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Effects of cannabidiol on brain excitation and inhibition
systems; a randomised placebo-controlled single dose trial
during magnetic resonance spectroscopy in adults with and
without autism spectrum disorder
Charlotte Marie Pretzsch
1
, Jan Freyberg
1
, Bogdan Voinescu
1
, David Lythgoe
2
, Jamie Horder
1
, Maria Andreina Mendez
1
,
Robert Wichers
1
, Laura Ajram
1
, Glynis Ivin
3
, Martin Heasman
3
, Richard A. E. Edden
4
, Steven Williams
2
, Declan G. M. Murphy
1
,
Eileen Daly
1
and Gráinne M. McAlonan
1
There is increasing interest in the use of cannabis and its major non-intoxicating component cannabidiol (CBD) as a treatment for
mental health and neurodevelopmental disorders, such as autism spectrum disorder (ASD). However, before launching large-scale
clinical trials, a better understanding of the effects of CBD on brain would be desirable. Preclinical evidence suggests that one
aspect of the polypharmacy of CBD is that it modulates brain excitatory glutamate and inhibitory γ-aminobutyric acid (GABA) levels,
including in brain regions linked to ASD, such as the basal ganglia (BG) and the dorsomedial prefrontal cortex (DMPFC). However,
differences in glutamate and GABA pathways in ASD mean that the response to CBD in people with and without ASD may be not
be the same. To test whether CBD ‘shifts’glutamate and GABA levels; and to examine potential differences in this response in ASD,
we used magnetic resonance spectroscopy (MRS) to measure glutamate (Glx =glutamate +glutamine) and GABA+(GABA +
macromolecules) levels in 34 healthy men (17 neurotypicals, 17 ASD). Data acquisition commenced 2 h (peak plasma levels) after a
single oral dose of 600 mg CBD or placebo. Test sessions were at least 13 days apart. Across groups, CBD increased subcortical, but
decreased cortical, Glx. Across regions, CBD increased GABA+in controls, but decreased GABA+in ASD; the group difference in
change in GABA +in the DMPFC was significant. Thus, CBD modulates glutamate-GABA systems, but prefrontal-GABA systems
respond differently in ASD. Our results do not speak to the efficacy of CBD. Future studies should examine the effects of chronic
administration on brain and behaviour, and whether acute brain changes predict longer-term response.
Neuropsychopharmacology (2019) 0:1–8; https://doi.org/10.1038/s41386-019-0333-8
INTRODUCTION
Autism spectrum disorder (ASD) affects up to 1 in 59 individuals
[1]. Of those affected, 70% also have co-occurring conditions such
as epilepsy [2], and mood and anxiety disorders [3]. This incurs a
high cost to the individual and society: on average the lifespan of
individuals with ASD is reduced by 20 years [4]. Given the lack of
effective pharmacological treatments, researchers have therefore
begun to explore alternative options. These include cannabis and
its major non-intoxicating component cannabidiol (CBD), which is
derived from the cannabis sativa plant [5].
CBD has already been trialled in several disorders. For instance,
preliminary evidence suggests that CBD may improve spasticity
[6], pain, sleep disturbances [7], and mobility [8] in multiple
sclerosis (MS); and alleviate anxiety symptoms in social phobia [9].
Moreover, alongside anecdotal accounts and case series reports of
benefits from medical marijuana in ASD [10], there is evidence
that CBD: (i) reduces seizure frequency in two epilepsy syndromes
associated with autistic symptoms: Dravet Syndrome and
Lennox–Gastaut syndrome [11–13]; and (ii) improves ASD-like
social deficits in a mouse model of Dravet Syndrome [14]. This
suggests that CBD may be worth further investigation in
idiopathic ASD. However, before embarking on large-scale clinical
trials, a better understanding of how CBD acts on the human
brain, and especially in ASD, would be desirable.
CBD has multiple targets, but one aspect of its polypharmacy
may be to help regulate excitatory glutamate (E) and inhibitory γ-
aminobutyric acid (GABA) (I) transmission, which may influence
the activity of excitatory and inhibitory signalling pathways: For
example, CBD facilitates glutamate and GABA neurotransmission
across the brain through agonism at the transient receptor
potential vanilloid type 1 (TRPV1) receptor [15,16]. Moreover, CBD
may increase GABAergic transmission by antagonism at the G
protein-coupled receptor 55 (GPR55), and especially in the basal
ganglia [14] (BG). In contrast, CBD is thought to be an agonist at
prefrontal 5-HT1A receptors, where it suppresses glutamate and
GABA transmission [17,18]. In sum, CBD may act on targets
Received: 18 September 2018 Revised: 14 January 2019 Accepted: 19 January 2019
1
Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK;
2
Department of
Neuroimaging Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK;
3
South London and Maudsley NHS Foundation Trust Pharmacy,
London, UK and
4
Russel H Morgan Department of Radiology and Radiological Science, Johns Hopkins Medical Institutions, Baltimore, MD, USA
Correspondence: Gráinne M. McAlonan (grainne.mcalonan@kcl.ac.uk)
These authors contributed equally: Declan G. M. Murphy, Eileen Daly, Gráinne M. McAlonan
www.nature.com/npp
©The Author(s) 2019
throughout the brain, but especially in the BG and the prefrontal
cortex. These actions of CBD upon glutamate-GABA pathways may
be especially important in ASD, where post mortem, genetic, and
in vivo proton magnetic resonance spectroscopy (MRS) studies
have shown abnormalities in both prefrontal and BG glutamate
and GABA pathways [19–21]; both regions have also been
repeatedly linked to ASD core symptoms [21,22]. Thus, CBD
could well impact on prefrontal and BG Glx and GABA levels in
ASD, but not necessarily in the same manner as in unaffected
individuals (with intact glutamate-GABA systems). However, no-
one has investigated this directly.
Therefore, in this study, we tested the hypotheses that CBD
impacts on human in vivo glutamate and GABA levels in the BG
and dorsomedial prefrontal cortex (DMPFC); but that the response
is atypical in ASD. To achieve this, we compared MRS measures of
glutamate and GABA in men with and without ASD following a
single oral dose of 600 mg CBD or a matched placebo (at least
2 weeks apart) in a randomised double-blind, cross-over design.
MATERIALS AND METHODS
Procedure
This research was conducted in accordance with the Declaration
of Helsinki, at the Institute of Psychiatry, Psychology, and
Neuroscience (IoPPN) at De Crespigny Park, SE5 8AF, London,
UK (August 2016 to August 2018). The Medicines and Health
Research Authority (MHRA) in the UK confirmed the study design
was not a Clinical Trial and ethical approval for this study was
provided by the King’s College London Research Ethics Commit-
tee, study reference HR15/162744. All participants provided
written informed consent. Every participant took part in all
aspects of this case-control study.
