Resting state electroencephalography microstates in autism spectrum disorder: A mini-review

Abstract

Atypical spatial organization and temporal characteristics, found via resting state electroencephalography (EEG) microstate analysis, have been associated with psychiatric disorders but these temporal and spatial parameters are less known in autism spectrum disorder (ASD). EEG microstates reflect a short time period of stable scalp potential topography. These canonical microstates (i.e., A, B, C, and D) and more are identified by their unique topographic map, mean duration, fraction of time covered, frequency of occurrence and global explained variance percentage; a measure of how well topographical maps represent EEG data. We reviewed the current literature for resting state microstate analysis in ASD and identified eight publications. This current review indicates there is significant alterations in microstate parameters in ASD populations as compared to typically developing (TD) populations. Microstate parameters were also found to change in relation to specific cognitive processes. However, as microstate parameters are found to be changed by cognitive states, the differently acquired data (e.g., eyes closed or open) resting state EEG are likely to produce disparate results. We also review the current understanding of EEG sources of microstates and the underlying brain networks.

Full text

fpsyt-13-988939 November 30, 2022 Time: 10:53 # 1
TYPE Mini Review
PUBLISHED 01 December 2022
DOI 10.3389/fpsyt.2022.988939
OPEN ACCESS
EDITED BY
David Quentin Beversdorf,
University of Missouri, United States
REVIEWED BY
Kazuyori Yagyu,
Hokkaido University Hospital, Japan
Anthony Zanesco,
University of Miami, United States
*CORRESPONDENCE
Pushpal Desarkar
pushpal.desarkar@camh.ca
SPECIALTY SECTION
This article was submitted to
Autism,
a section of the journal
Frontiers in Psychiatry
RECEIVED 07 July 2022
ACCEPTED 09 November 2022
PUBLISHED 01 December 2022
CITATION
Das S, Zomorrodi R, Enticott PG,
Kirkovski M, Blumberger DM, Rajji TK
and Desarkar P (2022) Resting state
electroencephalography microstates
in autism spectrum disorder:
A mini-review.
Front. Psychiatry 13:988939.
doi: 10.3389/fpsyt.2022.988939
COPYRIGHT
© 2022 Das, Zomorrodi, Enticott,
Kirkovski, Blumberger, Rajji and
Desarkar. This is an open-access
article distributed under the terms of
the Creative Commons Attribution
License (CC BY). The use, distribution
or reproduction in other forums is
permitted, provided the original
author(s) and the copyright owner(s)
are credited and that the original
publication in this journal is cited, in
accordance with accepted academic
practice. No use, distribution or
reproduction is permitted which does
not comply with these terms.
Resting state
electroencephalography
microstates in autism spectrum
disorder: A mini-review
Sushmit Das1,2 , Reza Zomorrodi1,3, Peter G. Enticott4,
Melissa Kirkovski4,5, Daniel M. Blumberger1,3,6,
Tarek K. Rajji1,3,6 and Pushpal Desarkar1,2,3,6*
1Centre for Addiction and Mental Health, Toronto, ON, Canada, 2Azrieli Adult Neurodevelopmental
Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada, 3Temerty Centre for
Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada,
4Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC, Australia,
5Institute for Health and Sport, Victoria University, Melbourne, VIC, Australia, 6Department of
Psychiatry, University of Toronto, Toronto, ON, Canada
Atypical spatial organization and temporal characteristics, found via resting
state electroencephalography (EEG) microstate analysis, have been associated
with psychiatric disorders but these temporal and spatial parameters are
less known in autism spectrum disorder (ASD). EEG microstates reflect a
short time period of stable scalp potential topography. These canonical
microstates (i.e., A, B, C, and D) and more are identified by their unique
topographic map, mean duration, fraction of time covered, frequency of
occurrence and global explained variance percentage; a measure of how well
topographical maps represent EEG data. We reviewed the current literature
for resting state microstate analysis in ASD and identified eight publications.
This current review indicates there is significant alterations in microstate
parameters in ASD populations as compared to typically developing (TD)
populations. Microstate parameters were also found to change in relation to
specific cognitive processes. However, as microstate parameters are found
to be changed by cognitive states, the differently acquired data (e.g., eyes
closed or open) resting state EEG are likely to produce disparate results. We
also review the current understanding of EEG sources of microstates and the
underlying brain networks.
KEYWORDS
electroencephalography, EEG microstates, neurophysiology, autism, mini review
Introduction
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder
characterized by persisting impairments in social communication and interaction, and
presence of repetitive behaviors, activities and interests (1). Electroencephalography
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(EEG) microstate analysis in ASD is an emerging field with
growing interest in the scientific community. Existing evidence
consistently indicates that both functional and structural
connectivity are atypical in individuals with ASD (2). These
networks include the social brain network (3) (amygdala
and superior temporal sulcus) (3), default mode network (4)
(posterior cingulate cortex, anterior cingulate cortex, medial
prefrontal cortex, medial temporal lobes, angular gyrus, and
precuneus) (5,6), and action observation network (7) (lateral
occipital cortex and fusiform gyrus) (7). Studying the spatial
and temporal characteristics of these atypical brain networks
and their relationship with the core behavioral characteristics
of ASD will provide more insights into the neurobiological
underpinnings of ASD. EEG is a technique that allows the
recording of electrophysiological activity by detecting the
fluctuating electrical potentials in the brain (8). One key
advantage of quantitative EEG over functional neuroimaging
scans is its excellent temporal resolution while investigating the
function of large-scale brain networks. While many methods
of quantitative EEG analysis exist, microstate analysis provides
an unique opportunity to investigate spatial organization and
temporal characteristics of large-scale cortical network activities
with excellent temporal resolution (9,10).
