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.

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

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

Accelerated Brain Aging in Adults With Major Depressive Disorder Predicts Poorer Outcome With Sertraline: Findings From the EMBARC Study.

BACKGROUND: Major depressive disorder (MDD) may be associated with accelerated brain aging (higher brain age than chronological age). This report evaluated whether brain age is a clinically useful biomarker by checking its test-retest reliability using magnetic resonance imaging scans acquired 1 week apart and by evaluating the association of accelerated brain aging with symptom severity and antidepressant treatment outcomes. METHODS: Brain age was estimated in participants of the EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care) study using T1-weighted structural magnetic resonance imaging (MDD n = 290; female n = 192; healthy control participants n = 39; female n = 24). Intraclass correlation coefficient was used for baseline-to-week-1 test-retest reliability. Association of baseline Δ brain age (brain age minus chronological age) with Hamilton Depression Rating Scale-17 and Concise Health Risk Tracking Self-Report domains (impulsivity, suicide propensity [measures: pessimism, helplessness, perceived lack of social support, and despair], and suicidal thoughts) were assessed at baseline (linear regression) and during 8-week-long treatment with either sertraline or placebo (repeated-measures mixed models). RESULTS: Mean ± SD baseline chronological age, brain age, and Δ brain age were 37.1 ± 13.3, 40.6 ± 13.1, and 3.1 ± 6.1 years in MDD and 37.1 ± 14.7, 38.4 ± 12.9, and 0.6 ± 5.5 years in healthy control groups, respectively. Test-retest reliability was high (intraclass correlation coefficient = 0.98-1.00). Higher baseline Δ brain age in the MDD group was associated with higher baseline impulsivity and suicide propensity and predicted smaller baseline-to-week-8 reductions in Hamilton Depression Rating Scale-17, impulsivity, and suicide propensity with sertraline but not with placebo. CONCLUSIONS: Brain age is a reliable and potentially clinically useful biomarker that can prognosticate antidepressant treatment outcomes.

Accelerated brain aging in individuals with diabetes: Association with poor glycemic control and increased all-cause mortality.

BACKGROUND: Diabetes has been linked to accelerated brain aging, i.e., neuroimaging-predicted age of brain is higher than chronological age. This report evaluated whether accelerated brain aging in diabetes is associated with higher levels of glycated hemoglobin (HbA1c) and increased mortality. METHODS: Brain age in Dallas Heart Study (n = 1949) was estimated using T1-weighted magnetic resonance imaging (MRI) scans and a previously-published Gaussian Processes Regression model. Accelerated brain aging (adjusted Δ brain age) was computed as follows: (brain age adjusted for chronological age)-minus-(chronological age). Mortality data until 12/31/2016 were obtained from the National Death Index. Associations of adjusted Δ brain age with diabetes in full sample and with HbA1c in individuals with diabetes were evaluated. Proportion of association between diabetes and all-cause mortality that was accounted for by adjusted Δ brain age were evaluated with mediation analyses. Covariates included Framingham 10-year risk score, race/ethnicity, income, body mass index, and history of myocardial infarction. RESULTS: Diabetes was associated with] higher adjusted Δ brain age [estimate= 1.79; 95% confidence interval (CI): 0.889, 2.68]. Among those with diabetes, higher HbA1c (log-base-2-transformed) was associated with higher adjusted Δ brain age (estimate=3.88; 95% CI: 1.47, 6.30). Over a median follow-up of 97.5 months, 24/246 (9.8%) with diabetes and 63/1703 (3.7%) without diabetes died. Adjusted Δ brain age accounted for 65.3 (95% CI: 39.3, 100.0)% of the association between diabetes and all-cause mortality. CONCLUSION: Accelerated brain aging may be related to poor glycemic control in diabetes and partly account for the association between diabetes and all-cause mortality.

Multimodal Magnetic Resonance Imaging Reveals Aberrant Brain Age Trajectory During Youth in Schizophrenia Patients.

Accelerated brain aging had been widely reported in patients with schizophrenia (SZ). However, brain aging trajectories in SZ patients have not been well-documented using three-modal magnetic resonance imaging (MRI) data. In this study, 138 schizophrenia patients and 205 normal controls aged 20-60 were included and multimodal MRI data were acquired for each individual, including structural MRI, resting state-functional MRI and diffusion tensor imaging. The brain age of each participant was estimated by features extracted from multimodal MRI data using linear multiple regression. The correlation between the brain age gap and chronological age in SZ patients was best fitted by a positive quadratic curve with a peak chronological age of 47.33 years. We used the peak to divide the subjects into a youth group and a middle age group. In the normal controls, brain age matched chronological age well for both the youth and middle age groups, but this was not the case for schizophrenia patients. More importantly, schizophrenia patients exhibited increased brain age in the youth group but not in the middle age group. In this study, we aimed to investigate brain aging trajectories in SZ patients using multimodal MRI data and revealed an aberrant brain age trajectory in young schizophrenia patients, providing new insights into the pathophysiological mechanisms of schizophrenia.

The additive impact of cardio-metabolic disorders and psychiatric illnesses on accelerated brain aging.

