Mind the gap: Performance metric evaluation in brain-age prediction.

Estimating age based on neuroimaging-derived data has become a popular approach to developing markers for brain integrity and health. While a variety of machine-learning algorithms can provide accurate predictions of age based on brain characteristics, there is significant variation in model accuracy reported across studies. We predicted age in two population-based datasets, and assessed the effects of age range, sample size and age-bias correction on the model performance metrics Pearson's correlation coefficient (r), the coefficient of determination (R2 ), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results showed that these metrics vary considerably depending on cohort age range; r and R2 values are lower when measured in samples with a narrower age range. RMSE and MAE are also lower in samples with a narrower age range due to smaller errors/brain age delta values when predictions are closer to the mean age of the group. Across subsets with different age ranges, performance metrics improve with increasing sample size. Performance metrics further vary depending on prediction variance as well as mean age difference between training and test sets, and age-bias corrected metrics indicate high accuracy-also for models showing poor initial performance. In conclusion, performance metrics used for evaluating age prediction models depend on cohort and study-specific data characteristics, and cannot be directly compared across different studies. Since age-bias corrected metrics generally indicate high accuracy, even for poorly performing models, inspection of uncorrected model results provides important information about underlying model attributes such as prediction variance.

Brain age in bipolar disorders: Effects of lithium treatment.

OBJECTIVE: Bipolar disorders increase the risk of dementia and show biological and brain alterations, which resemble accelerated aging. Lithium may counter some of these processes and lower the risk of dementia. However, until now no study has specifically investigated the effects of Li on brain age. METHODS: We acquired structural magnetic resonance imaging scans from 84 participants with bipolar disorders (41 with and 43 without Li treatment) and 45 controls. We used a machine learning model trained on an independent sample of 504 controls to estimate the individual brain ages of study participants, and calculated BrainAGE by subtracting chronological from the estimated brain age. RESULTS: BrainAGE was significantly greater in non-Li relative to Li or control participants, F(2, 125) = 10.22, p < 0.001, with no differences between the Li treated and control groups. The estimated brain age was significantly higher than the chronological age in the non-Li (4.28 ± 6.33 years, matched t(42) = 4.43, p < 0.001), but not the Li-treated group (0.48 ± 7.60 years, not significant). Even Li-treated participants with partial prophylactic treatment response showed lower BrainAGE than the non-Li group, F(1, 64) = 4.80, p = 0.03. CONCLUSIONS: Bipolar disorders were associated with greater, whereas Li treatment with lower discrepancy between brain and chronological age. These findings support the neuroprotective effects of Li, which were sufficiently pronounced to affect a complex, multivariate measure of brain structure. The association between Li treatment and BrainAGE was independent of long-term thymoprophylactic response and thus may generalize beyond bipolar disorders, to neurodegenerative disorders.

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

BACKGROUND: Prenatal 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. METHODS: We 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. RESULTS: Exposure 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). INTERPRETATION: The 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.

Changes of individual BrainAGE during the course of the menstrual cycle.

Brain morphology varies during the course of the menstrual cycle, with increases in individual gray matter volume at the time of ovulation. This study implemented our previously presented BrainAGE framework to analyze short-term neuroanatomical changes in healthy young women due to hormonal changes during the menstrual cycle. The BrainAGE approach determines the complex multidimensional aging pattern within the whole brain by applying established kernel regression methods to anatomical brain MRIs. The "Brain Age Gap Estimation" (i.e., BrainAGE) score is then calculated as the difference between chronological age and estimated brain age. Eight women (21-31 years) completed three to four MRI scans during their menstrual cycle (i.e., at (t1) menses, (t2) time of ovulation, (t3) midluteal phase, (t4) next menses). Serum levels of estradiol and progesterone were evaluated at each scanning session. Individual BrainAGE scores significantly differed during the course of the menstrual cycle (p<0.05), with a significant decrease of -1.3 years at ovulation (p<0.05). Moreover, higher estradiol levels significantly correlated with lower BrainAGE scores (r=-0.42, p<0.05). In future, the BrainAGE approach may serve as a sensitive as well as easily implementable tool to further explore the short-term and maybe long-term effects of hormones on brain plasticity and its modulating effects in lifestyle-related diseases and dementia.

Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge.

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n=30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org.

Brain maturation: predicting individual BrainAGE in children and adolescents using structural MRI.

