A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment
Alzheimer’s disease (AD) is the leading cause of dementia, accounting for 70% of cases worldwide. By 2050, dementia prevalence will have tripled, with most new cases occurring in low- and middle-income countries. Mild cognitive impairment (MCI) is a stage between healthy aging and dementia, marked b...
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| Format: | Article |
| Language: | English |
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Elsevier
2020-07-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811920302822 |
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| author | F.R. Farina D.D. Emek-Savaş L. Rueda-Delgado R. Boyle H. Kiiski G. Yener R. Whelan |
| author_facet | F.R. Farina D.D. Emek-Savaş L. Rueda-Delgado R. Boyle H. Kiiski G. Yener R. Whelan |
| author_sort | F.R. Farina |
| collection | DOAJ |
| description | Alzheimer’s disease (AD) is the leading cause of dementia, accounting for 70% of cases worldwide. By 2050, dementia prevalence will have tripled, with most new cases occurring in low- and middle-income countries. Mild cognitive impairment (MCI) is a stage between healthy aging and dementia, marked by cognitive deficits that do not impair daily living. People with MCI are at increased risk of dementia, with an average progression rate of 39% within 5 years. There is urgent need for low-cost, accessible and objective methods to facilitate early dementia detection. Electroencephalography (EEG) has potential to address this need due to its low cost and portability. Here, we collected resting state EEG, structural MRI (sMRI) and rich neuropsychological data from older adults (55+ years) with AD, amnestic MCI (aMCI) and healthy controls (~60 per group). We evaluated a range of candidate EEG markers (i.e., frequency band power and functional connectivity) for AD and aMCI classification and compared their performance with sMRI. We also tested a combined EEG and cognitive classification model (using Mini-Mental State Examination; MMSE). sMRI outperformed resting state EEG at classifying AD (AUCs = 1.00 vs 0.76, respectively). However, both EEG and sMRI were only moderately good at distinguishing aMCI from healthy aging (AUCs = 0.67–0.73), and neither method achieved sensitivity above 70%. The addition of EEG to MMSE scores had no added benefit relative to MMSE scores alone. This is the first direct comparison of EEG and sMRI for classification of AD and aMCI. |
| first_indexed | 2024-12-10T22:31:28Z |
| format | Article |
| id | doaj.art-a38a841096884469b702db7d8e499a49 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-10T22:31:28Z |
| publishDate | 2020-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-a38a841096884469b702db7d8e499a492022-12-22T01:31:02ZengElsevierNeuroImage1095-95722020-07-01215116795A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairmentF.R. Farina0D.D. Emek-Savaş1L. Rueda-Delgado2R. Boyle3H. Kiiski4G. Yener5R. Whelan6Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland; Corresponding author.Department of Psychology, Faculty of Letters, Dokuz Eylul University, Izmir, 35160, Turkey; Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, 35340, Turkey; Global Brain Health Institute, Trinity College Dublin, Dublin 2, IrelandTrinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, IrelandTrinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, IrelandTrinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, IrelandDepartment of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, 35340, Turkey; Department of Neurology, Dokuz Eylul University Medical School, Izmir, 35340, TurkeyTrinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland; Global Brain Health Institute, Trinity College Dublin, Dublin 2, Ireland; Corresponding author. Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland.Alzheimer’s disease (AD) is the leading cause of dementia, accounting for 70% of cases worldwide. By 2050, dementia prevalence will have tripled, with most new cases occurring in low- and middle-income countries. Mild cognitive impairment (MCI) is a stage between healthy aging and dementia, marked by cognitive deficits that do not impair daily living. People with MCI are at increased risk of dementia, with an average progression rate of 39% within 5 years. There is urgent need for low-cost, accessible and objective methods to facilitate early dementia detection. Electroencephalography (EEG) has potential to address this need due to its low cost and portability. Here, we collected resting state EEG, structural MRI (sMRI) and rich neuropsychological data from older adults (55+ years) with AD, amnestic MCI (aMCI) and healthy controls (~60 per group). We evaluated a range of candidate EEG markers (i.e., frequency band power and functional connectivity) for AD and aMCI classification and compared their performance with sMRI. We also tested a combined EEG and cognitive classification model (using Mini-Mental State Examination; MMSE). sMRI outperformed resting state EEG at classifying AD (AUCs = 1.00 vs 0.76, respectively). However, both EEG and sMRI were only moderately good at distinguishing aMCI from healthy aging (AUCs = 0.67–0.73), and neither method achieved sensitivity above 70%. The addition of EEG to MMSE scores had no added benefit relative to MMSE scores alone. This is the first direct comparison of EEG and sMRI for classification of AD and aMCI.http://www.sciencedirect.com/science/article/pii/S1053811920302822Alzheimer’s diseaseMild cognitive impairmentEEGResting stateStructural MRIMachine learning |
| spellingShingle | F.R. Farina D.D. Emek-Savaş L. Rueda-Delgado R. Boyle H. Kiiski G. Yener R. Whelan A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment NeuroImage Alzheimer’s disease Mild cognitive impairment EEG Resting state Structural MRI Machine learning |
| title | A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment |
| title_full | A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment |
| title_fullStr | A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment |
| title_full_unstemmed | A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment |
| title_short | A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment |
| title_sort | comparison of resting state eeg and structural mri for classifying alzheimer s disease and mild cognitive impairment |
| topic | Alzheimer’s disease Mild cognitive impairment EEG Resting state Structural MRI Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S1053811920302822 |
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