Severe mental illnesses (SMI) including major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia spectrum disorder (SSD) elevate accelerated brain aging risks. Cardio-metabolic disorders (CMD) are common comorbidities in SMI and negatively impact brain health. We validated a linear quantile regression index (QRI) approach against the machine learning "BrainAge" index in an independent SSD cohort (N = 206). We tested the direct and additive effects of SMI and CMD effects on accelerated brain aging in the N = 1,618 (604 M/1,014 F, average age = 63.53 ± 7.38) subjects with SMI and N = 11,849 (5,719 M/6,130 F; 64.42 ± 7.38) controls from the UK Biobank. Subjects were subdivided based on diagnostic status: SMI+/CMD+ (N = 665), SMI+/CMD- (N = 964), SMI-/CMD+ (N = 3,765), SMI-/CMD- (N = 8,083). SMI (F = 40.47, p = 2.06 × 10-10 ) and CMD (F = 24.69, p = 6.82 × 10-7 ) significantly, independently impacted whole-brain QRI in SMI+. SSD had the largest effect (Cohen's d = 1.42) then BD (d = 0.55), and MDD (d = 0.15). Hypertension had a significant effect on SMI+ (d = 0.19) and SMI- (d = 0.14). SMI effects were direct, independent of MD, and remained significant after correcting for effects of antipsychotic medications. Whole-brain QRI was significantly (p < 10-16 ) associated with the volume of white matter hyperintensities (WMH). However, WMH did not show significant association with SMI and was driven by CMD, chiefly hypertension (p < 10-16 ). We used a simple and robust index, QRI, the demonstrate additive effect of SMI and CMD on accelerated brain aging. We showed a greater effect of psychiatric illnesses on QRI compared to cardio-metabolic illness. Our findings suggest that subjects with SMI should be among the targets for interventions to protect against age-related cognitive decline.

Brain age in mood and psychotic disorders: a systematic review and meta-analysis.

OBJECTIVE: To evaluate whether accelerated brain aging occurs in individuals with mood or psychotic disorders. METHODS: A systematic review following PRISMA guidelines was conducted. A meta-analysis was then performed to assess neuroimaging-derived brain age gap in three independent groups: (1) schizophrenia and first-episode psychosis, (2) major depressive disorder, and (3) bipolar disorder. RESULTS: A total of 18 papers were included. The random-effects model meta-analysis showed a significantly increased neuroimaging-derived brain age gap relative to age-matched controls for the three major psychiatric disorders, with schizophrenia (3.08; 95%CI [2.32; 3.85]; p < 0.01) presenting the largest effect, followed by bipolar disorder (1.93; [0.53; 3.34]; p < 0.01) and major depressive disorder (1.12; [0.41; 1.83]; p < 0.01). The brain age gap was larger in older compared to younger individuals. CONCLUSION: Individuals with mood and psychotic disorders may undergo a process of accelerated brain aging reflected in patterns captured by neuroimaging data. The brain age gap tends to be more pronounced in older individuals, indicating a possible cumulative biological effect of illness burden.

Accelerated Brain Aging in Mild Traumatic Brain Injury: Longitudinal Pattern Recognition with White Matter Integrity.

Mild traumatic brain injury (mTBI) initiating long-term effects on white matter integrity resembles brain-aging changes, implying an aging process accelerated by mTBI. This longitudinal study aims to investigate the mTBI-induced acceleration of the brain-aging process by developing a neuroimaging model to predict brain age. The brain-age prediction model was defined using relevance vector regression based on fractional anisotropy from diffusion tensor imaging of 523 healthy individuals. The model was used to estimate the brain-predicted age difference (brain-PAD) between the chronological and estimated brain age in 116 acute mTBI patients and 63 healthy controls. Fifty patients were followed for 6 ∼ 12 months to evaluate the longitudinal changes in brain-PAD. We investigated whether brain-PAD was greater in patients of older age, post-concussion complaints, and apolipoprotein E (APOE) ɛ4 genotype, and whether it had the potential to predict neuropsychological outcomes. The brain-age prediction model predicted brain age accurately (r = 0.96). The brains of mTBI patients in the acute phase were estimated to be "older," with greater brain-PAD (2.59 ± 5.97 years) than the healthy controls (0.12 ± 3.19 years) (p < 0.05), and remained stable 6-12 month post-injury (2.50 ± 4.54 years). Patients who were older or who had post-concussion complaints, rather than APOE ɛ4 genotype, had greater brain-PADs (p < 0.001, p = 0.024). Additionally, brain-PAD in the acute phase predicted information processing speed at the 6 ∼ 12 month follow-up (r = -0.36, p = 0.01). In conclusion, mTBI accelerates the brain-aging process, and brain-PAD may be capable of evaluating aging-associated issues post-injury, such as increased risks of neurodegeneration.

Brain aging in temporal lobe epilepsy: Chronological, structural, and functional.

The association of epilepsy with structural brain changes and cognitive abnormalities in midlife has raised concern regarding the possibility of future accelerated brain and cognitive aging and increased risk of later life neurocognitive disorders. To address this issue we examined age-related processes in both structural and functional neuroimaging among individuals with temporal lobe epilepsy (TLE, N = 104) who were participants in the Epilepsy Connectome Project (ECP). Support vector regression (SVR) models were trained from 151 healthy controls and used to predict TLE patients' brain ages. It was found that TLE patients on average have both older structural (+6.6 years) and functional (+8.3 years) brain ages compared to healthy controls. Accelerated functional brain age (functional - chronological age) was mildly correlated (corrected P = 0.07) with complex partial seizure frequency and the number of anti-epileptic drug intake. Functional brain age was a significant correlate of declining cognition (fluid abilities) and partially mediated chronological age-fluid cognition relationships. Chronological age was the only positive predictor of crystallized cognition. Accelerated aging is evident not only in the structural brains of patients with TLE, but also in their functional brains. Understanding the causes of accelerated brain aging in TLE will be clinically important in order to potentially prevent or mitigate their cognitive deficits.