Article

Resting-State Quantitative Electroencephalography Reveals Increased Neurophysiologic Connectivity in Depression

Laboratory of Brain, Behavior, and Pharmacology, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, California, United States of America.
PLoS ONE (Impact Factor: 3.23). 02/2012; 7(2):e32508. DOI: 10.1371/journal.pone.0032508
Source: PubMed

ABSTRACT

Symptoms of Major Depressive Disorder (MDD) are hypothesized to arise from dysfunction in brain networks linking the limbic system and cortical regions. Alterations in brain functional cortical connectivity in resting-state networks have been detected with functional imaging techniques, but neurophysiologic connectivity measures have not been systematically examined. We used weighted network analysis to examine resting state functional connectivity as measured by quantitative electroencephalographic (qEEG) coherence in 121 unmedicated subjects with MDD and 37 healthy controls. Subjects with MDD had significantly higher overall coherence as compared to controls in the delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), and beta (12-20 Hz) frequency bands. The frontopolar region contained the greatest number of "hub nodes" (surface recording locations) with high connectivity. MDD subjects expressed higher theta and alpha coherence primarily in longer distance connections between frontopolar and temporal or parietooccipital regions, and higher beta coherence primarily in connections within and between electrodes overlying the dorsolateral prefrontal cortical (DLPFC) or temporal regions. Nearest centroid analysis indicated that MDD subjects were best characterized by six alpha band connections primarily involving the prefrontal region. The present findings indicate a loss of selectivity in resting functional connectivity in MDD. The overall greater coherence observed in depressed subjects establishes a new context for the interpretation of previous studies showing differences in frontal alpha power and synchrony between subjects with MDD and normal controls. These results can inform the development of qEEG state and trait biomarkers for MDD.

Full-text

Available from: Andrew F Leuchter
Resting-State Quantitative Electroencephalogr aphy
Reveals Increased Neurophysiologic Connectivity in
Depression
Andrew F. Leuchter
1,2
*, Ian A. Cook
1,2
, Aimee M. Hunter
1,2
, Chaochao Cai
3,4
, Steve Horvath
3,4
1 Laboratory of Brain, Behavior, and Pharmacology, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, California,
United States of America, 2 Depression Research and Clinic Program, Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human
Behavior, University of California Los Angeles, Los Angeles, California, United States of America, 3 Department of Human Genetics, David Geffen School of Medicine,
Gonda (Goldschmied) Neuroscience and Genetics Research Center, University of California Los Ang eles, Los Angeles, California, United States of America, 4 Department of
Biostatistics, School of Public Health, University of California Los Angeles, Los Angeles, Caliornia, United States of America
Abstract
Symptoms of Major Depressive Disord er (MDD) are hypothesized to arise from dysfunction in brain network s linking the
limbic system and cortical regions. Alterations in brain functional cortical connec tivity in resting-state networks have been
detected with functional imaging techniques, but neurophysiologic connectivity measures have not been systematica lly
examin ed. We used weighted network analysis to exa mine resting state functional connectivity as measured by
quantitative electroencephalographic (qEEG) coherence in 121 unmedicated subjects with MDD and 37 healthy controls.
Subjects with MDD had significantly higher overall coherence as compared to controls in the delta (0.5–4 Hz), theta (4
8 Hz), alpha (8–12 Hz), and beta (12–20 Hz) frequency bands. The frontopolar region contained the greatest number of
‘‘hub nodes’’ (surface recording locations) with high connectivity. MDD subjects expressed higher theta and alpha
coherence primarily in longer distance connect ions between frontopolar and temporal or parietooccipital regions, a nd
higher beta coherence primarily in connections within and between electrodes overlying the dorsolateral prefrontal
cortical (DLPFC) or temporal regions. Nearest centroid analysis indicated that MDD subjects were best c haracterized by six
alpha band connections primarily involving the prefrontal region. The pres ent finding s indicate a loss of selectivity in
resting functional connectivity in MDD. The overall greater coherence obse rved in depressed subjects establish es a new
context for the interpretation of previo us studies showing differences in frontal alpha power and synchrony between
subjects with MDD and normal controls. These results can inform the development of qEEG state and trait biomarkers for
MDD.
Citation: Leuchter AF, Cook IA, Hunter AM, Cai C, Horvath S (2012) Resting-State Quantitative Electroencephalography Reveals Increased Neurophysiologic
Connectivity in Depression. PLoS ONE 7(2): e32508. doi:10.1371/journal.pone.0032508
Editor: Wael El-Deredy, University of Manchester, United Kingdom
Received August 16, 2011; Accepted January 31, 2012; Published February 24, 2012
Copyright: ß 2012 Leuchter et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provide d the original author and source are credited.
Funding: Eli Lilly and Company (www.lilly.com), Wyeth Pharmaceuticals (now owned by Pfizer), and Pfizer (www.Pfizer.com) contributed to the funding for this
research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: AFL@UCLA.EDU
Introduction
Major Depressive Disorder (MDD) is characterized by dysphoric
and anxious mood, difficulties in concentration and decision making,
ruminative and self-referential thinking, as well as anhedonia and lack
of motivation [1,2]. These symptoms are consistent with deficits seen
in experimental paradigms, in which patients with MDD show
deficits in emotional and cognitive information processing [3,4].
Aberrant emotional processing has been demonstrated in the context
of reactions to emotional facial expression or startle in the context of
pleasant stimuli [5,6]. Cognitive deficits have been reported in
memory processing, learning, attention, and executive function [7,8].
While clusters of these symptoms are used to define MDD, their
neurobiological origins are not w ell understood [9]. Elucidating the
linkage between the symptoms and pathophysiology of MDD could
lead to more accurate and meaningful diagnoses that would have
greater prognostic significance [10].
Many of the symptoms and deficits of MDD have been
hypothesized to arise from dysfunction in brain networks linking
the limbic system and cortical regions [7,11]. Disruptions in both
top-down and bottom-up information processing have been
observed with task-activated functional magnetic resonance
imaging (fMRI), with altered functional connectivity between
dorsolateral prefrontal cortex (DLPFC) and subcortical limbic
structures (i.e., amygdala, thalamus) as well as subgenual anterior
cingulate cortex [11–13]. In addition to task activation studies,
resting-state fMRI has been used to examine ‘‘resting state
networks’’ (RSNs) that subserve a range of brain processes
including executive control, emotional saliency, self-referential
information processing, and the default mode network (DMN)
[14–17]. Studies of the resting state provide an important
opportunity to examine connectivity unbiased by any task, and
to examine the role that regions may play as parts of multiple
networks. Few studies have specifically examined RSNs in MDD.
