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Current Alzheimer Research
ISSN: 1567-2050
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Current Alzheimer Research, 2019, 16, 67-89
67
REVIEW ARTICLE
Visual Event-Related Potentials in Mild Cognitive Impairment and
Alzheimer’s Disease: A Literature Review
Cassandra Morrison1,*, Sheida Rabipour1, Vanessa Taler1, Christine Sheppard2 and Frank Knoefel3
1School of Psychology, University of Ottawa, Canada, & Bruyère Research Institute, Ottawa, Canada; 2Bruyère Re-
search Institute, Ottawa, Canada & School of Public Health and Health Systems, University of Waterloo, Waterloo,
Canada; 3Faculty of Medicine, University of Ottawa, Canada, Bruyère Research Institute, & Carleton University, Ot-
tawa, Canada
A R T I C L E H I S T O R Y
Received: May 22, 2 018
Revised: August 26, 2018
Accepted: October 15, 2018
DOI:
10.2174/156720501566618102210103
6
Abstract: Background: Cognitive deficits are correlated with increasing age and become more pro-
nounced for people with mild cognitive impairment (MCI) and dementia caused by Alzheimer’s disease
(AD). Conventional methods to diagnose cognitive decline (i.e., neuropsychological testing and clinical
judgment) can lead to false positives. Tools such as electroencephalography (EEG) offer more refined,
objective measures that index electrophysiological changes associated with healthy aging, MCI, and AD.
Objective: We sought to review the EEG literature to determine whether visual event-related potentials
(ERPs) can distinguish between healthy aging, MCI, and AD.
Method: We searched Medline and PyscInfo for articles published between January 2005 and April
2018. Articles were considered for review if they included participants aged 60+ who were healthy older
adults or people with MCI and AD, and examined at least one visually elicited ERP component.
Results: Our search revealed 880 records, of which 34 satisfied the inclusion criteria. All studies com-
pared cognitive function between at least two of the three groups (healthy older adults, MCI, and AD).
The most consistent findings related to the P100 and the P3b; while the P100 showed no differences be-
tween groups, the P3b showed declines in amplitude in MCI and AD.
Conclusion: Visually elicited ERPs can offer insight into the cognitive processes that decline in MCI and
AD. The P3b may be useful in identifying older adults who may develop MCI and AD, and more re-
search should examine the sensitivity and specificity of this component when diagnosing MCI and AD.
Keywords: Visual event-related potentials, Alzheimer’s disease, Mild cognitive impairment, Electroencephalography, P300,
N200
1. INTRODUCTION
Aging is associated with changes in both cognitive func-
tioning and brain activity patterns (event-related potentials;
ERPs). These ERP changes may be more prominent in those
who experience cognitive decline due to mild cognitive im-
pairment (MCI) and Alzheimer’s disease (AD). MCI is a
condition that causes cognitive decline and is gaining interest
among researchers because of its role as a transitional stage
between healthy aging and dementia caused by AD [1-3].
People with MCI who go on to develop AD experience pro-
gressive deterioration in a variety of cognitive domains
including attention, language, reasoning, and behavior [4, 5].
Although around 12% of people who have MCI progress
to AD annually, others remain stable or develop other types
of dementia [2]. These differing trajectories may be reflected
in differing MCI profiles, based on whether the primary
*Address correspondence to this author at the University of Ottawa, School
of Psychology, Vanier Hall, 136 Jean Jacques Lussier, Ottawa, ON K1N
6N5, Canada; Tel: 613-562-6262 ext:1096; E-mail: cmorr083@uottawa.ca
deficit is in memory (amnestic MCI, aMCI) or in another
cognitive domain such as language (non-amnestic MCI,
naMCI) [6, 7]. MCI may further be classified as stable
(sMCI), where the person does not progress, to AD (i.e.,
their cognitive functions remain stable after the initial de-
cline), progressive (pMCI), where the person goes on to de-
velop AD or another dementia [8]; multiple domain amnestic
MCI (mdaMCI), where there are impairments in other cogni-
tive domains in addition to memory; and single domain am-
nestic MCI (sdaMCI) [9], a decline in one other cognitive
domain in addition to memory [9].
Diagnosis of both MCI and AD is rising due to increased
life expectancy; however, as mentioned in the previous
installment of this review [10], the current methods of diag-
nosis for MCI and AD have many limitations. For example,
tests may accurately diagnose one subtype of MCI but not
others [7]. Additionally, using only neuropsychological tests
can generate many false positives, and multiple testing
methods are therefore needed [11]. One possible avenue to
more accurate diagnosis is the use of electroencephalography
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68 Current Alzheimer Research, 2019, Vol. 16 , No. 1 Morrison et al.
(EEG). By examining variances in brain activity patterns
(ERPs) during performance of cognitively-demanding visual
tasks, it may be possible to differentiate between these
groups with higher sensitivity and specificity than existing
methods.
Event-related potentials allow researchers to measure
brain responses to specific cognitive functions [12], and to
detect subtle changes in cognitive function between groups.
Each ERP component has a specific amplitude associated
with the intensity of the cognitive process, and a latency that
is associated with the processing time required for the task
[12]. ERPs can be elicited during both passive and active
tasks. Passive tasks do not require the participant to attend to
the stimuli being presented, while active tasks require the
participant to actively engage in the task (e.g., pressing a
button in response to a stimulus) [13, 14]. The components
elicited by passive and active tasks differ, as discussed be-
low. The present review focuses on ERPs elicited during
visual tasks. For a review of ERPs elicited during auditory
tasks, see our previous paper [10].
1.1. ERPs Measured in Visual Tasks
ERP components involved with perceptual and sensory
processing include the N70, N100, P100, N150, N160, and
P200. While the N70 and N150 ERP components are elicited
during visual paradigms involving pattern reversal [15, 16],
the N150 is also involved with early perceptual processing
[17]. Similarly, the P100 is believed to underlie early visual
processing [18] because it is evoked by a variety of visual
stimuli, including colors [19], flashes of light [20], shapes
[21], movement [22], and even the appearance of images
[15]. Both the N100 and P200 components are involved with
primary perceptual processing of incoming information and
early attentional allocation to visual stimuli [23-26]. The
N160 has been identified as ideal for studying the allocation
of attention to visual stimuli [27]. Despite the importance of
these components in studying age-related cognitive decline,
these early visual ERP components are often overlooked in
studies examining ERPs in aging and cognitive decline [e.g.,
28-31]
Middle to long-latency ERP components are among the
most widely-studied and well-understood components. These
include the N2b, N2pc, mismatch negativity (MMN), and
P3b - important components for higher order cognitive proc-
essing in response to a visual stimulus. The N2b reflects
processing of visual stimuli during active attention [32], in-
cluding the processing of motion [33, 34], whereas the N2pc
is specifically related to the processing of a target stimulus
and the ability to shift attention to a target [35, 36]. Another
N2 subcomponent is the negativity central contralateral
(N2cc), which is a measure of cognitive control [36]. The
MMN is associated with the ability to detect stimulus change
without direct attention [37, 38], and is measured with a pas-
sive task. Lastly, the P3b is involved in essential cognitive
processes such as attention and memory [13, 39], and is one
of the most common components analyzed in relation to vis-
ual tasks.
Cognitive functions related to facial recognition, lan-
guage, and memory processing are also associated with ERP
components. The viewing of faces elicits greater amplitudes
in the N170 and N250(r) compared to the viewing of scenes
or non-face objects [40-43]. The N400 reflects processing
involved with lexical/semantic integration, with larger am-
plitudes in response to semantic irregularities [12]. Memory
processing is reflected in several different ERP components,
including the positive negative working memory (WM)
component (PNwm), N300, P450, P600, and contralateral
delay activity (CDA). The PNwm, P450, and CDA are also
associated with WM processing. The PNwm is a relatively
newly-defined component that indexes WM capacity [44].
Similarly, the CDA is a newly-defined component [45]
thought to reflect the encoding and maintenance of items in
visual WM [46]; this component is a good index of an indi-
vidual’s WM capacity [47, 48]. The P450 is involved with
sustained attention and the ability to accommodate new and
relevant information into WM [49]. Finally, the P600 (also
called the late positive component; LPC) is a key component
in the processing of memory encoding [50].
1.2. Age-Related Changes in ER Ps Found in Visual Tasks
This literature review aims to discuss changes in visu-
ally-elicited ERP components in cognitive decline due to
MCI and AD. Prior to addressing the literature on these
populations, we briefly review some of the changes seen in
these components with normal aging.
