This Research Topic will feature models of predicting brain age from healthy individuals based on Structural Magnetic Resonance Imaging (sMRI) data. Many of the articles featured in this collection were submitted to the Predictive Analytics Competition (PAC) 2019, which featured 274 participants from 79 teams across the globe.
The brain changes as we age, and these changes are associated with cognitive decline, neurodegenerative disease and dementia. Although brain ageing is universal, rates of brain ageing differ markedly; some people suffer cognitive decline in later middle-adulthood, while others remains cognitively normal into their tenth decade. The process of brain ageing includes morphological and functional changes to the brain, which can be assessed using neuroimaging. This raises the possibility that the variability in brain ageing can be measured, and research has focused on developing such a neuroimaging biomarker of brain ageing; the so-called ‘brain-age’ paradigm.
The idea with brain-age is that if statistical models can be developed to accurately predict chronological age in healthy people (using neuroimaging data), then the apparent age of a new individual’s brain can be calculated. Where someone’s brain-age is older than their real age, this is thought to reflect poorer brain health, relative to their age. Older-appearing brains have been associated with psychiatric and neurological diseases, with greater risk of developing dementia and a shorter lifespan. Younger-appearing brains have been found in people who exercise more, have greater years of education, meditate or play musical instruments.
The hope is that brain-age can provide a sensitive, if unspecific, global measure of brain health, that could be used in many contexts. These include clinical trials of neuroprotective therapies, screening groups of people at-risk of poorer cognitive ageing, and providing mechanistic insights into the downstream consequences of different diseases.
Critical to the success of brain-age models, is the accuracy of the healthy training model. Hence, the goal of this year’s PAC is to build the most accurate model, using the training data supplied. Specifically, we would like to minimize brain predicted age difference (brain-PAD) which is calculated as brain-predicted age minus chronological age.
This Research Topic will feature models of predicting brain age from healthy individuals based on Structural Magnetic Resonance Imaging (sMRI) data. Many of the articles featured in this collection were submitted to the Predictive Analytics Competition (PAC) 2019, which featured 274 participants from 79 teams across the globe.
The brain changes as we age, and these changes are associated with cognitive decline, neurodegenerative disease and dementia. Although brain ageing is universal, rates of brain ageing differ markedly; some people suffer cognitive decline in later middle-adulthood, while others remains cognitively normal into their tenth decade. The process of brain ageing includes morphological and functional changes to the brain, which can be assessed using neuroimaging. This raises the possibility that the variability in brain ageing can be measured, and research has focused on developing such a neuroimaging biomarker of brain ageing; the so-called ‘brain-age’ paradigm.
The idea with brain-age is that if statistical models can be developed to accurately predict chronological age in healthy people (using neuroimaging data), then the apparent age of a new individual’s brain can be calculated. Where someone’s brain-age is older than their real age, this is thought to reflect poorer brain health, relative to their age. Older-appearing brains have been associated with psychiatric and neurological diseases, with greater risk of developing dementia and a shorter lifespan. Younger-appearing brains have been found in people who exercise more, have greater years of education, meditate or play musical instruments.
The hope is that brain-age can provide a sensitive, if unspecific, global measure of brain health, that could be used in many contexts. These include clinical trials of neuroprotective therapies, screening groups of people at-risk of poorer cognitive ageing, and providing mechanistic insights into the downstream consequences of different diseases.
Critical to the success of brain-age models, is the accuracy of the healthy training model. Hence, the goal of this year’s PAC is to build the most accurate model, using the training data supplied. Specifically, we would like to minimize brain predicted age difference (brain-PAD) which is calculated as brain-predicted age minus chronological age.
