Alzheimer’s disease (AD) is a neurodegenerative disorder that affects a significant proportion of the older population worldwide. It causes irreparable damage to the brain and severely impairs the quality of life in patients. Unfortunately, AD cannot be cured, but early detection can allow medication to manage symptoms and slow the progression of the disease.
Functional magnetic resonance imaging (fMRI) is a noninvasive diagnostic technique for brain disorders. It measures minute changes in blood oxygen levels within the brain over time, giving insight into the local activity of neurons. Despite its advantages, fMRI has not been used widely in clinical diagnosis. The reason is twofold. First, the changes in fMRI signals are so small that they are overly susceptible to noise, which can throw off the results. Second, fMRI data are complex to analyze. This is where deep-learning algorithms come into the picture.
In a recent study published in the Journal