In a seminal work, Lehman et al. (11) showed that although
the topographic maps of resting state EEG signals varied over
time, it consisted of few quasi-stable maps called “microstates.”
An analysis of 496 participants from ages 6–80 years by Koenig
et al. (12) found microstates to be stable for around ∼65–
105 ms. Of note, the average microstate duration was found
to be higher in children and it decreased with age. It was
demonstrated by Liu et al. (13) and Zanesco et al. (14) that
depending on the amount of reliable data collected, microstates
can show high inter-individual variability. Through cluster
analysis, it was noted that segmented EEG signals collapse
into temporarily stable spatial patterns; otherwise known as
“microstates.” In a recent review, Khanna et al. (15) reports that
there are at minimum four main microstate maps consistently
reported in the literature, lasting anywhere between 80 and
120 ms before immediately switching to another microstate.
These few number of microstates account for 70–80% of all
EEG activity during both eyes open and closed resting states
and some task related resting state EEG (15). The number of
topographical maps are chosen to reflect how well clusters of
EEG data resemble them and is denoted by the Global Explained
Variance percentage (GEV%) (15). Given the complexity of
the central nervous system, having only a handful of these
topographical maps explaining up to 80% of resting state
EEG data provides a parsimonious account, simplicity, and a
compelling reason to study them further. Currently, the four
most consistently reported microstates, A, B, C, and D, represent
the minimum number of microstates that are revealed by the
data (15) (see Figure 1). The orientations of the microstates
are as follows, A: right frontal to left posterior; B: left frontal
to right posterior; C: frontal to occipital; D: frontal medial
to occipital. The lettering conventions are generally accepted
in the literature to identify the orientation of the microstates.
However, in a recent study, Custo et al. (16) demonstrated
that new topographies may be revealed by using a data-driven
approach. They also showed that using a priori number of
clusters could force algorithms to combine microstates, leading
to potentially misleading results. Consequently, in recent studies
now, microstates E and F are also being reported; with
orientations, E: left to right (16); F: posterior medial to frontal
(16). Studies can report up to eight microstates in some cases
based on their data; however, 4–6 microstates being reported is
more common.
Microstates in psychiatric,
neurodevelopmental and neurological
disorders
Lehmann et al. (17) originally suggested that these quasi-
stable microstates can be interpreted as “atoms of thought,”
where different microstates of varying topographies are types
or stages of information processing. Subsequently, microstate
parameters were linked with personality differences (18) and
cognitive ability (19).
Recently, there has been an increasing interest in
studying microstates in different psychiatric disorders. It
has been found that microstates are significantly altered
in diseased states. For example, in Alzheimer’s dementia,
the decrease in average duration of each microstate is
proportional to cognitive decline (20–22). It was found
that microstate A could be the first affected microstate in
Alzheimer’s disease (23) and an increase in the duration
and frequency of microstate A differentiated patients with
Alzheimer’s disease and mild cognitive impairment from
healthy participants (23).
A wide variance in topographies and shortening of
prominent microstates were found in patients with clinical
depression (24). In a recent study, Yan et al. (25) were able
to predict the clinical outcomes of major depressive disorder
with microstates; a decrease in microstate B duration predicted
patient response to antidepressants after 3 months. Further,
increased frequency and average duration of microstate A
has been found in Tourette’s syndrome and panic disorder,
respectively (26,27). A number of studies also found that
an increase in the occurrence of microstates A and C, and a
decrease in the occurrence in microstates B and D, correlated
with hallucinatory symptoms in patients with schizophrenia
(28–30). A meta-analysis by Rieger et al. (31) revealed that the
most stable finding in schizophrenia microstate research is an
increase in frequency in microstate C and decrease in duration
in microstate D.
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FIGURE 1
Electroencephalography (EEG) Microstates (A–F). The orientations of the microstates are as follows, (A): right frontal to left posterior; (B): left
frontal to right posterior; (C): frontal to occipital; (D): frontal medial to occipital; (E): left to right; (F): posterior medial to frontal.
Studying electroencephalography
microstates in autism spectrum
disorder
The study of microstates has its advantages over other
forms of EEG analyses. First, EEG signals in general are
complex in nature due to their non-stationary and non-linear
nature. However, microstate analysis is independent of these
two factors, as within a given microstate the dynamics of
brain activity are not taken into account. In other words,
microstates can consist of any temporal dynamics given that
they are consistent throughout their 60–120 ms duration.
