scholarly journals Individual variations in ‘brain age’ relate to early-life factors more than to longitudinal brain change

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Didac Vidal-Pineiro ◽  
Yunpeng Wang ◽  
Stine K Krogsrud ◽  
Inge K Amlien ◽  
William FC Baaré ◽  
...  
Keyword(s):  
Early Life ◽  
Brain Aging ◽  
Average Rate ◽  
Aging Brain ◽  
Imaging Data ◽  
Cross Sectional ◽  
Polygenic Scores ◽  
Longitudinal Imaging ◽  
Brain Age ◽  
Brain Change

Brain age is a widely used index for quantifying individuals’ brain health as deviation from a normative brain aging trajectory. Higher-than-expected brain age is thought partially to reflect above-average rate of brain aging. Here, we explicitly tested this assumption in two independent large test datasets (UK Biobank [main] and Lifebrain [replication]; longitudinal observations ≈ 2750 and 4200) by assessing the relationship between cross-sectional and longitudinal estimates of brain age. Brain age models were estimated in two different training datasets (n ≈ 38,000 [main] and 1800 individuals [replication]) based on brain structural features. The results showed no association between cross-sectional brain age and the rate of brain change measured longitudinally. Rather, brain age in adulthood was associated with the congenital factors of birth weight and polygenic scores of brain age, assumed to reflect a constant, lifelong influence on brain structure from early life. The results call for nuanced interpretations of cross-sectional indices of the aging brain and question their validity as markers of ongoing within-person changes of the aging brain. Longitudinal imaging data should be preferred whenever the goal is to understand individual change trajectories of brain and cognition in aging.

scholarly journals Individual variations in “Brain age” relate to early life factors more than to longitudinal brain change

2021 ◽  
Author(s):  
D. Vidal-Piñeiro ◽  
Y. Wang ◽  
SK. Krogsrud ◽  
IK. Amlien ◽  
WFC. Baaré ◽  
...  
Keyword(s):  
Early Life ◽  
Brain Aging ◽  
Average Rate ◽  
Aging Brain ◽  
Individual Change ◽  
Imaging Data ◽  
Cross Sectional ◽  
Polygenic Scores ◽  
Longitudinal Imaging ◽  
Brain Age

AbstractBrain age is a widely used index for quantifying individuals’ brain health as deviation from a normative brain aging trajectory. Higher than expected brain age is thought partially to reflect above-average rate of brain aging. We explicitly tested this assumption in two large datasets and found no association between cross-sectional brain age and steeper brain decline measured longitudinally. Rather, brain age in adulthood was associated with early-life influences indexed by birth weight and polygenic scores. The results call for nuanced interpretations of cross-sectional indices of the aging brain and question their validity as markers of ongoing within-person changes of the aging brain. Longitudinal imaging data should be preferred whenever the goal is to understand individual change trajectories of brain and cognition in aging.

scholarly journals Cross-Sectional and Longitudinal Brain Scans Reveal Accelerated Brain Aging in Multiple Sclerosis

2018 ◽  
Author(s):  
Einar A. Høgestøl ◽  
Tobias Kaufmann ◽  
Gro O. Nygaard ◽  
Mona K. Beyer ◽  
Piotr Sowa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Age Estimation ◽  
Brain Aging ◽  
Chronological Age ◽  
Aging Brain ◽  
Inflammatory Disorder ◽  
Cross Sectional ◽  
Chronological Aging ◽  
Brain Age

ABSTRACTMultiple sclerosis (MS) is an inflammatory disorder of the central nervous system. By combining longitudinal MRI-based brain morphometry and brain age estimation using machine learning, we tested the hypothesis that MS patients have higher brain age relative to chronological age than healthy controls (HC) and that longitudinal rate of brain aging in MS patients is associated with clinical course.Seventy-six MS patients, 71 % females and mean age 34.8 years (range 21-49) at inclusion, were examined with brain MRI at three time points with a mean total follow up period of 4.4 years. A machine learning model was applied on an independent training set of 3208 HC, estimating individual brain age and calculating the difference between estimated brain age and chronological age, termed brain age gap (BAG). We also assessed the longitudinal change rate in BAG in MS individuals. We used additional cross-sectional MRI data from 235 HC for case-control comparison.MS patients showed increased BAG (4.4 ±6.6 years) compared to HC (Cohen’s D = 0.69, p = 4.0 × 10−6). Longitudinal estimates of BAG in MS patients suggested an accelerated rate of brain aging corresponding to an annual increase of 0.41 (±1.23) years compared to chronological aging for the MS patients (p = 0.008).On average, patients with MS have significantly higher BAG compared to HC and accelerated rate of brain aging compared to chronological aging. Brain age estimation represents a promising method for evaluation of brain changes in MS, with potential for predicting future outcome and guide treatment.

