Clinical trial NCT02080819
Conditions: Cocaine Dependence
Keywords: drug abuse cocaine impulsivity dopamine
Sponsor: Virginia Commonwealth University
Start date: 2014-02-01; Completion date: 2017-01-21
Enrollment: 131 participants; Allocation: RANDOMIZED; Intervention model: PARALLEL

Brief summary:
This project proposes to investigate the role of brain connectivity in the mechanism of treatment response to dopaminergic medications in cocaine dependence.

Detailed description:
This project will use stochastic DCM, which is a recent DCM extension that takes into account hidden fluctuations in neuronal and vascular responses, and thus is especially suited for investigating effects of disease or drugs. In addition, this project will use nonlinear DCM, a DCM extension that can measure gating effects by striatum on cortico-cortical pathways. The overall aims of this project are: (1) To conduct functional magnetic resonance imaging-based DCM studies of working memory and impulsivity in order to determine the effective (directional) connectivity between PFC and striatum in treatment-seeking Cocaine Dependent (CD) subjects compared to non-drug using controls. We hypothesize that DLPFC causally affects ventral striatum in CDs, and that the strength of this connection is lower in CDs compared to controls. (2) To determine whether the pretreatment gating effect by the dorsal striatum, as a reflection of pretreatment hypodopaminergic state associated with chronic compulsive drug use, predicts the treatment response to dopaminergic pharmacotherapy in CDs. We hypothesize that lower pretreatment gating by the dorsal striatum on prefrontal-parietal effective connectivity predicts greater 8-week improvement from treatment of CDs with DA enhancing medications (combined with cognitive behavioral therapy \[CBT\]), but not from treatment with placebo (combined with CBT).

Arms: arm 1: Non drug using healthy controls arm 2: Placebo BID for 7 weeks arm 3: Levodopa/carbidopa 400/100 BID for 7 weeks
Interventions: intervention 1: levodopa/carbidopa 400/100 BID
Intervention descriptions: intervention 1: Levodopa dose escalation (1 week): Days 1-2, one 50/12.5 mg tablet BID; Days 3-4, one 100/25 mg tablet BID; Days 5-6, one 200/50 mg tablet BID; Day 7, one 400/100 mg tablet BID.

Maintenance phase (7 weeks): One 400/100 mg Levodopa/Carbidopa tablet BID or placebo in conjunction with once weekly individual cognitive behavioral therapy plus contingency management for attendance.

Protocol documents:
Prot_000.pdf: 
Title: Striatal Effective Connectivity to Predict Treatment Response in Cocaine Misuse 
 
PI: Ma Liangsuo, Ph.D 
 
NCT #: NCT02080819 
 
Unique Protocol ID: HM20000079 
 
Document approval date: October 1, 2013 
 
Document Type: Protocol  
 
 
 


Program Director/Principal Investigator (Last, First, Middle): Ma, Liangsuo 
 
Research Strategy: 
A. Significance  
. 
A.1. Cognitive enhancement as target for pharmacotherapy of cocaine dependence. Cocaine dependence 
is associated with several cognitive deficits [33, 95-97]. Cognitive enhancement has been suggested as a 
target for pharmacotherapy of stimulant addiction [97]. Investigation of the neurobiological mechanisms of 
therapeutic interventions to improve cognition has the potential for predicting and/or evaluating treatment 
outcomes, improving pharmacotherapy, and aiding the development of new treatment protocols [44]. Several 
studies [35, 98-100] have investigated the relationship between baseline cognitive functions and treatment 
outcome, and indicate that treatment outcome is influenced by baseline neuronal functioning. Thus, methods 
are needed to measure accurately alterations in neuronal systems underlying cognitive functions. This would 
allow development of potential biomarkers that could be predictive of results from more time consuming and 
costly clinical trial involved in the development of new treatments for cocaine dependence. There is a pressing 
need to develop clinically useful biomarkers to characterize subpopulations among disease populations and to 
assess efficiency of candidate therapies [101, 102]: To date, however, it is still one of the major challenges in 
drug addiction research to identify such biomarkers [103]. In this proposal, we will use an fMRI-based brain 
connectivity approach that may ultimately lead to biomarkers for cocaine dependence treatment response. 
A.2. Regional fMRI activation studies in cocaine dependence. One of the traditional applications of fMRI in 
drug addiction is to identify regional neural substrates mediating impaired cognitive functions by using the 
analysis of regional activations (please see [90, 104] for review). Several research groups [7, 34-36] have used 
this method to study cognitive deficits in cocaine dependence, and found differences in fMRI blood 
oxygenation level dependent (BOLD) signal between CDs and controls while the subjects performed cognitive 
tasks. For example, using a working memory task, Moeller et al. [35] found that CDs showed significantly lower 
working-memory load-dependent activation in several cortical and subcortical regions compared to controls, 
including caudate nucleus, DLPFC, medial orbitofrontal cortex, and thalamus. Using a Go/NoGo task, Kaufman 
et al. [7] found that, relative controls, cocaine users had significant hypoactivity in cingulate, pre-supplementary 
motor and insula regions, during both successful response inhibition and errors of commission. FMRI analysis 
of regional activations has been used to measure cognitive functions in CDs and showed that baseline regional 
activations in CDs were predictive of treatment outcome [35, 99]. 
A.3. Brain connectivity analysis in cocaine dependence. Although traditional analysis of regional fMRI 
activation has been widely used in characterizing the pathology and treatment response of cocaine 
dependence and has produced valuable findings, this method has several limitations when it is applied to study 
disease or medications. First, cognitive processes essentially depend on interactions among brain regions [92, 
105-110], however the analysis of regional activation does not directly measure these interactions. In addition, 
the sensitivity of traditional fMRI analysis to the presence or severity of disease and/or medications can be 
lower than that of connectivity analysis (please see [93] for review). Moreover, chan·ges in the BOLD signal 
could be confounded by the possible disruption by disease or medications on neurovascular coupling and/or 
hemodynamic  response [94], and therefore caution should be exercised when comparing populations with 
different physiologic or pharmacologic  conditions [111, 112]. A previous study [113] has shown that the direct 
effects of dopamine (DA) upon the vasculature need to be considered when measuring the hemodynamic 
coupling associated with dopaminergic drugs. This is especially relevant in cocaine dependence, for which 
studies have suggested is a disorder associated with reduced DA neurotransmission in the brain [43, 44]. 
These limitations suggest that methods are needed that measure the connectivity between brain regions [114] 
and that are more closely associated with the underlying neuronal activity [79, 93, 115, 116]. 
Functional connectivity and effective connectivity analyses are two methods that can be used to measure 
functional integration (interactions among brain regions) [78]. Functional connectivity refers to correlations 
among spatially remote neurophysiological  events [78]. Unlike functional connectivity, effective connectivity 
measures the causal (hence directional) effect that one region's neuronal activity has on another region [78]. 
Several functional connectivity studies [117-123] have been conducted to investigate cocaine dependence. 
These studies demonstrated abnormal functional connectivity in cocaine users during tasks [119, 121, 122]. 
A.4. Dynamic Causal Modeling of effective connectivity. One limitation of functional connectivity is that it is 
unable to establish causal relationships because it is unable to measure directional changes between brain 
regions. The DCM measurement of effective connectivity that will be used in the proposed study can overcome 
t/Jis limitation [78]. Positron emission tomography (PET) studies have demonstrated a significant correlation 
between dopamine D2 receptor function and glucose metabolism in orbitofrontal cortex, anterior cingulate, and 
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OLPFC in subjects with cocaine dependence and other addictions [44, 83]. Because these were correlational 
analyses, it is unclear whether PFC is causing changes in striatum, or striatum is causing changes in PFC. 
This question is relevant because a prefrontal-striatal glutamatergic pathway may regulate striatal OA, and thus 
decreased function of this pathway may lead to a hypodopaminergic state in COs [44, 86]. Low OA may disrupt 
frontal inhibition, resulting in poor inhibitory control over compulsive drug intake [84, 85]. Thus, prefrontal- 
striatal connectivity may be a potential target for developing new pharmacotherapies for COs. Using OCM to 
establish the directionality of this connectivity and its degree of impairment relative to controls is a first step 
towards answering this clinically significant question. 
The possible confounding effects by disease or medications are reduced in OCM, as reflected by the 
following three points: (a) Effective connectivity in OCM is modeled at the underlying neuronal level rather than 
the observed hemodynamic level [75, 91]. (b) The parameters of the effective connectivity (modeled at the 
underlying neuronal level) and the hemodynamic response functions are jointly optimized in OCM [91]. (c) 
Stochastic OCM [77, 124], an advance in OCM technique that has been recently implemented by the authors 
of OCM! takes into account hidden and random fluctuations in neuronal and vascular responses that may have 
profound effects on effective connectivity [77]. 
A.5. OA enhancement as a target for pharmacotherapy of cocaine dependence. The impaired cognitive 
functions observed in COs may be related to decreased OA function [54, 125, 126], which is associated with 
cocaine dependence [43, 44, 127-129]. The hypodopaminergic state  is considered as one of the main 
factors that triggers drug-seeking and taking [130]. PET studies in humans suggest that decreased DA 
function in COs contribute to repeated  drug  intake  [43, 44, 46, 84]. This is supported by PET studies in 
primates, which have shown that there is an inverse relationship between  02 receptor availability and 
vulnerability to the reinforcing effects  of cocaine [45, 131]. Studies in rodents indicate that reduced DA 
function is a major contributor to the transition from  drug  use to drug  dependence [132]. 
OA enhancement has been proposed to be a target for pharmacotherapy of stimulant addiction [81, 88, 
89]. A recent PET study by Martinez et al. [87] showed that low pretreatment striatal OA transmission in COs 
was associated with treatment failure from behavioral therapy alone (without any pharmacological treatment). It 
has been theorized that COs with low pretreatment OA, such as in [87], would benefit from the addition of OA 
enhancing medication to the behavioral therapy [44]. The results of Martinez et al. [87] in addition to our 
preliminary findings (section C.2.4) are consistent with the theory that preexisting differences between COs in 
OA circuits may underlie variability in responsiveness to different treatments [44]. 
Previous published studies in our center [133-135] and other studies [136, 137] showing feasibility, safety 
and tolerability of levodopa/carbidopa in treatment of cocaine dependence. Previous studies using the DA 
drugs levodopa/carbidopa [135] have showed significant treatment effects, as evidenced by 
significal)tly reduced cocaine positive urine  and craving. In the current project, it is hypothesized that the 
effects of levodopa/carbidopa  [133-135] will have effects on therapeutic action (OA enhancement) and lead to 
optimal treatment efficacy. This clinical trial provides an opportunity for the present proposal to investigate the 
role of effective connectivity in the mechanism of treatment response to OA medications in COs. 
A.6. Striatal gating effects and DA function. An important issue that is relevant to the development of new 
treatments for cocaine dependence is how OA neurotransmission interacts with striatum and PFC during 
cognitive tasks. One possible way that OA regulates cognition is via gating effects (i.e., positive modulation 
effect) of striatum that regulate connections between the PFC and posterior regions [22, 23]. A cognitive model 
developed by O'Reilly and Frank [22] addresses this striatal gating mechanism. In brief, OA exerts its effects 
via firing of the "Open Gate" neurons in the dorsal striatum [22]. When "Open Gate" neurons in the dorsal 
striatum fire, they facilitate connections between frontal cortex and posterior cortical regions [22]. Nonlinear 
OCM can be used to evaluate this striatal gating model [76] as an indicator of OA function. We propose to use 
OCM with the stochastic and nonlinear extensions (snOCM) to test our hypotheses. 
We propose to test snDCM on working memory and response inhibition in separate  fMRI studies 
within the same CD subjects. Working memory is chosen for the task as it is well documented that DA 
plays a key role in working memory, and previous studies found altered working memory in COs. 
Cocaine .use is associated with  altered working memory function at both  the behavioral [32, 33] and 
neuronal [34-37] levels  in humans, and in animal studies [38-41]. Cocaine use is associated with 
altered DA function [42-51]. DA neurotransmission is involved in working memory [52-58]; supporting 
evidence comes from  human [59-68] and animal [69-73] studies. There is evidence that working 
memory function is predictive of DA synthesis capacity in humans [74]. Our published data [35] and 


