Related papers: OpenMAP-BrainAge: Generalizable and Interpretable …
Understanding distinct neurological aging patterns across various populations is vital in the context of a globally aging populace. This study seeks to unravel the structural variations in the aging brain, taking into consideration…
Brain aging is a regional phenomenon, a facet that remains relatively under-explored within the realm of brain age prediction research using machine learning methods. Voxel-level predictions can provide localized brain age estimates that…
Background: Brain maturation and aging involve significant microstructural changes, resulting in functional and cognitive alterations. Quantitative MRI (qMRI) can measure this evolution, distinguishing the physiological effects of normal…
Age is one of the major known risk factors for Alzheimer's Disease (AD). Detecting AD early is crucial for effective treatment and preventing irreversible brain damage. Brain age, a measure derived from brain imaging reflecting structural…
Brain aging is a complex and dynamic process, leading to functional and structural changes in the brain. These changes could lead to the increased risk of neurodegenerative diseases and cognitive decline. Accurate brain-age estimation…
The brain's white matter (WM) undergoes developmental and degenerative processes during the human lifespan. To investigate the relationship between WM anatomical regions and age, we study diffusion magnetic resonance imaging tractography…
Brain age estimation based on magnetic resonance imaging (MRI) is an active research area in early diagnosis of some neurodegenerative diseases (e.g. Alzheimer, Parkinson, Huntington, etc.) for elderly people or brain underdevelopment for…
Machine learning is a rapidly evolving field with a wide range of applications, including biological signal analysis, where novel algorithms often improve the state-of-the-art. However, robustness to algorithmic variability - measured by…
Human brain development is rapid during infancy and early childhood. Many disease processes impair this development. Therefore, brain developmental age estimation (BDAE) is essential for all diseases affecting cognitive development. Brain…
The human brain undergoes dynamic, potentially pathology-driven, structural changes throughout a lifespan. Longitudinal Magnetic Resonance Imaging (MRI) and other neuroimaging data are valuable for characterizing trajectories of change…
Deep learning can provide rapid brain age estimation based on brain magnetic resonance imaging (MRI). However, most studies use one neural network to extract the global information from the whole input image, ignoring the local fine-grained…
Normative aging trends of the brain can serve as an important reference in the assessment of neurological structural disorders. Such models are typically developed from longitudinal brain image data -- follow-up data of the same subject…
Lesion detection in brain Magnetic Resonance Images (MRIs) remains a challenging task. MRIs are typically read and interpreted by domain experts, which is a tedious and time-consuming process. Recently, unsupervised anomaly detection (UAD)…
How will my face look when I get older? Or, for a more challenging question: How will my brain look when I get older? To answer this question one must devise (and learn from data) a multivariate auto-regressive function which given an image…
Estimating brain age (BA) from T1-weighted magnetic resonance images (MRIs) provides a powerful framework for quantifying anatomical brain aging. Whereas global BA (GBA) summarizes overall brain health, local BA (LBA) provides cortically…
Deep learning methods based on Convolutional Neural Networks (CNNs) have shown great potential to improve early and accurate diagnosis of Alzheimer's disease (AD) dementia based on imaging data. However, these methods have yet to be widely…
In this study we propose a deformation-based framework to jointly model the influence of aging and Alzheimer's disease (AD) on the brain morphological evolution. Our approach combines a spatio-temporal description of both processes into a…
Functional connectivity (FC) derived from resting-state fMRI plays a critical role in personalized predictions such as age and cognitive performance. However, applying foundation models(FM) to fMRI data remains challenging due to its high…
Predictive modeling using structural magnetic resonance imaging (MRI) data is a prominent approach to study brain-aging. Machine learning algorithms and feature extraction methods have been employed to improve predictions and explore…
Brain age prediction serves as a powerful framework for assessing brain status and detecting deviations associated with neurodevelopmental and neurodegenerative disorders. However, most existing approaches emphasize whole-brain age…