Toward brain imaging clinical applications in psychiatry: Longitudinal imaging and multivariate statistics

2014 ◽  
Vol 29 (S3) ◽  
pp. 553-554
Author(s):  
G. Robert
Keyword(s):  
Machine Learning ◽  
Multivariate Statistics ◽  
Depressive Disorders ◽  
Diffusion Tensor ◽  
Brain Structures ◽  
Clinical Applications ◽  
Imaging Data ◽  
Cross Sectional ◽  
Cerebral Imaging ◽  
Longitudinal Imaging

Cerebral imaging is now acknowledged as a crucial research topic in Psychiatry. However, a gap remains between scientific results and clinical applications. For example, a large number of studies have focused on statistical associations with a disease, symptoms or treatment effects on a cross-sectional design. Results are thus informative at a specific time point whereas the disease and its cerebral phenotypes change overtime. Longitudinal imaging enables to identify brain structures and functions changes over time but requires specific preprocessing to avoid bias such as interpolation and registration asymmetries [1]. By creating a midpoint average image, patients’ scans are equally manipulated and statistics are unlikely to be biased.So far, cerebral imaging do not provide information on diagnosis and/or prognosis and clinicians do not use cerebral imaging in everyday practice. However, recent improvements in modeling cerebral imaging data using multivariate statistics and pattern recognition (i.e. machine learning) might offer the possibility to use imaging in clinical settings. Indeed, it has been shown that machine learning enables to distinguish patients with depressive disorders to controls based on cerebral activation during sad faces visualization [2]. Using a prognostic approach, Tognin et al. [3] were able to predict symptoms progression based on cortical thickness among ultra-high risk for psychosis. However, these applications need to be carefully interpreted in order to preclude inflated optimism [4]. On the basis on this literature, we propose to expose our preliminary results based on combining basal arterial spin labeling and diffusion tensor imaging to improve diagnosis performances of depression in 30 patients and controls.

scholarly journals Lack of association between proton pump inhibitor use and brain aging: a cross-sectional study

2021 ◽  
Author(s):  
Nayeon Ahn ◽  
Stefan Frenzel ◽  
Katharina Wittfeld ◽  
Robin Bülow ◽  
Henry Völzke ◽  
...  
Keyword(s):  
Cognitive Function ◽  
Proton Pump ◽  
Brain Aging ◽  
Verbal Learning ◽  
Brain Magnetic Resonance Imaging ◽  
Primary Analysis ◽  
Cross Sectional ◽  
Brain Volumes ◽  
Increased Risk ◽  
Brain Age

Abstract Purpose Due to conflicting scientific evidence for an increased risk of dementia by intake of proton pump inhibitors (PPIs), this study investigates associations between PPI use and brain volumes, estimated brain age, and cognitive function in the general population. Methods Two surveys of the population-based Study of Health in Pomerania (SHIP) conducted in Northeast Germany were used. In total, 2653 participants underwent brain magnetic resonance imaging (MRI) and were included in the primary analysis. They were divided into two groups according to their PPI intake and compared with regard to their brain volumes (gray matter, white matter, total brain, and hippocampus) and estimated brain age. Multiple regression was used to adjust for confounding factors. Cognitive function was evaluated by the Verbal Learning and Memory Test (VLMT) and the Nuremberg Age Inventory (NAI) and put in relation to PPI use. Results No association was found between PPI use and brain volumes or the estimated brain age. The VLMT score was 1.11 lower (95% confidence interval: − 2.06 to − 0.16) in immediate recall, and 0.72 lower (95% CI: − 1.22 to − 0.22) in delayed recall in PPI users than in non-users. PPI use was unrelated to the NAI score. Conclusions The present study does not support a relationship between PPI use and brain aging.

scholarly journals Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations

2019 ◽  
Author(s):  
Stephen M. Smith ◽  
Lloyd T. Elliott ◽  
Fidel Alfaro-Almagro ◽  
Paul McCarthy ◽  
Thomas E. Nichols ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Brain Aging ◽  
Functional Change ◽  
Genetic Associations ◽  
Imaging Data ◽  
Brain Health ◽  
Health And Disease ◽  
Brain Imaging Data ◽  
Structural Brain Change ◽  
Brain Age

