BrainAGE Score Research Articles

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.

Accelerated Aging after Traumatic Brain Injury: An ENIGMA Multi-Cohort Mega-Analysis.

The long-term consequences of traumatic brain injury (TBI) on brain structure remain uncertain. Given evidence that a single significant brain injury event increases the risk of dementia, brain-age estimation could provide a novel and efficient indexing of the long-term consequences of TBI. Brain-age procedures use predictive modeling to calculate brain-age scores for an individual using structural magnetic resonance imaging (MRI) data. Complicated mild, moderate, and severe TBI (cmsTBI) is associated with a higher predicted age difference (PAD), but the progression of PAD over time remains unclear. We sought to examine whether PAD increases as a function of time since injury (TSI) and if injury severity and sex interacted to influence this progression. Through the ENIGMA Adult Moderate and Severe (AMS)-TBI working group, we examine the largest TBI sample to date (n = 343), along with controls, for a total sample size of n = 540, to replicate and extend prior findings in the study of TBI brain age. Cross-sectional T1w-MRI data were aggregated across 7 cohorts, and brain age was established using a similar brain age algorithm to prior work in TBI. Findings show that PAD widens with longer TSI, and there was evidence for differences between sexes in PAD, with men showing more advanced brain age. We did not find strong evidence supporting a link between PAD and cognitive performance. This work provides evidence that changes in brain structure after cmsTBI are dynamic, with an initial period of change, followed by relative stability in brain morphometry, eventually leading to further changes in the decades after a single cmsTBI. ANN NEUROL 2024;96:365-377.

Is musical engagement enough to keep the brain young?

Music-making and engagement in music-related activities have shown procognitive benefits for healthy and pathological populations, suggesting reductions in brain aging. A previous brain aging study, using Brain Age Gap Estimation (BrainAGE), showed that professional and amateur-musicians had younger appearing brains than non-musicians. Our study sought to replicate those findings and analyze if musical training or active musical engagement was necessary to produce an age-decelerating effect in a cohort of healthy individuals. We scanned 125 healthy controls and investigated if musician status, and if musical behaviors, namely active engagement (AE) and musical training (MT) [as measured using the Goldsmiths Musical Sophistication Index (Gold-MSI)], had effects on brain aging. Our findings suggest that musician status is not related to BrainAGE score, although involvement in current physical activity is. Although neither MT nor AE subscales of the Gold-MSI are predictive for BrainAGE scores, dispositional resilience, namely the ability to deal with challenge, is related to both musical behaviors and sensitivity to musical pleasure. While the study failed to replicate the findings in a previous brain aging study, musical training and active musical engagement are related to the resilience factor of challenge. This finding may reveal how such musical behaviors can potentially strengthen the brain’s resilience to age, which may tap into a type of neurocognitive reserve.

Neuroimaging‐based Biomarkers of Brain Aging in Healthy Older Adults

AbstractBackgroundWith increasing longevity, the incidence of age‐associated functional decline and dementia is on the rise, with Alzheimer’s disease, the third most common type of dementia, projected to affect 13.8 million people by 2050. Previous studies on aging populations have reported that while people chronologically age at the same rate, an individual’s brain maturation/aging trajectory, which can be quantified by measuring the deviation of their brain’s structure and function from normative data, can be predictive of more serious forms of cognitive decline. Software capable of accurately computing this trajectory based on commonly acquired clinical scans, such as BrainAGE R, could provide reliable biomarkers of pre‐clinical disease states thereby enhancing clinicians’ ability to make early diagnosis of cognitive decline. In this study, we evaluate the relationship between resting‐state functional connectivity (rsFC) and BrainAGE measures in healthy older adults.MethodSixty participants (40 Female, 20 Male) aged between 60‐80 (M = 66.78, SD = 6.98) were recruited as part of the Aging Brain Cohort dataset at the University of South Carolina (ABC@UofSC). Participants completed structural T1‐weighted (T1w) and resting‐state fMRI (rsfMRI) scans. Preprocessing of the data was done using default preprocessing steps specified by the CONN toolbox. Following image preprocessing and denoising, ROI‐to‐ROI connectivity matrices in CONN were created for each participant. Individual brain age was estimated using BrainAGE R computer algorithm based on their T1w scans. BrainAGE difference scores were then calculated by computing the difference between each participant’s chronological age and their estimated brain age. Finally, a univariate general linear model analysis was used to identify significant relationships between BrainAGE difference scores and rsFC while controlling for participants’ chronological age.ResultOur analysis revealed a significantly negative association between BrainAGE scores and rsFC of several brain networks. In particular, higher BrainAGE scores were associated with decreased connectivity between 1) default mode and salience networks, 2) default mode and fronto‐parietal networks (bilateral), and 3) visual and salience networks.ConclusionOur results provide novel evidence that accelerated brain maturation/aging, as indexed by BrainAGE scores, is associated with specific patterns of decreased rsFC in healthy older adults.