This placebo-controlled, randomised, double-blind, repeated-
measures, cross-over case-control study was conducted as part of
a larger investigation into the role of phytocannabinoids in ASD;
clinicaltrials.gov (identifier: NCT03537950, entry name: HR15-
162744). Placebo (PLC) or CBD was allocated in a pseudo-
randomised order, so that approximately half in each group
attended a placebo visit before CBD; and half attended a CBD visit
before placebo. The randomisation was implemented by Prof
McAlonan using https://www.random.org/. Participants and
researchers directing the study were blind to the assignment.
Participants attended for two visits. To allow for drug wash-out,
visits were separated each by a minimum of 13 days, with all
attempts made to keep between-visit time consistent across all
visits and participants. Moreover, the acquisition of data from both
groups was mostly overlapping during the same period. On each
visit, urine samples were taken to screen for illicit substances (a full
list is included below). Subsequently, participants underwent a
brief health check, received a liquid oral dose of the pharmaco-
logical probe (600 mg of CBD; in line with previous single dose
studies of CBD adults (e.g. [23]) or a matched placebo, both
provided by GW Research Ltd, Cambridge, UK), and a second brief
health check to test for potential acute adverse reactions/side
effects. Participants underwent scanning timed to coincide with
peak plasma (2 h) concentration. After the scan, participants
received a third health check to ensure they had experienced no
ill-effects and were fit to leave the department.
Participants
Potential participants were excluded if they had a comorbid major
psychiatric or medical disorder affecting brain development (e.g.
schizophrenia or epilepsy), a history of head/brain injury, a genetic
disorder associated with ASD (e.g. tuberous sclerosis or Fragile X
syndrome), or an IQ below 70. We also excluded participants who
were reliant on receiving regular medication known to directly
modulate glutamate and GABA systems. However, we included
participants on other medications which are commonly prescribed
in ASD: one person with ASD who took a single dose of Ritalin on
the morning of each study visit, and one person with ASD who
took a single dose of sertraline on the morning of each study visit.
We asked participants to abstain from using cannabis and/or other
illicit substances in the month prior to scanning, and from drinking
alcohol on the day prior to testing. We also carried out Urine Drug
Screening on each test day. Data from individuals who screened
positive for these substances were excluded. Thus, we initially
retained data from 34 subjects (neurotypical control n=17, ASD
n=17) (see Table 1for demographics); this sample size was
sufficient to detect a 10% E-I shift (where ‘shift’means a change in
a component of the Glx-GABA metabolite pool) at a power of 0.8
and a significance level of α=0.05, based on a power analysis
using previous findings in the department [19]. All participants
had an IQ over 70. All participants in the ASD group had a clinical
diagnosis of ASD made according to ICD10 research criteria
[24–26], and severity of symptoms was confirmed using standar-
dised research diagnostic instruments (Autism Diagnostic Obser-
vation Schedule, ADOS; and Autism Diagnostic Interview-Revised,
ADI-R).
Imaging data acquisition
All imaging data were acquired on a 3T GE Excite II magnetic
resonance imaging (MRI) scanner (GE Medical Systems, Milwau-
kee, WI, USA). The scanning protocol included a structural MRI
scan acquired using a 3D inversion recovery prepared fast spoiled
gradient recalled (IR-FSPGR) sequence (slice thickness =1.1 mm,
spatial positions =124, flip angle =20°, field of view (FoV) =280
mm, echo time (TE) =2.844 ms, repetition time (TR) =7.068 ms,
inversion time =450 ms, matrix =256 × 256). This structural scan
was conducted to obtain information used during the preproces-
sing of the spectroscopy scan. The scanning protocol further
included a spectroscopy scan based on the MEshcher-GArwood
Point RESolved Spectroscopy (MEGA-PRESS) sequence [27]. We
acquired data (44 averages) from two voxels: the first was
positioned in the BG (echo time (TE) =68 ms, repetition time (TR)
=1800 ms, voxel size =35*30*25 mm
3
).
This voxel was placed with the anterior border initially abutting
the anterior portion of the left lentiform nucleus, and as medial as
possible, to avoid the ventricles as much as possible. Thus, taking
Table 1. Participant demographics for all subjects are reported including standard deviations (except for N)
Demographic measure TD ASD F(dof) p-value
N(M/F) 17 (17/0) 17 (17/0)
Age in years 28.47 (6.55) 31.29 (9.94) F(1) =0.956 p=0.335
Days between visits 34.82 (24.99) 36.44 (20.53) F(1) =0.041 p=0.841
FSIQ 124.59 (12.7) 111.35 (18.80) F(1) =5.781 p=0.022
Significant between-group differences are highlighted in bold
ASD autism spectrum disorder, F(dof) F statistic and degrees of freedom, Ffemale, FSIQ full scale intelligence quotient, Mmale, N, participant number, TD
typically developing individuals
Effects of cannabidiol on brain excitation and inhibition systems; a. . .
CM Pretzsch et al.
2
Neuropsychopharmacology (2019) 0:1 – 8
1234567890();,:
into account slight inter-individual anatomical differences, this
voxel was on average composed as follows: the white matter
(WM), primarily included the internal capsule and part of the
corpus callosum. The grey matter (GM), included the BG [~55%],
the thalamus [~25%] and the insula [~20%].
The second voxel was positioned in the DMPFC (TE =68 ms,
TR =2000 ms, voxel size =25*40*30 mm
3
). This voxel was placed
in the midline, avoiding the corpus callosum. Resultantly, given
inter-individual variance, this voxel was composed as follows: the
WM included the corpus callosum and cingulum; while the GM
included the anterior part of the cingulate gyrus.
Representative voxel positions are shown in Fig. 1.
Urine test
To evaluate presence or absence of illicit substances that could
confound potential effects of the pharmacological probes tested
in this study, we performed liquid chromatography-mass spectro-
metry (LC-MS) analysis on urine samples provided by each
participant before the drug administration. Participants that
showed positive results for any of the drugs tested, including
Amphetamines (Amphetamine, Methamphetamine, MDMA/
Ecstasy), Benzodiazepines, Cannabis, Cocaine (as benzoylecgo-
nine), Methadone and its metabolite EDDP, and Opioids
(6-Monoacetylmorphine, Morphine, Codeine, Dihydrocodeine),
were excluded from the analysis, resulting in the exclusion
of four subjects (two controls, two ASD) from the original sample.