AbstractBrain imaging can be used to study how individuals’ brains are aging, compared against population norms. This can inform on aspects of brain health; for example, smoking and blood pressure can be seen to accelerate brain aging. Typically, a single “brain age” is estimated per subject, whereas here we we identified 62 modes of subject variability, from 21,407 subjects’ multimodal brain imaging data in UK Biobank. The modes represent different aspects of brain aging, showing distinct patterns of functional and structural brain change, and distinct patterns of association with genetics, lifestyle, cognition, physical measures and disease. While conventional brain-age modelling found no genetic associations, 34 modes had genetic associations. We suggest that it is important not to treat brain aging as a single homogeneous process, and that modelling of distinct patterns of structural and functional change will reveal more biologically meaningful markers of brain aging in health and disease.

scholarly journals Improving brain age estimates with deep learning leads to identification of novel genetic factors associated with brain aging

2020 ◽  
Author(s):  
Kaida Ning ◽  
Ben A. Duffy ◽  
Meredith Franklin ◽  
Will Matloff ◽  
Lu Zhao ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
Brain Aging ◽  
Nucleotide Polymorphisms ◽  
Genetic Associations ◽  
Imaging Data ◽  
Factors Associated ◽  
Genome Wide ◽  
A Genome ◽  
Validation Tests ◽  
Brain Age

AbstractBrain aging trajectories among those of the same chronological age can vary significantly. Statistical models have been created for estimating the apparent age of the brain, or predicted brain age, with imaging data. Recently, convolutional neural networks (CNNs) have shown the potential to more accurately predict brain age. We trained a CNN on 16,998 UK Biobank subjects, and in validation tests found that it was more accurate than a regression model for predicting brain age. A genome-wide association study was conducted on CNN-derived predicted brain age whereby we identified single nucleotide polymorphisms from four independent loci significantly associated with brain aging. One locus has been previously reported to be associated with brain aging. The three other loci were novel. Our results suggest that a more accurate brain age prediction enables the discovery of novel genetic associations, which may be valuable for identifying other lifestyle factors associated with brain aging.

scholarly journals Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Stephen M Smith ◽  
Lloyd T Elliott ◽  
Fidel Alfaro-Almagro ◽  
Paul McCarthy ◽  
Thomas E Nichols ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Brain Aging ◽  
Functional Change ◽  
Genetic Associations ◽  
Imaging Data ◽  
Brain Health ◽  
Health And Disease ◽  
Brain Imaging Data ◽  
Structural Brain Change ◽  
Brain Age

Brain imaging can be used to study how individuals’ brains are aging, compared against population norms. This can inform on aspects of brain health; for example, smoking and blood pressure can be seen to accelerate brain aging. Typically, a single ‘brain age’ is estimated per subject, whereas here we identified 62 modes of subject variability, from 21,407 subjects’ multimodal brain imaging data in UK Biobank. The modes represent different aspects of brain aging, showing distinct patterns of functional and structural brain change, and distinct patterns of association with genetics, lifestyle, cognition, physical measures and disease. While conventional brain-age modelling found no genetic associations, 34 modes had genetic associations. We suggest that it is important not to treat brain aging as a single homogeneous process, and that modelling of distinct patterns of structural and functional change will reveal more biologically meaningful markers of brain aging in health and disease.

scholarly journals Sex- and age-specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort

2021 ◽  
Author(s):  
Sivaniya Subramaniapillai ◽  
Sana Suri ◽  
Claudia Barth ◽  
Ivan Maximov ◽  
Irene Voldsbekk ◽  
...  
Keyword(s):  
White Matter ◽  
Body Fat ◽  
Cardiometabolic Risk ◽  
Brain Aging ◽  
Chronological Age ◽  
Uk Biobank ◽  
Imaging Data ◽  
Increased Risk ◽  
Males And Females ◽  
Brain Age

Cardiometabolic risk factors (CMRs) are associated with accelerated brain aging and increased risk for sex-dimorphic illnesses such as Alzheimer’s Disease (AD). Yet, it is unknown how CMRs interact with sex and apolipoprotein E-ε4 (APOE4), a known genetic risk factor for AD, to influence brain age across different life stages. Using age prediction based on multi-shell diffusion-weighted imaging data in 21,308 UK Biobank participants, we investigated whether associations between white matter Brain Age Gap (BAG) and body mass index (BMI), waist-to-hip ratio (WHR), body fat percentage (BF%), and APOE4 status varied i) between males and females, ii) according to age at menopause in females, and iii) across different age groups in males and females. We report sex differences in associations between BAG and all three CMRs, with stronger positive associations among males com- pared to females. Higher BAG (older brain age relative to chronological age) was associated with greater BMI, WHR, and BF% in males, whereas in females, higher BAG was associated with greater WHR, but not BMI and BF%. These divergent associations were most prominent within the oldest group of females (66-81yrs), where higher BF% was linked to lower BAG (younger brain age relative to chronological age). Earlier menopause transition was associated with higher BAG, but no interactions were found with CMRs. APOE4 status was not significantly associated with BAG, and no significant interactions with CMRs were found. In conclusion, the findings point to sex- and age-specific associations between body fat composition and brain age. Incorporating sex as a factor of interest in studies addressing cardiometabolic risk may promote sex-specific precision medicine, consequently improving health care for both males and females.