Examination of the resting-state blood oxygen level-dependent
(BOLD) signal in MDD shows primarily broad increases in
functional connectivity in the DMN and other networks [18–21],
although other studies have found decreased resting connectivity
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Page 1
between some regions [22–24] or complex reciprocal relationships
between cortical and subcortical structures [25].
Neurophysiologic tools are complementary to fMRI for
examining brain network activity. Electroencephalographic
(EEG) signals oscillate on a faster time course than BOLD signals
[26] with the EEG oscillations actually eliciting the BOLD signal
activations within several RSNs [27]. Synchronous EEG oscilla-
tions appear to bind together BOLD responses within RSNs in a
frequency-dependent manner: long-distance integration of the
BOLD response is coordinated by lower frequency (e.g., alpha, or
8–12 Hz) activity, while shorter-distance BOLD responses are
coordinated by higher frequency (e.g., beta, or 12–20 Hz) activity
[26,28–29]. BOLD signal fluctuations within each RSN are
accounted for by different combinations of rhythmic neuronal
firing in the delta (0.5–4 Hz), theta (4–8 Hz), alpha, beta, and
gamma (.20 Hz) frequency bands, and multiple frequencies are
coupled to mediate brain operations [30–31]. Each functional
network therefore has a distinct electrophysiological signature that
is characterized by the synchronous oscillations of the neurons in
that network [31–32]. In a combined fMRI/qEEG resting state
study, Sadaghiani and colleagues showed that spontaneous fMRI
fluctuations were strongly positively correlated with alpha band
oscillations in a cingulo-insular-thalamic network, and negatively
correlated in the dorsal attention network [29]. They concluded
that the alpha synchronization plays a key global role in top-down
network control, as proposed by Klimesch and colleagues [33].
It has been established that subjects with MDD have
dysregulation of neural oscillatory synchrony, but comprehensive
information is limited. There is consistent support for increased
synchrony in the alpha band, as evidenced by increases within
single regions of alpha band power on quantitative electroenceph-
alography (qEEG) [34–39]. Studies are inconsistent, however, in
identifying which region(s) show this abnormality, with increases
reported over the frontal or parietooccipital regions, either on the
right or left [39–41]. One report found that patterns of alpha
asymmetry fluctuated over the span of weeks in subjects with
MDD as compared to normal controls [35], suggesting that
disturbed synchrony in MDD may reflect a broadly distributed
dysregulation [42–44]. Disturbed synchrony in other frequency
bands has not been consistently reported.
It has been suggested that the disturbed synchrony in neural
oscillations may reflect dysfunction within RSNs in subjects with
MDD [45]. Most studies of neural synchrony in MDD, however,
have examined brain function within a single region over a
relatively short distance. Few studies have examined synchronous
oscillations from sites spanning greater distances, across brain
regions, to provide information regarding the neurophysiology of
larger scale networks [37,43,46–47]. qEEG coherence is a
measure that is well suited to examine synchrony across brain
regions. While a peak in qEEG power indicates oscillatory
synchrony at a single point, coherence is a well-established
indicator of connectivity between two points, or ‘‘nodes,’’ that
have a fixed oscillatory phase relationship. Coherence therefore
represents the coupling of activity between two nodes that are
functionally linked, but not time-locked to a specific event [48–50].
Coherence values range between 0 (no shared activity between
nodes) and 1 (completely synchronous). This measure thus is well-
adapted for assessing functional connectivity in RSNs: it has been
successfully used to examine spatial integration both at short- and
long-distances in the brain [51,52], and functional connections
among sites overlying disparate cortical areas involved in sensory,
motor, and cognitive tasks, both during tasks and at rest [51,53–
54]. Coherence was first examined in clinical populations with
depression or dementia by O’Connor and colleagues [55], but has
not been extensively studied in subjects with MDD compared to
healthy controls. The most systematic previous study of connec-
tivity in MDD was conducted by Fingelkurts and colleagues [43],
who examined 12 medication-free depressed outpatients and used
the index of structural synchrony to analyze nine categories of
functional connectivity (e.g., short left/right, short anterior/
posterior, long left/right, long anterior/posterior, long interhemi-
spheric) separately for the theta and alpha frequency bands).
In the present study, we extend the earlier work of Fingelkurts
and colleagues by examining synchrony across brain regions with
qEEG coherence using a denser electrode array, a broader range
of frequency bands, and a greater number of subjects. We
compared the global resting functional connectivity of subjects
with MDD and healthy controls utilizing the novel method of
weighted network analysis [56], which obviates the need to
threshold the observed coherence values. The nodes in the
weighted (whole brain) network corresponded to pairs of
neighboring qEEG recording electrodes (Figure 1), and the
coherence between each pair of nodes was considered as a
connection or ‘‘edge’’ of the network. Coherence values
representing the strength of the network connections (i.e., value
of the edges) were examined in each frequency band to identify
any differences in the strength of the resting functional
connectivity between groups. To further elucidate patterns of
difference in functional connections between MDD and control
subjects and characterize brain connectivity in the depressed state,
we also examined the mean length of the edges showing significant
differences, as well as the locations of the nodes most commonly
linked by significant edges.
Materials and Methods
Ethics statement
This study was approved by the University of California Los
Angeles (UCLA) Office of the Human Research Protection
Program. Informed consent was taken via an approved consent
form before any study procedures were done. All clinical
investigation was conducted according to the principles expressed
in the Declaration of Helsinki. Further, all clinical investigations
are reviewed in accordance with FDA (Food and Drug
Administration) regulations at 21CFR Parts 50 and 56.
