! ! ! !
125!|!www.neuroregulation.org Vol. 4(3–4):125–137 2017 doi:10.15540/nr.4.3-4.125
Small-World Network Analysis of Cortical Connectivity
in Chronic Fatigue Syndrome Using Quantitative EEG
Mark A. Zinn1*, Marcie L. Zinn1, and Leonard A. Jason1
1Center for Community Research, DePaul University, Chicago, Illinois, USA
The aim of this study was to explore the relationship between complex brain networks in people with Chronic
Fatigue Syndrome (CFS) and neurocognitive impairment. Quantitative EEG (qEEG) recordings were taken from
14 people with CFS and 15 healthy controls (HCs) during an eye-closed resting condition. Exact low resolution
electromagnetic tomography (eLORETA) was used to estimate cortical sources and perform a functional
connectivity analysis. The graph theory approach was used to characterize network representations for each
participant and derive the “small-worldness” index, a measure of the overall homeostatic balance between local
and long-distance connectedness. Results showed that small-worldness for the delta band was significantly
lower for patients with CFS compared to HCs. In addition, delta small-worldness was negatively associated with
neurocognitive impairment scores on the DePaul Symptom Questionnaire (DSQ). Finally, delta small-worldness
indicated a greater risk of complex brain network inefficiency for the CFS group. These results suggest that CFS
pathology may be functionally disruptive to small-world networks. In turn, small-world characteristics might serve
as a neurophysiological indicator for confirming a biological basis of cognitive symptoms, treatment outcome, and
neurophysiological status of people with CFS.
Keywords: chronic fatigue syndrome; myalgic encephalomyelitis; qEEG; eLORETA; electrical neuroimaging;
lagged coherence; functional connectivity; graph theory; complex networks; small-world
Citation: Zinn, M. A., Zinn, M. L., & Jason, L. A. (2017). Small-world network analysis of cortical connectivity in Chronic Fatigue Syndrome
using quantitative EEG. NeuroRegulation, 4(3–4), 125–137. http://dx.doi.org/10.15540/nr.4.3-4.125
*Address correspondence to: Mark A. Zinn, Center for Community
Research, DePaul University, 990 W. Fullerton Avenue, Chicago, IL
60614-3504, USA. Email: email@example.com
Copyright: © 2017. Zinn et al. This is an Open Access article
distributed under the terms of the Creative Commons Attribution
Rex L. Cannon, PhD, Knoxville Neurofeedback Group, Knoxville,
Wesley D. Center, PhD, Liberty University, Lynchburg, Virginia, USA
Randall Lyle, PhD, Mount Mercy University, Cedar Rapids, Iowa,
Chronic fatigue syndrome (CFS) is a complex multi-
system disease characterized by unexplained
persistent or relapsing fatigue, post-exertional
malaise, flu-like symptoms, and neurocognitive
impairments not relieved by rest and worsened by
physical or mental activity (Carruthers et al., 2003;
Fukuda et al., 1994). Neurocognitive impairment
is a hallmark symptom in CFS (Jason, Zinn, & Zinn,
2015) and one of the primary factors involved in the
etiology of the condition (Johnson, DeLuca, &
Natelson, 1996). Approximately 90% of
patients with CFS report having cognitive symptoms,
anecdotally referred to in the clinic as “brain fog,”
profoundly affecting health and quality of life
(Grafman et al., 1993; Hopkins & Jackson, 2006;
Komaroff & Buchwald, 1991; Ocon, 2013). A meta-
analysis found cognitive deficits in CFS pertaining to
memory, attention, and information processing
speed, particularly during sustained working memory
tasks (Cockshell & Mathias, 2010). In addition,
patients have been shown to have slower reaction
times in many studies (Busichio, Tiersky, DeLuca, &
Natelson, 2004; Constant et al., 2011; Majer et al.,
2008; Thomas & Smith, 2009; Van Den Eede et al.,
2011), particularly under conditions of increasing
task complexity (Dobbs, Dobbs, & Kiss, 2001).
DeLuca, Johnson, and Natelson (1994) proposed
that most memory deficits seen in patients are due
to slower information processing rather than
impairment in storage/retrieval mechanisms.
Zinn et al. NeuroRegulation! !
126!|!www.neuroregulation.org Vol. 4(3–4):125–137 2017 doi:10.15540/nr.4.3-4.125!
Functional MRI studies have observed that patients
with CFS show signs of brain compensation in
response to verbal working memory tasks (Cook,
O'Connor, Lange, & Steffener, 2007; Lange et al.,
2005). This suggests that dynamic reorganization of
brain network topology in CFS with subsequent
reductions in neural efficiency could be contributing
to cognitive impairment indirectly. Thus, examining
changes in overall brain information processing
speed and neural efficiency factors in CFS may
elucidate the relationship between cortical
dysregulation and cognitive symptoms.
Knowledge of general principles of self-organization
in real-word systems has prompted a paradigm shift
in neuroscience away from localization of brain
responses toward a deeper understanding of brain
connectivity influences on information processing
efficiency (Sporns, 2013). In the past decade, graph
theoretical analysis has been increasingly used in
neuroscience as a framework for understanding how
dynamic processes are involved in the emergence of
cognition and behavior (Menon, 2012; Stam, 2014).
This approach has a number of distinct advantages
which allow researchers to: 1) quantify and model a
wide range of varying network attributes, 2)
characterize the balance of local and global trade-
offs that operate within systems, 3) examine
weakened elements of the system and
compensatory dynamics responding to pathological
processes, and 4) simultaneously account for
relationships between all the network elements and
a given cognitive function (Rubinov & Sporns, 2010).
In this sense, the application of graph theoretical
analysis can extend our understanding of the key
aspects of brain function in patients with CFS.
Complex networks are ubiquitous to the real world
(e.g., social networks, airline routes, power grids,
protein networks; Watts & Strogatz, 1998), and the
brain itself is a complex network comprised of many
subnetworks of distributed brain regions which
instigate even the most basic behaviors (Deco, Jirsa,
& Friston, 2012; Sepulcre, 2014; Stam, 2010). The
coordinated activity within complex networks of the
brain gives rise to fundamental aspects of
neurocognitive domains involving attention,
perception, memory, language, and motor
processing (van den Heuvel & Sporns, 2013; Wig,
Schlaggar, & Petersen, 2011). A homeostatic
balance exists within complex brain networks
between random neuronal growth processes and
activity-dependent modification of those processes
(Minati, Varotto, D'Incerti, Panzica, & Chan, 2013).
This state of affairs can be explained in terms of
parsimony; there is a continual drive in the system to
negotiate trade-offs to the costs involved in
supporting and to create adaptively valuable
functional connectivity (Bullmore & Sporns, 2012).
The number of connections in the system is
relegated by wiring cost (biological energy and
materials), and there are evolutionary reasons for
keeping the demand for long distance connections,
which are more “expensive,” to a minimum (Stam,
2010). Peculiar trade-offs in the topological
properties of complex brain networks can therefore
serve as a marker for specific neurobiological
adaptions to the CFS condition, modeling disease
course and spread, aberrant plasticity, indexing
overall information processing efficiency—all of
which could aid clinical diagnosis of patients and
even identify clinical subtypes (Crossley et al.,
The “small-world” network model was introduced in a
landmark study by Watts and Strogatz (1998)
demonstrating for the first time that small-world
properties exist in central nervous systems. The
topology of small-world networks is characterized by
high clustering (segregation) and short path lengths
(integration), representing a homeostatic balance
between local and global processing in order to
satisfy opposing demands which maximize
processing speed at minimal neurobiological energy
cost (Sporns & Honey, 2006). Segregation refers to
the tendency of nearest neighbor elements to cluster
together, whereas integration refers to the amount of
interconnectedness and efficient information
exchange within the entire network. The clustering
coefficient is a measure of functional segregation or
local connectedness, whereas the characteristic
path length is a measure of functional integration
describing global, large-scale activity and
coactivation of neuronal populations within the
network (Telesford, Simpson, Burdette, Hayasaka, &
Laurienti, 2011). The clustering coefficient and the
characteristic path length constitute properties of the
small-world network model. Taken together, they
are an indicator of small-worldness, an index
representing the suitable balance between functional
integration and segregation of dynamic system
organization (Humphries & Gurney, 2008; Stam,
2010; Thatcher, 2016; van Straaten & Stam, 2013).