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unpublished pilot data (Section C.2.4) have shown that the baseline fMRI working memory activation 
and the effective connectivity elicited by the working memory task are predictive of treatment outcome 
with DA enhancing medications in COs. Previous studies have shown that cocaine use can induce 
impaired response inhibition in both human ([1-8] and see [9] for review)  and animals ([10-12) and see 
[13] for review). In addition, there is evidence ([15-19) and see [20, 21] for review) supporting the 
hypothesis that  DA modulates response inhibition. Furthermore, impulsive personality traits predict 
DA-dependent changes in frontostriatal activity during component processes of working memory 
[117). A study [100) conducted by our group has shown that trait  impulsivity is predictive of cocaine 
addiction treatment outcome. The relationship between  cognitive domains and DA is relevant because 
the major  objective of this  study is to predict treatment outcome of DA enhancing medications. 
Summary of Significance: (a) Establishing the directional relationship between PFC and striatum and the 
role of striatal gating in cocaine treatment response will significantly improve our understanding of dopaminergic 
and pharmacotherapeutic  mechanisms in COs. (b) The pilot data (Section C.2.4) suggest that treatment 
outcome in COs with low pretreatment striatal OA can be improved with OA enhancing treatments by increasing 
striatal OA function, which we theorize increases striatal gating of cortico-cortico connections. Thus the evidence 
supporting the hypothesized striatal gating mechanism will have significant implication in cocaine addiction 
treatment. (c) An issue in cocaine dependence research is the lack of clinically useful biomarkers for 
pharmacotherapies. Effective connectivity as measured by snOCM may ultimately lead to clinically useful 
biomarkers for cocaine dependence and its treatment. 
B. Innovation: 
There is a paucity of effective connectivity studies in the field of cocaine dependence, and we are unaware 
of any published studies of cocaine dependence that have used effective connectivity in predicting treatment 
response in COs. Thus, the proposed study is novel and will provide evidence on whether cocaine-related 
alterations in effective connectivity are related to treatment outcomes. In addition, the testing of the striatal 
gating model and the application of snOCM to cocaine dependence are also novel. 
C. Approach: 
C.1 Overview of Study Design. The objective of this study is to use baseline (pretreatment) snDCM to 
predict the treatment response to DA medication. 
C.2 Preliminary Studies:  C.2.1. An analysis of regional fMRI activations showed that pretreatment thalamic 
activation predicted treatment response in COs [35]. This study examined the relationship between 
pretreatment working-memory elicited regional activations and subsequent treatment response in treatment- 
seeking early abstinence COs. Nineteen COs who were randomized to treatment studies and 14 non-drug 
using controls underwent fMRI while performing a working memory task with variable memory load, as 
parameterized by two levels of memory delay and three levels of digit load (number of digits presented in each 
visual stimulus). Group comparison  showed that COs had significantly lower regional activation in caudate, 
putamen, cingulate gyrus, middle and superior frontal gyri, inferior frontal gyrus pars triangularis and pars 
opercularis, medial orbitofrontal cortex, precentral gyrus, and thalamus compared to non-drug using controls. 
These brain regions are known to be part of a circuit associated with reward, conditioning/memory, executive 
control, and motivation/drive  [92]. Within COs, pretreatment thalamic activation significantly correlated with the 
subsequent treatment response. Subjects with pretreatment thalamic deactivation showed the poorest 
treatment response, possibly related to thalamic involvement in mesocortical and mesolimbic OA projections. 
This study supports our research group's capacity to carry out fMRI studies in COs and our research group's 
expertise in treatment response prediction research. 
C.2.2 A DCM effective connectivity study showed that working memory load modulated brain connectivity 
in a parieto-frontal network in normal healthy subjects [138]. This study used fMRI-based OCM to investigate 
how working memory load modulates effective connectivity in normal working memory system. FMRI data 
were acquired from eighteen non-drug-using  healthy subjects while they performed the aforementioned 
working memory task [35]. Based on regional activations and consistent with working memory literature, eight 
regions of interest, i.e., bilateral (LR) OLPFC, LR anterior cingulate cortex (ACC), LR inferior frontal cortex 
(IFC), and LR posterior parietal cortex (PPC), were chosen for OCM analyses. Bayesian inference indicated 
that a bilinear OCM in which the digit load (3, 5, or 7 digits) modulated several parieto-frontal connections was 
the optimal model. Analysis of model parameters showed that higher digit load enhanced connection from left 
(L) PPC to L IFC, and lower digit load inhibited connection from right (R) PPC to L ACC. These findings 


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- 
0
 
suggest that working memory load modulates effective connectivity in a parieto-frontal network, and may reflect' 
altered neuronal processes, e.g., information processing or error monitoring, with the change in working 
memory load. This study supports our research group's expertise in the use of OCM. 
C.2.3 A stochastic OCM effective connectivity study showed that the DLPFC-striatal effective connectivity 
was compromised in COs during working memory task [37]. This study used fMRI-based stochastic DCM to test 
whether there is altered effective connectivity of working memory pathways in cocaine dependence. Nineteen 
COs and fourteen non-drug using controls were scanned while performing the aforementioned working memory 
task [35]. Nodes for DCM analysis were chosen based on areas that significantly activated common to both 
groups, according to Seghier et al [79]. Bayesian inference on model parameters showed that the COs had large 
and reliable differences compared to the control subjects in the strength of the modulation effects on prefrontal-
prefrontal and prefrontal-striatal connections exerted by the memory delay conditions. The strength of the 
modulation effects on connection from DLPFC to striatum was lower in the COs compared to controls (Baysian 
confidence probability > 0.999). These findings are consistent with altered cortical-striatal networks related to 
reduced OA function in cocaine dependence. As far as we know, this is the first between- 
group effective connectivity study using stochastic OCM. This study supports our research group's expertise in · 
the use of stochastic OCM in addiction research. 
C.2.4 Our pilot snOCM study showed: (a) causal (i.e., directional) relationship from OLPFC to striatum in 
COs; (b) the strength of the endogenous connection from OLPFC to ventral striatum was lower in COs 
compared to controls; and (c) low pretreatment gating by the dorsal 
striatum on the IFC-PPC connection predicted subsequent 
improvement after treatment wih OA enhancing medications. Six 
treatment-seeking COs and six age- and sex-matched controls, who 
had been included in our previous OCM study [37], were included. 
The baseline (pretreatment) fMRI data acquired while the subjects 
performed the aforementioned working memory task [35] were used 
for testing Hypotheses 1 and 2. The COs subsequently received DA 
enhancing treatment (d-amphetamine: 1 subject, levodopa: 2 
subjects, and modafinil: 3 subjects). Based on the significant 
regional activations, seven regions of interest were chosen as 
nodes for the snDCM analysis: L OLPFC, L IFC, L PPC, R PPC, LR 
dorsal striatum, LR ventral striatum, and LR thalamus. OCMs were 
 
