excitatory/inhibitory imbalance in autism spectrum … imbalance in autism spectrum disorders:...
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The World Journal of Biological Psychiatry
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Excitatory/inhibitory imbalance in autismspectrum disorders: Implications for interventionsand therapeutics
Genoveva Uzunova, Stefano Pallanti & Eric Hollander
To cite this article: Genoveva Uzunova, Stefano Pallanti & Eric Hollander (2015): Excitatory/inhibitory imbalance in autism spectrum disorders: Implications for interventions andtherapeutics, The World Journal of Biological Psychiatry, DOI: 10.3109/15622975.2015.1085597
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THE WORLD JOURNAL OF BIOLOGICAL PSYCHIATRY, 2015http://dx.doi.org/10.3109/15622975.2015.1085597
REVIEW ARTICLE
Excitatory/inhibitory imbalance in autism spectrum disorders: Implications forinterventions and therapeutics
Genoveva Uzunova1, Stefano Pallanti1,2,3,4 and Eric Hollander1
1Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, USA, 2Psychiatry and Behavioural Sciences, UC DavisHealth System, CA, USA, 3Department Psychiatry, University of Florence, Florence, Italy, and 4Icahn School of Medicine at Mount Sinai,New York, NY, USA
ABSTRACTObjectives: Imbalance between excitation and inhibition and increased excitatory-inhibitory (E-I)ratio is a common mechanism in autism spectrum disorders (ASD) that is responsible for thelearning and memory, cognitive, sensory, motor deficits, and seizures occurring in these disorders.ASD are very heterogeneous and better understanding of E-I imbalance in brain will lead to betterdiagnosis and treatments. Methods: We perform a critical literature review of the causes andpresentations of E-I imbalance in ASD. Results: E-I imbalance in ASD is due primarily to abnormalglutamatergic and GABAergic neurotransmission in key brain regions such as neocortex,hippocampus, amygdala, and cerebellum. Other causes are due to dysfunction of neuropeptides(oxytocin), synaptic proteins (neuroligins), and immune system molecules (cytokines). At theneuropathological level E-I imbalance in ASD is presented as a ‘‘minicolumnopathy’’. E-I imbalancealters the manner by which the brain processes information and regulates behaviour. Newdevelopments for investigating E-I imbalance such as optogenetics and transcranial magneticstimulation (TMS) are presented. Non-invasive brain stimulation methods such as TMS fortreatment of the core symptoms of ASD are discussed. Conclusions: Understanding E-I imbalancehas important implications for developing better pharmacological and behavioural treatments forASD, including TMS, new drugs, biomarkers and patient stratification.
ARTICLE HISTORYReceived 14 October 2014Revised 17 July 2015Accepted 18 August 2015
KEYWORDSAutistic disorder; transcranialmagnetic stimulation; elec-troencephalography; bio-markers; translationalmedical research
I. Introduction. A heuristic model for develop-ment of interventions and therapeutics
A widely-accepted hypothesis on the aetiology of autism
spectrum disorders (ASD) proposes that there is excita-
tory-inhibitory imbalance (E-I imbalance) in brain neural
circuits (Rubenstein and Merzenich 2003). This imbal-
ance underlies the social, behavioural, emotional, cog-
nitive, sensory and motor control abnormalities.
Increased E-I ratio in prefrontal cortex is shown to
result in behavioural and social impairments character-
istic of ASD using optogenetics (Yizhar et al. 2011).
Decreased E-I ratio is found in Rett syndrome (RS), a
form of ASD (Eichler and Meier 2008). We will refer to
these mechanisms collectively as E-I imbalance. Several
reviews highlight the significance of E-I imbalance in
ASD (Rubenstein and Merzenich 2003; Polleux and
Lauder 2004; Eichler and Meier 2008; Baroncelli et al.
2011; Ecker et al. 2013; Zikopoulos and Barbas 2013).
Studies show local hyperconnectivity and long-range
hypoconnectivity and disconnection between neural
circuits in ASD (Courchesne and Pierce 2005; Peters et al.
2013; Tye and Bolton 2013). The literature on ASD has
greatly expanded and we consider it is necessary to
write a review highlighting new advances on E-I imbal-
ance in ASD such as the role of the immune system, new
methods to study E-I, biomarkers, patient stratification
and selection of appropriate treatments. We will focus
on developments with significance for neuroscience-
informed and research-based clinical therapeutic
approaches.
Factors controlling the formation and functioning of
excitatory and inhibitory synapses affect the E-I ratio. The
earliest time point when E-I imbalance occurs in ASD is
important to identify in order to develop early thera-
peutic interventions. There are challenges to studying E-I
imbalance in human brain and much of our knowledge
derives from animal models. Although this knowledge
CONTACT Dr Eric Hollander [email protected] Clinical Professor of Psychiatry and Behavioural Sciences and Director – Autism and OCD Spectrum& Anxiety and Depression Research Programs, Bronx, NY, USA.
