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Research report
Alpha absolute power measurement in panic disorder with
agoraphobia patients
Marcele Regine de Carvalho a,b,c,n, Bruna Brandão Velasques c,d,g, Rafael C. Freire a,b,
Maurício Cagy c,e, Juliana Bittencourt Marques c, Silmar Teixeira c,h,i, Bernard P. Rangé j,
Roberto Piedade c, Pedro Ribeiro c,d,f, Antonio Egidio Nardi a,b, Hagop Souren Akiskal k
a
Laboratory of Panic and Respiration, Institute of Psychiatry of Federal University of Rio de Janeiro (IPUB/UFRJ), Rio de Janeiro, Brazil
National Institute of Translational Medicine (INCT-TM), Rio de Janeiro, Brazil
c
Brain Mapping and Sensory Motor Integration, Institute of Psychiatry of Federal University of Rio de Janeiro (IPUB/UFRJ), Rio de Janeiro, Brazil
d
Institute of Applied Neuroscience (INA), Rio de Janeiro, Brazil
e
Division of Epidemiology and Biostatistics, Institute of Community Health, Federal, Fluminense University (UFF), Rio de Janeiro, Brazil
f
Bioscience Department (EEFD/ UFRJ), School of Physical Education, Rio de Janeiro, Brazil
g
Neuromuscular Research Laboratory, National Institute of Traumatology and Orthopaedics (NITO), Rio de Janeiro, Brazil
h
Physiotherapy Department – Piquet Carneiro Policlinic – State University of Rio de Janeiro, Brazil.
i
Laboratory of Physical Therapy, Veiga de Almeida University, Rio de Janeiro, Brazil.
j
Institute of Psychology of Federal University of Rio de Janeiro (IP/UFRJ), Rio de Janeiro, Brazil.
k
International Mood Center, University of California, San Diego, La Jolla, USA
b
art ic l e i nf o
a b s t r a c t
Article history:
Received 12 March 2013
Received in revised form
5 June 2013
Accepted 5 June 2013
Background: Panic attacks are thought to be a result from a dysfunctional coordination of cortical and
brainstem sensory information leading to heightened amygdala activity with subsequent neuroendocrine, autonomic and behavioral activation. Prefrontal areas may be responsible for inhibitory top-down
control processes and alpha synchronization seems to reflect this modulation. The objective of this study
was to measure frontal absolute alpha-power with qEEG in 24 subjects with panic disorder and
agoraphobia (PDA) compared to 21 healthy controls.
Methods: qEEG data were acquired while participants watched a computer simulation, consisting of
moments classified as “high anxiety”(HAM) and “low anxiety” (LAM). qEEG data were also acquired
during two rest conditions, before and after the computer simulation display.
Results: We observed a higher absolute alpha-power in controls when compared to the PDA patients
while watching the computer simulation. The main finding was an interaction between the moment and
group factors on frontal cortex. Our findings suggest that the decreased alpha-power in the frontal cortex
for the PDA group may reflect a state of high excitability.
Conclusions: Our results suggest a possible deficiency in top-down control processes of anxiety reflected
by a low absolute alpha-power in the PDA group while watching the computer simulation and they
highlight that prefrontal regions and frontal region nearby the temporal area are recruited during the
exposure to anxiogenic stimuli.
& 2013 Elsevier B.V. All rights reserved.
Keywords:
Absolute alpha-power
Panic disorder
qEEG
Frontal cortex
Neurobiology
Brain mapping
1. Introduction
Panic attacks (PA) are defined as sudden periods of intense fear or
discomfort, where various somatic and cognitive symptoms are
experienced, such as accelerated heart rate, sweating, trembling,
smothering, chest pain, nausea, dizziness, fear of losing control, and
fear of dying. Panic disorder (PD) patients experience recurrent PA and
fear their future repetition and consequences (APA, 2000). The PA also
n
Corresponding author at: Rua Desembargador Izidro, 40/504, 20521-160 Rio de
Janeiro, Brazil. Tel.: +55 21 2436 8202; cell: +55 21 9658 7080;
fax: +55 21 2523 6839.
