Mobile-EEG based profiling of cognitive impairment in PD and dementia
Research type
Research Study
Full title
The feasibility of mobile-EEG to profile cognitive impairment in Parkinson's disease and dementia.
IRAS ID
242823
Contact name
Aoife Sweeney
Contact email
Clinicaltrials.gov Identifier
N/A, N/A
Duration of Study in the UK
1 years, 6 months, 30 days
Research summary
Summary of Research
Diagnosis of dementia is difficult and often at a late stage, when cognitive deterioration is advanced and the loss of neurons irreversible. Globally, the number of people living with dementia is set to increase from 50 million in 2018 to 152 million in 2050, a 204% increase (WHO, 2017). Early diagnosis of dementia is crucial, however it typically involves the use of expensive imaging techniques or painful lumbar punctures. As such, new diagnostic methodologies are required if we are to improve differential diagnosis rates.In this study, a wireless, mobile dry-electrode EEG headset will be used. The headset will record patterns of brain activity evoked by the processing of information while participants complete computerised cognitive tasks. This study will focus on cognitive impairment due to subcortical syndromes or “Lewy body dementias”. Lewy body dementias manifest either idiopathically as ‘Dementia with Lewy Bodies’ (DLB) or else in the later stages of Parkinson’s disease (PD) as Parkinson’s disease dementia (PDD). Both dementia syndromes share a number of overlapping symptoms, but are clinically distinct and require different treatment strategies. We are interested in examining the basic electroencephalography (EEG) correlates of these syndromes and contrasting these recordings with Alzheimer’s disease (AD) patients, as DLB is frequently misdiagnosed as AD in its early stages.
As PD is primarily thought of as a motor disorder, there is limited research into cognitive aspects, particularly those using objective neuroimaging methods. This study aims to evaluate the feasibility of using mobile EEG to profile cognition in PD over a one year period. It is expected that some PD patients will present with mild cognitive impairment (PD-MCI) which is an intermediate stage between normal cognition and dementia. PD-MCI greatly increases a patient’s risk for PDD (Pederson et al., 2013). Machine learning methods will be used to interrogate the follow-up data to identify the most important factors which contribute to cognitive status in PD.
Summary of Results
In addition to the hallmark motor symptoms of Parkinson’s disease, a host of non-motor symptoms, including mild cognitive impairment (MCI) are often present. The presence of MCI in Parkinson’s disease (PD-MCI) has previously been associated with increased dementia risk. The aim of this study was to find out more about brain activity patterns in Parkinson’s disease and to investigate if any changes to these patterns could be used to detect early signs of PD-MCI. In order to do this, a method called electroencephalography (EEG), which records brain activity, was employed. EEG is safe and non-invasive and it is widely used in hospitals and universities. It works by measuring the electrical signals which brain cells use to communicate with each other. By measuring this activity while cognitive tasks, for example, matching words, are completed, we may obtain a more accurate picture of cognition. In this study, an “Enobio” wireless mobile EEG headset was used. A further aim of this study was to determine if this mobile EEG headset was an effective tool to capture brain activity, as it would be useful and convenient for clinicians and patients if this tool could be used in a variety of settings. A number of Parkinson’s disease patients (n=47) and people without Parkinson’s disease (n=37) completed the study. Traditional neuropsychological tasks which measure multiple cognitive domains such as memory and processing speed were completed in addition to EEG recordings. The results of the neuropsychological tests were then compared to brain activity patterns to determine if mobile EEG provided a more sensitive marker of PD-MCI. Although people with Parkinson’s disease demonstrated greater levels of MCI than people without Parkinson’s disease on traditional neuropsychological testing, statistical analysis revealed that mobile EEG did not prove to be a more effective measure of PD-MCI. A limitation of this study is the that the EEG recordings were only performed once. Follow-up recordings with larger groups may predict future changes to cognition more accurately than neuropsychological assessment and should be investigated in future studies.REC name
HSC REC B
REC reference
18/NI/0205
Date of REC Opinion
15 Nov 2018
REC opinion
Favourable Opinion