Brain age estimation by structural MRI in superagers: a BrainAGE approach in elderly people with youthful episodic memory

AbstractBackgroundAge‐related episodic memory decline is not the only trajectory of healthy ageing. Superagers are the living proof that an 80yo can have the memory of a 50‐60yo. We aim to estimate the biological brain age of superagers using the BrainAGE approach to understand the underlying ageing process. In the context of normal ageing, resistance refers to the avoidance of ageing effects and will translate into a deceleration of the ageing process, whereas resilience is the successful coping with age‐related changes and will translate into a similar estimated brain age and chronological age. We tested these hypotheses in this study.MethodA sample of 63 superagers (mean age 81.8 years ± 1.8 std, 60.3% women) and 55 age‐matched controls (mean age 82.4 years ± 1.9 std, 63.6% women) from the Vallecas Project cohort in Madrid, Spain, were selected according to their episodic memory score in the Free and Cued Selective Reminding Test. Superagers scored at or above the mean value of a 50‐56yo person, whereas controls scored within one standard deviation for their age. BrainAGE (“Brain Age Gap Estimation”) aggregates to a single value the complex multidimensional aging pattern within the whole brain by applying established kernel regression methods to anatomical brain magnetic resonance images (Franke and Gaser 2019). The BrainAGE score was calculated for both groups as the difference between chronological age and estimated brain age. Positive values indicate an acceleration of the ageing process and negative values indicate a deceleration of the ageing process.ResultThe BrainAGE score of superagers was ‐0.5 years, showing that their estimated brain age is lower than their chronological age whereas the estimated brain age of the control group is similar to their chronological age. The difference in BrainAGE between the superager and the control group is significant (p = 1x10‐6). This result indicates a deceleration in the brain ageing process of superagers compared to age‐matched controls.ConclusionSuperagers showed a slowing of the structural brain age‐related changes compared to the control group. These results suggest that superagers, selected for their youthful episodic memory, show whole‐brain resistance to the ageing process.

Alzheimer resemblance atrophy index, BrainAGE, and normal pressure hydrocephalus score in the prediction of subtle cognitive decline: added value compared to existing MR imaging markers

ObjectivesEstablished visual brain MRI markers for dementia include hippocampal atrophy (mesio-temporal atrophy MTA), white matter lesions (Fazekas score), and number of cerebral microbleeds (CMBs). We assessed whether novel quantitative, artificial intelligence (AI)–based volumetric scores provide additional value in predicting subsequent cognitive decline in elderly controls.MethodsA prospective study including 80 individuals (46 females, mean age 73.4 ± 3.5 years). 3T MR imaging was performed at baseline. Extensive neuropsychological assessment was performed at baseline and at 4.5-year follow-up. AI-based volumetric scores were derived from 3DT1: Alzheimer Disease Resemblance Atrophy Index (AD-RAI), Brain Age Gap Estimate (BrainAGE), and normal pressure hydrocephalus (NPH) index. Analyses included regression models between cognitive scores and imaging markers.ResultsAD-RAI score at baseline was associated with Corsi (visuospatial memory) decline (10.6% of cognitive variability in multiple regression models). After inclusion of MTA, CMB, and Fazekas scores simultaneously, the AD-RAI score remained as the sole valid predictor of the cognitive outcome explaining 16.7% of its variability. Its percentage reached 21.4% when amyloid positivity was considered an additional explanatory factor. BrainAGE score was associated with Trail Making B (executive functions) decrease (8.5% of cognitive variability). Among the conventional MRI markers, only the Fazekas score at baseline was positively related to the cognitive outcome (8.7% of cognitive variability). The addition of the BrainAGE score as an independent variable significantly increased the percentage of cognitive variability explained by the regression model (from 8.7 to 14%). The addition of amyloid positivity led to a further increase in this percentage reaching 21.8%.ConclusionsThe AI-based AD-RAI index and BrainAGE scores have limited but significant added value in predicting the subsequent cognitive decline in elderly controls when compared to the established visual MRI markers of brain aging, notably MTA, Fazekas score, and number of CMBs.Key Points• AD-RAI score at baseline was associated with Corsi score (visuospatial memory) decline.• BrainAGE score was associated with Trail Making B (executive functions) decrease.• AD-RAI index and BrainAGE scores have limited but significant added value in predicting the subsequent cognitive decline in elderly controls when compared to the established visual MRI markers of brain aging, notably MTA, Fazekas score, and number of CMBs.

