Neurodegenerative diseases target and progress along distinct neural networks that normally support healthy brain function, say scientists at the University of California, San Francisco and Stanford University.. The discovery, they say, could lead to earlier diagnoses, novel treatment-monitoring strategies, and, possibly, recognition of a common disease process among all forms of neurodegeneration. It also provides “an important new framework for understanding neurodegenerative disease.”

 

Researchers have known that neurodegenerative diseases are associated with misfolded proteins that aggregate within specific populations of neurons in the brain. Alzheimer's disease, for instance, results from misfolding events involving beta-amyloid and tau proteins. In all neurodegenerative diseases, synapses between nerve cells falter, and damage spreads to new regions.

 

In most cases, however, scientists have not known what determines the specific brain regions affected by a disease. The current study, which used neuroimaging to examine patients with five forms of early age-of-onset dementia—Alzheimer's disease, behavioral variant frontotemporal dementia, semantic dementia, progressive nonfluent aphasia, and corticobasal syndrome—as well as two groups of healthy controls, showed that each disease targets a different neural network. The findings suggest that network degeneration represents a class-wide neurodegenerative disease phenomenon.

 

“The study suggests that these diseases don't spread across the brain like a wave but instead travel along established neural network pathways,” says William Seeley, the lead author of the study and assistant professor of neurology at the UCSF Memory and Aging Center.

 

“Something about a network's architecture or biology is either bringing the disease to networked regions or propagating disease between network nodes,” says Seeley. At this point, the scientists have shown that the diseases cause atrophy in networked regions. “We still need to determine how the diseases impact connectivity, and we don't yet know how, at the molecular level, disease spreads between networked areas,” says Seeley.

 

The researchers plan to test neural network-based diagnostic and disease-monitoring studies in younger people with genetic predispositions to Alzheimer's disease and frontotemporal dementia. The goal is to try to track changes in neural network connectivity and, ultimately, to track how well new experimental drugs can repair or maintain connectivity once an individual begins to show signs of dysfunction.

 

"Our hope is to develop tools that can detect these diseases even before symptoms emerge, so that disease-modifying therapies can get started before it is too late," Seeley says.