Studies that have been conducted on age-related changes
in the P100 reported mixed findings: some have reported a
decrease in amplitude [51, 52], while others have found no
age-related changes in amplitude [53, 54]. P100 latency has
also shown mixed results, with one study reporting no
changes due to age [54], and one indicating a P100 prolonga-
tion due to age [53].
Similarly, research on age-related changes in the P200
has been inconsistent. Two studies have reported decreased
amplitude [51, 53], while another found no amplitude change
with age [54]. With respect to P200 latency, two studies re-
ported similar latency between young and older adults [51,
53], and one reported a prolongation with age [54]. The ma-
jority of studies have reported no change in N100 amplitude
with older age [51, 54-56], although some findings are
mixed, with reports of a decrease [22], and an increase [53]
in amplitude. Due to the lack of and inconsistent findings,
more research is needed to understand the influence of age
on the P100 and P200.
The MMN, N2b, and P3b have generated more interest in
aging research than earlier components. The MMN decreases
in amplitude with age [57], suggesting declines in pre-
attentive processing to visual stimuli, and the N2b is delayed
[33, 58, 59], that older adults require more time to process
and attend to a stimulus. Similarly, numerous studies have
found prolonged P3b latency [56, 58-63] with age, indicating
a potential reduction in processing speed [63, 64]. In addi-
tion, decreased amplitudes are found at parietal regions in
older adults compared to young controls [60, 65, 66]. How-
ever, in frontal regions P3b amplitudes appear to increase in
older adults compared to young controls [51, 56, 61-63]. The
amplitude decline in parietal regions is associated with less
efficient performance [56], whereas the increase in frontal
amplitude suggests the need for older adults to recruit addi-
tional regions to complete a task of the same difficulty [51,
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Visual Event-Related Potentia ls in MCI and AD Current Alzheimer Research, 2019, Vol. 16, No. 1 69
58]. This frontal switch is consistent with the posterior-
anterior shift in aging (PASA): older adults tend to engage
other networks in the frontal region to compensate for de-
clines in processing in the posterior regions [67]. The consis-
tent findings with respect to the N2b and P3b suggest that
these two components may be good indicators of age-related
cognitive declines.
The N170 and N400 have also been examined in the
healthy aging population. One study reported that N170 la-
tency appears to remain stable, whereas the amplitude be-
comes smaller during facial recognition tasks in older adults
compared to young controls [68]. This finding suggests a
decline in the processing of facial stimuli with age. Studies
examining language processing in older adults have reported
smaller amplitudes and prolonged latencies in older adults
relative to young controls [69, 70], suggesting diminished
processing of semantic information with healthy cognitive
aging.
Lastly, the PNwm, N300, P450, P600, and CDA are com-
ponents elicited during tasks requiring memory processing.
Initial research on the PNwm has shown that healthy older
adults exhibit a prolonged and attenuated PNwm compared to
young controls; this difference also becomes more pro-
nounced as task demands increase [44]. Similarly, the P450
shows both decreases in amplitude and prolongations in la-
tency due to age [28]. Smaller P600 [22] and N300 [28] am-
plitudes are also found with increased age, representing de-
creases in memory encoding and retrieval, respectively.
Lastly, the CDA has also shown to decline with age [71],
especially in those at risk of developing MCI [72]. These
component changes are related to declines with memory,
speed, and accuracy declines that occur with healthy aging
[28, 73].
1.3. Present Review
Current literature on ERPs elicited from visual paradigms
in cognitively healthy older adults indicate that some ERP
components (such as the P3b and N2b) can clearly distin-
guish between younger and older adults. Therefore, these
components may also be useful in distinguishing between
healthy aging, MCI, and AD. As discussed in our first litera-
ture review [10], other reviews have begun to examine the
influence of progressive cognitive decline on ERP compo-
nents [74-76]. However, no review to date has examined
ERP components as a function of the type of paradigm used
to elicit the responses (visual paradigm versus auditory para-
digm) or examined (in depth) the effects of MCI on ERP
components. In the present review, we sought to examine
whether visual paradigms can detect declines in different
aspects of cognition (e.g., facial processing, WM, semantic
abilities, visual motion perception) in MCI and AD. The
goals of this paper were to: i) update the literature on visu-
ally-elicited ERPs in MCI and AD; and ii) examine the util-
ity of ERPs as a diagnostic tool for both AD and MCI. This
review will further the existing research by identifying spe-
cific components that may be useful in clinical settings and
future experimental protocols.
2. METHODS
2.1. Search Protocol
A literature search on PsycINFO and Medline was con-
ducted using the following keywords: “Evoked potentials,
Alzheimer’s disease, Cognitive Impairment, and Electroen-
cephalography”. Titles and abstracts were searched using the
terms: “event related potential, ERP, mild cognitive impair-
ment, MCI, EEG, and Alzheimer”. A hand search of refer-
ences in key articles was also conducted to identify addi-
tional articles of relevance. Table 1 for an example of the
search protocol in PsycInfo.
Table 1. Example of the literature search run in PsycInfo.
Abbreviations. ti, title; ab, abstract.
# Searches # of Results
1) Evoked Potentials/ 18737
2) Erp.ti,ab. 11143
3) Event related potential*.ti,ab. 13648
4) Alzheimer’s Disease/ 42023
5) Cognitive Impairment/ 32079
6) Mci.ti,ab. 5718
7) Mild cognitive impairment.ti,ab 8341
8) Electroencephalography/ 23192
9) Eeg.ti,ab. 32591
10) Alzheimer*.ti,ab. 53228
11) 1 or 2 or 3 or 8 or 9 59489
12) 4 or 5 or 6 or 7 or 10 79502
13) 11 or 12 1538
14) Limit 13 to (English and human and last 13 years) 880
2.2. Article Selection
Articles were considered if they were available in Eng-
lish and published between January 2005 and April 2018.
Articles were included if they included participants 60 years
and older with a diagnosis of MCI or AD and if they re-
ported at least one ERP component elicited by visual para-
digms. Articles were excluded if they reported EEG data
with a paradigm (i.e., did not extract ERPs) or used somato-
sensory stimuli to elicit ERPs. Literature reviews, meta-
analyses, and case studies were also excluded. Finally, stud-
ies that only examined differences between young adults and
older adults but did not include an MCI or AD group were
also excluded. Two independent reviewers screened titles
and abstracts to assess inclusion and extract relevant study
data. The remaining articles underwent full text reading by
two independent reviewers to assess inclusion. Discrepancies
were resolved by discussion, including a third reviewer when
required.
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70 Current Alzheimer Research, 2019, Vol. 16, No. 1 Morrison et al.
3. RESULTS
After removing duplicates, we identified 880 titles and
abstracts from our search and selected 84 for full text review
(Fig. 1). A total of 34 articles met the full inclusion criteria
and were included in the review.
Six studies used a visual motion paradigm, 11 used vari-
ous WM tasks (n-back, Simon task, Go-NoGo, Eriksen
flanker task, and delayed matching to sample), four pre-
sented faces or scene pictures, six used a visual oddball task,
one study used a double flash paradigm requiring the partici-
pant to press a button when they saw a strobe flash, and
seven studies examined language processing using word
recognition and repetition, word memory paradigms, seman-
tic priming, or presentation of lexically ambiguous stimuli. A
summary of the ERP components elicited from the above
tasks can be found in Appendix I.
We included only papers that compared at least two of
the three participant groups (healthy older adults, and people
with MCI or AD). Eleven papers compared ERPs between
healthy older adults and people with MCI, 13 compared
healthy older adults to those with AD, and 10 compared
healthy older adults to both people with MCI and people
with AD. There were no studies examining the differences
between MCI and AD without a healthy older adult group.
The studies are summarized in Table 2.
3.1. ERPs in Sensory Processing
3.1.1. N70, N150, & N160
Only one study examined the N70 [15], N150 [15], and
N160 [77]. This study compared healthy older adults to peo-
ple with MCI using a visual pattern reversal paradigm, and
found no group differences in N70 or N150 amplitude or
latency [15]. Within the MCI population, medication did not
impact the N70 or N150 components [15], suggesting that
cholinesterase inhibitors do not affect these early sensory
processes. A second study examined the N160 using oddball
and n-back tasks in healthy older adults, as well as people
with sMCI, pMCI, and AD, and found no difference in N160
latency between any of the four groups [77].
