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S T U D Y P R O T O C O L Open Access
Multimodal neuroimaging of frontal white matter
microstructure in early phase schizophrenia: the
impact of early adolescent cannabis use
Denise Bernier
1*
, Jacob Cookey
1
, David McAllindon
1
, Robert Bartha
2
, Christopher C Hanstock
3
, Aaron J Newman
1,4
,
Sherry H Stewart
1,4
and Philip G Tibbo
1,4
Abstract
Background: A disturbance in connectivity between different brain regions, rather than abnormalities within the
separate regions themselves, could be responsible for the clinical symptoms and cognitive dysfunctions observed in
schizophrenia. White matter, which comprises axons and their myelin sheaths, provides the physical foundation for
functional connectivity in the brain. Myelin sheaths are located around the axons and provide insulation through
the lipid membranes of oligodendrocytes. Empirical data suggests oligodendroglial dysfunction in schizophrenia,
based on findings of abnormal myelin maintenance and repair in regions of deep white matter. The aim of this
in vivo neuroimaging project is to assess the impact of early adolescent onset of regular cannabis use on brain
white matter tissue integrity, and to differentiate this impact from the white matter abnormalities associated with
schizophrenia. The ultimate goal is to determine the liability of early adolescent use of cannabis on brain white
matter, in a vulnerable brain.
Methods/Design: Young adults with schizophrenia at the early stage of the illness (less than 5 years since
diagnosis) will be the focus of this project. Four magnetic resonance imaging measurements will be used to assess
different cellular aspects of white matter: a) diffusion tensor imaging, b) localized proton magnetic resonance
spectroscopy with a focus on the neurochemical N-acetylaspartate, c) the transverse relaxation time constants of
regional tissue water, d) and of N-acetylaspartate. These four neuroimaging indices will be assessed within the same
brain region of interest, that is, a large white matter fibre bundle located in the frontal region, the left superior
longitudinal fasciculus.
Discussion: We will expand our knowledge regarding current theoretical models of schizophrenia with a more
comprehensive multimodal neuroimaging approach to studying the underlying cellular abnormalities of white
matter, while taking into consideration the important confounding variable of early adolescent onset of regular
cannabis use.
Keywords: Schizophrenia, Cannabis, White matter, N-acetylaspartate, Oligodendrocytes, Transverse relaxation time
constants, Proton magnetic resonance spectroscopy, Diffusion tensor imaging
* Correspondence: dcbernie@dal.ca
1
Department of Psychiatry, Dalhousie University, 5909 Veterans’Memorial
Lane, Abbie J. Lane Building, Room 3030, Halifax B3H 2E2, Nova Scotia,
Canada
Full list of author information is available at the end of the article
© 2013 Bernier et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.
Bernier et al. BMC Psychiatry 2013, 13:264
http://www.biomedcentral.com/1471-244X/13/264
Background
Rationale
One neurodevelopmental model of schizophrenia [1]
postulates a ‘two-hit’hypothesis. A ‘first hit’is said to
disrupt the trajectory of normal neural development,
rendering the brain vulnerable to a ‘second hit’which
then precipitates the onset of psychosis. Similarly, an-
other model postulates that an underlying neuropatho-
logical vulnerability is necessary but not sufficient for
the development of the illness, and that full disease ex-
pression may require a trigger such as an environmental
or biological stressor [2]. For both models, early age at
onset of regular cannabis use may represent one possible
‘second hit’or biological stressor associated with full dis-
ease expression [3-5].
There is a growing body of evidence suggesting that a
disturbance in connectivity between different brain regions,
rather than abnormalities within the separate regions
themselves, are responsible for the clinical symptoms and
cognitive dysfunctions observed in schizophrenia [6].
White matter, which comprises axons and their myelin
sheaths, provides the physical foundation for functional
connectivity in the brain; it is therefore increasingly be-
coming a focus of research in order to better understand
the underpinnings of schizophrenia. Myelin sheaths are lo-
cated around the axons and provide insulation through the
lipid membranes of oligodendrocytes [7]. Several different
lines of empirical data [8,9] have suggested oligodendroglial
dysfunction in schizophrenia, based on atypical findings
in terms of myelin maintenance and repair in deep
white matter regions. Observed oligodencrocyte abnor-
malities in schizophrenia consist of more dispersed ar-
rangement and lower densities [10,11], reduced absolute
numbers [10,12,13] as well as aberrant morphology, ne-
crosis and apoptosis along with damaged myelin sheaths
[14,15]. This situation is not likely caused by chronic
antipsychotic medications as these drugs have been
reported to alter the numbers of astrocytes, not of oligo-
dendrocytes [16].