These microstates can also be studied at any defined frequency
band (i.e., delta, theta, alpha, beta, and gamma) (9). As
microstates provide a superior temporal resolution in tandem
with functional magnetic resonance imaging (fMRI) scan’s
high spatial resolution, it provides a unique opportunity
to study the brain dynamics in neuropsychiatric disorders
with more precision. Three groups, Britz et al. (32), Musso
et al. (33), and Yuan et al. (34) have reported different
approaches to identify fMRI correlates of EEG resting-state scalp
topographies. Recently, Endo et al. (35) reported that fast EEG
microstate transitions and slow blood oxygen level-dependent
(BOLD) fluctuations changed based on structural connectivity
across whole brain region and regions with strong structural
connectivity. In another recent a recent study by Abreu et al.
(36), EEG microstates were found to reflect large scale brain
dynamics collected from fMRI. The group found that the four
canonical microstates (i.e., microstates A–D) predicted fMRI
dynamical functional connectivity states with 90% accuracy.
Lastly, EEG microstates analyses have been shown to have high
test–retest reliability. Such analyses provide consistent results
with as little as eight electrodes and they have been found to be
consistent in different epoch lengths in records with as little as
2 min of recorded EEG data (9,13).
Khanna et al. (15), suggested that if microstates are thought
to reflect coordinated neural activity, then abnormalities can
be seen as a breakdown of this activity. There has been
consistent evidence that indicates that connectivity in the brain
in individuals with ASD is atypical and there is evidence of
considerable heterogeneity (37). Further, evidence indicates
significant network dysfunction in ASD and some resting
state networks (RSNs) such as networks involved with the
phonological processing and self-representation, were found
to be atypical in ASD (37). Studying spatial and temporal
characteristics of these atypical brain networks and their
relationship with the core behavioral characteristics of ASD
is likely to provide key insights into the neurobiological
underpinnings of ASD. Thus, the aim of this mini review is to
identify temporal and spatial properties of microstates in ASD
and their relationship with cognition and/or core behavioral
characteristics in ASD. Further, we will briefly discuss the
current evidence of source localizations of EEG microstates and
its connection to brain networks and how it relates to ASD.
Current literature on resting state
microstates in autism spectrum
disorder
Method
We searched PubMed, EMBASE and PsychINFO, and
included articles published until May 30th, 2022. The following
combination of search items was used: EEG, MICROSTATE
AND (autism OR autism spectrum disorder OR pervasive
developmental disorder OR Asperger disorder OR Asperger
syndrome). Additionally, references of selected studies were
manually searched. The searched was performed independently
by PD and SD and agreement was established after discussion.
After screening the title, abstract and full text, articles
were included if they studied microstates in ASD. We
included studies using human participants and published in
English language only.
We recorded the following variables from each article:
author, year of publication, diagnosis and comorbidities, study
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population characteristics (e.g., sample size, sex/gender, age
etc.), method used for EEG microstate analysis and, type
of EEG recording (eyes open vs. eyes closed rest EEG),
number of microstate clusters, microstate parameters (i.e., mean
duration, occurrence rate, coverage, and GEV%), relationship of
microstate parameters with clinical characteristics, relationship
of microstate parameters with cognition and core behavioral
characteristics of ASD. This was done by two authors (PD and
SD) with disagreements resolved by discussion.
Findings on resting state microstate
parameters in autism spectrum
disorder
Microstates extracted from resting state EEGs from the
selected studies were found to study brain activity in typically
developing (TD) or neurotypical (NT) and ASD groups. The
microstates found across the studies explained 72.88–80.44%
of the variance of clustering results of ASD and TD or
NT EEG data. This percentage represents the clusters at the
time samples when global field power (GFP) peaks were
calculated; GFP representing the time points with highest
signal to noise ratio. Among all microstates, microstate C
was found to have the highest GEV% among both groups;
however, this was higher in TD groups (25,38) and NT
groups (39).
Across all eight studies, there were significant differences
between the TD or NT and ASD groups. In particular,
microstate C stood out the most as it was consistently reported
to be less frequent in the ASD group in three studies (40–
42) (see Tables 1,2). This finding was consistent even
during free viewing of cartoons displaying social interactions
(40). Jia and Yu (38) and D’Croz-Baron et al. (39) reported
decreased microstate C duration, coverage (39) and GEV%
(39) in the ASD group. Nagabhushan Kalburgi et al. (42)
also reported increased duration of microstate C in the ASD
group. Microstate B also stood out as 4 studies found duration
(42,43), occurrence (38,39,43) rate, coverage (39,43), and
GEV% (39,43) of microstate B to be higher in the ASD
group across different age groups (see Tables 1,2). There were
discrepancies regarding the remaining microstates. A higher
duration of microstate A was reported in the TD group
by Jia and Yu (38). Furthermore, Jia and Yu (38) reported
higher microstate D occurrence rate in the ASD group and
Nagabhushan Kalburgi et al. (42) reported higher duration
in ASD and higher occurrence in TD group. D’Croz-Baron
et al. (39) reported higher occurrence, coverage and GEV% of
microstate E in the ASD group and Bochet et al. (43) reported
higher coverage, occurrence and GEV% of microstate E in
the TD group; although it did not survive false discovery rate
correction (43).