E-mail address: [email protected] (M.R. de Carvalho).
produces behavioral changes and decrease the quality of life of those
with PD (APA, 2000). PD subjects have elevated prevalence of
comorbid mental disorders (Goodwin and Gotlib, 2004). Agoraphobia
(AG) is associated with substantial clinical severity and impairment
relative to those with PD uncomplicated by agoraphobia (Pollack and
Smoller, 1995; White and Barlow, 2002; Kessler et al., 2006). PD affects
3–4% of the general population and the lifetime prevalence estimates
are 22.7% for PA, 3.7% for PD without AG and 1.1% for Panic Disorder
with Agoraphobia (PDA) (Kessler et al., 2006).
Gorman et al. (2000) developed one of the most influential
hypotheses of the PD neurocircuitry. They suggest that PA result
from a dysfunctional coordination of cortical and brainstem
sensory information leading to a heightened amygdala activity
0165-0327/$ - see front matter & 2013 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.jad.2013.06.002
Please cite this article as: de Carvalho, M.R., et al., Alpha absolute power measurement in panic disorder with agoraphobia patients.
Journal of Affective Disorders (2013), http://dx.doi.org/10.1016/j.jad.2013.06.002i
M.R. de Carvalho et al. / Journal of Affective Disorders ∎ (∎∎∎∎) ∎∎∎–∎∎∎
2
with subsequent neuroendocrine, autonomic and behavioral activation. Gorman et al. (2000) states that the medial prefrontal
cortex (PFC), along with other cortical sites that process higher
order sensory information is important in modulating anxiety
responses and inhibiting amygdala firing. The PFC's activity is
associated with attempts to regulate the outcome of attentional,
interpretive and associative processes triggered by the occurrence
of potentially threat related cues. Amygdala activity (directly
activated by the thalamus or activated by the lack of PFC inhibition), in turn, may trigger activity of some subcortical sites, typical
of PA symptoms (Dresler et al., 2013; Martin et al., 2009;
De Carvalho et al., 2010; Stein, 2005). Lateral PFC and orbitofrontal
cortex have been associated with cognitive strategies to regulate
emotion, such as reappraisal; dorsolateral prefrontal activity has
been related to the use of proactive metacognitive strategies
aimed at self-regulating the fear and anxiety evoked by the
anxiogenic stimuli (Aupperle et al., 2009). More generally, the
PFC is believed to govern executive functioning, which refers to a
heterogeneous and wide-ranging set of cognitive operations,
including attention allocation, inhibitory control, hypothesis generation, and self-monitoring, as well as other skills (Mohlman,
2005).
The alpha band (8–13 Hz) reflects top-down, inhibitory control
processes (Klimesch et al., 2007). Moreover, a decrease in absolute
alpha-power is related to neural excitation, such as cognitive
processing (Klimesch et al., 2007). The major findings about alpha
band showed a low alpha rhythm in anxiety (Siciliani et al., 1975;
Enoch et al., 1995; Kalashnikova and Sorokina, 1995; Wiedemann
et al., 1998; Gordeev, 2008; Wise et al., 2011). Thus, an absolute
alpha-power decrease in the frontal cortex observed in PD may
reflect a dysfunction in thalamic–cortical circuits that is associated
with incapacity to inhibit irrelevant information, role played
especially by the PFC (Klimesch et al., 2007).
In this context, the aim of this study is to observe absolute
alpha-power in the scalp frontal region as a whole (F3, F7, Fz, F4,
F8, Fp1, Fp2 electrodes) in PDA patients compared to healthy
controls while watching an anxiogenic computer simulation
(Freire et al., 2010) comprised of high anxiety moments (HAM)
and low anxiety moments (LAM). We were expecting a low
absolute alpha-power in PDA patients on all electrodes when
compared to healthy controls. Moreover, we formulated the
hypothesis that, in high anxiogenic moments, absolute alphapower may be different than in low anxiety moments.
