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Intrinsic Functional Hypoconnectivity in Core Neurocognitive Networks Suggests Central Nervous System Pathology in Patients with Myalgic Encephalomyelitis: A Pilot Study

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Intrinsic Functional Hypoconnectivity in Core Neurocognitive Networks Suggests Central Nervous System Pathology in Patients with Myalgic Encephalomyelitis: A Pilot Study

Abstract and Figures

Exact low resolution electromagnetic tomography (eLORETA) was recorded from nineteen EEG channels in nine patients with myalgic encephalomyelitis (ME) and 9 healthy controls to assess current source density and functional connectivity, a physiological measure of similarity between pairs of distributed regions of interest, between groups. Current source density and functional connectivity were measured using eLORETA software. We found significantly decreased eLORETA source analysis oscillations in the occipital, parietal, posterior cingulate, and posterior temporal lobes in Alpha and Alpha-2. For connectivity analysis, we assessed functional connectivity within Menon triple network model of neuropathology. We found support for all three networks of the triple network model, namely the central executive network (CEN), salience network (SN), and the default mode network (DMN) indicating hypo-connectivity in the Delta, Alpha, and Alpha-2 frequency bands in patients with ME compared to controls. In addition to the current source density resting state dysfunction in the occipital, parietal, posterior temporal and posterior cingulate, the disrupted connectivity of the CEN, SN, and DMN appears to be involved in cognitive impairment for patients with ME. This research suggests that disruptions in these regions and networks could be a neurobiological feature of the disorder, representing underlying neural dysfunction.
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Intrinsic Functional Hypoconnectivity in Core Neurocognitive
Networks Suggests Central Nervous System Pathology in Patients
with Myalgic Encephalomyelitis: A Pilot Study
Marcie L. Zinn
1
Mark A. Zinn
1
Leonard A. Jason
1
Ó Springer Science+Business Media New York 2016
Abstract Exact low resolution electromagnetic tomog-
raphy (eLORETA) was recorded from nineteen EEG
channels in nine patients with myalgic encephalomyelitis
(ME) and 9 healthy controls to assess current source den-
sity and functional connectivity, a physiological measure of
similarity between pairs of distributed regions of interest,
between groups. Current source density and functional
connectivity were measured using eLORETA software. We
found significantly decreased eLORETA source analysis
oscillations in the occipital, parietal, posterior cingulate,
and posterior temporal lobes in Alpha and Alpha-2. For
connectivity analysis, we assessed functional connectivity
within Menon triple network model of neuropathology. We
found support for all three networks of the triple network
model, namely the central executive network (CEN), sal-
ience network (SN), and the default mode network (DMN)
indicating hypo-connectivity in the Delta, Alpha, and
Alpha-2 frequency bands in patients with ME compared to
controls. In addition to the current source density resting
state dysfunction in the occipital, parietal, posterior tem-
poral and posterior cingulate, the disrupt ed connectivity of
the CEN, SN, and DMN appears to be involved in cogni-
tive impairment for patients with ME. This research sug-
gests that disruptions in these regions and networks could
be a neurobiological feature of the disorder, representing
underlying neural dysfunction.
Keywords eLORETA Myalgic encephalomyelitis
Chronic fatigue syndrome Lagged phase synchronization
Triple network model
Introduction
According to current theories of brain function, cognitive
abilities (Fuster 2009; Koziol and Budding 2009; Naglieri
and Das 1997) are supported by functionally linked, cor-
related and spatially distributed neurophysi ological events,
sharing information in real time (Friston 2002; Hacker
et al. 2013; Jann et al. 2012). Consistent with this view,
within the past half-decade, hundreds of studies have
demonstrated brain function is best understood through
functional integration models showing the time-dependent
patterns in neural activation of anatomically separ ated
brain regions (Friston 2012; Menon 2012). These models
contrast with traditional brain mapping procedures (func-
tional segregation approach) utilizing regional cerebral
activation changes to identify abnormalities (Fuster and
Bressler 2012; Rabinovich et al. 2012a). As a result of a
paradigm shift in neural assessment, methods used to
evaluate the neurobiology of cognition currently measure
the brain’s intrinsic activity using multivariate functional
connectivity approaches rather than relying on discrete
brain regions to expl ain many aspects of neurobiology and
cognition. To better understand this viewpoint, one has to
go beyond classical information processing theory, seeing
the brain as an information processing device, dependent
upon multiple time series of continuous information flow to
& Marcie L. Zinn
mzinn1@depaul.edu
1
Department of Community Psychology, Center for
Community Research, DePaul University, Chicago,
IL 60614, USA
123
Appl Psychophysiol Biofeedback
DOI 10.1007/s10484-016-9331-3
maintain steady-state homeostasis (Perlovsky 2012 ; Rabi-
novich et al. 2012a).
Myalgic encephalomyelitis (ME)
1
is a complicated
disorder characterized by extreme fatigue not otherwise
explained by an underlying medical condition. However,
mild to moderate neurocognitive impairment (DSM-V), is
present and often worsens after physical or mental activity,
not improving with rest, rendering daily activities such as
cooking meals, taking care of oneself, etc. difficult or
impossible. Many with ME experience hyper-sensitivity to
environmental events such as chemicals, noise or lights,
and also experience persistent viral symptoms (sore throat,
headache, nausea, etc.) (Jason et al. 2013). The most severe
patients are bed-bound. Therefore, a crucial issue in the
study of ME is to discover better methods to measure
patient symptoms. Here, we focus on neurocognitive
symptoms of the disorder (Jason et al. 2015).
Paradoxically, there is a considerable gap between cur-
rent empirical findings which assess brain function using
neuropsychological testing in ME, and patient self-reports
(Cockshell and Mathias 2014; Hawk et al. 2006). Within
neuroimaging literature, a similar situation exists whereby
imaging studies have historically been conducted to
examine the neurocognition in ME with only inconsistent
and/or weak findings (DeLuca et al. 2009). For example, a
recent meta-analysis of 50 studies covering 1544 patients
with ME found that the neurocognitive deficits were only
seen in memory, attention, and reduced responsiveness,
failing to find support for many other symptoms and
complaints routinely reported by patients. Typically,
patients wi th ME describe symptomatology using con-
structs such as hypersensitivity to environmental events,
deficits in motor functioning, selective and sustained
attention, speech, planning, decision making, error cor-
rection, reading and speech comprehension, information
processing speed, and visuospatial ability (Dickinson 1997;
Jason et al. 2010a, b; Thomas and Smith 2009). The
unfortunate outcome of the diverse findings discussed here
have contributed to a wide schism in medicine and science
regarding ME; that is, some believe that the absence of
clear, consistent findings supports the hypothesis that ME
is simply a form of somatization disorder with very little or
no pathophysiology, while others believe that ME is a
medical disorder whose etiology is not fully known (Lange
et al. 1998 , 2005; Tiersky et al. 1997; Twisk 2014). This
confusion has in turn led to a debate about how to best
investigate, classify, and treat ME (Twisk 2014 ). Another
substantial literature indicates that neurocognitive deficits
largely exist independently of ME (are not part of the
illness) (Afari and Buchwald 2003; Claypoole et al. 2007;
Constant et al. 2011; DeLuca et al. 1997; Sandman et al.
1993; Thomas and Smith 2009). Research indicates, how-
ever, that psychological antecedents, triggers, or mediators
of ME may be present as in any medical problem. Several
investigators have therefore shown that ME is not a pri-
mary psychological condition (Broderick et al. 2010; Hawk
et al. 2006; Maes et al. 2012; Wilson et al. 2002) and
though medical and/or psychological treatments may
reduce symptomatology, they have never been shown to
cure ME.
The use of quantitative electroencephalography (QEEG)
to assess neurocognition in ME has a more consistent
history. Known as the ‘gold standard’ measure of brain
states (Thatcher 2012), it is a core assessment in
polysomnography, epilepsy (Ropper and Samiuels 2014
),
as well as numerous disorders of cognition (Westmoreland
2005). In 1990, QEEG event-r elated potentials were used
to assess the slowed speed of information processing in ME
(Prasher et al. 1990). Since that time, a small but growing
number of QEEG studies have been conducted, reporting
oscillatory abnormalities (particularly delta and theta) and
indications of homeostatic dysregulation in patients during
wakefulness (Billiot et al. 1997; Decker et al. 2009; Flor-
Henry et al. 2004; Hammond 2001; James and Folen 1996;
Kishi et al. 2011; Le Bon et al. 2012; Sherlin et al. 2007;
Siemionow et al. 2004; Van Hoof et al. 2007). In sum,
QEEG and electrical neuroimaging may hold promise for
use in evaluation of brain dysregulation in ME, especially
since several authors now believe that the pathology of ME
will be found at the cellular level (Broderick et al. 2010,
2011; Dinkel et al. 2002; Light et al. 2012; Wilson et al.
