Multimodal brain age prediction fusing morphometric and imaging data and association with cardiovascular risk factors
Introduction The difference between the chronological and biological brain age, called the
brain age gap (BAG), has been identified as a promising biomarker to detect deviation from …
brain age gap (BAG), has been identified as a promising biomarker to detect deviation from …
An analysis of the effects of limited training data in distributed learning scenarios for brain age prediction
Objective Distributed learning avoids problems associated with central data collection by
training models locally at each site. This can be achieved by federated learning (FL) …
training models locally at each site. This can be achieved by federated learning (FL) …
Synthetic data in generalizable, learning-based neuroimaging
Synthetic data have emerged as an attractive option for developing machine-learning
methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)—a …
methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)—a …
Providing clinical context to the spatio-temporal analysis of 4D CT perfusion to predict acute ischemic stroke lesion outcomes
Acute ischemic stroke is a leading cause of mortality and morbidity worldwide. Timely
identification of the extent of a stroke is crucial for effective treatment, whereas spatio …
identification of the extent of a stroke is crucial for effective treatment, whereas spatio …
Analysis and visualization of the effect of multiple sclerosis on biological brain age
CJA Romme, EAM Stanley, P Mouches… - Frontiers in …, 2024 - frontiersin.org
Introduction The rate of neurodegeneration in multiple sclerosis (MS) is an important
biomarker for disease progression but can be challenging to quantify. The brain age gap …
biomarker for disease progression but can be challenging to quantify. The brain age gap …
Identifying biases in a multicenter MRI database for Parkinson's disease classification: Is the disease classifier a secret site classifier?
Sharing multicenter imaging datasets can be advantageous to increase data diversity and
size but may lead to spurious correlations between site-related biological and non-biological …
size but may lead to spurious correlations between site-related biological and non-biological …
A machine learning approach using conditional normalizing flow to address extreme class imbalance problems in personal health records
Background Supervised machine learning models have been widely used to predict and get
insight into diseases by classifying patients based on personal health records. However, a …
insight into diseases by classifying patients based on personal health records. However, a …
Biological Brain Age Estimation using Sex-Aware Adversarial Variational Autoencoder with Multimodal Neuroimages
Brain aging involves structural and functional changes and therefore serves as a key
biomarker for brain health. Combining structural magnetic resonance imaging (sMRI) and …
biomarker for brain health. Combining structural magnetic resonance imaging (sMRI) and …
Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems
Artificial Intelligence (AI) has paved the way for revolutionary decision-making processes,
which if harnessed appropriately, can contribute to advancements in various sectors, from …
which if harnessed appropriately, can contribute to advancements in various sectors, from …
Deep interpretability methods for neuroimaging
MM Rahman - 2022 - scholarworks.gsu.edu
Brain dynamics are highly complex and yet hold the key to understanding brain function and
dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging …
dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging …