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Research Article
Neurodegener Dis 2020;20:12–19
Analysis of Neuropsychiatric Symptoms
in Patients with Alzheimer’s Disease
Using Quantitative EEG and sLORETA
Yong S. Shim
a Hae-Eun Shin
b
a Department of Neurology, The Catholic University of Korea Eunpyeong St. Mary’s Hospital, Seoul, South Korea;
b Department of Neurology, The Catholic University of Korea Bucheon St. Mary’s Hospital, Bucheon, South Korea
Received: March 20, 2020
Accepted: April 23, 2020
Published online: July 1, 2020
Yong S. Shim
Department of Neurology
The Catholic University of Korea Eunpyeong St. Mary’s Hospital
1021 Tongil-ro, Eunpyeon-gu, Seoul 03312 (South Korea)
ysshim @ catholic.ac.kr
© 2020 The Author(s)
Published by S. Karger AG, Basel
karger@karger.com
www.karger.com/ndd
DOI: 10.1159/000508130
Keywords
Neuropsychiatric symptoms · Alzheimer’s disease ·
Electroencephalography
Abstract
Objective: The electrocortical activities associated with the
neuropsychiatric symptoms (NPSs) of Alzheimer’s disease
(AD) were investigated using frequency-domain electroen-
cephalography (EEG) spectral source analysis, and the po-
tential electrocortical indices identified. Materials and
Methods: Scalp EEG data were obtained from 51 patients
with AD to investigate the presence of four NPS subdomains,
hyperactivity, psychosis, affective symptoms, and apathy.
EEG power spectra and the standardized low-resolution
brain electromagnetic tomography (sLORETA)-localized EEG
cortical sources were compared between the groups with
and without the four NPS subdomains in eight frequency
bands: 1–4, 4–8, 8–10, 10–12, 12–18, 18–20, 20–30, and 30–
45 Hz. Results: The power spectral analysis of EEG data
showed that AD patients with psychosis had lower values at
the α2-band in most areas. In patients with apathy, the θ-to-β
power ratio showed a greater activity over the frontal and
central regions. The cortical source analysis using sLORETA
revealed that patients with psychosis showed decreased val-
ues in the α2-band and patients with apathy showed higher
δ-values, especially in the right frontal and temporal regions.
Conclusion: The results of the present study showed that
both classical EEG spectral and EEG source analysis could dif-
ferentiate patients with and without NPSs, especially psy-
chosis and apathy subdomains. Spectral and sLORETA analy-
ses provided information helpful for a better characteriza-
tion in patients with NPSs. © 2020 The Author(s)
Published by S. Karger AG, Basel
Introduction
Alzheimer’s disease (AD) is a progressive neurodegen-
erative disorder that usually starts slowly and gradually
worsens over time. Episodic memory loss is one of the
earliest manifestations and is accompanied by word-find-
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qEEG and sLORETA in Neuropsychiatric
Symptoms of AD
13
Neurodegener Dis 2020;20:12–19
DOI: 10.1159/000508130
ing difficulty, visuospatial dysfunction, and executive
function impairment [1, 2]. In addition to the cognitive
symptoms, various neuropsychiatric symptoms (NPSs)
emerge during the disease course, including delusions,
depression, agitation, sleep disturbances, and eating
problems, which can cause significant burden and stress
to caregivers [3–6]. However, few therapies exist for the
treatment of NPSs in dementia to date [7].
For many years, electroencephalography (EEG) has
been used in the field of dementia as a noninvasive, less
expensive, and easily available diagnostic and research
method [8]. In previous studies, patients with AD exhib-
ited slow EEG waves, reduced complexity of EEG signals,
and loss in EEG synchrony [9]. Although visual inspec-
tion of EEG remains useful in clinical practice, one of the
most common methods in the research field is quantita-
tive EEG (qEEG) analysis [8, 10, 11]. In numerous stud-
ies, AD and mild cognitive impairment were associated
with an increased power in low frequencies (e.g., δ- and
θ-bands), and a decreased power in higher frequencies
(α- and β-bands) [11]. In addition, increased global slow-
to-fast power ratios were strongly correlated with AD
progression [12–14].