2002) and aberrant neural oscillations are a function of
structural and functional abnor malities, often existing at
the cellular level. It is therefore important to explore ME
using QEEG methods as this distinctive modality will
likely provide a more complete picture of neurocognitive
symptoms associated with true physiological events.
Another advantage of QEEG over fMRI, PET, SPECT,
MRI, CT and similar methods is that EEG measures
directly asse ss neuronal activity at a high time-resolution
(at the millisecond level), thereby detecting subtle time
differences in neuronal communication through examina-
tion of oscillatory patterns generated by cortical and sub-
cortical regions (Buzsaki 2006; Steriade 2005; Thatcher
2012). This is a distinct advantage given toda y’s neural
assessment models that emphasize the perpetual dynamic
nature of the brain and how neuropsychiatric issues can,
and often do, stem from dysregulated dynamic systems.
The electroencephalograph is able to detect functional
changes even in situations when MRI detects no or few
structural problems. Exact low resolution electromagnetic
tomography (eLORETA) is a linear, discrete, three-
1
For the sake of clarity, throughout this article we will use ME even
though a number of studies use Chronic Fatigue Syndrome (CFS) to
describe their patient samples.
Appl Psychophysiol Biofeedback
123
dimensional weighted minimal norm inverse solution and
the latest iteration in a family of well-established EEG
inverse methods (Pascual-Marqui 2002; Pascual-Marqui
et al. 1994, 2011a). eLORETA has the advantage of
allowing a non-invasive study of intra-cortical interactions
with accurat e spatial resolution that is similar to fMRI even
after spatial filtering which is commonly applied, to
increase the signal-to-noise ratio of the hemodynamic
signal (Poldrack et al. 2012). Lagged phase synchroniza-
tion, one measure of information transfer between two
brain regions, is a real-t ime animation of the information
transfer that links patients’ symptoms and complaints to
functional systems in the brain. Here, we used this tech-
nology to assess the hypothesized functional system dys-
regulation in the brains of patients with ME.
Triple Network Model of Brain Pathology
Functional connectivity approaches have ushered in a
substantial paradigm shift in the study of cognitive
impairment (Menon 2011). These approaches aid in the
understanding of how functionally connected systems
produce pathology through alterations in connectivity pat-
terns or brain dynamics (de Pasquale et al. 2012; Sporns
2013). However, brain networks do not operate in isolation.
The Menon Triple Network model of brain pathology
(Greicius et al. 2008, 2009; Menon 2011, 2012; Supekar
and Menon 2012) offers such a system to assess cognitive
dysfunction in a variety of neurocognitive disorders
(Menon 2012; Rabinovich et al. 2012b). It hypothesizes
that there are three primary networks which operate syn-
ergistically to regulate shifts in arousal, attention, and
general access to cognitive abilities (Menon 2012; Raichle
2010; Uddin et al. 2011). Predictions from the model
include that dysregulation in one of the three core networks
will significantly impact the other two networks, producing
dysregulation of, and symptoms in, all three networks. The
complex symptom structures of these networks will then
vary according to their source (environmental events,
internal states, genetics), and can yield a number of levels
of prominence by time and individual.
For exampl e, in a healthy brain, the central executive
network (CEN) and the salience network (SN) activity
increases as a function of cognitive and affective process-
ing (Uddin et al. 2013) while the Default Mode Network
(DMN) decreases activity during the same processing; the
opposite occurs during activation of the DMN activity
(Greicius and Menon 2004; Supekar and Menon 2012).
According to the model, when all three networks display
deficient context-dependent engagement and/or disen-
gagement signaling, they create imbalances leading to
neuropsychological symptomology produced by deficits
SN, DMN, and CEN activation and coactivation (Chand
and Dhamala 2015; Chiong et al. 2013; George and Pearce
2012).
The anterior insula (AI), a crucial hub in brain net-
works (Laird et al. 2011; Seo and Choo 2015) has been
shown to produce patterns of structural and functional
changes during cognitive impairment (Bora et al. 2010;
Nickl-Jockschat et al. 2012). The anterior insula is in the
SN, which is primarily made up of the dorsal anterior
cingulate and anterior insula cortices (Laird et al. 2011;
Thatcher 2012) playing a key role in sorting out relevant
stimuli, both external and internal (Haase et al. 2016;
Nguyen et al. 2016; Romero-Grimaldi et al. 201 5 ); this
switching mechanism, between all three networks, aids in
focused attention to environmental events, allowing the
stimuli to be interpreted with incr eased importance (Hu
et al. 2015; Makovac et al. 2016; Qin et al. 2015; Srid-
haran et al. 2008). In pathological states, the SN not only
is impaired in its ability to sufficiently switch between the
CEN and the DMN, but it inappropriately assigns
importance to inconsequential events or too little impor-
tance to significant events, both internal and external
(Greicius et al. 2009) thereby producing deregulated sig-
nals of pain, anxiety, and/or other negative states (Yang
et al. 2012 ).
The CEN includes the dorsolateral prefrontal cortex and
the posterior parietal cortex (Menon 2012; Sridharan et al.
2008). Its key roles include maintenance of working
memory, goal-directed behavior, judgment and decision-
making, activating during executive functioning, then
deactivating during the self-referential thought including
autobiographical episodic memory and mentation of the
DMN (Kim et al. 2016; Varela 2014). The DMN, the most
studied network, characterizes basic neural activity which
negotiates self-referential thought, mentation, and intro-
spection (McCormick et al. 2013), decreasing activity with
task demands (Bonnelle et al. 2011, 2012). The DMN is
metabolically ‘expensive,’ invol ving a high number of
brain regions, and is implicated independently in a number
of neurocognitive disorders (Bonnel le et al. 2011, 2012;
Crone et al. 2011; Damoiseaux et al. 2006, 2008). Deficits
in this process may play a substantial role in neurocogni-
tive disorders (Menon 2011; Putcha et al. 2015), creating
phenotypic deficits in executive functioning (memory,
information processing speed, learning capability, etc.) as
well as the ability to self-reflect and process personal
information (Menon 2012).
The CEN is engaged during external cognitive tasks
(e.g. planning, attention, adaptive cognitive processes to
meet environmental demands) (Varela 2014) and is nega-
tively correlated with DMN activity (Putcha et al. 2015).
The SN is involved in awareness of body states (Chiong
et al. 2013; Menon and Uddin 2010) and in switching states
between the DMN and CEN (Daniels et al. 2010
). Taken
Appl Psychophysiol Biofeedback
123
together, in neurocognitive disorders, the 3 networks pre-
sented here, display deficits in access, commitment, and
separation of resources (Greicius et al. 2003, 2004; Gre-
icius and Menon 2004).
The purpose of the present study was therefore to first
examine the differences in cortical source density between
patients with ME and healthy controls, then using the same
individuals, assess lagged phase synchronization (or phase
lock) in the same individuals, within the Menon Triple
Network model. We hypothesized that abnormal neural
function would be evidenced by dysregulated rhythms in
the delta, theta and/or alpha frequency bands. Based on
previous work finding dysregulation in these bands using
source localization methods (Canuet et al. 2011; Flor-
Henry et al. 2010; Lehm ann et al. 2012; Sherlin et al.
2007). Given its high sensitivity and specificity with pre-
cise localization, eLORETA (Pascual-Marqui et al. 2011a)
was chosen to extract the most clinically relevant infor-
mation from the QEEG data. eLORETA lagged phase
synchronization was used to assess functional connectivity
within the triple network model, due to previous observa-
tions of slowed phase lock duration in this population (Zinn
et al. 2016). We also sought to determine whether the
eLORETA lagged phase synchronization may be a viable
tool in the study of ME in clinical applications to aid in the
diagnosis and treatment planning.
Method
Participants
Eighteen adults were included in this study (9 individuals
with ME, 9 healthy controls) ranging in age from 23 to
79 years and the mean age was 42.4 years (SD = 20.5).
There were 3 males and 15 females, 17 participants were
right handed, and all participants were Caucasian. All
participants visited the DePaul University Center for
Community Psychology Research. The ME group met the
Canadian Clinical Criteria (Carruthers et al. 2003) and had
been diagnosed with ME (some physicians used the term
CFS). No participants were taking medications that would
affect the EEG.
