Researchers Use MRI and ASL Techniques to Diagnose Alzheimer’s

Functional changes in the brain caused by Alzheimer’s disease are already present before structural changes become evident in imaging scans.

The study that included 260 participants was published on July 6, 2016, in the online issue of the journal Radiology. The researchers taught an automated machine learning program to recognize patterns in the perfusion maps so that they could identify the levels of cognitive impairment of a patient. One hundred patients were diagnosed with probable Alzheimer’s disease, 60 were diagnosed with MCI, and 100 with Subjective Cognitive Decline (SCD). The study included 26 healthy control subjects.

Using the new technique the researchers were able to differentiate between patients with Alzheimer’s disease, and those with MCI and SCD. Using automated machine learning training the researchers could then predict the diagnosis of a single patient with an accuracy of between 82% and 90%.

Alle Meije Wink, PhD, principal investigator of the study from the VU Medical Center (VUmc; Amsterdam, Netherlands), said, “An MRI can help with the diagnosis of Alzheimer’s disease. However, the early diagnosis of Alzheimer’s disease is problematic. With standard diagnostic MRI, we can see advanced Alzheimer’s disease, such as atrophy of the hippocampus. But at that point, the brain tissue is gone and there’s no way to restore it. It would be helpful to detect and diagnose the disease before it’s too late. ASL is a promising alternative functional biomarker for the early diagnosis of Alzheimer’s disease. ASL MRI can identify brain changes that appear early in disease process, when there’s a window of opportunity for intervention. If the disease process from SCD to MCI to Alzheimer’s disease could be intercepted or slowed, this technique could play a role in screening.”

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