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largely overlaps with humans, there are differences,
and it is necessary to uncover the mechanisms in human
ASD with techniques such as transcranial magnetic
stimulation (TMS), electroencephalography (EEG), mag-
netoencephalograpgy (MEG) and post-mortem brain
studies.
II. Mechanisms and presentation of E-Iimbalance
1. Mechanisms by which E-I imbalance impacts
the clinical presentation of ASD
ASD are neurodevelopmental disorders with symptoms
in two core domains – social-communication deficits
and repetitive behaviours and restricted interests.
Associated symptoms are irritability, hyperactivity,
movement abnormalities, language delay, sensory
issues, seizures, learning disabilities, developmental
regression and gastrointestinal symptoms. Most ASD
present without an apparent cause (i.e. idiopathic). One
group, ‘‘syndromic forms’’, result from single-gene
defects such as Tuberous Sclerosis Complex 1 and 2
(TSC1 and 2), Fragile X syndrome (FXS), RS and
Angelman syndrome. Toxins, infections, immunological,
nutritional, epigenetic factors and stress may play roles
in ASD.
E-I imbalance may be due to increase in glutama-
tergic or decrease in GABAergic signalling. The E-I
ratio in neocortex is determined by the activity of
pyramidal glutamatergic neurones and inhibitory
GABAergic parvalbumin (PV)-positive interneurons, and
is modulated by minicolumns. They consist of self-
contained neuronal aggregations and their afferent,
efferent and interneuronal connections, mediating the
interactions of neuronal microcircuits, and are patho-
logically altered in ASD (Casanova 2007; Opris and
Casanova 2014). In ASD there is activation of microglia,
patches of disorganised cortex, focal cortical dysplasia
(smaller pyramidal neurones, reduced numbers of inter-
neurons), and altered radial migration of neurones
resulting in heterotopias, i.e. normal cells in an abnor-
mal location (Casanova et al. 2013). Subependymal and
subcortical heterotopias may cause seizures, develop-
mental delay, dyslexia, obsessive–compulsive disorder
(OCD) and ASD. ASD is proposed to be a minicolumno-
pathy with increased numbers of minicolumns and
decreased minicolumn width (Casanova 2006; Casanova
et al. 2002, 2003a, 2003b, 2010). The sheath around the
minicolumns provides inhibitory control and may be
decreased due to dense packing (Casanova 2008). These
alterations cause inhibitory deficit, sensory noise over-
load and lack of stimulus discrimination evidenced by
evoked gamma EEG power at frontal cortical sites.
Importantly, after repetitive TMS, ASD individuals show
significant improvement in cortical discrimination of
stimuli (Sokhadze et al. 2009).
The E-I ratio of synaptic inputs in developing and
adult cortical neurones is dynamically regulated (Zhang
et al. 2011). One hypothesis underscores the importance
of altered connectivity of excitatory and inhibitory
cortical circuits in ASD (Zikopoulos and Barbas 2013).
E-I imbalance occurs due to altered neuronal migration,
development and network formation. This may result
from lack of the extracellular matrix protein reelin
supporting GABAergic neuronal development (Folsom
and Fatemi 2013).
Brain synapses may be modified by immune proteins
such as Major Histocompatibility Class I (MHCI) antigens
that are present on axons and dendrites of cortical
neurons during early development, negatively regulate
cortical synapse density, establishment of neuronal
connections, strength of excitatory but not inhibitory
synapses, and control the E-I balance onto cortical
neurons (Glynn et al. 2011). Through MHCI the immune
system may regulate the E-I balance in ASD. The immune
system may modulate E-I balance through proinflamma-
tory cytokines such as IL-1b, IL-6 and TNFa which play
roles in synaptic plasticity (Beattie et al. 2002), trafficking
of AMPA (Beattie et al. 2010), GABAA receptors and
learning (Miller and Fahey 1994; Lai et al. 2006;
Boulanger 2009; Besedovsky and del Rey 2011; del Rey
et al. 2013; Wei et al. 2011, 2012). Mice with elevated
brain IL-6 display autistic features and E-I imbalance.
TNFa modulates AMPA receptor trafficking in neurons
and glia (Beattie et al. 2010; He et al. 2012).
The E-I ratio depends on levels of ionotropic and
metabotropic glutamate, GABA receptors (Banerjee et al.
2013; Billingslea et al. 2014), extracellular glutamate and
GABA concentrations (Bejjani et al. 2012), oxytocin,
reelin, Arc, neuronal translation (Gkogas et al. 2013;
Gkogas and Sonenberg 2013), signal transduction, cell
adhesion molecules and PSD proteins such as neuroli-
gins (Bourgeron 2007; Kohl et al. 2013). Neuroendocrine
factors such as oestrogens may cause E-I imbalance,
evidenced in reeler mice (Macri et al. 2010).