Currently, few studies have reported on the associa-
tion of EEG with the NPSs in patients with AD [15]. In a
study on EEG complexity, identifying the neuropsychiat-
ric correlates provided insight into how brain-specific re-
gional activity is associated with these symptoms; disin-
hibition was associated with frontal EEG complexities,
depression and anxiety were correlated with temporal
and parietal complexity, and apathy, aberrant behavior,
and sleep changes were correlated with parieto-occipital
EEG changes [15]. NPSs can always be present during the
disease course; however, specific symptoms are more
common at different stages. Almost all symptoms worsen
with disease severity; however, certain symptoms such as
delusions, agitation, and apathy tend to become much
more prevalent [16]. Evaluation of the frequency and re-
gion-specific qEEG changes associated with NPSs based
on qEEG and brain structural change would be informa-
tive.
In the present study, the qEEG of patients with AD was
evaluated to investigate the presence of NPSs such as hy-
peractivity, psychosis, affective symptoms, and apathy to
determine whether qEEG frequencies and spectral pow-
ers differ by scalp region in patients with AD. Based on
the hypothesis that NPSs can provide insight into possible
relationships between clinical features and their underly-
ing causal and/or risk factors, different therapeutic strat-
egies targeting subsyndromes may be more effective.
Materials and Methods
Participants
The study group included 51 patients with AD (34 females, 17
males) who were recruited from the Dementia Clinic at the Catho-
lic University of Korea, Bucheon St. Mary’s Hospital, from January
2018 to December 2018. The diagnosis for AD was based on the
criteria of the National Institute of Neurological and Communica-
tive Disorders and the Stroke/Alzheimer’s Disease and Related
Disorders Association [17]. All patients received neurological ex-
aminations, laboratory tests, EEG monitoring, and neuroimaging
evaluation during the diagnostic process. Retrospective analysis of
the patients’ clinical and EEG data was approved by the Institu-
tional Review Board of the Catholic University of Korea, Bucheon
St. Mary’s Hospital. Subjects provided informed consent. Patients
who had other conditions that might cause cognitive impairment,
such as vascular dementia, Parkinson’s disease, hypothyroidism,
vitamin B12 deficiency, syphilis, and history of major psychiatric
illness (e.g., major depression, bipolar disorder, or schizophrenia)
were excluded.
The severity of dementia was assessed using the Clinical De-
mentia Rating (CDR) scale [18], and general cognition was evalu-
ated using the Mini-Mental State Examination (MMSE) [19].
NPSs were evaluated using the Neuropsychiatric Inventory (NPI)
[3], and a score > 3 was considered to indicate the presence of clin-
ically relevant symptoms [20–24]. The 12 NPI items were classified
into four subsyndromes, corresponding to hyperactivity (agita-
tion, disinhibition, irritability, aberrant motor behavior), psycho-
sis (delusions, hallucinations, night-time behavior disturbances),
affective behaviors (depression, anxiety), and apathy (apathy, ap-
petite and eating abnormalities) [25].
Patients who underwent qEEG and did not show an EEG ab-
normality were included in the present study. An EEG abnormal-
ity was defined as asymmetry of background activity, continuous
θ-range slow waves, generalized or focal δ-range slow waves, or
epileptiform discharges. Patients taking cholinesterase inhibitors,
benzodiazepines, or antidepressants that could influence EEG
rhythms were excluded [26]. There was no one taking antiepileptic
drugs in the study participants.
EEG Recording
All participants received a routine EEG (COMET plus EEG,
Grass Technologies Inc., West Warwick, RI, USA) in a specialized
examination room as a part of the initial assessment before starting
any specific AD therapy (e.g., acetylcholinesterase inhibitors). Pro-
cedures were performed on weekdays between 9 a.m. and 5 p.m.
