Differential Associations of White Matter Brain Age With Language-Related Mechanisms in Word-Finding Ability Across the Adult Lifespan

Research on cognitive aging has established that word-finding ability declines progressively in late adulthood, whereas semantic mechanism in the language system is relatively stable. The aim of the present study was to investigate the associations of word-finding ability and language-related components with brain aging status, which was quantified by using the brain age paradigm. A total of 616 healthy participants aged 18–88 years from the Cambridge Centre for Ageing and Neuroscience databank were recruited. The picture-naming task was used to test the participants’ language-related word retrieval ability through word-finding and word-generation processes. The naming response time (RT) and accuracy were measured under a baseline condition and two priming conditions, namely phonological and semantic priming. To estimate brain age, we established a brain age prediction model based on white matter (WM) features and estimated the modality-specific predicted age difference (PAD). Mass partial correlation analyses were performed to test the associations of WM-PAD with the cognitive performance measures under the baseline and two priming conditions. We observed that the domain-specific language WM-PAD and domain-general WM-PAD were significantly correlated with general word-finding ability. The phonological mechanism, not the semantic mechanism, in word-finding ability was significantly correlated with the domain-specific WM-PAD. In contrast, all behavioral measures of the conditions in the picture priming task were significantly associated with chronological age. The results suggest that chronological aging and WM aging have differential effects on language-related word retrieval functions, and support that cognitive alterations in word-finding functions involve not only the domain-specific processing within the frontotemporal language network but also the domain-general processing of executive functions in the fronto-parieto-occipital (or multi-demand) network. The findings further indicate that the phonological aspect of word retrieval ability declines as cerebral WM ages, whereas the semantic aspect is relatively resilient or unrelated to WM aging.

Download Full-text

Advances in transcriptome analysis of human brain aging

Experimental & Molecular Medicine ◽

10.1038/s12276-020-00522-6 ◽

2020 ◽

Vol 52 (11) ◽

pp. 1787-1797

Author(s):

Seokjin Ham ◽

Seung-Jae V. Lee

Keyword(s):

Gene Expression ◽

Human Brain ◽

Cognitive Aging ◽

Brain Aging ◽

Noncoding Rnas ◽

Transcriptome Profiling ◽

Synaptic Function ◽

Whole Transcriptome ◽

Brain Age ◽

Physiological And Biochemical

AbstractAging is associated with gradual deterioration of physiological and biochemical functions, including cognitive decline. Transcriptome profiling of brain samples from individuals of varying ages has identified the whole-transcriptome changes that underlie age-associated cognitive declines. In this review, we discuss transcriptome-based research on human brain aging performed by using microarray and RNA sequencing analyses. Overall, decreased synaptic function and increased immune function are prevalent in most regions of the aged brain. Age-associated gene expression changes are also cell dependent and region dependent and are affected by genotype. In addition, the transcriptome changes that occur during brain aging include different splicing events, intersample heterogeneity, and altered levels of various types of noncoding RNAs. Establishing transcriptome-based hallmarks of human brain aging will improve the understanding of cognitive aging and neurodegenerative diseases and eventually lead to interventions that delay or prevent brain aging.

Download Full-text

Age 20 Cognitive Ability Moderates the Long-Term Influence of Lifestyle Behaviors on Brain Aging in Late Midlife

Innovation in Aging ◽

10.1093/geroni/igaa057.384 ◽

2020 ◽

Vol 4 (Supplement_1) ◽

pp. 116-117

Author(s):

Carol Franz ◽

Teresa Warren ◽

William Kremen

Keyword(s):