 
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constructed according to Ma et al. [37] with the following additions: 
(a) for each CD, the following putative OCMs were constructed: a 
model in which DLPFC causally (directionally) affects dorsal and 
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TES 
ventral striatum, and a model in which the dorsal and ventral striatum causally affects the OLPFC. 
Anatomically, OLPFC projects to the caudate, whereas limbic and paralimbic regions project to the 
ventral striatum [139, 140]. However, snOCM effective connectivity can be validly determined by 
indirect anatomical connectivity [141]. Thus, the connection from  OLPFC to ventral striatum can be 
indirect via corticocortical intermediate connections [142, 143]. (b) The activity in the dorsal striatum was 
hypothesized to gate (positive modulation effect) the connectivity between the L IFC and the L PPC, based on 
O'Reilly and Frank [22]. Bayesian inference on model structure showed that the model in which the OLPFC 
causally affects the dorsal and ventral striatum had higher Bayesian exceedance probability (<1>=0.98) than the 
model in which the ventral and dorsal striatum causally affects the OLPFC. The strength of the endogenous 
connection from DLPFC to ventral striatum was lower in the COs (-0.025 Hz) compared to controls (0.019 Hz) 
(Baysian confidence probability> 0.999; Cohen's d = 1.89), but there was no significant difference between 
groups for dorsal striatum. These results are consistent with the corticostriatal circuits in addiction 
proposed in [85], in which increasingly repetitive or compulsive cocaine use is associated with 
progressively greater  involvement of dorsal striatum but stable  involvement of ventral striatum [144, 
145). Thus it is possible that in COs there is relatively less heterogeneity as a group  in ventral striatum 
and relatively more  heterogeneity in dorsal striatum. Heterogeneity in dorsal striatum is consistent with 
the above figure. This may explain why a significant average  group difference was found in the 
connection from  OLPFC to ventral striatum but not in the connection from  OLPFC to dorsal striatum. 
Based on evidence [146-150] showing that glutamate has an excitory effect  on OA in striatum, it can be 
speculated that the lower  (negative)  strength of the connection from  OLPFC to striatum in COs reflects 


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decreased DA caused by decreased prefrontal-striatal glutamatergic neurotransmission [44, 85, 86, 92]. 
Nonparametric Spearman correlation analysis showed that the pretreatment gating effect by the dorsal 
striatum on the connection from IFC to PPC was inversely related to the Treatment Effectiveness score (TES), 
a measure of relative abstinence from cocaine use, after 8 weeks of treatment with OA enhancing medications 
(r = -0.69, p=0.12, see figure in this section). These preliminary findings are consistent with the theory that OA 
enhancement may be a useful target for pharmacotherapy  of cocaine dependence in COs who have 
pretreatment hypodopaminergic state [81, 88, 89]. A recent study by Martinez et al. [87] showed that low 
pretreatment striatal OA transmission in COs was associated with treatment failure from behavioral therapy 
alone (without any pharmacological treatment). One may speculate that COs with low pretreatment OA in that 
study would benefit from the addition of OA enhancing medication [44]. The results of [87] and our preliminary 
findings are consistent with the theory that preexisting differences between COs in OA circuits may be a 
mechanism underlying variability in responsiveness to different treatments [44]. This pilot study supports our 
expertise in the use of snDCM and Hypotheses 1 and 2. 
C.2.5 Our pilot fMRI study testing response inhibition in COs showed: (a) activation in an 
orbitofrontal-striatal-thalamica/ circuit; and (b) positive correlation of caudate activation with 
performance, and negative correlation of caudate activation with severity of cocaine addiction. Eleven 
COs (cocaine group) and ten age-matched controls (control group)  were included in an event-related 
fMRI study of response inhibition. The fMRI data were acquired while  the subjects performed a Go/NoGo 
task [1], in which there are two levels  of difficulty in NoGo trials (Easy NoGo and Hard NoGo trials), (see 
section C.4.6 and reference [1] for description). An SPM univariate 2nd level analysis of the fMRI data 
across both  groups revealed that the main  effects  of correct NoGo trials relative to Go trials showed 
activation in four  clusters (uncorrected 2-tail cluster P < 0.05, cluster size > 238 voxels) that were found 
in portions of LR thalamus, L posterior cingulate, LR supplemental motor area, L superior frontal gyrus 
(g), R superior parietal g, R angular g, R inferior parietal lobule, L fusiform g, and L parahippocampal g. 
There was no significant interaction of activation with task  difficulty. Within COs, whole  brain  voxel-
wise SPM regression analysis showed positive correlation of NoGo activation with NoGo accuracy (d') 
in portions of L caudate, R middle cingulate, R inferior parietal lobule, R medial orbital g, R putamen, R 
pallidum, R rectus  g, and R lateral orbitofrontal cortex  (cluster 2-tail false discovery rate [FOR) corrected 
P < 0.10, cluster size> 465 voxels). Whole  brain  voxel-wise SPM regression analysis also  showed 
negative correlation of NoGo activation with severity of cocaine addiction (total KMSK score [151]) in 
portions of L caudate, L fusiform g, L angular g, L insula, L olfactory g, L medial orbital g, L rectus g, L 
putamen, L parahippocampal g, L middle temporal g, and L inferior temporal g (cluster 2-tail FOR 
corrected P < 0.10, cluster size> 443 voxels). This pilot study supports our expertise in the use of a 
Go/NoGo  fMRI task in studying response inhibition in COs, and the results support the regions of 
interest selected for snDCM analysis to test Hypothesis 3 (C.4.19.2). 
C.3 Description of the Clinical Trial. The objective of this proposal is to use pretreatment snOCM to predict 
the treatment response to OA enhancing medication in COs. 
C.3.1 Participants and Recruitment for the Clinical Trial: Male and female treatment-seeking individuals, 18 
to 55 years old, who meet current OSM-IV criteria for cocaine dependence and report using cocaine within the 
past 30 days are enrolled. Eligible subjects are required to submit at least one positive urine toxicology screen 
for the cocaine metabolite benzoylecgonine  (BE) > 300 ng/ml,during the one-week screening and pretreatment 
assessment  period. Subjects currently dependent on any psychoactive substance other than cocaine, nicotine, 
or marijuana are excluded. 60 treatment-seeking COs will be recruited. 
C.3.2 Screening, Pretreatment, Randomization for the Clinical Trial: Candidates signing written informed 
· consent enter a one-weeintake and assessment phase to evaluate eligibility and administer baseline 
pretreatment measures. Those who fail to return for required visits during this period are not randomized to 
treatment so as to reduce early treatment dropout rates [152]. An urn randomization [153] procedure 
assigns subjects to one of the four  treatment groups. This procedure balances groups on pretreatment 
characteristics that  may influence measures of cognitive performance and/or  treatment outcome. The 
variables include (a) severity of cocaine use, based  on days using cocaine in past 30 days and KMSK 
score  [151]; (b) lifetime duration of cocaine use; (c) level of marijuana use, based on days using in past 
30 days; (d) Barratt Impulsiveness Scale (BIS) [202], measuring trait  impulsivity. 
In the Clinical Trial, there is one active medication arm and one placebo arm. Equal allocation to treatment 
conditions is expected, as follows: 30 COs assigned levodopa/carbidopa 400/100 BID, and 30 COs 


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Program Director/Principallnvestigator(Last, First, Middle):  Ma, Liangsuo 
 
 
 