2. Methods
2.1. Participants
We selected a sample by convenience of 24 PDA patients (8 male
and 16 female; ages varying between 25 and 61 years old, mean:
38.75, SD: 710.09), who were in psychopharmacological treatment at
the Laboratory of Panic and Respiration at the Institute of Psychiatry
and were evaluated in the Department of Applied Psychology at the
Institute of Psychology before treatment; these are both institutes of
the Federal University of Rio de Janeiro (UFRJ). The recruitment of
subjects was done through posters with information about the
research in the outpatient institute of psychiatry and psychology at
UFRJ. All patients that met the study inclusion criteria were invited to
participate. The patients were interviewed with the M.I.N.I. 5.0
(Sheehan et al., 1998; Amorim, 2000) and fulfilled DSM-IV [1] criteria
for PDA. Another inclusion criterion was the occurrence of at least two
panic attacks in a 30-day period before the visit. Patients with
comorbid dysthymia (n¼1), generalized anxiety disorder (n¼2), social
phobia (n¼1) or depression (n¼ 3) were included only when PDA was
judged to be the primary diagnosis. Some of them began the
treatment unmedicated (n¼7), while others were already taking
antidepressants (n¼3), benzodiazepines (n¼ 5) or both antidepressants and benzodiazepines (n¼9). The patients performed three selfevaluation questionnaires to measure the severity of anxiety, depression and PDA symptoms: Beck Anxiety Inventory (BAI) (Beck et al.,
1988) (mean score: 22.68 and SD: 714,17; which means moderate
anxiety); Beck Depression Inventory (BDI) (Beck et al., 1961) (mean
score: 16.37 and SD: 710,99; which means mild depression). Seven of
the 24 subjects had BDI scores above the relevant clinical threshold for
depression and Panic and Agoraphobia Scale (PAS) (Bandelow, 1995)
(mean score: 23.82 and SD:79.96; which means moderate PDA
symptoms).
There was also a control group with 21 healthy participants (4
male and 17 female; ages from 23 to 61 years old, mean: 40.52,
SD: 7 12.47) who were screened with the M.I.N.I. 5.0 (Sheehan
et al., 1998; Amorim, 2000) and did not fulfill criteria for any
psychiatric disorder. Subjects with other psychiatric disorders,
neurological, cardiologic or respiratory diseases were not included
in this study, neither in the patient nor in the control group.
Patient and control group did not differ from each other in age
(p ¼0.848).Our local Ethics Committee (Comitê de Ética em Pesquisa do Instituto de Psiquiatria da Universidade Federal do Rio de
Janeiro—CEP-IPUB/UFRJ) approved the protocol, which complied
with the principles of the Declaration of Helsinki. After the
experiment was fully explained, the subjects signed a voluntary
written consent.
2.2. Computer simulation
The simulation consisted of a 4-min three-dimensional computer
animation developed by Triptyque LAB (www.triptyquelab.com). Two
30-s periods in which a gray screen was displayed, one before and the
other after the animation per se, were included in this animation. This
was in a first person perspective (a graphical perspective rendered
from the viewpoint of observer of the computer simulation) and there
was a camera movement as if the subject was walking inside/outside a
bus and looking at different directions during a bus ride. The
animation starts at a bus stop: the bus arrives, the subject gets on
the bus and sits down, the bus moves through city streets, it stops
again and is filled by many people, it moves through the streets, goes
in a tunnel, stops inside the tunnel because of traffic, it starts moving
again, gets out the tunnel, stops at a bus stop, and the subject gets off
the bus and watches the bus leave. The simulation included sound,
which consisted of ordinary street noises associated with the images
(Freire et al., 2010). In a previous study, this computer simulation
demonstrated to be a useful method to induce anxiety and somatic
symptoms in PDA patients. Compared to health controls, they had
higher scores in anxiety self-evaluation scales and had higher skin
conductance level, electrodermal response magnitude, respiratory
rate, tidal volume, and respiratory rate irregularities. Two of 10
patients had PA. The heart rate means were higher for PDA patients
who had PA (Freire et al., 2010).