3.1.2. P100
Most studies reported no changes in P100 amplitude in
people with MCI [15, 54, 77-81] and AD [54, 82-85], com-
pared to healthy older adults. However, one study found an
increase in amplitude in MCI and AD [86], and one study
also reported a decline in AD [81]. The lack of P100 differ-
ences between groups extends to latency effects during vis-
ual tasks; none of the studies reported differences in P100
latency across groups [15, 54, 77, 78, 80, 81, 84, 86]. Al-
though one study reported a decrease in latency between
healthy older adults and people with AD, this difference was
only present at one electrode site (O1) [83]).
3.1.3. N100
Four studies indicated no difference in amplitude [54, 79,
80, 85] or latency [54, 80] between people with MCI [79,
80)] or AD [(54, 85]. Two studies reported smaller N100
amplitudes in people with MCI [81] or AD [81, 87)] com-
pared to healthy older adults. No group differences in N100
latency were found in any study [54, 80, 81, 87].
3.1.4. P200
Four of the seven studies examining the P200 focused on
amplitude differences between groups. Two studies found
that people with amnestic MCI (aMCI) [80] and AD [54]
Fig. (1). Flow diagram of search protocol.
Records identified through
database searching
(n = 1538)
Screenin
g
Included Eligibility Identification
Additional records identified
through manual search
(
n = 77
)
Records after duplicates removed
(n = 880)
Records screened
(n = 880)
Records excluded after
title and abstracts
reviewed
(n = 797)
Full-text articles
assessed for eligibility
(n = 83)
Full-text articles
excluded, with reasons
(n = 49)
n = 2 components
obtained through PCA
n = 11 only compared
YA to HC
n = 4 used specified time
frames not components
n = 30 used auditory
components
n = 2 joint auditory and
visual tas
k
Studies included in
qualitative synthesis
(
n = 34
)
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Table 2. Summary of reviewed articles.
Reference Sample: N Method of
diagnosis
Task Component Amplitude Latency Behaviour
Wolk et al.,
(2005)
[97]
N=24
n=12 HC
8 males
Age range:65-86
M= 75.0
n=12 AD
6 males
Age range:55-80
M= 71.8
AD: NINCDS-
ADRDA
Word recogni-
tion/ familiar-
ity (active)
N400:
325-625 ms
AD < HC N.A. HC > discrimina-
tion abilities than
AD
Olichney et
al., (2006)
[87]
N=22
n=11 HC
7 males
Age range: N.R.
M=77.1
n=11 AD
8 males
Age range: N.R.
M=79.4
AD: extensive
NP testing,
laboratory tests.
MMSE
avg:22.9 ± 3.9
Semantic
congruent and
incongruent
statement
pairs – word
repetition
effects (ac-
tive)
N100:
100-250 ms
N400:
400-550 ms
P600:
550-800 ms
AD<HC
1) AD<HC (poste-
rior &midline)
2) AD>HC
(frontal)
3) AD no repeti-
tion effect
AD decreased
repetition effect &
over left channels
(HC was over
right)
AD=HC
AD=HC
N.A.
ACC: HC >AD
Tales et al.,
(2006)
[57]
N=42
n=11 YA
3 males
Age range:20-43
M=28
n=12 HC
2 males
Age range:51-84
M=73
n=8 probable AD
0 males
Age range:52-84
M=73
AD: DSM-IV
& NINCDS-
ADRDA crite-
ria
Press button
to target,
ignore stan-
dard, and
deviant stim-
uli
(active)
vMMN:
250-400 ms
AD=HC
N.A. N.R.
(Table 2) contd….
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72 Current Alzheimer Research, 2019, Vol. 16 , No. 1 Morrison et al.
Reference Sample: N Method of
diagnosis
Task Component Amplitude Latency Behaviour
Fernandez et
al., (2007)
[82]
N=26
n=14 HC
Age range: N.R.
M=70.60
n=6 ADhs
Age range: N.R.
M=73.33
n=6 ADls
Age range: N.R.
M=75.00
AD: NINCDS-
ADRDA crite-
ria
Forced choice
visual motion
discrimination
task, press
button indicat-
ing way of
motion
(active)
Pattern:
P100:
60-110 ms,
P200:
210-300 ms, &
N2b:140-250 ms
Visual motion:
N2b:
140-250 ms
Attention:
P3b:
250-700 ms
ADhs=ADls=
HC
1) ADhs&
ADls <HC
2) ADhs = ADls
ADhs&ADls <HC
N.R.
N.R.
N.R.
N.R.
Irimajiri et
al., (2007)
[15]
N=30
n=15 HC
6 males
Age range: N.R.
M=76.3
n=8 T-MCI
6 males
Age range: N.R.
M=73.4
n=7 NT-MCI
4 males
Age range: N.R.
M=74.3
MCI: neuro-
logical and NP
examinations,
family inter-
views and brain
imaging
Visual pattern
reversal (pas-
sive)
N70:
50-90 ms
P100:
90-120 ms
N150:
100-165 ms
MCI=HC
MCI=HC
MCI=HC
MCI=HC
MCI=HC
MCI=HC
N.A.
Liddell et
al., (2007)
[28]
N=1123
n=1008 HC
51.1% males
Age range:6-80
M= 31.63
n=76 SMC
40.8% males
Age range:52-88
M= 64.92
n=20 MCI
50% males
Age range:54-85
M= 73.6
MCI: CDR of
0.5 and referred
by clinician
AD: DSM-IV
criteria by
neurologist
N-back task
(active)
N300:
200-400 ms
P450:
300-540 ms
1) MCI > HC
2) AD < HC
AD<MCI, SMC,
& HC
1) MCI > HC
2) AD > HC
AD=MCI=SM
C=HC
RT and error
rates did not
differ between
groups
(Table 2) contd….
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Reference Sample: N Method of
diagnosis
Task Component Amplitude Latency Behaviour
n=19 AD
45% males
Age range:58-95
M= 76.61
Missonnier
et al., (2007)
[77]
N=55
n=16 HC
6 males
Age range: N.R.
M= 71.63
n=13 sMCI
3 males
Age range:57-91
M= 82.00
n=16 pMCI
3 males
Age range:57-91
M= 82.13
n=10 AD
3 males
Age range: N.R.
M= 76.60
MCI: CDR 0.5
and 1.5 SD
below on NP
tests. MMSE
between 25-28
AD: CDR 1
and 2 S.D.
below on NP
tests &
NINCDS-
ADRDA.
MMSE below
25
Oddball or n-
back
(active)
P100
Measured at time
of max. peak
N160
Measured at time
of max. peak
P200
Measured at time
of max. peak
N2b
Measured at time
of max. peak
N.R.
N.R.
N.R.
N.R.
AD=pMCI=
sMCI=HC
AD=pMCI=
sMCI=HC
AD & pMCI
>HC & sMCI
AD & pMCI
>HC & sMCI
RT & ACC:
HC=sMCI=
pMCI=AD
Tales et al.,
(2008) [94]
N=28
n=10 HC
4 males
Age range:65-81
M=71.2
n=8 aMCI
6 males
Age range:65-82
M=74.5
n=10 probable
AD
2 males
Age range:67-81
M=75.2
MCI & AD:
DSM-IV &
NINCDS-
ADRDA
Press button
to target,
ignore stan-
dard and
deviant stim-
uli
(active)
vMMN:
140- 250 ms
vMMN:
250-400 ms
AD=aMCI >HC
(HC did not elicit a
vMMN)
AD=aMCI=
HC
N.A.
N.A.
N.R.
(Table 2) contd….
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74 Current Alzheimer Research, 2019, Vol. 16 , No. 1 Morrison et al.
Reference Sample: N Method of
diagnosis
Task Component Amplitude Latency Behaviour
Beuzeron-
Mangina et
al., (2009)
[29]
N=75 (33 males)
n=25 HC
Age range: N.R.
M=70.4
n=25 vEAD
Age range: N.R.
M=69.9
n=25 MVD
Age range: N.R.
M=70.1
HC: 6 steps
e.g., MMSE
=30
MCI: 8 steps
e.g., DSM-IV
& MMSE
AD: 7 steps
e.g., DSM-IV
& NINCDS-
ADRDA
Word memory
paradigm -
press button
for target
word
(active)
P450:
300-650 ms
vEAD>HC vEAD>HC
(anterior)
vEAD<HC
(posterior)
No effect of
memory load on
group
Taler et al.,
(2009)
[98]
N=49
n=19 HC
8 males
Age range: N.R.