scholarly journals Multifaceted Brain Age Measures Reveal Premature Brain Aging and Associations with Clinical Manifestations in Schizophrenia

2020 ◽  
Author(s):  
Chang-Le Chen ◽  
Tzung-Jeng Hwang ◽  
Yu-Hung Tung ◽  
Li-Ying Yang ◽  
Yung-Chin Hsu ◽  
...  
Keyword(s):  
Intelligence Quotient ◽  
Brain Aging ◽  
Clinical Manifestations ◽  
Mean Diffusivity ◽  
Onset Age ◽  
Imaging Data ◽  
Temporal Lobes ◽  
Illness Duration ◽  
Brain Age ◽  
Premature Brain

AbstractSchizophrenia is a mental disorder with extensive alterations of cerebral gray matter (GM) and white matter (WM) and is known to have advanced brain aging. However, how the structural alterations contribute to brain aging and how brain aging is related to clinical manifestations remain unclear. Here, we estimated the bias-free multifaceted brain age measures in patients with schizophrenia (N=147) using structural and diffusion magnetic resonance imaging data. We calculated feature importance to estimate regional contributions to advanced brain aging in schizophrenia. Furthermore, regression analyses were conducted to test the associations of brain age with illness duration, onset age, symptom severity, and intelligence quotient. The patients with schizophrenia manifested significantly old-appearing brain age (P<.001) in both GM and WM compared with the healthy norm. The GM and WM structures contributing to the advanced brain aging were mostly located in the frontal and temporal lobes. Among the features, the GM volume and mean diffusivity of WM were most sensitive to the neuropathological changes in schizophrenia. The WM brain age index was associated with a negative symptom score (P=.006), and the WM and multimodal brain age indices demonstrated negative associations with the intelligence quotient (P=.037; P=.040, respectively). Moreover, brain age exhibited associations with the onset age (P=.006) but no associations with the illness duration, which may support the early-hit non-progression hypothesis. In conclusion, our study reveals the structural underpinnings of premature brain aging in schizophrenia and its clinical significance. The brain age measures might be a potentially informative biomarker for stratification and prognostication of patients with schizophrenia.

CLOCK genetic variations are associated with age-related changes in sleep duration and brain volume

2021 ◽  
Author(s):  
Song E Kim ◽  
Soriul Kim ◽  
Hyeon Jin Kim ◽  
Regina E Y Kim ◽  
Sol Ah Kim ◽  
...  
Keyword(s):  
Circadian Rhythm ◽  
Sleep Duration ◽  
Sleep Disturbances ◽  
Brain Volume ◽  
Brain Aging ◽  
Genetic Variations ◽  
Analysis Of Covariance ◽  
Imaging Data ◽  
Cross Sectional ◽  
Age Related

Abstract Background Although a connection between sleep disruption and brain aging has been documented, biological mechanisms need to be further clarified. Intriguingly, aging is associated with circadian rhythm and/or sleep dysfunction in a key gene regulating circadian rhythm, CLOCK, have been linked to both aging-related sleep disturbances and neurodegenerative diseases. This study aims to investigate how CLOCK genetic variation associates with sleep duration changes and/or volumetric brain alteration. Methods This population-based cross-sectional study used data from the Korean Genome Epidemiology Study (KoGES), and analyzed sleep characteristics and genetic and brain imaging data in 2,221 subjects (mean 58.8±6.8 years, 50.2% male). Eleven single-nucleotide polymorphisms (SNPs) in CLOCK were analyzed using PLINK software v1.09 to test for their association with sleep duration and brain volume. Haplotype analysis was performed by using pair-wise linkage disequilibrium (LD) of CLOCK polymorphisms, and multivariate analysis of covariance was for statistical analysis. Results Decreased sleep duration was associated with several SNPs in CLOCK intronic regions, with the highest significance for rs10002541 (P=1.58x10 -5). Five SNPs with the highest significance (rs10002541-rs6850524-rs4580704- rs3805151-rs3749474) revealed that CGTCT was the most prevalent. In the major CGTCT haplotype, decreased sleep duration over time was associated with lower cortical volumes predominantly in frontal and parietal regions. Less common haplotypes (GCCTC/CGTTC) had shorter sleep duration and more decreases in sleep duration over 8 years, which revealed smaller total and gray matter volumes, especially in frontal and temporal regions of the left hemisphere. Conclusion CLOCK genetic variations could be involved in age-related sleep and brain volume changes.

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