimally preprocessed 3D T1-weighted brain scans of healthy subjects from eight publicly available sources (N = 2160; mean age = 33 years [range 4-86]), we trained a model predicting chronological age based on a DenseNet architecture and used it to generate brain-age predictions in the internal cohort. Within a linear modeling framework, brain-PAD was tested for age/sex-adjusted associations with diagnostic group (FD vs. HC), FASTEX score, and both global and voxel-level neuroimaging measures. We studied 52 FD patients (40.6 ± 12.6 years; 28F) and 58 HC (38.4 ± 13.4 years; 28F). The brain-age model achieved accurate out-of-sample performance (mean absolute error = 4.01 years, R2 = .90). FD patients had significantly higher brain-PAD than HC (estimated marginal means: 3.1 vs. -0.1, p = .01). Brain-PAD was associated with FASTEX score (B = 0.10, p = .02), brain parenchymal fraction (B = -153.50, p = .001), white matter hyperintensities load (B = 0.85, p = .01), and tissue volume reduction throughout the brain. We demonstrated that FD patients' brains appear older than normal. Brain-PAD correlates with FD-related multi-organ damage and is influenced by both global brain volume and white matter hyperintensities, offering a comprehensive biomarker of (neurological) disease severity.

Differences in brain aging between sexes in Parkinson’s disease

Parkinson’s disease (PD) is linked to faster brain aging. Male sex is associated with higher prevalence, severe symptoms, and a faster progression rate in PD. There remains a significant gap in understanding the function of sex in the process of brain aging in PD. The structural T1-weighted MRI-driven brain-predicted age difference (i.e., Brain-PAD: the actual age subtracted from the brain-predicted age) was computed in a group of 373 people with PD (mean age ± SD: 61.37 ± 9.81, age range: 33–85, 34% female) from the Parkinson’s Progression Marker Initiative database using a robust brain-age estimation framework that was trained on 949 healthy subjects. Linear regression models were used to investigate the association between Brain-PAD and clinical variables in PD, stratified by sex. Males with Parkinson’s disease (PD-M) exhibited a significantly higher mean Brain-PAD than their female counterparts (PD-F) (t(256) = 2.50, p = 0.012). In the propensity score-matched PD-M group (PD-M*), Brain-PAD was found to be associated with a decline in general cognition, a worse degree of sleep behavior disorder, reduced visuospatial acuity, and caudate atrophy. Conversely, no significant links were observed between these factors and Brain-PAD in the PD-F group. Having ‘older’ looking brains in PD-M than PD-F supports the idea that sex plays a vital function in PD, such that the PD mechanism may be different in males and females. This study has the potential to broaden our understanding of dissimilarities in brain aging between sexes in the context of PD.

Evaluation of brain age changes in patients with liver cirrhosis and hepatic encephalopathy with deep learning models based on structural magnetic resonance imaging

Objective: To investigate the brain aging in patients with cirrhosis and hepatic encephalopathy(HE), constructed a prediction model of brain age based on deep learning and T1 high-resolution MRI, and try to reveal the specific regions where cirrhosis and HE accelerating brain aging. Methods: A cross-sectional study. A brain age prediction model based on the 3D full convolutional neural network was constructed through T1 high-resolution MRI data from 3 609 healthy individuals across eight global public datasets. The mean absolute error (MAE) between actual age and predicted brain age, Pearson correlation coefficient (r) and determination coefficient (R2) were calculated to evaluate the accuracy of the model's predictions. A test set (n=555) from the Human Connectome Project was used to assess the accuracy of the model. A total of 136 patients with cirrhosis were recruited from Tianjin First Central Hospital as the case group (79 patients with cirrhosis without HE and 57 patients with cirrhosis with HE), and 70 healthy individuals were recruited from the society as the healthy control group during the same period. Brain-predicted age difference (Brain-PAD), digital connection-A (NCT-A) and digital-symbol test (DST) scores of all subjects were calculated for all subjects to assess brain aging and cognitive function in the healthy control group, the cirrhosis without HE group, and the cirrhosis with HE group. The network occlusion sensitivity analysis method was employed to assess the importance of each brain region in predicting brain age. Results: As for the prediction model, in the training set, MAE=2.85, r=0.98, R2=0.96. In the test set, MAE=4.45, r=0.96, R2=0.92. In the local data set of the healthy control group, MAE=3.77, r=0.85, R2=0.73. The time of NCT-A in both cirrhosis groups was longer than healthy control group, while the DST scores were lower than healthy control group, and the differences were statistically significant (both P<0.001); the Brain-PAD of healthy control group was (0.8±4.5) years, the Brain-PAD of no-HE group was (6.9±8.1) years, and the HE group was (10.2±7.7) years. The differences between the three groups were statistically significant (P<0.001), and the differences between any two groups were statistically significant (all P<0.05). The importance ratio of visual network in predicting brain age increased in cirrhosis patients, and the HE group was higher than no-HE group. Conclusions: In patients with cirrhosis, the cognitive function is reduced, brain aging is accelerated, and these changes are more obvious in patients with HE. The importance differences of each brain network in predicting brain aging provide a new direction for identifying the specific regions where cirrhosis and HE accelerate brain aging.

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.

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. 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. 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. 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. ClinicalTrials.gov Identifier: ChiCTR-IOR-16008191.

Cerebrovascular lesion loads and accelerated brain aging: insights into the cognitive spectrum.