Data processing
Structural data processing. T1-weighted structural MRI volumes
were inspected manually to ensure adequate signal-to-noise ratio
(SNR) and absence of motion artefacts. Subsequently, structural
volumes were normalised to Montreal Neurological Institute (MNI)
space, and segmented into GM, WM, and CSF, to obtain
percentage measures of tissue composition in each individual
MRS voxel, using positional coordinates embedded in the raw
spectra data files.
MRS data processing. MRS data were pre-processed using in-
house scripts adapted from FID-A [28], which prepared the data
for reading into the main processing software. This included
conversion of data to the required file format, combination of
receiver channels, removal of ‘bad’averages (>4 standard devia-
tions), frequency drift correction (alignment of averages), separa-
tion and visualisation of the edit on/off spectra, and their
subtraction to generate the difference spectrum. Each spectrum
was manually inspected to ensure adequate SNR, as well as the
absence of artefacts [26,28]. Representative example spectra are
displayed in Fig. 1.
MRS data were then processed using LCModel v6.3-1 L software
(Stephen Provencher Incorporated, Oakville, Canada). LCModel
uses a linear combination of model spectra derived from
metabolite solutions in vitro to analyse the major resonances of
in vivo spectra. For this analysis, we used a basis set (mega-press-
3T-1) to determine the concentrations of GABA+(which
comprises GABA plus macromolecules), glutamine, glutamate,
glutathione (GSH), N-acetyl-aspartate (NAA), N-acetyl-
aspartylglutamate (NAAG), NAA +NAAG, Glx (Glu +Gln), and
GSH +Glu +Gln in each voxel; however, for this analysis, we
focused solely on GABA+and Glx.
In MRS, partial volume effects (different proportions of GM, WM,
and CSF in the MRS voxels) are a potential confound, especially
given previously reported volumetric differences between autistic
and neurotypical individuals [29]. To account for partial volume
effects, we therefore corrected all metabolites for GM, WM, and
CSF percentages. Assuming that CSF only contains negligible
quantities of the metabolites of interest, the calculations were as
follows: LCModel assumes a voxel is 100% WM with a water
concentration (WCONC) of 35880 mM and corrects each metabo-
lite value (where F stands for fraction) using the factor:
(43300*F
GM
+35880*F
WM
+55556*F
CSF
)/(1-F
CSF
). To correct for
the value of water concentration being used in the processing
through LCModel, we divided values by an individual correction
factor (35880), arriving at (1.207*F
GM
+F
WM
+1.548*F
CSF
)/(1-F
CSF
).
Fig. 1 Magnetic resonance spectroscopy (MRS) representative voxel placement and example spectra. aMRS voxel of interest (outlined in
white) in the basal ganglia and the dorsomedial prefrontal cortex. bExample spectroscopy spectra from each voxel. Glx glutamate +
glutamine, GABA +γ-aminobutyric acid +macromolecules, NAA N-acetyl-aspartate, p.p.m parts per million
Effects of cannabidiol on brain excitation and inhibition systems; a. . .
CM Pretzsch et al.
3
Neuropsychopharmacology (2019) 0:1 – 8
Therefore, in summary, the corrected metabolite values were
obtained by multiplying the raw metabolite values by this
correction. Since we did not measure relaxation times for tissue
water and metabolites, these were not corrected for—with the
exception of assuming the tissue water relaxation time (T
2
=80
ms) [30].
To further ensure the robustness of our findings, we excluded
all measurements of GABA+and Glx (Glx =glutamate +gluta-
mine) where the LCModel Cramér-Rao lower bound (CRLB)
estimates exceeded 15% from further analysis (LCModel manual,
Stephen Provencher Incorporated, Oakville, Canada). This resulted
in the exclusion of a total of eleven data points from six ASD
participants from the original sample. The spread across voxels
and conditions (placebo/CBD) was as follows: 1: BG Glx
PLC
;2:
DMPFC GABA +
PLC
& Glx
PLC
; 3: DMPFC GABA +
CBD
& Glx
CBD
;4:
DMPFC GABA +
CBD
& Glx
CBD
; 5 (also excluded due to positive
drug screening): DMPFC GABA +
PLC
& Glx
CBD
; 6: DMPFC Glx
CBD
&
GABA +
CBD
.
Statistical analysis
Demographic measures (age, IQ) and baseline levels of Glx and
GABA+in each region of interest were compared using a one-way
ANOVA (significance level p< 0.05).
To test the primary hypothesis that CBD impacts on E-I balance
in our two brain regions of interest (BG and DMPFC), differences in
mean metabolite concentrations were calculated using two 2 ×
2 × 2 mixed-model ANOVAs with group (neurotypicals, ASD) as
between-subject factor, voxel (BG, DMPFC) and drug (PLC, CBD) as
within-subject factors, and the respective metabolite (Glx, GABA+)
as the dependent variable. Our planned comparisons tested a
priori predictions that CBD would impact upon Glx and GABA+;
and that there would be differences in the response of
participants with and without ASD. With the caveat that
Bonferroni testing can be overly conservative, for completeness
however, we also report a Bonferroni corrected p-value alongside
any significant (uncorrected) results.
However, this repeated-measures approach is impacted by
missing data (one missing/poor quality data point from either
voxel during either placebo or drug condition results in data from
that individual being omitted); also there is a possibility that our
results were influenced by the different T1-weighting in the
cortical and subcortical voxels. Therefore, following this overall
analysis we conducted secondary post hoc two-by-two mixed-
model ANOVAs with group (neurotypicals, ASD) as a between-
subject factor, and drug (PLC, CBD) as a within-subject factor for
each metabolite in each region separately and examined any
group difference in the change in each using as much of the
available data as possible. Thus, for Glx measures in both BG and
DMPFC, ASD n=13, neurotypicals n=17; for GABA+measures in
the DMPFC, ASD n=11, neurotypicals n=17; and for GABA+
measures in BG, ASD n=16, neurotypicals n=17.
All analyses were performed using SPSS 24.00 software (SPSS,
Chicago, IL, USA). Graphs displaying results were produced using
GraphPad Prism version 7 for Mac, GraphPad Software, La Jolla,
CA, USA, www.graphpad.com.
RESULTS
Demographics
Groups did not differ significantly in age (F(1) =0.956, p=0.335);
but, as is commonly reported, individuals with ASD had a slightly
lower IQ than neurotypical controls, and this difference was
significant (F(1) =5.781, p=0.022) (as summarised in Table 1).