E-I imbalance in brain may give rise to:
(a) altered synaptic plasticity, learning and memory
discussed in Sections II.2 (Bateup et al. 2013) and
III.2.b (Oberman et al. 2010);
(b) seizures (Moavero et al. 2010) due to increase in
glutamatergic or decrease in GABAergic neuronal
activity, frequent in syndromic ASD such as TSC, RS
and FXS;
(c) neural network oscillatory abnormalities such as
abnormal gamma oscillations (Grice et al. 2001;
Orekhova et al. 2008; Gandal et al. 2010). Gamma
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oscillations are instrumental for the synchronisation
of neuronal discharges in cortical networks and
sensory processing. Research suggests that they
may be linked to cognitive functions such as
selective attention, short- and long-term memory
and multisensory integration;
(d) visual system abnormalities such as atypical visual
perception (sensitivity to bright light, atypical face
perception), attributed to E-I imbalance in occipital,
parietal and frontal cortex (Milne et al. 2009).
Gamma oscillations (30–90 Hz) depend on E-I
balance and can be recorded using EEG.
Measurement of the gamma response to contextual
modulation of visual stimuli in control and ASD
subjects has shown that controls exhibit a gamma
response in the 60-Hz range influenced by context-
ual modulation. ASD subjects show a smaller
gamma response not influenced by contextual
modulation (Snijders et al. 2013). These findings
indicate that E-I imbalance in ASD is due to
GABAergic deficit, extending earlier studies showing
atypical gamma response to visual stimuli in ASD
(Grice et al. 2001; Sun et al. 2012; Wright et al. 2012).
Gamma oscillations may be a biomarker to stratify
patients and follow the responses to therapy.
Another measurement of E-I imbalance is binocular
rivalry in which perception reflects the contrasting
effects of excitation and inhibition in cortex (Laing and
Chow 2002; Said et al. 2013). Adult high-functioning ASD
subjects show a slower rate of binocular rivalry than
controls, longer duration of mixed percepts and
increased likelihood to revert to the previously perceived
object when exiting a mixed percept (Robertson et al.
2013). The atypical dynamics of binocular rivalry in ASD
predict the autistic symptoms measured with the Autism
Diagnostic and Observation Schedule (ADOS), suggest-
ing that binocular rivalry may be a clinical biomarker.
Another study found normal binocular rivalry in ASD
subjects in comparison with controls and no corres-
pondence between binocular rivalry and ASD severity
(Said et al. 2013), indicating minimal E-I imbalance in
visual system.
Children with high-functioning ASD exhibit early-
stage visual processing abnormalities manifested by
augmented evoked potentials elicited by task-irrelevant
stimuli which disrupt stimulus discrimination (Baruth
et al. 2010a). These abnormalities result from E-I imbal-
ance within parietal-occipital and frontal cortical regions
and can be reversed using TMS.
(e) general dyspraxia (impaired performance of skilled
gestures) due to abnormalities in frontal-parietal-
subcortical neural circuits important for learning of
sensory representations of movement and/or the
motor sequences necessary to execute them
(Mostofsky et al. 2006; Dowell et al. 2009; MacNeil
and Mostofsky 2012). This reflects an E-I imbalance in
neural circuits within parietal, premotor and motor
cortices. Praxis is associated with social, communica-
tive and behavioural impairments suggesting that
dyspraxia may be a biomarker of neurological
abnormalities in ASD (Dziuk et al. 2007);
(f) behavioural changes and social dysfunction such as
repetitive and disruptive behaviours, irritability,
social avoidance/withdrawal.
Notably, E-I imbalance is not ASD-specific and is found
in other related neurodevelopmental conditions such as
attention deficit hyperactivity disorder (ADHD) and OCD
(Zimmermann et al. 2014). The specific changes and
brain localisation in the E-I imbalance are likely charac-
teristic for each disorder.
2. Syndromic forms of ASD with marked E-I
imbalance
TSC is a genetic disorder due to inactivating mutations
in the TSC1 or 2 genes. It is associated with learning
abnormalities, intellectual disabilities, developmental
delay, autism, tubers, and epilepsy (Jeste et al. 2008,
2014). The pathological processes result from loss of the
TSC1 or TSC2 proteins regulating the mammalian target
of rapamycin (mTOR) pathway: abnormal protein syn-
thesis, synaptic plasticity, reduced neuronal connectivity
and CNS myelination, and E-I imbalance (Han and Sahin
2011; Tsai and Sahin 2011; Peters et al. 2012; Julich and
Sahin 2014). Loss of Tsc1 in mice leads to upregulation of
mTOR, abnormal protein synthesis and enhanced exci-
tatory synaptic function that alter circuit information
processing (Bateup et al. 2011). Loss of Tsc1 in mice is
accompanied with weakened inhibition resulting from
insensitivity of the glutamatergic neurons to signals
from GABAergic neurons due to mTOR deficiency
(Bateup et al. 2013), suggesting that drugs enhancing
GABA functions may not be effective in TSC. Tsc2
heterozygous knockout mice show deficiency in CA1
hippocampal long-term potentiation (LTP) as result of
mTOR dysregulation and learning and memory deficits
that are corrected using the mTOR inhibitor rapamycin
(Ehninger et al. 2008, 2009). TSC1/2 are important for
axon specification, guidance and regeneration, and have
effects on neuronal connectivity and E-I balance (Choi
et al. 2008; Tsai and Sahin 2011). The deficits in mTOR,
GABAergic neurotransmission and E-I imbalance in TSC
may be overcome by the neuropeptide oxytocin or
mTOR inhibitors (Julich and Sahin 2014).
