printable versionBrain diseases shown to target neural networks
Scientists are reporting the strongest evidence to date that neurodegenerative diseases target and progress along distinct neural networks that normally support healthy brain function. The discovery could lead to earlier diagnoses, novel treatment-monitoring strategies and, possibly, recognition of a common disease process among all forms of neurodegeneration.
The study, reported in the April 16 issue of Neuron and featured on that issue's cover, was conducted by scientists at the School of Medicine and UC-San Francisco, who characterized their finding as "an important new framework for understanding neurodegenerative disease."
In all neurodegenerative diseases, synapses between nerve cells falter, and damage spreads to new regions, accompanied by worsening clinical deficits. In most cases, however, scientists have not known what determines the specific brain regions affected by a disease.
The Stanford and UCSF investigators examined 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. Their findings showed that these diseases don't spread across the brain willy-nilly, but instead travel along established neural network pathways, causing progressive atrophy in one after another networked region of a circuit. Each disease studied appears to progress along its own characteristic neural network.
Earlier work performed by senior author Michael Greicius, MD, assistant professor of neurology and neurological sciences at Stanford, had indicated that this was true for Alzheimer's disease. The new findings suggest that network degeneration represents a phenomenon that may apply to a host of additional dementias.
"These results suggest that brain-imaging measures of network strength should be sensitive enough to detect these diseases at an early stage and specific enough to reliably distinguish one disease from the others," Greicius said.
Greicius and his colleagues plan to perform 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 incipient 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," said William Seeley, MD, assistant professor of neurology at the UCSF Memory and Aging Center and the study's lead author.
The study was funded by the National Institute of Aging, the National Institute of Neurological Diseases and the Larry L. Hillblom Foundation.