Therefore, to be sure that our findings were not influenced by IQ,
we investigated the relationship between drug-induced shifts
(CBD-PLC) in metabolite levels (Glx and GABA+) and IQ. As
expected, there were no significant correlations across the whole
group (r< 0.095, p> 0.350), in ASD alone (r<−0.008, p> 0.698)
nor in the neurotypicals alone (r< 0.068, p> 0.235), suggesting
that the difference in IQ did not influence the results. No
participant experienced any subjective or objective ill-effects/
harm following administration of the study drug.
Tissue composition and data quality
Tissue percentage (not excluding omitted spectra) differed
between groups for BG PLC GM (F(1) =7.307, p=0.011) and for
BG PLC WM (F(1) =9.345, p=0.004), but not for other tissues or
drug conditions (as summarised in Table 2). This is unsurprising, as
previous studies have suggested morphological differences in the
BG in autistic compared to neurotypical individuals [31]. In our
statistical analysis we corrected all metabolite values accordingly.
To ensure that the [H]MRS data quality was equal between
groups, we compared CRLB estimates for each metabolite (Glx,
GABA+) in each voxel (excluding omitted spectra), using a one-
way ANOVA. As expected, we found no significant differences (all
F(1) ≤4.102, all p≥0.052) (as summarised in Table 3).
In extended MRS studies, there is often a risk of ‘drift’, where the
metabolite estimates on the same scanner change over long
Table 2. Absolute values (and standard deviations) for percentages of grey and white matter and cerebrospinal fluid in the voxels of interest
Voxel Drug Tissue TD ASD F(dof) p-value
BG PLC GM 42.53% (3.03%) 45.43% (3.21%) F(1) =7.307 p=0.011
WM 50.47% (3.39%) 46.54% (4.08%) F(1) =9.345 p=0.004
CSF 6.92% (1.33%) 7.95% (1.95%) F(1) =3.222 p=0.082
CBD GM 43.24% (3.54%) 45.07% (3.64%) F(1) =2.157 p=0.152
WM 49.20% (4.66%) 47.29% (4.32%) F(1) =1.482 p=0.233
CSF 7.46% (2.16%) 7.56% (1.93%) F(1) =0.19 p=0.890
DMPFC PLC GM 52.93% (2.21%) 52.38% (3.49%) F(1) =0.299 p=0.589
WM 27.24% (3.36%) 28.11% (3.65%) F(1) =0.527 p=0.473
CSF 19.73% (4.05%) 19.41% (2.57%) F(1) =0.076 p=0.784
CBD GM 52.69% (2.86%) 52.84% (3.69%) F(1) =0.017 p=0.898
WM 27.32% (3.54%) 27.41% (4.14%) F(1) =0.004 p=0.947
CSF 19.89% (3.72%) 19.63% (2.53%) F(1) =0.048 p=0.827
Significant between-group differences are highlighted in bold
ASD autism spectrum disorder, BG basal ganglia, CBD cannabidiol, CSF cerebrospinal fluid, DMPFC dorsomedial prefrontal cortex, F(dof) F statistic and degrees
of freedom, GM grey matter, PLC placebo, TD typically developing individuals, WM white matter
Effects of cannabidiol on brain excitation and inhibition systems; a. . .
CM Pretzsch et al.
4
Neuropsychopharmacology (2019) 0:1 – 8
periods of time. For this reason, we compared the duration
between scans (days between PLC and CBD scan) across the two
groups. There was no significant difference in duration between
visits (F(1) =0.041, p=0.841) in controls (34.82 ± 24.99) and ASD
(36.44 ± 20.53) (see Table 1). Furthermore, scan date for each drug
condition (PLC, CBD) was not correlated with the value of any
metabolite at that drug condition (all Pearson’sr≤0.299, all p≥
0.115), confirming that data acquisition was stable over time.
Metabolite differences
Glx (glutamate+glutamine). There were no significant between-
group differences in baseline Glx in the BG (F(1) =0.000, p=0.993,
n=29) or in the DMPFC (F(1) =0.196, p=0.661, n=32). There
was however a significant voxel × drug interaction effect
(F(1,21) =5.235, p
uncorr
=0.033, partial eta squared (η
2
)=0.200):
in both groups, CBD increased Glx in the BG and decreased Glx in
the DMPFC (as depicted in Fig. 2). This effect did not survive
stringent Bonferroni-correction. Nonetheless, p
corr
=0.126 indi-
cates at least an 87% likelihood that the observed effect was real.
Results of post hoc testing within each voxel separately were
consistent with these findings, albeit at trend level. In the BG, CBD
increased Glx in both groups (F(1,24) =3.593, p
uncorr
=0.070, η
2
=
0.130); in the DMPFC, CBD decreased Glx in both groups
(F(1,26) =4.030, p
uncorr
=0.055, η
2
=0.134). Thus, differences in
the acquisition parameters for each region were unlikely to
explain the overall results. Moreover, post-hoc within-subject
comparisons of Glx changes (CBD-PLC) showed that there was no
group-difference in Glx responsivity to CBD in the BG (F(1) =0.602,
p
uncorr
=0.445) nor in the DMPFC (F(1) =0.006, p
uncorr
=0.937),
confirming that Glx in adults with and without ASD responded to
CBD in the same way.
GABA+. There were no significant between-group differences in
baseline GABA+in the BG (F(1) =0.000, p
uncorr
=0.987, n=33) or
in the DMPFC (F(1) =0.408, p
uncorr
=0.528, n=30). There was
however a significant group × drug interaction in both brain
regions (F(1,22) =13.506, p
uncorr
=0.001, η
2
=0.380). CBD
increased GABA+in the control group and decreased GABA+in
autistic individuals. This effect survived Bonferroni-correction
(p
corr
=0.004). These findings are displayed in Fig. 2.
Post hoc testing in each voxel separately indicated that this
result was largely driven by changes in the DMPFC, where there
was a significant group × drug interaction effect (F(1,23) =4.864,
p
uncorr
=0.038, η
2
=0.175); and the group difference in CBD-
induced change in GABA+was significant in the DMPFC (F(1) =
6.510, p
uncorr
=0.017), but not in the BG.
Post hoc within-subject analyses of GABA+changes (CBD-PLC)
also confirmed a significant group difference in the DMPFC (F1) =
4.864, p
uncorr
=0.038), and but not the BG. This effect did not
survive stringent Bonferroni-correction (p
corr
=0.14), but there was
at least a 86% chance (p
corr
=0.14) it was real.