The computer simulation consisted of situations classified as “high
anxiety” and “low anxiety”. They were classified as being “high” or “low
anxiety” by patients that participated in the cited previous study
(Freire et al., 2010). The high anxiety situations were when the bus
gets filled with people, when the bus gets in a tunnel and when it
stops inside the tunnel because of traffic. And the low anxiety
situations were those when the camera just moves around and the
subject sees the bus, when the bus moves through the streets but is
not filled with people, when the bus leaves the tunnel and there is no
traffic and when the subject gets off the bus and watches the bus go
away. These low anxiety situations refer to the situations where the
difficulty of exposure to anxiogenic events tends to be smaller (but it
still exists), that is, moments when the patient is about to leave the
situations of greater discomfort and for this reason may experiment
Please cite this article as: de Carvalho, M.R., et al., Alpha absolute power measurement in panic disorder with agoraphobia patients.
Journal of Affective Disorders (2013), http://dx.doi.org/10.1016/j.jad.2013.06.002i
M.R. de Carvalho et al. / Journal of Affective Disorders ∎ (∎∎∎∎) ∎∎∎–∎∎∎
less anxiety. All these situations were connected with EEG recordings
through a computer software designed by the Brain Mapping and
Sensory Motor Integration Laboratory of the Psychiatry Institute of the
Federal University of Rio de Janeiro.
2.3. Experimental procedures
The experiment was fully explained to the subjects and they
signed a voluntary written consent. Patients with PDA filled out
the BAI, BDI and PAS scales. Subjects were seated on a comfortable
chair in a darkened and sound-protected room in order to
minimize sensory interference. The subjects were positioned in
front of a 32-inch monitor and the distance between the participants and the monitor was 30 cm. Speakers were positioned
around the room and the experiment was divided into three
stages: (1) rest condition 1 (RC1): 4 min of open eyes rest qEEG
recording; (2) computer simulation (low anxiety and high anxiety
situations)—the participants watched the movie, and concomitant
signal qEEG was recorded; and (3) rest condition 2 (RC2): 4 min of
open eyes rest qEEG recording. All qEEG recordings, for both
patients and healthy controls, were made in the afternoon, from
1 PM to 4 PM. Subjects were oriented to have at least 8 h of sleep
before recordings.
2.4. EEG data acquisition recording
The International 10/20 EEG electrode system (Jasper, 1958)
was used with a 20-channel EEG system (Braintech-3000, EMSA
Medical Instruments, Brazil). The 20 electrodes were arranged on a
nylon cap (ElectroCap Inc., Fairfax, VA, USA) yielding monopolar
derivation using the earlobes reference. Impedance of EEG and
EOG electrodes was kept between 5 and 10 kΩ. The data recorded
had total amplitude of less than 70 mV. The EEG signal was
amplified with a gain of 22,000, analogically filtered between
0.01 Hz (high-pass) and 80 Hz (low-pass), and sampled at 200 Hz.
The software Data Acquisition (Delphi 5.0) from the Brain Mapping
and Sensory Motor Integration Lab was employed with the digital
filter notch (60 Hz).
2.5. Data processing and analysis
We applied a visual inspection and independent component
analysis (ICA) to remove possible sources of artifacts produced by
the task (i.e., blinking and muscle -related artifacts) (Onton et al.,
2006). The data were collected using the bi-auricular reference
and they were transformed (re-referenced) using the average
reference after we conducted the artifact elimination using ICA.