Kim et al. (2015) demonstrated small-world
abnormality in CFS using resting-state fMRI to
examine a sample of 18 women with CFS and 18
age-matched female controls. They assessed
global efficiency, the inverse of the mean shortest
characteristic path length, relating to the functional
efficiency of information flow between any two nodes
in the network. They also assessed local efficiency
Zinn et al. NeuroRegulation! !
127!|!www.neuroregulation.org Vol. 4(3–4):125–137 2017 doi:10.15540/nr.4.3-4.125!
which quantifies the fault tolerance of the network
proportional to the clustering coefficient (Bassett &
Bullmore, 2006). They found that global efficiency
(integration) was lower in CFS compared to the HC
group, while there were no differences in local
efficiency (segregation). Increased demand for long
distance connections in CFS suggests there is an
added wiring expense for compensatory systems
which negatively affects global efficiency of the
network information processing. The degree of
perturbation to small-world dynamics was linked to
the amount of neurocognitive impairment in patients
and brain processes found to be compromised
reflected an underlying disturbance to small-world
propensity. However, these investigators did not
examine small-worldness, an overall indicator of
optimal brain functioning and neural efficiency and
neurocognitive impairment (e.g., memory, attention,
slow thought, etc.) may represent a combination of
pathology in the overall small-worldness measure
with the concomitant overt behavioral changes in
Quantitative electroencephalography (qEEG)
involves numeric analysis of local field potentials
resulting from the summation of neuronal electrical
activity that arises from the cell bodies and
associated dendrites of large populations of
synchronously active cortical pyramidal neurons
(Niedermeyer & Lopes da Silva, 2005). The
electrical currents are dependent on the integrity of
the neural sodium/potassium and calcium ion
pumps, reflecting metabolic activity and rendering
qEEG a useful tool for quantifying and exploring
electrophysiological correlates of both normal and
abnormal neurological function (Thatcher, 2016).
The frequency, phase, and amplitude of band-limited
EEG oscillations relates to the specific information
processing taking place at different spatiotemporal
scales at any given moment (Le Van Quyen, 2011).
Higher order cognitive processes appear to call
upon even more temporal precision for sustained
neuronal activity between neuronal populations
(Nunez, Srinivasan, & Fields, 2015). Temporal
resolution of qEEG on a millisecond timescale allows
fine-grained detection of subtle differences in speed
and efficiency within the relay of information flow via
cooperative sequencing of oscillatory patterns and
their phase differences (Buzsáki & Freeman, 2015;
Steriade, 2005; Thatcher, North, & Biver, 2008).
This is important given that even the most basic
cognitive processes depend on precise timing of
phase relationships in the brain occurring through
large populations of spontaneously synchronized
neurons communicating among distributed brain
regions (Buzsáki, 2006; Sauseng & Klimesch, 2008;
Steriade & Paré, 2006).
Tomographic EEG methods (electrical
neuroimaging) use inverse methods to accurately
map current source density in a three-diminensional
brain volume, allowing the ability to visualize EEG
abnormality in deeper brain structures (Grech et al.,
2008; Thatcher, 2016). A growing number of studies
are using electrical neuroimaging methods to
elucidate information processing in the brain and
small-world network organization in response to
neurological conditions including epilepsy
(Adebimpe, Aarabi, Bourel-Ponchel,
Mahmoudzadeh, & Wallois, 2016; Vecchio, Miraglia,
Curcio, Della Marca, et al., 2015), multiple sclerosis
(Vecchio et al., 2017), and Alzheimer’s disease
(Hata et al., 2016; Vecchio, Miraglia, Curcio,
Altavilla, et al., 2015; Vecchio et al., 2016). A
comprehensive review on the role of electrical
neuroimaging techniques for studying the brain in
CFS can be found in Jason, Zinn, et al. (2015).
Using low resolution electromagnetic tomography
(LORETA) to investigate 17 monozygotic twins with
one twin with CFS vs. one healthy co-twin, Sherlin et
al. (2007) showed that twins affected with CFS had
increased delta sources in the left uncus and
parahippocampal gyrus, deeper structures of the
limbic system. Sherlin et al. also found higher theta
sources in the cingulate gyrus and right superior-
frontal gyrus. Using eLORETA (where “e” stands for
exact), Zinn et al. (2014) found significantly elevated
delta sources in a widespread portion of the frontal
lobe and limbic lobe as well as decreased beta
sources in the parietal lobe bilaterally. Higher delta
sources were also associated with the reduced
motivation scores on the Multidimensional Fatigue
Inventory, a measure of fatigue severity commonly
used in CFS studies. Increased delta in limbic
structures is consistent with the findings of Sherlin et
al., and rhythmic alterations in these regions could
be indicators of blunted emotional processing in
CFS possibly related to reduced motivation and
attentional difficulties. Interestingly, symptoms
manifested by brain pathology within the medial
prefrontal cortex, anterior cingulate, and orbitofrontal
cortex are largely undetected by most traditional
neuropsychological tests (Koziol & Budding, 2009).
Finally, using a Beamformer source analysis
method, Flor-Henry, Lind, and Koles (2010) found
sources that were globally reduced in the alpha and
beta bands in those with CFS (delta band was not
examined). Together, the various qEEG and
tomographic EEG investigations mentioned here
Zinn et al. NeuroRegulation! !
128!|!www.neuroregulation.org Vol. 4(3–4):125–137 2017 doi:10.15540/nr.4.3-4.125!
demonstrate a relationship between EEG and CFS
which lay the foundations for this study.
Zinn, Zinn, and Jason (2016) performed eLORETA
functional connectivity analysis in CFS to examine
three fundamental neurocognitive networks based
on Menon’s triple network model of brain pathology
(Menon, 2011). This model posits there are three
primary large-scale brain networks that operate
dynamically to regulate shifts in arousal, attention,
and general access to cognitive abilities. It includes
the central executive network, salience network, and
the default mode network and predicts that aberrant
activity within any one of these networks will
significantly impact the other two networks resulting
in pathological states. Using lagged phase
synchronization (Pascual-Marqui, 2007a),
hypoconnectivity was found in the delta and alpha
frequency bands between nodes for all three
networks in the group with CFS in comparison to
health controls. This finding is consistent with
several functional connectivity studies using
magnetic resonance which reported decreased
connectivity involving key nodes of the salience
network (Boissoneault et al., 2016; Gay et al., 2016;
Wortinger et al., 2016). Disruptions to the salience
network could underlie primary cognitive symptoms
in CFS involving attention to internal/external events
and adaptive engagement of systems responsible
for processing of working memory and executive
control. The above findings show that functional
connectivity approaches including electrical
neuroimaging methods are promising avenues for
studying brain dysfunction in CFS.