Cognitive Ability ◽

Social Engagement ◽

Cognitive Reserve ◽

Brain Aging ◽

Apoe Genotype ◽

Age Difference ◽

Lifestyle Behaviors ◽

Brain Health ◽

Positive Behaviors ◽

Brain Age

Abstract We examined whether the longitudinal association between lifestyle behaviors and brain age is moderated by early general cognitive ability (GCA). The sample comprises 356 participants from the Vietnam Era Twin Study of Aging (VETSA). At mean age 40 (SD 2.7; range 34-44) a positive lifestyle index was created comprising three self-reported behaviors: not smoking, zero to moderate alcohol consumption, and high social engagement. GCA at mean age 20 was assessed with the Armed Forces Qualification Test. At mean age 68 (SD 2.6; range 61-72), participants underwent structural magnetic resonance imaging which was used to create predicted brain age difference (PBAD) scores. Multivariate models included GCA, lifestyle and their interaction as IVs, adjusted for age, ethnicity, APOE genotype, height, and family membership. Age 20 GCA and age 40 lifestyle significantly predicted age 68 PBAD [F=5.83; p=.02 and F=15.14; p<.001, respectively]. Both positive behaviors and higher age 20 GCA were associated with less brain aging. The GCA-lifestyle interaction was also significant. Those with both lower age 20 GCA and fewer positive behaviors had older brains relative to chronological age [F=5.00; p=. 03]. When GCA was high, however, participants had younger brains, regardless of lifestyle behaviors, suggesting a protective effect of early high GCA or cognitive reserve on later brain health. However, for those with lower cognitive reserve, positive lifestyle behaviors appeared to be protective against brain aging nearly three decades later. Results highlight the important role of cognitive reserve and lifestyle factors for later life brain health.

Download Full-text

Linking objective measures of physical activity and capability with brain structure in healthy community dwelling older adults

10.1101/2021.01.28.21250529 ◽

2021 ◽

Author(s):

Anne-Marthe Sanders ◽

Geneviève Richard ◽

Knut Kolskår ◽

Kristine M. Ulrichsen ◽

Tobias Kaufmann ◽

...

Keyword(s):

Physical Activity ◽

Older Adults ◽

White Matter ◽

Cognitive Aging ◽

Walking Speed ◽

Community Dwelling ◽

Brain Morphology ◽

Brain Health ◽

Physical Capability ◽

Brain Age

AbstractMaintaining high levels of daily activity and physical capability have been proposed as important constituents to promote healthy brain and cognitive aging. Studies investigating the associations between brain health and physical activity in late life have, however, mainly been based on self-reported data or measures designed for clinical populations. In the current study, we examined cross-sectional associations between physical activity, recorded by an ankle-positioned accelerometer for seven days, physical capability (grip strength, postural control, and walking speed), and neuroimaging based surrogate markers of brain health in 122 healthy older adults aged 65-88 years. We used a multimodal brain imaging approach offering two complementary structural MRI based indicators of brain health: white matter diffusivity and coherence based on diffusion tensor imaging and subcortical and global brain age based on brain morphology inferred from T1-weighted MRI data. The analyses revealed a significant association between global white matter fractional anisotropy (FA) and walking speed, indicating higher white matter coherence in people with higher pace. We also found a significant interaction between sex and brain age on number of daily steps, indicating younger-appearing brains in more physically active women, with no significant associations among men. These results provide insight into the intricate associations between different measures of brain and physical health in old age, and corroborate established public health advice promoting physical activity.

Download Full-text

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

10.1101/440412 ◽

2018 ◽

Cited By ~ 1

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.

Download Full-text

Longitudinal trajectories of brain age in young individuals at familial risk of mood disorder from the Scottish Bipolar Family Study

Wellcome Open Research ◽

10.12688/wellcomeopenres.15617.3 ◽

2020 ◽

Vol 4 ◽

pp. 206

Author(s):

Laura de Nooij ◽

Mathew A. Harris ◽

Emma L. Hawkins ◽

Toni-Kim Clarke ◽

Xueyi Shen ◽

...

Keyword(s):

Mood Disorder ◽

Brain Structure ◽

Family Study ◽

Brain Aging ◽

Familial Risk ◽

Age Difference ◽

Age Related ◽

Prospective Longitudinal ◽

Brain Age

Background: Within young individuals, mood disorder onset may be related to changes in trajectory of brain structure development. To date, however, longitudinal prospective studies remain scarce and show partly contradictory findings, with a lack of emphasis on changes at the level of global brain patterns. Cross-sectional adult studies have applied such methods and show that mood disorders are associated with accelerated brain aging. Currently, it remains unclear whether young individuals show differential brain structure aging trajectories associated with onset of mood disorder and/or presence of familial risk. Methods: Participants included young individuals (15-30 years, 53%F) from the prospective longitudinal Scottish Bipolar Family Study with and without close family history of mood disorder. All were well at time of recruitment. Implementing a structural MRI-based brain age prediction model, we globally assessed individual trajectories of age-related structural change using the difference between predicted brain age and chronological age (brain-predicted age difference (brain-PAD)) at baseline and at 2-year follow-up. Based on follow-up clinical assessment, individuals were categorised into three groups: (i) controls who remained well (C-well, n = 93), (ii) high familial risk who remained well (HR-well, n = 74) and (iii) high familial risk who developed a mood disorder (HR-MD, n = 35). Results: At baseline, brain-PAD was comparable between groups. Results showed statistically significant negative trajectories of brain-PAD between baseline and follow-up for HR-MD versus C-well (β = -0.60, pcorrected < 0.001) and HR-well (β = -0.36, pcorrected = 0.02), with a potential intermediate trajectory for HR-well (β = -0.24 years, pcorrected = 0.06). Conclusions: These preliminary findings suggest that within young individuals, onset of mood disorder and familial risk may be associated with a deceleration in brain structure aging trajectories. Extended longitudinal research will need to corroborate findings of emerging maturational lags in relation to mood disorder risk and onset.