M=74.68
n=20 MCI
10 males
Age range: N.R.
M=75.80
n=10 AD
3 males
Age range: N.R.
M=82.4
MCI: Petersen
criteria
AD: APA
diagnostic
criteria for
dementia
Exp. 1: Read
lexically am-
biguous items
(passive)
0-350 ms
N400:
300-650 ms
500-650 ms
600-650 ms
AD=MCI=HC
1) AD >HC
2) trend for MCI
>HC
AD >MCI
trend for AD >HC
N.A.
N.A.
N.A.
N.A.
N.A.
Cheng et al.,
(2010)
[83]
N=37
n=17 HC
9 males
Age range: N.R.
M=69.47
n=20 AD
12 males
Age range: N.R.
M=71.05
AD: DSM-IV,
NINCDS-
ADRDA
Familiar/
novel faces &
scenes
(active)
P100:
80-120 ms
N170:
150-180 ms
N250r:
280-330 ms
AD=HC
AD<HC @ P3 and
P4
AD>HC for famil-
iar scene & novel
faces
1) AD=HC @
Oz & O2
2) AD<HC @
O1
AD=HC
AD=HC
AD RT to scene >
face
HC RT to scene =
face
(Table 2) contd….
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Reference Sample: N Method of
diagnosis
Task Component Amplitude Latency Behaviour
Kubová et
al., (2010)
[84]
N=30
n=15 HC
# males N.R.
Age range:59-85
Median=78.00
n=15 AD
9 males
Age range:58-87
Median=75.00
HC: MMSE
between 28-30
AD: diagnosed
by a neurologic
department &
MMSE be-
tween 12-23
Pattern and
motion
(active)
P100:
Peak latency and
inter-peak abso-
lute mean ampli-
tude
N2b:
Peak latency and
inter-peak abso-
lute mean ampli-
tude
P3b:
Mean inter-peak
latency & ampli-
tudes measured
AD=HC
AD<HC
AD=HC
AD=HC
AD=HC
AD>HC
RT: AD=HC
ACC: HC made
fewer mistakes to
target stimuli
Kurita et al.,
(2010) [90]
N=84
n=20 HC
11 males
Age range: N.R.
M=71.4
n=21 AD
11 males
Age range: N.R.
M=71.7
n=41 other
25 males
AD: DSM-IV
criteria
Visual
discrimination
with human
faces
(active)
N1:
N2b:
Second peak
following P2
P200:
Largest peak
following N1
P3b:
300-800 ms
N.R.
N.R.
N.R.
N.R.
AD=HC
AD=HC
AD=HC
AD>HC
N.R.
Fernandez et
al., (2012)
[33]
N= 43
n=12 YC
# males N.R.
n=16 HC
# males N.R.
Age range: N.R.
M=76.2
n=15 EAD
# males N.R.
Age range: N.R.
M=78.6
AD: NINCDS-
ADRDA crite-
ria
Visual motion
(VM)
(active)
N2b (motion
coherence)
N2b (Speed para-
digm)
Peak deflection
±50 ms for each
person
AD<HC
AD<HC
AD=HC
AD=HC
RT: AD=HC
ACC: AD<HC
(Table 2) contd….
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76 Current Alzheimer Research, 2019, Vol. 16 , No. 1 Morrison et al.
Reference Sample: N Method of
diagnosis
Task Component Amplitude Latency Behaviour
Parra et al.,
(2012) [30]
N= 30
n=10 HC
3 males
Age range: N.R.
M=64.70
n=10 MCI
4 males
Age range: N.R.
M=72.60
n=10 AD
4 males
Age range: N.R.
M=74.10
MCI: Petersen
criteria
AD: NINCDS-
ADRDA
Visual oddball
(active)
P3b
Avg latency:
HC 331.78 ms
MCI 537.48 ms
AD 564.31 ms
AD&MCI<HC @
Fz not Pz
AD&MCI>HC N.R.
Saavedra et
al., (2012a)
[96]
N=43
n=16 YC
Age range: N.R.
M=25
n=15 HC
Age range: N.R.
M=83.5
n=5 aMCI
n=7 mild AD
Age range: N.R.
M=81.8
aMCI: Petersen
criteria
AD: NINCDS-
ADRDA
Face familiar-
ity
(active)
N170: (posterior)
160-240 ms
Vertex Positive
Potential:
160-240 ms
AD< HC& YC
AD >YC
N.R.
N.R.
ACC: HC=
MCI=AD
Saavedra et
al., (2012b)
[86]
N=27
n=15 HC
4 males
Age range:65+
M=83.5
n= 12 CI
4 probable AD,
3 possible AD, &
5 MCI
0 males
Age range: N.R.
M=81.8
MCI: Petersen
criteria
AD: NINCDS-
ADRDA
Face familiar-
ity
(active)
P100:
80-120 ms
N170:
150-200 ms
N250:
330-400 ms
N400:
600-680 ms
CI>HC
CI<HC
CI<HC @T6
CI=HC
N.A.
N.A.
N.A.
N.A.
ACC: CI=HC
(Table 2) contd….
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Reference Sample: N Method of
diagnosis
Task Component Amplitude Latency Behaviour
Yamasaki et
al., (2012)
[54]
N= 72
n=18 YA
6 males
Age range: N.R.
M=28.2
n=18 HC
6 males
Age range: N.R.
M=71.8
n=18 aMCI
6 males
Age range: N.R.
M=72.4
n=18 AD
6 males
Age range: N.R.
M=75.5
aMCI: Petersen
criteria
AD: NINCDS-
ADRDA
Neuropsyc-
holgical as-
sessment also
done: e.g.,
EEG, MMSE,
MRI
Coherent
motion stimuli
-moving white
dots
(passive)
P100& N100
N170
P200(optic flow)
All components:
peak latencies
measured from
the stimulus
onset, and peak
amplitudes meas-
ured from base-
line to peak
AD=aMCI=
HC
AD=aMCI=
HC
1)AD=aMCI
2)AD<HC
AD=aMCI=
HC
1)AD >aMCI
& HC
2)aMCI =HC
AD>aMCI
>HC
N.A.
Cespón et
al., (2013)
[35]
N=55
n=25 HC
14 males
Age range:51-85
M=65.2
n=17 sdaMCI
10 males
Age range:51-85
M=67.0
n=13 mdaMCI
6 males
Age range:51-85
M=71.0
MCI: MMSE,
CVLT, CAM-
DEX-r, IADL
scale, GDS, &
questionnaire
on SMC
Simon Task
(active)
N2pc:
Latency
200-375 ms
Amplitude
250-350ms
LRP-r
-125 ms &
-25 ms regarding
the response
mdaMCI <HC
mdaMCI &
sdaMCI <HC
HC=mdaMCI=
sdaMCI
HC=mdaMCI=
sdaMCI
RT: MCI=HC
ACC:
mdaMCI<
sdaMCI
Fernandez et
al., (2013)
[59]
N=42
n=15 YC
15.47% males
Age range: N.R.
M=26.00
AD: NINCDS-
ADRDA
Visual motion
(VM)
(active)
N200(flow)
N200(VM)
EAD<HC & YC
EAD<HC & YC
N.R.
EAD=HC<YC
EAD=HC<YC
RT: AD=HC
ACC: AD=HC
(Table 2) contd….
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78 Current Alzheimer Research, 2019, Vol. 16 , No. 1 Morrison et al.
Reference Sample: N Method of
diagnosis
Task Component Amplitude Latency Behaviour
n=15 HC
15.33 % males
Age range: N.R.
M=75.07
n=12 EAD
12.58% males
Age range: N.R.
M=78.42
N2b(VM)
P3b(VM)
EAD<HC
EAD>HC
EAD>HC
Grieder et
al., (2013)
[85]
N=38
n=19 HC
Age range: N.R.
M=69.5
n=14 AD
Age range: N.R.
M=66.5
n=5 SD
Age range: N.R.
M=65.8
AD: ICD-10
criteria
Semantic
priming para-
digm
(active)
P100 & N100:
94-302 ms
N400:
382-546 ms
AD=HC
AD<HC
N.R.
N.R.