The present study addressed the question of
whether fundamental neurobiological relationships
and adaptions could underlie cognitive symptoms in
CFS. Our primary hypothesis was that patient
networks would show deviations from normal in
small-world network characteristics as measured by
the small-worldness index, thus demonstrating a
pathological imbalance affecting network efficiency
and information processing due to the trade-offs
associated with adaptive reconfiguration of network
topology in CFS. Using graph theoretical analysis of
small-world networks with eLORETA connectivity
data was used for exploring the linkage of brain
topology with cognitive impairments that are
commonly associated with CFS (John, 2005; van
Straaten & Stam, 2013). Secondly, changes in the
small-worldness index were hypothesized to be
associated with subjective levels of cognitive
impairment due to maladaptive reconfigurations in
network topology needed for supporting efficient
brain processing in patients with CFS. Lastly, the
small-worldness index was tested as a way to look
at risk in patients with CFS compared to HC
participants. At the present time, there is no
physiological marker that represents risk for
neurocognitive impairment in patients with CFS.
Having an accurate method for identifying risk of
cognitive impairment in CFS would help establish
the utility for this approach for identifying
epidemiological factors relating to patient health.
The participants in this investigation were 29 adults
(14 individuals with CFS, 15 HCs) ranging in age
from 20 to 80 years old and the mean age was
43.97 years (SD = 20.32). The effects of age were
statistically adjusted since the mean age between
groups was significantly different and physiological
aging is a significant factor within the EEG (Kirk,
2013; Rossini, Rossi, Babiloni, & Polich, 2007;
Vysata et al., 2014). All participants visited the
Center for Community Research at DePaul
University to have their EEG recorded. The
participants with CFS all met the Fukuda criteria
(Fukuda et al., 1994) and they had been diagnosed
with CFS by their physician. No participants were
taking medications that would affect the EEG. This
study was approved by the Institutional Review
Board at DePaul University in Chicago.
Eyes-closed, resting state EEG data for each
participant was recorded for 5 min from 19 electrode
locations (Fp1, Fp2, F3, F4, F7, F8, Fz, C3, C4, Cz,
P3, P4, Pz, T3, T4, T5, T6, O1, and O2) positioned
on the scalp according to the international 10/20
system using standardized electrode caps (Jurcak,
Tsuzuki, & Dan, 2007) with a linked-ears reference.
During cap preparation, impedances for all electrode
sites were measured and brought to within 5 kΩ.
Once cap preparation was completed, participants
were shown their raw EEG signals and trained to
minimize artifact by relaxing muscles in their
forehead, jaws, and face to the best of their ability
while they observed corresponding changes in the
raw EEG. The data collection apparatus involved
Neuroguide qEEG signal processing software
(Version 2.8.7, 2016) together with the BrainMaster
Discovery 24 (Bedford, OH) qEEG acquisition
module, which allows up to 19 channels of EEG
signals to be recorded simultaneously at 256 Hz.
During the EEG recording session, each participant
was seated upright in a comfortable chair in a room
that was well lit. Participants were given instructions
to relax to the best of their ability while keeping their
Zinn et al. NeuroRegulation! !
129!|!www.neuroregulation.org Vol. 4(3–4):125–137 2017 doi:10.15540/nr.4.3-4.125!
eyes closed until the recording session has ended.
EEG data were acquired at a 256 Hz sampling rate
and filtered offline between 1 and 40 Hz. Artifact
removal procedures were as follows: 1) visual
inspection and manual deletion of visible artifact by
an EEG technician; 2) automated Z-score artifact
removal using rejection algorithms built into
Neuroguide set for high sensitivity at two standard
deviations for immediate exclusion of EEG segments
with eye movement, muscle, and drowsiness artifact;
and 3) second visual inspection and manual deletion
of the artifact by an EEG technician. Since this
study was directed toward understanding changes in
phase relationships of the original time-series data,
independent components analysis (ICA) was not
performed. ICA/regression procedures intended to
remove artifact actually produce distortion of phase
relationships between channels by reconstructing
the EEG time series. This methodological problem,
which essentially invalidates the EEG data, has
been empirically proven in several studies
(Castellanos & Makarov, 2006; Kierkels, van Boxtel,
& Vogten, 2006; Wallstrom, Kass, Miller, Cohn, &
Fox, 2004). The EEG segments that were included
for analysis showed greater than 95% split-half
reliability and greater than 90% test–retest reliability
coefficients instantaneously computed by
Neuroguide, and each record had a minimum total
edit time of at least 1 minute. For each participant,
the artifact-free data were then fragmented into 2-
sec EEG segments. Due to theoretical
considerations, all analyses were limited to the delta
(1–3 Hz), alpha-1 (8–10 Hz), and alpha-2 (10–12
Hz) frequency bands. Each frequency band
provides an added layer of physiological significance
to brain function.
All participants completed the DePaul Symptom
Questionnaire (DSQ; Jason, So, Brown, Sunnquist,
& Evans, 2015), and data for the DSQ were
collected and managed using the Research
Electronic Data Capture (REDCap) hosted at
DePaul University (Harris et al., 2009). The DSQ is
a self-report instrument that measures 54 symptoms
related to criteria specified in the CDC criteria
(Fukuda et al., 1994), the Canadian Criteria for
ME/CFS (Carruthers et al., 2003), and the CFS
International Consensus Criteria (Carruthers et al.,
2011). For each symptom item, respondents are
asked to separately rate the frequency and severity
over the last 6 months on a 5-point Likert scale (0 =
none of the time, 1 = a little of the time, 2 = about
half the time, 3 = most of the time, and 4 = all of the
time). The DSQ has good test–retest reliability with
Pearson’s correlation coefficients above 0.70 and
test–retest correlations for classified symptom
categories (fatigue, post-exertional malaise,
neurocognitive, and autonomic) at 0.80 or higher
(Jason, So, et al., 2015). Results of factor analysis
on the DSQ support at least three distinct symptom
factors: 1) post-exertional malaise, 2) neurocognitive
dysfunction, and 3) neuroendocrine/autonomic
/immune dysfunction (Jason, Sunnquist, et al.,
2015). Murdock et al. (2016), an independent group
using the DSQ, found that it demonstrated excellent
internal reliability and that among patient-reported
symptom measures it optimally differentiated
between patients and controls. The cognitive
variable of this proposal was the aggregate average
of nine items that fall under the neurocognitive
dysfunction factor: problems remembering things,
difficulty paying attention for a long period of time,
difficulty with word finding or expressing thoughts,
difficulty understanding things, only able to focus on
one thing at a time, unable to focus vision attention,
slowness of thought, absentmindedness or
forgetfulness, and loss of depth perception (Jason,
Sunnquist, et al., 2015).
Functional connectivity was analyzed using
coherence, a measure of the consistency of phase
differences in the time-series corresponding to
different spatial locations (Lehmann, Faber, Gianotti,
Kochi, & Pascual-Marqui, 2006; Pascual-Marqui,
2007a, 2007b). Coherence is interpreted as an
indicator of “connectivity” which quantifies the
degree to which phase differences remain stable
over time either between electrode sites, when
measured at the scalp when using surface EEG
(Buzsáki & Watson, 2012; Klimesch, Freunberger,
Sauseng, & Gruber, 2008; Thatcher, 2016), or
between two brain regions, in the case of eLORETA
(Pascual-Marqui et al., 2011). An advantage of
eLORETA is that it uses lagged coherence, a
specialized measure of functional connectivity that
controls for physiological artifact by removing zero-
lag contributions from volume conduction and spatial
blurring effects (Pascual-Marqui, 2007a, 2007c).
Functional connectivity analyses of coherence was
conducted using the LORETA-KEY software
package (Pascual-Marqui, 2015). This software is
freely provided for download by the KEY Institute for
Brain-Mind Research at http://www.uzh.ch/keyinst
/loreta.htm. eLORETA is based on the stereotactic
space provided by the Montreal Neurological
Institute (MNI) template and offers a highly accurate
estimate of the intracortical current source density
within a three-dimensional cortical volume consisting
of 6,239 voxels of unambiguous grey matter at 5
mm3 spatial resolution. Complete mathematical
details of this inverse solution are provided in
Zinn et al. NeuroRegulation! !