Through visual inspection, we removed all the trials which clearly
showed blinking and a muscle-related artifacts “influence”, and
through ICA we removed the components that showed blinking
and muscle-related artifacts “contamination”. A classic estimator
was applied for the power spectral density (PSD) performed by
MATLAB 5.3 (Matworks, Inc.). Eight hundred (4 s 200 Hz)
samples with rectangular windowing were analyzed. For the
computer simulation, we extracted qEEG parameters within a
time frame of 1 s before and 2 s after each situation. As the
anxiogenic events do not have an instantaneous beginning, rather,
they are gradual, the gap of 1 s prior to labeling served as a
guarantee that we did not lose snippets of information signal due
to a failure in marking the exact start of events. Therefore, the
situations were classified according to their characteristics and
they were grouped into two different moments: low anxiety
moments (LAM) and high anxiety moments (HAM). LAM and
HAM moments were 3 s length periods spread along the computerized simulation. Six moments were marked during the computer simulation (a total of 9 s of LAM and 9 s of HAM). The Fourier
3
Transform resolution was 1/4 s–0.25 Hz (FFT). The “Run-test” and
“Reverse-Arrangement test” were applied to examine a stationary
process, which was accepted for every 1 s (epoch's duration). In
this manner, based on artifact-free EEG epochs, the threshold was
defined by the mean plus three standard deviations; epochs which
showed a total power higher than this threshold were not
included into the analysis.
2.6. Statistical analysis
Statistical analysis was performed using SPSS for Windows—
version 17.0 (SPSS Inc., Chicago, USA) and absolute alpha-power
(8–13 Hz) was the dependent variable of interest. Absolute alphapower values during the computer simulation presentation were
assessed in 24 PDA patients and were compared with 21 healthy
controls. An ANOVA three-way was performed among the independent variables: group (2 levels: patients and control group),
moment (4 levels: RC1, LAM, HAM, and R2) and electrode (7 levels:
F7, F3, Fz, F8, F4, Fp1 and Fp2). Scheffé post hoc test was used.
In cases where we found interaction, we examined the interaction
with a t-test or with an ANOVA one-way in order to understand
further results. We also reported the effect size using Eta Partial
Squared (ηp²).
Additional analysis was performed excluding four subjects that
presented PDA with comorbid Major Depressive Disorder (MDD)
and Generalized Anxiety Disorder (GAD). The same statistical
analysis procedure was performed with the remaining 20 PDA
patients and the 21 healthy controls.
3. Results
We analyzed absolute alpha-power on the frontal cortex.
An ANOVA three-way was performed between the independent
variables: group, moment and electrode. We did not find an
interaction among the three factors (F¼ 0.739; p ¼ 0.774; ηp² ¼
0.001), but we found an interaction between group vs. moment
(F¼23.572; p ¼0.001; ηp² ¼ 0.003) (Fig. 1), moment vs. electrode
(F¼3.512; p ¼0.001; ηp² ¼ 0.003) (Fig. 2) and group vs. electrode
(F¼3.284; p ¼0.003; ηp²¼ 0.001) (Fig. 3).
As the interaction between group and moment on the frontal
cortex was the main finding according to our objectives, between
groups t-tests were performed to examine this interaction. We
detected a difference between RC1 (p ¼0.001), LAM (p¼ 0.001) and
RC2 (p¼ 0.001), but no difference was detected on HAM
(p ¼0.363). We found the greater absolute alpha power for RC2
and the lowest for LAM. From RC1 to LAM the absolute alpha
power diminished and it rose from LAM to RC2.
We performed an ANOVA one-way for each moment to
examine the interaction between moment and electrode. We
Fig. 1. Mean and standard deviation of absolute alpha power on the frontal cortex.
The statistical analysis revealed an interaction between group and moment
(p ¼ 0.001).
Please cite this article as: de Carvalho, M.R., et al., Alpha absolute power measurement in panic disorder with agoraphobia patients.