Materials and Procedure
Eyes-closed, resting state EEG was recorded for 5 min
using the Discov ery 19-channel acquisition amplifier
(BrainMaster Technologies, Bedford, Ohio) with Neu-
roguide (Applied Neuroscience, Inc.) software (version
2.8.5) from 19 scalp electrode locations (Fp1, F3, F4, F7,
F8, Fz, C3, C4, Cz, P3, P4, Pz, T3, T4, T5, T6, O1, O2)
positioned according to the international 10/20 system
using standardized electrode caps (Jurcak et al. 2007)
employing passive electrodes for the linked ears references
(2). During electrode preparation, impedances for all sites
were maintained below 5 kX. Participants were 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. Each
participant was seated upright in a comfortable chair in a
well-lit room. Participants were given instructions to ‘relax
to the best of your ability while keeping your eyes closed
until the recording session has ended.’ EEG data were
acquired at a 256 Hz sampling rate and filtere d offline
between 1 and 40 Hz. Deartifacting was conducted as
follows: first , by visually inspecting and manually editing
to remove any visible artifact. Then, using Neuroguide
automated Z-score artifact rejection algorithms, set for
high sensitivity as well as amplitude selection set at 2
standard deviations for immediate exclusion of EEG seg-
ments, eye movement, muscle, and drowsiness artifact
were eliminated. Third, a second visual inspection and
manual removal of the artifact was done by the EEG
technician. Since this study was directed toward under-
standing changes in phase relationships of the original
time-series data, independent components anal ysis was not
used. The methodological problem of distorting time and
phase relations present in the original time series from
using ICA/Regression procedures has been empirically
validated in several studies (Castellanos and Makarov
2006; Kierkels et al. 2006; Wallstrom et al. 2004). Only
epochs with [95 % split-half reliability and [90 % test–
retest reliability coefficients compu ted by Neuroguide with
total measurement for at least 1 min were subjected to the
analysis. Split-half reliability is the ratio of variance
between the even and odd seconds of the time series of
selected digital EEG. Test–retest reliability is the ratio of
variance between the first half versus the second half of the
selected EEG segments (Thatcher 2012). For each partic-
ipant, artifact-free data were then exported to text files
containing 2-s EEG segments with a 75 % cosine taper
window to minimize leakage (Sterman and Kaiser 2000).
Further procedures were performed on the exported surface
EEG data using LORETA-KEY software (R. Pascual-
Marqui 2015) as freely provided by the Key Institute for
Brain-Mind Research, University Hospital of Psychiatry,
Zurich at http://www.uzh.ch/keyinst/loreta.htm.
eLORETA Source Localization
Eyes-closed resting EEG data were analyzed using
eLORETA to compute the 3-dimensional distribution of
intracortical brain electrical activity (Pascual-Marqui 2007;
Pascual-Marqui et al. 2011b). The eLORETA inverse
solution has zero localization error under ideal, noise-free
Appl Psychophysiol Biofeedback
123
conditions and the solution space has a volume of 6239
voxels at 5 mm
3
spatial resolution. Computations of cortical
current source density are restricted to unambiguous cortical
grey matter (Mazziotta 2001) using Montreal Neurological
Institute (MNI) coordinates for the significantly active
regions of interest with neuroanatomical labels and Brod-
mann areas based on ‘corrected’ Talairach coordinates
(Lancaster et al. 2000; Talairach and Tournoux 1988).
Implementation is based on a 3-shell spherical head model
and EEG electrode coordinates derived from spherical and
realistic Talairach head geometry (Towle et al. 1993). A
detailed report of this inverse solution, together with the
proof of its exact zero-error localization property, can be
found in an article by Grech et al. (2008). eLORETA func-
tional images of current source density was computed from 1
to 40 Hz for the following nine frequency bands: delta
(1–3 Hz), theta (4–7 Hz), alpha-1 (8–10 Hz), alpha-2
(10–12 Hz), alpha (8–12 Hz), beta-1(13–18 Hz), beta-2
(19–21 Hz), beta-3 (22–30 Hz) and gamma (30–40 Hz).
Functional Connectivity Analysis
Cortical regions of interest (ROIs) within each of the core
neurocognitive networks were defined a priori, chosen on
the basis of previously published research on resting-state
networks (Raichle 201 1) derived from BOLD fMRI signals
(coordinates shown in Tables 1, 2 , 3). All connec tivity
analyses were conducted within the ROI’s specific to the
network being analyzed. Each ROI under investigation was
assigned one 5-mm
3
voxel and all of its nearest adjacent
voxels (5 9 5 voxels/15 9 15 mm
3
maximum) to repre-
sent each corresponding Brodmann area. To conduct the
functional connectivity analysis, we used eLORETA to
evaluate group differences in lagged phase synchronization
for all nine frequency bands between each of the 6 pairs of
ROIs within the SN (135 connections), 6 pairs of ROI’s
within the CEN (135 connections), and within 7 pairs of
ROI’s in the DMN (189 connections), for each of the nine
frequency bands (total ROIs X 9 = n connections). We
chose lagged phase synchronization to assess the functional
similarity in the multivariate time series of signaling
between all pairs of regions of interest within each net-
work. Lagged phase synchronization measures the
nonlinear dependence between two signals in the frequency
domain while correcting for the instantaneous zero-lag
component to remove artifact. This phase synchrony cor-
rection is necessary to exclude contam ination due to non-
physiological effects, and physics artifact from low spatial
resolution and volume conduction. Therefore, lagged phase
synchronization is considered to be an index of true
physiological functional connectivity information (Pascual-
Marqui et al. 2011b). Lagged phase synchronization was
calculated for each participant from 1 to 40 Hz in the
following nine frequency bands: delta (1–3 Hz), theta
(4–7 Hz), alpha-1 (8–10 Hz), alph a-2 (10–12 Hz), alpha
(8–12 Hz), beta-1(13–18 Hz), beta-2 (19–21 Hz), beta-3
(22–30 Hz) and gamma (30–40 Hz) for each network. This
produced a text output file for each person with a corre-
lation matrix showing columns equal to the number of
ROIs, and rows equal to the number of time frames.
eLORETA Statistics and Multiple Comparison Corrections
The eLORETA software package was used to evaluate
group differences in current source density in cortical
source localization between groups within each frequency
band. To create three-dimensional statistical images for all
nine frequency bands, we conduc ted voxel-by-voxel inde-
pendent sample F-ratio-tests to evaluate the differences,
based on eLORETA log-transformed current source den-
sity power. To control for potential global experimental
effects, a subject-wise normalization was performed to
scale the data for each subject by dividing the value of
every single voxel by the total power of all voxels of each
image. Source voxels with significant differences were then
identified using a nonparametric permutation/randomiza-
tion procedure (Fisher 1971), with a threshold set at the
0.05 probability level. To control for Type 1 error, we
applied a statistical non-parametric mapping (SnPM) pro-
cedure to estimate the empirical probability distribution
and find the ‘maximal-statistic’ at the 95th percentile
under the null hypothesis. SnPM has been shown elsewhere
to be effective in controlling the Type I error in neu-
roimaging studies (particularly when evaluating electro-
physiological data) without the need to rely on Gaussianity
(Holmes et al. 1996; Nichols and Holmes 2002). Another
Table 1 eLORETA
coordinates used for default
mode network regions of
interest (adapted from Raichle
2011)
Orientation Brodmann area X, Y, Z coordinates
a
Neuroanatomical label
Left medial 23 0, -52, 27 Posterior cingulate
Left 9 -1, 54, 27 Medial frontal gyrus
Left 39 -46, -66, 30 Left angular gyrus
Right 39 49, -63, 33 Right angular gyrus
Left 21 -61, -24, -9 Middle temporal gyrus
Right 21 58, -24, -9 Middle temporal gyrus
a
x,y,z coordinates provided in MNI space. Neuroanatomical labels taken from eLORETA
Appl Psychophysiol Biofeedback
123
advantage of permutation strategies is that they can be
applied to any statistic (t-tests, r values, F-ratios) to find its
critical probability value under the null hypothesis. In our
study, we utilized eLORETA software to compute 5000
data randomizations to create an approximate permutation
distribution needed to determin e the critical threshold value
at the p = 0.05 alpha level for the observed log of F-ratio
statistic to correct for Type 1 error across all voxels and for
all frequencies. The initial procedure describ ed here, the
use of SnPM for creating eLORETA single-voxel statistical
images, has been confirmed in studies (Anderer et al. 1998;
Pascual-Marqui et al. 1999). The value for the critical
threshold is then entered into the ‘scale-max’ parameter of
the LORETA viewer for showing the comparative analysis
with positive/negative color coded significant statistical
values pertaining to the ‘surviving’ voxels (those rejecting
the omnibus null hypothesis). SnPM procedures in
eLORETA also perform exceedance proportion tests to for
determining the critical probability thresholds for supra-
threshold voxels based on spatial extent for cluster-based
inference (cluster statistics). This appro ach yields greater
sensitivity over the singe-voxel test while trading off
specificity.