130!|!www.neuroregulation.org Vol. 4(3–4):125–137 2017 doi:10.15540/nr.4.3-4.125!
Pascual-Marqui et al. (2011). To obtain a
topographic view of the whole cortex, coordinates
were computed for 42 separate Brodmann areas
(BAs: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 17, 18, 19
20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34,
35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47) for
the left and right hemispheres (84 total ROIs) using
a single voxel to define each ROI centroid. Given
that eLORETA has low spatial resolution based on
the spatial smoothness assumption, the single
center voxel is considered an accurate
representation of activity within the ROI while
minimizing the possibility of signal contamination
from neighboring ROIs.
eLORETA lagged coherence was then calculated for
all 84 ROIs for each participant, generating text files
with output containing a separate weighted 84 x 84
coherence matrix for each frequency band. The
coherence matrix contains the entire set of network
connections whereby each cell has a value
representing the magnitude of the statistical
correlation (coherence) between any pair of nodes.
In each coherence matrix, the table rows and
columns represent the ROIs and the cell values
represent the coherence magnitude of dependency
between each pair of ROIs. Figure 1 illustrates the
workflow for all the analyses that were implemented
in this study.
Figure 1. The workflow of all analyses in this thesis summarized as an overview.
Graph Theoretical Analysis
The coherence matrix for each frequency band for
each participant was subjected to graph theoretical
analysis using the MATLAB Brain Connectivity
Toolbox (BCT; Rubinov & Sporns, 2010). The BCT
has functions that take into account the weighted
undirected strength or magnitude of all the network
connections. Descriptions and code for the
mathematical functions in the BCT are freely
available for download at https://sites.google.com
/site/bctnet/. BCT functions were applied to each
participant’s coherence matrix to calculate small-
world characteristics. The weighted clustering
coefficient around a given node varies from 0 to 1
and is quantified by the number of triangles formed
by that node and its neighboring nodes. The
weighted characteristic path length is defined as the
average shortest weighted path between two given
nodes using the sum of the individual weighted
lengths. Path lengths with conversions based on
values of the coherence matrix were stored
separately as a distance matrix with sequences of
edges that connect nodes indirectly to form neural
paths. The path length values in the distance matrix
are not physical distances, but instead they
represent the degree of topological separation
between any two given nodes (Rubinov & Sporns,
2010). The GraphVar toolbox in Matlab (Kruschwitz,
Zinn et al. NeuroRegulation! !
131!|!www.neuroregulation.org Vol. 4(3–4):125–137 2017 doi:10.15540/nr.4.3-4.125!
List, Waller, Rubinov, & Walter, 2015) was used to
calculate small-worldness, which is the ratio
between the clustering coefficient and characteristic
path length compared to their values for equivalent
randomly generated graphs (Humphries & Gurney,
2008). The small-worldness index variable (SW)
was computed using Csw = (Cw/Cwrand)/(Lw/Lwrand)
as a comparative marker of efficient brain
functioning for each participant.
The graph theory output that was produced using
BCT functions in MATLAB was subsequently
imported to SPSS version 23 for conducting further
statistical analyses. The data were screened for
outliers, missing data, skewness, and kurtosis in
meeting the assumptions for parametric statistics.
Continuous variables were log-transformed to meet
the assumption of Gaussianity.
Demographic characteristics by study group and
descriptives of key study variables are shown in
Tables 1 and 2. Most patients with CFS were older
than HCs and the potential confound of age was
controlled for in all models. Given that some
secondary outcomes were considered
corresponding to the study hypotheses, this study is
considered exploratory, and the p values considered
descriptive. All data were evaluated with tests which
were two-sided at the .05 level of significance.
Demographic and Clinical Data.
All 29 Participants
DSQ Cognitive Composite score*
* p < .01
Means and Confidence Intervals for Small-worldness
Indices by Experimental Group.
The primary outcome of interest was to determine
whether small-world network values deviate from
normal in a sample of patients with CFS. Analysis of
Variance (ANOVA) was conducted to assess
whether networks of patients with CFS deviated
significantly from those of HCs, adjusting for age.
We first identified statistically significant ANOVA
values in an overall test, F(2, 80) = 4.915, p = .029,
which indicated a significantly lower small-worldness
index z-value for patients with CFS (M = −.181, SD =
1.047) than HCs (M = .164, SD = .950). To identify
the differences between small-worldness within each
frequency band in this study, follow-up tests were
conducted with the Bonferroni correction for multiple
comparisons. These estimates identified SW delta
as statistically different between patients with CFS
and HCs, p = .014; however, the SW alpha-1 and
Zinn et al. NeuroRegulation! !
132!|!www.neuroregulation.org Vol. 4(3–4):125–137 2017 doi:10.15540/nr.4.3-4.125!
SW alpha-2 were not significant between both
groups (p = .622 for alpha-1; p = .099 for alpha-2;
Figure 2). Within the HC group, a significant
difference was found between SW delta and SW
alpha-1 (p = .001), but not between SW delta and
SW alpha-2 (p = .177). Within the CFS group,
however, there was no significant difference
between any SW frequency bands (p = .355).
Figure 2. Small-worldness results of group comparisons
by frequency band. The CFS group was 1 SD lower than
the HC group for SW delta (p = .014).
Next, hierarchical regression techniques were used
to determine the linear relationship of small-world
network organization (measured by SW delta, SW
alpha-1, and SW alpha-2 combined) with
neurocognitive impairment. Two models were fit for
estimating this relationship, age-adjusted, and found
that small-worldness significantly predicted the
neurocognitive impairment scores, F(2, 84) =
53.482, p = .000, adjusted R2 = .550 for model 1 and
F(2, 83) = 122.546, p = .000, adjusted R2 = .809.
These strong effect sizes suggest that deviations
from small-worldness affect neurocognitive
impairment. For model 2 in particular, 80.9% of
neurocognitive impairment was predicted by the
combination of small-worldness, experimental group,
and age (Figure 3).
Figure 3. Small-worldness results of regression analysis
by frequency band.
Our third outcome of interest was the development
of prediction models to estimate odds ratios and
95% CIs for patients with CFS in SW delta, SW
alpha-1, and SW alpha-2. Fixed-effects multinomial
logistic regression allowed us to appropriately model
the relationship between group membership and
small-world effects at each frequency band. All
models were adjusted for the potential confounder of
age. To estimate differences between patients with
CFS in our study cohort, the deviated small-world
values (small-worldness index variable) in the fixed-
effects logistic regression model were associated
with increased risk in CFS of SW delta (OR 1.425;
95% CI: 0.500–3.75) but not for SW alpha-1 (OR
0.702; 95% CI: 0.310–1.590) or SW alpha-2 (OR
0.786; 95% CI: 0.386–1.601). According to this data
set, the group with CFS was 1.425 times as likely to
have deviations from normal in small-worldness in
the delta frequency band but not in the alpha-1 or
alpha-2 band. The overall regression model was
significant at p = .05.
To our knowledge, this is the first study to evaluate
an association between small-world characteristics
and cognitive symptoms reported in CFS. These
findings of functional connectivity alterations suggest
the importance of applying graph theory to
connectome-scale analysis of network topology to
detect subtle disruptions incurred by CFS sequelae.
Zinn et al. NeuroRegulation! !
133!|!www.neuroregulation.org Vol. 4(3–4):125–137 2017 doi:10.15540/nr.4.3-4.125!