Download Full-text

HEAVY ALCOHOL CONSUMPTION IN MIDLIFE IS ASSOCIATED WITH ACCELERATED BRAIN AGING SIX YEARS LATER

Innovation in Aging ◽

10.1093/geroni/igz038.3324 ◽

2019 ◽

Vol 3 (Supplement_1) ◽

pp. S911-S911

Author(s):

Riki E Slayday ◽

Carol E Franz ◽

Sean N Hatton ◽

Linda K McEvoy ◽

Michael J Lyons ◽

...

Keyword(s):

Alcohol Consumption ◽

Brain Aging ◽

Age Difference ◽

Protective Effects ◽

Heavy Alcohol ◽

Heavy Drinkers ◽

Heavy Alcohol Consumption ◽

Excessive Alcohol ◽

Brain Age ◽

Late Midlife

Abstract Excessive alcohol consumption is associated with cognitive decline, exacerbated brain atrophy, and dementia in older adults, but associations with midlife brain health are less well understood. We hypothesized that heavy drinkers would have older-looking brains in late midlife. We examined alcohol consumption at mean age 56 (range 51-59) in 364 men from the Vietnam Era Twin Study of Aging (VETSA) and their predicted brain age at mean age 62 (range 56-67). We created five midlife alcohol consumption groups based on drinks consumed over the past two weeks: never, former, light (1-14), moderate (15-28), and heavy (>28). Participants underwent structural magnetic resonance imaging at mean age 62. Predicted brain age was measured using the Brain-Age Regression Analysis and Computation Utility software (BARACUS). Models adjusted for age, scanner, race/ethnicity, SES, smoking, health, depressive symptoms, alcohol dependence, general cognitive ability at age 20, and non-independence of twins within pairs. Heavy drinkers had a significantly older predicted than chronological brain age (M= 5.93, SE= 0.88) compared to each of the other four groups (p’s < 0.05). There were no significant differences among the never (M= 2.88, SE= 0.98), former (M= 2.76, SE= 0.74), light (M= 3.00, SE= 0.94), or moderate (M= 5.93, SE= 0.88) consumption groups. Heavy alcohol consumption at age 56 was associated with an approximately 3-year greater predicted brain age difference at age 62. There was no evidence of protective effects of light/moderate drinking over non-drinking. The neurotoxic effects of excessive alcohol may exacerbate brain aging in late midlife.

Download Full-text

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

10.31234/osf.io/7xcun ◽

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.

Download Full-text

Assessing distinct patterns of cognitive aging using tissue-specific brain age prediction based on diffusion tensor imaging and brain morphometry

PeerJ ◽

10.7717/peerj.5908 ◽

2018 ◽

Vol 6 ◽

pp. e5908 ◽

Cited By ~ 33

Author(s):

Geneviève Richard ◽

Knut Kolskår ◽

Anne-Marthe Sanders ◽

Tobias Kaufmann ◽

Anders Petersen ◽

...

Keyword(s):

Diffusion Tensor Imaging ◽

White Matter ◽

Cognitive Aging ◽

Diffusion Tensor ◽

Imaging Features ◽

Tissue Specific ◽

Brain Morphometry ◽

White Matter Microstructure ◽

Brain Age ◽

Age Prediction

Multimodal imaging enables sensitive measures of the architecture and integrity of the human brain, but the high-dimensional nature of advanced brain imaging features poses inherent challenges for the analyses and interpretations. Multivariate age prediction reduces the dimensionality to one biologically informative summary measure with potential for assessing deviations from normal lifespan trajectories. A number of studies documented remarkably accurate age prediction, but the differential age trajectories and the cognitive sensitivity of distinct brain tissue classes have yet to be adequately characterized. Exploring differential brain age models driven by tissue-specific classifiers provides a hitherto unexplored opportunity to disentangle independent sources of heterogeneity in brain biology. We trained machine-learning models to estimate brain age using various combinations of FreeSurfer based morphometry and diffusion tensor imaging based indices of white matter microstructure in 612 healthy controls aged 18–87 years. To compare the tissue-specific brain ages and their cognitive sensitivity, we applied each of the 11 models in an independent and cognitively well-characterized sample (n = 265, 20–88 years). Correlations between true and estimated age and mean absolute error (MAE) in our test sample were highest for the most comprehensive brain morphometry (r = 0.83, CI:0.78–0.86, MAE = 6.76 years) and white matter microstructure (r = 0.79, CI:0.74–0.83, MAE = 7.28 years) models, confirming sensitivity and generalizability. The deviance from the chronological age were sensitive to performance on several cognitive tests for various models, including spatial Stroop and symbol coding, indicating poorer performance in individuals with an over-estimated age. Tissue-specific brain age models provide sensitive measures of brain integrity, with implications for the study of a range of brain disorders.