RT: AD>HC
ACC: HC>AD
Schefter et
al., (2013)
[78]
N=32
n=17 HC
7 males
Age range: N.R.
M=68.00
n=15 aMCI
4 males
Age range: N.R.
M=67.13
MCI: reported
memory com-
plaint, cogni-
tive testing, not
normal nor
demented, and
intact ADL
(Winbald et al.,
2004 criteria)
Recognition
memory of
words and
pictures
(active)
P100:
Latency
90-140 ms
Amplitude
104-134 ms
N170:
Latency
160-240 ms
Amplitude
HC:172-202 ms
MCI:189-219 ms
P200:
Latency
240-380 ms
Amplitude
290-320 ms
aMCI=HC
aMCI<HC (angry)
Occurred in oppo-
site hemisphere in
aMCI
aMCI=HC
aMCI>HC
aMCI=HC
RT: aMCI>HC
ACC: aMCI<HC
(Table 2) contd….
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Reference Sample: N Method of
diagnosis
Task Component Amplitude Latency Behaviour
Spironelli et
al., (2013)
[31]
N=22
n=11 HC
2 males
Age range:69-83
M= 76.36
n=11 AD
2 males
Age range:70-88
M=78.18
AD: 4 step
method e.g.,
evidence of
cortical atrophy
though MRI
Go/NoGo
with words
(active)
N400:
400-600 ms
For non-words AD
< HC (anterior)
AD=HC (poste-
rior)
N.A. RT: AD<HC
AD omissions >
HC
Wang et al.,
(2013) [91]
N=38
n=16 HC
9 males
Age range: N.R.
M=69.3
n=15 MCI
9 males
Age range: N.R.
M=72.9
n=7 AD
3 males
Age range: N.R.
M=68.59
MCI: Petersen
criteria,
CDR=0
AD: NINCDS-
ADRDA &
CDR =1
Modified
Eriksen
flanker task
(active)
N2b:
200-350 ms
P3b:
300-550 ms
AD&MCI< HC
AD&MCI<HC
1)MCI>HC
2)AD=HC
(trend)
AD&MCI
>HC
RT: AD=MCI=
HC
ACC: AD<MCI<
HC
Asaumi et
al.,(2014)
[95]
N=48
n=12 HC
3 males
Age range: N.R.
M=71.0
n=12 HRMG
3 males
Age range: N.R.
M=74.3
n=12 LRMG
6 males
Age range: N.R.
M=71.6
Intermediate
Group: MMSE,
HDS-R, &
CDR
AD: NINCDS-
ADRDA
Oddball with
faces
(active)
P3b:
300-600 ms
Crying face:
1) AD <
LRMG&HC @ Fz
2) AD<HC @ Cz,
Pz, Oz
3) AD&HRMG <
HC @ Cz
Smiling Face:
1) AD<HC @ Cz,
Oz
2) AD<HRMG @
Oz
Crying face:
AD > HRMG,
LRMG, & HC
@ Fz, Pz, Cz,
& Oz
Smiling face:
AD > HRMG,
LRMG, & HC
RT crying: 1)
AD > LRMG,
HRMG, & HC
RT Smiling:
1) AD > LRMG,
HRMG, & HC
2) HRMG >
LRMG
(Table 2) contd….
Bentham Science Publishers
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80 Current Alzheimer Research, 2019, Vol. 16 , No. 1 Morrison et al.
Reference Sample: N Method of
diagnosis
Task Component Amplitude Latency Behaviour
n= 12 AD
7 males
Age range: N.R.
M=74.1
Cespón et
al., (2015a)
[92]
N=53 (22 males)
n=15 HC
Age range: N.R.
M=66.0
n=11 mdnaMCI
Age range: N.R.
M=66.5
n=16 sdaMCI
Age range: N.R.
M=67.1
n=11 mdaMCI
Age range: N.R.
M=69.6
MCI: MMSE,
CVLT, CAM-
DEX-r, subjec-
tive memory
complaints,
IADLs
Simon Task
(active)
N2b:
Latency 200-375
ms
Amplitude ±20
ms peak
N2pc:
Latency
200-375 ms
Amplitude ±20
ms peak
P3b:
Latency 375-700
ms
Amplitude ±20
ms peak
mdaMCI =
mdnaMCI =
sdaMCI = HC
mdaMCI<HC
mdaMCI =
mdnaMCI =
sdaMCI = HC
mdaMCI >
mdnaMCI,
sdaMCI & HC
mdaMCI =
mdnaMCI =
sdaMCI = HC
mdaMCI =
mdnaMCI =
sdaMCI = HC
RT and errors not
influenced by
group
Cespón et
al., (2015b)
[36]
N=43
n= 18 HC
7 males
Age range:60-83
M=68.3
n=13 sdaMCI
8 males
Age range:60-83
M=69.1
n=12 mdaMCI
5 males
Age range:60-83
M=71.2
MCI: MMSE,
CVLT, CAM-
DEX-r, subjec-
tive memory
complaints,
IADLs
Simon Task
(active)
N2cc
Latency
200-400 ms
Amplitude ±20
ms peak
N2pc
Latency
200-400 ms
Amplitude ±20
ms peak
mdaMCI =
mdnaMCI =
sdaMCI = HC
mdaMCI < HC
mdaMCI > HC
& sdaMCI
mdaMCI =
mdnaMCI =
sdaMCI = HC
Errors higher in
mdaMCI
Deiber et
al., (2015)
[79]
N=142
n=55 sCON
22 males
Age range: N.R.
M=73.7
MCI: MMSE,
HAD, IADL, &
CDR.
dCON: 0.5
S.D. lower at
follow up
Attentional
and 2-back
WM task
(active)
PNwm
P100&
N100
1) MCI<sCON
2) sCON= dCON
MCI=dCON=
sCON
N.A.
N.R.
RT & ACC:
MCI=dCON=
sCON
(Table 2) contd….
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Visual Event-Related Potentia ls in MCI and AD Current Alzheimer Research, 2019, Vol. 16, No. 1 81
Reference Sample: N Method of
diagnosis
Task Component Amplitude Latency Behaviour
n=42 dCON
12 males
Age range: N.R.
M=73.7
n=45 MCI
30 males
Age range: N.R.
M=75.3
P3b
MCI< sCON &
dCON
N.R.
Fix et al.,
(2015) [89]
N=13
n=8 HC
4 males
Age range: N.R.
M=65.57
n=5 aMCI
4 males
Age range: N.R.
M=78.00
aMCI: used
Albert et al.,
(2011) criteria
Double flash -
press when
see strobe
(active)
P200:
100-300 ms
N.R.
aMCI>HC in
single flash but
not when ISI
was longer
than refractory
period
N.R.
Stothart et
al., (2015)
[81]
N=71
n=26 HC
14 males
Age range:62-88
M=76.0
n=25 aMCI
16 males
Age range:62-91
M=77.3
n=20 AD
7 males
Age range:60-91
M=79.2
AD/MCI: clini-
cal staff evalua-
tion on 6 dif-
ferent areas
according to
DSM-IV &
NINCDS-
ADRDA guide-
lines
Visual oddball
(active)
P100:
75-100 ms
N100:
160-260 ms
vMMN:
difference wave
following N1
P3b:
230-500 ms
1) AD<aMCI&
HC
2) aMCI=HC
1) AD&aMCI
<HC
2) AD=aMCI
1) aMCI<HC
2) AD=HC
AD=aMCI=
HC
AD=aMCI
=HC
AD=aMCI=
HC
N.A.
AD=aMCI=
HC
R.T & ACC:
AD=MCI
=HC
Li et al.,
(2016) [80]
N=46
n=22 HC
14 males
Age range: N.R.
M=69.17
Delayed
match to
sample
(active)
P200:
150-250 ms &
P3b:
250-450 ms
aMCI <HC
aMCI=HC
RT: MCI>HC
ACC:
MCI<HC
(Table 2) contd….
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82 Current Alzheimer Research, 2019, Vol. 16 , No. 1 Morrison et al.
Reference Sample: N Method of
diagnosis
Task Component Amplitude Latency Behaviour
n=24 MCI
13 males
Age range: N.R.