Subjects
This study examined adult subjects ages 21–70 with MDD who
had participated in one of four placebo-controlled antidepressant
treatment trials conducted over four years in the UCLA
Laboratory of Brain, Behavior, and Pharmacology (n = 121) and
healthy control subjects who were recruited for a study of the
effects of antidepressant medication on normal brain function
(n = 37). All depression trials were of similar size, utilized
comparable recruitment procedures and inclusion/exclusion
criteria, and subjects among the four trials did not differ
significantly with respect to age, gender, or symptom severity at
intake, so that the data were pooled for these analyses. Healthy
control subjects had no current or prior history of any psychiatric
or neurologic disorder [57]. All subjects were recruited by
community advertisement and were screened for eligibility using
a standard clinical evaluation, a structured clinical interview
(Structured Clinical Interview for Axis I DSM-IV Disorders
Patient Edition: SCID-I/P, version 2.0) [58], and the 17-item
Hamilton Depression Rating Scale (HamD
17
) [59]. Depressed
subjects had HamD
17
scores $16 at entry. Exclusion criteria
included psychotic symptoms, cluster A or B Axis II disorders,
prior suicidal ideation, or any serious medical conditions known to
Brain Functional Connectivity in Major Depression
PLoS ONE | www.plosone.org 2 February 2012 | Volume 7 | Issue 2 | e32508
Page 2
affect brain function or to contraindicate use of the active
medication. Subjects were free of psychotropic medications for at
least two weeks prior to enrollment. There was no significant
difference in the mean age or gender or handedness ratios between
the two subject groups, although predictably, the MDD subjects
had significantly higher mean HamD
17
score than did control
subjects (Table 1).
qEEG recordings
Resting EEG was recorded while subjects lay quietly with eyes
closed in a sound attenuated room. Subjects were alerted
frequently to avoid drowsiness, and were instructed to remain
still and inhibit blinks or eye movements during each recording
period. EEG was recorded using a 35-channel enhanced version of
the International 10–20 System of Electrode Placement with
additional electrodes located over prefrontal and parietooccipital
regions (indicated by red dots and labels in Figure 1A). Ag|AgCl
electrodes were placed using an electrode cap (ElectroCap, Inc.;
Eaton, OH) referenced to Pz. Electrode impedances were
balanced and under 5 kV for all electrodes. Vertical and
horizontal electro-oculograms (EOG) were recorded for identifi-
cation of eye movement artifact using bipolar electrodes placed at
the supraorbital and infraorbital ridge of the right eye and the
outer canthi of the left and right eye, respectively.
A minimum of 10 minutes of EEG data were recorded using a
16-bit resolution Neurodata QND system (Neurodata, Inc.;
Pasadena, CA) at a sampling rate of 256 Hz, a low-pass filter of
70 Hz, and a high-pass filter of 0.3 Hz, as well as a notch filter at
60 Hz. Data were stored in digital format and imported into Brain
Vision Analyzer (BVA) software (Brain Products GmbH; Gilching,
Germany) in order to remove offsets, optimize scaling, and re-
reference the data through amplitude subtraction into a series of
66 nearest-neighbor bipolar electrode pairs. These pairs are
indicated by the lines between electrodes (punctuated by blue dots)
Figure 1. Topographic locations for the electrode montage used in EEG recordings and coherence calculations. Electrode locations
were based upon an enhanced version of the International 10–20 System of electrode placement, with additional electrodes placed over the frontal
and parietal regions (1A). Locations were projected through Cartesian coordinates onto a two-dimensional representation of the brain, using a central
electrode (Cz) as the origin, with locations labeled and indicated by red dots. Recordings were performed referenced to the Pz electrode, and data
were recalculated by subtraction offline for a bipolar montage consisting of 66 nearest-neighbor electrode pairs (signified by the lines connecting
individual electrodes). Bipolar pairs were considered as nodes of a brain network, with the nodes located at the midpoint between the electrode pairs
shown (indicated by the blue dots and the oval labels in 1B). Coherence was calculated between all pairs of nodes as described in the methods.
doi:10.1371/journal.pone.0032508.g001
Table 1. Mean age, gender and handedness ratios, and HamD
17
scores for MDD and healthy control subjects.
MDD subjects (n = 121) Healthy controls (n = 37) Test Statistic, p-value
Age mean yrs. (SD) 41.5 (12.6) 37.4 (13.4) t
(156)
= 21.73, p = .09, N.S.
Gender (F:M) 75:46 20:17 Chi-square = .73, p = .39, N.S.
Handedness (R:L:ambi) 99:21:1 33:4:0 Chi-square = 1.09, p = .58, N.S.
HamD
17
mean(SD) 21.9 (3.6) 0.70 (0.97) Student t-test p,E-22
doi:10.1371/journal.pone.0032508.t001
Brain Functional Connectivity in Major Depression
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Page 3
in Figure 1A, and are labeled within the ovals in Figure 1B. The
data then were segmented into 2-second non-overlapping epochs,
and any epochs containing eye movement, muscle, or movement-
related artifacts, or amplifier drift were removed using a
semiautomated interactive process. Two technologists inspected
the data independently using multiple bipolar and referential
montages, and isolated and removed data segments containing
artifacts. In addition, data were processed using the BVA artifact
rejection module that removed data according to standard
thresholds likely to represent artifact based upon voltage step
gradient (i.e., 100
mV), absolute values of difference within the
epoch, and persistent low activity.
Power and coherence calculations
The power spectral density of the artifact-free bipolar pair EEG
data was calculated using the BVA fast Fourier transform (FFT)
function. The 512-point FFT was calculated for artifact-free two-
second epochs with a rectangular window, DC de-trending
applied to each segment of data, and 0.5 Hz overlap at the limits
of the band, yielding a frequency resolution of 0.5 Hz. Power was
calculated in four frequency bands, corresponding to delta (0.5–
4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–20 Hz), for all
nearest neighbor bipolar pairs of electrodes. For the purposes of
these analyses, each pair of nearest neighbor electrodes represents
a node of a brain network. The 66 nodes were mapped onto a
Cartesian projection of the head in a two-dimensional plane, with
each node located at the geometric midpoint (indicated by blue
dots) between the two individual electrodes (indicated by red dots)
in Figure 1B. This mapping allowed calculation of the relative
physical distance between nodes in Cartesian coordinate space
relative to the origin (in this case, the location of electrode Cz).
qEEG coherence is a measure of the consistency of the phase
relationship between two signals and uses surface EEG to make
inferences about underlying brain functional connectivity [49].
Coherence was calculated between pairs of nodes, and represents a
normalized measure of the functional coupling between the signals
at the nodes at any given frequency [60–62]. Coherence was
calculated as a function of the power spectral outputs for the
signals from the separate nodes for each frequency l:
C
x,yðÞ
IðÞ~
S
xy IðÞ
2
S
xIðÞ
S
yIðÞ
or the square of the cross-spectrum of the two signals x and y
divided by the product of the spectra of the individual channels, at
the frequency l. This procedure yields a real number between 0
(no coherence) and 1 (maximal coherence). Coherence values from
individual bins within a frequency band were averaged to obtain
the coherence value for that band.