For the functional connectivity analysis, eLORETA
performed using an independent sample t-tests for gener-
ating t-statistic values of brain connectivity. The ROI’s for
each network can be seen in Tables 1, 2, and 3. As men-
tioned above, we applied the same permutation/random-
ization strategy (SnPM) with 5000 randomizations to find
the critical probability thresholds at sign ificant alpha levels
and correct for Type 1 error.
Results
Source Analysis Using eLORETA
To capture the spatial extent of cortical source activations,
statistical images were assessed for cluster-wise signifi-
cance (Nichols and Holmes 2002). Independent groups
t-tests were performed to compare group differ ences in all
6239 cortical grey matter voxels within the entire eLOR-
ETA solution space. Deviant current source density values
were found in alpha (ME: 0.065, HC: 0.429) and alpha-2
bands (ME: 0.075, HC: 0.305), (log-F-ratio thresh-
old =-1.65, p = 0.033, two-tailed, corrected) in the
bilateral parietal, occipital and posterior temporal lobes
(Figs. 1, 2). No other significant differences or significant
relationships in source localization were found betwee n the
patient and control groups in the above analyses with
respect to the delta, theta, beta or gamma frequency bands
as defined in this study.
Functional Connectivity Analysis Using eLORETA
In the assessment of the triple network model, functional
connectivity in patients with ME compared with healthy
controls showed significantly decreased lagged phase
synchronization for Delta, Alpha, and Alpha-2 in most
cortical regions: DMN (threshold: t =-1.84; p = 0.021,
one-tailed, corrected), the SN (threshold: t =-1.9;
p = 0.037, one-tailed, corrected), and the CEN (threshold:
t =-1.36; p = 0.024, one-tailed, corrected) (Figs. 3, 4, 5,
6, 7, 8). One-tailed tests were chosen a priori due to
Table 2 eLORETA
coordinates used for central
executive network regions of
interest (adapted from Raichle
2011)
Orientation Brodmann area X, Y, Z coordinates
a
Neuroanatomical label
Left medial 8 0, 24, 46 Medial frontal gyrus
Left 8 -33, 22, 53 Superior frontal gyrus
Left 10 -44, 45, 0 Inferior frontal gyrus
Right 10 44, 45, 0 Inferior frontal gyrus
Left 40 -50, -51, 45 Inferior parietal lobule
Right 40 50, -51, 45 Inferior parietal lobule
a
x,y,z coordinates provided in MNI space. Neuroanatomical labels taken from eLORETA
Table 3 eLORETA
coordinates used for salience
network regions of interest
(adapted from Raichle 2011)
Orientation Brodmann area X, Y, Z coordinates
a
Neuroanatomical label
Left medial 32 0, 21, 36 Cingulate gyrus
Left 10 -35, 45, 30 Middle frontal gyrus
Right 10 32, 45, 30 Middle frontal gyrus
Left 13 -41, 3, 6 Insula
Right 13 41, 3, 6 Insula
Left 40 -62, -45, 30 Supramarginal gyrus
Right 40 62, -45, 30 Supramarginal gyrus
a
x,y,z coordinates provided in MNI space. Neuroanatomical labels taken from eLORETA
Appl Psychophysiol Biofeedback
123
evidence of cortical hypofunction found in our source
analysis in addition to our prior investigations (Zinn et al.
2014a, b).
Discussion
In the present study, we applied two new methodological
approaches to investigate resting-state neurological differ-
ences in people with ME compared to healthy controls
(source analysis and functional connectivity analysis).
First, abnor malities in current source densities were found
with our patient group, displaying decreased alpha and
alpha-2 current sources primarily in the bi-lateral parieto-
occipital region (Figs. 1, 2). Alpha rhythm, the dominant
oscillation in the human brain, is especially prominent in
the posterior regions, representing a distinctive feature of
the normal brain in the waking resting state. The alpha
rhythm has been shown to modulate inhibition, timing,
attention, memory processes including consolidation,
detection of irrelevant stimuli, and information processing
speed (Capotosto et al. 2009; Ishii et al. 2010; Klimesch
Fig. 1 eLORETA source current density in Alpha 8–12 Hz in ME patients compared to healthy controls
Appl Psychophysiol Biofeedback
123
1996, 1997, 1999; Klimesch et al. 1997, 2010; Schabus
et al. 2011). Abnormalities in alpha are typically seen in
the parietal-occipital regions which can represent signs of
cerebral dysfunction in neurocognitive disorders (Babiloni
et al. 2014). Many of these symptoms corroborate those
commonly reported by patients with ME, especially the
slowed information processing speed.
The alpha frequency band is currently regarded as
important in cognitive function due its strong correlation
with general cognitive abilities. Alpha activity matures in
early adolescence (Simkin et al. 2014; Thatcher et al. 2008),
declines in old age (Klimesch 2012) and is a reliable pre-
dictor of many aspects of memory (Angelakis et al. 2004;
Vogt et al. 1998). The alpha frequency band has reliably
demonstrated predictive power of individual differences in a
large number of studies involving cognitive and perceptual
processes (Osaka 1984; Osaka et al. 1999), including visual
encoding (Klimesch et al. 2011), response sel ection (Kli-
mesch et al. 2011) and motor prepa ration (Holz et al. 2008;
Sauseng et al. 2009). Taken together, studies such as these
find a strong positive relationship between the alpha fre-
quency and executive functioning. For example, Klimesch
Fig. 2 eLORETA source current density in Alpha 2, 10–12 Hz in ME patients compared to healthy controls
Appl Psychophysiol Biofeedback
123
Fig. 3 CEN high Alpha 2 (10–12 Hz). eLORETA wire diagram indicating cortical regions with significantly decreased alpha 2 lagged phase
synchronization in patients with Myalgic Encephalomyelitis compared to healthy controls
Fig. 4 CEN delta (1–3 Hz). eLORETA wire diagram indicating cortical regions with significantly decreased delta lagged phase synchronization
in patients with Myalgic Encephalomyelitis compared to healthy controls
Appl Psychophysiol Biofeedback
123
Fig. 5 SN high Alpha 2 (10–12 Hz). eLORETA wire diagram indicating cortical regions with significantly decreased alpha 2 lagged phase
synchronization in patients with Myalgic Encephalomyelitis compared to healthy controls
Fig. 6 SN Delta (1–3 Hz). eLORETA wire diagram indicating cortical regions with significantly decreased delta lagged phase synchronization
in patients with Myalgic Encephalomyelitis compared to healthy controls
Appl Psychophysiol Biofeedback
123
Fig. 7 DMN high Alpha 2 (10–12 Hz). eLORETA wire diagram indicating cortical regions with significantly decreased alpha 2 lagged phase
synchronization in patients with Myalgic Encephalomyelitis compared to healthy controls
Fig. 8 DMN Delta (1–3 Hz). eLORETA wire diagram indicating cortical regions with significantly decreased delta lagged phase
synchronization in patients with Myalgic Encephalomyelitis compared to healthy controls
Appl Psychophysiol Biofeedback
123
et al. (1993) characterized alpha phase synchronization as a
control process that organizes top-down modulation of
working memory and attention as well as access to long-
term memory. Other authors find relationships between
human memory, alpha amplitude and network dynamics
(Hughes and John 1999). The effects of modulation of
cognition within the alpha band are strong regardless of the
nature or direction of the cognitive domain assessed. Our
source density finding showing reduced alpha reflects the
types of cognitive impairment that it is associated with (e.g.
memory, attention, concentration, information processing
speed). Furthermore, alpha rhythms are known to have
involvement in ME (Billiot et al. 1997).
Specific locations of the reduced alpha band activity
included the cuneus region, precuneus, lingual gyrus,
posterior cingulate, parahippocampal gyrus, fusiform
gyrus, superior parietal lobule, premotor and primary
motor areas (precentral gyrus), middle temporal gyrus,
inferior temporal gyrus, and angular gyrus. Taken together,
abnormal delta and alpha-2 rhythms in occipital, parietal,
temporal, and limbic regions provide objective evidence
for ME neurocognitive symptoms with disruption to the
perception–action cycle (Fuster 2009) and associated sen-
sorimotor deficits.