M=69.27
P100:
84-140 ms,
N100:
150-210 ms, &
N2b:
230-300 ms
aMCI=HC
aMCI=HC
Mudar et al.,
(2016) [93]
N=50
n=25 HC
9 males
Age range:57-82
M=65.4
n=25 aMCI
9 males
Age range:54-86
M=68.5
MCI: Petersen
criteria
Go/NoGo
(active)
N2b:
150-300 ms
P3b:
250-650 ms
aMCI=HC
aMCI=HC
aMCI>HC
aMCI=HC
RT: MCI=HC
ACC:
MCI > commis-
sion errors than
HC
Lopez-
Zunini et al.,
(2016) [88]
N=32
n=17 HC
6 males
Age range: N.R.
M=72.75
n=15 aMCI
7 males
Age range: N.R.
M=75.60
MCI: diag-
nosed by phy-
sician
N-back
(active)
P200:
100-220 ms
N2b:
200-380 ms
P3b:
400-500 ms
aMCI=HC
aMCI=HC
aMCI<HC
aMCI>HC
@Cpz &Pz
aMCI>HC @
all midline
sites
N.A.
RT: MCI>HC
ACC: MCI<HC
Bagattini et
al., (2017)
[46]
N= 56
n= 20 HC
Age range:60-85
M=69.40
n= 16 aMCI
Age range:60-85
M=75.00
n= 20 AD
Age range:60-85
M=76.30
AD: MMSE ≥
20 & CDR
score less ≤ 2
HC: MMSE >
24 & CDR = 0
MCI: MMSE ≥
24 & CDR =
0.5
Multiple
Object Proc-
essing (active)
N2pc:
250-250 ms
CDA:
450-800 ms
AD=MCI=HC
1) AD < MCI &
HC
2) MCI> AD &
HC
N.A.
N.A.
ACC: AD< MCI
< HC
YC, young controls; HC, healthy controls; sCON, stable controls; dCON, deteriorated controls; SMC, subjective memory complaints; MCI, mild cognitive
impairment; aMCI; amnestic MCI; sMCI, stable mild cognitive impairment; pMCI, progressive mild cognitive impairment; sdMCI, single domain mild cogni-
tive impairment-single; mdMCI, multiple domain-mild cognitive impairment; sdaMCI, single domain amnestic mild cognitive impairment; sdnaMCI, single
domain non-amnestic mild cognitive impairment; mdaMCI multiple domain amnestic mild cognitive impairment; mdnaMCI, multiple domain non-amnestic
mild cognitive impairment; (V)EAD, (very) early Alzheimer’s disease; AD, Alzheimer’s Disease; ADhs, Alzheimer’s disease high sensitivity; ADls, Alz-
heimer’s disease low sensitivity; SD, semantic dementia; MVD, mild vascular dementia; HRMG, high risk intermediate group; LRMG, low risk intermediate
group; SMC, subjective memory complains; NT-MCI, no treatment MCI; T-MCI, treated MCI; IADL, instrumental activities of daily living; GDS; geriatric
depression scale; CVLT, California verbal learning test; CAMDEX-r; Cambridge Examination for Mental Disorders in Elderly; APA; American Psychological
Association; CDR; Clinical Dementia Rating; NINCDS-ADRDA, National Institute of Neurological and Communicative Disorders and Stroke and
the Alzheimer's Disease and Related Disorders Association; DSM-IV & DSM-V, Diagnostic and Statistics Manual fourth & fifth version; HAD, The Hospital
and Anxiety Depression scale; NP; neuropsychological; MoCA, Montreal Cognitive Assessment; MMSE, Mini-Mental Status Examination; MRI, magnetic
resonance imaging; EEG, electroencephalography; vMMN; visual mismatch negativity; PNwm, Positive negative working memory component; CDA, contra-
lateral delay activity; RT, reaction time; ACC, accuracy; N.A., not applicable; N.R., not reported; S.D., standard deviation.
Bentham Science Publishers
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Visual Event-Related Potentia ls in MCI and AD Current Alzheimer Research, 2019, Vol. 16, No. 1 83
exhibited smaller P200 amplitudes compared to healthy
older adults. In contrast, two other studies reported no differ-
ences in P200 amplitude between healthy older adults and
people with aMCI [78, 88] and AD [82]. One study exam-
ined scalp distributions of brain activity and found that those
with MCI showed activity in the left hemisphere, whereas in
healthy older adults right hemisphere activity was observed
[78].
Changes in P200 latency due to cognitive decline were
also inconsistent. Four studies indicated that P200 latency is
prolonged in aMCI [54, 88, 89], pMCI [77], and AD [54, 77]
compared to healthy older adults, while three found no group
differences [78, 80, 90]. One study found that the P200 la-
tency could discriminate between people with progressive
and stable MCI, with a sensitivity of 88% and a specificity of
77% [77].
3.2. N200
Five studies found declines in N2b amplitude in people
with MCI and AD compared to healthy controls [33, 59, 82,
84, 91]. Conversely, four studies reported no differences in
the N2b amplitude based on cognitive status, indicating that
attention (or effort required to complete the task) may not
differ between healthy older adults and those with MCI [80,
88, 92, 93]. The differing findings between these studies may
be due to task differences. For example, the tasks that found
similar group performance focused on attention related to
memory [80, 88, 92, 93], whereas the studies that found
group differences focused primarily on attention during vis-
ual motion [33, 59, 82, 84] and perceptual interference [91]
tasks.
Findings relating to N2b latency in MCI and AD have
also been inconsistent. Six studies found a prolonged N2b
component due to MCI [88, 91-93], pMCI [77], and AD [59,
77], whereas five studies have reported no N2b latency dif-
ferences between healthy older adults and those with MCI
(80) and AD [33, 59, 84, 90]. Similarly, N2b latency has
poor sensitivity and specificity (75% and 69% respectively)
in discriminating between stable and progressive MCI [77].
However, one study found higher sensitivity and specificity
(83% and 81%) in discriminating mdaMCI from healthy
older adults, multiple domain non-amnestic MCI, and
sdaMCI [92].
With respect to the N2pc subcomponent of the N200,
three studies have found that people with multiple domain
amnestic MCI have smaller amplitudes but similar latencies
[35, 36, 92] compared to healthy older adults. The same re-
searchers found that N2pc amplitude could distinguish be-
tween healthy older adults and those with multiple domain
amnestic MCI with a sensitivity of 77-91% and a specificity
of 53-76% [35, 36, 92]. However, one study reported no
group differences in N2pc amplitude between healthy older
adults, MCI, and AD [46]. The N2cc showed similar ampli-
tudes but prolonged latencies in people with mdaMCI com-
pared to healthy older adults [36]. When discriminating be-
tween healthy older adults and those with mdaMCI, the N2cc
latency was found to have high sensitivity (92%) and good
specificity (84%) [36].
3.3. MMN
Although the MMN is often used to measure differences
in an automatic response to a change in stimulus presenta-
tion, few studies looked at this component when examining
changes in cognitive functioning. One study reported that
people with MCI have smaller amplitudes compared to
healthy older adults [81]. In contrast, three studies reported
comparable MMN amplitudes between healthy older adults
and people with MCI [94] and AD [57, 81, 94] and between
people with MCI and AD [81, 94]. However, people with
MCI and AD exhibited larger MMN amplitudes than healthy
older adults after repeated exposed to more deviants [57, 94],
suggesting that the ability to detect change decreases in MCI
and AD, and increased exposure to the stimulus is needed for
MCI and AD participants to detect the deviant [57, 94].
3.4. P3b
Twelve studies compared P3b amplitude in healthy older
adults and people with MCI and/or AD. Eight studies re-
ported smaller P3b amplitudes in people with MCI [30, 79,
80, 88, 91] and AD [30, 59, 82, 91, 95] compared to healthy
older adults, while four studies reported no changes in P3b
amplitude as a result of MCI [81, 92, 93] and AD [81, 84].
Despite these promising findings, P3b amplitude has been
found to discriminate between healthy aging and MCI with a
sensitivity and specificity of only 70%, and between healthy
aging and AD with a sensitivity of 80% and specificity of
70% [30].
Findings have been inconsistent with respect to whether
P3b latency changes are observed in MCI. While two studies
have reported increased latency due to MCI [30, 91], four
found no latency differences between healthy older adults
and people with MCI [80, 81, 92, 93]. Only one study found
that P3b latency accurately discriminated between healthy
older adults and people with MCI (sensitivity of 80% and
specificity of 100%) [30]. In AD, latency changes are re-
ported more consistently, with most studies finding that peo-
ple with AD had prolonged latencies compared to healthy
older adults [30, 59, 84, 90, 91, 95], and only one reporting
similar latencies in healthy older adults and people with AD
[81]. One research group reported that people with AD had
longer latencies than both healthy older adults and an inter-
mediate group1 [95]. In another study, P3b latency was found
to discriminate between healthy older adults and people with
AD with a sensitivity and specificity of 100% [30].