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FXS is a genetic disorder associated with ASD
affecting primarily boys. It results from loss-of-function
of the X-linked gene FMR1 encoding the RNA-binding
protein FMRP (Ronesi and Huber 2008). Associated
symptoms are anxiety, sensory abnormalities, hyper-
activity, irritability, seizures (Tranfaglia 2012). The E-I
imbalance changes in FXS are brain region-specific.
Major mechanism is enhanced synaptic protein synthesis
due to overactivation of group I metabotropic glutamate
receptors (gpI mGluRs; Dolen and Bear 2008; D’Antoni
et al. 2014). The functions of the affected synaptic
proteins are abnormal leading to changes in excitation
and inhibition (Ronesi et al. 2012). Proteins with
enhanced translation are PSD-95, Arc, GluA1 and GluA2
receptors (Muddashetty et al. 2007), microtubule-asso-
ciated protein 1B, striatal-enriched protein tyrosine
phosphatase, calcium-calmodulin kinase II (Luscher and
Huber 2010). There are alterations in synaptic plasticity
(Huber et al. 2002), activity-dependent formation and
refinement of neuronal circuits (Paluskiewitz et al. 2011;
Doll and Broadie 2014) and learning. There is enhanced
gpI mGluR-LTD expressed with AMPA receptor internal-
isation. Besides gpI mGluRs, M1 muscarinic acetylcholine
Gq-coupled receptors trigger LTD expressed with AMPA
receptor internalisation which is enhanced in FXS (Volk
et al. 2007). There are GABAergic deficits in cortex,
hippocampus, amygdala, striatum, and subiculum,
decreased GABAA receptors and alterations in GABA
production and metabolism in the Fmr1 KO mouse
(Paluskiewitz et al. 2011). Human ASD post-mortem
brain studies report decreased GABAA and GABAB
receptors in anterior cingulate cortex (Oblak et al.
2009, 2010). There is hyperexcitability in layer 4 of
somatosensory neocortex of the Fmr1 KO mouse (Gibson
et al. 2008). The expression of NMDA receptors is
increased in PFC and hippocampus of Fmr1 KO mice
(Krueger et al. 2011; Uzunova et al. 2014). These findings
provided basis for development of drugs decreasing gpI
mGluR activity such as MTEP, MPEP, CTEP (Michalon
et al. 2012), AFQ056 (Pop et al. 2014; Jacquemont et al.
2011), increasing GABAergic functions such as
Arbaclofen (Berry-Kravis et al. 2012), and decreasing
NMDA receptor functions such as memantine. They aim
to restore the E-I imbalance and target the core
symptoms of ASD. Clinical trials with these drugs have
yielded mixed results. This may be due to the hetero-
geneity of E-I imbalance and lack of appropriate
outcome measures.
RS is due to loss-of-function mutations in the X-linked
gene for the methyl-CpG binding protein 2 (MeCP2), a
transcriptional activator and repressor. RS is associated
with autism, regression of language, cognitive functions,
social and motor skills, stereotypies, seizures, breathing
difficulties, affecting primarily girls. Similarly to TSC and
FXS, RS is a disorder of synaptic protein synthesis. There is
disrupted development and functions of neuronal circuits
and E-I imbalance. Using transgenic mice it is established
that MeCP2 is critical for normal function of GABA-
releasing neurons (Chao et al. 2010). Human RS post-
mortem brain studies show abnormal minicolumns
(Casanova et al. 2003a, 2003b). Experimental therapies
aiming to restore the E-I imbalance are Insulin-like growth
factor 1 (Tropea et al. 2009) for correcting the synaptic
plasticity deficits and the ampakine CX546 (Kwaja and
Sahin 2011) for correcting the protein synthesis deficits
and increasing Brain-derived neurotrophic factor.
III. Methods to interrogate E-I imbalance inautistic brain
1. Preclinical (animal models)
(a) Optogenetics allows manipulating the activity of
neurons and neuronal networks (Fenno et al. 2011).