BACKGROUND: Neural development during human childhood and adolescence involves highly coordinated and sequenced events, characterized by both progressive and regressive processes. Despite a multitude of results demonstrating the age-dependent development of gray matter, white matter, and total brain volume, a reference curve allowing prediction of structural brain maturation is still lacking but would be clinically valuable. For the first time, the present study provides a validated reference curve for structural brain maturation during childhood and adolescence, based on structural MRI data. METHODS AND FINDINGS: By employing kernel regression methods, a novel but well-validated BrainAGE framework uses the complex multidimensional maturation pattern across the whole brain to estimate an individual's brain age. The BrainAGE framework was applied to a large human sample (n=394) of healthy children and adolescents, whose image data had been acquired during the NIH MRI study of normal brain development. Using this approach, we were able to predict individual brain maturation with a clinically meaningful accuracy: the correlation between predicted brain age and chronological age resulted in r=0.93. The mean absolute error was only 1.1 years. Moreover, the predicted brain age reliably differentiated between all age groups (i.e., preschool childhood, late childhood, early adolescence, middle adolescence, late adolescence). Applying the framework to preterm-born adolescents resulted in a significantly lower estimated brain age than chronological age in subjects who were born before the end of the 27th week of gestation, demonstrating the successful clinical application and future potential of this method. CONCLUSIONS: Consequently, in the future this novel BrainAGE approach may prove clinically valuable in detecting both normal and abnormal brain maturation, providing important prognostic information.

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.

Obesity as a Risk Factor for Accelerated Brain Ageing in First-Episode Psychosis-A Longitudinal Study.

BACKGROUND: Obesity is highly prevalent in schizophrenia, with implications for psychiatric prognosis, possibly through links between obesity and brain structure. In this longitudinal study in first episode of psychosis (FEP), we used machine learning and structural magnetic resonance imaging (MRI) to study the impact of psychotic illness and obesity on brain ageing/neuroprogression shortly after illness onset. METHODS: We acquired 2 prospective MRI scans on average 1.61 years apart in 183 FEP and 155 control individuals. We used a machine learning model trained on an independent sample of 504 controls to estimate the individual brain ages of study participants and calculated BrainAGE by subtracting chronological from the estimated brain age. RESULTS: Individuals with FEP had a higher initial BrainAGE than controls (3.39 ± 6.36 vs 1.72 ± 5.56 years; ß = 1.68, t(336) = 2.59, P = .01), but similar annual rates of brain ageing over time (1.28 ± 2.40 vs 1.07±1.74 estimated years/actual year; t(333) = 0.93, P = .18). Across both cohorts, greater baseline body mass index (BMI) predicted faster brain ageing (ß = 0.08, t(333) = 2.59, P = .01). For each additional BMI point, the brain aged by an additional month per year. Worsening of functioning over time (Global Assessment of Functioning; ß = -0.04, t(164) = -2.48, P = .01) and increases especially in negative symptoms on the Positive and Negative Syndrome Scale (ß = 0.11, t(175) = 3.11, P = .002) were associated with faster brain ageing in FEP. CONCLUSIONS: Brain alterations in psychosis are manifest already during the first episode and over time get worse in those with worsening clinical outcomes or higher baseline BMI. As baseline BMI predicted faster brain ageing, obesity may represent a modifiable risk factor in FEP that is linked with psychiatric outcomes via effects on brain structure.

Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: exploring the influence of various parameters.

The early identification of brain anatomy deviating from the normal pattern of growth and atrophy, such as in Alzheimer's disease (AD), has the potential to improve clinical outcomes through early intervention. Recently, Davatzikos et al. (2009) supported the hypothesis that pathologic atrophy in AD is an accelerated aging process, implying accelerated brain atrophy. In order to recognize faster brain atrophy, a model of healthy brain aging is needed first. Here, we introduce a framework for automatically and efficiently estimating the age of healthy subjects from their T(1)-weighted MRI scans using a kernel method for regression. This method was tested on over 650 healthy subjects, aged 19-86 years, and collected from four different scanners. Furthermore, the influence of various parameters on estimation accuracy was analyzed. Our age estimation framework included automatic preprocessing of the T(1)-weighted images, dimension reduction via principal component analysis, training of a relevance vector machine (RVM; Tipping, 2000) for regression, and finally estimating the age of the subjects from the test samples. The framework proved to be a reliable, scanner-independent, and efficient method for age estimation in healthy subjects, yielding a correlation of r=0.92 between the estimated and the real age in the test samples and a mean absolute error of 5 years. The results indicated favorable performance of the RVM and identified the number of training samples as the critical factor for prediction accuracy. Applying the framework to people with mild AD resulted in a mean brain age gap estimate (BrainAGE) score of +10 years.