Finally, given that we excluded ASD participants, but not
neurotypicals, on the basis of low CRLB estimates, we also reran
our analysis including CRLB measures as a covariate and this did
Table 3. Absolute values (and standard deviations) for Cramér-Rao Lower Bound estimates for each metabolite in each voxel
Voxel Drug Metabolite TD ASD N(TD, ASD) F(dof) p-value
BG PLC GABA+4.56 (0.66) 4.82 (0.81) 17, 17 F(1) =1.095 p=0.303
Glx 7.50 (2.09) 7.56 (2.25) 17, 16 F(1) =0.007 p=0.935
CBD GABA+4.59 (0.62) 4.25 (1.43) 17, 16 F(1) =0.782 p=0.383
Glx 7.82 (3.00) 6.75 (2.48) 17, 16 F(1) =1.236 p=0.275
DMPFC PLC GABA+6.47 (0.87) 7.38 (2.06) 17, 16 F(1) =2.751 p=0.107
Glx 5.68 (0.73) 5.88 (1.02) 17, 16 F(1) =0.416 p=0.524
CBD GABA+6.29 (0.85) 7.69 (2.69) 17, 13 F(1) =4.102 p=0.052
Glx 6.24 (1.95) 6.31 (1.55) 17, 13 F(1) =0.12 p=0.913
ASD autism spectrum disorder, BG basal ganglia, CBD cannabidiol, DMPFC dorsomedial prefrontal cortex, F(dof) F statistic and degrees of freedom, GABA +
g-aminobutyric acid +macromolecule, Glx glutamate +glutamine, Nnumber of individuals, PLC placebo, TD typically developing individuals
Fig. 2 Glx (glutamate +glutamine) (14 neurotypicals, 9 autistic individuals) (a) and GABA+(γ-aminobutyric acid +macromolecules) (16
neurotypicals, 8 autistic individuals) (b) in the basal ganglia and the dorsomedial prefrontal cortex for both groups in both drug conditions.
Glx (a) and GABA+(b) concentration represents the ratio of the Glx and GABA+metabolite resonance area to the unsuppressed water
resonance area, respectively. Dotted lines connect group means, which are indicated by black horizontal bars. Error bars represent standard
deviations. ASD autism spectrum disorder, BG basal ganglia, CBD cannabidiol, DMPFC dorsomedial prefrontal cortex, PLC placebo, TD typically
developed controls; * indicates a significance level at p≤0.05; *** indicates a significance level at p≤0.001
Effects of cannabidiol on brain excitation and inhibition systems; a. . .
CM Pretzsch et al.
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Neuropsychopharmacology (2019) 0:1 – 8
not materially alter the findings. Thus, GABA+in adults with and
without ASD responded to CBD in opposite directions, and
especially in the cortex.
We note that secondary analyses confirmed that CBD did not
alter the levels of other metabolites within the spectrum; namely
we observed no significant group and drug main effects, and no
group × drug interaction effects for GSH, NAA, NAAG, NAA +
NAAG, and GSH +Glx.
DISCUSSION
Here we report that acute (single dose) CBD ‘shifts’levels of the
brain’s primary excitatory and inhibitory neurotransmitters in
adults with and without ASD. In both groups, CBD increased Glx in
the BG voxel and decreased it in the DMPFC voxel. In contrast,
CBD had opposite effects on GABA+in each group. Specifically,
both in prefrontal and subcortical regions, CBD increased GABA+
in the controls but decreased GABA+in ASD. Moreover, in line
with some [19,21], but not all previous MRS studies of glutamate
and GABA in ASD [21,32] in the BG and DMPFC voxel, there were
no differences in baseline metabolite levels. Thus, our study
suggests that excitatory (E) glutamate response mechanisms to
CBD are comparable regardless of diagnosis; whereas inhibitory (I)
GABA response pathways are altered in ASD.
Effect of CBD on Glx
The region including and surrounding the BG is richly innervated
by a web of excitatory pyramidal neurons alongside GABAergic
inhibitory projection neurons and glia cells [33]. The increase in
Glx triggered by CBD in both groups could therefore have resulted
from CBD binding to neuronal TRPV1 receptors. Subsequent
activation of pyramidal neurons [15] may potentially have
contributed to the altered Glx metabolite levels in the BG
captured by MRS. Cannabinoid activation of TRPV1 receptors on
microglia could also theoretically upregulate microglial activity
and migration, leading to extracellular vesicular shedding and
augmentation of Glx levels [34]. However, this is speculative, given
the rapid desensitisation of TRPV1 receptors after activation [35].
In the DMPFC, glutamatergic pyramidal neurons predominate,
with relatively fewer GABAergic interneurons (ratio ~4.7:1) [17].
Here, CBD reduced Glx in each group. One possible explanation
for this is that CBD suppressed the activity of prefrontal
glutamatergic neurons via their 5-HT
1A
receptors [17,18], thereby
reducing Glx levels. Preliminary evidence has linked impaired
TRPV1 signalling to the ASD risk gene SHANK3, and 5-HT
anomalies, including 5-HT
1a
receptor dysfunction, to ASD [36].
Despite this, we found no group difference in Glx response to
CBD. This implies that glutamate targets of CBD in the BG and
DMPFC in idiopathic ASD are no different from those in
neurotypicals.
Effect of CBD on GABA+
In contrast, CBD increased GABA+levels in the BG and DMPFC
voxel in neurotypicals, but decreased GABA+levels in the BG and
(markedly so) in the DMPFC voxel of autistic adults. The causes of
group differences in GABA+response are unknown. However, it
may be partially explained by ASD-related alterations in CBD
targets. For example, the expression of the CBD interneuron
GPR55 receptor is reduced in the cortex in the valproic rat model
of ASD [37]. Another explanation could be more general
disruption to GABA pathways in ASD. For instance, a reduction
in the activity of the rate-limiting GABA synthesising enzyme
glutamic acid decarboxylase (GAD) [38], and genetic anomalies in
GABA receptors [39] have been reported in ASD. Since MRS
GABA+is thought to reflect metabolic (intracellular) and
extracellular GABA+levels, rather than GABAergic synaptic
transmission [40,41], further studies are required to back-
translate our results into preclinical models to dissect exactly
what underpins the atypical cortical and sub-cortical GABA+
response to CBD in ASD; and what is the impact on excitatory and
inhibitory system activity. For example, Kaplan and colleagues
have reported that CBD appears to restore GABAergic neuro-
transmission in an animal model of Dravet syndrome [14]. Despite
the limitations of resolution using MRS, the present findings,
together with our previous finding of atypical prefrontal GABA
responsivity to the glutamate-GABA acting drug riluzole, clearly
point to an alteration in the dynamics of GABA, but not glutamate,
systems in ASD. This observation may not only have aetiological
relevance, but also add to the evidence that the GABA system may
be a tractable treatment target in ASD [42,43].