Sherlin et al. (2007) found increased delta in the left
uncus, left parahippocampal gyrus, and increased theta in
the cingulate gyrus and right superior frontal gyrus, and in
2014, Zinn et al. found increased delta in over 50 % of the
frontal-limbic regions in the superior frontal gyrus, entire
cingulate gyrus, medial frontal gyrus, orbito-frontal cortex,
middle frontal gyrus, insula, superior temporal gyrus and in
the rectal gyrus. Our overall findings extended previous
ME neuroimaging research by providing a more complete
analysis of neurocognitive deficits. First, alpha, alpha 1,
and alpha-2 curr ent source density was deviant from nor-
mal in patients with ME involving the entire occipital lobe,
extending into portions of the parietal, temporal and limbic
lobe (Figs. 1, 2). The divergent results most likely indicate
severity levels of disease and individual differences in
brain pathology.
Second, there was significantly reduced lagged phase
synchronization in the DMN, the SN and the CEN in delta
and alpha bands, supporting the triple network model.
Evidence of psychomotor slowing in ME was found by
Van Den Eede et al. (2011) who demonstrated delayed
reaction time and movement time in patient s with ME, a
confirmation of earlier ME studies showing psychomotor
slowing, persistent motor impairment, and impaired corti-
cal motor area excitability in patients (Gaudino et al. 1997;
Majer et al. 2008; Prasher et al. 1990 ). Taken together,
these studies support our finding of hypoconnectivity in
lagged phase synchronization in all three networks of the
Menon triple network model in the occipital, parietal,
posterior cingulate, and posterior temporal lobes. Func-
tional connectivity disruptions between nodes and hubs of
all three networks in the triple network model reveal the
presence of decreased phase lag synchronization affecting
the delta and alpha bands. This is consistent with other
neuropsychological studies finding significantly decreased
delta band connectivity using both linear (coherence)
(Burroughs et al. 2014) and nonlinear (phase lag synchro-
nization) measures (Bosma et al. 2009; Cooray et al. 2011;
Zeng et al. 2015). Decreased functional connectivity within
the SN, DMN and CEN could indicate a biomarker if
commonly found in patients with ME. Finding support for
the triple network model has far-reaching implications for
ME, given that it may explain many of the symptoms
reported by patients. Some of those symptoms include the
feelings of derealization (brain fog) commonly experienced
in the disease. Other implications include issues in complex
spatial patterns, deductive reasoning, mental navigation,
visual rotation and similar spatial issues. ME patients are
known for their inability to drive a car, and often become
lost even in their own neighborhood. Hypoactive mental
states interrupt consciousness, sleep and can bring on
vegetative states, underpinning changes in behavior and
general self- and other-awareness (Ramos Reis et al. 2013).
These combined findings present support for inefficient
allocation of resources dependent on three factors: (1) that
the incomplete switching between the DMN and CEN were
likely present (but not directly measured). Recent studies
have observed the fluctuations in activation of all networks,
including the SN in both task-based and task-free states.
For example, in normal, healthy individuals, switching
occurs whenever the task performance occurs and is cor-
related with the CEN. However, during pathological states,
the switching effect is compromised, creating significant
group differences between engagement and disengagement
of the CEN (Daniels et al. 2010). In this manner, aberrant
switching would prevent optimal cognitive states in our
sample. (2) We found weakened coupling within the SN
suggests hypersensitivity within many domains, another
facet of ME symptom presentation. Though we did not
measure hypersensitivity, it is an exceedingly common
complaint of patients with ME. (3) Due to aberrant SN
switching and weakened connectivity, the DMN would
likely never be fully active, resulting in patients having
considerable trouble engaging in self-reflection and men-
tation, thereby interrupting normal brain dynamics within
moving time windows. We believe this may contribute to
‘brain fog,’ slowed information processing speed and
possibly deregulation in other aspects of cognitive func-
tion. Overall, these deficits would be seen as cognitive
decline.
Appl Psychophysiol Biofeedback
123
Limitations
Although the present findings taken from eLORETA neu-
roimaging method reveal dysregulation in the alpha band,
as well as support for the triple network model, we
acknowledge limitations of this study. First the eLORETA
neuroimaging method can only examine cortical areas but
does not look at subcortical structures such as the
hypothalamus, thalamus, amygdala, hippocampus, basal
ganglia, cerebellum, and brainstem. However, cortical
pathology within brain circuits indirectly implicates these
subcortical structures by inference. For this pilot study,
results should be cautiously interpreted because of the
relatively small sample size. Future studies should include
larger sample sizes with more experimental groups such as
a group with co-morbid depression. Future research should
also include neuropsychological measures for comparison
to connectivity findings, such as measures of sleep distur-
bance and executive functioning. Such measur es would be
most usef ul if they are found to predict the most commonly
found pathological states in patients with ME, such as
slowed information processing speed, memory and con-
centration disturbances and overall feelings of derealiza-
tion and depersonalization.
Conclusion
The present study suggests that a clear pattern of sub-
stantial CNS hypoactivation in ME patients, finding sup-
port for aberrant source localization. Current ME research
points to a common finding of cognitive slowing in ME and
we identified this with quantifiable reductions in delta and
alpha frequency bands as well as relating delta and alpha
cortical sources to reduc ed functional connectivity. By
finding support for the Menon Tripe Network model of
pathology, we provide one possible explanation for known
cognitive deficits in ME, such as incomplete engagement of
executive functioning in the awake state.
Our study used eLORETA to explore EEG indices of
ME pathophysiology with findings implicating profound
CNS involvement. Our results support the hypothesis that
there is significant brain dysregulation overall seen in the
parietal, occipital, posterior temporal, posteri or cingulate
and parahippocampal gyrus. Dysregulation is also present
within the 3 core networks of the human brain as defined
by the triple network model, within ME. Based on high
concordance of our findings with other ME source analysis
studies, it is possible that eLORETA can provide clinically
relevant information about patients with ME, and may be
therefore a viable tool for use in clinical as well as research
settings.
Acknowledgments Financial Disclosure: This study was supported
by Linda Clark.
Compliance with Ethical Standards
Conflict of interest The authors declare that there is no conflict of
interest. The funder played no role in the design or conduct of this
study.
Ethical Standards All procedures performed in studies involving
human participants were in accordance with the ethical standards of
the institutional and/or national research committee and with the 1964
Helsinki declaration and its later amendments or comparable ethical
standards. This study was approved by the Institutional Review Board
at DePaul University in Chicago, Protocol # LJ052615 PSY.
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... EEG measures in general represent direct neuronal activity with high time-resolution at the millisecond level, thereby detecting fast, ongoing neural processes underlying oscillatory dynamics [55,56]. The eLORETA, a linear, discrete, three-dimensional weighted minimal norm inverse solution method [42], enables the non-invasive examination of intra-cortical interactions with interpretable spatial resolution [57]. Specifically, the lagged phase synchronization of the eLORETA is predominantly used to assess functional connectivity (e.g., [45,58]), as it represents physiological (neural) information and is minimally affected by the low spatial resolution [42]. ...
... Localization capabilities and concordance of LORETA based methods have been reported by multimodal imaging studies of fMRI [92,93], structural MRI [94], and positron emission tomography (PET) [95,96], including studies with intracranial recordings in humans [97]. A growing number of studies are using eLORETA methods to examine current density activations and functional connectivity across brain regions to understand neurocognitive functioning and abnormalities (e.g., [43][44][45]57,58,[98][99][100]). ...
... It is likely that resting state hyperconnectivity across DMN connections may be suggestive of neural hyperexcitability and disinhibition in AUD individuals [108][109][110][111][112], possibly modulated by GABAergic and glutamatergic mechanisms underlying neural excitability reflected in EEG and acute and chronic effects of alcohol in the brain [113][114][115][116][117]. While neural disinhibition in other electrophysiological measures (e.g., low P3 amplitude and suppressed delta and theta oscillations underlying P3 during cognitive processing, and increased resting state beta power) have been reported [108,[118][119][120], resting state EEG source FC may serve as an important and novel index of neural disinhibition in AUD and other externalizing disorders, as it is a direct measure of neural communication and brain (dys)function [57]. Since there is only a single eLORETA study on AUD [59], and it is worth comparing our findings to it. ...