Relationship of microstate parameters
with clinical characteristics and
cognition
Of the eight studies reviewed, two studies were found
to include female participants (42,43). Participants’ ages
ranged from 6 months to 30 years old. No relationship
between microstate parameters and clinical characteristics such
as age, sex, cognition or treatment were reported across the
studies studying resting state microstates. No other clinical
characteristic was studied in these studies. In the study by
Nagabhushan Kalburgi et al. (42), no significant correlation
between the mentioned clinical characteristics (see Section
“Electroencephalography source localization of microstates”)
and microstates was reported. In the study by Bochet et al.
(43), the team discovered multiple correlation to clinical
measures. First, a negative correlation was found between the
Autism Diagnostic Observation Schedule social affects severity
score and mean duration of microstate E. Second, a negative
correlation was found between the fine motor domain of Mullen
Scales of Early Learning and GEV%, coverage, occurrence
of microstate D. Next, a positive correlation was found
between children’s affective problems in the Child Behavior
Checklist and GEV%, coverage, occurrence of microstate B.
Lastly, a negative correlation between children’s attention
deficits and hyperactivity problems in the Child Behavior
Checklist and duration of microstate C. However, it is worth
noting that these findings do not survive false discovery
rate corrections and are thus exploratory in nature. Bochet
et al. (43) also conducted a male-only subgroup analysis
and compared to the larger analysis and concluded that
their findings were not biased by gender. Bochet et al.
(43) further correlated the age of participants with temporal
parameters and reported no correlation. Takarae et al. (41)
reported the duration of microstate C positively correlated
with age in the TD group but not the ASD group. Takarae
et al. (41) reported a significant negative correlation between
ADOS Social Communication scores and mean duration
of microstate C.
Discussion
From the studies reviewed here, there seems to be significant
alterations in microstate parameters in ASD populations as
compared to TD or NT populations. Specifically, increased
(42) and decreased microstate C duration (38,39), coverage
(39), occurrence (40–42), and GEV% (39) and increased
duration (42,43), occurrence (38,39,43) rate, coverage (39,
43), and GEV% (39,43) of microstate B in the ASD group.
Microstate parameters were also found to change in relation to
specific cognitive processes; such as eyes open or eyes closed
(see Table 1).
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TABLE 1 Summary of key findings related to microstate analysis in ASD research.
Citation Objective Sample size Clinical assessments in ASD
group
Microstate and statistical
parameters
Results and key findings to microstates
Malaia et al. (62) To characterize
spatiotemporal network
dynamics in EEG of ASD
and TD groups
14 Diagnosis of Asperger syndrome or
high-functioning ASD
Individuals (does not specify sex) (age
8–15)
14 TD age and sex-matched participants
ASD:
Pragmatic Language Observation Scale
Score 84 and below
TD:
Pragmatic Language Observation Scale
Score 90 and above.
(1) Mean duration of quasi-stable
networks (ms)
(2) Mean number of quasi-stable
networks
(3) Mean quasi-stable network
diameter
ASD group had a larger mean network diameter and longer
mean duration of quasi-stable networks during resting state
Higher frequency of quasi-stable networks during resting
state in both TD and ASD groups
D’Croz-Baron et al.
(39)
Compare resting state
microstates between ASD
and TD groups.
15 ASD
16 NT
Ages 18–30 years old
(Does not specify sex)
No assessments reported. (1) Mean duration (ms)
(2) Frequency of occurrence
(3) Fraction of time covered
(4) GEV%
First step in the 2-step spatial cluster analysis identified
between 4 and 7 microstate maps in individuals
Group cluster analysis revealed six microstates that best
describe the dataset with ∼80% total variance
Microstate B (left frontal to right posterior): higher
occurrence, (p-pairwise = 0.008; p-corrected = 0.030);
higher coverage (p-pairwise = 0.021; p-corrected = 0.063);
higher GEV% (p-pairwise = 0.018; p-corrected = 0.054) in
the ASD group.
Microstate C (frontal to occipital): higher duration
(p-pairwise = 0.026; p-corrected = 0.156), coverage
(p-pairwise = 0.042; p-corrected = 0.084) and GEV%
(p-pairwise = 0.049; p-corrected = 0.098) in the TD group.
Microstate E (left to right): higher occurrence
(p-pairwise = 0.010; p-corrected = 0.030); coverage
(p-pairwise = 0.008; p-corrected = 0.048); GEV%
(p-pairwise = 0.010; p-corrected = 0.054) in the ASD group.
ASD and TD GEV% (6 maps) = ∼80%
ASD and TD GEV% (4 maps) = ∼76%
Jan et al. (40) Investigate neural
activation between ASD
and TD groups during free
viewing of dynamic
semi-naturalistic stimuli
containing social
interactions.