assigned placebo. Treatment assignment is concealed to all staff, including the investigators. Only the study 
pharmacist who dispenses the study medications is unblinded to participants' assigned condition. At each clinic 
visit (Monday, Wednesday, Friday) subjects are administered the medication at the dispensing window and 
given take-home doses for intervening days. 
C.3.3 Treatment for the Clinical Trial: During the 12-week active trial participants are scheduled to attend 3 
clinic visits per week (Monday, Wednesday, and Friday). Weekly cognitive behavioral therapy (CBT), an. 
evidenced-based  psychosocial treatment for cocaine dependence, is used as the behavioral therapy platform. 
Participants receive individual cognitive behavioral therapy in 50-minute weekly sessions. The CBT is based on 
the model of relapse proposed by Marlatt and Gordon [154]. 
C.3.4 Treatment Outcome: The primary outcome measure of cocaine use/nonuse is based on urine drug 
screen levels (acquired 3 x per week) of the cocaine metabolite benzoylecgonine  (BE), coded as "positive" for 
cocaine use if BE ;;:: 300 ng/ml.However, urine-based definitions of cocaine use/nonuse have limitations in 
terms of data loss (due to missed visits) and carryover effect. Recent procedures for combining self-report and 
urine BE have been recommended [159, 160]. Thus, as a secondary measure of cocaine use/nonuse, the new 
SRPHK1 (SelfReportPHarmacoKinetic1) coding method is used. This classifies daily cocaine use based on 
evaluation of self-report (SUR) (obtained 3 x per week), quantitative urine BE levels, and participants' 
concordance rate (agreement between self-report and urine result). Retention in treatment is measured in two 
ways: (a) As a binary variable, defined as "completing" 12 weeks of cognitive behavioral treatment, since this 
cutoff is regarded as standard duration of treatment [161] and predictive of better outcome [162]. (b) A 
secondary continuous measure of retention is the total number of weeks a subject remained in treatment. 
C.3.5 Retention of Participants: As part of participation in the clinical trial, participants receive incentives for 
attending the in-person intake ($50.00 in gift cards) and end of study ($50.00 in gift cards). They also receive 
$5 for each medication p·ack returned and bus/parking tokens as needed to help defray the costs of travel. Also 
during treatment, for each visit attended, subjects earn a draw from the attendance voucher "fishbowl", with 
prizes ranging in value from $0 to $100 (one slip). Prizes are exchanged for vouchers. In addition, they receive 
approximately $50.00 in gift cards for completing behavioral/cognitive laboratory assessments. All parking is 
free. Subjects receive an additional $130 for completion of the MRI scans. 
C.3.6 Assessment of Medication Compliance: Riboflavin (50 mg) is added to the medication (and 
placebo) capsules and used as a marker to monitor compliance. The compliance to the treatment . 
regimens is assessed by pill-counting and by urine  riboflavin determination. 
C.4 Experimental Design and Methods for the snDCM Study: C.4.1 Subjects for the snDCM Study: There 
are 3 groups in the snDCM study: (a) medication group, {b) placebo group, and (c) control group. 
Conservatively estimating approximately 20% unusable scans in each group because of excessive head 
motion, clinical structural abnormalities, artifacts, or subject no-show for the scan, this project will need to 
recruit a total of 30 treatment-seeking CDs for the medication group (15 male and 15 female), 30 treatment- 
seeking CDs for placebo group (15 male and 15 female), and 30 age-, handedness-, and education-matched 
non-drug-using  healthy control subjects (15 male and 15 female) in order to achieve 24 subjects per group for 
final analysis (Power Analysis section C.4.21). Thus, we need to recruit 24 subjects each year over the first 3 
years and 18 subjects during Year 4. Fewer subjects will be recruited during Year 4 because more time in Year 
4 will be devoted to final data analysis, write-up, presentation of findings, and subsequent grant applications. 
C.4.2 Subject Recruitment and Screening: The medication group will consist of 30 subjects assigned to 
levodopa/carbidopa. The placebo group will consist of 30 subjects assigned to placebo in the clinical trial. The 
30 non-drug-using  healthy control subjects (control group) will be recruited through advertising in local 
newspapers and word of mouth. Written informed consent form will be obtained from all subjects. 
All control subjects undergo an initial screening that consists of a physical examination, routine blood work, 
urinalysis, electrocardiogram (EKG), metal screening, structured diagnostic interview (SCID), addiction severity 
index (ASI), and human immunodeficiency virus (HIV) screening. We anticipate screening 45 potential control 
subjects in order to recruit 30 control subjects (yielding 24 control subjects in the final analysis). 
C.4.3 Subject inclusion/exclusion criteria: The patients who meet DSM-IV criteria for current cocaine 
dependence, have at least 1 positive urine benzoylecgonine  (BE) specimen (> 300 ng/ml)during intake, and 
are assigned medications or placebo, will be the candidates for this study. In addition, the included subjects 
must simultaneously meet the following criteria: (a) Be between 18 and 50 years of age; (b) Right handed; (c) 
Have no past history of Psychiatric or non-Psychiatric medical disorders which could affect the central nervous 
system as assessed by SCID and physical examination; (d) Be in acceptable health on the basis of interview, 


 
 
Program Director/Principal Jnvestigator(Last, First, Middle): Ma, Liangsuo 
 
medical history, labs, and physical exam; (e) Be able to understand the consent form and provide written 
informed consent; (f) Have no metal fragments or other bodily metal (e.g., pacemaker) or significant 
claustrophobia that would put the subjects at risk for MRI scanning. (For a complete list of inclusion and 
exclusion criteria see Human Subjects section below). 
C.4.4 Coordination between the Proposed Study and the Clinical Trial: For eacCD, the MRI session will 
be conducted in Week 1, which is before the treatment is initialized (baseline pretreatment). The treatment 
outcome (see Section C.3.4 Treatment Outcome) will be shared for the snDCM study. 
C.4.5 Working Memory Task for fMRI: The immediate memory task/delayed memory task (IMTIOMT) fMRI 
protocol [35, 138, 165] is used as the working memory test. The IMT and DMT are delayed-matching-to- 
sample tasks [166]. In both IMT and DMT, each stimulus consists of a string of numbers that is displayed for 
0.5 s, followed by an inter-stimulus interval of 0.5 s, at a rate of 1 stimulus per second. In DMT, the target and 
probe stimuli are separated by distracter stimuli, consisting of a string of all zeros (e.g., 00000) that is repeated 
three times at the same rate and duration as the target and probe stimuli. Thus, in DMT, the memory delay 
between the end of the target stimulus and beginning of the probe stimulus is 3.5 s. In IMT, there are no 
distracter stimuli, and thus the memory delay between target and probe is 0.5 s. A nonsalient stimulus 
consisting of all ones (e.g., 11111) is presented once after each DMT trial and four times after each IMT trial. 
Therefore the sum of the distracter stimuli and the nonsalient intertrial stimuli is the same (four) during DMT and 
IMT conditions, and hence the number of trials is same (seven) for IMT blocks and DMT blocks. In both IMT and 
DMT, the probability of a match and the probability of a catch are 50%. In a catch, the probe differs 
from target in only one of the digits. Subjects are instructed to press a button using the right index finger only 
when the probe matches the target  The A' score [167] is used as ari accuracy measure. The A' ranges from 
0.5 to 1.0, corresponding to chance to perfect discriminability, respectively. There are 12 blocks alternating 
between IMT and DMT within each run. The number of digits in the stimulus string can be 3, 5, or 7 and is held 
constant within each block. The digit-length conditions represent three levels of digit load. All digit conditions are 
presented within each run, in counterbalanced order between runs and subjects. Each block is 42.5 s; there is 
10 s rest between blocks and 20 s rest at the beginning of each run. 
C.4.6 Response Inhibition Task for fMRI: An event-related Go/NoGo task [1] is used for fMRI of 
response inhibition. In each run, the subjects look at a total of 224 visual stimuli that are sequentially 
presented in random order. Each stimulus is displayed for 500 ms, and any two neighbor stimuli are 
separated by a blank  screen  lasting 1900, 2100, or 2300 ms Uittered  randomly). Each visual stimulus 
consists of lines  enclosed within two boxes  that are presented simultaneously side by side. The 
subjects are instructed to look  at both boxes  and to discriminate the direction of the lines. The 
subjects are instructed to press  a button using their right  index  finger  only  when they see that both 
boxes  show  diagonal lines  in the same direction in both boxes  (Go trials). The subjects are instructed 
not to press  any button when they see that both boxes  show  horizontal lines (easy-NoGo  trials), or 
when they see that one box contains diagonal lines  that are in the opposite direction of the diagonal 
lines  in the other  box (hard-NoGo trials). For Go trials, a correct response is defined as a key-press 
completed within 600 ms. For NoGo trials, an incorrect response is defined as a key-press completed 
within 600 ms. There are 168 Go trials (75%), 28 easy-NoGo  trials  (12.5%), and 28 Hard-NoGo trials 
(12.5%) in each fMRI run (10 min 40 s per run). For detailed descripton, please  see reference [1]. 
C.4.7 Pre- and Post-treatment Behavioral Performance: Because  fMRI sessions in this project are 
conducted only  during pretreatment baseline, this project will also acquire out-of-scanner pre- 
treatment and post-treatment (week 12) behavioral data for the cognitive tasks. Pre-treatment 
performance will  be considered as a possible confounding factor  and will be controlled as a covariate 
in the statistical analysis (Section C.4.20). Post-treatment behavioral performance is a treatment 
outcome and will be included in the treatment prediction analysis (C.4.20). In addition, Dr. Scott Lane, 
who is an expert  in the behavioral science of drug  addiction, will be a Consultant on this project to 
advise  on the behavioral data implementation and analysis. 
C.4.8 MRI Scanning Site and Devices: MRI sessions are conducted at the Magnetic Resonance Imaging 
Center of the UTHSC-H, using a research-dedicated Philips lntera 3.0 T MRI scanner with an 8-channel 
receive head coil (Philips Medical Systems, Best, Netherlands). 
C.4.9 Task Practice and Mock MRI Simulation: Prior to the real MRI scan, the subjects undergo at least 10 
min of practice of the fMRI tasks until it is clear that the procedures are understood and that the majority of the 
practice responses are correct. After that, the subjects undergo a mock MRI scan using a Philips product MRI 
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Procedure 
Location 
Timing 
Consent for participation, screening (physical examination, 
routine blood work, urinalysis, EKG, metal screening, SCID, ASI, 
HIV, and urine drug screen) 
VCU Collaborative 
Advanced Research Imaging 
(CARl) program 
Day 1 
Training session on the cognitive·tasks, mock scan, pre- 
treatment behavioral performance 
MR Imaging Center Mock 
Scanner at CARl 
Day 2 
Urine drug screen, urine pregnancy screen, breath alcohol 
screen 
CARl 
Day 3 
Sagittal and SENSE localizers, 3-D MPRAGE, IMT/DMT fMRI 
(x 3), Go/NoGo fMRI (x 3), 3D-FLAIR, T2-weighted fast spin 
echo 
CARl Research Dedicated 
3T MRI Scanner 
Day 3 
 