Neurocognitive impairment, as measured by the
DSQ cognitive composite score, was negatively
associated with small-worldness index for the delta
band under observation. Group-level differences
were also found, but only for small-worldness in the
delta band. Finally, the risk of having small-
worldness deviations in the delta band is
increasingly greater in CFS.
Small-world models of the brain systems explore the
balance between high clustering of local systems
with short path lengths of global systems; these
attributes are considered to be vital to the efficiency
of information processing within neurocognitive
networks (Menon, 2012; Rubinov & Sporns, 2010).
This model emphasizes the morphological
adaptations (e.g., changes in axonal diameter, white
matter pathways, conduction velocities, and energy
transport mechanisms) governed by trade-offs within
components and compensation necessary for
maintaining the multiscale spatial-temporal patterns
for which the brain operates. Differences in neural
resource allocation in CFS were reported in three
fMRI studies investigating compensatory responses
to cognitive tasks (Caseras et al., 2006; Cook et al.,
2007; Lange et al., 2005). The findings of our study
explain these differences in terms of peculiarities to
these trade-offs with subsequent weakness to small-
worldness structure that could account for loss of
cognitive function in people with CFS.
Secondarily, it was found that small-worldness in the
delta band accounted for the greatest amount of
variance in cognitive composite scores for the
hierarchical regression model equation. Delta is a
slow oscillation that plays a key role in the dynamic
coordination of large-scale cortical networks and
modulation of faster rhythms through cross-
frequency coupling (Buzsáki & Freeman, 2015). In
the case of inflammatory disorders of the CNS, the
most prominent change in large-scale network
dynamics is the occurrence of cortical slowing (e.g.,
delta activity) during the waking state
(Westmoreland, 2005). Furthermore, delta cortical
slowing can result from a decrease in the afferent
drive due to white matter or subcortical lesions to
deep midline areas (Gloor, Ball, & Schaul, 1977;
Schaul, Gloor, & Gotman, 1981). Finding abnormal
small-worldness in delta suggests there may be
some similarities between CFS and Alzheimer’s
disease (Babiloni et al., 2013; Hata et al., 2016),
multiple sclerosis (Babiloni et al., 2016), and
Parkinson’s disease (Babiloni et al., 2011), where
abnormal delta sources have been detected.
This is the first study to measure small-world
properties in CFS in terms of the small-worldness
index. Using resting-state fMRI data, Kim et al.
(2015) found that functional integration (global
efficiency) was decreased in CFS and disruption to
global efficiency suggests that, with fewer
biologically “expensive” long distance connections,
added burden is being placed on the system for
satisfying opposing demands. The “costs” to
chronically reduced functional integration in CFS
include: 1) a lowered ability to rapidly combine
specialized information from distributed brain
regions, 2) slowed information processing speed
due to compensatory responses, and 3) a
generalized impairment to domains of cognitive
function. However, our study found differences
using the small-worldness index as a ratio of
individual small-world properties (clustering and path
lengths), a measure of both global and local
properties which are salient in CFS depending on
frequency band. This underscores the need for
considering a combination of graph theory metrics
for a more comprehensive examination of CFS.
There are some limitations in the present study. The
results of this study should be interpreted with
caution due to small sample size. Although
significant deviations in the reported small-
worldness phenomena were found in people with
CFS, neurological disorders are invariably
associated with diffuse network changes. However,
it was beyond the scope of this study to report the
individual nodes, hub, and modules that may be
involved in suboptimal information processing
efficiency and prone to failure in CFS. Although the
outcome of brain function following individual hub
failure would likely go beyond discrete local regions,
future research could explore a more
comprehensive inspection of hub strength,
distribution, and participation within modular
structures to identify ROIs that serve as potential
targets for treatment. As another limitation of this
study, the examination of small-world differences
was kept within the delta, apha-1, and alpha-2
frequency bands. Frequency-dependent changes to
cortical arrangements occurring in other frequency
bands (e.g., theta, beta) could also be explored.
Finally, insignificant findings in alpha-1 and alpha-2
could reflect a deficiency in the diagnostic criteria for
CFS, a deficiency in the coherence-based measure
itself, a problem with the way the ROIs were defined,
and/or unexplored levels of complex network
analysis using other graph theory metrics.
Functional connectivity EEG markers associated
with neurocognitive impairment and small-worldness
Zinn et al. NeuroRegulation! !
134!|!www.neuroregulation.org Vol. 4(3–4):125–137 2017 doi:10.15540/nr.4.3-4.125!
in different frequency bands should be verified in
The present findings support the concept that small-
worldness is altered in CFS. This has important
implications for this field of study. For example,
system-dependent coupling of oscillations has
fundamental importance to CNS function and may
be strongly influenced by delays in conduction
velocity and myelin plasticity. Changes to white
matter have been reported in CFS (Puri et al., 2012),
also associated with clinical measures (Barnden et
al., 2011), and a severity-dependent increase in
myelination has also been found (Barnden, Crouch,
Kwiatek, Burnet, & Del Fante, 2015). Disruption to
white matter could explain the relationship between
abnormal eLORETA coherence patterns over large-
scale complex systems in CFS. Furthermore, the
linkage between cognitive symptoms and small-
worldness demonstrates the fundamental
importance of timing, stability, and adaptation of
complex systems to CFS which could be related to
findings of neuroinflammation in patients (Nakatomi
et al., 2014). Understanding the network dynamics
of CFS in terms of eLORETA coherence is an
important way of comprehending compensatory
mechanisms and could serve as a practical tool for
investigating large-scale loss of cognitive function
related to adaptive reconfiguration of brain networks.
There is a need for future research that models the
activity-dependent modifications of brain connectivity
in CFS with disruption to neurocognitive processes.
Adebimpe, A., Aarabi, A., Bourel-Ponchel, E., Mahmoudzadeh,
M., & Wallois, F. (2016). EEG resting state functional
connectivity analysis in children with benign epilepsy with
centrotemporal spikes. Frontiers in Neuroscience, 10, 143.
Babiloni, C., Carducci, F., Lizio, R., Vecchio, F., Baglieri, A.,
Bernardini, S., ... Frisoni, G. B. (2013). Resting state cortical
electroencephalographic rhythms are related to gray matter
volume in subjects with mild cognitive impairment and
Alzheimer's disease. Human Brain Mapping, 34(6), 1427–
Babiloni, C., De Pandis, M. F., Vecchio, F., Buffo, P., Sorpresi, F.,
Frisoni, G. B., & Rossini, P. M. (2011). Cortical sources of
resting state electroencephalographic rhythms in Parkinson's
disease related dementia and Alzheimer's disease. Clinical
Neurophysiology, 122(12), 2355–2364. http://dx.doi.org
Babiloni, C., Del Percio, C., Capotosto, P., Noce, G., Infarinato,
F., Muratori, C., ... Lupattelli, T. (2016). Cortical sources of
resting state electroencephalographic rhythms differ in
relapsing–remitting and secondary progressive multiple
sclerosis. Clinical Neurophysiology, 127(1), 581–590.
Barnden, L. R., Crouch, B., Kwiatek, R., Burnet, R., & Del Fante,
P. (2015). Evidence in chronic fatigue syndrome for severity-
dependent upregulation of prefrontal myelination that is
independent of anxiety and depression. NMR in Biomedicine,
28(3), 404–413. http://dx.doi.org/10.1002/nbm.3261
Barnden, L. R., Crouch, B., Kwiatek, R., Burnet, R., Mernone, A.,
Chryssidis, S., ... Del Fante, P. (2011). A brain MRI study of
chronic fatigue syndrome: Evidence of brainstem dysfunction
and altered homeostasis. NMR in Biomedicine, 24(10), 1302–
Bassett, D. S., & Bullmore, E. (2006). Small-world brain networks.