The brain region of interest for this study is the left
superior longitudinal fasciculus (SLF), a large bundle of
white matter fibre tract located in the frontal lobe, trav-
elling between the dorsal prefrontal and caudal-inferior
parietal regions of the brain. Frontal regions undergo
substantial myelination during the periods of adoles-
cence and early adulthood [17,18], especially in the
bilateral SLF [19]. Abnormal maturation of the SLF in
adolescence may thus be crucial in the development
of schizophrenia. Several DTI studies have found ab-
normalities in the SLF in schizophrenia as well as in
asymptomatic cannabis users, as reviewed below. We
will investigate the SLF microstructure in early phase
schizophrenia (less than five years since diagnosis)
[20], focusing on the potential role of early adolescent
onset of regular cannabis, and using neuroimaging
modalities that are sensitive to cellular changes.
Neuroimaging modalities and review of the literature
Diffusion tensor imaging (DTI)
Water diffusion can occur equally in all directions (iso-
tropic diffusion), for example in cerebrospinal fluid where
diffusion is not restricted or in brain tissue where water
diffusion is restricted similarly in all directions (e.g., gray
matter tissue which has a complex cellular structure).
Water diffusion is called anisotropic (preferentially diffus-
ing in one direction) where the brain tissue microstructure
contains fibres that are aligned (e.g., white matter fibre
tracts); in that case, water diffusion will preferentially
occur along the axis of the fibre tracts. An ellipsoid model
of anisotropic water diffusion tensor (describing linear as-
sociations between vectors) can be calculated for each
anatomical voxel (the smallest volumetric unit of brain
images).
DTI is thus an in vivo brain imaging tool that provides
an index of the micro-structural integrity of white mat-
ter tissue [21]. Mean diffusivity (MD) provides a general
measure of water diffusion without differentiating the
direction of diffusivity. Another measure called fractional
anisotropy (FA), when found to be high, will indicate a pre-
ferred direction of water diffusion in the region of interest
[22,23]; when found to be reduced relative to normative
data, it broadly suggests reduced white matter integrity
[24]. Ultrastructural studies directly comparing DTI pa-
rameters with tissue pathology have associated changes in
DTI water diffusivity measures with dysmyelination of
white matter tracts; other tissue alterations that influence
water diffusivity are axonal pathology and changes in cell
densities [24].
There have been over 60 studies using DTI to evaluate
white matter integrity in schizophrenia [25]. The major-
ity of these studies have focussed on chronic schizo-
phrenia and have reported evidence of multiple areas of
white matter disruption most notably in the corpus
callosum, prefrontal white matter, SLF, and cingulum
bundle [26,27]. There have been fewer DTI studies of
early phase schizophrenia. This cohort is however ex-
tremely important, as it allows for the investigation of
pathology core to the illness, with minimal impact of
confounders such as medication, age, and length of time
with illness.
DTI in early phase schizophrenia DTI studies of early
phase schizophrenia have yielded inconsistent findings.
In a review of this literature (2010), it was observed that
for each white matter fibre tract that was found abnor-
mal in the clinical sample relative to the normative sam-
ple, there was at least one other, negative research report
[27]. In more recent studies (2010–2013), the pattern of
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mixed findings remains, however with fewer reports of
negative findings [28,29] than positive findings [30-38].
Altogether, several different white matter tracts have
been reported as disrupted in schizophrenia at different
stages of the illness, supporting the hypothesis that white
matter deficits could possibly be widespread throughout
the whole brain [29]. In early phase schizophrenia more
specifically, the three white matter tracts most often
implicated are the SLF [31,34,37,39-42], the splenium
of the corpus callosum [31,33,34,39,40,43-45], and the
fronto-occipital fasciculus [25,31,34,37,39,41-43,46].
DTI in cannabis users without schizophrenia Differ-
ent lines of evidence support the assumption that early
cannabis use in a developing (adolescent) brain could be
markedly more damaging than in a more mature brain
(with ‘early users’defined as those below age 17 years;
[4,47]). In healthy volunteers, a greater detrimental im-
pact of early initiation of regular cannabis use (relative
to a later initiation) has been reported for visual reaction
times [48], cognitive performance [49], and volumetric
brain tissue abnormalities [50].
DTI studies have reported that early adolescent regular
cannabis use in otherwise healthy young adults is associ-
ated with reduced FA values in white matter tracts
involving fronto-temporal connections [51], and with in-
creased mean diffusivity (MD) in the prefrontal section
of the corpus callosum [52]. MD quantifies water diffu-
sion in each voxel and is increased when there is re-
duced white matter integrity. It is thus possible that
early onset cannabis use in adolescence might decrease
white matter structural integrity in otherwise healthy
individuals. The white matter fibre tracts most often
reported as abnormal in cannabis users include the SLF
[51,53-56], the corpus callosum [52,53,57,58], and more
broadly defined temporal [51,53,56,58] and frontal re-
gions [53,54,57].
DTI in cannabis users with schizophrenia A recent re-
view of epidemiological evidence found that onset of
cannabis use in early adolescence is associated with a
particularly increased risk of developing schizophrenia
[47], while the lifetime rate of cannabis use use in adults
with schizophrenia is associated with earlier onset of the
illness [59].