Simulator. Lying in the mock scanner, the subject performs the fMRI tasks as in the real scan, while listening to 
a recording of MRI sounds. The fMRI tasks are written in E-Prime 1.2 (Psychology Software Tools). 
C.4.10 Pre-scan Screening: Immediately prior to the real MRI scan, all subjects undergo urine drug screen 
and breath alcohol screen. Female subjects also undergo urine pregnancy screen. The subjects with a breath 
alcohol result of greater-than-zero do not proceed for the MRI scan that day, and are rescheduled. The control 
subjects with a positive urine drug screen are excluded from the study. The female subjects with a positive 
urine pregnancy test are excluded from the study. Subjects who regularly smoke tobacco are asked to smoke 
two hours prior to the first fMRI scan, and not to smoke after that until the fMRI scans are finished. 
C.4.11 MRI Scans: The MRI session is approximately 90 min, with a 10 min break in the middle. The first 2 
series are a T1-weighted spin echo 3-plane localizer (scout), and a SENSE localizer. A high resolution T1- 
weighted 3D-MPRAGE series is acquired with the following parameters: 256 x 256 acquisition and 
reconstruction matrix, in-plane resolution= 0.94 mm, 170 sagittal slices, 0.94 slice thickness. This is followed by 
3 working memory (IMT/DMT) and 3 response inhibition (Go-NoGo) fMRI series (runs) with 
approximately 2 minutes rest between runs. Each fMRI series is a single shot spin-echo echoplanar (EPI) pulse 
sequence (see next section for rationale), with SENSE factor= 2.0, repetition time= 2212 ms for IMT/DMT and 
2500 ms for Go-NoGo, echo time= 75 ms (optimized for spin echo), flip angle= 90 degrees, number of axial 
slices = 22 for IMT/DMT and 25 for Go-NoGo, field-of-view= 240 mm x 240 mm, in-plane resolution 3.75 mm 
x  3.75 mm, slice thickness= 3.75 mm, gap= 1.25 mm, repetitions= 294 for IMT/DMT and 
256 for Go-NoGo after 10 dummy acquisitions, total duration = 10 min 47 s for IMT/DMT and 10 min 40 s for 
Go-NoGo. A 3D-FLAIR and T2-weighted fast spin echo are acquired to rule out incidental intracranial findings. 
C.4.12 Spin-Echo EPI: Spin-echo EPI, rather than gradient-echo EPI, is used for fMRI in this study to avoid 
signal losses caused by through-slice dephasing in regions that are affected by strong susceptibility gradients at 
3 T [168-170]. The regions affected by strong susceptibility gradients near air-tissue interfaces include 
medial orbitofrontal cortex and medial temporal areas that are important for drug addiction research. In addition 
to its advantage of eliminating signal dropout, spin-echo EPI has other advantages over gradient-echo EPI 
including greater extravascular signal component, less spatial distortion, and more specific spatial localization 
at 3T [169, 171]. Although the magnitude of the BOLD effect is somewhat lower in spin echo EPI compared to 
gradient echo EPI at 3T (in areas of the brain that are not affected by susceptibility dropout), a cognitive fMRI 
study using spin-echo EPI at 3T [169] found significant activation in the same cortical areas that were found 
using gradient echo EPI (i.e., in the areas not affected by susceptibility dropout). Importantly, that study found 
significant activation in predicted medial orbitofrontal areas that were not found with gradient echo EPI [169]. 
Likewise, our fMRI [35] and fMRI-based DCM [138] studies, which used the same working memory task and 
scanning parameters as in the proposed project, found significant findings in medial orbitofrontal cortex, as well 
as in expected cortical areas that are not affected by magnetic susceptibility dropout. 
C.4.13 fMRI presentation system: Stimulus presentation and recording of behavioral performance during 
fMRI scanning is managed through the Eloquence fMRI System (lnvivo Corporation, Orlando, Florida), an 
update of the Integrated Functional Imaging System-Stand Alone system (IFIS-SA, InVivo Corporation, 
Orlando, Florida). The subject views the task on an LCD video display unit that is built into the head coil. The 
subject responds to the task by pressing a keyboard-like button with the right index finger. 
C.4.14 Flowchart and Timeline of Procedures for All Aims· 
 
 
 
 
 
 
 
 
 
 
 
 
 
C.4.15/ncJdental Fmdmgs on MRI: The FLAIR 1mages and T2 weighted spm echo 1mages from all subJects 
will be read by the study Radiologist. The subjects who have clinically significant abnormalities will be excluded 
from the study and referred for appropriate treatment. 


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C.4.16 FMRI Preprocessing: The fMRI data is preprocessed using Statistical Parametric Mapping (SPM8, 
http://www.fil.ion.ucl.ac.uk/spm/) software from the Wellcome Department of Cognitive Neurology, London, UK, 
implemented in Matlab (Mathworks Inc.). After slice-timing correction, each fMRI time series is realigned to 
correct for head motion. Runs with head motion greater than 1 voxel (3.7S mm translation on any axis) or 
rotation greater than 3.7S degrees are eliminated from analysis. After coregistering the high-resolution 30- 
MPRAGE to the fMRI images, the 30-MPRAGE is warped to the coordinates of the Montreal Neurological 
Institute (MNI) standard space [172, 173] using the SPM8 Normalisation procedure, consisting of affine 
followed by non-linear transformations. These transformations are applied to the fMRI series to convert the 
fMRI images to MNI space, and the fMRI images are then resliced to 2 mm isotropic resolution and spatially 
smoothed with a Gaussian filter of 8 mm isotropic full width at half maximum (FWHM). 
C.4.17 SPM univariate analysis of working memory fMRI data: First-level voxel-wise univariate statistical 
analysis of the fMRI data is conducted using SPM8. The IMT and DMT blocks for each digit condition are 
modeled by boxcar functions convolved with the SPM8 canonical hemodynamic response function. The 
parameters for each condition are estimated using the General Linear Model (GLM) [174] at each voxel without 
global normalization. The fMRI time series is high-pass filtered with an optimized cut-off period of 330 s 
determined by the Fourier transformation of each condition's time model, which shows that the signal from the 
experimental condition is retained at this value·but not at shorter cut-offs. Activation for each digit condition is 
defined as the contrast of DMT minus IMT parameter estimates ("DI") for that digit condition (i.e., 013, DIS, and 
017 for 3 digits, S digits, and 7 digit conditions respectively). In addition, the following contrast images are 
defined to determine the interaction effects between the memory delay (IMT and DMT) and the digit load (3 
digits, S digits, and 7 digits): DIS minus 013, 013 minus 015, 017 minus 013, 013 minus 017, 017 minus DIS, and 
DIS minus 017. The following contrast images are defined to determine the main effects of memory delay 
(pooled across 3 digits, S digits, and 7 digits): combined 013, DIS, and 017. The summary contrast images are 
output for each subject for entry into the second-level analysis. Because a between-group DCM study focuses 
on the characterization of abnormal connectivity in a common network of regions, Seghier et al. [79] 
recommended that only commonly activated regions in patients and controls should be included as nodes in 
DCM [79]. Accordingly, a second-level Random Effects [17S] SPM8 GLM analysis is conducted in order to 
determine the nodes of the working memory system that are common to both groups for purpose of DCM._ 
analysis. For each type of summary contrast image, the group difference and the main effects across the 
means for both groups are determined with the default non-sphericity correction for unequal variance between 
groups. The cluster-defining threshold is voxel t = 2.S. Statistical significance of the fMRI analysis is false 
discovery rate (FOR) corrected cluster P less than O.OS. 
C.4.18 SPM univariate analysis of response inhibition fMRI data: This processing is similar to that 
for working memory, except for the following. (a) The cut-off period of the high-pass temporal filter is 
128 s, which is an optimized setting determined by Fourier transform of the GoiNoGo task time-series 
model. (b) The summary contrast images of the first level fixed effects analysis are: correct Easy NoGo 
trials minus correct Go trials, correct Hard NoGo trials minus correct Go trials, correct Hard NoGo 
trials minus correct Easy NoGo trials and combined correct NoGo trials (correct Hard NoGo trials plus 
correct Easy NoGo trials) relative to correct Go tri()ls (in brain regions not showing significant 
interaction of activation with task difficulty). The last contrast image is used to investigate the overall 
effects of the NoGo trials. 
C.4.19 Effective Connectivity Analysis: The effective connectivity analysis, described below, is conducted 
using stochastic and nonlinear DCM (snDCM) as implemented in the latest update to SPM8. 
C.4.19.1 Working memory DCM analysis 
Regions-of-interest (ROis): L IFC, L DLPFC, L PPC, R PPC, LR dorsal striatum, LR ventral striatum, 
and LR thalamus are the ROis. The LR dorsal striatum (or LR ventral striatum) is treated as a single ROI 
because we found that the DCM parameters on the connections from or to the striatum were similar for left and 
right striatum [37]. The ROis are based on the following criteria: (a) these regions showed significant activation 
in the SPM second-level analysis of the main effect of memory delay for the combined groups in our previous 
study [37]; (b) meta-analyses have shown that these regions activate consistently during working memory 
tasks [176, 177]; (c) these regions have been implicated in working memory processes, e.g., encoding [178], 
storage [179], maintenance [177-179], and executive control [179]; and (d) some regions (e.g., thalamus) have 
been implicated in treatment response in COs [3S].  · 