The Neuroscientist, 12(6), 512–523. http://dx.doi.org/10.1177
Boissoneault, J., Letzen, J., Lai, S., O'Shea, A., Craggs, J.,
Robinson, M. E., & Staud, R. (2016). Abnormal resting state
functional connectivity in patients with chronic fatigue
syndrome: An arterial spin-labeling fMRI study. Magnetic
Resonance Imaging, 34(4), 603–608. http://dx.doi.org
BrainMaster Discovery 24 [Apparatus]. (2011). Bedford, OH:
BrainMaster Technologies, Inc.
Bullmore, E., & Sporns, O. (2012). The economy of brain network
organization. Nature Reviews Neuroscience, 13(5), 336–349.
Busichio, K., Tiersky, L. A., DeLuca, J., & Natelson, B. H. (2004).
Neuropsychological deficits in patients with chronic fatigue
syndrome. Journal of the International Neuropsychological
Society, 10(2), 278–285. http://dx.doi.org/10.1017
Buzsáki, G. (2006). Rhythms of the Brain. New York, NY: Oxford
Buzsáki, G., & Freeman, W. (2015). Editorial overview: Brain
rhythms and dynamic coordination. Current Opinion in
Neurobiology, 31, v–ix. http://dx.doi.org/10.1016
Buzsáki, G., & Watson, B. O. (2012). Brain rhythms and neural
syntax: Implications for efficient coding of cognitive content
and neuropsychiatric disease. Dialogues in Clinical
Neuroscience, 14(4), 345–367.
Carruthers, B. M., Jain, A. K., De Meirleir, K. L., Peterson, D. L.,
Klimas, N. G., Lerner, A. M., ... van de Sande, M. I. (2003).
Myalgic encephalomyelitits/chronic fatigue syndrome: Clinical
working case definition, diagnostic and treatment protocols.
Journal of Chronic Fatigue Syndrome, 11(1), 7–115.
Carruthers, B. M., van de Sande, M. I., De Meirleir, K. L., Klimas,
N. G., Broderick, G., Mitchell, T., ... Stevens, S. (2011).
Myalgic encephalomyelitis: International Consensus Criteria.
Journal of Internal Medicine, 270(4), 327–338.
Caseras, X., Mataix-Cols, D., Giampietro, V., Rimes, K. A.,
Brammer, M., Zelaya, F., ... Godfrey, E. L. (2006). Probing
the working memory system in chronic fatigue syndrome: A
functional magnetic resonance imaging study using the n-
back task. Psychosomatic Medicine, 68(6), 947–955.
Castellanos, N. P., & Makarov, V. A. (2006). Recovering EEG
brain signals: Artifact suppression with wavelet enhanced
independent component analysis. Journal of Neuroscience
Methods, 158(2), 300–312. http://dx.doi.org/10.1016
Cockshell, S. J., & Mathias, J. L. (2010). Cognitive functioning in
chronic fatigue syndrome: A meta-analysis. Psychological
Medicine, 40(8), 1253–1267. http://dx.doi.org/10.1017
Constant, E. L., Adam, S., Gillain, B., Lambert, M., Masquelier, E.,
& Seron, X. (2011). Cognitive deficits in patients with chronic
fatigue syndrome compared to those with major depressive
disorder and healthy controls. Clinical Neurology and
Zinn et al. NeuroRegulation! !
135!|!www.neuroregulation.org Vol. 4(3–4):125–137 2017 doi:10.15540/nr.4.3-4.125!
Neurosurgery, 113(4), 295–302. http://dx.doi.org/10.1016
Cook, D. B., O'Connor, P. J., Lange, G., & Steffener, J. (2007).
Functional neuroimaging correlates of mental fatigue induced
by cognition among chronic fatigue syndrome patients and
controls. NeuroImage, 36(1), 108–122. http://dx.doi.org
Crossley, N. A., Mechelli, A., Scott, J., Carletti, F., Fox, P. T.,
McGuire, P., & Bullmore, E. T. (2014). The hubs of the human
connectome are generally implicated in the anatomy of brain
disorders. Brain, 137(8), 2382–2395. http://dx.doi.org/10.1093
Deco, G., Jirsa, V., & Friston, K. J. (2012). The dynamical
structural basis of brain activity. In M. I. Rabinovich, K.
Friston, & P. Varona (Eds.), Principles of Brain Dynamics:
Global State Interactions (pp. 1–23). Cambridge, MA: MIT
DeLuca, J., Johnson, S. K., & Natelson, B. H. (1994).
Neuropsychiatric status of patients with chronic fatigue
syndrome: An overview. Toxicology and Industrial Health,
Dobbs, B. M., Dobbs, A. R., & Kiss, I. (2001). Working memory
deficits associated with chronic fatigue syndrome. Journal of
the International Neuropsychological Society, 7(3), 285–293.
Flor-Henry, P., Lind, J. C., & Koles, Z. J. (2010). EEG source
analysis of chronic fatigue syndrome. Psychiatry Research,
181(2), 155–164. http://dx.doi.org/10.1016
Fukuda, K., Straus, S. E., Hickie, I., Sharpe, M. C., Dobbins, J.
G., & Komaroff, A. (1994). The chronic fatigue syndrome: A
comprehensive approach to its definition and study. Annals of
Internal Medicine, 121(12), 953–959.
Gay, C. W., Robinson, M. E., Lai, S., O'Shea, A., Craggs, J. G.,
Price, D. D., & Staud, R. (2016). Abnormal resting-state
functional connectivity in patients with chronic fatigue
syndrome: Results of seed and data-driven analyses. Brain
Connectivity, 6(1), 48–56. http://dx.doi.org/10.1089
Gloor, P., Ball, G., & Schaul, N. (1977). Brain lesions that produce
delta waves in the EEG. Neurology, 27(4), 326–333. http://dx.
Grafman, J., Schwartz, V., Dale, J. K., Scheffers, M., Houser, C.,
& Straus, S. E. (1993). Analysis of neuropsychological
functioning in patients with chronic fatigue syndrome. Journal
of Neurology, Neurosurgery, & Psychiatry, 56(6), 684–689.
Grech, R., Cassar, T., Muscat, J., Camilleri, K. P., Fabri, S. G.,
Zervakis, M., ... Vanrumste, B. (2008). Review on solving the
inverse problem in EEG source analysis. Journal of
NeuroEngineering and Rehabilitation, 5, 25. http://dx.doi.org
Harris, P. A., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., &
Conde, J. G. (2009). Research electronic data capture
(REDCap)—A metadata-driven methodology and workflow
process for providing translational research informatics
support. Journal of Biomedical Informatics, 42(2), 377–381.
Hata, M., Kazui, H., Tanaka, T., Ishii, R., Canuet, L., Pascual-
Marqui, R. D., ... Takeda, M. (2016). Functional connectivity
assessed by resting state EEG correlates with cognitive
decline of Alzheimer's disease: An eLORETA study. Clinical
Neurophysiology, 127(2), 1269–1278. http://dx.doi.org
Hopkins, R. O., & Jackson, J. C. (2006). Long-term
neurocognitive function after critical illness. Chest, 130(3),
Humphries, M. D., & Gurney, K. (2008). Network 'small-world-
ness': A quantitative method for determining canonical
network equivalence. PloS ONE, 3(4), e0002051.
Jason, L. A., So, S., Brown, A. A., Sunnquist, M., & Evans, M.