3D Scaffold-Based Macrophage Fibroblast Coculture Model Reveals IL-10 Dependence of Wound Resolution Phase.

Persistent inflammation and impaired repair in dermal wound healing are frequently associated with cell-cell and cell-matrix miscommunication. A direct coculture model of primary human myofibroblasts (MyoFB) and M-CSF-differentiated macrophages (M-Mɸ) in fibrillar three-dimensional Collagen I (Coll I) matrices is developed to study intercellular interactions. The coculture experiments reveal the number of M-Mɸ regulated MyoFB dedifferentiation in a dose-dependent manner. The amount of MyoFB decreases in dependence of the number of cocultured M-Mɸ, even in the presence of MyoFB-inducing transforming growth factor ß1 (TGF-ß1 ). Gene expression analysis of matrix proteins (collagen I, collagen III, ED-A-fibronectin) confirms the results of an altered MyoFB phenotype. Additionally, M-Mɸ is shown to be the main source of secreted cytokine interleukin-10 (IL-10), which is suggested to affect MyoFB dedifferentiation. These findings indicate a paracrine impact of IL-10 secretion by M-Mɸ on the MyoFB differentiation status counteracting the TGF-ß1 -driven MyoFB activation. Hence, the in vitro coculture model simulates physiological situations during wound resolution and underlines the importance of paracrine IL-10 signals by M-Mɸ. In sum, the 3D Coll I-based matrices with a MyoFB-M-Mɸ coculture form a highly relevant biomimetic model of late stages of wound healing.

Effects of maternal stress and nutrient restriction during gestation on offspring neuroanatomy in humans.

Cognitive and mental health are major determinants of quality of life, allowing integration into society at all ages. Human epidemiological and animal studies indicate that in addition to genetic factors and lifestyle, prenatal environmental influences may program neuropsychiatric disorders in later life. While several human studies have examined the effects of prenatal stress and nutrient restriction on brain function and mental health in later life, potentially mediating effects of prenatal stress and nutrient restriction on offspring neuroanatomy in humans have been studied only in recent years. Based on neuroimaging and anatomical data, we comprehensively review the studies in this emerging field. We relate prenatal environmental influences to neuroanatomical abnormalities in the offspring, measured in utero and throughout life. We also assess the relationship between neuroanatomical abnormalities and cognitive and mental disorders. Timing- and gender-specific effects are considered, if reported. Our review provides evidence for adverse effects of an unfavorable prenatal environment on structural brain development that may contribute to the risk for cognitive, behavioral and mental health problems throughout life.

Ten Years of BrainAGE as a Neuroimaging Biomarker of Brain Aging: What Insights Have We Gained?

With the aging population, prevalence of neurodegenerative diseases is increasing, thus placing a growing burden on individuals and the whole society. However, individual rates of aging are shaped by a great variety of and the interactions between environmental, genetic, and epigenetic factors. Establishing biomarkers of the neuroanatomical aging processes exemplifies a new trend in neuroscience in order to provide risk-assessments and predictions for age-associated neurodegenerative and neuropsychiatric diseases at a single-subject level. The "Brain Age Gap Estimation (BrainAGE)" method constitutes the first and actually most widely applied concept for predicting and evaluating individual brain age based on structural MRI. This review summarizes all studies published within the last 10 years that have established and utilized the BrainAGE method to evaluate the effects of interaction of genes, environment, life burden, diseases, or life time on individual neuroanatomical aging. In future, BrainAGE and other brain age prediction approaches based on structural or functional markers may improve the assessment of individual risks for neurological, neuropsychiatric and neurodegenerative diseases as well as aid in developing personalized neuroprotective treatments and interventions.

Brain Age in Early Stages of Bipolar Disorders or Schizophrenia.

