[HTML][HTML] Contextualizing adolescent structural brain development: Environmental determinants and mental health outcomes
The spatiotemporal group-level patterns of brain macrostructural development are relatively
well-documented. Current research emphasizes individual variability in brain development …
well-documented. Current research emphasizes individual variability in brain development …
Explainable artificial intelligence for magnetic resonance imaging aging brainprints: Grounds and challenges
Marked changes occur in the brain during people's lives, and individual rates of aging have
revealed pronounced differences, giving rise to subject-specific brainprints that are the …
revealed pronounced differences, giving rise to subject-specific brainprints that are the …
Brain age prediction: A comparison between machine learning models using brain morphometric data
Brain structural morphology varies over the aging trajectory, and the prediction of a person's
age using brain morphological features can help the detection of an abnormal aging …
age using brain morphological features can help the detection of an abnormal aging …
Brain age vector: A measure of brain aging with enhanced neurodegenerative disorder specificity
Neuroimaging‐driven brain age estimation has become popular in measuring brain aging
and identifying neurodegenerations. However, the single estimated brain age (gap) …
and identifying neurodegenerations. However, the single estimated brain age (gap) …
[HTML][HTML] Linking brain age gap to mental and physical health in the Berlin aging study II
From a biological perspective, humans differ in the speed they age, and this may manifest in
both mental and physical health disparities. The discrepancy between an individual's …
both mental and physical health disparities. The discrepancy between an individual's …
Gray matter volume drives the brain age gap in schizophrenia: a SHAP study
Neuroimaging-based brain age is a biomarker that is generated by machine learning (ML)
predictions. The brain age gap (BAG) is typically defined as the difference between the …
predictions. The brain age gap (BAG) is typically defined as the difference between the …
Brain age prediction across the human lifespan using multimodal MRI data
Measuring differences between an individual's age and biological age with biological
information from the brain have the potential to provide biomarkers of clinically relevant …
information from the brain have the potential to provide biomarkers of clinically relevant …
eXplainable Artificial Intelligence (XAI) in aging clock models
XAI is a rapidly progressing field of machine learning, aiming to unravel the predictions of
complex models. XAI is especially required in sensitive applications, eg in health care, when …
complex models. XAI is especially required in sensitive applications, eg in health care, when …
Sex differences in predictors and regional patterns of brain age gap estimates
N Sanford, R Ge, M Antoniades… - Human Brain …, 2022 - Wiley Online Library
The brain‐age‐gap estimate (brainAGE) quantifies the difference between chronological
age and age predicted by applying machine‐learning models to neuroimaging data and is …
age and age predicted by applying machine‐learning models to neuroimaging data and is …
Accelerated functional brain aging in major depressive disorder: evidence from a large scale fMRI analysis of Chinese participants
Major depressive disorder (MDD) is one of the most common mental health conditions that
has been intensively investigated for its association with brain atrophy and mortality. Recent …
has been intensively investigated for its association with brain atrophy and mortality. Recent …