(2015). Test–retest reliability of the DePaul Symptom
Questionnaire. Fatigue Biomedicine Health & Behavior, 3(1),
Jason, L. A., Sunnquist, M., Brown, A., Furst, J., Cid, M., Farietta,
J., ... Strand, E. B. (2015). Factor analysis of the DePaul
Symptom Questionnaire: Identifying core domains. Journal of
Neurology and Neurobiology, 1(4). http://dx.doi.org/10.16966
Jason, L. A., Zinn, M. L., & Zinn, M. A. (2015). Myalgic
encephalomyelitis: Symptoms and biomarkers. Current
Neuropharmacology, 13(5), 701–734. http://dx.doi.org
John, E. R. (2005). From synchronous neuronal discharges to
subjective awareness? In S. Laureys (Ed.), Progress in Brain
Research (Vol. 150, pp. 55–68). London, England: Elsevier.
Johnson, S. K., DeLuca, J., & Natelson, B. H. (1996). Assessing
somatization disorder in the chronic fatigue syndrome.
Psychosomatic Medicine, 58(1), 50–57. http://dx.doi.org
Jurcak, V., Tsuzuki, D., & Dan, I. (2007). 10/20, 10/10, and 10/5
systems revisited: Their validity as relative head-surface-
based positioning systems. NeuroImage, 34(4), 1600–1611.
Kierkels, J. J. M., van Boxtel, G. J. M., & Vogten, L. L. M. (2006).
A model-based objective evaluation of eye movement
correction in EEG recordings. IEEE Transactions on
Biomedical Engineering, 53(2), 246–253. http://dx.doi.org
Kim, B.-H., Namkoong, K., Kim, J.-J., Lee, S., Yoon, K. J., Choi,
M., & Jung, Y.-C. (2015). Altered resting-state functional
connectivity in women with chronic fatigue syndrome.
Psychiatry Research, 234(3), 292–297. http://dx.doi.org
Kirk, R. E. (2013). Experimental design: Procedures for the
behavioral sciences (4th ed.). Los Angeles, CA: SAGE
Klimesch, W., Freunberger, R., Sauseng, P., & Gruber, W.
(2008). A short review of slow phase synchronization and
memory: Evidence for control processes in different memory
systems? Brain Research, 1235, 31–44. http://dx.doi.org
Komaroff, A. L., & Buchwald, D. (1991). Symptoms and signs of
chronic fatigue syndrome. Reviews of Infectious Diseases,
13(Suppl. 1), S8–S11. http://dx.doi.org/10.1093/clinids
Koziol, L. F., & Budding, D. E. (2009). Subcortical structures and
cognition: Implications for neuropsychological assessment.
New York, NY: Springer.
Kruschwitz, J. D., List, D., Waller, L., Rubinov, M., & Walter, H.
(2015). GraphVar: A user-friendly toolbox for comprehensive
graph analyses of functional brain connectivity. Journal of
Neuroscience Methods, 245, 107–115. http://dx.doi.org
Lange, G., Steffener, J., Cook, D. B., Bly, B. M., Christodoulou,
C., Liu, W.-C., ... Natelson, B. H. (2005). Objective evidence
of cognitive complaints in Chronic Fatigue Syndrome: A
BOLD fMRI study of verbal working memory. NeuroImage,
26(2), 513–524. http://dx.doi.org/10.1016
Le Van Quyen, M. (2011). The brainweb of cross-scale
interactions. New Ideas in Psychology, 29(2), 57–63.
Lehmann, D., Faber, P. L., Gianotti, L. R. R., Kochi, K., &
Pascual-Marqui, R. D. (2006). Coherence and phase locking
in the scalp EEG and between LORETA model sources, and
microstates as putative mechanisms of brain temporo-spatial
functional organization. Journal of Physiology–Paris, 99(1),
Zinn et al. NeuroRegulation! !
136!|!www.neuroregulation.org Vol. 4(3–4):125–137 2017 doi:10.15540/nr.4.3-4.125!
Majer, M., Welberg, L. A., Capuron, L., Miller, A. H., Pagnoni, G.,
& Reeves, W. C. (2008). Neuropsychological performance in
persons with chronic fatigue syndrome: Results from a
population-based study. Psychosomatic Medicine, 70(7),
Menon, V. (2011). Large-scale brain networks and
psychopathology: A unifying triple network model. Trends in
Cognitive Sciences, 15(10), 483–506. http://dx.doi.org
Menon, V. (2012). Functional connectivity, neurocognitive
networks, and brain dynamics. In M. I. Rabinovich, K. J.
Friston, & P. Varona (Eds.), Principles of Brain Dynamics:
Global State Interactions (pp. 27–47). Cambridge, MA: MIT
Minati, L., Varotto, G., D'Incerti, L., Panzica, F., & Chan, D.
(2013). From brain topography to brain topology: Relevance
of graph theory to functional neuroscience. Neuroreport,
24(10), 536–543. http://dx.doi.org/10.1097
Murdock, K. W., Wang, X. S., Shi, Q., Cleeland, C. S., Fagundes,
C. P., & Vernon, S. D. (2016). The utility of patient-reported
outcome measures among patients with myalgic
encephalomyelitis/chronic fatigue syndrome. Quality of Life
Research, 26(4), 913–921. http://dx.doi.org/10.1007/s11136-
Nakatomi, Y., Mizuno, K., Ishii, R., Wada, Y., Tanaka, M.,
Tazawa, S., ... Watanabe, Y. (2014). Neuroinflammation in
patients with Chronic Fatigue Syndrome/Myalgic
Encephalomyelitis: An 11C-(R)-PK11195 PET study. Journal
of Nuclear Medicine, 55(6), 945–950. http://dx.doi.org
Neuroguide (Version 2.8.7) [Computer software]. (2016). St.
Petersburg, FL: Applied Neuroscience, Inc.
Niedermeyer, E., & Lopes da Silva, F. H. (2005).
Electroencephalography: Basic principles, clinical applications
and related fields (5th ed.). Philadelphia, PA: Lippincott
Williams and Wilkins.
Nunez, P. L., Srinivasan, R., & Fields, R. D. (2015). EEG
functional connectivity, axon delays and white matter disease.
Clinical Neurophysiology, 126(1), 110–120. http://dx.doi.org
Ocon, A. J. (2013). Caught in the thickness of brain fog: Exploring
the cognitive symptoms of Chronic Fatigue Syndrome.
Frontiers in Physiology, 4, 63. http://dx.doi.org/10.3389
Pascual-Marqui, R. D. (2007a). Coherence and phase
synchronization: Generalization to pairs of multivariate time
series, and removal of zero-lag contribution
(arXiv:0706.1776v3 [stat.ME]). Retrieved from http://arxiv.org
Pascual-Marqui, R. D. (2007b). Discrete, 3D distributed linear
imaging methods of electric neuronal activity. Part 1: Exact,
zero error localization (arXiv:0710.3341 [math-ph]). Retrieved
Pascual-Marqui, R. D. (2007c). Instantaneous and lagged
measurements of linear and nonlinear dependence between
groups of multivariate time series: Frequency decomposition
(arXiv:0711.1455[stat.ME]). Retrieved from https://arxiv.org
Pascual-Marqui, R. D. (2015). LORETA-KEY software (Version
2015-12-22). Zurich, Switzerland: KEY Institute for Brain-Mind
Research. Retrieved from http://www.uzh.ch/keyinst
Pascual-Marqui, R. D., Lehmann, D., Koukkou, M., Kochi, K.,
Anderer, P., Saletu, B., ... Kinoshita, T. (2011). Assessing
interactions in the brain with exact low-resolution
electromagnetic tomography. Philosophical Transactions of
the Royal Society A, Mathematical, Physical, and Engineering
Sciences, 369(1952), 3768–3784. http://dx.doi.org/10.1098
Puri, B. K., Jakeman, P. M., Agour, M., Gunatilake, K. D. R.,
Fernando, K. A. C., Gurusinghe, A. I., ... Gishen, P. (2012).