EEG was recorded under waking-rest conditions (eyes closed)
from 19 scalp electrodes positioned based on the International 10-
20 System (Fp1, Fp2, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, P7, P3,
Pz, P4, P8, O1, and O2) with linked ear reference, 256 Hz sampling
rate, high-pass filter of 1 Hz, low-pass filter of 70 Hz, notch filter
of 60 Hz, and impedance below 5 kΩ. Activation maneuvers, in-
cluding opening of eyes, hyperventilation, or photic stimulation
were performed under instruction from technicians. Vigilance was
monitored by the EEG technician who alerted patients when signs
of drowsiness appeared in the tracings; 9–12 artifact-free 4-s ep-
ochs were finally obtained per patient. One independent investiga-
tor blinded to the diagnosis of participants visually confirmed the
EEG segments were acceptable for further analyses.
Shim/Shin
Neurodegener Dis 2020;20:12–19
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DOI: 10.1159/000508130
EEG Analysis
For preprocessing steps, the EEG data were high-pass filtered
offline above 1 Hz, low-pass filtered below 45 Hz, and recomputed
to the common average reference. Transient artifacts were rejected
with a visual inspection, and stationary artifact components origi-
nating from eye movement, muscle tensions, or heartbeat were
removed using adaptive mixture independent component analysis
[27].
Following the removal of artifacts from the EEG signals, sensor
level analysis using spectopo function based on EEGLAB [20] was
performed in the following eight spectral bands: δ (1–4 Hz), θ (4–8
Hz), α1 (8–10 Hz), α2 (10–12 Hz), β1 (12–15 Hz), β2 (15–20 Hz), β3
(20–30 Hz), and γ (30–45 Hz). In the power spectral density 2-di-
mensional map, topomaps of absolute and relative power in 1-Hz
bins (1–45 Hz) as well as each frequency band are presented. Ab-
solute power is the sum of the component powers for each fre-
quency band. Relative power is the absolute power in a specific
frequency band divided by the total power. This feature provides
absolute and relative power based on five brain regions (frontal,
temporal, central, parietal, and occipital). The power spectra for
each of the 19 channels are shown in the power spectral density
spectrum, which presents the amplitude of power in units of mi-
crovolts squared per hertz or decibels per hertz to obtain the fre-
quency characteristics of the α-band or β-band, respectively. Fur-
thermore, the following band power ratios were calculated: θ-to-α
(TAR), δ-to-α (DAR), and θ-to-β (TBR). Source reconstructions
were performed using standardized low-resolution brain electro-
magnetic tomography (sLORETA) plugin [21] using a Colin 27
Head model [22] with 68 regions of interest segmentations based
on the Desikan and Killiany atlas [23]. All preprocessing steps, de-
noising using adaptive mixture independent component analysis,
sensor level feature extractions, and source level feature extrac-
tions were performed on iSyncBrain® [24].
Statistical Analysis
For the four NPSs (hyperactivity, psychosis, affective behav-
iors, and apathy), clinical characteristics and qEEG analyses were
compared between patients with and without NPSs using the
Mann-Whitney U test. Statistical significance was accepted for a p
value < 0.05 and performed automatically using the iSyncBrain®
[24] program.
Results
Clinical Characteristics
A total of 51 patients (17 males, 34 females) with AD
were analyzed in the present study. The clinical charac-
teristics were as follows: mean age 77.73 ± 7.28 years; du-
ration of education 4.44 ± 3.90 years; MMSE score 16.36
± 6.43; CDR score 1.16 ± 0.66, range 0.5–3.0; CDR sum
of boxes score 6.01 ± 3.83, range 0–16.
Differences in age, sex, duration of education, MMSE
score, CDR score, and CDR sum of boxes score were not
observed between groups with and without the four NPSs
(hyperactivity, psychosis, affective symptoms, and apa-
thy), except for the distributions of sex between groups
with and without affective symptoms (9 males: 28 females
vs. 8 males: 6 females, p = 0.028). Although duration of
education of patients was low, below 6 years, there was no
difference between groups with and without the NPSs.