Data analysis
Weighted network analysis. The data were analyzed
according to the principles of weighted network analysis
(WGCNA) using the methods implemented in the WGCNA R
package [56,63]. Weighted networks preserve the continuous
nature of the underlying coherence information and do not
require one to choose a threshold value. For the network analyses
performed here, the relative locations of the electrodes were
mapped in Cartesian coordinate space with electrode Cz at the
origin, and each electrode’s coordinates specified as T
x,y
relative to
the origin. Nodes were formed from pairs of adjacent electrodes,
such that the 35 individual electrodes yielded 66 separate nodes.
Nodes N
X,Y
were specified as located at the midpoint between the
two electrodes T
X,Y
and T
X9,Y9
comprising the node (Figure 1), with
the locations N
X
and N
Y
calculated as:
N
X
~ T
X
zT
X
ðÞ
=
2 and N
Y
~ T
Y
zT
Y
ðÞ
=
2
Each pair of nodes was considered a connection or network
‘‘edge’’ E
N
X ,Y
N
X
0
,Y
0
, where N
X,Y
and N
X9,Y9
represent the two
nodes whose connection comprised the edges, for a total of 2,145
edges between each pair of nodes in each frequency band. Each
edge was characterized by two complementary measures: 1)
connection strength CE
N
X ,Y
N
X
0
,Y
0
, which is the coherence value
between the two nodes; and, 2) connection length LE
N
X ,Y
N
X
0
,Y
0
,
which represents the physical distance between the two nodes
comprising the connection. Lengths were calculated from the
Cartesian coordinate map (Figure 1) according to the formula
LE
N
x,y
N
x,y
~
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
X{XðÞ
2
z Y{YðÞ
2
q
Finally, the degree of each node (also known as overall node
connectivity) was calculated, defined as the average of the
coherence values for a node with the other 65 nodes according
to the formula
DN
x,y
~
X
N
X,
_
YY
CE
N
X,Y
.
65
The median values of connection strengths (CE) were compared
for the two groups in each frequency band using the Kruskal
Wallis test. The median length values (LE) of those connections
that showed differences in strength also were compared for the two
groups in each frequency band using the Kruskal Wallis test. To
identify MDD related hub nodes, Student’s t-test was used to test
whether the mean node degree (DN) in MDD subjects differed
from that in controls. A strict Bonferroni correction of 2.33610
25
(0.05/2,145) was imposed on all analyses involving network
connections, and of 7.57610
24
(0.05/66) was applied to all
analyses involving hub nodes, to protect against false positive
findings. Locations of significant connections and hub nodes were
tabulated. Associations between significant edges, hub nodes, and
severity of depression (as measured by HamD
17
scores) and by age
were examined using Pearson correlations. Differences in edge and
hub node values by gender were examined using the Kruskal
Wallis test. In addition, other tests for statistical significance (e.g.,
the Kruskal Wallis test p-value and the q-value) were performed
and are reported as supplementary data (Table S1).
The nearest centroid analysis method as implemented in the
WGCNA R library [63], was used to determine which
combination of edges best characterized MDD subjects and
differentiated them from normal controls. Because the subject pool
was unbalanced with regard to group size (121 MDD versus 37
control subjects), the MDD subjects were divided into four datasets
reflecting their original study source (groups of 31, 29, 25, and 36
subjects), such that each of the datasets contained roughly the
same number of subjects and was comparable to the number of
control subjects. The final nearest centroid categorization used the
rankings from the four separate datasets and combined them with
the metaanalysis method implemented in the rankPvalue function
(pValueLowScale) of the WGCNA R library. Within each of the
four datasets, we performed five-fold cross validation in which the
data were split into five bins, with four of these used at any one
time as a training set and the remaining bin used as a test set. Edge
Brain Functional Connectivity in Major Depression
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Page 4
connectivity selection (based on the correlation test) in each of the
training sets was performed separately in order to avoid biasing the
results. Thus, each of the four datasets led to cross-validated
estimates of the classification accuracy (percentage of subjects
correctly classified). The four cross-validated estimates were
averaged to arrive at a final unbiased estimate of the classification
accuracy. Results of supervised clustering based on the most
significant edges that defined the nearest centroid predictor of group
membership were displayed in a hierarchical cluster tree map.
Results
MDD subjects showed statistically significantly greater connec-
tion strength CE (higher overall median coherence across all edges)
than controls in each of the four frequency bands, but most notably
in the beta band (Kruskal Wallis p = 0.000035) (Figure 2). The
topography of individual connections (edges) showing significantly
greater strength CE in MDD subjects is displayed by frequency
band in Figures 3A–D. In the delta and theta bands, relatively few
highly significant differences in connection strength were found
after applying Bonferroni correction. The most significant differ-
ences in CE in the delta band (17 edges) and theta band (42 edges)
were seen between the frontopolar and temporal regions. In the
theta band, highly significant edges also were found between the
frontopolar and parietooccipital regions, and between the temporal
regions bilaterally (Figures 3A–B). The alpha band contained the
greatest number of significantly different connections (141 edges)
and these linked brain regions that were more widely separated,
including connections between the frontopolar or DLPFC and the
temporal or parietooccipital regions bilaterally (Figure 3C). The
beta band also contained a large number of significantly different
connections (121 edges) that formed a dense network within the
frontal and temporal regions, both within and across hemispheres.
There were fewer differences in long distance connections between
the prefrontal and posterior regions in this band compared with
lower frequency bands (Figure 3D). Further detail on the differences
in connection strength CE between MDD and control subjects, with
results for each edge in each frequency band, are shown in Table
S1. This Table S1 reports details of Student’s-t and Kruskal Wallis
tests, associated p-values, and q-values. For all significant edges, the
mean coherence values were higher in the MDD than in the control
group.
The median physical length LE of those connections that
showed differences in strength also differed significantly across
frequency bands (p = 0.00001) (Figure 4). Edge length LE was
significantly greater in alpha than in any other band, and beta
length was significantly greater than that for the delta band.
A number of nodes were identified as hub nodes that had
significantly different degree DN (i.e., average coherence with all
other nodes) between the MDD group and normal controls
(Table 2). Two frontopolar hub nodes, Fp1-Fpz and Fp2-Fpz, met
the Bonferroni threshold for significance in each of the four
frequency bands. In the theta and alpha band, these same nodes
plus two DLPFC nodes, Af1-Fpz and Af2-Fpz, had significantly
higher degree DN in subjects with MDD. These four nodes
showed broadly higher connectivity CE in the alpha band with all
brain regions in MDD subjects compared with healthy controls. In
the beta band, these same four plus 21 additional hub nodes
showed greater connectivity in MDD than in controls. Across all
four frequency bands, no hub node had higher connectivity in
controls than in MDD subjects. Maps showing the median
connectivity CE between one of these nodes, Fp1-Fpz, and all
other nodes in all frequency bands for MDD and control subjects
separately are presented in Figure 5.