It is an invasive method in which light-sensitive ion
channels (opsins) are introduced in neurons by viral
(AAV or lentiviral) transfection or genetic means.
Using light the neurons are activated or inhibited
with temporal and spatial precision and the effects
of these manipulations on behaviour of living
animals is observed (Tye and Deisseroth 2012;
Yizhar 2012). Optogenetic manipulation of principal
excitatory or inhibitory parvalbumin-positive inter-
neurons in the mouse medial prefrontal cortex
(mPFC) shows that elevated (by activating the
principal neurons) and not reduced (by activating
the inhibitory neurons) E-I balance in the mPFC
impairs social behaviour and conditioning (Yizhar
et al. 2011). The elevation of the E-I ratio is
accompanied by elevated baseline gamma oscilla-
tions, a clinical ASD biomarker (Sohal 2012).
The social deficits can be reduced by increasing
the interneuron inhibitory tone. This study provides
support for E-I imbalance in ASD and indicates that
the changes are reversible.
2. Clinical
(a) EEG and MEG may be used to determine E-I
imbalance in ASD alone, or together with TMS,
and provide useful biomarkers and outcome meas-
ures. Characteristic EEG changes manifesting as
excess oscillations in the high frequency range are
found in ASD children (Cantor et al. 1986; Orekhova
et al. 2007). The EEG findings alone in ASD can be
non-specific and resemble those of toddlers indicat-
ing maturational lag (Cantor et al. 1986).
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MEG studies find abnormal oscillations in ASD children
and adolescents suggesting E-I imbalance (Wilson et al.
2007; Cornew et al. 2012). Children and adolescents with
ASD exhibit reduced left hemispheric gamma oscilla-
tions (Wilson et al. 2007). ASD children exhibit elevations
in delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz) and
high frequency (20–120 Hz) power (Cornew et al. 2012).
The increased alpha power in temporal and parietal
regions correlates with the severity of ASD symptoms
and is of particular interest because inhibitory inter-
neurons play role in its maintenance. MEG studies report
that gamma activation underlying face processing is
abnormal in ASD (Wright et al. 2012). This is likely due to
E-I imbalance in occipital regions. MEG oscillations may
depend on medications (SSRIs, methylphenidate), state of
arousal, anxiety, eyes open or closed condition and high-
or low-functioning ASD. Nevertheless, the findings of
abnormal brain activity using MEG and EEG partially
correspond and may have clinical significance for
biomarkers.
(b) TMS is a technique for non-invasive brain stimula-
tion (NIBS) that can be applied to study and
modulate cortical excitability and synaptic plasticity
in vivo in humans (Pascual-Leone et al. 2011; Vicario
and Nitsche 2013). TMS consists of application of
rapidly changing magnetic fields to discrete brain
regions using a coil positioned above the targeted
region. The magnetic fields penetrate into the brain
and induce electric currents depolarising groups of
neurons. If applied to motor cortex, the depolarisa-
tion can produce contralateral muscle contraction,
motor evoked potential (MEP), measured using
electromyography (EMG). If applied to non-motor
cortical regions, TMS may evoke field potentials
which are recorded using EEG. In addition to effects
in the targeted neurons, TMS may produce transsy-
naptic distributed network effects that can be
detected using neuroimaging. TMS may be com-
bined with EEG or functional magnetic resonance
imaging (fMRI; Oberman et al. 2010). TMS is
particularly useful because it detects changes in
synaptic plasticity (Oberman et al. 2013). TMS is
subdivided into single-pulse (spTMS), paired-pulse
(ppTMS) or repetitive (rTMS). SpTMS is a measure of
cortical excitability when applied together with EMG
or EEG. ppTMS can be used to study the E-I ratio
(Kobayashi and Pascual-Leone 2003) whereas theta
burst stimulation (TBS) may be used to study LTP,
LTD and metaplasticity (Huang et al. 2005). TBS can
measure synaptic plasticity in cortex determined by
GABA and glutamate receptors. Using ppTMS it is
possible to measure intracortical inhibition
mediated by GABA(A) or GABA(B) receptors
(Oberman et al. 2010). TMS studies in ASD and FXS
subjects and controls show that ASD patients have
increased cortical excitability and that ASD and FXS
patients have altered synaptic plasticity. The find-
ings of hyperplasticity in ASD are consistent
irrespective of the aetiology, suggesting that this
may be a biomarker (Oberman et al. 2010). The
findings of altered synaptic plasticity in ASD and FXS
patients are consistent with animal models.
Combination of TMS and EEG shows that in ASD
there are robust patterns of over- and under
connectivity in frontal and temporal cortical regions
in the eyes closed resting state (Murias et al. 2007).
In the theta frequency range (3–6 Hz) there is locally
elevated coherence in frontal and temporal cortex
within the left hemisphere, and in the lower alpha
range (8–10 Hz) there is reduced coherence within
frontal regions in adults with ASD (Murias et al.
