Determining brain age from an MRI scan has always been a time-consuming business. Now an AI machine gives the answer in seconds
Human cognitive abilities decline with age. And neuroscientists have long known that this decline correlates with anatomical changes in the brain as well. So it’s no surprise to learn that it is possible to spot the signs of aging in MRI images of the brain and even to determine a “brain age.” The difference between brain age and chronological age can reveal the onset of conditions such as dementia.
But the analysis is lengthy because the MRI data has to be heavily processed before it is suitable for automated aging. This pre-processing includes the removal from the image of non-brain tissue such as the skull, the classification of white matter, gray matter, and other tissue, and the removal of image artefacts along with various data-smoothing techniques.
All this data crunching can take more than 24 hours, and that is a serious obstacle for doctors hoping to take into account a patient’s brain age when making a clinical diagnosis.
Today, all that changes thanks to the work of Giovanni Montana at King’s College London and a few pals who have trained a deep-learning machine to measure brain age using raw data from an MRI scanner. The deep-learning technique takes seconds and could give clinicians an accurate idea of brain age while the patient is still in the scanner.
The method is a standard deep-learning technique. Montana and co use MRI brain scans of over 2,000 healthy people between 18 and 90 years old. None had any kind of neurological condition that might influence their brain age. So their brain age should match their chronological age.
Each scan is a standard T1-weighted MRI scan of the type produced by most modern MRI machines. Each scan is labeled with the chronological age of the patient.
The team used 80 percent of these images to train a convolutional neural network to determine a person’s age, given their brain scan. They used a further 200 images to validate this process. Finally, they tested the neural network on 200 images it hadn’t seen to determine how well it could measure brain age.
At the same time, the team compared the deep learning approach to the conventional method of determining brain age.