Journal of Affective Disorders (2013), http://dx.doi.org/10.1016/j.jad.2013.06.002i
4
M.R. de Carvalho et al. / Journal of Affective Disorders ∎ (∎∎∎∎) ∎∎∎–∎∎∎
MDD and GAD, as it could bias the finding results. We found that
these comorbid conditions could not account for the findings, and,
for this reason, these analyses were not reported here.
4. Discussion
Fig. 2. Mean and standard deviation of absolute alpha power on the frontal cortex.
The statistical analysis revealed an interaction between moment and electrode
(p¼ 0.001).
Fig. 3. Mean and standard deviation of absolute alpha power on the frontal cortex.
The statistical analysis revealed an interaction between group and electrode
(p¼ 0.003).
detected a difference among electrodes for each moment: RC1
(F¼ 9.993; p ¼0.000; ηp² ¼0.006), LAM (F¼ 15.420; p ¼0.000;
ηp²¼ 0.047), HAM (F¼ 13.164; p ¼0.000; ηp²¼ 0.035) and RC2
(F¼ 11.726; p ¼0.000; ηp²¼ 0.007). For RC1, according to Scheffé
post hoc test, the differences were found between the following
pair of electrodes: F3–Fz (p¼ 0.000), F3–Fp2 (p ¼0.045), Fz–F4
(p ¼0.000), Fz–F8 (p¼ 0.000) and Fz–Fp1 (p¼ 0.003). For LAM:
F7–F3 (p ¼0.000), F7–Fz (p ¼0.001), F7–F4 (p ¼0.000), F3–F8
(p ¼0.044), F3–Fp1 (p ¼0.000), F3–Fp2 (p ¼0.000), Fz–Fp1
(p ¼0.001), Fz–Fp2 (p ¼ 0.037), F4–Fp1(p ¼0.000) and F4–Fp2 (p ¼
0.000). For HAM: F7–F3 (p ¼0.015), F7–F4 (p ¼0.012), F3–Fp1
(p ¼0.000), F3–Fp2 (p ¼0.000), Fz–Fp1 (p ¼ 0.011), Fz–Fp2
(p ¼0.000), F4–Fp1 (p¼ 0.000) and F4–Fp2 (p ¼0.000). For RC2:
F7–Fz (p ¼0.000), F3–Fz (p ¼ 0.000), F3–Fp2 (p ¼0.007), Fz–F4
(p ¼0.000), Fz–F8 (p ¼0.000) and Fz–Fp1 (p ¼0.001). We observed
that absolute alpha power for Fp1 and Fp2 were constantly higher
for all moments.
We performed an ANOVA one-way for each group to examine
the interaction between group and electrode. We detected
a difference among electrodes for each group: Healthy Controls
(F¼ 13.118; p ¼0.000; ηp² ¼0.006) and PDA (F¼15.694; p ¼0.000;
ηp²¼ 0.008). For controls, according to Scheffé post hoc test, the
differences were found between the following pair of electrodes:
F7–F3 (p¼ 0.010), F3–Fz (p¼ 0.000), F3–Fp2 (p ¼0.023), Fz–F4
(p ¼0.000), Fz–F8 (p ¼0.000), Fz–Fp1 (p¼ 0.000) and F4–Fp2 (p ¼
0.021). For PDA: F7–F3 (p ¼0.010), F3–Fz (p¼ 0.000), F3–Fp1
(p ¼0.000), F3–Fp2 (p ¼0.000), Fz–F4 (p ¼0.023), Fz–F8 (p ¼
0.002), F4–Fp1 (p ¼0.034), F4–Fp2 (p ¼0.002), F8–Fp1 (p ¼0.003)
and F8–Fp2 (0.000). In both groups, absolute alpha power for Fz,
Fp1 and Fp2 were constantly higher.