G1
G2
α2
α2
α2α2
α2α2
α2
α2
α2
p = 0.044
p = 0.003
p = 0.008p = 0.043
p = 0.045 p = 0.034 p = 0.008
p = 0.012
p = 0.025
α2
α2
α2α2
α2α2
α2
α2
α2
p = 0.014
p = 0.024
p = 0.010p = 0.021
p = 0.032 p = 0.024 p = 0.014
p = 0.037
p = 0.012
a b
Fig. 1. Absolute (a) and relative (b) electroencephalogram power spectra for each of the 19 channels in groups
without (G1) and with (G2) psychosis. Blue letters indicate decreased power in G2.
qEEG and sLORETA in Neuropsychiatric
Symptoms of AD
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Neurodegener Dis 2020;20:12–19
DOI: 10.1159/000508130
Power Spectral Analysis
Figure 1 shows the absolute and relative EEG power
spectra for each of the 19 channels in groups without (G1)
and with (G2) psychosis. Blue letters indicate decreased
power in G2. Differences between groups with and with-
out hyperactivity, affective symptoms, and apathy were
not observed. The EEG power spectrum had lower values
at the α2 (10.0–12.0 Hz) frequency band in the channels
of Fp1, Fp2, Fz, T3, Cz, T5, and Pz in AD patients with
psychosis compared with patients without psychosis.
This result was further confirmed by the scalp topogra-
phies of EEG power differences between groups with and
without psychosis in the α2-frequency band (Fig.2). Pa-
tients with psychosis showed decreased power of the α2-
band in frontal (1.580 ± 1.395 vs. 2.590 ± 1.897 μV2, p =
0.016), central (1.915 ± 1.917 vs. 2.838 ± 2.354 μV2, p =
0.044), and temporal (2.219 ± 1.787 vs. 3.480 ± 1.962 μV2,
p = 0.009) areas. Over most scalp locations, the average
power in α2 (10–12 Hz) largely decreased in patients with
psychosis.
Figure 3 shows power ratios (TBR, TAR, and DAR)
between groups with and without apathy. Patients with
apathy had increased TBR values in frontal (3.634 ± 2.568
vs. 3.197 ± 4.266 AU, p = 0.032) and central (2.772 ± 2.061
vs. 2.799 ± 3.567 AU, p = 0.030) areas. Scalp topographies
of TBR showed prominent differences between patients
with and without apathy over the frontal and central re-
gions. Patients with apathy had a greater slow-to-fast-
wave power ratio. A further comparison between groups
with and without apathy based on the Mann-Whitney U
test did not show significance when TAR or DAR was
used as the power ratio parameter.
Results of absolute power spectrum are usually similar
to relative power measure. However, in the present study,
the results with significance in both the absolute and rel-
ative power spectra were presented to attenuate the inter-
individual variability of the absolute spectral power value
[28].
Cortical Source Analysis
Neuronal sources with significant power changes in 1–4,
4–8, 8–10, 10–12, 12–18, 18–30, and 30–45 Hz between pa-
tients with and without psychosis and apathy were localized
in widespread brain regions based on sLORETA (Table 1).
Specifically, the significantly decreased source activities in-
cluded α2-wave oscillatory activity in the right transverse
a
b
Mean (G1) Mean (G2) Difference (G2–G1) p value
9.78
5.59
1.40
9.78
5.59
1.40
5.06
0
–5.06
0.14
0.07
0
Mean (G1) Mean (G2) Difference (G2–G1) p value
0.13
0.09
0.06
0.13
0.09
0.05
0.05
0
–0.05
0.16
0.09
0.01
Unit: %
Unit: %
Fig. 2. Scalp topographies of absolute (a) and relative (b) electroencephalogram power differences between
groups with (G2) and without (G1) psychosis in the α2-frequency band.
Shim/Shin
Neurodegener Dis 2020;20:12–19
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DOI: 10.1159/000508130
temporal area in patients with psychosis. The significantly
increased source activities included δ-waveoscillatory ac-
tivity in the right superior, middle, and inferior temporal
areas and the left parahippocampal regions in patients with
apathy. The default mode network showed increased
δ-changes in the right temporal (superior and middle) and
bilateral entorhinal and parahippocampal areas in patients
with apathy. These significant source activities were com-
bined into 68 regions of interest based on anatomic location
and frequency band.
