Mobile electroencephalography & computational modelling to understand the role of sleep in disease progression in amnestic mild cognitive impairment
Lead Research Organisation:
University of Bath
Department Name: Psychology
Abstract
Using mobile electroencephalography (EEG) and computational modelling to understand the role of sleep in disease progression in amnestic mild cognitive impairment
The disruption of slow wave sleep (SWS) is implicated in the pathogenesis and functional consequences of dementia. SWS is critical for clearing toxic dementia-linked proteins, and plays an essential role in memory consolidation. Amnestic Mild Cognitive Impairment (aMCI) is the earliest identifiable stage of dementia, with the majority of patients progressing to Alzheimer's disease. Sleep quality is altered in aMCI, and linked to subsequent waking cognitive performance, however, study samples have been small and lacking EEG measures of sleep. Consequently, the link between SWS, aMCI and disease progression is unknown. Identifying SWS changes in aMCI could provide a significant therapeutic target for both disease-modification and symptom management. Historically sleep has been measured using polysomnography, requiring participants to stay overnight in sleep clinics, wearing sleep disruptive equipment. This project will be the first of its kind to harness cutting edge mobile, wearable EEG technology to revolutionise the clinical assessment of sleep in the home environment, with no experimenter present. DreemTM are market leader and project partner.
The aims are to:
1. Characterise changes to sleep in aMCI using remote, home-based EEG data collection.
2. . Examine the relationship between SWS and waking cognitive performance
3. Develop mathematical models using sleep EEG and predict the conversion of MCI to dementia
Methods:
100 aMCI patients will be recruited from co-supervisor, Dr Coulthard's, ReMemBr Memory Clinic volunteer database of 1500 patients, over 2 years. They will complete standardised tests of cognition and sleep (ACE-III, PSQI), and be provided with a Dreem EEG headset and laptop. I will provide instructions to the patient and partner/carer via an online video call each night, for 1 acclimatisation + 7 test nights. Patients will also complete a novel EEG test battery (also via Dreem EEG), developed by Dr Stothart to assess attention, memory and language function. Patient clinical outcome, e.g. conversion to dementia, will be updated at 6 monthly intervals. Computational models will be developed using the spectral characteristics of sleep EEG to understand network dysfunction and predict conversion to dementia.
Relevance to MRC:
This research project fits within MRC's Neuroscience and Mental Health theme, which supports work that aims to advance our understanding of disorders of the brain and central nervous system so that new interventions and treatments can be developed.
The disruption of slow wave sleep (SWS) is implicated in the pathogenesis and functional consequences of dementia. SWS is critical for clearing toxic dementia-linked proteins, and plays an essential role in memory consolidation. Amnestic Mild Cognitive Impairment (aMCI) is the earliest identifiable stage of dementia, with the majority of patients progressing to Alzheimer's disease. Sleep quality is altered in aMCI, and linked to subsequent waking cognitive performance, however, study samples have been small and lacking EEG measures of sleep. Consequently, the link between SWS, aMCI and disease progression is unknown. Identifying SWS changes in aMCI could provide a significant therapeutic target for both disease-modification and symptom management. Historically sleep has been measured using polysomnography, requiring participants to stay overnight in sleep clinics, wearing sleep disruptive equipment. This project will be the first of its kind to harness cutting edge mobile, wearable EEG technology to revolutionise the clinical assessment of sleep in the home environment, with no experimenter present. DreemTM are market leader and project partner.
The aims are to:
1. Characterise changes to sleep in aMCI using remote, home-based EEG data collection.
2. . Examine the relationship between SWS and waking cognitive performance
3. Develop mathematical models using sleep EEG and predict the conversion of MCI to dementia
Methods:
100 aMCI patients will be recruited from co-supervisor, Dr Coulthard's, ReMemBr Memory Clinic volunteer database of 1500 patients, over 2 years. They will complete standardised tests of cognition and sleep (ACE-III, PSQI), and be provided with a Dreem EEG headset and laptop. I will provide instructions to the patient and partner/carer via an online video call each night, for 1 acclimatisation + 7 test nights. Patients will also complete a novel EEG test battery (also via Dreem EEG), developed by Dr Stothart to assess attention, memory and language function. Patient clinical outcome, e.g. conversion to dementia, will be updated at 6 monthly intervals. Computational models will be developed using the spectral characteristics of sleep EEG to understand network dysfunction and predict conversion to dementia.
Relevance to MRC:
This research project fits within MRC's Neuroscience and Mental Health theme, which supports work that aims to advance our understanding of disorders of the brain and central nervous system so that new interventions and treatments can be developed.
Organisations
People |
ORCID iD |
| Mason TAYLOR (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| MR/W006308/1 | 01/10/2022 | 30/09/2028 | |||
| 2749694 | Studentship | MR/W006308/1 | 01/10/2022 | 30/09/2026 | Mason TAYLOR |