Article
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: Individuals with alcohol use disorder (AUD) manifest a variety of impairments that can be attributed to alterations in specific brain networks. The current study aims to identify features of EEG-based functional connectivity, neuropsychological performance, and impulsivity that can classify individuals with AUD (N = 30) from unaffected controls (CTL, N = 30) using random forest classification. The features included were: (i) EEG source functional connectivity (FC) of the default mode network (DMN) derived using eLORETA algorithm, (ii) neuropsychological scores from the Tower of London test (TOLT) and the visual span test (VST), and (iii) impulsivity factors from the Barratt impulsiveness scale (BIS). The random forest model achieved a classification accuracy of 80% and identified 29 FC connections (among 66 connections per frequency band), 3 neuropsychological variables from VST (total number of correctly performed trials in forward and backward sequences and average time for correct trials in forward sequence) and all four impulsivity scores (motor, non-planning, attentional, and total) as significantly contributing to classifying individuals as either AUD or CTL. Although there was a significant age difference between the groups, most of the top variables that contributed to the classification were not significantly correlated with age. The AUD group showed a predominant pattern of hyperconnectivity among 25 of 29 significant connections, indicating aberrant network functioning during resting state suggestive of neural hyperexcitability and impulsivity. Further, parahippocampal hyperconnectivity with other DMN regions was identified as a major hub region dysregulated in AUD (13 connections overall), possibly due to neural damage from chronic drinking, which may give rise to cognitive impairments, including memory deficits and blackouts. Furthermore, hypoconnectivity observed in four connections (prefrontal nodes connecting posterior right-hemispheric regions) may indicate a weaker or fractured prefrontal connectivity with other regions, which may be related to impaired higher cognitive functions. The AUD group also showed poorer memory performance on the VST task and increased impulsivity in all factors compared to controls. Features from all three domains had significant associations with one another. These results indicate that dysregulated neural connectivity across the DMN regions, especially relating to hyperconnected parahippocampal hub as well as hypoconnected prefrontal hub, may potentially represent neurophysiological biomarkers of AUD, while poor visual memory performance and heightened impulsivity may serve as cognitive-behavioral indices of AUD.
... 47 of the 55 identified papers included participants that fulfilled the requirements of being diagnosed with the Fukuda criteria [12,14,[16][17][18][19][21][22][23][24][25][26][27][28]30,31,[34][35][36][37][38][39][40][41][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61]. Three of the papers used the more stringent CCC criteria to classify ME/CFS patients [15,29,42]. The remaining five research papers used either of the two aforementioned criteria [11,13,20,33,62]. ...
... There were four different neuroimaging techniques used to assess neural changes in ME/ CFS compared with HCs. Out of the 55 studies: 16 studies utilised MRI [11,[13][14][15]20,[31][32][33][34][35][36]43,53,58,62,63], 17 used functional MRI (fMRI) [16,18,[21][22][23][24]26,34,[40][41][42][43]45,49,[49][50][51], five used PET scans [17,25,28,30,44] and 11 used EEG [12,27,29,38,39,[46][47][48]54,55]. The remaining studies used magnetic resonance spectrometry (MRS) [19,52,59,60,64,65]. ...
... One of the most frequently reported structural or functional differences occurred in the cingulate region. This feature was described in 15 studies [18,19,[22][23][24][25][26][27][28][29][30]44,50,57,62]. A PET study reported a 199% greater binding potential of 1C-R-PK11195, which shows neuroinflammation, in the cingulate region of ME/CFS patients compared with HCs. ...
Article
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Background Myalgic encephalomyelitis/ Chronic Fatigue Syndrome (ME/CFS) is a multi-system illness characterised by a diverse range of debilitating symptoms including autonomic and cognitive dysfunction. The pathomechanism remains elusive, however, neurological and cognitive aberrations are consistently described. This systematic review is the first to collect and appraise the literature related to the structural and functional neurological changes in ME/CFS patients as measured by neuroimaging techniques and to investigate how these changes may influence onset, symptom presentation and severity of the illness. Methods A systematic search of databases Pubmed, Embase, MEDLINE (via EBSCOhost) and Web of Science (via Clarivate Analytics) was performed for articles dating between December 1994 and August 2019. Included publications report on neurological differences in ME/CFS patients compared with healthy controls identified using neuroimaging techniques such as magnetic resonance imaging, positron emission tomography and electroencephalography. Article selection was further refined based on specific inclusion and exclusion criteria. A quality assessment of included publications was completed using the Joanna Briggs Institute checklist. Results A total of 55 studies were included in this review. All papers assessed neurological or cognitive differences in adult ME/CFS patients compared with healthy controls using neuroimaging techniques. The outcomes from the articles include changes in gray and white matter volumes, cerebral blood flow, brain structure, sleep, EEG activity, functional connectivity and cognitive function. Secondary measures including symptom severity were also reported in most studies. Conclusions The results suggest widespread disruption of the autonomic nervous system network including morphological changes, white matter abnormalities and aberrations in functional connectivity. However, these findings are not consistent across studies and the origins of these anomalies remain unknown. Future studies are required confirm the potential neurological contribution to the pathology of ME/CFS.
... The use of networks that go beyond individual cytokine analyses can help us identify predisposing biological factors in the development and maintenance of symptoms, and thus lay the groundwork for a biological explanation of ME/CFS. In addition to identifying biological immune markers that could help legitimize patients with ME/CFS, psychologists have also used systems theory to identify irregularities within brain networks of these patients (Zinn et al., 2016). Such dysregulation evidence could be used by practicing psychologists to build rapport with patients who feel demoralized after experiencing skepticism of their illness by employers, friends, and family members (Komaroff, 2021) who are not aware of these findings. ...
... In order to evaluate the connectivity among the RS neurocognitive networks, Regions of Interest (ROIs) for DMN, CEN and SN were defined (Table 1) according to a previous exact Low-Resolution Brain Electromagnetic Tomography (eLORETA) study (Zinn et al., 2016). The connectivity analysis was performed by computing the Lagged Phase Synchronization (LPS; Pascual-Marqui et al., 2011), one of the main neurophysiological indices used for investigating inter-regional functional connectivity (Hata et al., 2016;Imperatori et al., 2017;Olbrich et al., 2014). ...
Article
Need for Cognitive Closure (NCC) is a construct referring to the desire for predictability, unambiguity and firm answers to issues. Neuroscientific literature about NCC processes has mainly focused on task-related brain activity. According to the Triple Network model (TN), the main aim of the current study was to investigate resting state (RS) electroencephalographic (EEG) intra-network dynamics associated with NCC. Fifty-two young adults (39 females) were enrolled and underwent EEG recordings during RS. Functional connectivity analysis was computed trough exact Low Resolution Electromagnetic Tomography (eLORETA) software. Our results showed that higher levels of NCC were associated with both i) decreased alpha EEG connectivity within the Central Executive Network (CEN), and ii) increased delta connectivity within the Default Mode Network (DMN). No significant correlations were observed between NCC and functional connectivity in the Salience Network (SN). Our data would seem to suggest that high levels of NCC are characterized by a specific communication pattern within the CEN and the DMN during RS. These neurophysiological patterns might reflect several typical NCC-related cognitive characteristics (e.g., lower flexibility and preference for habitual and rigid response schemas).
... Patients frequently report brain fog, which is the subjective experience of slowed thinking, difficulty focusing, and forgetfulness [117][118][119]. Furthermore, neuroimaging studies in ME/CFS patients have identified hypoconnectivity in the brainstem and core neurocognitive networks [120,121]. ME/CFS patients additionally display an increased incidence of autonomic impairments [4]. Single nucleotide polymorphisms (SNPs) affecting ACh receptors as well as TRP ion channel members have been identified in ME/CFS patients, suggesting an association between ACh receptors and TRP family members in ME/CFS [122,123]. ...
Article
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The transient receptor potential (TRP) superfamily of ion channels is involved in the molecular mechanisms that mediate neuroimmune interactions and activities. Recent advancements in neuroimmunology have identified a role for TRP cation channels in several neuroimmune disorders including amyotropic lateral sclerosis, multiple sclerosis, and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). ME/CFS is a debilitating disorder with an obscure aetiology, hence considerable examination of its pathobiology is warranted. Dysregulation of TRP melastatin (TRPM) subfamily members and calcium signalling processes are implicated in the neurological, immunological, cardiovascular, and metabolic impairments inherent in ME/CFS. In this review, we present TRPM7 as a potential candidate in the pathomechanism of ME/CFS, as TRPM7 is increasingly recognized as a key mediator of physiological and pathophysiological mechanisms affecting neurological, immunological, cardiovascular, and metabolic processes. A focused examination of the biochemistry of TRPM7, the role of this protein in the aforementioned systems, and the potential of TRPM7 as a molecular mechanism in the pathophysiology of ME/CFS will be discussed in this review. TRPM7 is a compelling candidate to examine in the pathobiology of ME/CFS as TRPM7 fulfils several key roles in multiple organ systems, and there is a paucity of literature reporting on its role in ME/CFS.
