Researchers have developed a new method for constructing personal brain networks using multiple structural features to improve the accuracy of diagnosing Alzheimer's disease (AD) and mild cognitive impairment (MCI). The personal networks accurately classified 96 percent of patients with AD or MCI from healthy control participants, a level similar to the current accuracy of clinical evaluations. The high performance of the method suggests it could be useful in clinics to enhance auto-diagnosis of AD and MCI based on brain imaging.