Discussion
The present study was the first in which a power spec-
tral analysis of the four NPSs, hyperactivity, psychosis,
affective symptoms, and apathy, was performed in AD
patients. In addition, NPSs at the cortical level were char-
acterized based on frequency-domain EEG source local-
ization using sLORETA.
The power spectral analysis of EEG data showed that
AD patients with psychosis had lower values at the α2-
Mean (G1)
TBR
TAR
DAR
Mean (G2) Difference (G2–G1) p value
4.42
3.46
2.49
4.42
3.46
2.49
0.77
0
–0.77
0.47
0.25
0.03
Mean (G1) Mean (G2) Difference (G2–G1) p value
1.27
0.99
0.70
1.27
0.99
0.70
0.29
0
–0.29
0.98
0.59
0.21
Unit: %
Unit: %
Mean (G1) Mean (G2) Difference (G2–G1) p value
2.52
1.49
0.45
2.52
1.49
0.45
1.37
0
–1.37
0.98
0.54
0.10
Unit: %
Fig. 3. θ-to-β (TBR), θ-to-α (TAR), and δ-to-α (DAR) power ratios between groups with (G2) and without (G1) apathy.
qEEG and sLORETA in Neuropsychiatric
Symptoms of AD
17
Neurodegener Dis 2020;20:12–19
DOI: 10.1159/000508130
band in most areas. Decreased α indicates failure to relax
and self-warming for external stimulation [29]. Patients
with apathy showed abnormal findings in the power ratio
where scalp topographies of TBR showed a greater slow-
to-fast-wave power ratio over the frontal and central re-
gions. TBR is the ratio of θ (4–8 Hz) to β (13–21 Hz) pow-
er during resting conditions and tends to reflect attention-
related functions [30, 31]. Increased TBR is associated
with increased reward responsiveness, risk taking, and
impulsiveness and tends to reflect attentional control
functioning and behavioral inhibition processes. Re-
searchers also demonstrated an increased slow-to-fast
band power ratio in advanced AD over all brain regions
except the frontal area, which may be caused by contami-
nation of the EEG signal by eye movement [14].
The cortical source analysis of EEG showed the NPSs
induced significant changes in source power across differ-
ent frequency bands in many brain regions. Patients with
psychosis showed decreased values in the α2-band and pa-
tients with apathy showed higher values in δ, especially in
the right frontal and temporal regions. In the present study,
sLORETA results were similar to the classical spectral anal-
ysis, which is consistent with previous reports regarding the
LORETA approach in AD [32–34]. The sLORETA results
of patients with apathy showed increased δ-power, primar-
ily in the right side and central areas. Both spectral and
sLORETA values for α-rhythm were relatively preserved
over the frontal areas in patients with psychosis compared
with patients without psychosis. This finding may indicate
the so-called “anteriorization” of α-rhythm in AD; in
healthy controls, α-wave generators are localized in the pos-
terior brain regions while decreased posterior α-activity
produces a shift of α-wave generator to more anterior re-
gions in patients with AD, as previously reported [35–37].
New neuroimaging techniques have been used in re-
cent neurobiological studies to elucidate behaviorally
relevant circuits and networks associated with these
subsyndromes [38]. Several frontosubcortical circuits,
corticocortical networks, and neurotransmitter sys-
tems have been proposed as regions and mechanisms
underlying NPS-AD. Common to most of these sub-
syndromes is the broad overlap of regions associated
with the salience network (anterior cingulate and in-
sula), mood regulation (amygdala), and motivated be-
havior (frontal cortex). Physiologically, EEG cortical
activity depends on a complex balance among different
neurotransmitter systems, primarily cholinergic path-
ways [39]. α-Rhythms are mainly modulated by thala-
mocortical interactions facilitating or inhibiting the
transmission of sensorimotor and cognitive informa-
tion among subcortical and cortical pathways [40–42].