2007). The TMS studies need to be expanded with
different brain areas and larger patient populations.
TMS may be used to assess physiological biomarkers
of E-I imbalance in ASD. It is possible to use
common measures of E-I imbalance such as som-
atosensory evoked potentials (SEP) at baseline to
stratify patients and understand responses to ther-
apy. TMS may be used in ASD to induce acute and
long-lasting changes in brain cortical excitability and
functions depending on the protocol. Examples of
use of TMS in ASD are discussed below.
Induction of paired association (PAS) using TMS in
high-functioning ASD patients is impaired implying
presence an LTP-like deficit (Jung et al. 2013). PAS
consists of peripheral electrical stimulation paired with
TMS applied to the contralateral motor cortex and
depends on intact sensorimotor integration. The short
intracortical interval inhibition (SICI) dependent on
GABAergic transmission was normal and did not correl-
ate with the PAS effect. These findings suggest that in
patients with high-functioning ASD there is reduced
excitatory synaptic connectivity and deficits in sensory-
motor integration (Jung et al. 2013). The motor learning
deficits are likely due to decreased connectivity within
basal ganglia, thalamus, primary motor cortex (M1) and
SMA (Mostofsky et al. 2009).
Low-frequency rTMS has been applied to modulate
the abnormal gamma oscillations resulting from sen-
sory-perceptual deficits in ASD (Baruth et al. 2010b). ASD
individuals show augmented gamma band activity
indiscriminative of stimulus type during the early
stages of visual processing, a consequence of cortical
E-I imbalance. Twelve sessions of bilateral slow rTMS to
the DLPFC in ASD individuals significantly improved the
discriminatory gamma band activity between relevant
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and irrelevant stimuli. This was associated with improve-
ment on the responses in the behavioural questionnaires
with respect to irritability and repetitive behaviour.
Therefore, it is possible to use EEG measurement of
stimulus discrimination in ASD as a physiological
biomarker of E-I imbalance before and after therapy.
This study shows that rTMS can be useful to treat the
core symptoms of ASD without major side effects.
In another study low frequency rTMS is applied to the
bilateral DLPFC in children with ASD to determine if this
will improve the error monitoring and correction deficit
(Sokhadze et al. 2012). There was beneficial effect of
rTMS and the study showed that the ERPs associated
with responses to errors and behavioural performance
measures may be used as functional outcome measures.
The brain regions affected by E-I imbalance in human
ASD determined in the studies discussed above are
indicated in Figure 1.
IV. Interventions for restoration of E-I balancein ASD
1. Pharmacological therapeutics
(a) Glutamatergic drugs. Group I mGlu (mGlu5) antag-
onists have not proven very effective in human ASD.
Ampakines (enhancing the activity of AMPA recep-
tors) have shown partial effectiveness in FXS.
Memantine, NMDA receptor antagonist, has been
tested in ASD treatment trials (Ghaleiha et al. 2013;
Uzunova et al. 2014). A multi-site double-blind clinical
trial with memantine tested the safety, tolerability and
efficacy in a paediatric ASD patient group
(NCT01592747). In the memantine study, similarly to
clinical trials with mGlu5 antagonists, results may be
different if patients were stratified on the basis of E-I
imbalance. This may be done using gamma oscillations
or SEPs as biomarkers. Biomarkers can identify patients
with greater E-I imbalance and select a subgroup likely
to have a favourable treatment response. Gamma
oscillations and SEPs can also be used as outcome
measures assessing the effects of therapy. Lack of
patient stratification may explain why single-site mem-
antine studies in which the patient population is
homogenous have shown positive results, not confirmed
in ASD multi-site studies.
(b) GABA agonists. The drug AZD7325 (AstraZeneca) is
a selective modulator of the GABAA2/3 receptor, is
well-tolerated and without tendency to induce cog-
nitive deficits. AZD7325 is tested in a Fast-Fail Trial
(FAST) as ASD treatment by restoring the E-I imbal-
ance. An EEG biomarker is used as patient selection
tool and means to assess the drug action, and side
effects, attention and learning are measured.
