Prevalence and risk factors of subjective cognitive decline in older adults in Baotou, China: a cross-sectional study DOI Creative Commons

Shang-Jia Ma,

Yan-Xue Yu,

Kai Tian

et al.

Frontiers in Aging Neuroscience, Journal Year: 2024, Volume and Issue: 16

Published: Oct. 9, 2024

Objectives Subjective cognitive decline (SCD) as a stage between healthy cognition and early neurocognitive disorders, has been proposed to be helpful in the diagnosis of prodromal disorders. To investigate prevalence SCD related risk factors on prevalence. Methods A cross-sectional study involving 1,120 elderly subjects residing Baotou, China. From June 2021 2023, data were gathered by research assistants with training utilizing standardized questionnaires. The following evaluated: subjective decline, physical activity levels, past medical history, demographics, instrumental activities daily living, function. Risk used chi-square tests multivariate logistic regression analysis. Results was 43.8%. Permanent residence, marital status, BMI, dietary habits, average sleep duration per night, smoking, diabetes, coronary heart disease, visual impairment significantly associated ( p < 0 0.05). Multivariable analysis showed obesity, vegetarian-based, smoking for long time, diabetes impairment, no spouse, night <6 h independent SCD. Based gender analysis, difference drinking, hypertension statistically significant 0.001). Conclusion high among elder adults. We discovered differences or men women based their sex. This provides more theoretical basis prevention screening diseases population.

Language: Английский

Brain asymmetries from mid- to late life and hemispheric brain age DOI Creative Commons
Max Korbmacher, Dennis van der Meer, Dani Beck

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Feb. 1, 2024

Abstract The human brain demonstrates structural and functional asymmetries which have implications for ageing mental neurological disease development. We used a set of magnetic resonance imaging (MRI) metrics derived from diffusion MRI data in N =48,040 UK Biobank participants to evaluate age-related differences asymmetry. Most regional grey white matter presented asymmetry, were higher later life. Informed by these results, we conducted hemispheric age (HBA) predictions left/right multimodal metrics. HBA was concordant conventional predictions, using both hemispheres, but offers supplemental general marker asymmetry when setting into relationship with each other. In contrast WM asymmetries, discrepancies are lower at ages. Our findings outline various sex-specific differences, particularly important estimates, the value further investigating role

Language: Английский

Citations

16
White matter microstructure links with brain, bodily and genetic attributes in adolescence, mid- and late life DOI Creative Commons
Max Korbmacher, Mario Tranfa, Giuseppe Pontillo

et al.

NeuroImage, Journal Year: 2025, Volume and Issue: 310, P. 121132 - 121132

Published: March 15, 2025

Advanced diffusion magnetic resonance imaging (dMRI) allows one to probe and assess brain white matter (WM) organisation microstructure in vivo. Various dMRI models with different theoretical practical assumptions have been developed, representing partly overlapping characteristics of the underlying biology potentially complementary value cognitive clinical neurosciences. To which degree metrics relate clinically relevant geno- phenotypes is still debated. Hence, we investigate how tract-based whole WM skeleton parameters from approaches associate matter-related (sex, age, pulse pressure (PP), body-mass-index (BMI), asymmetry) genetic markers UK Biobank (UKB, n=52,140) Adolescent Brain Cognitive Development (ABCD) Study (n=5,844). In general, none could explain all examined phenotypes, though were overall similar explaining variability phenotypes. Nevertheless, particular used stood out some important known correlate general human health outcomes. A multi-compartment Bayesian approach provided strongest associations together tensor imaging, largest accuracy for sex-classifications. We find a pattern metric tract-dependent asymmetries across datasets, stronger ABCD data. The magnitude polygenic scores as well PP depended more on sample, likely than metrics. However, kurtosis was most indicative BMI bipolar disorder scores. conclude that differentially associated pheno- genotypes at points life.

Language: Английский

Citations

1
Conformal Prediction for Uncertainty Quantification in Brain Age Estimation Using Random Forests Quantile Regression on MRI Features of the HCP Young Adult DOI
Alessia Sarica, Chiara Camastra, Assunta Pelagi

et al.

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 132 - 146

Published: Jan. 1, 2025

Language: Английский

Citations

0
Association of Psychological Resilience with Decelerated Brain Aging in Cognitively Healthy World Trade Center Responders DOI Creative Commons
Saren H. Seeley, Rachel Fremont,

Zoe Schreiber

et al.

Biological Psychiatry Global Open Science, Journal Year: 2025, Volume and Issue: unknown, P. 100489 - 100489

Published: March 1, 2025

Language: Английский

Citations

0
Cross‐Sectional Brain Age Assessments Are Limited in Predicting Future Brain Change DOI Creative Commons
Max Korbmacher, Dídac Vidal-Piñeiro, Mengyun Wang

et al.

Human Brain Mapping, Journal Year: 2025, Volume and Issue: 46(6)

Published: April 15, 2025

ABSTRACT The concept of brain age (BA) describes an integrative imaging marker health, often suggested to reflect aging processes. However, the degree which cross‐sectional MRI features, including BA, past, ongoing, and future changes across different tissue types from macro‐ microstructure remains controversial. Here, we use multimodal data 39,325 UK Biobank participants, aged 44–82 years at baseline 2,520 follow‐ups within 1.12–6.90 examine BA their relationship anatomical changes. We find insufficient evidence conclude that reflects rate aging. modality‐specific differences in ages state brain, highlighting diffusion as potentially useful markers.