Association of Treatable Health Conditions During Adolescence With Accelerated Aging at Midlife

Biological aging is a distinct construct from health; however, people who age quickly are more likely to experience poor health. Identifying pediatric health conditions associated with accelerated aging could help develop treatment approaches to slow midlife aging and prevent poor health in later life. To examine the association between 4 treatable health conditions in adolescence and accelerated aging at midlife. This cohort study analyzed data from participants in the Dunedin Study, a longitudinal investigation of health and behavior among a birth cohort born between April 1, 1972, and March 31, 1973, in Dunedin, New Zealand, and followed up until age 45 years. Participants underwent an assessment at age 45 years and had data for at least 1 adolescent health condition (asthma, smoking, obesity, and psychological disorders) and outcome measure (pace of aging, gait speed, brain age, and facial age). Data analysis was performed from February 11 to September 27, 2021. Asthma, cigarette smoking, obesity, and psychological disorders were assessed at age 11, 13, and 15 years. The outcome was a midlife aging factor composite score comprising 4 measures of biological aging: pace of aging, gait speed, brain age (specifically, BrainAGE score), and facial age. A total of 910 participants (459 men [50.4%]) met the inclusion criteria, including an assessment at age 45 years. Participants who had smoked daily (0.61 [95% CI, 0.43-0.79] SD units), had obesity (0.82 [95% CI, 0.59-1.06] SD units), or had a psychological disorder diagnosis (0.43 [95% CI, 0.29-0.56] SD units) during adolescence were biologically older at midlife compared with participants without these conditions. Participants with asthma were not biologically older at midlife (0.02 [95% CI, -0.14 to 0.19] SD units) compared with those without asthma. These results remained unchanged after adjusting for childhood risk factors such as poor health, socioeconomic disadvantage, and adverse experiences. This study found that adolescent smoking, obesity, and psychological disorder diagnoses were associated with older biological age at midlife. These health conditions could be treated during adolescence to reduce the risk of accelerated biological aging later in life.

The effect of Alzheimer’s disease risk factors on brain aging in normal Chineses: Cognitive aging and cognitive reserve

Aging has been recognized as a major driving force of the Alzheimer’s disease’s (AD) progression, however, the relationship between brain aging and AD is still unclear. There is also a lack of studies investigating the influence of AD risk factors on brain aging in cognitively normal people. Here, the “Brain Age Gap Estimation” (BrainAGE) framework was applied to investigate the effects of AD risk factors on individual brain aging. Across a total of 165 cognitively normal elderly subjects, although no significant difference was observed in the BrainAGE scores among the three groups, AD risk dose (i.e., the number of AD risk factors) is tend to associated with an increased BrainAGE scores (high-risk > middle risk > low risk). Female exhibited more advanced brain aging (P = 0.004), and higher education years were associated with preserved brain aging (P < 0.001). APOE-ɛ4 (P = 0.846) and family history (FH) of dementia (P = 0.209) did not increase BrainAGE scores. When comparing 52 aMCI patients with 38 cognitively normal controls from ADNI dataset, aMCI patients showed significantly increased BrainAGE scores. BrainAGE scores were negatively correlated with CSF Aβ42 levels in the aMCI group (r = −0.275, P = 0.048). With an accuracy of 68.9%, BrainAGE outperformed APOE-ɛ4 and hippocampus gray matter volume (GMV) in predicting aMCI. In conclusion, AD is independently associated with structural changes in the brain that reflect advanced aging. Potentially, BrainAGE combined with APOE-ɛ4 and hippocampus GMV could be used as a pre-screening tool in early-stage AD.