Arbaclofen, a GABA(B) agonist, has shown promise in
treatment of FXS and ASD (Berry-Kravis et al. 2012;
Henderson et al. 2012). A clinical trial with STX209
(Arbaclofen) on the neurobehavioral functions of children
and adults with FXS has not shown difference from
Figure 1. Brain regions affected by E-I imbalance in ASD targeted for therapeutic purposes using TMS. (A) Lateral view of the humanbrain. Areas targeted by TMS are: (1) dorsolateral prefrontal cortex (DLPFC) important for social cognition and cognition in general; (2)inferior frontal gyrus (IFG), important for language and verbal comprehension; (3) supplementary motor area (SMA), important forcontrol of movements and coordinating the temporal sequence of actions; (4) premotor cortex (Prem Ctx) and 5) primary motorcortex (Motor Ctx). Other regions such as parietal, temporal, occipital cortex (Occ Ctx) and cerebellum (Crb) can also be affected by E-Iimbalance in ASD. It is possible to target them with TMS in the future as this technique is further developed. Among them, thecerebellum is of particular interest. (B) Medial view of the human brain. Areas affected by E-I imbalance currently targeted by TMS areindicated: (1) dorsomedial prefrontal cortex (DMPFC) that contains mirror neurones important for social interactions; (2) the anteriorcingulate cortex (ACC) involved in empathy, emotion and impulse control; and (3) the SMA. Other regions that are affected by E-Iimbalance in ASD are cerebellum, thalamus (Th), basal ganglia, hippocampus and amygdala.
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placebo on the primary endpoint measure – ABC-I
(Aberrant Behaviour Checklist-Irritability) (Berry-Kravis
et al. 2012). However, analysis using ABC – Social
Avoidance Scale showed significant beneficial treatment
effect. Subsequent study of STX209 in ASD subjects ages
5–21 (ClinicalTrials.gov Identifier: NCT01706523) was
terminated due to absence of effects on the primary
outcome measure – ABC. Future studies will benefit from
patient stratification.
(c) mTOR inhibitors. Rapamycin and its analogue
everolimus used for management of the tumours
and epilepsy in TSC (Franz 2011, Franz et al. 2013)
may be beneficial for ameliorating neurocognitive
manifestations in TSC and ASD (de Vries 2010),
based on the hypothesis that mTOR overactivation
is responsible for neurocognitive symptoms.
Rapamycin has improved measures of learning and
memory (immediate and delayed recall) by 20%
from baseline in 5 of 8 TSC subjects (de Vries 2010).
(d) Oxytocin (OT) has improved ASD symptoms in
clinical trials (Soorya et al. 2008; Green and
Hollander 2010; Anagnostou et al. 2012). OT has
advantages over mTOR inhibitors such as lower
toxicity, lack of immunosuppression, effects on the
phosphatidylinositol 3-kinase (PI3K) pathway and
enhancement of GABAergic neurotransmission. In
humans OT modulates a variety of behaviours
relevant to ASD including anxiety, stress, social
memory, recognition, bonding (affiliation), sexual
and aggressive behaviours. In CA1 neurons OT
stimulates protein synthesis and enhances LTP (Lin
et al. 2012). There is relationship between OT and
GABAergic neurotransmission in brain. GABAergic
abnormalities are established in ASD (Yip et al. 2007;
Fu et al. 2012; Mori et al. 2013). OT activates
hippocampal interneurons (Ogier et al. 2008) and
increases inhibitory GABAergic synapses in brain
(Theodosis et al. 2006). OT receptor knockout mice
exhibit reduced hippocampal GABAergic synapses,
increased ratio glutamatergic/GABAergic synapses,
increased seizure susceptibility, impaired social
behaviour, increased aggression, reduced cognitive
flexibility, and are a neurobehavioral model of ASD
(Sala et al. 2011). Intracerebral administration of OT
and vasopressin rescues these deficits.
(e) Implication of E-I imbalance for development of
pharmacologic drugs for treatment of ASD. It is
difficult to predict before treatment which patients
will respond favourably. To predict the treatment
response it is possible to administer a test drug dose
and monitor for early efficacy biomarker effect to
demonstrate target engagement of the drug. If
there is positive effect, the drug can be administered
for a longer period of time. If there is no effect on
early efficacy biomarker or if there are side effects,
the drug will not be administered.
2. Clinical somatic treatments
TMS can be applied as a therapeutic modality in ASD to
restore the imbalance of excitation and inhibition,
modulate synaptic plasticity and gamma oscillations. In
future it will be possible to target additional brain
regions affected by E-I imbalance such as cerebellum
(Demirtas-Tatlidede et al. 2013). Pharmacological, behav-
ioural or neuromodulation treatments that reverse the
altered E-I balance may be developed. Various TMS
protocols in terms of location of stimulation (i.e. dorso-
lateral prefrontal cortex, DLPFC, right operculus, supple-
mentary motor area, SMA), intensity of stimulation (high
frequency to activate – 20 Hz, and low frequency to
inhibit – 1 Hz) have been tested in ASD clinical trials. With
respect to the optimal brain area to target, no consensus
is reached. In summary, the results of TMS in ASD are
promising but inconclusive. Knowledge of the underlying
mechanisms will allow clinicians to design TMS protocols
so that the pathological changes in excitation, inhibition
and neural connectivity are reversed. TMS may be
preferred in patients who cannot take medications, are
not responsive to therapies, and may be combined with
medications, neurofeedback or behavioural challenges.