3.4. Object/Facial Recognition
3.4.1. N170
Although the N170 is a commonly-studied ERP compo-
nent in facial recognition paradigms, only four studies have
examined this component in relation to cognitive impair-
ment. People with MCI [78, 86] and AD [83, 86, 96] exhib-
ited smaller N170 amplitudes relative to healthy older adults
1 The term “intermediate group” was used to describe a group that scored
between healthy older adults and those with AD, as determined based on the
Revised Hasegawa Dementia scale (scores of 20-28), Mini-Mental State
Examination scores (scores of 23-28), and Clinical Dementia Rating (scores
of 0-1).
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during facial recognition, although one reported that this
decline was only in response to angry faces [78]. One study
examined the N170 using a visual motion task and found
similar amplitudes across groups, but longer latencies in AD
relative to MCI and healthy aging [54]. With respect to la-
tency changes, one study reported that N170 latency was
delayed in aMCI during recognition memory tasks [78], but
two others found that people with AD [83] and MCI [54] had
similar latencies compared to healthy older adults.
3.4.2. N250
The N250 component has been examined in only two
studies using a visual paradigm. Both of these studies com-
pared healthy older adults to people with MCI and AD and
found conflicting results in terms of amplitude: one reported
an increase in amplitude with AD [83], and the other found
that people with MCI and AD had decreased amplitudes
compared to healthy older adults [86]. Only one of these
studies examined latency between the groups, finding that
healthy older adults and those with MCI and AD had similar
latencies [83].
3.5. Semantic Processing
Research into the N400 component has consistently
found that people with AD exhibit smaller N400 amplitudes
compared to healthy older adults [31, 85, 87, 97], with some
researchers finding these differences only in frontal [31] or
posterior regions [87]. When discriminating between healthy
older adults and AD, N400 amplitude appeared to have an
excellent specificity (91%), although sensitivity was only
55% [87]. Two additional studies reported a more negative
(larger) N400 in AD and MCI patients [87, 98], than healthy
older adults, even though the difference was only found in
frontal sites for AD [87] and only trended towards signifi-
cance for MCI patients [98]. Only one study measured N400
latency, and found similar latencies in healthy older adults
and people with AD [87]. The N400 has also been elicited
during face-feature matching tasks [99] and was similar in
amplitude and latency in healthy older adults and those with
AD during facial recognition tasks [86].
3.6. Memory-related Components
3.6.1. PNwm
People with sMCI exhibited a PNwm; however, those
with pMCI failed to show this component during a WM task
[100]. Although latency results were not reported, one study
found that people MCI exhibited lower PNwm amplitude
than cognitively stable healthy older adults [79].
3.6.2. N300
Only one study has examined memory retrieval deficits
using the N300. These researchers found that people with
MCI had increases in N300 amplitude, whereas those with
AD had decreased amplitudes compared to healthy older
adults [28]. Both those with MCI and those with AD showed
prolonged N300s compared to healthy controls.
3.6.3. P450
We found only two studies that reported on P450 differ-
ences between healthy older adults and people with cognitive
decline, with contradictory findings. One study, using an n-
back task, found smaller amplitudes and similar latencies in
AD compared to healthy older adults and people with MCI
[28] while the other, which examined WM for words, re-
ported increased amplitudes in AD, with prolonged latencies
in anterior regions but shorter latencies in posterior regions
compared to healthy older adults [29].
3.6.4. P600
The one study examining the P600 used a word repetition
task and found that people with AD exhibited a smaller repe-
tition effect that was a topographically atypical (i.e., greater
left frontal amplitude) compared to healthy older adults [87].
In this sample, P600 amplitude discriminated between
healthy aging and AD with a sensitivity of 91% and a speci-
ficity of 73% [87].
3.6.5. CDA
While there have been many studies examining CDA in
healthy aging, we found only one report that examined the
CDA component in people with MCI and AD [46]. People
with MCI showed an increased CDA amplitude compared to
healthy older adults, whereas those with AD showed reduc-
tions in their amplitudes compared to both MCI and healthy
controls [46]. These authors also found that CDA amplitude
could discriminate between healthy older adults and people
with MCI with a specificity of 85% and a sensitivity of 63%,
and between MCI and AD with 80% specificity and 69%
sensitivity [46].
4. DISCUSSION
This review examined 34 studies that measured visually
elicited ERP components in healthy older adults and people
with MCI or AD. After full text review, we categorized the
studies based on the type of processing that the stimulus re-
quired and the cognitive status of the participants involved.
We focused on identifying the visually-elicited ERP compo-
nents that could discriminate between healthy older adults
and people with MCI or AD. This review included seventeen
different components from a wide range of visual tasks.
Although the P100, N2b and P3b were the most com-
monly-studied components, only the P100 and P3b yielded
consistent findings. While conflicting findings have been
reported with respect to the effect of healthy aging on the
P100 [51-54], no differences were observed between healthy
older adults and people with cognitive impairment in P100
amplitude [15, 54, 78-80, 82-85] and latency [15, 54, 77, 78,
80, 81, 84], across all tasks employed. This suggests that
early perceptual processing, indexed by the P100, might not
be affected by MCI and AD. This lack of difference also
indicates that the participants are able to perceive a stimulus,
and that differences in later components are due to changes
in cognitive processing rather than perception.
In addition to the age-related prolongations in P3b la-
tency [56, 58-63] and decreased amplitudes [60, 65, 66],
people experiencing cognitive decline had more reductions
in their P3b amplitudes, suggesting that the P3b may be sen-
sitive to declines due both to age and to decreased cognitive
functioning. The P3b latency appears to be prolonged in
people with AD compared to healthy older adults [30, 59,
84, 90, 91, 95], with less consistent results in MCI [30, 80,
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81, 91-93]. The prolongation in AD but not MCI is likely
due to the severity of the cognitive decline; the reduction in
processing speed observed in MCI may not be severe enough
to differentiate people with MCI from healthy older adults.
The prolongation of the P3b in people with AD indicates that
the time required to process incoming information may in-
crease with cognitive impairment due to AD. Smaller P3b
amplitudes were also consistently found in people with MCI
and AD compared to healthy older adults [30, 59, 79, 80, 82,
88, 91, 95], reflecting declines in memory and attention in
people with cognitive impairment. Although increases in the
frontal regions have been found with healthy older adults
[60, 65, 66], this increase is no longer present in MCI and
AD, perhaps because sufficient cognitive resources are not
available to compensate for declines in memory.
4.1. Limitations in Current Research
Many of the limitations in the visual ERP literature are
similar to those reported in our recent review of studies using
auditory paradigms [10]. Firstly, many researchers fail to
report or control for medication usage in participants [28, 30,
33, 35, 36, 54, 59, 82, 83, 92, 95, 97, 98]. It is essential to
control for medications that influence cognitive and central
nervous system function when measuring brain activity and
cognitive processes, because it is well-established that medi-
cations such as cholinesterase inhibitors influence brain ac-
tivity patterns [101-104]. These medications could mitigate
differences between healthy older adults and those with MCI
and AD because the medications are intended to restore the
brain functioning of those with cognitive impairment. Al-
though the effectiveness of these medications is quite con-
troversial, with some research showing no differences be-
tween those who are on medications and those who are not
[15, 105], it is nonetheless important to control for medica-
tion use in studies including participants with MCI and AD.
Secondly, the exclusion of behavioural measures in the
studies examined in this review [30, 57, 82, 90, 94], is a
cause for concern when studying clinical populations. While
some studies (such as the passive visual paradigms to meas-
ure the MMN) do not require behavioural measures, tasks
that explicitly measure cognitive functioning (i.e., active
tasks) should always examine behavior to ensure that par-
ticipants are completing the task appropriately [106]. For
example, if someone with cognitive impairment forgets the
purpose of the task and obtains a low score (e.g., 50%) then
the brain activity is not representing the cognitive functions
they require to do the task. Without measuring and analyzing
both behavior and brain activity it is difficult to determine
what the changes in brain activity actually represent [107].