Regional grey and white matter volumetric changes in
myalgic encephalomyelitis (chronic fatigue syndrome): A
voxel-based morphometry 3 T MRI study. British Journal of
Radiology, 85(1015), e270–e273. http://dx.doi.org/10.1259/bjr
Rossini, P. M., Rossi, S., Babiloni, C., & Polich, J. (2007). Clinical
neurophysiology of aging brain: From normal aging to
neurodegeneration. Progress in Neurobiology, 83(6), 375–
Rubinov, M., & Sporns, O. (2010). Complex network measures of
brain connectivity: Uses and interpretations. NeuroImage,
52(3), 1059–1069. http://dx.doi.org/10.1016
Sauseng, P., & Klimesch, W. (2008). What does phase
information of oscillatory brain activity tell us about cognitive
processes? Neuroscience & Biobehavioral Reviews, 32(5),
Schaul, N., Gloor, P., & Gotman, J. (1981). The EEG in deep
midline lesions. Neurology, 31(2), 157–167. http://dx.doi.org/
Sepulcre, J. (2014). Functional streams and cortical integration in
the human brain. The Neuroscientist, 20(5), 499–508.
Sherlin, L., Budzynski, T., Kogan Budzynski, H., Congedo, M.,
Fischer, M. E., & Buchwald, D. (2007). Low-resolution
electromagnetic brain tomography (LORETA) of monozygotic
twins discordant for chronic fatigue syndrome. NeuroImage,
34(4), 1438–1442. http://dx.doi.org/10.1016
Sporns. (2013). Structure and function of complex brain networks.
Dialogues in Clinical Neuroscience, 15(3), 247–262.
Sporns, O., & Honey, C. J. (2006). Small worlds inside big brains.
Proceedings of the National Academy of Sciences, 103(51),
Stam, C. J. (2010). Characterization of anatomical and functional
connectivity in the brain: A complex networks perspective.
International Journal of Psychophysiology, 77(3), 186–194.
Stam, C. J. (2014). Modern network science of neurological
disorders. Nature Reviews Neuroscience, 15(10), 683–695.
Steriade, M. (2005). Cellular substrates of brain rhythms. In E.
Niedermeyer & F. H. Lopes de Silva (Eds.),
Electroencephalography: Basic principles, clinical applications
and related fields (5th ed., pp. 31–83). Philadelphia, PA:
Lippincott Williams and Wilkins.
Steriade, M., & Paré, D. (2007). Gating in cerebral networks. New
York, NY: Cambridge University Press.
Telesford, Q. K., Simpson, S. L., Burdette, J. H., Hayasaka, S., &
Laurienti, P. J. (2011). The brain as a complex system: Using
network science as a tool for understanding the brain. Brain
Connectivity, 1(4), 295–308. http://dx.doi.org/10.1089
Thatcher, R. W. (2016). Handbook of Quantitative
Electroencephalography and EEG Biofeedback. St.
Petersburg, FL: ANI Publishing.
Thatcher, R. W., North, D. M., & Biver, C. J. (2008). Intelligence
and EEG phase reset: A two compartmental model of phase
shift and lock. NeuroImage, 42(4), 1639–1653.
Thomas, M., & Smith, A. (2009). An investigation into the
cognitive deficits associated with chronic fatigue syndrome.
The Open Neurology Journal, 3, 13–23. http://dx.doi.org
Van Den Eede, F., Moorkens, G., Hulstijn, W., Maas, Y.,
Schrijvers, D., Stevens, S. R., ... Sabbe, B. G. C. (2011).
Psychomotor function and response inhibition in chronic
Zinn et al. NeuroRegulation! !
137!|!www.neuroregulation.org Vol. 4(3–4):125–137 2017 doi:10.15540/nr.4.3-4.125!
fatigue syndrome. Psychiatry Research, 186(2–3), 367–372.
van den Heuvel, M. P., & Sporns, O. (2013). An anatomical
substrate for integration among functional networks in human
cortex. The Journal of Neuroscience, 33(36), 14489–14500.
van Straaten, E. C. W., & Stam, C. J. (2013). Structure out of
chaos: Functional brain network analysis with EEG, MEG,
and functional MRI. European Neuropsychopharmacology,
23(1), 7–18. http://dx.doi.org/10.1016
Vecchio, F., Miraglia, F., Curcio, G., Altavilla, R., Scrascia, F.,
Giambattistelli, F., ... Rossini, P. M. (2015). Cortical brain
connectivity evaluated by graph theory in dementia: A
correlation study between functional and structural data.
Journal of Alzheimer's Disease, 45(3), 745–756.
Vecchio, F., Miraglia, F., Curcio, G., Della Marca, G., Vollono, C.,
Mazzucchi, E., ... Rossini, P. M. (2015). Cortical connectivity
in fronto-temporal focal epilepsy from EEG analysis: A study
via graph theory. Clinical Neurophysiology, 126(6), 1108–
Vecchio, F., Miraglia, F., Porcaro, C., Cottone, C., Cancelli, A.,
Rossini, P. M., & Tecchio, F. (2017).
Electroencephalography-Derived Sensory and Motor Network
Topology in Multiple Sclerosis Fatigue. Neurorehabilitation
and Neural Repair, 31(1), 56–64. http://dx.doi.org/10.1177
Vecchio, F., Miraglia, F., Quaranta, D., Granata, G., Romanello,
R., Marra, C., ... Rossini, P. M. (2016). Cortical connectivity
and memory performance in cognitive decline: A study via
graph theory from EEG data. Neuroscience, 316, 143–150.
Vysata, O., Kukal, J., Prochazka, A., Pazdera, L., Simko, J., &
Valis, M. (2014). Age-related changes in EEG coherence.
Neurologia i Neurochirurgia Polska, 48(1), 35–38.
Wallstrom, G. L., Kass, R. E., Miller, A., Cohn, J. F., & Fox, N. A.
(2004). Automatic correction of ocular artifacts in the EEG: A
comparison of regression-based and component-based
methods. International Journal of Psychophysiology, 53(2),
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of
"small-world" networks. Nature, 393(6684), 440–442.
Westmoreland, B. (2005). The EEG in Cerebral Inflammatory
Processes. In E. Niedermeyer & F. H. Lopes da Silva (Eds.),
Electroencephalography: Basic principles, clinical applications
and related fields (5th ed., pp. 323–337). Philadelphia, PA:
Lippincott Williams and Wilkins.
Wig, G. S., Schlaggar, B. L., & Petersen, S. E. (2011). Concepts
and principles in the analysis of brain networks. Annals of the
New York Academy of Sciences, 1224, 126–146.
Wortinger, L. A., Endestad, T., Melinder, A. M. D., Øie, M. G.,
Sevenius, A., & Bruun Wyller, V. (2016). Aberrant resting-
state functional connectivity in the salience network of
adolescent chronic fatigue syndrome. PLoS ONE, 11(7),
Zinn, M. A., Zinn, M. L., Norris, J. L., Valencia, I., Montoya, J. G.,
& Maldonado, J. R. (2014). Cortical hypoactivation during
resting EEG suggests central nervous system pathology in
patients with Chronic Fatigue Syndrome. Paper presented at
the Symposium conducted at the meeting of IACFS/ME 2014
Biennial Conference, San Francisco, CA, USA.
Zinn, M. L., Zinn, M. A., & Jason, L. A. (2016). Intrinsic functional
hypoconnectivity in core neurocognitive networks suggests
central nervous system pathology in patients with myalgic
encephalomyelitis: A pilot study. Applied Psychophysiology
and Biofeedback, 41(3), 283–300. http://dx.doi.org/10.1007
Received: August 18, 2017
Accepted: August 30, 2017
Published: December 8, 2017