1. Components Studied in Literature Discussion and Conclusion
Using Event-Related Potentials to Help Diagnose Mild Cognitive Impairment and Alzheimer's disease: A Systematic Review Cassandra Morrison1,2,Sheida Rabipour1, Frank Knoefel1,2, Vanessa Taler1,2 1University Of Ottawa, 2Bruyère Research Institute
Stephanie’s note for Figure 4:
These bounds were applied to all data (and therefore both images). They are the sensor levels below which I determined there was no person on the bed and beyond which I assumed there was more than one person on the bed. The top plot is the average exerted pressure, whereas the bottom plot is the standard deviation. The standard deviation represents the amount of movement each foot experienced - so, theses plots show that the left foot exerted more pressure and moved less when compared to the right foot.
Introduction
Results
Increased age is associated with increased risk of cognitive impairment ranging from healthy aging to mild cognitive impairment (MCI) and Alzheimer’s disease (AD)
Neuropsychological tests offer a sensitivity of only 75-80% when attempting to diagnose MCI and AD1
There is currently no accepted diagnostic tool for MCI or AD
Electroencephalography (EEG) generates event-related potentials (ERPs) through cognitive tasks
ERPs enable the monitoring of electrophysiological changes associated with cognitive performance2,3
Common cognitive tasks include: Oddball, Go/NoGo, and N-back to elicit ERPs such as the P300, and N200
Components Studied in Literature
P50
P100
PNwm
N100
N160
N170
P200
N200
P300
N300
N400
P450
P600
Type of Tasks Used
Auditory Oddball
Visual Oddball
N-back
Go/NoGo
Auditory Other
Visual Other
Groups Examined
Healthy adults over 60 vs young controls
Individuals with MCI vs healthy older adults
Individuals with AD vs those with MCI and/or healthy older adults
Right Foot
Left Foot
Key Findings
The P300 amplitude and latency appear to differentiate healthy older adults from young adults, MCI from HC, and AD from MCI and HC
The N200 latency appear to discriminate MCI from healthy aging and AD from HC and MCI
The N100 & P200 components do not appear to differentiate between healthy aging, MCI and AD
Many other components have begun showing differences between groups, but have minimal research completed on them
Limitations
Many studies do not report both behavioural and electrophysiological responses to the tasks
Not reporting sensitivity and specificity between the groups
The use of anticholinergic medications or cholinesterase inhibitors is not controlled for
Natural and environmental influences on brain activity patterns are often not controlled for
Some studies compare AD to healthy controls but not those with MCI
Left Foot
Objective
To review the literature on the degree to which brain activity patterns (ERPs) help distinguish between healthy aging, MCI and AD
Methods
Electronic search: PsycINFO & Medline for peer-reviewed articles published between 2005 and June 2016
Keywords: Event-related potentials, Alzheimer’s disease, Electroencephalography, Mild cognitive impairment
Articles: English, humans 60 years of age and older
Discussion and Conclusion
Inclusion Criteria:
Older adults (healthy aging, MCI or AD patients)
Compare to younger controls or each other
Peer-reviewed journal
At least one ERP component elicited by visual or auditory paradigms
Many ERP components have minimal research completed on them making it difficult to determine whether or not they are acceptable components to diagnose MCI and AD
The current literature suggests that the P300 and N200 are sensitive to electrophysiological alterations associated with cognitive impairment and may serve as a potential biomarker for diagnosing MCI and AD
Some research suggests that the P300 and N200 latency can provide a sensitivity and specificity ranging from 70%-100% when discriminating between the groups
Future research may benefit from combining neuropsychological testing with ERP components to obtain more accurate diagnosis of healthy cognitive decline compared to MCI and AD
Future research should further explore ERP components that remain poorly understood, to better elucidate how brain activity changes over time 
Examining the relationships between multiple ERP components may increase diagnostic accuracy 
Bibliography:
1Liu, Y., Paajanen, T., Zhang, Y., Westman, E., Wahlund, L.O., Simmons, A., et al., (2011) Neurobiology of Aging. 32(7): Papaliagkas, V.T., Kimiskidis, V.K., Tsolaki, M.N., Anogianakis G. . Clinical Neurophysiology 122(7): Liddell, B.J., Paul, R.H., Arns, M., Gordon, N., Kukla, M., Rowe, D., et al. (2007) Integrative neuroscience 6(1): Daltrozzo, J., & Conway, C. M. (2014) Frontiers in human neuroscience 8:437