In early adolescent onset of schizophrenia, cannabis-
positive patients showed reduced FA values relative to
cannabis-negative patients in several white matter tracts
including the SLF [34]. In people with first episode psych-
osis, cannabis-naive patients had reduced FA in the corpus
callosum, relative to patients with early onset of cannabis
use and healthy controls [33]. On the other hand, patients
with recent onset schizophrenia and early adolescent can-
nabis use had increased FA values in temporal and frontal
regions, relative to healthy controls; no differences were
found between controls and patients without early adoles-
cent cannabis use [60].
Although some findings go in opposite direction (de-
creased and increased FA values), altogether the empir-
ical DTI data supports the assumption of a greater
detrimental effects of cannabis on an immature brain in
both healthy volunteers and patients with recent onset
schizophrenia. In addition to being a potential “second
hit”for psychosis in a vulnerable brain, failure to control
for this confounding variable could underlie the incon-
sistent findings in previous DTI studies of early phase
schizophrenia and would demand for this variable to be
factored into future studies of white matter abnormal-
ities in psychosis.
Proton magnetic resonance spectroscopy (
1
H-MRS)
Another neuroimaging technique that will be used in
this study is
1
H-MRS, which will be acquired from the
same targeted brain region (the left SLF). The neuro-
chemical of interest is N-acetylaspartate (NAA), a free
amino acid that produces the most prominent resonance
in
1
H-MRS of the human brain: a peak located at 2.02
ppm on the spectral profile [61].
In vivo concentration levels of NAA are slightly higher
in white matter relative to gray matter tissue [62]. Post-
mortem studies have demonstrated that NAA is synthe-
sized in neurons, transported into white matter and then
catabolized into aspartate and acetate in oligodendrocytes
via aspartoacylase [63]. NAA catabolism is therefore
closely linked to myelin lipid metabolism, as it provides a
very important source of acetate which is crucial for mye-
lin lipid production and maintenance [64,65].
The assumption of abnormal myelin biosynthesis in
schizophrenia, strongly supported by several different
lines of evidence [8,9,66], can thus be examined by
1
H-
MRS studies as long as the targeted brain region in-
volves a single tissue type (white matter) that allows a
meaningful interpretation of findings in terms of the
catabolic cycle of NAA.
Noteworthy for this study, the cannabinoid receptors
CB1 are present on astrocytes and oligodendrocytes and
may thus be implicated in the detrimental impact of
early adolescent cannabis use by affecting the trajectory
of white matter development in the critical period of
adolescence [4,53].
1
H-MRS: technical limitation Due to the low concen-
tration (mM) of the neurochemicals detected by
1
H-
MRS, localized spectroscopy studies generally sample a
relatively large brain volume and require averaging several
acquisitions to build up a reasonable signal-to noise ratio
from the brain volume of interest. As such, most previous
1
H-MRS clinical studies have reported on NAA signals
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originating from both gray and white matter tissues taken
together as a whole; unfortunately, this approach has
prevented the interpretation of findings in regards to the
specific anabolic and catabolic activities of NAA.
In this proposed study, we will sample a large brain
volume comprised of 95% white matter. Our previous
1
H-MRS data acquired in the same brain region has
demonstrated that across more than 150 brain scans ac-
quired with these anatomical landmarks, the mean (SD)
fractional content of white matter was 95(2.8)% (unpub-
lished). These
1
H-MRS data will thus provide insight
about the specific catabolic cycle of NAA in early phase
schizophrenia and consequently, about the regional avail-
ability of acetate which is required for biosynthesis of
myelin. In this context, regional levels of NAA can be con-
sidered a marker of myelin integrity.
Previous relevant
1
H-MRS studies In adolescent chronic
cannabis users, reductions in NAA concentration levels
were reported, relative to non-user controls, in the an-
terior cingulate region encompassing mainly gray matter
[67]. Levels of NAA might also be altered in schizophre-
nia but findings are inconsistent across studies, thus
inconclusive. Indeed, if we compile previous
1
H-MRS
studies of schizophrenia while selecting studies with the
best contemporary methods (those that referenced neuro-
chemical levels to internal water and that used a sample
size of 20 subjects or more in each group in order to de-
crease probabilities of “noise discoveries”) [68,69], no con-
sensus can be reached in the current literature [70].
In the frontal/prefrontal regions of the brain, the focus
of this study, some
1
H-MRS studies have reported that
concentrations were reduced in established schizophrenia
relative to healthy controls for levels of NAA [71-74], and
age-adjusted NAA [75]. On the other hand, several other
studies have reported normal levels of NAA in the same
frontal/prefrontal regions in never treated first episode
psychosis [76], medicated first episode psychosis [77,78],
and established schizophrenia [79-86]. These studies,
for the most part, sampled a brain volume of interest
encompassing both gray matter and white matter tissue
types, consequently precluding any specific interpret-
ation of findings in terms of the precise anabolic or
catabolic cycle of NAA. Obviously there is a need to
search for confounding variables that might impact on
the current mixed
1
H-MRS findings reported across
population samples and laboratories. Early adolescent
onset of regular cannabis use certainly has the potential
to be one such factor [3,5].