Cortico-striatal systems (in ASD)
The CBD-induced shift in cortical and subcortical Glx and GABA+
levels may influence excitation and inhibition, although MRS does
not tell us directly about excitation or inhibition at the level of the
synapse. Nevertheless, this shift in metabolites could potentially
have widespread implications for brain function and behaviour.
This is because the BG (and the thalamus and insula) and DMPFC
form part of a circuit that is heavily dependent on glutamatergic
excitation and GABAergic inhibition and supports and regulates a
range of cognitive processes. In brief, in the neurotypical brain, the
BG receive input from the (insular) cortex, brainstem, and
thalamus. Cortical input is predominantly excitatory [44], but BG
output nuclei act via a direct monosynaptic GABAergic and an
indirect polysynaptic glutamatergic pathway [45]. Projection
neurons from the output nuclei provide GABAergic tonic
inhibition to thalamocortical and brainstem neurons to complete
a‘loop’[45,46]. In ASD, however, neuroimaging studies have
revealed reduced WM ‘integrity’especially in prefrontal tracts [47],
and abnormal ‘functional integration’of the BG and the DMPFC.
This is thought to partly explain why multiple processes
dependent upon cortico-striatal loop integrity, such as socio-
emotional, motor, and reward processing, are altered in ASD [48,
49]. Our results suggest that the structural and functional
differences previously reported in MRI studies of cortical-
subcortical systems in ASD extend to atypical E-I response to
pharmacological challenge.
Implications
The corollary of our observations is that because CBD ‘shifts’
glutamate and GABA+, it may affect glutamatergic excitation and
GABAergic inhibition, and thereby impact on brain function. We
did not directly test this here, but some support for this
proposition comes from a recent report that CBD increases
prefrontostriatal functional connectivity in neurotypical controls
[50]. However, our results predict that the direction of a functional
response to CBD may be distinct in autistic individuals, and this
warrants further investigation.
Our results reinforce the fact that we cannot expect the
actions of a drug tested in a typically developing population to
be replicated in people with neurodevelopmental conditions.
For example, we have previously reported a link between
disrupted functional connectivity and an atypical MRS GABA+
response to pharmacological E-I challenge through riluzole in
ASD but not in controls [19]. However, unlike CBD, riluzole
increased prefrontal GABA+in ASD. Together with our current
findings, this suggests that GABA+can be shifted bi-
directionally in cortical-subcortical systems in adults with ASD.
This is encouraging, as we can now begin to build a repertoire of
drugs that elicit a biological response in ASD. This tactic will be
critical given the heterogeneity of the autism spectrum, where a
‘one-drug-fits-all’approach is unlikely to succeed. Thus, our next
steps will be to examine whether acute drug response allows us
to (i) identify more pharmacologically homogeneous sub-groups
within ASD; and (ii) predict clinical responsiveness to
sustained treatment.
Effects of cannabidiol on brain excitation and inhibition systems; a. . .
CM Pretzsch et al.
6
Neuropsychopharmacology (2019) 0:1 – 8
Limitations
We acknowledge that our study has important limitations. First,
here we measured MRS bulk amounts of Glx and GABA+in the
chosen voxels of interest. This did not allow us to reliably discern
the specific contributions of different compounds (glutamate and
glutamine) contributing to the Glx signal. Moreover, at 3T, we
were limited to draw inferences about intra-cellular and extra-
cellular metabolite levels from our findings. Future studies with
higher resolutions and magnetic field strengths are required to
address these questions.
Second, we only included adult male subjects with an IQ above
70, and with no epilepsy or comorbid psychiatric conditions. This
step was taken to ensure the homogeneity of our study sample
and to make sure that observed effects were related to ASD rather
than a comorbidity of ASD. However, this also limits our ability to
extend our findings to the general ASD population, which is
characterised by heterogeneity and the presence of psychiatric
and neurological comorbidities. Future studies should attempt to
replicate our findings in larger and more diverse population
samples, and especially include women.
Third, our participant sample was relatively small. This can be
attributed to our strict recruitment criteria (e.g. no use of illicit
substances in the month leading up to and during the study). It is
also influenced by difficulties inherent in time-intensive repeated-
measures studies involving drug administration, e.g. high drop-out
rates. Finally, also contributing to the modest sample size were our
rigorous data quality criteria, e.g. exclusion of scans based on head
motion, known to be a difficulty in ASD. That said, our sample size
was comparable (or bigger than) previous MRS studies in ASD [19,
21]. Moreover, each individual in our study had two scans and
thus acted as their own ‘control’to reduce inter-subject variability
and to increase statistical power.
Fourth, in this study we only investigated the impact of acute
CBD administration on brain. We cannot extrapolate from the
effects of a single dose to the impact of repeated administrations,
e.g. as a therapeutic option in ASD, for several reasons. For
instance, chronic CBD administration may result in a steady state,
wherein the brain system plasticity equilibrates to the presence of
CBD. Future studies should thus investigate the impact of long-
term treatment with CBD on brain and behaviour.
CONCLUSIONS
In summary, we report that CBD can ‘shift’levels of Glx and
GABA+. These metabolites contribute to the regulation of
excitatory and inhibitory neurotransmission in both the typical
and the autistic brain. However, our study also demonstrated that
the atypical (autistic) brain reacts differently to CBD challenge of
GABA+. Our findings that the GABAergic system is distinct in ASD,
but can be shifted, is relevant both to our understanding of causal
mechanisms and to the discovery of treatment targets in ASD.
Additional studies will be required to (i) identify the neural basis of
the response to acute CBD challenge, including potential
pharmacologically homogeneous sub-groups within the autistic
spectrum; (ii) examine potential functional consequences of CBD
challenge in terms of inhibition, brain network activity, cognition,
and behaviour; and (iii) investigate whether an acute response to
CBD could predict the effects of sustained treatment in ASD.
FUNDING AND DISCLOSURE
This study was an Investigator Initiated Study (G.M.) which received funding and
product from GW Research Ltd (Cambridge, UK). GW Research Ltd (Cambridge, UK)
had no role in the data collection or analysis of results, nor in the decision to publish.
