Machine learning in mental health: a scoping review of methods and applications
BackgroundThis paper aims to synthesise the literature on machine learning (ML) and big
data applications for mental health, highlighting current research and applications in …
data applications for mental health, highlighting current research and applications in …
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 …
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 …
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 …
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 …
Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker
Abstract Machine learning analysis of neuroimaging data can accurately predict
chronological age in healthy people. Deviations from healthy brain ageing have been …
chronological age in healthy people. Deviations from healthy brain ageing have been …
Brain age predicts mortality
Age-associated disease and disability are placing a growing burden on society. However,
ageing does not affect people uniformly. Hence, markers of the underlying biological ageing …
ageing does not affect people uniformly. Hence, markers of the underlying biological ageing …
Support vector regression
F Zhang, LJ O'Donnell - Machine learning, 2020 - Elsevier
This chapter provides an overview of the support vector regression (SVR), an analytical
technique to investigate the relationship between one or more predictor variables and a real …
technique to investigate the relationship between one or more predictor variables and a real …
Mapping the heterogeneous phenotype of schizophrenia and bipolar disorder using normative models
Importance Schizophrenia and bipolar disorder are severe and complex brain disorders
characterized by substantial clinical and biological heterogeneity. However, case-control …
characterized by substantial clinical and biological heterogeneity. However, case-control …