Transverse (T
2
) relaxation time constants
Another neuroimaging modality involved in this study tar-
gets the same white matter brain region, while maintaining
the focus at the cellular level. The transverse relaxation
time constants of regional tissue water (involving both
intracellular and extracellular tissue water) and of NAA
(intracellular) provide an index of the integrity of the
microcellular environment of the brain region studied.
In fact, T
2
relaxation time constants are dependent on
the morphological parameters of cell size and cell pack-
ing density in the brain region studied; they also reflect
intracellular molecular mobility as they are dependent
on the frequency of molecule-microenvironment inter-
actions [87]. As such, prolonged T
2
time constants are
associated with reduced cell densities.
Transverse time constants of NAA in the context of
~95% white matter tissue will provide an index of intracel-
lular density of cells in this brain region, oligodencrodytes
being an important target among these cells [65]. Trans-
verse time constants of water will provide an index of
intracellular plus extracellular cell packing densities with-
out differentiation of which type of cells are present in the
specific region of interest.
Previous studies Different from DTI and
1
H-MRS stud-
ies of schizophrenia, the studies assessing T
2
relaxation
time constants in this illness have, to this date, reported
consistent findings. The T
2
time constants of water were
found to be prolonged relative to healthy controls in pre-
frontal white matter [75,88] and anterior corpus callosum
[89], in adults with established schizophrenia [75,88,89]
and first episode psychosis [89]. No differences were ob-
served between adults with first episode psychosis and
those with established schizophrenia [89]. These findings
altogether support the assumption of abnormal axonal mi-
lieu and myelin structures in schizophrenia.
Transverse relaxation time constants of NAA, on the
other hand, were found to be shortened relative to
healthy controls in prefrontal white matter of adults
with established schizophrenia [75,88] and first episode
psychosis [89], thus supporting the assumption of in-
creased intracellular (oligodendrocytes) cell density. In
gray matter (anterior cingulate cortex), T
2
time con-
stants of NAA were also shortened in adults with
schizophrenia relative to healthy controls but the group
difference did not reach statistical significance [90].
Because of the consistency of reported findings with
these particularly sensitive neuroimaging indices of cell
packing densities, T
2
relaxation time constants acquired
in this proposed study will be used as an anchor point
against which DTI and
1
H-MRS measures will be
interpreted. The aim is to generate, from these in vivo
data, a plausible interpretation of the specific cellular
abnormalities associated with schizophrenia, which
might differ from those associated with early adolescent
onset of regular cannabis use.
Noteworthy for this project, the T
2
relaxation time
constants of NAA and tissue water have never been used
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to help differentiate the detrimental impact of cannabis
use from the white matter cellular abnormalities associ-
ated with schizophrenia. We expect that these sensitive
measures of intracellular and extracellular cell packing
densities will be related to DTI (FA) values, as water dif-
fusivity is also influenced by cell densities [24].
Aims
We propose a multimodal neuroimaging study of frontal
white matter microstructure in patients in the early
phase of schizophrenia, while taking into account the
detrimental impact of early adolescent onset of regular
cannabis use. The brain region of interest is the left SLF
from which FA values, NAA levels as well as T
2
relax-
ation time constants of tissue water and of NAA will be
measured, providing novel insight into the specific cellu-
lar pathology associated with early phase schizophrenia,
and into the potential confounding impact of early ado-
lescent onset of regular cannabis use.
Methods/Design
Sample size and groups/subgroups
This study will involve 240 participants overall. The two
main conditions are a) young adults in their early phase
of schizophrenia (n = 120) and b) healthy controls (n =
120). Each condition will be further subdivided into
three subgroups (n = 40 each) based on age at initiation
of regular cannabis use: a) prior to age 17, b) after age
17, or c) with no lifetime exposure or very minimal ex-
perimentation with cannabis.
A priori premises
1. How are we going to interpret the findings?
The particular neuroimaging indices that will show
anomalies relative to normative data (non-user
healthy controls) in regards to each of the four
potential situations outlined below will permit a
meaningful micro-structural interpretation of the
specific and potentially different cellular
abnormalities existing in each group and subgroup
of participants.
a) Given the specific online selection of white matter
tissue, reduction in regional levels of NAA will lead
to the assumption of insufficient availability of
acetate, which is the main ‘building block’necessary
for myelin repair and maintenance. Thus, we would
assume reduced integrity of myelin sheaths.
b) In the case of reduction in FA values, we would
assume reduced integrity of axonal fibres in this
same brain region (more disorganized axonal fibres).
Integrity of myelin sheaths has less impact on FA
values than integrity of axonal fibres.
c) In the case of prolonged T
2
relaxation time
constants of regional tissue water, we would assume
reductions in intracellular and extracellular cell
packing density of axonal fibres (myelin water is not
included in this measure; see next section).
d) T
2
time constants of NAA are sensitive to
intracellular density; as such, shorter T
2
time
constants would yield the assumption of greater
intracellular density of regional white matter cells,
oligodendrocytes being involved.
