It's also important that “the regions that we found that were involved had been identified in group analyses,” said Dr. Resnick.
Dr. Resnick noted that one of the study's strengths is how early patients' mild cognitive impairment was detected, showing the tool's potential for very early diagnosis.
“The people [whose conditions] we're calling 'mild cognitive impairment' in this sample would not really have come to clinical attention,” she said.
One advantage of this type of tool is that clinicians typically don't have serial data on patient cognition. Rather, a patient usually comes into the office with a complaint about memory and the clinician has to determine if this is a result of normal aging or a more pathologic process.
The ability to use an assessment of brain structure to help determine MCI could be particularly important for high-functioning individuals, who may have suffered significant cognitive declines by the time they meet clinical criteria for impairment, said Dr. Resnick.
In fact, one participant in this study was considered cognitively normal by clinical measure at the time of the most recent scan. However, when previous scans were evaluated using this method, the patient's abnormality scores rose over time. This patient subsequently died and autopsy revealed moderate AD pathology.
“Although we only evaluated the autopsy results of this one patient who seemed to be an outlier, it shows that the patterns on MRI were more in agreement with the underlying pathology than with the clinical status of the patient,” said Dr. Davatzikos.
The study is limited by the small sample size.
So is this new technique going to solve the problem of predicting which individuals will eventually develop MCI and progress to Alzheimer disease? Probably not. It's unlikely that any one tool or test is going to be able to definitively predict which individuals will develop Alzheimer disease.
“We're talking about risk factors here,” said Dr. Davatzikos. “It's like cardiac disease. … It's a collection of information that the clinician then has to evaluate [to] make a decision.”
Still, each additional piece of information that can help a clinician identify individuals potentially on the road to Alzheimer disease as early in the process as possible will be essential for making treatment decisions, especially should disease progression-halting drugs becomes available.
The above image shows the regions in which brain atrophy was evaluated by the pattern classifier to get an abnormality score. The color-coding reflects how much each region contributed to the discrimination between mild cognitive impairment and cognitively normal individuals. Larger numbers indicate a larger contribution. Courtesy Dr. Christos Davatzikos and Dr. Susan M. Resnick