Background: The greater presence of neurodevelopmental antecedants may differentiate schizophrenia from bipolar disorders (BD). Machine learning/pattern recognition allows us to estimate the biological age of the brain from structural magnetic resonance imaging scans (MRI). The discrepancy between brain and chronological age could contribute to early detection and differentiation of BD and schizophrenia. Methods: We estimated brain age in 2 studies focusing on early stages of schizophrenia or BD. In the first study, we recruited 43 participants with first episode of schizophrenia-spectrum disorders (FES) and 43 controls. In the second study, we included 96 offspring of bipolar parents (48 unaffected, 48 affected) and 60 controls. We used relevance vector regression trained on an independent sample of 504 controls to estimate the brain age of study participants from structural MRI. We calculated the brain-age gap estimate (BrainAGE) score by subtracting the chronological age from the brain age. Results: Participants with FES had higher BrainAGE scores than controls (F(1, 83) = 8.79, corrected P = .008, Cohen's d = 0.64). Their brain age was on average 2.64 ± 4.15 years greater than their chronological age (matched t(42) = 4.36, P < .001). In contrast, participants at risk or in the early stages of BD showed comparable BrainAGE scores to controls (F(2,149) = 1.04, corrected P = .70, η2 = 0.01) and comparable brain and chronological age. Conclusions: Early stages of schizophrenia, but not early stages of BD, were associated with advanced BrainAGE scores. Participants with FES showed neurostructural alterations, which made their brains appear 2.64 years older than their chronological age. BrainAGE scores could aid in early differential diagnosis between BD and schizophrenia.

Obesity, dyslipidemia and brain age in first-episode psychosis.

INTRODUCTION: Obesity 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). METHODS: 120 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. RESULTS: In 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. CONCLUSIONS: Overweight/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.

Engineered matrix coatings to modulate the adhesion of CD133+ human hematopoietic progenitor cells.

Interactions of hematopoietic progenitor cells (HPC) with their local microenvironments in the bone marrow are thought to control homing, differentiation, and self-renewal of the cells. To dissect the role of extracellular matrix (ECM) components of the niche microenvironment, a set of well-defined ECM coatings including fibronectin, heparin, heparan sulphate, hyaluronic acid, tropocollagen I, and co-fibrils of collagen I with heparin or hyaluronic acid was prepared and analysed with respect to the attachment of human CD133+ HPC in vitro. The extension of the adhesion areas of individual cells as well as the fraction of adherent cells were assessed by reflection interference contrast microscopy (RICM). Intense cell-matrix interactions were found on surfaces coated with fibronectin, heparin, heparan sulphate, and on the collagen I based co-fibrils. Insignificant adhesion was found for tropocollagen I and hyaluronic acid. The strongest adhesion of HPC was observed on fibronectin with contact areas of about 7 microm(2). Interaction of HPC with coatings consisting of heparin, heparan sulphate, and co-fibrils result in small circular shaped contact zones of 3 microm(2) pointing to another, less efficient, adhesion mechanism. Analysing the specificity of cell-matrix interaction by antibody blocking experiments suggests an integrin(alpha(5)beta(1))-specific adhesion on fibronectin, while adhesion on heparin was shown to be mediated by selectins (CD62L). Taken together, our data provide a basis for the design of advanced culture carriers supporting site-specific proliferation or differentiation of HPC.

Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers.

The brain changes as we age and these changes are associated with functional deterioration and neurodegenerative disease. It is vital that we better understand individual differences in the brain ageing process; hence, techniques for making individualised predictions of brain ageing have been developed. We present evidence supporting the use of neuroimaging-based 'brain age' as a biomarker of an individual's brain health. Increasingly, research is showing how brain disease or poor physical health negatively impacts brain age. Importantly, recent evidence shows that having an 'older'-appearing brain relates to advanced physiological and cognitive ageing and the risk of mortality. We discuss controversies surrounding brain age and highlight emerging trends such as the use of multimodality neuroimaging and the employment of 'deep learning' methods.

Quantitative label-free single cell tracking in 3D biomimetic matrices.

Live cell imaging enables an observation of cell behavior over a period of time and is a growing field in modern cell biology. Quantitative analysis of the spatio-temporal dynamics of heterogeneous cell populations in three-dimensional (3D) microenvironments contributes a better understanding of cell-cell and cell-matrix interactions for many biomedical questions of physiological and pathological processes. However, current live cell imaging and analysis techniques are frequently limited by non-physiological 2D settings. Furthermore, they often rely on cell labelling by fluorescent dyes or expression of fluorescent proteins to enhance contrast of cells, which frequently affects cell viability and behavior of cells. In this work, we present a quantitative, label-free 3D single cell tracking technique using standard bright-field microscopy and affordable computational resources for data analysis. We demonstrate the efficacy of the automated method by studying migratory behavior of a large number of primary human macrophages over long time periods of several days in a biomimetic 3D microenvironment. The new technology provides a highly affordable platform for long-term studies of single cell behavior in 3D settings with minimal cell manipulation and can be implemented for various studies regarding cell-matrix interactions, cell-cell interactions as well as drug screening platform for primary and heterogeneous cell populations.