The nearest centroid classification analysis identified six edges in
the alpha band that best characterized the depressed state. Five of
these edges involved a hub node, with higher coherence values in
the MDD group: three of these edges connected pairs of nodes
between the left and right frontopolar and DLPFC regions (Af1-Fz
and Fp2-Fpz, Af1-Fpz and Af2-Fpz, and Fp1-Af1 and Fp2-Fpz),
one connected a pair between the left frontopolar and DLPFC
regions (Fp1-Fpz and Af1-Fpz), and one a pair between the right
frontopolar and right temporal regions (Fp2-Fpz and T4-Fc6). The
sixth edge connected a pair of nodes in the right parietooccipital
region which were not hub nodes, and for which coherence was
lower in the MDD group than controls (Po2-Pz and O2-Oz).
Supervised hierarchical clustering (Figure 6) shows that MDD
cases (shown in black in the top color bar) tend to cluster together,
indicating that the combinations of these edges discriminate cases
from controls. The cluster tree on the left side shows the
relationship among the edges. The bottom five edges (indicated
by green in the left color bar) are over-expressed in cases and are
positively correlated with each other. The sixth edge (Po2-Pz and
O2-Oz) is anti-correlated with the other five edges. This classifier
was cross-validated across the four sets of subjects, corresponding
to the four datasets that were pooled for this study. On average,
the classifier accurately distinguished 81% of MDD from healthy
control subjects.
There was no significant difference between the mean number
or values of significant edges or hub nodes between male and
female MDD subjects. There also was no significant association
between mean edge or hub node value and severity of depression
as measured by the 17-item Hamilton Depression Rating Scale, or
between edge or hub node value and age (data not presented).
Discussion
These results indicate that subjects with MDD differ signifi-
cantly from healthy control subjects in patterns of brain functional
connectivity. A large number of highly significant edges, in all
frequency bands, showed higher functional connectivity in MDD
as compared to controls. These differences were most notable in
the alpha and beta bands. The hub nodes most often involved in
increased connectivity were located in the frontopolar and DLPFC
Figure 2. Boxplots of median coherence for MDD and healthy
control groups (by frequency band). The short horizontal line
within each box shows the median values, and the notches represent
95% confidence intervals for the median values. Statistical significance
listed for each frequency band is based upon the Kruskal Wallis test.
doi:10.1371/journal.pone.0032508.g002
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Page 5
regions, although the patterns of connectivity involving these
nodes differed by frequency: in the alpha band, these nodes were
involved in significantly longer distance edges than in the beta
band. Examination of the most significant edges in the alpha band
showed that the connections were between the frontopolar or
DLPFC regions and the temporal or parietooccipital regions,
whereas in the beta band, the connections were most often within
the prefrontal, temporal, or less often the parietooccipital regions.
Figure 3. Map of connection strengths CE
N
X ,Y
N
X
0
,Y
0
showing significant differences between groups (by frequency band). Red lines
represent connections (edges) whose strength remained significantly different between MDD and control subjects after Bonferroni correction
(p#2.33610
25
). All red edges represent coherence values that were greater in the MDD group with line thickness proportional to the magnitude of
the difference. The nodes most commonly involved in significant edges across frequency bands were located in the prefrontal region.
doi:10.1371/journal.pone.0032508.g003
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Nearest centroid analysis indicated that six connections in the
alpha band, five of which showed higher connectivity between the
frontopolar and DLPFC or frontopolar and temporal regions, and
one of which showed lower connectivity within the parietooccipital
region, differentiated MDD from control subjects with 81%
accuracy.
The patterns of difference between MDD and control subjects,
which are consistent with earlier results from Fingelkurts and
colleagues [43], should be interpreted within the context of prior
research regarding the role of rhythmic oscillations in regulating
brain activity. Rhythmic activity overall helps to bind cell
assemblies together into functional units: lower frequency
oscillations (in the alpha and theta range) operate at a broader
level across the brain, binding more distant areas into functional
units through ‘‘top-down’’ control, and modulating the activity of
local functional units that are bound together by faster oscillations
[33,64–65]. The present findings are consistent with this
functional topography of alpha and beta oscillations in the brain.
Increased alpha coherence was observed in edges that span
relatively greater distances (e.g., between prefrontal nodes and
more distant temporal or parietooccipital regions), whereas
increased beta coherence was evidenced in shorter distance edges
(e.g., within frontal or temporal regions).
These findings, which suggest a broad loss of selectivity in
functional connections in MDD, are consistent with the reports of
Sheline and colleagues [20] as well the Zhou [21] and Greicius
[18] groups, which showed significant increases in resting-state
cortical functional connectivity in MDD using fMRI. The location
of the prefrontal hub nodes that showed the most frequent
involvement in increased coherence in the present study
approximately coincides with the dorsomedial prefrontal cortical
area found by Sheline’s group to constitute a ‘‘dorsal nexus’’ of
increased connectivity [20]. The fact that the most significant
increases in coherence were found in the alpha frequency band
could be interpreted as a failure of the top-down control exerted
by rhythmic alpha activity. This rhythm is generated by the cortex
under the influence of corticothalamic neuronal loops [66].
Greicius and colleagues showed significantly increased thalamic
Figure 4. Boxplots of edge lengths LE
N
X ,Y
N
X
0
,Y
0
of connections
that showed significant difference between groups (by fre-
quency band). Edge length was determined from the relative physical
distance between nodes on a two-dimensional plane as shown in
Figure 1B. Edges with significantly different connection strength
differed significantly in length across frequency bands (p = 0.00001).
Significance level represents the p value for the Kruskal Wallis test
examining the equality of the median edge length values between
groups. Short horizontal lines within boxes show the median edge
length, with notches indicating 95% confidence intervals of the
medians. Median edge length was significantly greater for alpha than
any other band. The width of the bars is proportional to the number of
edges that were significantly different between groups in the frequency
band: in the delta band, there were 17 significant edges; in theta, 42; in
alpha, 141; and in beta, 121.
doi:10.1371/journal.pone.0032508.g004
Table 2. Mean node connectivity
DN
(degree) for hub nodes
for MDD and control subjects.
