The brain has amazing capacity to overcome large deficits, which means that by the time a neurodegenerative disease becomes apparent, the damage is extensive. In the case of Parkinson’s disease, for example, more than half of the midbrain region called the substantia nigra can be completely destroyed without any evidence of a problem. But once a threshold of 65- to 70-percent damage has been crossed, the trajectory goes over a cliff and the telltale motor symptoms of Parkinson’s appear.
Alzheimer’s disease is similar. Plaques and tangles can be developing in the brain and neurons can be dying, but the compensatory mechanisms of the brain are so great that there are no behavioral symptoms—until the brain can no longer overcome the damage. By that time, there may be no hope for treatment. Unfortunately, we are a long way away from having any sort of treatment that can repair the massive destruction that has occurred by the time the disease is detected.
In SRI’s Center for Neuroscience, my colleagues and I are searching for an indicator that could help diagnose neurodegenerative diseases much earlier, hopefully years before the damage is so extensive that nothing can be done. If a marker is found in both humans and animal models, it could also help guide the development of new treatments and be used to monitor patients to determine if potential treatments are effective.
Our search for a translational biomarker for measuring the presence and progression of neurodegenerative disease centers on the use of the electroencephalogram (EEG). Put simply, an EEG is a measure of the sum of all electrical activity across different regions of the cerebral cortex. Healthy neurons produce electric currents in the process of releasing neurotransmitters and relaying signals. When neurons get sick, their electrical patterns can change long before they die. When large numbers of cells start changing their electrophysiological properties due to the progression of a neurodegenerative disease, we believed there would be measurable changes in the readout of the EEG. And that is what we found in Huntington’s disease, as described below.
Using EEG to diagnose brain dysfunction is not a new idea. Numerous studies have found changes in the EEGs of patients suffering from various neurodegenerative diseases as well as in animal models of neurodegenerative diseases. However, finding EEG changes that are indicative of the presence of disease prior to the onset of symptoms has been difficult to ascertain.
Huntington’s disease is unique among the neurodegenerative diseases in that it is the only major one for which we know the major cause with certainty. In Huntington’s disease patients, a genetic mutation in the Huntingtin gene produces a change in a protein that makes it toxic to neurons. Because Huntington’s disease has a known inheritable cause, we are able to determine if a person has the disease decades before they become symptomatic. Thus, we recognized the opportunity to study the diagnostic value of EEG in Huntington’s disease patients at various stages of the development of the disease.
UCLA Professor Andrew Leuchter has studied EEG patterns in Huntington’s disease patients and several years ago published an important finding about how EEG patterns changed in pre-symptomatic Huntington’s disease patients compared to controls. These changes progressed as patients got closer to being symptomatic. Dr. Leuchter was able to use EEG as a biomarker that could identify a patient having the disease 10 years prior to its onset. This work validated the concept that the EEG can be used as a biomarker for the presence and progression of a neurodegenerative disease.
For the last three years, SRI researchers have been studying EEG patterns in mice that have a condition similar to human Huntington’s disease. Our findings that an EEG “signature” could indeed identify early changes in the brain were recently published in the journal Brain . SRI’s findings and Dr. Leuchter’s findings both support the hypothesis that the EEG can be used to diagnose neurodegenerative disease and that it might also be used as a biomarker to help measure the efficacy of potential new therapeutics.