2. Specific hypotheses:
a) In terms of T
2
relaxation time constants of NAA
and of regional tissue water:
In the clinical group (n = 120), T
2
time constants
of NAA will be reduced relative to healthy
controls (n = 120), while T
2
time constants of
tissue water will be prolonged (replication data)
[75,88,89]. In addition, the clinical subgroup of
early cannabis users (n = 40) will display a greater
level of deviation from normative data (non-user
healthy controls; n = 40) compared with the two
other clinical subgroups (novel data).
In the healthy control group, similar findings (as
above) will be observed in early cannabis users
(n = 40) relative to non-users (n = 40), but not in
late cannabis users (n = 40) relative to non-users
(novel data).
b) In terms of FA values and NAA levels: We expect
these two measures to correlate with each other [82].
In the clinical group (n = 120), reduced FA values
and NAA levels might or might not be observed
relative to healthy controls (n = 120), as previous
findings are mixed and inconclusive; however, the
clinical subgroup of early cannabis users will
display reduced FA values and NAA levels
relative to normative data and relative to the two
other clinical subgroups (novel data).
In healthy controls, reduced FA values and NAA
levels will be found in early cannabis users
relative to non-users (replication data) [51,53-56],
but not in late cannabis users relative to non-
users (novel data).
3. Associations between neuroimaging indices:
We expect that correlations between abnormalities
in neuroimaging indices will be stronger in early
cannabis users with schizophrenia relative to non-
users with schizophrenia and relative to early
cannabis users without symptoms of schizophrenia.
4. Associations with symptom/function measures:
We expect that in the clinical group, abnormalities
in neuroimaging indices will correlate with more
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pronounced clinical/functional abnormalities
according to symptom/function measures (see
section Questionnaires and interviews, below).
There are few studies investigating the relationship
between different aspects of white matter cellular
integrity and symptom/function measures in early
phase schizophrenia. These analyses will be
exploratory and will be used for the generation of
hypotheses for future studies.
Statistical power analyses
To our knowledge, this study is the first one to compare
and contrast four cellular neuroimaging indices acquired
from the exact same brain region, while targeting a sin-
gle tissue type. As such, these data will help establish
statistical power calculations for future studies. We com-
puted power estimations using data from studies with
contemporary neuroimaging methods and relatively
good sample sizes, while selecting those studies that
were very close to our own research question. Our pur-
pose was to ensure that our planned sample size was
reasonable even in this context of a pioneer study.
From previous data reporting T
2
relaxation time con-
stants of NAA in patients with schizophrenia relative to
healthy controls [75], we estimated that a sample size of
35 participants in each group would provide adequate
power to detect differences between independent
groups, with a two-tailed test at an alpha level of .05 and
power of .8.
From previous data reporting T
2
relaxation time con-
stants of water in patients with schizophrenia relative to
healthy controls [88], we estimated that a sample size of
23 participants in each group will provide adequate power
to detect differences between independent groups.
From frontal DTI FA values previously reported in
patients with schizophrenia who started cannabis use
prior to age 17 versus healthy non-users controls [60],
sample size calculations revealed that 23 participants in
each group will yield adequate power to detect group
differences.
With the reduced levels of NAA in anterior cingulate
previously reported in adolescent marijuana users versus
non-user controls [67], computations yielded a sample
size of 24 participants in each group in order to have ad-
equate power.
Our planned sample size of 40 participants per sub-
group will thus yield adequate power for all planned
analyses.
Operational definitions
Early phase schizophrenia: less than 5 years since
diagnosis of psychosis with initiation of appropriate
medical treatment [20].
Regular cannabis use: usage occurring on 3 or more
days per week, maintained for a period of 6 months
or more [34].
Early adolescent onset of cannabis use: start of regular
cannabis use prior to age 17 years old [33,60].
Minimal or non-cannabis users: people who are
cannabis-naive or who had minimal experimentation
with cannabis (less than 10 experimentations over
lifetime) [57].
Recruitment and diagnosis
Recruitment of patients will be conducted at the Nova
Scotia Early Psychosis Program (NSEPP). Currently
NSEPP has about 220 individuals who are active in the
clinic and within 5 years of illness onset (meeting our
criteria); approximately 60-70% of these patients have a
history of cannabis use, as assessed at time of referral.
Approximately 50 new incoming patients are accepted
at the clinic every year, adding to the current pool of pa-
tients. Recruitment of healthy controls will be conducted
through advertisements. Diagnosis of patients (using
DSM-IV) will be confirmed by consensus between the
treating psychiatrist and one of the authors (PT).
Inclusion and exclusion criteria
Healthy controls will be 19–35 years of age; with no life-
time diagnosis of psychiatric disorder; healthy; and tak-
ing no prescribed medications. They will have no first
degree relatives (sibling, mother, or father) with a life-
time diagnosis of psychosis or bipolar disorder. They will
be matched with patients in regards to age, gender and
history of cannabis use. Patients will be 19–35 years of
age and within five years of diagnosis of schizophrenia.