Instructing Human Macrophage Polarization by Stiffness and Glycosaminoglycan Functionalization in 3D Collagen Networks.

Dynamic alterations of composition and mechanics of the extracellular matrix are suggested to modulate cellular behavior including plasticity of macrophages (MPhs) during wound healing. In this study, engineered 3D fibrillar matrices based on naturally occurring biopolymers (collagen I, glycosaminoglycans (GAGs)) are used to mimic matrix stiffening as well as modification by sulfated and nonsulfated GAGs at different stages of wound healing. Human MPhs are found to sensitively respond to these microenvironmental cues in terms of polarization toward proinflammatory or wound healing phenotypes over 6 days in vitro. MPhs exhibit a wound healing phenotype in stiffer matrices as determined by protein and gene expression of relevant cytokines (IL10, IL12, and TNFα). Presence of sulfated and nonsulfated GAGs inhibits this polarization effect. Furthermore, control experiments on 2D matrices stress the relevance of using stiffness-controlled 3D matrices, as MPhs show a reciprocal polarization behavior depending on GAG presence. Hence, the results indicate a strong influence of dimensionality, stiffness, and GAG presence of the biomaterial scaffold on MPh polarization and emphasize the need for matrices closely mimicking the 3D in vivo context with a variable stiffness and GAG composition in in vitro studies.

Premature Brain Aging in Baboons Resulting from Moderate Fetal Undernutrition.

Contrary to the known benefits from a moderate dietary reduction during adulthood on life span and health, maternal nutrient reduction during pregnancy is supposed to affect the developing brain, probably resulting in impaired brain structure and function throughout life. Decreased fetal nutrition delivery is widespread in both developing and developed countries, caused by poverty and natural disasters, but also due to maternal dieting, teenage pregnancy, pregnancy in women over 35 years of age, placental insufficiency, or multiples. Compromised development of fetal cerebral structures was already shown in our baboon model of moderate maternal nutrient reduction. The present study was designed to follow-up and evaluate the effects of moderate maternal nutrient reduction on individual brain aging in the baboon during young adulthood (4-7 years; human equivalent 14-24 years), applying a novel, non-invasive neuroimaging aging biomarker. The study reveals premature brain aging of +2.7 years (p < 0.01) in the female baboon exposed to fetal undernutrition. The effects of moderate maternal nutrient reduction on individual brain aging occurred in the absence of fetal growth restriction or marked maternal weight reduction at birth, which stresses the significance of early nutritional conditions in life-long developmental programming. This non-invasive MRI biomarker allows further longitudinal in vivo tracking of individual brain aging trajectories to assess the life-long effects of developmental and environmental influences in programming paradigms, aiding preventive and curative treatments on cerebral atrophy in experimental animal models and humans.

The Effect of the APOE Genotype on Individual BrainAGE in Normal Aging, Mild Cognitive Impairment, and Alzheimer's Disease.

In our aging society, diseases in the elderly come more and more into focus. An important issue in research is Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) with their causes, diagnosis, treatment, and disease prediction. We applied the Brain Age Gap Estimation (BrainAGE) method to examine the impact of the Apolipoprotein E (APOE) genotype on structural brain aging, utilizing longitudinal magnetic resonance image (MRI) data of 405 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We tested for differences in neuroanatomical aging between carrier and non-carrier of APOE ε4 within the diagnostic groups and for longitudinal changes in individual brain aging during about three years follow-up. We further examined whether a combination of BrainAGE and APOE status could improve prediction accuracy of conversion to AD in MCI patients. The influence of the APOE status on conversion from MCI to AD was analyzed within all allelic subgroups as well as for ε4 carriers and non-carriers. The BrainAGE scores differed significantly between normal controls, stable MCI (sMCI) and progressive MCI (pMCI) as well as AD patients. Differences in BrainAGE changing rates over time were observed for APOE ε4 carrier status as well as in the pMCI and AD groups. At baseline and during follow-up, BrainAGE scores correlated significantly with neuropsychological test scores in APOE ε4 carriers and non-carriers, especially in pMCI and AD patients. Prediction of conversion was most accurate using the BrainAGE score as compared to neuropsychological test scores, even when the patient's APOE status was unknown. For assessing the individual risk of coming down with AD as well as predicting conversion from MCI to AD, the BrainAGE method proves to be a useful and accurate tool even if the information of the patient's APOE status is missing.