14 male ASD
14 male TD
Ages 2–5 years old
ASD:
ADOS-2/ADOS-G: 7.4 ±1.9
(1) Mean duration (ms)
(2) Frequency of occurrence
(3) Fraction of time covered
(4) GEV%
Group-level k-means cluster analysis identified four
microstates maps;
Significant difference within maps with respect to mean
duration, frequency of occurrence, fraction of time covered
and GEV% (p<0.001)
ASD GEV% = 80.44%
TD GEV% = 77.43%
Jia and Yu (38) Compare resting state
microstates between ASD
and TD groups.
15 male ASD (mean age = 11.6 years,
SD = 4.4) (age 5–18 years)
18 male TD (mean age = 8.9 years, SD
2.4 years) (aged 5–15 years)
ASD:
Schedule for Affective Disorders and
Schizophrenia–Children’s version and
(ADOS). All ASD patients were reported
to be high-functioning (i.e., IQ >66).
No score reported.
(1) Mean Duration (ms)
(2) Frequency of Occurrence
(3) Fraction of time covered
(4) GEV%
Microstate A: higher duration in TD group; (p= 0.006)
Microstate B (left frontal to right posterior): higher
occurrence, (p= 0.001); higher coverage%, (p= 0.012) in the
ASD group.
Microstate C (frontal to occipital): higher duration,
(p= 0.002); higher coverage%, (p= 0.008) in the TD group.
Microstate D (frontal medial to occipital): higher occurrence
in the ASD group; (p= 0.013)
ASD GEV% = 78.60% (SD = 2.67%)
TD GEV% = 77.11% (SD = 3.08%)
(Contiuned)
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TABLE 1 (Contiuned)
Citation Objective Sample size Clinical assessments in ASD
group
Microstate and statistical
parameters
Results and key findings to microstates
Nagabhushan
Kalburgi et al. (42)
To examine eyes-closed and
eyes-open resting state EEG
microstates of ASD and TD
groups
11 male ASD, 2 female ASD
(age = 9.7 ±1.5) 11 male TD, 2 female
TD (age = 10.4 ±1.4)
ASD:
ADOS-2: 7.8 ±1.6.
6 of the ASD participants took various
ADHD medications; 2 of these 6 also
took SSRIs, 1 took risperidone.
SCQ: 17.7 ±6.2
SRS T-score: 73 ±10
RBS-R: 34.9 ±21.1 in the Repetitive
Behavior Scale–Revised.
ASD group had an Abbreviated Battery
IQ score of 101.3 ±19.8 in the Stanford
Binet–5 Abbreviated IQ Test.
TD:
SCQ: 3.5 ±2.5
RBS-R: 1.8 ±2
(1) Mean duration (ms)
(2) Frequency of occurrence
(3) Fraction of time covered
(4) GEV%
Microstate B (left frontal to right posterior): higher duration
(p= 0.008) in ASD group
Microstate C (frontal to occipital): higher duration
(p= 0.012) in ASD group in eyes closed condition; higher
occurrence (p= 0.0014) in TD group in eyes closed
condition
Microstate D (frontal medial to occipital): higher duration
(p= 0.004) in ASD group; higher occurrence (p= 0.013) in
TD group
ASD GEV% (eyes closed) = 77.68% (SD = 3.59%)
ASD GEV% (eyes open) = 75.08% (SD = 3.26%)
TD GEV% (eyes closed) = 73.23% (SD = 4.42%)
TD GEV% (eyes open) = 72.88% (SD = 5.00%)
Portnova et al. (63) To examine difference of
resting state EEG between
children with
low-functioning autism
receiving ABA therapy, no
ABA therapy and TD
group.
10 ASD + ABA therapy group
(age = 3.9 ±1.1)
25 ASD, no ABA (age = 4.1 ±1.2)
30 TD (age = 4.0 ±0.9)
ASD:
ADOS-2: 13.9 ±3.8
CARS: 43.8 ±6.8
Non-verbal scale of WPPSI: 101.6 ±9.9
ASD + ABA therapy group:
ADOS-2: 14.5 ±3.7
CARS: 45.2 ±7.1
Non-verbal scale of WPPSI: 100.7 ±6.1
TD:
ADOS-2: 2.7 ±1.5
CARS: 23 ±5
Non-verbal scale of WPPSI: 104.6 ±7.3
(1) Total duration of each cluster (s)
calculated before and after ABA
therapy
Microstate Cluster left-frontal to right posterior
Microstate Cluster right-frontal to left posterior
Microstate Cluster parietal
Bochet et al. (43) Compare resting state
microstates between ASD
and TD groups.
55 male ASD, 11 female ASD
(age = 3.3 ±1.0) 39 male TD, 8 female
TD (age = 3.3 ±1.2)
ASD:
ADOS-2/ADOS-G total severity score:
7.67 ±1.83.
MSEL total DQ: 73.4 ±24.5
TD:
MSEL total DQ: 110.4 ±13.7
(1) Mean duration (ms)
(2) Frequency of occurrence
(3) Fraction of time covered
(4) GEV%
Microstate B (left frontal to right posterior): higher GEV%,
(p<0.001); higher duration, (p<0.001); higher coverage,
(p<0.001); higher occurrence, (p<0.001) in the ASD
group. Persisted even when all females were removed from
analysis. Survived false discovery rate correction.
