Abstract
In neuroimaging, the difference between chronological age and predicted brain age, also known as brain age delta, has been proposed as a pathology marker linked to a range of phenotypes. Brain age delta is estimated using regression, which involves a frequently observed bias due to a negative correlation between chronological age and brain age delta. In brain age prediction models, this correlation can manifest as an overprediction of the age of young brains and an underprediction for elderly ones. We show that this bias can be controlled for by adding correlation constraints to the model training procedure. We develop an analytical solution to this constrained optimization problem for Linear, Ridge, and Kernel Ridge regression. The solution is optimal in the least-squares sense i.e., there is no other model that satisfies the correlation constraints and has a better fit. Analyses on the PAC2019 competition data demonstrate that this approach produces optimal unbiased predictive models with a number of advantages over existing approaches. Finally, we introduce regression toolboxes for Python and MATLAB that implement our algorithm.
Highlights
As the world’s population ages, early detection and prevention of neurological aspects of aging, such as cognitive decline and dementia, is a public health priority and challenge
A predictive bias manifesting as an overprediction of the age of young individuals and an underprediction for elderly individuals has led to much speculation and investigation [2, 3, 9, 12,13,14,15]
A predictive bias manifesting as an overprediction of the age of young individuals and an underprediction of the age of elderly individuals has been consistently reported in the brain age literature [2, 3, 14, 15]
Summary
As the world’s population ages, early detection and prevention of neurological aspects of aging, such as cognitive decline and dementia, is a public health priority and challenge. There has been growing interest in developing statistical approaches in order to identify individuals deviating from a healthy brain aging trajectory [2]. To this end, a metric referred to as brain age delta, defined as the difference between brain-predicted age and chronological age, has been proposed as an index of the level of neuropathology in aging [2,3,4]. Investigating the association between this metric with demographics, and lifestyle and cognitive variables can deepen the understanding of the processes that underpin healthy aging [5]. Brain age delta has the potential to index the severity of premature aging in patients suffering from disease. A higher delta has been associated with lower fluid intelligence and higher mortality [1], risk for developing Alzheimer’s disease [6], severity of schizophrenia and depression [7]
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