The authors also acknowledge infrastructure and training support from the National
Institute for Health Research (NIHR) Mental Health Biomedical Research Centre (BRC)
at South London and Maudsley NHS Foundation Trust and King’s College London.
The views expressed are those of the authors and not necessarily those of the NHS,
the NIHR or the Department of Health, U.K. Additional sources of support included
the Sackler Institute for Translational Neurodevelopment at King’s College London,
Autistica, the Medical Research Council (MRC) Centre grant (MR/N026063/1) and EU-
AIMS (European Autism Interventions)/EU AIMS-2-TRIALS, a European Innovative
Medicines Initiative Joint Undertaking under Grant Agreements No. 115300 and
777394, the resources of which are composed of financial contributions from the
European Union’s Seventh Framework Programme (Grant FP7/2007–2013). RAEE
receives salary support from NIH R01 MH106564 and U54 HD079123. The remaining
authors have nothing to disclose. Finally, the authors sincerely thank all the
participants.
ADDITIONAL INFORMATION
Supplementary Information accompanies this paper at (https://doi.org/10.1038/
s41386-019-0333-8).
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims
in published maps and institutional affiliations.
REFERENCES
1. Baio J, Wiggins L, Christensen DL, Maenner MJ, Daniels J, Warren Z, et al. Pre-
valence of Autism Spectrum Disorder Among Children Aged 8 Years - Autism and
Developmental Disabilities Monitoring Network, 11 Sites, United States, 2014.
Mmwr-Morbid Mortal W. 2018;67:1–23.
2. Tuchman R, Rapin I. Epilepsy in autism. Lancet Neurol. 2002;1:352–8.
3. Joshi G, Wozniak J, Petty C, Martelon MK, Fried R, Bolfek A, et al. Psychiatric
comorbidity and functioning in a clinically referred population of adults with
autism spectrum disorders: a comparative study. J Autism Dev Disord.
2013;43:1314–25.
4. Hirvikoski T, Mittendorfer-Rutz E, Boman M, Larsson H, Lichtenstein P, Bolte S.
Premature mortality in autism spectrum disorder. Br J Psychiatry. 2016;208:232–8.
5. Fetterman PS, Turner CE. Constituents of Cannabis sativa L. I. Propyl homologs of
cannabinoids from an Indian variant. J Pharm Sci. 1972;61:1476–7.
6. Wade DT, Makela P, Robson P, House H, Bateman C. Do cannabis-based medicinal
extracts have general or specific effects on symptoms in multiple sclerosis? A
double-blind, randomized, placebo-controlled study on 160 patients. Mult Scler.
2004;10:434–41.
7. Rog DJ, Nurmikko TJ, Friede T, Young CA. Randomized, controlled trial of
cannabis-based medicine in central pain in multiple sclerosis. Neurology.
2005;65:812–9.
8. Zajicek J, Fox P, Sanders H, Wright D, Vickery J, Nunn A, et al. Cannabinoids for
treatment of spasticity and other symptoms related to multiple sclerosis (CAMS
study): multicentre randomised placebo-controlled trial. Lancet. 2003;362:
1517–26.
9. Bergamaschi MM, Queiroz RH, Chagas MH, de Oliveira DC, De Martinis BS,
Kapczinski F, et al. Cannabidiol reduces the anxiety induced by simulated public
speaking in treatment-naive social phobia patients. Neuropsychopharmacology.
2011;36:1219–26.
10. Campbell CT, Phillips MS, Manasco K. Cannabinoids in Pediatrics. J Pediatr
Pharmacol Ther. 2017;22:176–85.
11. Devinsky O, Marsh E, Friedman D, Thiele E, Laux L, Sullivan J, et al. Cannabidiol in
patients with treatment-resistant epilepsy: an open-label interventional trial.
Lancet Neurol. 2016;15:270–8.
12. Devinsky O, Cross JH, Laux L, Marsh E, Miller I, Nabbout R, et al. Trial of Canna-
bidiol for Drug-Resistant Seizures in the Dravet Syndrome. N Engl J Med.
2017;376:2011–20.
13. Thiele EA, Marsh ED, French JA, Mazurkiewicz-Beldzinska M, Benbadis SR, Joshi C,
et al. Cannabidiol in patients with seizures associated with Lennox-Gastaut
syndrome (GWPCARE4): a randomised, double-blind, placebo-controlled phase 3
trial. Lancet. 2018;391:1085–96.
14. Kaplan JS, Stella N, Catterall WA, Westenbroek RE. Cannabidiol attenuates sei-
zures and social deficits in a mouse model of Dravet syndrome. Proc Natl Acad
Sci USA 2017;114:11229–34.
15. Musella A, De Chiara V, Rossi S, Prosperetti C, Bernardi G, Maccarrone M, et al.
TRPV1 channels facilitate glutamate transmission in the striatum. Mol Cell Neu-
rosci. 2009;40:89–97.
16. Ho KW, Ward NJ, Calkins DJ. TRPV1: a stress response protein in the central
nervous system. Am J Neurodegener Dis. 2012;1:1–14.
17. Santana N, Bortolozzi A, Serrats J, Mengod G, Artigas F. Expression of serotonin1A
and serotonin2A receptors in pyramidal and GABAergic neurons of the rat pre-
frontal cortex. Cereb Cortex. 2004;14:1100–9.
18. Russo EB, Burnett A, Hall B, Parker KK. Agonistic properties of cannabidiol at 5-
HT1a receptors. Neurochem Res. 2005;30:1037–43.
Effects of cannabidiol on brain excitation and inhibition systems; a. . .
CM Pretzsch et al.
7
Neuropsychopharmacology (2019) 0:1 – 8
19. Ajram LA, Horder J, Mendez MA, Galanopoulos A, Brennan LP, Wichers RH, et al.
Shifting brain inhibitory balance and connectivity of the prefrontal cortex of
adults with autism spectrum disorder. Transl Psychiatry. 2017;7:e1137.
20. El-Ansary A, Al-Ayadhi L. GABAergic/glutamatergic imbalance relative to exces-
sive neuroinflammation in autism spectrum disorders. J Neuroinflamm.
2014;11:189.
21. Horder J, Lavender T, Mendez MA, O’Gorman R, Daly E, Craig MC, et al. Reduced
subcortical glutamate/glutamine in adults with autism spectrum disorders: a [(1)
H]MRS study. Transl Psychiatry. 2013;3:e279.