Node p MDD Mean (± SE) Normal Mean (± SE)
Delta
Fp1-Fpz 0.00021 0.1 (0.0022) 0.085 (0.0026)
Fp2-Fpz 0.00034 0.1 (0.0022) 0.085 (0.0024)
Theta
Fp1-Fpz 7.90E-06 0.12 (0.0031) 0.091 (0.0036)
Af1-Fpz 5.00E-04 0.15 (0.0034) 0.13 (0.0055)
Fp2-Fpz 2.30E-05 0.12 (0.0031) 0.091 (0.0033)
Af2-Fpz 5.90E-05 0.15 (0.0033) 0.13 (0.0048)
Alpha
Fp1-Fpz 5.40E-08 0.16 (0.0062) 0.097 (0.0029)
Af1-Fpz 4.50E-06 0.2 (0.005) 0.15 (0.0073)
Fp2-Fpz 1.20E-08 0.16 (0.0059) 0.097 (0.0031)
Af2-Fpz 9.30E-06 0.2 (0.0054) 0.15 (0.0063)
Beta
Fp1-Fpz 1.50E-06 0.11 (0.0028) 0.087 (0.0027)
Af1-Fpz 3.30E-07 0.14 (0.0028) 0.11 (0.0033)
F3-Af1 1.00E-04 0.11 (0.0023) 0.095 (0.0026)
F7-Fc5 2.80E-04 0.12 (0.0024) 0.1 (0.0032)
F3-Fc1 2.40E-04 0.13 (0.0026) 0.11 (0.0041)
Fc1-Fz 4.00E-04 0.12 (0.0027) 0.11 (0.0032)
T3-Fc5 6.50E-04 0.13 (0.0024) 0.11 (0.0029)
T3-Cp5 7.30E-05 0.13 (0.0026) 0.11 (0.0029)
Cp5-C3 3.60E-04 0.14 (0.0026) 0.12 (0.0035)
Po1-Pz 7.80E-05 0.14 (0.0026) 0.12 (0.0039)
Po1-Oz 4.60E-50 0.14 (0.0024) 0.12 (0.0035)
Fp2-Fpz 8.70E-06 0.11 (0.0026) 0.088 (0.0024)
Af2-Fpz 4.30E-06 0.13 (0.0026) 0.11 (0.0036)
F8-F4 6.70E-04 0.13 (0.0025) 0.12 (0.0037)
Af2-Fz 1.50E-04 0.13 (0.0025) 0.11 (0.0032)
F8-Fc6 7.10E-05 0.12 (0.0023) 0.1 (0.0031)
F4f-C2 1.70E-04 0.13 (0.0025) 0.11 (0.0038)
Fc6-C4 3.00E-04 0.13 (0.0025) 0.12 (0.0032)
Fc2-Cz 6.80E-04 0.13 (0.0028) 0.11 (0.0034)
T4-Cp6 1.20E-04 0.13 (0.0026) 0.11 (0.0026)
C4-Cp2 3.90E-04 0.13 (0.0024) 0.12 (0.0035)
O2-Po2 5.00E-04 0.13 (0.0024) 0.12 (0.0038)
Hub nodes are those that had significantly different average coherence with all
other nodes between the two groups after Bonferroni correction. For each of
these nodes, connectivity was greater in the MDD subjects.
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functional connectivity with the default mode network at rest in
MDD, supporting the concept of dysfunction in the top-down
control circuit that is mediated by rhythmic alpha activity [18].
The increases in longer-distance alpha coherence could in turn
mediate the local increases seen in beta coherence; there is
significant cross-frequency interaction, such that top-down alpha
band oscillatory processes and bottom-up high frequency oscilla-
tory processes may be functionally coupled [67]. The possibility
that increased alpha coherence may in part be the result of a
bottom-up input from local processes in the beta frequency band,
however, cannot be ruled out.
The present findings establish a new context for interpretation
of previous studies showing differences in frontal alpha band
power and synchrony between subjects with MDD and normal
controls [34–36,38]. Studies have shown increases in synchronized
frontal alpha activity and qEEG alpha power, although the
lateralization has varied, with relatively greater alpha power
reported both over left and right anterior regions [34,36,68]. It is
possible that shifting power asymmetries previously reported [35]
may reflect the effects of significantly increased functional
connectivity in subjects with MDD. Recent results indicate that
interhemispheric interactions are related to shifting lateralization
on a moment-to-moment basis in MDD [69]. Future studies
should examine the role of increased connectivity in modulating
asymmetries in frontal power.
Few previous studies have assessed resting state functional
connectivity in MDD. Winterer and colleagues reported that
depressed alcoholic patients had significant increases in coherence
in the alpha and beta bands in the posterior regions, although
alcoholics without depression did not [70]. Fingelkurts and
colleagues examined the ‘‘index of structural synchrony,’’ a
different measure of signal synchronization, and found that
subjects with MDD had broad significant increases in alpha and
theta band functional connectivity [43]. These differences
consisted primarily of increased short distance functional connec-
tions in the left and long-range connections in the right
hemisphere. They interpreted these increases as adaptive and
compensatory mechanisms aimed at overcoming deficient seman-
tic integration. Hinrikus and colleagues found that depressed
subjects had increased coherence between some brain regions, but
examined only interhemispheric coherence between small num-
bers of locations and detected no statistically significant difference
[71]. Other studies of coherence have used methods that differ
from the current study, and have obtained disparate results. Knott
Figure 5. Maps showing the median connectivity
CE
(coherence) between hub node Fp1-Fpz and all other nodes in all frequency
bands, separately for MDD and healthy control subjects. This node demonstrates broadly higher median connectivity in the MDD subjects (A,
C, E, and G) compared to the control subjects (B, D, F, and H). Coherence values are indicated by the color bar on the left of the maps. Coherence
values decrease with distance from the hub node in both MDD and control subjects, but show greater decrease with distance in control subjects.
doi:10.1371/journal.pone.0032508.g005
Figure 6. Nearest centroid classification of MDD and healthy control subjects. Six edges (listed on the right) selected using nearest
centroid analysis classified subjects into MDD and control groups, with classification indicated by the dendrogram at the top of the figure. Individual
subjects are represented by the terminal branches of the dendrogram, with MDD subjects clustering toward the right (indicated by black bars in the
top row) and control subjects clustering toward the left (indicated by red bars) in the supervised cluster analysis. Data values for each subject are
indicated by a color column in the heatmap corresponding to a terminal branch. MDD subjects tended to have higher coherence values than controls
on edges involving frontopolar electrodes, while controls tended to have higher coherence on the edge involving parietooccipital electrodes
(indicated by green-to-yellow colors in the heatmap). As part of the clustering algorithm, the coherence values were scaled to have zero mean and
unit variance across the subjects (as shown in colorbar).
doi:10.1371/journal.pone.0032508.g006
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and colleagues [37] found decreased coherence in MDD subjects
compared to normal controls, but calculated coherence between a
limited number of individual electrodes, a technique that may not
characterize regional measures of brain activity as well as the
electrode pairs in the present study [61]. Armitage and colleagues
have examined coherence during sleep and shown that it is
decreased among adolescents with MDD, and is a predictor of
recurrence and risk of developing illness [72–74]. The relationship
between sleep and resting awake state coherence is unknown.