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Volumes of interest (VO!s) and time series extraction: We follow the method of constructing the VOis as 
described in Ma et al. [138]. The significant group level activation clusters form the VO/s, which are further 
constrained [141] to be within the boundaries of the aforementioned seven ROts, as defined by anatomical 
atlases. The atlas-derived binary masks of the regions are obtained from the Anatomical Automatic Labeling 
atlas [180] as implemented in the WFU (Wake Forest University) PickAtlas SPM toolbox [181, 182]. The binary 
mask of DLPFC is the middle frontal gyrus. The binary mask of IFC is the set-theoretic union of the atlas-based 
binary masks of inferior frontal gyrus pars opercularis and inferior frontal gyrus pars triangularis. The binary 
mask of PPC is the union of the atlas-based binary masks of superior parietal lobule and inferior parietal 
lobule. The binary mask of dorsal striatum is the union of the atlas-based binary masks of dorsal caudate and 
dorsal putamen, and the binary mask of ventral striatum is the union of the atlas-based binary masks of ventral 
caudate, ventral putamen, and nucleus accumbens. Following Postuma and Dagher [183], z = 7 mm (Talairach 
space) is used as the border between dorsal caudate and ventral caudate, and z = 2 mm (Talairach space) is 
used as the border between dorsal putamen and ventral putamen. The atlas-derived binary mask of the 
nucleus accumbens is obtained from the Harvard-Oxford Subcortical Structural atlas 
(http://www.cma.mgh.harvard.edu/) as provided in the FSL software [184]. Each VOl is obtained by the 
intersection of the atlas-based binary masks with the significant activation clusters in common for both groups, 
as determined by the SPM8 second level random effects univariate analysis. For each subject, the time series 
from the first-level univariate analysis for that subject is extracted from all voxels within the VOl, and the 
principal eigenvariate [141] of this time series, which is adjusted for effects of interest, is input into DCM for 
model estimation [141]. The same VOis are used for all subjects. 
Striatal gating DCM: Based on Landau et al. [64] showing that striatal DA function is related to working- 
memory capacity and activation in left inferior PFC, and Honey et al. [185] showing that working memory 
capacity is related to effective connectivity from L IFC to L PPC, we use L IFC and L PPC as the prefrontal and 
posterior regions, respectively, in the striatal gating model proposed by O'Reilly and Frank [22]. The activity in 
the dorsal striatum is hypothesized to gate the connectivity via thalamus, between the L IFC and the L PPC, 
based on the striatal gating models [22, 23] and our pilot study results (section C.2.4). 
C.4.19.2 Response Inhibition DCM analysis. ROis are: R bilateral OFC, LR pre-supplementary 
motor area (pre-SMA), R PPC, LR dorsal striatum, LR ventral striatum, and LR thalamus are the ROis, 
which are based on our pilot data (Section C.2.5); and consistent with: (a) existing striatal gating 
models [22, 23] and theoretical models on impulsivity [24-26, 186]; (b) review  articles on Go-NoGo  fMRI 
studies [27-30] and cocaine addiction Go-NoGo fMRI studies [7, 8, 31, 187, 188]; and (c) DA modulation 
effect  on response inhibition [15). Except for the striatum, our proposed ROis are different from those 
used in a published effective connectivity study [188] using Go/NoGo  task, possibly due to different 
subject samples (children were studied in [188] and we studied adults) and different fMRI design (a 
block  design was used in [188] and we used event-related design). Since these regions are based on 
our pilot data, other  regions, if they show  significant activation on fMRI using the larger  sample  size 
that is proposed in the grant, win be considered as ROis in the future. VOis and time series  extraction: 
This part  of processing is essentially same as that in the working memory DCM analysis. Striatal 
gating  DCM: Based  on the study [83] showing significant correlation between dopamine 02 receptor 
function and glucose metabolism in orbitofrontal cortex, and the study [15] showing that levodopa 
affected the magnitude of Go/NoGo task-elicited fMRI response in right parietal cortex, we use R lateral 
OFC and R PPC as the prefrontal and posterior regions, respectively, in the striatal gating  models [22, 
23]. The activity in the dorsal striatum is hypothesized to gate, via thalamus, the connectivity between 
the R lateral OFC and the R PPC, based  on the striatal gating  models [22, 23). 
C.4.19.3 Inference on DCM model structure: In DCM analysis, the number of candidate DCMs, after 
permutation and combination of possible endogenous connections and exogenous inputs, is very large. 
Therefore, the network discovery technique (NDT) [189], as implemented in the latest release of DCM, is used 
as a first step to determine the model structure for each subject. The NDT scores the relative posterior 
probability of each candidate DCM in order to evaluate efficiently the large number of possible DCMs and to 
find the optimal model for each subject. The initial full model to be reduced by NDT includes all possible bi- 
directional endogenous connections between each node (see C.4.19.1 and C.4.19.2 VOis). The contextual 
conditions of the cognitive tasks  are putative exogenous inputs and bilinear modulators at each connection 
in the initial full model. In addition, nonlinear gating by dorsal striatum on PFC-PPC is hypothesized in the initial· 
full model (see section C.4.19.1 and C.19.2). The NDT results for each subject are checked first to see 


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whether the causal (directional) relationship between the OLPFC and the striatum has been determined and 
whether the striatal gating OCM is optimal for all subjects in each group. The inference on model structure is 
further conducted using Bayesian model selection (BMS) [190] and Bayesian family level inference (BFLI) [191], 
as described in detail in Ma, et al. [138]. BMS is an established procedure for selecting an optimal OCM from 
alternative OCMs. This is realized by comparing model evidence, which simultaneously quantifies how 
accurately a model explains the data and model complexity [190]. An "optimal"  model is reflected by the 
Bayesian exceedance probability [190], which denotes the probability that a model is more likely to have 
generated the observed data than the other models considered. BFLI is an extension of BMS that can be used 
to select the optimal family from candidate families of models [138, 191]. 
C.4.19.4 Inference on DCM parameters: The inference on OCM parameters is conducted using Bayesian 
model averaging (BMA) [191]. BMA is a Bayesian approach that averages each OCM parameter across 
subjects and across models such that the contribution of each model (of each subject) for that parameter is 
weighted by each model'$ posterior probability for that subject. Thus, BMA avoids the rigid assumption of an 
identical single OCM for all subjects that is required in classical "Frequentist" analysis of OCM parameters 
[191]. The same model space, comprised of the optimal OCM for the COs group plus the optimal OCM for the 
control group, is used in the BMA analysis of each group to determine the posterior distribution of each OCM 
parameter for each subject within the group [138]. 
C.4.20 Statistical analysis: 
Specific Aim 1: To conduct an snOCM study of working memory to determine the baseline effective 
connectivity between OLPFC and striatum in treatment-seeking COs compared to non-drug using controls. 
Initial analyses will evaluate group differences on demographic and baseline variables, will use contingency 
tables with chi-square testing, ANOVA's, and examination of correlations between baseline variables and 
snOCM results. The Friston et al. [189] network discovery technique, BMS, and BFLI (C.4.19.3) will be used to 
select the optimal model based on the exceedance probability of the OCM (or family of OCMs) in which OLPFC 
causally affects ventral striatum vs. exceedance probability of the OCM (or a family of OCMs) in which the 
ventral striatum causally affects the OLPFC. The parameter estimates for the endogenous OLPFC-striatal 
connectivity will be compared between groups using BMA and also compared using analysis of covariance. 
Specific Aim 2: To determine·whether the pretreatment gating effect by dorsal striatum on IFC-PPC 
effective connectivity (reflecting low OA) predicts treatment response to OA pharmacotherapy in COs. 
Urn randomization [153], a form  of stratified randomization, will help  ensure comparability of 
treatment groups on baseline severity and duration of cocaine abuse. Pretreatment task behavioral 
performance or demographic variables on which group  differences are detected, and which  are 
correlated with  outcomes, meet the definition of confounders [192, 193]. In such  cases, analyses will 
evaluate the robustness of findings after statistically controlling for the confounder. If conclusions 
remain unchanged after covarying for the confounder, the more parsimonious model will be reported 
along  with a statement indicating its robustness to covariation for potential confounding. If inclusion of 
the covariate alters  the conclusions of the analysis, both  models will be reported. Treatment success is a 
function of two elements: (a) retention and (b) demonstrating cocaine-free status using the SRPHK1 criteria 
(see section C.3.4). The Treatment Effectiveness Score (TES) provides a metric for this composite outcome 
[194]. TES is the sum of all cocaine-negative tests (according to SRPHK1 rules). Higher TES indicates that 
participants both continue to participate in the study and are negative for cocaine. For each group, BMA 
(section C.4.19.4) is conducted across all OCMs in the model space. Group difference in the gating effect by 
the dorsal striatum is assessed using Bayesian confidence probability [195]. In addition, BMA is conducted 
across all OCMs in the model space for each subject in the medication group and in the placebo 
group. Evaluation of differential treatment response, measured by the TES, as a function of the averaged 
(across the OCM model space) gating effect, will use Poisson regression. Violations of assumptions of 
dispersion will result in the use of more appropriate approaches such as zero-inflated Poisson, negative 
binomial, or zero-inflated negative binomial models. Regression of treatment, averaged gating, and their 
interaction will permit evaluation of differential treatment response as function of gating. The critical element in 
doing so is the evaluation of the interaction term. Given that the current study is powered to detect group 
differences, it is likely underpowered to detect an interaction. Brookes, et al. [196] demonstrate that for studies 
with 80% power to detect a between-groups effect, power to detect an interaction effect of the same magnitude 
as the main effect is approximately 29%. One solution is to adopt a Bayesian subgroup analysis approach 
[197-201]. This approach utilizes the posterior distribution of the interaction parameter to estimate the 