We did additional analyses, following the same procedures
described above, that excluded the individuals with comorbid
This study aimed to shed light on the relationship between
electrocortical activity on frontal cortex in PDA patients and a healthy
control group. Specifically, we investigated the absolute alpha-power
difference between the PDA and the control group on the frontal
cortex while watching a computer simulation (Freire et al., 2010)
with HAM and LAM. Based on previous electrophysiological findings
in PDA patients, we hypothesized that these would present a low
absolute alpha-power for all frontal electrodes. We expected that
frontal region would react differently between the HAM and LAM.
Moreover, we anticipated the frontal region would participate more
actively in the modulation of high anxiogenic emotional stimuli
processing.
The interactions between moment and group in frontal cortex
demonstrate that absolute alpha power fluctuation depends on
the relationship between both factors: moment and group.
A greater absolute alpha-power for healthy subjects when compared to the PDA patients was found in the frontal area. We can
highlight that in moment and group interaction RC1, LAM and RC2
differed from each other, with a greater absolute alpha power for
RC2 and a lower absolute alpha power for LAM. It was interesting
to find out that LAM was more significant than HAM for both
groups. We can hypothesize that it might have happened because
PDA patients tend to be hypersensitive even for lower anxiety
stimulus, exhibiting hyperousal responses, as they are hypervigilant to danger cues (Beck et al., 1992). Hoehn-Saric et al. (1991)
reported that PD patients with frequent PA exhibited heightened
cardiovascular arousal and decreased electrodermal flexibility,
even in nonthreatening situations, when compared to controls.
Besides, we cannot ignore that in HAM there was an increase in
the mean of absolute alpha power plus a large variability in PDA
patients in relation to controls, which did not happen in LAM.
Our results are in agreement with previous studies that report
a low absolute alpha-power in PD patients (Siciliani et al., 1975;
Enoch et al., 1995; Kalashnikova and Sorokina, 1995; Wiedemann
et al., 1998; Gordeev, 2008; Wise et al., 2011). The decrease of
alpha activity has been associated with a state of high excitability
and lower inhibitory control (Gordeev, 2008; Pavlenko et al.,
2009), while an increase of alpha power represents a low excitability state, related to a relaxed condition (Cahn and Polich, 2006;
Gordeev, 2008; Pavlenko et al., 2009). Thus, our results of greater
absolute alpha-power for healthy subjects when compared to the
PDA patients in the frontal area can be interpreted as a greater
frontal activation and this may be related to an impaired frontal
attempt to regulate downstream excitability (although we cannot
direct test this hypothesis) or to the reflection of the excitation
originated from deeper subcortical regions.
Alpha is elicited in situations where subjects withhold or
control the execution of a response and it is obtained over sites
that probably are under or exert top-down control. Thus, it is
assumed that alpha is related to top-down and inhibitory control
processes (Siciliani et al., 1975). Alpha may also be considered an
index for measuring emotional stability; as previous studies have
shown the appearance of alpha during meditation, which indicates
a state of low brain excitability with a reduction of stress and
anxiety (Cho et al., 2011). Further, the lateral frontal region is
located nearby the temporal cortex, an important region that is
correlated with the limbic system activity, especially with amygdala activity (LeDoux, 1992). The medial temporal lobe consists of
the amygdala and the hippocampus (including the entorhinal,
Please cite this article as: de Carvalho, M.R., et al., Alpha absolute power measurement in panic disorder with agoraphobia patients.
Journal of Affective Disorders (2013), http://dx.doi.org/10.1016/j.jad.2013.06.002i
M.R. de Carvalho et al. / Journal of Affective Disorders ∎ (∎∎∎∎) ∎∎∎–∎∎∎
perirhinal and parahippocampal cortices); these structures are
known to be involved in memory and emotional learning (LeDoux,
1992). The role of such structures in emotional learning and
memory is particularly well characterized in classical fear conditioning (Liu et al., 2010). The amygdala plays a special central
role in fear learning processes. It is known that the amygdala is a
center where different impulses converge, and it conducts excitatory efferent outputs to the hypothalamus, midbrain and brainstem areas, which subsequently select suitable behavioral and
autonomic programs (Globish et al., 1999). The amygdala also
participates in fear extinction along with the medial PFC and
hippocampus. The medial PFC is thought to regulate extinction of
long-term memory. It has been seen that with constant presentation of the conditioned stimuli, the lateral amygdala is responsible
for decreased firing, and the medial PFC inhibits firing of amygdala
neurons, under the modulation of the hippocampus (Garakani
et al., 2006). If the PFC top-down modulation is not working
properly, anxiety symptoms are likely to be more prominent.