Greicius and colleagues speculated that the increased functional
connectivity in mood regulating networks might be associated with
impaired cognitive processing in MDD [18]. This speculation is
consistent with the established role of oscillatory activity in
regulating cognitive networks [32,75]. The ability to modulate
alpha rhythmicity and coherence has been linked to the ability to
shift and focus attention, and meet working memory and executive
demands [51,53–54,76]. Successful modulation of beta activity has
been related to response preparation and cognitive control [77–
78]; ‘‘pathological’’ increases in beta activity are associated with
deterioration in cognitive flexibility and control [78]. Several
neurophysiologic measures of synchronization, including coher-
ence, phase synchronization, and synchronization likelihood, have
been related to deficits on measures of attention and working
memory, as well as processing of auditory, visual, linguistic, and
social cognition information in psychiatric and neurologic illnesses
[75]. This wide range of cognitive activities overlaps with the
cognitive domains and functions that have been reported to be
deficient in some subjects with MDD [3,79]. Theta oscillations
play a significant role in memory function, with modulated
coupling of theta oscillations between the prefrontal, parietal, and
temporal cortices prominently involved in memory encoding and
recall [80–82]. In the present study, those edges showing
significantly increased coherence in the theta band involved
connections between prefrontal and temporal regions. These
connections may have special functional significance related to
memory dysfunction in MDD, and should be explored in future
studies.
Experimental data also link synchronization of neuronal
oscillations to the ability to process emotional information.
Kostandov and colleagues reported that processing of the
emotional content of facial expression was associated with
increases in coherence in the theta and alpha frequency ranges,
particularly involving the dorsolateral frontal and temporal
cortices [83]. Similarly, Balconi and colleagues found that
processing of positive and negative visual images, or masked
emotional facial expressions [84], was associated with increases in
coherence in the delta, theta, and alpha bands, depending on the
nature of the task and stimulus, and particularly from the frontal
regions. In addition to processing of emotional content, the
subject’s internal emotional state may be mediated by the degree
of synchronization. Andersen and colleagues reported that anxious
rumination in healthy volunteers was associated with increases in
theta and alpha band coherence [80]. This finding is consistent
with the results reported here that MDD is associated with an
increase in theta and alpha coherence, and also is consistent with
Greicius’ speculation that increased connectivity associated with
MDD may operate to the detriment of other types of brain
processing [18]. If networks are saturated with the load of
processing emotional information, there may be limited capacity
to modulate synchronization in response to other processing
demands.
Previous reports have highlighted disruption of brain regulatory
mechanisms in MDD, focusing on ‘‘hubs’’ of the mood regulatory
network such as the rostral anterior cingulate (rACC) [85] or the
dorsal nexus posited by Sheline and colleagues [20]. Disruption of
normal connectivity patterns could explain many of the regulatory,
cognitive, neurovegetative, and emotional symptoms of MDD
[75,86–87]. It remains unclear what fundamental mechanism
underlies and perpetuates network dysregulation. The current
results are consistent with a growing body of literature implicating
disturbed brain oscillatory activity in the pathogenesis of MDD
[42,88–90]. Modulation of cerebral oscillatory activity plays a
central role in regulation of mood, and processing of affective
information and emotional stimuli [91–93]. Interestingly, syn-
chronization of oscillatory activity is strongly influenced by central
serotonergic tone [89]. Serotonergic projections from the medial
septal area inhibit hippocampal theta oscillatory synchrony [94],
while alpha synchrony is modulated by serotonergic projections
from the raphe nuclei to the intralaminar and medial thalamic
nuclei [95]. Furthermore, oscillatory activity and related behaviors
are modulated by administration of antidepressant medication in
animals [94,96–97]. Oscillatory synchrony could represent the
neurophysiologic link between neurochemical activity and brain
network functions that regulate mood, affect, and processing of
emotional information. Oscillatory dysregulation may similarly
represent the pathophysiologic link between disturbances in
monoaminergic neurotransmission and brain network dysfunction
in MDD. Future research should more closely examine the
regulation of oscillatory synchrony in subjects at risk for or
recovering from MDD, as well as the effect of antidepressant
treatments on oscillatory synchrony in MDD.
There are several limitations to the current study. First, limited
information was available on the specific symptoms of the MDD
subjects and the number of prior episodes they may have had, so
we cannot relate the increased connectivity to specific subtypes of
the illness. Second, because all subjects in the present study either
were experiencing a current major depressive episode or were
healthy controls, it is unclear whether elevated connectivity would
resolve with treatment or it would be a persistent trait marker for
those with a predisposition to the illness. Third, in this study we
examined only a single measure of neurophysiologic connectivity,
coherence, which indicates the linear association between time-
series curves in a frequency band [60]. Absence of a statistical
association between two processes does not necessarily exclude a
physiologic connection [98–99]; conversely, presence of an
association does not necessarily indicate a physiologic connection,
as EEG signals show a finite correlation even when recorded from
separate subjects (secondary to the finite epoch time and similar
bandwidth of signal pairs) [100]. Finally, although there is strong
evidence of correspondence between surface EEG and brain
functional activity in underlying structures [26–29], EEG
coherence, like any metric derived from electrical recordings from
the scalp does not directly measure brain activity. Connectivity of
brain regions is inferred from electrical activity recorded at surface
sites overlying the various cortical regions.
There is no single technique that has proven to be ideal to study
the interaction between two brain signals from scalp recordings.
Coherence measures are susceptible to both volume conduction
and electrode reference effects [101–103], although in the present
study both effects were minimized through calculating coherence
from closely spaced bipolar electrode pairs [102]. This strategy
renders these confounding influences negligible for close bipolar
pairs separated from one another by more than 4–5 cm [104–
106], although volume conduction still may increase coherence for
shorter distances depending upon the frequency band and the
orientation of the dipole source [103]. It is highly unlikely,
however, that any of the differences reported between the MDD
and healthy control groups in the current study would arise from
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Page 10
volume conduction or reference effects because the electrode
montage and recording techniques were identical for both
depressed and control groups. Nevertheless, future studies also
should consider use of surface Laplacian [107] and Independent
Component Analysis (ICA) [108–109] EEG methods, as well as
phase synchrony [110] connectivity measures, that may help
further minimize the effects of volume conduction. Use of high-
density electrode arrays in future studies also would help to define
more clearly the brain regions showing differences in brain
connectivity between MDD and control subjects.