Microstate E (left to right): higher GEV%, (p= 0.031); higher
coverage, (p= 0.034); higher occurrence, (p= 0.019) in the
TD group. Did not survive false discovery rate correction.
ASD GEV% = 80.8%
TD GEV% = 81.9%
Takarae et al. (41) Compare resting state
microstates between ASD
and TD groups.
34 male ASD, 4 female ASD
(age = 12.19 ±2.39)
34 male TD, 14 female TD
(age = 11.68 ±3.15)
ASD:
ADOS total severity score: 11 ±3.39
VIQ Score: 106.11 ±18.00
PIQ Score: 105.06 ±17.96
TD:
VIQ Score: 109.64 ±11.75
PIQ Score: 106.15 ±12.32
(1) Mean duration (ms)
(2) Frequency of occurrence
(3) Fraction of time covered
(4) GEV%
Microstate C (frontal to occipital): higher occurrence
(p<0.01) in the TD group.
ASD GEV% = 84.05%
TD GEV% = 85.28%
ASD, autism spectrum disorder; TD,typically de veloping; NT, neurotypical; MSEL, mullen scales of early learning; WPPSI, the wechsler preschool and primary scale of intelligence; CARS, child autism rating scale; SCQ, social communication questionnaire;
SRS, social responsiveness scale; RBS-R, repetitive behavior scale–revised; IQ, intelligent quotient; PIQ, performance IQ; VIQ, verbal IQ; ABA, applied behavior analysis; GEV%, global explained variance %.
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TABLE 2 Visualization of key microstate findings.
Citation A B C D E Microstates
Malaia et al. (62) Not applicable Not applicable
D’Croz-Baron et al. (39)↑Occurrence, coverage,
GEV%
↓Duration,
coverage, GEV%
↑Occurrence,
coverage, GEV%
Jan et al. (40) Significant difference within maps with respect to mean duration,
frequency of occurrence, fraction of time covered and GEV%
Jia and Yu (38)↓Duration ↑Occurrence, coverage ↓Duration, coverage ↑Occurrence
Nagabhushan Kalburgi et al. (42)↑Duration ↑Duration
↓Occurrence
↑Duration
↓Occurrence
Portnova et al. (63)
Bochet et al. (43)↑Duration, coverage,
occurrence
↓Coverage,
occurrence, GEV%
Takarae et al. (41)↓Occurrence
(1) No studies reported any statistical differences between groups with regards to microstates F and G. (2) Eyes closed microstates were extracted from the study by Nagabhushan Kalburgi et al. (3) Microstates extracted represent ASD or ASD + NT/TD only.
(4) Arrows represent how ASD data is related to NT/TD.
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TABLE 3 Summary of methodology related to microstate analysis in ASD research.
Citation Microstate analysis
method
Sampling rate Software for
microstate analysis Filtering Additional filtering
approaches (spatial filtering
as implemented in Cartool)
Malaia et al. (62) Network approach to
characterization of spatiotemporal
dynamics of EEG data in ASD and
TD groups
512 Hz N/a N/a N/a
D’Croz-Baron et al. (39) K-means clustering algorithm
(modified version)
500 Hz (Downsampled
to 125 Hz)
Cartool Software (64) 1–50 Hz Cartool spatial filter used
Jan et al. (40) K-means clustering algorithm 1 kHz (Downsampled to
125 Hz)
Cartool Software (64) 1–40 Hz Cartool spatial filter used
Jia and Yu (38) T-AAHC 500 Hz Cartool Software (64) 2–20 Hz Cartool spatial filter used
Nagabhushan Kalburgi et al.
(42)
T-AAHC 1 kHz Microstate EEGLab Toolbox
(65)
2–20 Hz N/a
Portnova et al. (63) K-means clustering algorithm
(modified version)
250 Hz Microstate EEGLab Toolbox
(65)
2–30 Hz N/a
Bochet et al. (43) K-means clustering algorithm 1 kHz (Downsampled to
125 Hz)
Cartool Software (64) 1–40 Hz Cartool spatial filter used
Takarae et al. (41) K-means clustering algorithm
(modified version)
256 Hz Cartool Software (64) 0.5–40 Hz Cartool spatial filter used
T-AAHC, Topographic Atomize and Agglomerate Hierarchical Clustering.
Discrepancies mentioned in Section “Future research
directions” (i.e., findings related to microstates A, D, and E)
could be related to several factors such as age, sample size, states
of cognition, and recording length, as more recorded EEG data
leads to higher reliability and reproducibility (see Tables 1,3).
For example, Liu et al. (13) found that microstate parameters
are revealed with higher intra-class correlation with longer EEG
recording length.