Program Director/Principa50 Investigator (Last, First, Middle):  Ma, 
 
 
 
probability that it exceeds some number (e.g., for Poisson regression a Risk Ratio of 1.0). Specification of 
vague neutral priors will reflect uncertainty regarding parameter values, utilizing prior distributions for the lower 
order effects that are neutral and diffuse with specification -N(O, 1 x 10"6)  on the log scale (i.e., centered at the 
null hypothesis with a 95% Credible Interval of± 1960) for all coefficients, and -U(0,100) for any required 
dispersion terms. Examination of the interaction of treatment and the subgroup index forms the crux of the 
evaluation of the subgroup effect. For evaluating the interaction representing the differential subgroup effect, 
analysis will be conducted in two ways: (a) utilizing diffuse, indifferent priors (-N(O, 1 x 10-6) on the log scale for 
the interaction coefficients, and (b) using informative skeptical priors for the coefficients will evaluate sensitivity 
of statistical conclusions to specification of priors. Informative, skeptical priors will be derived via the meth.od 
first proposed by Dixon and Simon [200]. Evaluation of resulting posterior distributions will permit conclusions 
regarding the probability that subgroup/interaction  effects of varying magnitudes obtain. Examples of this use 
of the posterior distribution may be found in Green et al. [197]. Bayesian modeling will use SAS v9.2 (Proc 
Genmod and Proc Monte-Carlo Markov chain [MCMC]), and in WinBugs 1.4.3 (http://www.mrc- 
bsu.cam.ac.uklbugs). Convergence of Bayesian analyses on the posterior distributions via MCMC will be 
assessed via graphical (Trace Plot, Autocorrelation Plot) and quantitative (Geweke Diagnostics, Gelman-Rubin 
Diagnostics, and Heidelberger-Welsh Diagnostics) evidence. Evaluation of posterior distributions will permit 
statements regarding the probability that effects of varying magnitudes exist, given the data. 
While the use of urn randomization [153] will likely result in comparable groups, substantial 
heterogeneity may still exist  within conditions on baseline variables such  as severity, duration of use, 
and impulsivity. We propose subgroup analyses following the procedures described above. This will 
determine the degree  to which baseline variables moderate the interaction of treatment and snOCM. 
Specific Aim 3: To conduct an snOCM of impulsivity to test Hypotheses 1 and 2 (above). 
The statistical analysis is essentially the same as in Specific Aims 1 and 2. 
C.4.21 Power Analysis: The power analysis was conducted using StudySize 2.0 software 
(http://www.studysize.com/). The power analysis for the correlation analysis between the DCM striatal gating 
effect and the subsequent treatment outcome (Specific Aim 2) is based on the preliminary data (Section C.2.4). 
We have shown an effect size of 0.69 in our correlation analysis, but this is probably inflated due to the small 
sample size. Therefore we have conservatively used a more modest effect size of 0.60 when calculating the 
power. For an effect size d = 0.60 and 2-tailed alpha = 0.05, a sample size of 24 subjects would achieve C:l 
power of 86% for the correlation analysis. For Specific Aim 1, our pilot data (Section C.2.4) showed an effect 
size of 1.89 for the between-group comparison of the strength of the endogenous connection from DLPFC to 
ventral striatum. Based on this effect size, we have calculated that 24 subjects per group would achieve a 
power of 99.9% at a 2-tail alpha = 0.05. Previous studies by our group [35, 37] (section C.2) found significant 
fMRI activation in predicted regions with 19 CDs and 14 controls. Thus we expect significant fMRI activation in 
the current project using the same fMRI task and scanning parameters with 24 subjects per group. 
C.4.22 Limitations: OCM is based  on several assumptions, especially in its original form [75]. 
However, we will use the OCM extensions, nonlinear [76] and stochastic [77] OCM, to reduce  the 
dependence on these assumptions. OCM is a relatively new approach to study effective connectivity. 
However, OCM is being  established as a primary effective connectivity technique in the neuroimaging 
literature, as evidenced by rapidly increased citation rates [78], overall publication number [203], and 
publication number in other  patient populations [79], and high  reliability has been shown in healthy 
subjects [80]. This  project will only  study COs who are treatment-seeking individuals, which  may affect 
generalization of the findings to all COs. We believe that this  is an acceptable trade-off because the 
major  objective of this  study is to develop a novel biomarker to predict treatment response in 
treatment-seeking  COs. 
 
 
 
 
 
 
 
 
 
 
 
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Program Director/Principa51 nvestigator {Last, First, Middle): Ma, Liangsuo 
 
 
 
1.   Be between 18 and 50 years of age; 
2.   Right handed; 
3.   Have no past history of Psychiatric or non-Psychiatric medical .disorders which could affect the central 
nervous system as assessed by SCID and physical examination. 
4.  Be in acceptable health on the basis of interview, medical history, labs, and physical exam; 
5.   Be able to understand the consent form and provide written informed consent; 
6.   Have a medical history and physical examination demonstrating no clinically significant 
contraindications for study participation; 
7.   Have no metal fragments or other bodily metal (pacemaker) or significant claustrophobia that would put 
the subjects at risk for MRI scanning. 
Cocaine-dependent subjects with medical conditions contraindicated to, or taking medications known to 
have significant drug interactions with the study medication(s) will be excluded from the ongoing clinical trial, 
and therefore will not participate this study. In addition, a subject will be excluded from this study if any of the 
following conditions apply: 
1.  Current DSM-IV diagnosis of any psychoactive substance dependence other than cocaine, marijuana, 
or nicotine (for cocaine-dependent subjects only); 
2.  Current DSM-IV diagnosis of any psychoactive substance dependence (for non-drug using control 
subjects only); 
3.   Have a DSM-IV axis I psychiatric disorder (other than psychoactive substance abuse or dependence as 
described in criteria 1  for cocaine dependent subjects) or neurological disease or disorder;; 
4.   Have cognitive impairment due to non-substance related factors (e.g., history of stroke, transient 
ischemic attacks, mental retardation, epilepsy, head injury); 
5.   Significant current suicidal or homicidal ideation; 
6.   Take CNS active concomitant medications; 
7.   Pregnant or nursing (for female subjects only); 
8.   Inability to read, write, or speak English; 
9.   Unwillingness to sign a written informed consent form; 
10. Have evidence of clinically significant heart disease or hypertension. 
 
1.b Sources of Materials 
 
Urine, blood, and breath samples will be collected from all recruited subjects. We will also collect 
demographic information as well as other information needed for screenings, e.g., family history information, 
and drug use information, from all recruited subjects. In addition, MRI data will be collected from all recruited 
subjects. All data collected from the subjects will remain confidential. Data recorded by paper will be kept in a 
locked cabinet in the Principal Investigator's laboratory. Electronic data will be stored in computer, COs or hard 
disks with password protection. Only the Principal Investigate and trained research assistants will have access. 
 
1.c. Potential Risks 
 
Phlebotomy: There is the potential risk of bruising at the site of the blood draw for the HIV test and for the 
blood chemistries and complete blood count. 
Contraindication to MRI scanner: Individuals who have pacemakers, metal or electromechanical 
implants or metallic foreign bodies can be injured if they undergo an MRI scan. 
Claustrophobia: Some individuals become anxious due to claustrophobia during MRI scans. 
Privacy  of individuals: There is potential risk to privacy of individuals. 
Pregnant women: There is unknown risk of MRI in pregnancy. 
 
2 Adequacy of Protection Against Risks 
 
2.a Recruitment and Informed Consent 
 
 
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Participants for the Ongoing Clinical Trial (R01 DA030787; Dr. Joy M. Schmitz,.EJj _will be self-referred in 
response to various study advertisements.  Individuals who call for information will be given a brief description 
of the study. Those interested will then be asked to answer questions about their current substance use.  A 
trained research assistant will conduct this telephone-screening interview. Interviewee responses will be 
evaluated by the Principal Investigator of the ongoing clinical trial (Dr .Joy Schmitz) , who is Co-Investigator on 
the current proposal, and a decision regarding study eligibility will be made. Eligible subjects will be scheduled 
for an in-person visit at the CNRA. The first appointment will begin with the presentation of the informed 
consent form for the ongoing clinical trial. The consent form will detail the requirements of study participation 
(e.g., number of visits, type of data collected, time commitment, etc.). Information about the potential risks and 
how these risks are minimized will be informed. Other information on the consent form will include a full 
description of study requirements, reimbursement, risks, benefits, alternatives, and the role of the local 
Institutional Review Board (IRS).  All questions will be answered before written consent is requested. 
A copy of the signed consent form for the ongoing clinical study is made and given to the subject, another 
copy is held in the Principal Investigator's (Dr. Joy M. Schmitz) records, and the original signed consent is kept 
in a separate, locked file accessible to the IRS upon request. 
Subjects for the current DCM MRI proposal will be recruited from participants in the ongoing clinical trial 
(above). Subjects will be told by the P.I. (Dr. Liangsuo Ma, or trained research assistant) that the purpose of 
the current proposed study is to evaluate the mechanism of pharmacotherapeutic response for cocaine 
addiction treatment using MRI. Information about the potential risks and how these risks are minimized will be 
given. Other information on the consent form will include a full description of study requirements, 
reimbursement, risks, benefits, alternatives, and the role of the local Institutional Review Board (IRS).  All 
questions will be answered before written consent is requested. 
A copy of the signed form is made and given to the subject, another copy is held in the Principal 
Investigator's (Dr. Liangsuo Ma) records, and the original signed consent is kept in a separate, locked file 
accessible to the IRS upon request. 
 