White matter hyperintensities (WMHs) and cerebral microbleeds are widespread among aging population and linked with cognitive deficits in mild cognitive impairment (MCI), vascular MCI (V-MCI), and Alzheimer's disease without (AD) or with a vascular component (V-AD). In this study, we aimed to investigate the association between brain age, which reflects global brain health, and cerebrovascular lesion load in the context of pathological aging in diverse forms of clinically-defined neurodegenerative conditions. We computed brain-predicted age difference (brain-PAD: predicted brain age minus chronological age) in the Comprehensive Assessment of Neurodegeneration and Dementia cohort of the Canadian Consortium on Neurodegeneration in Aging including 70 cognitively intact elderly (CIE), 173 MCI, 88 V-MCI, 50 AD, and 47 V-AD using T1-weighted magnetic resonance imaging (MRI) scans. We used a well-established automated methodology that leveraged fluid attenuated inversion recovery MRIs for precise quantification of WMH burden. Additionally, cerebral microbleeds were detected utilizing a validated segmentation tool based on the ResNet50 network, utilizing routine T1-weighted, T2-weighted, and T2* MRI scans. The mean brain-PAD in the CIE cohort was around zero, whereas the four categories showed a significantly higher mean brain-PAD compared to CIE, except MCI group. A notable association trend between brain-PAD and WMH loads was observed in aging and across the spectrum of cognitive impairment due to AD, but not between brain-PAD and microbleed loads. WMHs were associated with faster brain aging and should be considered as a risk factor which imperils brain health in aging and exacerbate brain abnormalities in the context of neurodegeneration of presumed AD origin. Our findings underscore the significance of novel research endeavors aimed at elucidating the etiology, prevention, and treatment of WMH in the area of brain aging.

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

BackgroundImpaired 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. MethodsParticipants 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. ResultsSlower 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. ConclusionThese results demonstrate that slower systolic and diastolic BP recovery in the early phase after standing is associated with accelerated brain ageing 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 ageing, relying on a simple procedure and devices that are easily accessible.

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

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). 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. 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. 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.

SEX DIFFERENCES IN THE LINK BETWEEN DNA METHYLATION–BASED BIOLOGICAL AGING AND HEALTH IN OLDER INDIVIDUALS

Abstract It is well-established that women live longer than men, however, sex differences in biological aging have not been consistently reported, and may differ depending on the measure used. This study aimed to determine the correlation between DNA methylation (DNAm) age acceleration (AA) measures, with system-wide frailty-index (FI) aging and brain-predicted age difference (brain-PAD), in an initially healthy population of men and women aged ≥70 years. We additionally explored the extent to which these AA measures were associated with clinical measures, and chronic conditions, separately in men and women. DNAm age (Horvath, Hannum, PhenoAge, GrimAge, and DunedinPACE) were estimated in blood from 560 Australians (women, 50.7%) enrolled in the ASPREE study. FI was a deficit accumulation of 67 items including cognition and morbidities. Brain age was estimated from T1-weighted MRI. Clinical measurements, laboratory tests, and self-reports were used to identify chronic illnesses. FI had the strongest correlation with GrimAA and DunedinPACE (range r: 0.20 to 0.24 in both sexes) but was not associated with HorvathAA. Brain-PAD was not correlated with any biological aging measure. Correlations between AA and clinical measures were more commonly found in women than men and were the strongest for DunedinPACE and clinical measures including grip strength, gait speed, and self-rated health (r: -0.23 to 0.24). AA measures, especially GrimAA and DunedinPACE, were significantly associated with hypertension, diabetes, chronic kidney disease in men, and obesity and depression in women. Our findings highlight the importance of considering sex differences when investigating the link between biological age and clinical measures.

Association between cardiovascular health and brain age: a large population‐based study in the UK Biobank

AbstractBackgroundCardiovascular health (CVH) has been associated with cognitive decline and dementia, but the extent to which CVH impact brain age remains unclear. We aimed to investigate the association of CVH assessed by Life’s Essential 8 (LE8) score with neuroimaging‐based brain age and brain predicted age difference (PAD).MethodThe study included a total of 34098 participants (mean age 54.82±7.50 years; 53.21% female) from the UK Biobank, who underwent brain magnetic resonance imaging (MRI) scans 9 years after baseline. A brain age prediction model with the best prediction performance was trained from 9 candidate models (Lasso, support vector regression, and XGBoost, with or without the addition of 2 variable selection methods, respectively) based on T1‐weighted MRI, T2‐FLAIR, T2∗, diffusion‐MRI, task fMRI, and resting‐state fMRI in 4355 healthy participants. Using the model, we calculated brain age and brain PAD (predicted age‐chronological age). Baseline LE8 score was calculated by the mean score of 8 metrics (diet, physical activity, nicotine exposure, sleep health, body mass index, blood lipids, blood glucose, and blood pressure) and tertiled as low, moderate, and high CVH. Data were analyzed using linear regression models.ResultAt baseline, LE8 score was ranged from 15.00 to 100.00. In linear regression adjusted for age, sex and education, better CVH was associated with younger brain age (β [95% CI]: ‐0.029 [‐0.032, ‐0.026]) and smaller brain PAD (β [95% CI]: ‐0.033 [‐0.035, ‐0.030]). Compared to moderate CVH, low CVH was associated with older brain age (β [95% CI]: 0.444 [0.345, 0.543]) and bigger brain PAD (β [95% CI]: 0.513 [0.422, 0.604]), whereas high CVH was associated with younger brain age (β [95% CI]: ‐0.360 [‐0.459, ‐0.261]) and smaller brain PAD (β [95% CI]: ‐0.398 [‐0.489, ‐0.307]). The additional adjustment did not alter the relationships observed. After stratification by age, cardiovascular disease (CVD), or APOE ε4, the association remained significant among adults aged 40‐60 years and 60+ years, among CVD and non‐CVD, as well as among APOE ε4 carriers and non‐ carriers.ConclusionBetter cardiovascular health is associated with younger brain age and reduced brain PAD. This association is significant even in middle‐aged, non‐CVD and non‐carriers adults.