They will be taking appropriate antipsychotic medica-
tions (these will be recorded and tested as potential
confounding variables). Participants will be excluded if
they have a lifetime history of a) more than minimal
experimentation with illicit drugs other than cannabis
(e.g., cocaine, ecstasy) or b) more than low risk alcohol
consumption as behaviourally defined by the Canada’s
Low-Risk Alcohol Drinking Guidelines [90]; we esti-
mated that approximately 10% of patients in our clinic
will be excluded based on this lifetime history.
Ethics approval and financial support
Full ethics approval to conduct this study was received
from the Capital Heath Research Ethics Board. Capital
Health is a public provider of health care services in
Halifax, Nova Scotia, Canada. Ethics approval was also
obtained from the IWK Research Ethics Board, as the
scanner is located in this hospital. The IWK Health
Centre is a public provider of health care in Halifax.
Each participant will be fully informed of the study prior
to signing the consent form. Seed funding for this study
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was obtained from the Department of Psychiatry Re-
search Fund at Dalhousie University, Halifax. This study
is also financially supported by the Dr. Paul Janssen
Chair in Psychotic Disorders (P. Tibbo).
Questionnaires and interviews
The Structured Clinical Interview for the Diagnostic
and Statistical Manual of Mental Disorders Axis 1
(SCID-1) [91] is a semi-structured interview used
for diagnosis.
The Structured Clinical Interview for the Positive and
Negative Syndrome Scale (SCI-PANSS) [92] assesses
30 different symptoms associated with schizophrenia
[93], grouped into three subscales: The Positive,
Negative and General Symptoms subscales.
The Personal and Social Performance scale (PSP)
was developed to measure social functioning in
schizophrenia, separate from psychological
symptoms [94].
Severity of anxiety and mood symptoms is assessed
with the Beck Anxiety Inventory (BAI) [95] and the
Calgary Depression Rating Scale for Schizophrenia
[96].
Detailed information about past and current use of
all types of illicit drugs, alcohol, and cigarette
smoking is collected using a custom Drug
Questionnaire, which includes all the questions
provided in the SCID but organized in a much more
detailed way. Cumulative usage of cannabis will be
estimated (in grams) for two time periods: before
age 17 (when applicable) and cumulative lifetime.
Neuroimaging
MR online acquisitions
Neuroimaging data will be acquired with a GE 1.5 Tesla
MRI and a multi-channel head coil. MR acquisition pa-
rameters are outlined in Table 1. A 6.5 cm volume of
interest (VOI) is prescribed in the left dorsal frontal white
matter, immediately anterior to the rostral part of the
inferior parietal lobe. VOI dimensions are 45 mm (A/P)
by 13 mm (S/I) and 11 mm (R/L) (Figure 1). Relaxometry
acquisitions for NAA start at a TE of 80 ms, in order to
minimize the contribution from macromolecules into the
estimation of neurochemicals (which would present at
shorter TE times). Relaxometry acquisitions for the water
files start at a TE of 50 ms, in order to minimize the con-
tribution from the myelin water signal into the estimations
of tissue water relaxation times[87].
MR offline analyses
1
H-MRS data Offline analysis of the neurochemical
spectra are performed with the program fitMAN [97].
The following signals will be quantified: N-acetylaspartate
plus N-acetylaspartylglutamate (NAA), choline-containing
compounds (Cho), and creatine plus phosphocreatine
(tCr). We will compile all the data acquired, even when
not part of the main research question. At 1.5 Tesla how-
ever, it is impossible to biologically meaningfully interpret
findings from the Cho signal, as its anabolic component
(phosphocholine; PC) cannot be resolved separately from
its catabolic component (glycerophosphocholine; GPC).
Neurochemical concentration levels will be adjusted to
account for the fractional content of tissue types within
each VOI, according to their known variation in this re-
spect [62], and they will be referenced to the estimation
of internal water signal extrapolated to TE = 0 ms. For
each individual participant, neurochemical levels will be
corrected for T
2
induced signal losses.
Noteworthy, the fitMAN program can be used to ana-
lyse spectra in the time domain, therefore allowing the
elimination of the tail of the free induction decay curve
(where mainly noise remains) from the fitting, which
strategy strikingly increases the signal-to-noise ratio
(SNR) of the spectra in the frequency domain. The
model function used is Equation 1 in Bartha et al. (1999)
[97], with parameters for both zero- and first-order phase.
As a result, there is no need to perform zero- or first
order-phasing of the data prior to quantification in the
time domain; these parameters are estimated as part of
Table 1 Neuroimaging acquisition sequences
Type Parameters Min
Localizer and calibration 2
3D SPGR T
1
-weighted, for online placement of VOI and
its offline tissue segmentation
256 x 256 matrix; 170 sagittal slices; 1 mm isotropic resolution, no inter-slice gap;
TR = 11.3 s; TE = 4.2 ms; flip angle = 20 deg.
