Researchers may now be able to predict memory problems far in advance by identifying certain brain activity patterns. In a new study out of Gladstone Institutes, scientists tested this new method in mice with the goal of identifying Alzheimer’s memory loss sooner—so interventions can be established as quickly as possible.

“Being able to predict [memory] deficits long before they appear could open up new opportunities to design and test interventions that prevent Alzheimer’s in people,” Yadong Huang, Gladstone Senior Investigator and an author of the study, said in a news release.

The study, published in Cell Reports, relied on research from a previous study on the APOE4 gene. The APOE4, or apolipoprotein E4, gene has been the subject of numerous recent investigations to better understand its role in neurodegenerative diseases. If someone carries the APOE4 gene, they have a much higher chance of developing Alzheimer’s disease.

In the most recent development on APOE4, scientists discovered that a woman carrying the APOE4 gene also had a genetic mutation that caused a delay in the disease developing in her. While the gene she carried highly increased her risk of developing Alzheimer’s in her 30s, she didn’t develop it until her 70s.

Other recent research out of the Banner Alzheimer’s Institute picked apart different types of the APOE gene and their varying impacts on Alzheimer’s risk.

In the previous Gladstone study, the researchers examined sharp-wave ripples (SRWs), a type of brain pattern involved in spatial learning and memory in mammals. In aging mice that carried the APOE4 gene—meaning they had a higher risk of developing Alzheimer’s—the researchers noticed SRWs were weaker compared to healthy aging mice. Impaired SRWs are associated with worsened memory and spatial learning.

In the latest study, they decided to test whether identifying weak SRWs in advance could help predict Alzheimer’s disease.

“We actually successfully replicated this experiment two years later with different mice,” Huang said. “What was striking is that we were able to use the results from the first cohort to predict with high accuracy the extent of learning and memory deficits in the second cohort, based on their SWR activity.”