Advances in data preprocessing for biomedical data fusion: An overview of the methods, challenges, and prospects
Due to the proliferation of biomedical imaging modalities, such as Photoacoustic
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …
The promise of music therapy for Alzheimer's disease: a review
AM Matziorinis, S Koelsch - Annals of the New York Academy of …, 2022 - Wiley Online Library
Alzheimer's disease (AD) is a progressive neurodegenerative disease associated with
cognitive decline. Memory problems are typically among the first signs of cognitive …
cognitive decline. Memory problems are typically among the first signs of cognitive …
[HTML][HTML] Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan
As medical imaging enters its information era and presents rapidly increasing needs for big
data analytics, robust pooling and harmonization of imaging data across diverse cohorts …
data analytics, robust pooling and harmonization of imaging data across diverse cohorts …
[HTML][HTML] Accurate brain age prediction with lightweight deep neural networks
Deep learning has huge potential for accurate disease prediction with neuroimaging data,
but the prediction performance is often limited by training-dataset size and computing …
but the prediction performance is often limited by training-dataset size and computing …
[HTML][HTML] Brain age prediction using deep learning uncovers associated sequence variants
Abstract Machine learning algorithms can be trained to estimate age from brain structural
MRI. The difference between an individual's predicted and chronological age, predicted age …
MRI. The difference between an individual's predicted and chronological age, predicted age …
[HTML][HTML] Ten Years of BrainAGE as a Neuroimaging Biomarker of Brain Aging: What Insights Have We Gained?
With the aging population, prevalence of neurodegenerative diseases is increasing, thus
placing a growing burden on individuals and the whole society. However, individual rates of …
placing a growing burden on individuals and the whole society. However, individual rates of …
[HTML][HTML] Machine learning for brain age prediction: Introduction to methods and clinical applications
The rise of machine learning has unlocked new ways of analysing structural neuroimaging
data, including brain age prediction. In this state-of-the-art review, we provide an …
data, including brain age prediction. In this state-of-the-art review, we provide an …
Predicting age using neuroimaging: innovative brain ageing biomarkers
The brain changes as we age and these changes are associated with functional
deterioration and neurodegenerative disease. It is vital that we better understand individual …
deterioration and neurodegenerative disease. It is vital that we better understand individual …
[HTML][HTML] Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning
Recent critical commentaries unfavorably compare deep learning (DL) with standard
machine learning (SML) approaches for brain imaging data analysis. However, their …
machine learning (SML) approaches for brain imaging data analysis. However, their …
[HTML][HTML] Multimodality neuroimaging brain-age in UK biobank: relationship to biomedical, lifestyle, and cognitive factors
JH Cole - Neurobiology of aging, 2020 - Elsevier
The brain-age paradigm is proving increasingly useful for exploring aging-related disease
and can predict important future health outcomes. Most brain-age research uses structural …
and can predict important future health outcomes. Most brain-age research uses structural …