7
1
H-MRS volume of interest (VOI) Online VOI placement; shimming (values are carried over to each subsequent
1
H-MRS acquisition)
6
NAA concentration levels and T
2
time constants of NAA PRESS; TR = 3 s; TEs = 80, 120, 180, 350, 600 ms; NEX = 64 25
T
2
time constants of water TR = 10 s; TEs = 50, 60, 80, 120, 180, 350, 600, 800, 1000 ms; NEX = 4 20
Diffusion-weighted images TR = 8.5 s; TE 80–90 ms; flip angle = 90 deg; 54 non-collinear diffusion weighting
directions, b-factor of 1000 s/mm
2
; 6 acquisitions, b-factor of ~ 0 s/mm
2
; 256 x
256 matrix; 260 FOV; 1.02 x 1.02 x 3 mm
3
voxels; NEX = 1; acquisition of field maps.
25
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the fitting process. When fitting in the time domain, we
simply specify the time interval over which to perform the
minimization; therefore, eliminating data points at the be-
ginning or end of the FID becomes straightforward. For
spectral fitting, we include data points that range from 1
to 512.
Prior knowledge basis set spectra are not required as the
three main singlets of the spectral profiles are quite ro-
bust. The quality criteria for
1
H-MRS data to be retained
for statistical analyses are the following: signal-to-noise ra-
tio (SNR) of 15 or greater, computed from the amplitude
of NAA divided by the standard deviation (SD) of the
noise; linewidth (FWHM) of 8 Hz or less; and uncertainty
in the estimation of the fit (Cramer Rao bounds) smaller
than 10 %SD. Our preliminary data shows that SNR for
NAA typically ranges from 20 to 55, depending on the
specific TE used for acquisition; FWHM is 6 Hz or less;
and % SD is consistently ≤5 (Figure 2).
T
2
measurements Area under the water signal is calcu-
lated from the frequency domain at each TE, using a
MATLAB script ([98]. From the decrease in water signal
amplitude as a function of TE, the corresponding T
2
re-
laxation time constants are estimated as a curve fit of a
two-component exponential decay using the Curve Fit-
ting Tool in MATLAB. The decay curve of water could
potentially involve three components: CSF (long T
2
), tis-
sue water (intermediate T
2
), and myelin water (very short
T
2
) [87]. With this study design however, signals from
myelin water mostly decayed at the echo times sampled;
the very short myelin component is therefore not identi-
fied by the fitting algorithm. As for NAA levels, signal loss
(as a function of TE) has a better fit with a mono-
exponential decay curve [99] (Figures 3 and 4).
DTI data
The scanner 3D coordinates used at time of
1
H-MRS
online acquisition will be used to precisely define a DTI
region of interest encompassing the exact same brain
volume that was prescribed for estimation of
1
H-MRS
neurochemical levels and T
2
relaxation time constants.
This strategy will permit the offline estimation of all
Figure 1 Online placement of the
1
H-MRS volume of interest. Note. The volume is parallel to the AC-PC line. Its posterior border is aligned
3 mm posterior to the point of posterior commissure using the sagittal view. Its inferior border is placed axially on the first slice where the corpus
callosum meets from the two hemispheres. In the right/left direction, it is centered on the white matter fibre tract using the inferior axial slice.
Bernier et al. BMC Psychiatry 2013, 13:264 Page 8 of 13
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neuroimaging indices (DTI,
1
H-MRS and T
2
time con-
stants) from the exact same brain region, as precisely de-
fined by online coordinates of
1
H-MRS VOI placement.
Fractional anisotropy (FA) images are calculated using
Bayesian estimation of diffusion parameters at each
voxel. Images are spatially normalized using nonlinear
registration to the MNI152 brain template. A mean FA
image is created and thinned to generate a mean FA
skeleton representing the maximum FA values of all
tracts common to all participants. A threshold of 0.2 is
applied to the skeleton to control for cross-subject
variability.
The VOI described above is used to mask the FA skel-
eton image; tracts passing through this VOI are statistically
analysed using nonparametric tract-based spatial statistics
[100] using 10000 permutations. Threshold-free cluster
Figure 2 Spectral profiles acquired at different times of echo (TE). Note. Yellow: raw data; blue: residuals; red: fitted data. No data filtering
has been applied. In the time domain, the number of data points included in the fits ranged from 1 to 512.
Figure 3 NAA signal decay curve. Note. NAA signal areas are fitted to a single exponential decay model using MATLAB.
Bernier et al. BMC Psychiatry 2013, 13:264 Page 9 of 13
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enhancement [101] is used to correct for multiple compar-
isons. Statistical significance is determined at p < .05,
corrected. Tracts are identified using the MRI Atlas of Hu-
man White Matter and the JHU DTI-based white matter
atlases included with the FSL software: the ICBM-DTI-81
white matter labels atlas and the JHU white matter
tractography atlas [102,103].
Statistical analyses
All statistical analyses will be two-tailed and computed
with SPSS version 17; alpha will be set at p < .05 unless
otherwise specified.