Language: Английский

Citations

0
Brain age as an imaging-based diagnostic and treatment biomarker of neurodegenerative disorders DOI Creative Commons
Max Korbmacher

Research Ideas and Outcomes, Journal Year: 2025, Volume and Issue: 11

Published: May 13, 2025

In the proposed project, we expect to improve diagnosis and treatment for patients suffering from neurodegenerative diseases by establishing a new biomarker based on deep learning big data outputs. We will use brain age, neuroimaging-derived marker of health which has previously rarely been tested longitudinally, but not in disorders. The analyses help assess response as well stratifying sub-typing disease, structural characteristics addition multiple other markers disease expression.

Language: Английский

Citations

0
Predicting brain age for veterans with traumatic brain injuries and healthy controls: an exploratory analysis DOI Creative Commons
John P. Coetzee,

Xiaojian Kang,

Victoria Liou‐Johnson

et al.

Frontiers in Aging Neuroscience, Journal Year: 2025, Volume and Issue: 17

Published: May 15, 2025

Background Traumatic brain injury (TBI) is associated with increased dementia risk. This may be driven by underlying biological changes resulting from the injury. Machine learning algorithms can use structural MRIs to give a predicted age (pBA). When estimated greater than chronological (CA), this called gap (BAg). We analyzed outcome in men and women without TBI. Objective To determine whether factors that contribute BAg, as using brainageR algorithm, differ between who are US military Veterans Methods In an exploratory, hypothesis-generating analysis, we data 85 TBI patients 22 healthy controls (HCs). High-resolution T1W images were processed FreeSurfer 7.0. pBAs calculated T1s. Differences two groups tested Mann-Whitney U. Associations BAg other partial Pearson’s r within groups, controlling for CA, followed construction of regression models. Results After correcting multiple comparisons, HCs differed on PCL score (higher patients) cortical thickness (CT) both hemispheres HCs). Among patients, was correlated pBA hippocampal volume (HV), among CT. HCs, only CA. Four hierarchical models constructed predict each group, which controlled CA excluded multicollinearity. These showed HV TBI, while CT HCs. Interpretation results offer tentative support view individuals women. Specifically, neuroanatomical factors, it reflect features process, or both.

Language: Английский

Citations

0
Considerations on brain age predictions from repeatedly sampled data across time DOI Creative Commons
Max Korbmacher, Mengyun Wang, Rune Eikeland

et al.

Brain and Behavior, Journal Year: 2023, Volume and Issue: 13(10)

Published: Aug. 16, 2023

Brain age, the estimation of a person's age from magnetic resonance imaging (MRI) parameters, has been used as general indicator health. The marker requires however further validation for application in clinical contexts. Here, we show how brain predictions perform same individual at various time points and validate our findings with age-matched healthy controls.We densely sampled T1-weighted MRI data four individuals (from two datasets) to observe corresponds is influenced by acquisition quality parameters. For validation, cross-sectional datasets. was predicted pretrained deep learning model.We found small within-subject correlations between age. We also evidence influence field strength on which replicated inconclusive effects scan quality.The absence maturation range presented sample, model bias (including training distribution strength), error are potential reasons relationships longitudinal data. Clinical applications models should consider possibility apparent biases caused variation process.

Language: Английский

Citations

9
Brain age as a biomarker for pathological versus healthy ageing – a REMEMBER study DOI Creative Commons
Mandy Melissa Jane Wittens, Stijn Denissen, Diana M. Sima

et al.

Alzheimer s Research & Therapy, Journal Year: 2024, Volume and Issue: 16(1)

Published: June 14, 2024

This study aimed to evaluate the potential clinical value of a new brain age prediction model as single interpretable variable representing condition our brain. Among many use cases, could be novel outcome measure assess preventive effect life-style interventions. The REMEMBER population (N = 742) consisted cognitively healthy (HC,N 91), subjective cognitive decline (SCD,N 65), mild impairment (MCI,N 319) and AD dementia (ADD,N 267) subjects. Automated volumetry global, cortical, subcortical structures computed by CE-labeled FDA-cleared software icobrain dm (dementia) was retrospectively extracted from T1-weighted MRI sequences that were acquired during routine at participating memory clinics Belgian Dementia Council. volumetric features, along with sex, combined into weighted sum using linear model, used predict 'brain age' predicted difference' (BPAD age-chronological age) for every subject. MCI ADD patients showed an increased compared their chronological age. Overall, outperformed BPAD in terms classification accuracy across spectrum. There weak-to-moderate correlation between total MMSE score both (r -0.38,p < .001) -0.26,p .001). Noticeable trends, but no significant correlations, found incidence conversion ADD, nor time ADD. heavy alcohol drinkers non-/sporadic (p .014) moderate .040) drinkers. Brain associated have serve indicators for, impact lifestyle modifications or interventions on, health.

Language: Английский

Citations

2
The validity of studying healthy aging with cognitive tests measuring different constructs DOI Creative Commons
Oula Hatahet, Mohamed L. Seghier

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 12, 2024

A clinically useful characterization of the cognitive aging process requires development valid and robust behavioral tests, with an emphasis on explaining understanding typical inter-individual variability in cognition. Here, using a dataset that includes scores collected National Institute Health Toolbox Cognition Battery (NIHTB-CB) other auxiliary we examined (1) differences between young old adults across different domains, (2) strength across-subject correlations test scores, (3) consistency low-dimensional representations age factor analysis, (4) accuracy predicting participants' age. Our results revealed elderly females had better verbal episodic memory than males, tests varied group, although three-factor model explained data both groups, some tasks loaded to factors two age-performance relationship (i.e. regression linking scores) one group cannot be extrapolated predict indicating inconsistency relationships groups. These findings suggest executive function might tap into processes which ultimately statistically significant between-group difference performance not always reflect same underlying processes. Overall, this study calls for more caution when interpreting age-related similarities groups abilities even are used.

Language: Английский

Citations

2