Functional magnetic resonance imaging (fMRI) is a noninvasive diagnostic technique for brain disorders, such as Alzheimer’s disease (AD).
fMRI has not been widely used in clinical diagnosis.
Their limited use is due to the fact fMRI data are highly susceptible to noise, and the fMRI data structure is very complicated compared to a traditional x-ray or MRI scan.
Scientists from Texas Tech University now report they developed a type of deep-learning algorithm known as a convolutional neural network (CNN) that can differentiate among the fMRI signals of healthy people, people with mild cognitive impairment, and people with AD.
“Deep learning CNNs could be used to extract functional biomarkers related to AD, which could be helpful in the early detection of AD-related dementia,” Parmar explained.
The researchers tested their CNN with fMRI data from a public database, and the classification accuracy of their algorithm was as high as or higher than that of other methods.
Their results demonstrate that deep learning-based approaches can help improve diagnosis not only for AD but also for other neurological disorders.
“Alzheimer’s has no cure yet.
Although brain damage cannot be reversed, the progression of the disease can be reduced and controlled with medication,” according to the authors.
“Our classifier can accurately identify the mild cognitive impairment stages which provide an early warning before progression into AD.”