Testing hypotheses 1 and 2:
A multivariate analysis of variance (MANOVA) will be
computed. Two between-group factors will be entered in
the model: Group with two levels (clinical and controls),
and Subgroup with three levels (Early, Late, and Min-
imal cannabis users). The four dependent variables will
be T
2
time constants of water and of NAA, FA values,
and NAA levels. Analyses of variance (ANOVAs) and
then t tests will be used to follow-up on significant main
effects and interaction effects.
Testing hypothesis 3:
Potential significant interaction effects between the
two factors entered in the MANOVA will be used as a
basis to determine whether or not hypothesis 3 is sup-
ported. Further follow-up analyses of these significant
interactions will involve Pearson’s correlations with rele-
vant neuroimaging indices.
Testing hypothesis 4:
Exploratory analyses will involve Pearson’s correlations
between neuroimaging variables and clinical variables; e.g.,
cumulative lifetime use of cannabis, gender, current age,
age at onset of psychosis, duration of untreated psychosis,
current stage of illness, severity of symptoms, medication
type, and length of time taking antipsychotic medications.
A p value of .01 or smaller will be necessary for an associ-
ation to be considered for discussion.
Discussion
We hereby propose to differentiate the detrimental im-
pact of early adolescent cannabis use from the cellular
changes associated with schizophrenia, in order to refine
the current understanding of the specific cellular mecha-
nisms involved in white matter abnormalities in the early
phase of schizophrenia [64]. This comparison will also
highlight the protective factors by which resiliency to
cannabis use occurs; that is, not all cannabis users de-
velop psychosis.
Abbreviations
Cho: Choline-containing compounds; MD: Mean diffusivity; DTI: Diffusion tensor
imaging; NAA: N-acetylaspartate; FA: Fractional anisotropy; SLF: Superior
longitudinal fasciculus; FWHM: Full width at half maximum of peak; tCr: Creatine
plus phosphocreatine;
1
H-MRS: Proton magnetic resonance spectroscopy;
TE: Time of echo; MANOVA: Multivariate analysis of variance; TR: Time of repetition.
Competing interests
The authors declare that they have no competing interests.
Authors’contributions
DB made a substantial contribution to the conception and design of the
proposal. DB and JC made a substantial contribution to the initial writing of
the draft proposal. JC is recruiting and screening participants. JC and DB are
acquiring data and performing MR offline analyses. DM made a substantial
contribution to all MR technical aspects of this proposal namely, pre-testing
of MR data quality; computing of the decay curves and T
2
relaxation time
constants; and all aspects of compilation of MR data. RB provided his
program for
1
H-MRS offline analyses (fitMAN) and also provided technical
support for the program in the context of 3-peak fits at 1.5T, along with
extensive teaching about offline analyses. CH has designed the relaxometry
Figure 4 Water signal decay curve. Note. Water signal areas are fitted to a two-component decay model using MATLAB.
Bernier et al. BMC Psychiatry 2013, 13:264 Page 10 of 13
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parameters for acquisition and offline analyses; he also provided a custom-
made program to assess the decay curves. RB and CH made a substantial
intellectual contribution to the writing of the
1
H-MRS technical part of the
proposal. AN has designed the DTI section of the proposal in terms of data
acquisition parameters and offline analyses as well as writing of this
technical section. SS brought expertise about the addiction part of this
study. PT provided help with recruitment and diagnosis of patients, and he
supported the research as the Director of the NSEPP and as the Dr. Paul
Janssen Chair in Psychotic Disorders. All authors have made intellectual
contributions to the writing and editing of the full study proposal. All
authors read and approved the final manuscript.
Acknowledgements
We are thankful to Gregory MacLean, Matthew Rogers and Sarah Sullivan
who are meticulously acquiring MR data at the IWK scanner site, Halifax,
Nova Scotia, Canada. We are grateful to all participants for their contributions
to this study. We also thank anonymous reviewers who provided helpful
comments and suggestions to this study proposal.
Author details
1
Department of Psychiatry, Dalhousie University, 5909 Veterans’Memorial
Lane, Abbie J. Lane Building, Room 3030, Halifax B3H 2E2, Nova Scotia,
Canada.
2
Robarts Research Institute, Western University, 100 Perth Drive,
London N6A 5K8, Ontario, Canada.
3
Department of Biomedical Engineering,
University of Alberta, 8308-114 Street, Edmonton T6G 2V2, Alberta, Canada.
4
Department of Psychology and Neuroscience, Dalhousie University, Box
15000, Life Sciences Centre, B3H 4R2 Halifax, Nova Scotia, Canada.
Received: 20 May 2013 Accepted: 14 October 2013
Published: 17 October 2013
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doi:10.1186/1471-244X-13-264
Cite this article as: Bernier et al.:Multimodal neuroimaging of frontal
white matter microstructure in early phase schizophrenia: the impact of
early adolescent cannabis use. BMC Psychiatry 2013 13:264.
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