The accuracy of T1-weighted voxel-wise and region-wise metrics for brain age estimation

IntroductionThe brain age score has recently been introduced for robust monitoring of brain morphological alterations throughout the lifespan, prediction of mortality risk, and early detection of neurological disorders. MethodsWe assessed the brain age prediction accuracy of the widely used T1-weighted voxel-wise and region-wise metrics (i.e., T1-weighted magnetic resonance imaging [MRI]-wise metrics)) separately and their integration. We assessed 788 healthy individuals (age, 18–94 years) in a training set to build a brain age estimation framework based on different T1-weighted MRI-wise metrics (15 different metrics in total) and then validated each T1-weighted MRI-wise metric in an independent test set comprising 88 healthy individuals. We also assessed the accuracy of each T1-weighted MRI-wise metric in a clinical set of 70 patients with mild cognitive impairment and another of 30 patients with Alzheimer's disease. ResultsIntegration of gray matter voxel-wise maps and all region-wise metrics achieved the highest brain age prediction accuracy (mean absolute error, 4.63 years). These metrics on their own achieved lower accuracy (mean absolute error, 4.97 years and 5.75 years, respectively). DiscussionFor tracing brain atrophy levels in neurological disorders at the clinical level, integration of voxel-wise and region-wise metrics may contribute to a more sensitive brain age framework than when these metrics are used on their own.

Increased Brain Age Gap Estimate (BrainAGE) in Young Adults After Premature Birth

Recent evidence suggests increased metabolic and physiologic aging rates in premature-born adults. While the lasting consequences of premature birth on human brain development are known, its impact on brain aging remains unclear. We addressed the question of whether premature birth impacts brain age gap estimates (BrainAGE) using an accurate and robust machine-learning framework based on structural MRI in a large cohort of young premature-born adults (n = 101) and full-term (FT) controls (n = 111). Study participants are part of a geographically defined population study of premature-born individuals, which have been followed longitudinally from birth until young adulthood. We investigated the association between BrainAGE scores and perinatal variables as well as with outcomes of physical (total intracranial volume, TIV) and cognitive development (full-scale IQ, FS-IQ). We found increased BrainAGE in premature-born adults [median (interquartile range) = 1.4 (−1.3–4.7 years)] compared to full-term controls (p = 0.002, Cohen’s d = 0.443), which was associated with low Gestational age (GA), low birth weight (BW), and increased neonatal treatment intensity but not with TIV or FS-IQ. In conclusion, results demonstrate elevated BrainAGE in premature-born adults, suggesting an increased risk for accelerated brain aging in human prematurity.

When your brain looks older than expected: combined lifestyle risk and BrainAGE

Lifestyle may be one source of unexplained variance in the great interindividual variability of the brain in age-related structural differences. While physical and social activity may protect against structural decline, other lifestyle behaviors may be accelerating factors. We examined whether riskier lifestyle correlates with accelerated brain aging using the BrainAGE score in 622 older adults from the 1000BRAINS cohort. Lifestyle was measured using a combined lifestyle risk score, composed of risk (smoking, alcohol intake) and protective variables (social integration and physical activity). We estimated individual BrainAGE from T1-weighted MRI data indicating accelerated brain atrophy by higher values. Then, the effect of combined lifestyle risk and individual lifestyle variables was regressed against BrainAGE. One unit increase in combined lifestyle risk predicted 5.04 months of additional BrainAGE. This prediction was driven by smoking (0.6 additional months of BrainAGE per pack-year) and physical activity (0.55 less months in BrainAGE per metabolic equivalent). Stratification by sex revealed a stronger association between physical activity and BrainAGE in males than females. Overall, our observations may be helpful with regard to lifestyle-related tailored prevention measures that slow changes in brain structure in older adults.

Can fetal heart rate variability obtained from cardiotocography provide the same diagnostic value like from electrophysiological interbeat intervals?