One major caveat noted is that only three studies (37–
39) directly examined the relationship between microstate
parameters and clinical characteristics. It is also possible that
as no significant difference was found, none was reported
in the other studies. D’Croz-Baron et al. (39) commented
that a significantly reduced occurrence of microstate C in
the ASD group could explain the social impairments faced
by the population. In the study by Nagabhushan Kalgurbi
et al. (42), no significant correlation between the clinical
measures: Autism Diagnostic Observation Schedule-2, Social
Communication Questionnaire, Social Responsiveness Scale,
Repetitive Behaviors Scale–Revised, Stanford-Binet–5, and
microstate parameters were reported. In the study by Bochet
et al. (43), multiple correlations were found between clinical
measures and microstates B, C, D, and E; however these findings
do not survive false discovery rate corrections and thus the
authors suggest the findings should be considered exploratory.
Taking the findings from Takarae et al. (41) and Bochet et al.
(43), it is suggestive that decreased microstate C duration is
indicative of greater presence of ASD symptoms.
Electroencephalography source
localization of microstates
In a recent work by Custo et al. (16), using source
localization algorithms, the team was able to estimate seven
resting-state topography sources. Among these, the four
canonical microstates commonly seen were also present. Of
note, microstate B saw strong activation in the cuneus and a
second weaker activation in the right insula, right claustrum,
and right frontal eye field; microstate C saw strong activation
in the precuneus, posterior cingulate cortex and a second
weaker activation in the left angular gyrus. Interestingly,
literature suggests reduced functional connectivity in the
precuneus/superior parietal lobule in ASD (44). Furthermore,
the posterior cingulate cortex, a region pivotal in cognitive,
social and emotional processing, is also reported to be atypical
in ASD populations (45). Both hypo- and hyper-connectivity
has been reported in insular regions (46), hypo-connectivity
between frontal eye fields and dorsal anterior cingulate cortex.
Taken together, these preliminary studies reviewed in this paper
are suggestive of microstates B and C being crucial to the
underlying neurophysiology of ASD.
As mentioned earlier, three groups, Britz et al. (32), Musso
et al. (33) and, Yuan et al. (34) have reported different
approaches to identify fMRI correlates of EEG resting-state scalp
topographies. Although, it is difficult to compare the methods
due to different methodological approaches, it still demonstrates
that EEG microstates closely describe RSNs identified by fMRI.
Of note, Michel and Koenig (10) cautions against making one-
to-one attributions of microstates to brain functions revealed
by fMRI. Only a handful of other groups (16,47,48) in
addition to Custo et al. (16) utilized EEG source localization
techniques to conclude that the four canonical microstates
represent components of the default mode network (48).
Pascual-Marqui et al. (48) reports that these four microstates
have common posterior cingulate generators, three microstates
also had activity in left occipital/parietal, right occipital/parietal
and, anterior cingulate cortices. Microstate C as found to be
atypical in this review, was reported to have maximum activity
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in anterior cingulate areas (48); a key atypical area in ASD as
discussed previously.
Future research directions
For future ASD microstate research, one crucial point to
take note of is that Custo et al. (16) reports when limiting
the number of microstate clusters to 4, both microstates C
and F seem to collapse into one. This is important for two
reasons. First, it limits new microstates to be discovered
when not using a data-driven approach and secondly, because
microstate F has strong activations in the dorsal anterior
cingulate cortex, superior frontal gyrus, bilateral middle frontal
gyrus and bilateral insula. ASD populations been reported
to show atypical connectivity in the dorsal anterior cingulate
cortex (49), superior frontal gyrus (50), middle frontal gyrus
(50), and insula (46) as mentioned above. Since we see
consistent atypical parameters in microstate C, it is possible
that microstate F once studied may also be atypical in
ASD. Coincidently, these regions: posterior cingulate cortex,
anterior cingulate cortex, medial prefrontal cortex, angular
gyrus and precuneus are implicated in the default mode
network. A network that has been extensively studied and
reported to be atypical in ASD (51). However, interestingly
no groups yet reported any significant findings with regards
to microstate F.
Although Bochet et al. (43) reported the findings from
the male-only analysis were similar to the larger dataset, the
team reported no correlation between microstate parameters
and age and sex. Thus, the effect of development and sex
on microstate parameters in ASD remains largely unclear.
In schizophrenia research, parameters of microstate A and
D were found to be significantly altered in younger patient
groups (52). Furthermore, in both NT and TD populations,
studies have reported significant developmental effect of age
and sex over microstate parameters (12,53,54). It is worth
noting that the ages of participants in Bochet et al. (43) did
not vary as much in comparison to all the studies combined
(see Table 1). Interestingly, in the study by Takarae et al.
(41), the duration of microstate C was reported to positively
correlate with 7–19 year old TD and NT participants but
not ASD participants. This finding may explain the aberrant
parameters of microstate C across studies. The overall variations
in microstate findings may still be explained by the large age
range, cognition, and core behavioral characters of ASD across
the studies. Future studies should also include a large dataset
with participants’ ages ranging from infancy to adulthood to
clearly investigate the developmental effect of age on microstates
in ASD. Future research studies should also be adequately
powered and control for some common sources of heterogeneity
in autism such as age and sex. There is a need to recruit
enough female participants to reflect the 3:1 ratio observed
in clinical sample (55). Also, similar to Bochet et al. (43), it
would be useful to conduct sex-stratified subgroup analysis to
examine the effect and interaction of sex on the microstate
parameters. The effect of development on age and sex on
microstate parameters should also be studied further in NT and
TD populations.