... Studies of alterations in the synchronization of neural networks reflect a marked hypoactivity in these patients. The analysis of electri-cal neuroimaging (eLORETA) helps to understand the dysfunctions associated with the syndrome in cognitive areas [38][39][40]. The study of cerebral perfusion using arterial spin labeling (ASL), a non-invasive method that is also used with MRI, shows a decrease in regional cerebral perfusion. ...
Article
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Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a disorder of unknown physiopathology with multisystemic repercussions, framed in ICD-11 under the heading of neurology (8E49). There is no specific test to support its clinical diagnosis. Our objective is to review the evidence in neuroimaging and dysautonomia evaluation in order to support the neurological involvement and to find biomarkers serving to identify and/or monitor the pathology. The symptoms typically appear acutely, although they can develop progressively over years; an essential trait for diagnosis is “central” fatigue together with physical and/or mental exhaustion after a small effort. Neuroimaging reveals various morphological, connectivity, metabolic, and functional alterations of low specificity, which can serve to complement the neurological study of the patient. The COMPASS-31 questionnaire is a useful tool to triage patients under suspect of dysautonomia, at which point they may be redirected for deeper evaluation. Recently, alterations in heart rate variability, the Valsalva maneuver, and the tilt table test, together with the presence of serum autoantibodies against adrenergic, cholinergic, and serotonin receptors were shown in a subgroup of patients. This approach provides a way to identify patient phenotypes. Broader studies are needed to establish the level of sensitivity and specificity necessary for their validation. Neuroimaging contributes scarcely to the diagnosis, and this depends on the identification of specific changes. On the other hand, dysautonomia studies, carried out in specialized units, are highly promising in order to support the diagnosis and to identify potential biomarkers. ME/CFS orients towards a functional pathology that mainly involves the autonomic nervous system, although not exclusively.
... We also performed the subject-wise normalization for different frequency bands by dividing the current source density value of every single voxel by the total activity of all voxels using the same approach described in Eq. (1) with N C representing the total number of cortical voxels (Paquette et al., 2009;Zinn et al., 2016). We further computed the average eLORETA solutions for the ROIs defined at the source level. ...
Article
Objective This study investigated age-dependent and subtype-related alterations in electroencephalography (EEG) power spectra and current source densities (CSD) in children with attention deficit and hyperactivity disorder (ADHD). Methods We performed spectral and cortical source (exact low-resolution electromagnetic tomography, eLORETA) analyses using resting state EEG recordings from 40 children (8-16 years) with combined and inattentive subtypes of ADHD and 41 age-matched healthy controls (HC). Group differences in EEG spectra and CSD were investigated at each scalp location, voxel and cortical region in delta, theta, alpha and beta bands. We also explored associations between topographic changes in EEG power and CSD and age. Results Compared to healthy controls, combined ADHD subtype was characterized with significantly increased diffuse theta/beta power ratios (TBR) with a widespread decrease in beta CSD. Inattentive ADHD subtype presented increased TBR in all brain regions except in posterior areas with a global increase in theta source power. In both ADHD and HC, older age groups showed significantly lower delta source power and TBR and higher alpha and beta source power than younger age groups. Compared to HC, ADHD was characterized with increases in theta fronto-central and temporal source power with increasing age. Conclusions Our results confirm that TBR can be used as a neurophysiological biomarker to differentiate ADHD from healthy children at both the source and sensor levels. Significance Our findings emphasize the importance of performing the source imaging analysis in order to better characterize age-related changes in resting-state EEG activity in ADHD and controls.
... Lange et al. (2005) found that patients with CFS had more widespread activation compared to healthy controls (HC) during a verbal working memory task, suggesting that compensatory mechanisms could be contributing to cognitive symptoms. Furthermore, decreased functional connectivity has been found in primary neurocognitive networks (Boissoneault et al., 2015;Gay et al., 2015;Zinn, Zinn, & Jason, 2016b). Aberrant connectivity, in turn, might contribute to slowed information processing speed and disruption to other aspects of cognitive function. ...
Article
We investigated central fatigue in 50 patients with chronic fatigue syndrome (CFS) and 50 matched healthy controls (HC). Resting state EEG was collected from 19 scalp locations during a 3 min, eyes-closed condition. Current densities were localized using exact low-resolution electromagnetic tomography (eLORETA). The Multidimensional Fatigue Inventory (MFI-20) and the Fatigue Severity Scale (FSS) were administered to all participants. Independent t-tests and linear regression analyses were used to evaluate group differences in current densities, followed by statistical non-parametric mapping (SnPM) correction procedures. Significant differences were found in the delta (1-3 Hz) and beta-2 (19-21 Hz) frequency bands. Delta sources were found predominately in the frontal lobe, while beta-2 sources were found in the medial and superior parietal lobe. Left-lateralized, frontal delta sources were associated with a clinical reduction in motivation. The implications of abnormal cortical sources in patients with CFS are discussed.
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CFS/ME International Conference 2018 National Centre for Neuroimmunology and Emerging Diseases Crowne Plaza, Surfers Paradise, Qld 26-27 November 2018 Abstract Title: ME/CFS: NDIS and the Disability Hurdle Authors: Geoffrey Hallmann Background: B.Bus.(Hons), LLB (Hons). DipLegPrac, DipFinPlan; Chair of ME/CFS (Australia), PhD Candidate (Southern Cross University); Masters of Public Health Student (Griffith University) Question: What is the position of the National Disability Insurance Scheme (‘NDIS’) with respect to claimants MyalgicEncephalomyelitis/Chronic Fatigue Syndrome (‘ME/CFS’)? Methods: A literature review was conducted with respect to the background and purposes of the NDIS, with a specific emphasis upon ME/CFS. Direct feedback from the National Disability Insurance Agency (‘NDIA’) with respect to its policy and the foundation for it was obtained. Publicly available grey literature was accessed to identify current issues affecting NDIS claimants with ME/CFS. Results: The NDIA is currently assessing claims on the basis that ME/CFS is not a permanent condition. The primary reason for the NDIA’s position is a misinterpretation of piece of 2006 research known colloquially as the Dubbo study and reliance upon long outdated guidelines. Conclusions: The NDIA is does not presently have an established policy for claimants with ME/CFS, and the current view of permanency is flawed, hence people with ME/CFS are being turned away from the scheme. Abstract Submission: Established by the National Disability Insurance Scheme Act 2013, the NDIS was formally rolled out on a national basis on 1 July 2016. The NDIS has not been without controversy, A recent report by Flinders University identified that NDIS recipients were received around half of the assistance that they had received prior to the introduction of the scheme (Mavromaras, et al., 2018). The scheme currently operates on a staffing cap, hence is heavily understaffed (Productivity Commission, 2017). The NDIS spends approximately $ 10 million a year defending matters involving claimants contesting the decisions of the NDIA. Approximately 40% of appeals yield an improved outcome (Productivity Commission, 2017). A review of the AAT caselaw reveals no cases have been decided with respect to ME/CFS presently. There are a number of cases pending (ME/CFS Legal Resources, 2017). The current anecdotal evidence identifies that ME/CFS applicants are not succeeding in their claims (Reilly & Buchanan, 2018; Emerge, 2018; Hutchinson, 2018; Ludlam, 2018). ME/CFS is a debilitating condition. For the majority, it is permanent (ME/CFS Legal Resources, 2017). The NDIA deny the condition is permanent and are relying upon the 2006 Dubbo Infection Outcomes study (Hickie, et al., 2006) to assert that “many individuals recover without intervention over weeks or months, but approximately 10% will meet the criteria of ME/chronic fatigue syndrome at six months”. The NDIA claim that “[o]f these, a small subset may go on to suffer from both severely disabling and prolonged (greater than 5 years) ME/chronic fatigue syndrome” (Faulkner, 2018). The NDIA rely upon the 2002 Australian CFS guidelines (Loblay, et al., 2002) and the UK’s NICE guidelines (Turnbull, et al., 2007). Cognitive Behavioural Therapy (CBT) and Graded Exercise therapy (GET) are