Predicting Outcomes in Parkinson Disease: Utility of EEG

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There remains a clear need for further research comprising larger cohorts to more effectively assess the utility of EEG for monitoring and predicting outcomes in PD.
There remains a clear need for further research comprising larger cohorts to more effectively assess the utility of EEG for monitoring and predicting outcomes in PD.

Parkinson disease (PD) is the fastest growing neurologic disorder worldwide in prevalence, disability, and deaths. The overall number of people affected by the disease was estimated to have more than doubled globally from 1990 to 2015.1

In high-income countries such as the United States, the median annual incidence rate is estimated to be 14 per 100,000 people in the general population and 160 per 100,000 in people 65 and older. Indeed, the most important risk factor for PD is age, but the disease is also associated with industrial chemicals and pollutants. As lifespans increase and industrialization expands, the prevalence of PD may also continue to increase.2

PD symptoms generally occur 5 to 15 years after molecular and cellular neuropathology begins in patients. As a result, current treatments for PD symptoms, including resting tremors, rigidity, and postural disabilities, provide limited benefits for patients. One of the most pressing areas of PD research involves the need for clinical biomarkers to assist with earlier diagnosis, as well as outcome predictions and treatment options.3

This search for reliable objective markers has resulted in numerous studies that validate the use of electroencephalogram (EEG), as interpreted via quantitative EEG (qEEG), to provide insight into PD.

EEG as a Diagnostic Tool

Quantitative biomarkers may be able to identify the risk for PD before symptoms of the disorder are expressed,4 which is achieved by the direct measurement of brain activity to ascertain cortical dysfunction. John Caviness, MD, a professor of neurology at the Mayo Clinic College of Medicine in Rochester, Minnesota, told Neurology Advisor, “The EEG is very useful. It is an objective physiological biomarker that is reliable, cheap, and easy to do relative to imaging. It has been validated in multiple studies across the world with similar results.”

Since EEGs can detect early signs of cortical dysfunction, they can potentially provide prognostic markers of future clinical deterioration, which can then speed up the diagnostic process. For example, changes in dopamine receptors correlate with the progression of PD, according to neurologist Juan Pablo Romero Muñoz, MD, PhD, from the brain damage unit at Hospital Beata Maria Ana in Madrid, Spain. EEGs can provide realistic information regarding what stage a patient with PD might be at or entering.

“PD has important functional changes depending on the dopaminergic stimulation state,” he said. “EEG techniques with advanced signal analysis as microstates may have great potential to differentiate degrees of dopaminergic stimulation, thus allowing [doctors] to detect fluctuations. These functional changes can be detected ahead of clinical manifestations, so in the future this can be a good way to study, in real time, the dopamine effects in the brain.”

EEG for Predicting Cognitive Impairment

According to a 2018 study published in Movement Disorders, an average of 26.7% (range 18.9% to 38.2%) of patients with PD meet the criteria for mild cognitive impairment, which can lead to reduced quality of life and increased healthcare costs.1 Non-motor symptoms, such as cognitive impairment, can present early in the disease and may more accurately reflect the severity and progression of the disease than motor symptoms. In particular, qEEG measurements — including decreased dominant frequency and increased θ power — correlate with cognitive impairment.4 The use of EEG “could be very suitable for a preliminary screening of a patient's cognitive profile and, thus, to quickly steer the patient to more in-depth exams according to their condition,” the researchers reported.

Dr Caviness, who was co-investigator in a 2018 study exploring the changes in the spectral domain of the EEG, said they found that as PD progresses the patient's frequency distribution spectrum shows less fast frequency and more slow frequency. This is particularly true as patients' cognitive abilities decline.5 “We found that slowing of the background rhythm could predict incident dementia,” he said. “And when combined with PD-related mild cognitive impairment, this EEG parameter added to the predictive power. EEG activity correlated with increasing pathological stage overall but particularly for cortical Lewy body pathology.”

EEG in Treatment Evaluations

In a study published in Frontiers in Neuroscience, Dr Muñoz and colleagues utilized EEG microstate analysis to study dopaminergic stimulation treatment. They administered levodopa, one of the most common drugs used to treat all stages of PD, and then measured the microstates and compared those findings with a control group.6

After the patients received 1 dose of levodopa, their microstate features and occurrence were closer to the controls' microstates than before taking the medication. Since only patients with typical PD experience good functional response to levodopa, the researchers are hopeful that EEGs can be used to differentiate typical vs atypical PD. Consequently, this differentiation could provide more insight into a patient's response to drug treatments. “We think we will be able to differentiate a typical from atypical Parkinson´s patient by administering a single levodopa dose and performing a resting state EEG, avoiding time-consuming therapeutic trials,” Dr Muñoz said.

There remains a clear need for further research comprising larger cohorts to more effectively assess the utility of EEG for monitoring and predicting outcomes in PD. With additional investigation, there is the potential for network analyses and functional connectivity to serve as biomarkers. In particular, EEGs have the capacity to facilitate early diagnosis of non-motor symptoms.4 In addition, EEG has proven to be a widely accessible, non-invasive, inexpensive, objective, and a reliable diagnostic tool that has the potential to significantly benefit patients with PD.

References

1. Betrouni N, Delval A, Chaton L, et al. Electroencephalography-based machine learning for cognitive profiling in Parkinson's disease: Preliminary results [published online. Mov Disord. doi:10.1002/mds.27528

2.  GBD 2016 Parkinson's Disease Collaborators. Global, regional, and national burden of Parkinson's disease, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2018;17(11):939-953.

3. Miller DB, O'Callaghan JP. Biomarkers of Parkinson's disease: present and future. Metab Clin Exp. 2015;64(3 Suppl 1):S40-S46.4. Geraedts VJ, Boon LI, Marinus J, et al. Clinical correlates of quantitative EEG in Parkinson disease: A systematic review. Neurology. 2018;91(19):871-883.

5. Caviness JN, Beach TG, Hentz JG, Shill HA, Driver-Dunckley ED, Adler CH. Association between pathology and electroencephalographic activity in Parkinson's disease. Clin EEG Neurosci. 2018;49(5):321-327.

6. Serrano JI, Del Castillo MD, Cortés V, et al. EEG microstates change in response to increase in dopaminergic stimulation in typical Parkinson's disease patients. Front Neurosci. 2018;12:714.

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