Our incomplete understanding of the causes and pathways involved in the onset and
progression of multiple sclerosis (MS) limits our ability to effectively treat this complex …

The field of psychiatry is hampered by a lack of robust, reliable and valid biomarkers that can
aid in objectively diagnosing patients and providing individualized treatment …

Biological aging of human organ systems reflects the interplay of age, chronic disease,
lifestyle and genetic risk. Using longitudinal brain imaging and physiological phenotypes …

Human brain structure changes throughout the lifespan. Altered brain growth or rates of
decline are implicated in a vast range of psychiatric, developmental and neurodegenerative …

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 …

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 …

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 …

The factor that is most relevant and strongly associated with the clinical course of multiple
sclerosis is chronological age. Very young patients exclusively have relapsing remitting …

Importance Physical health and chronic medical comorbidities are underestimated,
inadequately treated, and often overlooked in psychiatry. A multiorgan, systemwide …

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 …