Objective. Fetal heart rate variability (HRV) is widely used for monitoring fetal developmental disturbances. Only expensive fetal magnetocardiography (fMCG) allows the precise recording of the individual fetal heart beat intervals uncovering also highly frequent vagal modulation. In contrast, transabdominal fetal electrocardiography (fECG) suffers from noise overlaying the fetal cardiac signal. Cardiotocography (CTG) is the clinical method of choice, however, based on Doppler ultrasound, improper to resolve single beats concisely. The present work addresses the transferability of established electrophysiological HRV indices to CTG recordings during the fetal maturation period of 20–40 weeks of gestation (WGA). Approach. We compared (a) HRV indices obtained from fMCG, CTG and fECG of short-term amplitude fluctuations (sAMPs) and long-term amplitude fluctuations (lAMPs) and complexity, and (b) their diagnostic value for identifying maturational age, fetal growth restriction (FGR) and small for gestational age (SGA). We used the functional brain age score (fABAS) and categories of long- and short-term regulation and complexity. Main results. Integrating all substudies, we found: (a) indices related to long-term regulation, and with modified meaning and values of short-term regulation and sympathovagal balance (SVB) according to electrophysiological HRV standards can be obtained from CTG. (b) Models using HRV indices calculated from CTG allow the identification of maturational age and discriminate FGR from controls with almost similar precision as electrophysiological means. (c) A modified set of HRV parameters containing short- and long-term regulation and long-term/short-term ratio appeared to be most suitable to describe autonomic developmental state when CTG data is used. Significance. Whereas the predominantly vagally modulated beat-to-beat precise high frequencies of HRV are not assessable from CTG, we identified relevant related HRV indices and categories for CTG recordings with diagnostic potential. They require further evaluation and confirmation with respect to any issues of fetal developmental and perinatal problems in subsequent studies. This methodology significantly extends the measures of established CTG devices. Novelty and significance HRV indices provide predestinated diagnostic markers of autonomic control in fetuses. However, the established CTG does not provide the temporal precision of electrophysiological recordings.Beat-to-beat related, mainly vagally modulated behavior is not exactly represented in CTG. However, a set of CTG-specific HRV indices that are mainly comparable to established electrophysiological HRV parameters obtained by magnetocardiography or electrocardiography provided almost similar predictive value for fetal maturational age and were helpful in characterizing FGR.These results require validation in the monitoring of further fetal developmental disturbances.We recommend a corresponding extension of CTG methodology.

Initial exploration of a brain age score based on validated computerized cognitive assessments in Japanese individuals

AbstractBackgroundDifferences between chronological age and biological age can be used to communicate health related information to the general population. For the brain, cognitive performance can be used to define biological age. We utilized data from performance on tests from the Cogstate Brief Battery (CBB) to develop a ‘Brain Age’ score for characterizing the presence of any cognitive impairment in Japanese older adults at risk of dementia.MethodsTo compute an Brain Age, data from the One Card Learning and One Back tests from the CBB was compared to age stratified normative data. Brain Age was computed as the difference in standardized score from age matched normative data. For every point better or worse than the normative mean (0), 0.5 years was added or subtracted from chronological age, to a maximum of 10 years (20 points, or 2 SD). For the current analysis, Brain Age scores were derived for 106 Japanese cognitively normal (CN) individuals from clinical trial screening data for the A4 study and 611 Japanese adults classified with a memory impairment during their screening for the elenbecestat MissionAD program (memory impaired). Differences between these groups were used to determine known groups validity.ResultsMean chronological age was equivalent in the CN (mean = 71.7, SD = 4.6) and memory impaired (mean =72.6, SD= 7.4) samples. Brain Age score was equivalent to chronological age in the CN sample (mean Brain Age = 72.4, SD = 7.45). However in the memory impaired group Brain Age was 6.3 years greater than chronological age (mean Brain Age = 78.9, SD = 8.2). Accordingly, Brain Age was significantly greater in the memory impaired than cognitively normal groups (p &lt; 0.01; d = 0.81).ConclusionBrain Age score is a valid construct for defining biological age impairment in Japanese individuals at risk for dementia. The current data suggest the presence of a difference between chronological and biological age of approximately six years is a criterion that would warrant more detailed investigations. The Brain Age score may be a useful means for communicating brain health information, including the need to adopt a healthier lifestyle.

The modern approaches for the assessment of fetal well-being

Fetal growth restriction is known as an intrauterine pathological condition that is associated with some diseases in a further lifetime. Fetal distress is a satellite of fetal growth restriction. Timely and true diagnosing of fetal distress is still an issue in perinatology. This case study showed that non-invasive fetal electrocardiography could contribute to better diagnosing of fetal distress. The variables of beat-to-beat variations, fetal heart rate variability and fetal autonomic brain age score should be investigated as the biophysical markers of fetal deterioration.

Accelerated brain ageing in sepsis survivors with cognitive long-term impairment.

