Since its discovery, Alzheimer's disease has been considered irreversible. Consequently, research has focused on preventing or slowing the disease, rather than reversing it.  Using different mouse models of Alzheimer’s and analysis of human Alzheimer’s brains, researchers showed that the brain’s failure to maintain normal levels of a central cellular energy molecule, known as NAD⁺, is a major driver of Alzheimer’s. They showed in animal models not only that preserving normal brain NAD⁺ balance blocks the onset of Alzheimer’s, but also that restoring brain NAD⁺ balance in advanced stages of Alzheimer’s enables the brain to reverse pathology and restore normal cognitive function. Therapies that restore brain energy balance could offer a path to recovery from Alzheimer’s. These findings encourage new research into complementary approaches and clinical testing in patients.

As people age, the brain undergoes multiple changes, increasing the vulnerability to neurological diseases. Early detection of neurological diseases is critical for effective treatment. In a recent study, researchers from China demonstrated that machine learning algorithms integrating imaging, genetic, and clinical data can significantly improve diagnostic accuracy and prediction, enabling timely interventions and better patient outcomes. This approach may transform clinical practice by improving precision, speed, and interpretability in assessing complex brain disorders.

As Alzheimer’s disease (AD) becomes more prevalent, scientists are exploring new methods for its early detection. In a recent review, researchers from India outline how neuron-derived extracellular vesicles—tiny membrane-bound particles released by neurons—are emerging as a promising diagnostic tool for AD. The contents of these vesicles carry molecular clues on the health of neurons, and it may soon be possible to use them to predict the onset of AD several years in advance.