Therefore, the magnitude reduction of fast cortical
rhythms in mild AD is hypothetically associated with
impairment of the cholinergic pathway resulting in an
abnormal increase in cortical excitation or disinhibi-
tion during the resting state. Developing rational thera-
Table 1. Neuronal sources with significant power changes in δ-
and α2-frequency bands between patients with and without psy-
chosis and apathy, localized based on standardized low-resolution
brain electromagnetic tomography in 68 brain regions of interest
Brain structure δ α2
L R L R
Frontal pole
Superior frontal
Rostral middle frontal
Caudal middle frontal
Pars opercularis
Pars orbitalis △
Pars triangularis △
Medial orbitofrontal
Lateral orbitofrontal △
Precentral
Paracentral
Rostral anterior cingulate
Caudal anterior cingulate
Temporal pole △
Superior temporal ▲
Middle temporal ▲
Inferior temporal ▲
Transverse temporal △ ⚫
Banks of superior temporal sulcus
Fusiform △ ⚪
Entorhinal △ △
Parahippocampal ▲ △
Insula △
Postcentral
Superior parietal
Inferior parietal
Supramarginal
Precuneus
Posterior cingulate
Isthmus
Lateral occipital ⚪
Cuneus
Pericalcarine ⚪ ⚪
Lingual ⚪
The number of voxels with significant power changes is listed
separately for the left (L) and right hemispheres (R) for each fre-
quency band. Blue colors indicate decreased power and red colors
indicate increased power in groups with neuropsychiatric symp-
toms. Circles indicate psychosis, triangles indicate apathy. Empty
symbols indicate borderline significance.
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Neurodegener Dis 2020;20:12–19
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DOI: 10.1159/000508130
peutic approaches for NPS-AD will require a better un-
derstanding of the underlying etiology, which can be
aided with qEEG analysis. Furthermore, qEEG can be
useful to predict the effects of treatment for NPSs. The
results of the present study are meaningful for the im-
plementation of EEG for the timely and proper treat-
ment based on pathophysiological aspects of the NPSs
of dementia although certain limitations remain.
The present study had several limitations. First, patho-
logical or biomarker confirmation was not obtained;
however, all the diagnoses were clinically performed with
supportive MRI findings. Use of biomarkers such as am-
yloid positron emission tomography or cerebrospinal flu-
id can help detect other causes of dementia than AD. Sec-
ond, the number of participants was relatively small. In
addition, EEG activity can be influenced by various fac-
tors such as age and the severity of dementia [43, 44].
Further studies with a larger study cohort, analysis of in-
dividual NPI items, and adjusted analysis of covariates
including age and MMSE can strengthen our results.
In summary, the results of the present study showed
that both classical EEG spectral analysis and EEG source
analysis can classify patients with and without NPSs in-
cluding hyperactivity, psychosis, affective symptoms, and
apathy. Spectral and sLORETA analyses provided helpful
information for a better characterization of qEEG in pa-
tients with NPSs. A more accurate analysis could be ob-
tained in further studies if a greater sample size and a
combination of spectral and sLORETA analyses with im-
aging techniques are used.
Acknowledgment
We would like to thank the research group in the iMediSync
Inc., South Korea, which developed source level feature extractions
and group statistics functionalities, and applied those to the re-
search data set on iSyncBrain. We would like to thank all their
valuable assistances for the analysis and reviewing of the result.
Statement of Ethics
This retrospective study was performed in accordance with the
World Medical Association Declaration of Helsinki and approved
by the Institutional Review Board of the Catholic University of
Korea, Bucheon St. Mary’s Hospital (HC18OESI0114). We con-
firm that we have read the position of Neurodegenerative Diseases
on issues involved in ethical publication and affirm that this work
is consistent with the guidelines.
Disclosure Statement
The authors have no conflicts of interest to declare.
Author Contributions
Yong S. Shim: concept and design, organization and execution
of the study, analysis and interpretation of data, and drafting of the
manuscript, review and critique of the manuscript. Hae-Eun Shin:
acquisition of data and drafting of the manuscript, review and cri-
tique of the manuscript. All authors (Yong S. Shim and Hae-Eun
Shin): final approval of the version to be published.
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