Another NIBS technique that has potential for
inducing changes in neuronal activity and management
of ASD is transcranial direct current stimulation (tDCS;
D’Urso et al. 2015). tDCS is still under development and
not FDA-approved method.
Evidence supports the gut–brain connection and roles
of inflammation and immunity ASD (Hsiao et al. 2013).
This is relevant to E-I imbalance because proinflamma-
tory cytokines released during gut–brain pathological
processes may affect trafficking of AMPA, GABA recep-
tors and synaptic plasticity. Based on this knowledge
novel treatments for ASD such as Trichuris suis ova
are currently tested by Dr. Eric Hollander’s team at
Montefiore Medical Centre in a clinical trial with
promising effects on repetitive and disruptive behav-
iours in ASD (Hollander et al. 2013).
3. Early detection using event-related potentials
(ERPs)
Clinical studies in the USA (Luyster et al. 2011) and UK
(Elsabbagh et al. 2012) evaluate early detection of ASD
by measurement of neural correlates of E-I imbalance in
infant brain as at 6–12 months of age using sensor nets
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to detect ERPs. ERPs are changes in brain electrical
activity resulting from simultaneous activation of a
group of neurons that can be time-locked to a stimulus.
They are often recorded in response to a face-processing
stimulus and are useful to test in infants because they do
not require verbal abilities. The ERP components N290
and P400 (occipito-temporal region components) are
precursors of the adult N170 component, a marker of
detection of faces. The Nc component (negative central,
observed over frontal regions) is a marker of attention.
The group of Luyster examined the N290, P400 and Nc
components in response to familiar and unfamiliar faces
in 12-month-old infants at high vs. low ASD risk. They
found that the N290 and P400 did not differ among the
high- and low-risk infants. However, there were differ-
ences in the Nc responses among groups. The group of
Elsabbagh determined in infants 6–10 months of age the
ERP components P1, N290 and P400 as measures of the
neural responses to dynamic eye gaze. The infant’s
sensitivity to eye gaze is a precursor to social and
communicative skills. The researchers hypothesised that
infants at risk for ASD have abnormal neural responses
to eye gaze correlating with the behavioural
impairments observed later and can be used for early
diagnosis. These studies found that face-related ERP
distinguish infants at risk for ASD from the controls and
that atypical brain function precedes the onset of
behavioural signs and overt ASD symptoms. Other
studies show that toddlers at risk for ASD, infants
whose siblings have ASD and whose siblings do not
have ASD exhibit variability in the early trajectories of
development measured using the ADOS and the Autism
Diagnostic Interview – Revised (ADI-R), suggesting that
there is need for early identification, regular monitoring
and standardised assessments of young children sus-
pected of having ASD (Lord et al. 2012). Studies on
development of toddlers with early and later diagnosis
of ASD show that ASD may involve developmental
arrest, slowing or even regression (Landa et al. 2007). By
early detection (3–6 months) it is possible to achieve
better therapeutic results. For early ASD management it
is feasible to apply a neurofeedback approach to retrain
the E-I imbalance (Pineda et al. 2012). The various
mechanism and clinical diagnostic and treatment
aspects of E-I imbalance in ASD discussed are schemat-
ically presented in Figure 2.
Figure 2. Mechanisms and clinical aspects of E-I imbalance in ASD. The figure summarises the various aspects of E-I imbalance asdiscussed in the review. Central to ASD is E-I imbalance in key neural circuits in brain. In the lower panel are shown in the left box themajor mechanisms currently thought to lead to E-I imbalance (Causative Mechanisms). In turn, they may give rise to variousfunctional changes shown in the lower right box such as altered synaptic plasticity, learning and memory, seizures, etc. (FunctionalConsequences). These functional changes can measured for research and diagnostic purposes with different techniques shown in thetop left box such as optogenetics and clinical methods such as EEG, MEG and NIBS (Methods to Interrogate). These latter methodscan also be suitable ASD biomarkers and outcome measures. Based on the knowledge from the three components (causativemechanisms, functional consequences and methods to interrogate E-I imbalance) new therapeutics may be developed that aresummarised in the top right box (Interventions for Reversal).
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V. Summary, conclusions and future directions
Can a single model of E-I imbalance consistent with the
various studies be proposed in ASD? ASD are very
heterogeneous (Hollander et al. 2011) and there is a
need to stratify patients based on clinical features and
biomarkers. Measurement of E-I imbalance is influenced
by the experimental method, subtype of ASD (e.g. FXS,
TSC, high-functioning vs. low-functioning) and develop-
mental period. We propose that E-I imbalance is a ‘‘final
common pathway’’ in ASD but the different patient
subgroups may have different mechanisms of E-I imbal-
ance and respond to different treatments.
Acknowledgements
None
Statement of interest
Dr Hollander has received research grants from Roche, Forest,Coronado, and Brainsway, and consulted to Roche, andCoronado.
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