In the last years, cognitive impairment was emphasized to be a prominent long-term sequelae of sepsis. The level of cognitive impairment is comparable with that in mild cognitive impairment (MCI) patients. Whether sepsis survivors also show a comparable brain atrophy is still unclear. For the analysis of brain atrophy, a novel method named brain age gap estimation (BrainAGE) was used. In this analysis approach, an algorithm identifies age-specific atrophy across the whole brain and calculates a BrainAGE score in years. In case of accelerated brain atrophy, the BrainAGE score is increased in comparison to the healthy age reference group, indicating a difference in estimated chronological age. 20 survivors of severe sepsis (longer than 2years post sepsis) with persistent cognitive deficits were investigated with a battery of neuropsychological tests. Their MRI images were compared to an age- and sex-matched control group. Sepsis survivors showed a significant higher BrainAGE score of 4.5years compared to healthy controls. We also found a close relationship between the BrainAGE score and severity of cognitive impairment (a higher BrainAGE score was associated with more severe cognitive impairment). Consequently, sepsis survivors with persistent cognitive impairment showed an accelerated brain ageing, which was closely associated with the severity of cognitive impairment (similar to MCI patients).

Brain-age in midlife is associated with accelerated biological aging and cognitive decline in a longitudinal birth cohort

An individual’s brainAGE is the difference between chronological age and age predicted from machine-learning models of brain-imaging data. BrainAGE has been proposed as a biomarker of age-related deterioration of the brain. Having an older brainAGE has been linked to Alzheimer’s, dementia, and mortality. However, these findings are largely based on cross-sectional associations which can confuse age differences with cohort differences. To illuminate the validity of brainAGE as a biomarker of accelerated brain aging, a study is needed of a large cohort all born in the same year who nevertheless vary on brainAGE. In the Dunedin Study, a population-representative 1972–73 birth cohort, we measured brainAGE at age 45 years, as well as the pace of biological aging and cognitive decline in longitudinal data from childhood to midlife (N = 869). In this cohort, all chronological age 45 years, brainAGE was measured reliably (ICC = 0.81) and ranged from 24 to 72 years. Those with older midlife brainAGEs tended to have poorer cognitive function in both adulthood and childhood, as well as impaired brain health at age 3. Furthermore, those with older brainAGEs had an accelerated pace of biological aging, older facial appearance, and early signs of cognitive decline from childhood to midlife. These findings help to validate brainAGE as a potential surrogate biomarker for midlife intervention studies that seek to measure dementia-prevention efforts in midlife. However, the findings also caution against the assumption that brainAGE scores represent only age-related deterioration of the brain as they may also index central nervous system variation present since childhood.

KEEP YOUR BRAIN YOUNG WITH MUSIC: INSIGHTS FROM MACHINE LEARNING AND BRAIN IMAGING

Music-making is a widespread leisure activity that has garnered interest over the years due to its effect on brain and cognitive development and its potential as a rehabilitative therapy of brain dysfunction. We investigated whether music-making has a potential age-protecting effect on the brain. For this, we studied anatomical magnetic resonance images in professional musicians, amateur musicians, and non-musicians. For each subject, we calculated a so-called BrainAGE score which corresponds to the discrepancy (in years) between chronological age and the ‘‘age of the brain’’, with negative values reflecting an age-decelerating brain and positive values an age-accelerating brain, respectively. The index of ‘‘brain age’’ was estimated using a machine-learning algorithm that was trained in a large independent sample to identify neuroanatomical correlates of brain-aging. Compared to non-musicians, musicians overall had lower BrainAGE scores, with amateur musicians having the lowest scores suggesting that music-making has an age-decelerating effect on the brain.

The association between "Brain-Age Score" (BAS) and traditional neuropsychological screening tools in Alzheimer's disease.

IntroductionWe present the Brain‐Age Score (BAS) as a magnetic resonance imaging (MRI)‐based index for Alzheimer's disease (AD). We developed a fully automated framework for estimating the BAS in healthy controls (HCs) and individuals with mild cognitive impairment (MCI) or AD, using MRI scans.MethodsWe trained the proposed framework using 385 HCs from the IXI and OASIS datasets and evaluated 146 HCs, 102 stable‐MCI (sMCI), 112 progressive‐MCI (pMCI), and 147 AD patients from the J‐ADNI dataset. We used a correlation test to determine the association between the BAS and four traditional screening tools of AD: the Mini‐Mental State Examination (MMSE), Clinical Dementia Ratio (CDR), Alzheimer's Disease Assessment Score (ADAS), and Functional Assessment Questionnaire (FAQ). Furthermore, we assessed the association between BAS and anatomical MRI measurements: the normalized gray matter (nGM), normalized white matter (nWM), normalized cerebrospinal fluid (nCSF), mean cortical thickness as well as hippocampus volume.ResultsThe correlation results demonstrated that the BAS is in line with traditional screening tools of AD (i.e., the MMSE, CDR, ADAS, and FAQ scores) as well as anatomical MRI measurements (i.e., nGM, nCSF, mean cortical thickness, and hippocampus volume).DiscussionThe BAS may be useful for diagnosing the brain atrophy level and can be a reliable automated index for clinical applications and neuropsychological screening tools.

