For the millions of people who carry the gene APOE4, which is the strongest known genetic risk factor for Alzheimer’s disease, their brain activity may begin changing long before any memory problems appear. Now, researchers at Gladstone Institutes have uncovered a precise chain of molecular events behind those early changes and identified a potential way to reverse them. Published in the journal Nature Aging, their new study in mouse models reveals how APOE4 triggers increased production of the protein Nell2, which makes neurons shrink and become hyperactive. The more hyperactive the neurons were in early life, the more severe were the memory problems the mice developed later in life.

A $3.3 million NIH-funded project at Pitt is exploring how brain metabolism shapes aging and cognitive decline. By linking cellular data in mice with whole-brain imaging in humans, researchers aim to identify early metabolic signals of Alzheimer’s and enable more personalized interventions.

The National Institutes of Health has renewed support for Artificial Intelligence for Alzheimer’s Disease, or AI4AD. The new $12.6 million award to advance the project’s next phase, AI4AD2, brings its total investment in AI4AD to $30.7 million. Led by Paul M. Thompson, PhD, associate director of the USC Mark and Mary Stevens Neuroimaging and Informatics Institute (Stevens INI) at the Keck School of Medicine of USC, the multi-institutional initiative will develop artificial intelligence (AI) tools to uncover the biological causes of Alzheimer’s and related dementias, improve predictions of disease progression, and help develop more precise treatment options. AI4AD2 unites 10 investigators and 23 co-investigators from 10 institutions in pursuit of four interconnected research goals. The consortium will analyze large-scale datasets, including whole-genome sequencing, brain imaging, cognitive testing, and other biological data, to advance the diagnosis and treatment of dementia. This work builds on the original AI4AD initiative launched in 2020, which developed AI tools to detect Alzheimer’s-related patterns in brain scans and showed how machine learning can link imaging findings to underlying genetic risk. AI4AD2 will also develop new “genomic language models,” a type of AI inspired by the same broad family of technology used in language-based artificial intelligence systems. Instead of analyzing words, these models will analyze genomic sequences to identify combinations of DNA changes associated with Alzheimer’s disease, disease progression, and key biomarkers. The project will train and evaluate these methods using data from over 58,000 participants across 57 cohorts. In practical terms, that involves teaching AI to search vast genetic datasets for patterns that traditional methods could not identify.