22. Cochran DM, Sikoglu EM, Hodge SM, Edden RA, Foley A, Kennedy DN, et al.
Relationship among glutamine, gamma-aminobutyric acid, and social cognition
in autism spectrum disorders. J Child Adolesc Psychopharmacol. 2015;25:314–22.
23. Bhattacharyya S, Falkenberg I, Martin-Santos R, Atakan Z, Crippa JA, Giampietro V,
et al. Cannabinoid modulation of functional connectivity within regions proces-
sing attentional salience. Neuropsychopharmacology. 2015;40:1343–52.
24. World Health Organisation. International Statistical Classification of Diseases and
Related Health Problems. Geneva, Switzerland 2016.
25. Lord C, Rutter M, Le Couteur A. Autism Diagnostic Interview-Revised: a revised
version of a diagnostic interview for caregivers of individuals with possible
pervasive developmental disorders. J Autism Dev Disord. 1994;24:659–85.
26. Lord C. Autism diagnostic observation schedule: a standardized observation of
communicative and social behavior. J Autism Dev Disord. 1989;19:185–212.
27. Mescher M, Merkle H, Kirsch J, Garwood M, Gruetter R. Simultaneous in vivo
spectral editing and water suppression. NMR Biomed. 1998;11:266–72.
28. Simpson R, Devenyi GA, Jezzard P, Hennessy TJ, Near J. Advanced processing and
simulation of MRS data using the FID appliance (FID-A)An open source, MATLAB-
based toolkit. Magn Reson Med. 2017;77:23–33.
29. Hollander E, Anagnostou E, Chaplin W, Esposito K, Haznedar MM, Licalzi E, et al.
Striatal volume on magnetic resonance imaging and repetitive behaviors in
autism. Biol Psychiatry. 2005;58:226–32.
30. Tong ZY, Yamaki T, Harada K, Houkin K. In vivo quantification of the metabolites
in normal brain and brain tumors by proton MR spectroscopy using water as an
internal standard. Magn Reson Imaging. 2004;22:735–42.
31. Schuetze M, Park MT, Cho IY, MacMaster FP, Chakravarty MM, Bray SL. Morpho-
logical alterations in the thalamus, striatum, and pallidum in autism spectrum
disorder. Neuropsychopharmacology. 2016;41:2627–37.
32. Bejjani A, O’Neill J, Kim JA, Frew AJ, Yee VW, Ly R, et al. Elevated glutamatergic
compounds in pregenual anterior cingulate in pediatric autism spectrum dis-
order demonstrated by 1H MRS and 1H MRSI. PLoS ONE 2012;7:e38786.
33. Conn PJ, Battaglia G, Marino MJ, Nicoletti F. Metabotropic glutamate receptors in
the basal ganglia motor circuit. Nat Rev Neurosci. 2005;6:787–98.
34. Marrone MC, Morabito A, Giustizieri M, Chiurchiu V, Leuti A, Mattioli M, et al.
TRPV1 channels are critical brain inflammation detectors and neuropathic pain
biomarkers in mice. Nat Commun. 2017;8:15292.
35. Touska F, Marsakova L, Teisinger J, Vlachova VA. “cute”desensitization of TRPV1.
Curr Pharm Biotechnol. 2011;12:122–9.
36. Veenstra-VanderWeele J, Muller CL, Iwamoto H, Sauer JE, Owens WA, Shah CR,
et al. Autism gene variant causes hyperserotonemia, serotonin receptor hyper-
sensitivity, social impairment and repetitive behavior. Proc Natl Acad Sci USA
2012;109:5469–74.
37. Kerr DM, Downey L, Conboy M, Finn DP, Roche M. Alterations in the endo-
cannabinoid system in the rat valproic acid model of autism. Behav Brain Res.
2013;249:124–32.
38. Yip J, Soghomonian JJ, Blatt GJ. Decreased GAD67 mRNA levels in cerebellar
Purkinje cells in autism: pathophysiological implications. Acta Neuropathol.
2007;113:559–68.
39. Buxbaum JD, Silverman JM, Smith CJ, Greenberg DA, Kilifarski M, Reichert J, et al.
Association between a GABRB3 polymorphism and autism. Mol Psychiatry.
2002;7:311–6.
40. Myers JFM, Evans CJ, Kalk NJ, Edden RAE, Lingford-Hughes AR. Measurement of
GABA using J-difference edited H-1-MRS following modulation of synaptic GABA
concentration with tiagabine. Synapse. 2014;68:355–62.
41. Stagg CJ, Bestmann S, Constantinescu AO, Moreno LM, Allman C, Mekle R, et al.
Relationship between physiological measures of excitability and levels of
glutamate and GABA in the human motor cortex. J Physiol-Lond.
2011;589:5845–55.
42. Lemonnier E, Degrez C, Phelep M, Tyzio R, Josse F, Grandgeorge M, et al. A
randomised controlled trial of bumetanide in the treatment of autism in children.
Transl Psychiatry. 2012;2:e202.
43. Han S, Tai C, Westenbroek RE, Yu FH, Cheah CS, Potter GB, et al. Autistic-like
behaviour in Scn1a+/−mice and rescue by enhanced GABA-mediated neuro-
transmission. Nature. 2012;489:385–90.
44. McHaffie JG, Stanford TR, Stein BE, Coizet W, Redgrave P. Subcortical loops
through the basal ganglia. Trends Neurosci. 2005;28:401–7.
45. DeLong MR, Wichmann T. Circuits and circuit disorders of the basal ganglia. Arch
Neurol. 2007;64:20–4.
46. DeLong M, Wichmann T. Changing views of basal ganglia circuits and circuit
disorders. Clin EEG Neurosci. 2010;41:61–7.
47. Mueller S, Keeser D, Samson AC, Kirsch V, Blautzik J, Grothe M, et al. Convergent
findings of altered functional and structural brain connectivity in individuals with
high functioning autism: a multimodal MRI study. PLoS ONE 2013;8:e67329.
48. D’Mello AM, Stoodley CJ. Cerebro-cerebellar circuits in autism spectrum disorder .
Front Neurosci. 2015;9:408.
49. Kennedy DP, Courchesne E. Functional abnormalities of the default network
during self- and other-reflection in autism. Soc Cogn Affect Neurosci.
2008;3:177–90.
50. Grimm O, Loffler M, Kamping S, Hartmann A, Rohleder C, Leweke M, et al. Probing
the endocannabinoid system in healthy volunteers: cannabidiol alters
fronto-striatal resting-state connectivity. Eur Neuropsychopharmacol. 2018;28:
841–849.
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