Download Full-text

Increased brain-predicted aging in treated HIV disease

Neurology ◽

10.1212/wnl.0000000000003790 ◽

2017 ◽

Vol 88 (14) ◽

pp. 1349-1357 ◽

Cited By ~ 91

Author(s):

James H. Cole ◽

Jonathan Underwood ◽

Matthan W.A. Caan ◽

Davide De Francesco ◽

Rosan A. van Zoest ◽

...

Keyword(s):

Hiv Infection ◽

Cognitive Performance ◽

Brain Aging ◽

Chronological Age ◽

Hiv Positive ◽

Age Difference ◽

Hiv Disease ◽

Hiv Positive Adults ◽

Brain Age ◽

Hiv Negative

Objective:To establish whether HIV disease is associated with abnormal levels of age-related brain atrophy, by estimating apparent brain age using neuroimaging and exploring whether these estimates related to HIV status, age, cognitive performance, and HIV-related clinical parameters.Methods:A large sample of virologically suppressed HIV-positive adults (n = 162, age 45–82 years) and highly comparable HIV-negative controls (n = 105) were recruited as part of the Comorbidity in Relation to AIDS (COBRA) collaboration. Using T1-weighted MRI scans, a machine-learning model of healthy brain aging was defined in an independent cohort (n = 2,001, aged 18–90 years). Neuroimaging data from HIV-positive and HIV-negative individuals were then used to estimate brain-predicted age; then brain-predicted age difference (brain-PAD = brain-predicted brain age − chronological age) scores were calculated. Neuropsychological and clinical assessments were also carried out.Results:HIV-positive individuals had greater brain-PAD score (mean ± SD 2.15 ± 7.79 years) compared to HIV-negative individuals (−0.87 ± 8.40 years; b = 3.48, p < 0.01). Increased brain-PAD score was associated with decreased performance in multiple cognitive domains (information processing speed, executive function, memory) and general cognitive performance across all participants. Brain-PAD score was not associated with age, duration of HIV infection, or other HIV-related measures.Conclusion:Increased apparent brain aging, predicted using neuroimaging, was observed in HIV-positive adults, despite effective viral suppression. Furthermore, the magnitude of increased apparent brain aging related to cognitive deficits. However, predicted brain age difference did not correlate with chronological age or duration of HIV infection, suggesting that HIV disease may accentuate rather than accelerate brain aging.

Download Full-text

Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study

10.1101/2020.01.28.923094 ◽

2020 ◽

Cited By ~ 5

Author(s):

Ann-Marie G. de Lange ◽

Melis Anatürk ◽

Tobias Kaufmann ◽

James H. Cole ◽

Ludovica Griffanti ◽

...

Keyword(s):

Cardiovascular Risk ◽

White Matter ◽

Functional Connectivity ◽

Prediction Accuracy ◽

Brain Aging ◽

Training Sample ◽

Resting State Functional Connectivity ◽

White Matter Microstructure ◽

Brain Age ◽

Age Prediction

AbstractBrain age is becoming a widely applied imaging-based biomarker of neural aging and potential proxy for brain integrity and health. We estimated multimodal and modality-specific brain age in the Whitehall II MRI cohort using machine learning and imaging-derived measures of gray matter morphology, diffusion-based white matter microstructure, and resting state functional connectivity. Ten-fold cross validation yielded multimodal and modality-specific brain age estimates for each participant, and additional predictions based on a separate training sample was included for comparison. The results showed equivalent age prediction accuracy between the multimodal model and the gray and white matter models (R2 of 0.34, 0.31, and 0.31, respectively), while the functional connectivity model showed a lower prediction accuracy (R2 of 0.01). Cardiovascular risk factors, including high blood pressure, alcohol intake, and stroke risk score, were each associated with more apparent brain aging, with consistent associations across modalities.

Download Full-text