Besides, there is some evidence that support that the lateral PFC
controls anxiety related limbic activity through connections with
ventromedial prefrontal cortex (Klumpers et al., 2010). Anatomical
studies demonstrated that the medial PFC and the 1ateral PFC have
direct connections with limbic structures, such as the amygdala, the
hypothalamus and the hippocampus (Groenewegen et al., 1990;
McDonald et al., 1996). Areas of the dorsolateral PFC are likely to be
recruited during deliberate emotional regulation through cognitive
appraisal paradigms' studies (Ochsner et al., 2002; Eippert et al.,
2007). There is also evidence that healthy subjects downregulate
their defensive states while recruiting ventromedial and right
lateral prefrontal areas (Klumpers et al., 2010). Thus, the lateral
PFC must be one site that contributes to the modulation of the
activation of the amygdala, exerting inhibitory effects on the
amygdaloid complex and being critical for the inhibition of conditioned fear (Lacroix et al., 2000). The constantly high absolute alpha
power for prefrontal region found on both interactions: moment vs.
electrode and group vs. electrode is also in line with the presented
hypothesis of an impaired top-down regulation.
The findings for frontal area demonstrated that both patients and
healthy controls presented higher alpha values at RC1 and RC2 when
compared to LAM and HAM. These findings suggest that the
computer simulation was effective in inducing anxiety, as also
demonstrated in a previous study that measured peripheral physiological alterations in PDA patients, where they demonstrated significant anxiety, electrodermal and respiratory alterations related to
control subjects (Freire et al., 2010). Another possible explanation to
be considered is that alpha was reduced during the increased
information processing associated with the computer simulation.
Our results confirm our hypothesis that the PDA patients
present a lower absolute alpha power in the frontal cortex when
compared to healthy controls. Our additional analyses that
excluded subjects with MDD and GAD showed that MDD and
GAD were conditions that did not bias the findings.
Panic disorder is almost never clinically manifested without
comorbidity, most of which is also characterized by over-arousal.
Research conducted in collaboration between International Mood
Center in San Diego and University of Pisa, Italy, has indeed
reported high levels of arousal in panic disorder and its comorbid
boundaries, especially bipolar II (Akiskal et al., 2006). In other
words, the specific findings of this paper have broader implications for anxious-bipolar patients and probably beyond.
The limitations of this study were the small sample, the wide
age range of participants, the gender of participants was not
balanced in the samples and the use of psychotropic medications
by most of the PDA patients. About only 29% of the patients
(seven) were drug-free during the experiment. Some of the
patients also have psychiatric comorbidities, and they represented
5
about 29% of the patients' sample. As Freire et al. (2010) also
pointed out in their study, the lack of interactivity between the
computer simulation and the participants may have been another
limitation, since interaction, like the one provided by virtual
reality environments, would probably contribute to the enhancement of the computer simulation anxiogenic properties.
Role of funding source
Purchase and maintenance of research equipment was supported by the Brazilian
Council for Scientific and Technological Development (CNPq) and INCT Translational
Medicine (CNPq).
Conflict of interest
None to declare.
Acknowledgments
Supported by the Brazilian Council for Scientific and Technological Development (CNPq) and INCT Translational Medicine (CNPq).
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Journal of Affective Disorders (2013), http://dx.doi.org/10.1016/j.jad.2013.06.002i