Only three (41–43) identified studies have examined the
association between microstates and clinical characteristics, in
particular core behavioral characteristics of autism.
There exist opportunities to use machine learning
methodologies in resting state microstate analysis. Future
studies can further investigate the potential relationship
between microstate parameters and clinical characteristics and
whether microstates can be used to classify individuals with
ASD (12,54).
There have been numerous studies examining microstates
in schizophrenia looking at deviate microstate characteristics,
microstate patterns, genomic effects and the association between
microstates and symptoms (28,30,56,57). Work is also being
done to use microstates as predictors of disease (58). Such
methodologies could be used to fill the gap that exists in
microstate research related to ASD.
There is potential utility of microstate research to fit
into future neuroimaging research, given the increased inter-
individual variability and idiosyncrasy seen in the brain in
individuals with ASD using neuroimaging data (59). Microstates
have also been found to display inter-individual variability.
Results reported by Nunes et al. (59) are suggestive of
idiosyncratic functional connectivity being a hallmark of the
ASD brain. Further, this team also found that the level of
idiosyncrasy was associated with core behaviors of ASD. Since
both microstates in general and neuroimaging data in ASD
display high inter-individual variability, they can be studied in
tandem to obtain greater understanding of the heterogeneity
and idiosyncrasies in the brain mechanisms in ASD with
a higher scale of sensitivity. Such combination will also
provide a greater degree of temporal and spatial resolution
at the same time. As mentioned previously, recent reports
by Endo et al. (35) and Abreu et al. (36) suggest that EEG
microstates reflect large scale brain dynamics collected from
fMRI and also fluctuate based on structural connectivity along
with BOLD fluctuations. Using these approaches, future ASD
EEG microstate researchers can study both fast and slow
resting-state network dynamics and their associations with
different cognitive states and core behavioral characteristics
of ASD. One other recent study reported a method to
investigate excitation/inhibition regulation in the brain using
fMRI, positron emission tomography and EEG microstates (60).
One widely cited neurophysiologic model in ASD is altered
excitation/inhibition balance in the brain (61); therefore, there
is an opportunity to utilize EEG microstate along with fMRI to
study the excitation/inhibition balance in the brain in autism
with increased precision.
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Conclusion
In summary, the current mini review of microstates in
ASD indicates that there is a significant difference between
microstates of ASD and TD or NT groups, however the data
are heterogeneous, the clinical significance of such altered
microstates has not been thoroughly examined, and major
sources of heterogeneity in ASD such as age and sex have not
satisfactorily been addressed. Combined, these results suggest
that EEG microstates can be used to further detect and study
atypical functionality in the brain in individuals with ASD. In
the future, resting state and event related potential microstate
analysis, in tandem with neuroimaging, can be utilized to study
atypical functional networks in ASD.
Author contributions
SD was involved in literature search, reviewing downloaded
articles, data extraction, assessment of quality, writing, and
reviewing the manuscript. RZ was involved in the design,
assessment of quality, writing, and critically reviewing the
manuscript. PE and MK were involved in writing and critically
reviewing the manuscript. DB and TR were involved in critically
reviewing the manuscript. PD was involved in the design,
literature search, data extraction, assessment of quality, writing,
and critically reviewing the manuscript. All authors approved
the submission of this manuscript.
Conflict of interest
PD is currently being supported by CAMH Discovery
Fund and the Academic Scholar Award from the Department
of Psychiatry, University of Toronto. PE was supported by
a Future Fellowship from the Australian Research Council
(FT160100077). MK is supported by an Alfred Deakin
Postdoctoral Research Fellowship. DB receives research support
from CIHR, NIH, Brain Canada and the Temerty Family
through the CAMH Foundation and the Campbell Research
Institute. He received research support and in-kind equipment
support for an investigator-initiated study from Brainsway
Ltd., and he is the site principal investigator for one sponsor-
initiated study for Brainsway Ltd. He also receives in-kind
equipment support from Magventure for investigator-initiated
studies. He received medication supplies for an investigator-
initiated trial from Indivior. TR has received research support
from Brain Canada, Brain and Behavior Research Foundation,
BrightFocus Foundation, Canada Foundation for Innovation,
Canada Research Chair, Canadian Institutes of Health Research,
Centre for Aging and Brain Health Innovation, National
Institutes of Health, Ontario Ministry of Health and Long-Term
Care, Ontario Ministry of Research and Innovation, and the
Weston Brain Institute. TR also received for an investigator-
initiated study in-kind equipment support from Newronika, and
in-kind research online accounts from Scientific Brain Training
Pro, and participated in 2021 in one advisory board meeting for
Biogen Canada Inc. TR is also an inventor on the United States
Provisional Patent No. 17/396,030 that describes cell-based
assays and kits for assessing serum cholinergic receptor activity.
The remaining authors declare that the research was
conducted in the absence of any commercial or financial
relationships that could be construed as a potential conflict
of interest.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed
or endorsed by the publisher.
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