These findings indicate that resting state neurophysiologic
connectivity is increased broadly across all brain regions in MDD.
Future studies also should more closely examine clinical features of
subjects with MDD, including cognitive profiles, functional status,
and response to treatment in relation to connectivity measures, in
order to determine the possible role of increased functional
connectivity as a diagnostic or prognostic marker for MDD.
Supporting Information
Table S1 Supplementary data on mean differences in
connection strength between MDD and control subjects
across frequency bands. The rows of the table correspond to
the connections (edges) between nodes. For each frequency band,
the columns report the following measures: the Pearson correla-
tion between edge coherence and MDD status; the Student t-test
statistic and p-value; the fold change defined as mean value in
MDD cases divided by the mean value in controls; the mean value
in the first group (i.e., MDD cases) and the corresponding standard
error; and, the value for the Kruskal Wallis test, which is a non-
parametric group comparison test that does not assume normality.
The q-value represents the expected False Discovery Rate (FDR),
controlled with the local FDR method/algorithm [111].
(XLS)
Acknowledgments
We acknowledge the technical support of Don Vince-Cruz for obtaining
EEGs on our research subjects, the administrative support of Kelly Nielson
and Jennifer Villalobos in project administration, and Melody Tran and
Jennifer Villalobos in preparation of the manuscript.
Author Contributions
Conceived and designed the experiments: AFL IAC AMH CC SH.
Performed the experiments: AFL IAC. Analyzed the data: AMH CC.
Contributed reagents/materials/analysis tools: AFL IAC AMH CC SH.
Wrote the paper: AFL IAC AMH CC SH.
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    • "Studies in animals and in humans have demonstrated that cerebral regions, when engaged in shared tasks, start synchronizing the neuronal firings of involved networks [42][43][44]. This behavior can be hampered by several pathological conditions that shift synchronization levels toward lower values, such as in acute stroke patients [45] or even toward higher values, such as in depression, where an augmented functional connectivity within the anterior medial cortex [46] and an overall greater coherence in the resting state are found [47]. Alternatively and non-mutually exclusive, the increase of synchronization in our cohort can also result from an increment in the number of recruited neurons during movement, as a probable compensatory mechanism of MS fatigue directed to achieve the assigned task, which in turn induces the perception of fatigue. "
    [Show description] [Hide description] DESCRIPTION: Fatigue in multiple sclerosis (MS) is a highly disabling symptom. Among the central mechanisms behind it, an involvement of sensorimotor networks is clearly evident from structural and functional studies. We aimed at assessing whether functional/structural balances of homologous sensorimotor regions—known to be crucial for sensorimotor networks effectiveness—decrease with MS fatigue increase. Functional connectivity measures at rest and during a simple motor task (weak handgrip of either the right or left hand) were derived from primary sensorimotor areas electroencephalographic recordings in 27 mildly disabled MS patients. Structural MRI-derived inter-hemispheric asymmetries included the cortical thickness of Rolandic regions and the volume of thalami. Fatigue symptoms increased together with the functional inter-hemispheric imbalance of sensorimotor homologous areas activities at rest and during movement, in absence of any appreciable parenchymal asymmetries. This finding supports the development of compensative interventions that may revert these neuronal activity imbalances to relieve fatigue in MS.
    Full-text · Research · Apr 2016
  • Source
    • "Studies in animals and in humans have demonstrated that cerebral regions, when engaged in shared tasks, start synchronizing the neuronal firings of involved networks [42][43][44]. This behavior can be hampered by several pathological conditions that shift synchronization levels toward lower values, such as in acute stroke patients [45] or even toward higher values, such as in depression, where an augmented functional connectivity within the anterior medial cortex [46] and an overall greater coherence in the resting state are found [47]. Alternatively and non-mutually exclusive, the increase of synchronization in our cohort can also result from an increment in the number of recruited neurons during movement, as a probable compensatory mechanism of MS fatigue directed to achieve the assigned task, which in turn induces the perception of fatigue. "
    [Show description] [Hide description] DESCRIPTION: Journal of Neurology. 2014. DOI 10.1007/s00415-014-7590-6
    Full-text · Research · Apr 2016
    • "Aside from the question about the underlying mechanisms of 2–4 Hz coherence changes is the question of how these observations can be used to inform clinical diagnosis or treatment. Previous work using electroencephalograms (EEGs) to examine inter-region coherence in depressed versus control patients have yielded complex results, with differences found in the proximity of electrode pairs, their hemisphere, and frequency bands (Fingelkurts et al., 2007; Leuchter et al., 2012). The effects on EEG of treatment with SCG DBS are also complex, although generally appear to make depressed brains appear more similar to non-depressed controls (Broadway et al., 2012; Quraan et al., 2014). "
    [Show abstract] [Hide abstract] ABSTRACT: Deep Brain Stimulation (DBS) of the subgenual cingulate gyrus (SCG) has been used to treat patients with treatment-resistant depression. As in humans, DBS applied to the ventromedial prefrontal cortex of rats induces antidepressant-like responses. Physiological interactions between structures that play a role in depression and antidepressant treatment are still unknown. The present study examined the effect of DBS on inter-region communication by measuring the coherence of local field potentials in the rat infralimbic cortex (IL; homologue of the SCG) and one of its major afferents, the ventral hippocampus (VH). Rats received daily IL DBS treatment (100μA, 90μs, 130Hz; 8h/day). Recordings were conducted in unrestrained, behaving animals on the day before treatment, after 1 and 10days of treatment, and 10days stimulation offset. VH-IL coherence in the 2-4Hz range was reduced in DBS-treated animals compared with shams after 10days, but not after only 1day of treatment. No effect of DBS was observed in the 6-10Hz (theta) range, where coherence was generally high and could be further evoked with a loud auditory stimulus. Finally, coherence was not affected by fluoxetine (10mg/kg), suggesting that the effects of DBS were not likely mediated by increased serotonin levels. While these data support the hypothesis that DBS disrupts communication between regions important for expectation-based control of emotion, they also suggest that lasting physiological effects require many days of treatment and, furthermore, may be specific to lower-frequency patterns, the nature and scope of which await further investigation. Copyright © 2015. Published by Elsevier Inc.
    No preview · Article · Apr 2015 · Experimental Neurology
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