Obesity, dyslipidemia and brain age in first-episode psychosis

IntroductionObesity and dyslipidemia may negatively affect brain health and are frequent medical comorbidities of schizophrenia and related disorders. Despite the high burden of metabolic disorders, little is known about their effects on brain structure in psychosis. We investigated, whether obesity or dyslipidemia contributed to brain alterations in first-episode psychosis (FEP). Methods120 participants with FEP, who were undergoing their first psychiatric hospitalization, had <24 months of untreated psychosis and were 18–35 years old and 114 controls within the same age range participated in the study. We acquired 3T brain structural MRI, fasting lipids and body mass index. We used machine learning trained on an independent sample of 504 controls to estimate the individual brain age of study participants and calculated the BrainAGE score by subtracting the chronological from the estimated brain age. ResultsIn a multiple regression model, the diagnosis of FEP (B = 1.15, SE B = 0.31, p < 0.001) and obesity/overweight (B = 0.92, SE B = 0.35, p = 0.008) were each additively associated with BrainAGE scores (R2 = 0.22, F(3, 230) = 21.92, p < 0.001). BrainAGE scores were highest in participants with FEP and obesity/overweight (3.83 years, 95%CI = 2.35-5.31) and lowest in normal weight controls (−0.27 years, 95%CI = −1.22-0.69). LDL-cholesterol, HDL-cholesterol or triglycerides were not associated with BrainAGE scores. ConclusionsOverweight/obesity may be an independent risk factor for diffuse brain alterations manifesting as advanced brain age already early in the course of psychosis. These findings raise the possibility that targeting metabolic health and intervening already at the level of overweight/obesity could slow brain ageing in FEP.

Premature brain aging in humans exposed to maternal nutrient restriction during early gestation

BackgroundPrenatal exposure to undernutrition is widespread in both developing and industrialized countries, causing irreversible damage to the developing brain, resulting in altered brain structure and decreased cognitive function during adulthood. The Dutch famine in 1944/45 was a humanitarian disaster, now enabling studies of the effects of prenatal undernutrition during gestation on brain aging in late adulthood. MethodsWe hypothesized that study participants prenatally exposed to maternal nutrient restriction (MNR) would demonstrate altered brain structure resembling premature brain aging in late adulthood, expecting the effect being stronger in men. Utilizing the Dutch famine birth cohort (n = 118; mean age: 67.5 ± 0.9 years), this study implements an innovative biomarker for individual brain aging, using structural neuroimaging. BrainAGE was calculated using state-of-the-art pattern recognition methods, trained on an independent healthy reference sample, then applied to the Dutch famine MRI sample, to evaluate the effects of prenatal undernutrition during early gestation on individual brain aging in late adulthood. ResultsExposure to famine in early gestation was associated with BrainAGE scores indicative of an older-appearing brain in the male sample (mean difference to subjects born before famine: 4.3 years, p < 0.05). Furthermore, in explaining the observed variance in individual BrainAGE scores in the male sample, maternal age at birth, head circumference at birth, medical treatment of hypertension, history of cerebral incidences, actual heart rate, and current alcohol intake emerged to be the most influential variables (adjusted R2 = 0.63, p < 0.01). InterpretationThe findings of our study on exposure to prenatal undernutrition being associated with a status of premature brain aging during late adulthood, as well as individual brain structure being shaped by birth- and late-life health characteristics, are strongly supporting the critical importance of sufficient nutrient supply during pregnancy. Interestingly, the status of premature brain aging in participants exposed to the Dutch famine during early gestation occurred in the absence of fetal growth restriction at birth as well as vascular pathology in late-life. Additionally, the neuroimaging brain aging biomarker presented in this study will further enable tracking effects of environmental influences or (preventive) treatments on individual brain maturation and aging in epidemiological and clinical studies.