Using Machine Learning to Differentiate between Healthy Aging, Mild Cognitive Impairment, & Alzheimer's Disease

2021 ◽  
Author(s):  
Monica Truelove-Hill
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Healthy Aging

scholarly journals A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment

2019 ◽  
Author(s):  
FR Farina ◽  
DD Emek-Savaş ◽  
L Rueda-Delgado ◽  
R Boyle ◽  
H Kiiski ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Resting State ◽  
Healthy Aging ◽  
Neurodegenerative Disorder ◽  
Low Cost ◽  
Structural Mri ◽  
Lower Accuracy

AbstractAlzheimer’s disease (AD) is a neurodegenerative disorder characterised by severe cognitive decline and loss of autonomy. AD is the leading cause of dementia. AD is preceded by mild cognitive impairment (MCI). By 2050, 68% of new dementia cases will occur in low- and middle-income countries. In the absence of objective biomarkers, psychological assessments are typically used to diagnose MCI and AD. However, these require specialist training and rely on subjective judgements. The need for low-cost, accessible and objective tools to aid AD and MCI diagnosis is therefore crucial. Electroencephalography (EEG) has potential as one such tool: it is relatively inexpensive (cf. magnetic resonance imaging; MRI) and is portable. In this study, we collected resting state EEG, structural MRI and rich neuropsychological data from older adults (55+ years) with AD, with MCI and from healthy controls (n~60 per group). Our goal was to evaluate the utility of EEG, relative to MRI, for the classification of MCI and AD. We also assessed the performance of combined EEG and behavioural (Mini-Mental State Examination; MMSE) and structural MRI classification models. Resting state EEG classified AD and HC participants with moderate accuracy (AROC=0.76), with lower accuracy when distinguishing MCI from HC participants (AROC=0.67). The addition of EEG data to MMSE scores had no additional value compared to MMSE alone. Structural MRI out-performed EEG (AD vs HC, AD vs MCI: AROCs=1.00; HC vs MCI: AROC=0.73). Resting state EEG does not appear to be a suitable tool for classifying AD. However, EEG classification accuracy was comparable to structural MRI when distinguishing MCI from healthy aging, although neither were sufficiently accurate to have clinical utility. This is the first direct comparison of EEG and MRI as classification tools in AD and MCI participants.

O1-02-06 Deactivation in healthy aging, mild cognitive impairment and Alzheimer's disease

2004 ◽  
Vol 25 ◽  
pp. S13
Author(s):  
Frederik Barkhof ◽  
Serge A. Rombouts ◽  
Rutger Goekoop ◽  
Philip Scheltens
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Healthy Aging

scholarly journals A machine learning-based linguistic battery for diagnosing mild cognitive impairment due to Alzheimer’s disease

PLoS ONE ◽  
2020 ◽  
Vol 15 (3) ◽  
pp. e0229460
Author(s):  
Sylvester Olubolu Orimaye ◽  
Karl Goodkin ◽  
Ossama Abid Riaz ◽  
Jean-Maurice Miranda Salcedo ◽  
Thabit Al-Khateeb ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment
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