considered the evidence based management of the condition – despite significant evidence to the contrary. Both guidelines are under review, with a particular focus upon the appropriateness of CBT and GET, both of which are of questionable value (Vink & Vink-Niese, 2018; Various Authors, 2017). ME/CFS is not currently a “List B” condition, hence not presumed to be a disability. The current stance is therefore making it near impossible for people with ME/CFS to succeed. The NDIS require more contemporary insight into the condition to assist them to a more realistic view of ME/CFS claimants. References: Emerge. (2018, October). National Disability Insurance Scheme (NDIS). Retrieved October 12, 2018, from Emerge: emerge.org.au/support-services/financial-services/ndis Faulkner, C. (2018, August 17). Personal Communication from NDIA. Hickie, I., Davenport, T., Wakefield, D., Vollmer-Conna, U., Cameron, B., Vernon, S. D., . . . Lloyd, A. (2006). Post-infective and chronic fatigue syndromes precipitated by viral and non-viral pathogens: prospective cohort study. British Medical Journal, 333(7568), 575. doi:http://doi.org/10.1136/bmj.38933.585764.AE Hutchinson, S. (2018, April 10). OPINION: NDIS must recognise chronic fatigue syndrome or suicide will follow. Retrieved October 12, 2018, from The Feed: https://www.sbs.com.au/news/the-feed/opinion-ndis-must-recognise-chronic-fatigue-syndrome-or-suicide-will-follow Loblay, R., Stewart, G., Bertouch, J., Cistulli, P., Darvenzia, P., Ellis, C., . . . Toulkidis, V. (2002). Chronic Fatigue Syndrome - Clinical Practice Guidelines. Medical Journal of Australia, 176(6 May), S17-S46. Ludlam, S. (2018, May 12). To the #MillionsMissing With ME/CFS Something Remarkable is Happening. Retrieved October 12, 2018, from The Guardian: https://www.theguardian.com/commentisfree/2018/may/12/to-the-millionsmissing-with-mecfs-something-remarkable-is-happening Mavromaras, K., Moskos, M., Isherwood, L., Goode, A., Walton, H., Smith, L., . . . Flavel, J. (2018). Evaluation of the NDIS. Finliders University, National Institute of Labour Studies. Adelaide: Department of Social Services. Retrieved October 12, 2018, from https://www.dss.gov.au/disability-and-carers/programs-services/for-people-with-disability/national-disability-insurance-scheme/ndis-evaluation-consolidated-report ME/CFS Legal Resources. (2017, November 13). Submission to the Joint Parliamentary Committee on the National Disability Insurance Scheme. Retrieved October 12, 2018, from Parliament of Australian: https://www.google.com.au/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=2ahUKEwjxjKXX-f7dAhUCIIgKHTR9Cr8QFjAAegQIAhAC&url=https%3A%2F%2Fwww.aph.gov.au%2FDocumentStore.ashx%3Fid%3D09902d19-3475-4f08-b222-62e15ed32dfe%26subId%3D561511&usg=AOvV Morton, R. (2018, May 17). NDIS legal bill hitting $10m a year. The Australian. Retrieved October 12, 2018, from https://www.theaustralian.com.au/national-affairs/health/ndis-legal-bill-hitting-10m-a-year/news-story/c048d6028a8363597a30115d3cdb921f Productivity Commission. (2017). National Disability Insurance Scheme (NDIS) Costs. Australian Government, Productivity Commission. Canberra: Commonwealth of Australia. Retrieved October 12, 2018, from https://www.pc.gov.au/inquiries/completed/ndis-costs/report/ndis-costs.pdf Reilly, A., & Buchanan, R. (2018, August 24). ME/CFS National Disability Agreement Review Submissio. Retrieved October 12, 2018, from Productivity Commission: https://www.pc.gov.au/__data/assets/pdf_file/0004/230953/sub023-disability-agreement.pdf Turnbull, N., Shaw, E. J., Dundson, S., Costin, N., Britton, G., Kuntze, S., & Norman, R. (2007). Chronic Fatigue Syndrome/Myalgic Encephalomyelitis (or Encephalopathy): Diagnosis and Management of Chronic Fatigue Syndrome/Myagic Encephalomyelitis (or Encepaholpathy) in Adults and Children. London: Royal College of General Physicians. Various Authors. (2017). Special Issue: The PACE Trial. Journal of Health Psychology, 22(9), 1103-1216. Retrieved from http://journals.sagepub.com/toc/hpqa/22/9 Vink, M., & Vink-Niese, A. (2018). Graded Exercise Therapy for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome is Not Effective and Unsafe. Re-Analysis of a Cochrane Review. Health Psychology Open, July-December, 1-12. doi:https://doi.org/10.1177/2055102918805187
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Prior research has found a heightened risk of suicide in patients with myalgic encephalomyelitis (ME) and chronic fatigue syndrome (CFS). It is possible that a number of factors including stigma, unsupportive social interactions, and severe symptoms could lead to the development of depression, suicidal ideation, and heightened risk of suicide in this patient population. Prior studies have indicated that patients often report the legitimacy of their illness being questioned by family, friends, and even their physicians. This study aimed to determine whether stigma experienced, social support, symptomology, and functioning may be associated with depression and endorsement of suicidal ideation (SI) in patients with a self‐reported diagnosis of ME or CFS. Findings indicated that participants that endorsed both SI and depression, in contrast to those that did not, experienced more frequent unsupportive social interactions in the form of blame for their illness, minimization of its severity, and social distancing from others. In addition, 7.1% of patients with ME and CFS endorsed SI but do not meet the criteria for clinical depression These findings highlight the importance of stigma and unsupportive social interactions as risk factors for suicidal thoughts or actions among patients with ME and CFS. Community psychologists have an important role to play in helping educate health care professionals and the public to these types of risk factors for patients marginalized by ME and CFS.
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Neuroimaging studies of mindfulness training (MT) modulate anterior cingulate cortex (ACC) and insula among other brain regions, which are important for attentional control, emotional regulation and interoception. Inspiratory breathing load (IBL) is an experimental approach to examine how an individual responds to an aversive stimulus. Military personnel are at increased risk for cognitive, emotional and physiological compromise as a consequence of prolonged exposure to stressful environments and, therefore, may benefit from MT. This study investigated whether MT modulates neural processing of interoceptive distress in infantry marines scheduled to undergo pre-deployment training and deployment to Afghanistan. Marines were divided into two groups: individuals who received training as usual (control) and individuals who received an additional 20-h mindfulness-based mind fitness training (MMFT). All subjects completed an IBL task during functional magnetic resonance imaging at baseline and post-MMFT training. Marines who underwent MMFT relative to controls demonstrated a significant attenuation of right anterior insula and ACC during the experience of loaded breathing. These results support the hypothesis that MT changes brain activation such that individuals process more effectively an aversive interoceptive stimulus. Thus, MT may serve as a training technique to modulate the brain’s response to negative interoceptive stimuli, which may help to improve resilience.
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Importance: Chronic Fatigue Syndrome (CFS) is a chronic disease resulting in considerable and widespread cognitive deficits. Accurate and accessible measurement of the extent and nature of these deficits can aid healthcare providers and researchers in the diagnosis of this condition, choosing interventions and tracking treatment effects. Here, we present a case of a middle-aged man diagnosed with CFS which began following a typical viral illness. Observations: LORETA source density measures of surface EEG connectivity at baseline were performed on 3 minutes of eyes closed deartifacted19-channel qEEG. The techniques used to analyze the data are described along with the hypothesized effects of the deregulation found in this data set. Nearly all (>90%) patients with CFS complain of cognitive deficits such as slow thinking, difficulty in reading comprehension, reduced learning and memory abilities and an overall feeling of being in a “fog.”Therefore, impairment may be seen in deregulated connections with other regions (functional connectivity); this functional impairment may serve as one cause of the cognitive decline in CFS. Here, the functional connectivity networks of this patient were sufficiently deregulated to cause the symptoms listed above. Conclusions and significance: This case report increased our understanding of CFS from the perspective of brain functional networks by offering some possible explanations for cognitive deficits in patients with CFS. There are only a few reports of using source density analysis or qEEG connectivity analysis for cognitive deficits in CFS. While no absolute threshold exists to advise the physician as to when to conduct such analyses, the basis of his or her decision whether or not to use these tools should be a function of clinical judgment and experience. These analyses may potentially aid in clinical diagnosis, symptom management, treatment response and can alert the physician as to when intervention may be warranted.