The studies reviewed also varied in the time frame and
methods used for measuring ERP components. Some re-
searchers measure amplitude differences using mean area,
while others use peak detection. Taking the average voltage
of a specific time frame generates mean amplitude whereas
the peak amplitude measures the most positive (or negative)
peak in a given time window [12]. In most cases the mean
amplitude offers a more reliable and appropriate measure
than peak detection, because measuring the peak does not
use the same time point across conditions and participants,
and is more susceptible to noise in the data [12]. The use of
peak vs. mean amplitude, as well as the differences in time
window, may contribute to the divergent findings in the dif-
ferent ERP components examined in this review. For exam-
ple, for the N400, where conflicting results have been re-
ported, four of the studies used mean area measures [31, 86,
97, 98] and two used peak detection [85, 87]. Furthermore,
when measuring the N400, all studies in our review meas-
ured the amplitude using different time windows: 325-625ms
[97], 400-550ms [87], 300-650ms [98], 600-680ms [86],
382-546ms [85], 400-600ms [31].
Moreover, many different paradigms can be used when
conducting research using visual tasks. This large range of
tasks facilitates measurement of the processes associated
with a variety of domains such as WM, attention, and dis-
tractibility. However, the use of different tasks is likely the
main contributor to divergent findings between research
studies. For example, the N2b reflects many cognitive proc-
esses such as task difficulty, effort required to complete the
task, conflict adaptation, and cognitive control [108]. There-
fore, comparing the N2 elicited from a variety of tasks with
many levels of task demands is a challenge. In our review,
studies examining the N2b related to memory [80, 88, 92,
93] have found no amplitude differences between healthy
older adults and those with cognitive impairment, but when
examining attention differences during a motion task, differ-
ences due to cognitive impairment were found [33, 59, 82,
84, 91]. Such findings indicate that researchers cannot sim-
ply assume that changes in components are universal when
different tasks are employed. Even within the same cognitive
process, differences in the tasks used can generate different
findings. In this review, two studies that both measured WM
functioning using different tasks (one using an n-back task
with letters [28], and the other using word stimuli [29]),
found opposing results. Although both these tasks measure
WM, they vary in level of difficulty and the amount of re-
sources needed to complete the task. These conflicting re-
sults suggest that differences (or lack thereof) between
groups and studies may be due to the type of task used, some
of which require more resources than others [109]. Future
research should continue to replicate current findings to de-
termine the influence of MCI and AD on varying tasks and
components.
Despite the numerous papers reviewed, minimal research
exists on a number of components: the PNwm, N70, N150,
N160, N300, P450, P600, and CDA. This paucity of studies
limits our ability to evaluate whether these components can
differentiate between people with differing degrees of cogni-
tive impairment. For example, discriminability of the P600
between healthy older adults and people with AD was only
supported by one study [87] and should therefore be repli-
cated to better understand the effects of cognitive impair-
ment on this component. Additionally, the PNwm and CDA
component were only identified in 2003 [44] and 2004 [45],
respectively. Thus, minimal research has been conducted
examining these two components in young and older adults
as well as those with cognitive impairment.
4.2. Recommendations for Future Directions
Future studies should continue to address the limitations
listed above regarding medications, behavioral measures,
and task differences. Medication usage in older adults and
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86 Current Alzheimer Research, 2019, Vol. 16 , No. 1 Morrison et al.
patients is common, and these medications may have an in-
fluence on the central nervous system, cognitive functioning,
and brain activity. Therefore, it is essential that researchers
control for these medications and balance their usage be-
tween groups. By completing an analysis with and without
the participants on medications, researchers can determine
whether these medications are in fact influencing the results
of their studies.
Behavioural measures should also be required for studies
using active tasks in healthy older adults and those with cog-
nitive impairment. Such an approach not only allows for
comparisons to other studies, but also ensures that the par-
ticipants understood the task and were completing it cor-
rectly. Additionally, measuring behavioural responses can
assist with the interpretation of the brain activity. The inter-
pretation of group differences in neural activity will vary
depending on whether or not the groups had similar per-
formance and accuracy. For example, P3b amplitude in-
creases when more attention is required and decreases with
increasing task difficulty, due to the availability of fewer
cognitive resources [110]. Therefore, if P3b amplitude is
larger during a more difficult task, it would suggest that
more resources are available to allocate to task completion
[110]. Without comparing P3b amplitudes to behavioural
measures, it is difficult to interpret the results, because a
larger amplitude could be due to either increased available
resources or the requirement of more attention to complete a
task. Increased resources would presumably lead to higher
performance, whereas more attention may result in decreased
accuracy.
It is also important that future research identify universal
tasks to measure specific aspects of cognitive functioning.
This approach would allow comparison of results across
studies, and would ensure that when components are meas-
ured in different studies they are indeed measuring the same
function.
Finally, future research should continue to report the sen-
sitivity and specificity of each component. Only seven of the
34 papers identified in this review reported the sensitivity
and specificity of the components of interest [30, 35, 36, 46,
77, 87, 92]. In addition to identifying group differences
through between-subjects effects, sensitivity and specificity
values are useful to aid in determining whether specific
components can be used for diagnosis on an individual basis.
By indicating a specific time (latency) or voltage (amplitude)
cut-off(s) that can differentiate between groups, the research
will become more clinically-based and may help determine if
ERPs can be used to identify cognitive decline. This data-
drive approach will ensure that appropriate cutoffs (or ranges
for cutoffs) are generated when attempting to diagnose peo-
ple experiencing cognitive decline.
CONCLUSION
Both P3b latency and amplitude showed promise in terms
of effectiveness in discriminating between healthy older
adults and people with MCI and AD. These results suggest
that the P3b may be useful on an individual basis to deter-
mine who will develop MCI and/or AD. However, future
research should continue to examine the sensitivity and
specificity of the P3b in discriminating between groups of
different cognitive status. Additionally, future research
should collect normative data and generate a standardization
of data collection to ensure comparison across groups and
studies is possible. Electrophysiological measures have the
potential to improve differentiation and diagnostic accuracy
between healthy older adults and people with MCI and AD.
Determining who may progress to MCI or AD could in-
crease the probability of people with cognitive impairment
receiving treatment that could slow the progression of their
disorder, as well as facilitating appropriate social, familial,
and financial interventions.
FUNDING
This work was supported by the Canadian Institutes of
Health Research [Grant numbers CNA 137794, 2015]
CONSENT FOR PUBLICATION
Not applicable.
CONFLICT OF INTEREST
The authors declare no conflict of interest, financial or
otherwise.
ACKNOWLEDGEMENTS
Declared none.
APPENDIX I
Pnwm: 2-back and attention task [79].
P100: Semantic priming [85], oddball [81] and n-back task
[77], motion [54, 82, 84] and pattern [15, 82, 84], 2-back and
attention [79], delayed match-to-sample [80], face & scene
recognition [83], face familiarity [86], and recognition mem-
ory tasks [78].
N70 & N150: Pattern tasks [15].
N100: Semantic congruency [87], semantic priming [85],
oddball [81], motion [54], 2-back and attention [79], and
match-to-sample tasks [80].
N160: oddball and n-back task [77].
N170: Motion [54], face and scene recognition [83], face
familiarity [86,96], and recognition memory tasks [78].
P200: oddball [77], pattern [82], motion [54], n-back
[77,88], delayed match-to-sample [80], discrimination of
faces [90], recognition memory [78], and flash tasks [89].
N2b: oddball [77], motion [33,59] and pattern [82,84], n-
back [77,88], delayed match-to-sample [80], discrimination
of faces [90], Simon task [92], go/no-go [93], and Flanker
tasks [91].
N2pc & N2cc: Simon task [35,36,92], and multiple object
processing tasks [46].
N250r: discrimination of faces and scenes [83], face famili-
arity [86].
VPP: Face familiarity [96] .
vMMN: oddball [57,81,94].
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LRP-r: Simon task [35].
P3b: oddball (with [95] and without faces [30,81]), attention
[82], pattern [84], motion [59,84], n-back [88], 2-back and
attention [79], delayed match-to-sample [80], discrimination
of faces [90], Simon task [92], go/no-go [93], and Flanker
task [91].
N300: n-back task [28].
N400: word recognition [97], semantic congruency [87],
semantic priming [85], reading of lexically ambiguous words
[98], face familiarity [86], and go/no-go task [31].
P450: n-back [28] and word memory paradigm [29].
P600: semantic congruency and word repetition effect [87].
CDA: multiple object processing [46].
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