2.b Protection Against Risk from the proposed DCM MRI study 
 
Phlebotomy: This risk will be minimized by having blood drawn by a trained phlebotomist or nurse. 
Contraindication to MRI scanner: The individuals under this risk will be carefully screened out prior to 
participation in the MRI experiment by careful history and physical examination. 
Claustrophobia: This risk will be minimized by having all subjects undergo a "mock" MRI scan using a 
simulator prior to the actual scan. Subjects with significant claustrophobia during the mock scan will be 
excluded from the actual MRI study. Subjects will be monitored and can communicate with the scanner 
operators at all times during scanning. The scanning will be stopped immediately at the subject's request or in 
case the subject appears uncomfortable. 
Privacy  of individuals: All subject related information will be coded and de-identified. Any information 
about the subjects gained during the course of the study will remain confidential. Data recorded by paper will 
be kept in a locked file cabinet in the principal investigator's laboratory. Electronic media (i.e., computer-stored 
data, and data stored on COs) will be password protected via encrypted code key known only to the study 
personnel. 
Pregnant women: Pregnancy will be carefully screened for female subjects. Pregnant women will be 
excluded from the study. 
 
3 Potential Benefits of the Proposed Research to the Subject  and Others 
All subjects will undergo a free thorough psychiatric and non-psychiatric medical screening during the 
study. Any clinically relevant abnormalities discovered during this screening will be discussed with subjects and 
they will be referred for treatment for these abnormalities. All subjects who undergo an MRI scan will have a 
structural scan read by a Radiologist. Any clinically significant abnormalities on the MRI scans will be 
discussed with the subjects by the study physician and will be referred for treatment. The optimal 
measurement of the mechanism of pharmacotherapeutic response has significant implications in cocaine 
addiction treatment, and would be a great benefit to society. 
 
4 Importance of the Knowledge to Be Gained 


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This study aims to optimally measure the mechanism  of pharmacotherapeutic  response in dopamine- 
related neuronal systems underlying impaired cognitive function in cocaine dependence. The completion of the 
proposed study is expected to improve our understanding of the dopaminergic mechanism and 
pharmacotherapeutic  mechanism in cocaine dependence, and ultimately allow development of a clinically 
useful biomarker that could be predictive of more time-consuming and costly clinical trial results. Compared to 
above-stated risks that are minimal, the knowledge to be gained outweighs the risks. 
 
5 Data and Safety Monitoring Plan 
 
The Principal Investigator will be responsible  of monitoring the safety and efficacy of this study, executing 
the data and safety monitoring (DSM) plan, and comply with the reporting requirements. The P.l. will provide a 
summary of the DSM report to NIDA on an annual basis as part of the progress  report. The DSM report will 
include the participants' sociodemographic  characteristics, expected versus actual recruitment rates, treatment 
retention rates, any quality  assurance  or regulatory  issues  that occurred  during  the past year,  summary  of 
adverse events (AEs) and serious (SAEs), and any actions or changes with respect to the protocol. The DSM 
report to NIDA will also include, if applicable, the results of any efficacy data analysis conducted. 
All data will be obtained for the specific purposes of this research. We will obtain information about subjects 
from structured interview evaluations, physical examinations,  self-report measures,  and collateral. informants. 
The  biological  specimens  obtained from  all  subjects  will  include  urine,  blood,  and  breath  samples.  Brain 
imaging studies will also be performed on all subjects. Collected data will be identified with the study's ID of the 
participant.  The  codes that link the name  of the participant  and the study ID will be kept confidential  in a 
secured cabinet. Data entered by trained research staff will be verified via second entry by independent  staff. 
The study statistician  will analyze the data, using SAS software. Primary outcome variables  will be derived 
through vital signs, self-reported behavioral effects, and brain imaging measures. 
The following procedures will be taken to safeguard against AEs: (1) careful initial screening to determine 
eligibility  based  on  inclusion/exclusion  criteria;  (2)  thorough  physical  evaluation  prior  to exposure  to study 
drugs consisting of physical examination, standard laboratory tests, electrocardiogram,  toxicology screen, 
pregnancy test and vital signs; (3) careful screening for presence of a pacemaker, metal or electromechanical 
implants or metallic foreign bodies. Specific criteria will be used to exclude potential  subjects for whom MR 
imaging is contraindicated. 
All  AEs  occurring  during  the  course  of  the  study  will. be  collected,  documented,  and  reported  to  the 
Principal Investigator. The occurrence of AEs will be assessed at baseline, on each day of testing, and on visit 
during the chronic administration phase of the study. 
AE's deemed to be serious, as defined by the FDA, will be systematically evaluated on each day of study 
participation. Any SAE, whether or not related to study, will be reported to the IRB and NIDA. A full written 
report to all institutions will follow as soon as possible but in no more than three days. The written report will be 
in the format required by the local IRB and will contain information regarding the date of the SAE, description of 
the SAE, severity rating (Grade 1 to 4), assessment of cause, whether the SAE indicates an increased risk for 
current or future subjects, and whether changes to the informed consent form are necessary. 
In cases of early termination from the study due to SAE, the participant will have appropriate follow-up 
medical monitoring. Monitoring will continue until the problem has resolved or stabilized with no further change 
expected, is clearly unrelated to study medication, or results in death. Outcome of SAEs will be periodically 
reported to NIDA. A summary of the SAEs that occurred during the previous year will be included in the annual · 
progress report to NIDA. 
 
 
Inclusion of Women  and Minorities 
 
Men and women of all ethnic backgrounds will be recruited to participate. It is anticipated that the subject 
demographic profile will closely _mirror the larger population from which they are recruited. In the previous 
cocaine clinical trials at our center, the percentage of females and males is approximately 30% and 70% 
respectively. We will continue recruitment efforts to achieve a 50 50 balance and thus permit meaningful 
analyses by sex across groups. 


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In the ongoing clinical trial, efforts are made to have the outreach coordinator present educational material 
and referral information at women's clinics in Houston and the surrounding communities. Advertising in papers 
especially directed to women, such as SingleFile (a publication with 53% female readership), and the Health 
and Beauty section of the Greensheet has helped in the past to enhance recruitment of women with cocaine 
dependence. 
The ethnic representation has been 55% African-American, 44% Caucasian, 15% Hispanic, and <1% 
Asian. In the absence of data on the prevalence of cocaine dependence disorder in the Houston area, we 
cannot define ethnic base rates. The medical center environment is not known to be uniquely avoided by any 
particular ethnic group and patients view it as favorable and neutral. Nevertheless, we are prepared to 
implement recruitment procedures to ensure a more diverse patient population:These procedures include: 1) 
Targeted advertising in newspapers which serve minority communities (e.g., Chance, a Hispanic publication). 
2) Distribution of flyers and notices in neighborhoods known to have a high minority population. 3) Engaging in 
outreach activities on an ongoing basis, e.g., contacting church and community leaders in the Hispanic 
communities to provide educational material about cocaine dependence and its consequences; providing 
contact information to aid inreferrals to clinic at the CNRA. 
 
 
Inclusion of Children 
 
Children between the age of 18 and 21 years will be included in this study. This age range was chosen as the 
vast majority of subjects who meet criteria for cocaine dependence are over 18 years of age. In addition, brain 
function in children under 18 is significantly different from that of adults. The inclusion of younger subjects  than 
this age level will increase variance in brain function. 

SAP_001.pdf: 
Principal Investigator/Program Director (Last, First, Middle):   Ma, Liangsuo 
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Title: Striatal Effective Connectivity to Predict Treatment Response in Cocaine Misuse 
 
PI: Ma Liangsuo, Ph.D 
 
NCT #: NCT02080819 
 
Unique Protocol ID: HM20000079 
 
Document approval date: October 1, 2013 
 
Document Type: SAP 
 
 


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Data analysis plan 
 
Initial analyses will evaluate group differences on demographic and baseline variables, will use contingency 
tables  with  chi-square  testing,  ANOVA’s,  and  examination  of correlations  between  baseline  variables  and 
snDCM  results.  The  Friston  et  al.  network  discovery  technique,  Bayesian  model  selection  (BMS),  and 
Bayesian  family level inference  (BFLI) will be used to select the optimal model based on the exceedance 
probability of the DCM (or family of DCMs) in which DLPFC causally affects ventral striatum vs. exceedance 
probability of the DCM (or a family of DCMs) in which the ventral striatum causally affects the DLPFC. The 
parameter estimates for the endogenous DLPFC-striatal connectivity will be compared between groups using 
Bayesian model averaging (BMA) and also compared using analysis of covariance. 
 
Urn randomization, a form of stratified randomization, will help ensure comparability of treatment groups on 
baseline severity and duration of cocaine abuse. Pretreatment task behavioral performance or demographic 
variables on which group differences are detected, and which are correlated with outcomes, meet the definition 
of confounders. In such cases, analyses will evaluate the robustness of findings after statistically controlling for 
the confounder. If conclusions remain unchanged after covarying for the confounder, the more parsimonious 
model will be reported along with a statement indicating its robustness to covariation for potential confounding. 
If  inclusion of the covariate alters the conclusions  of the analysis, both models will be reported. Treatment 
success  is  a  function  of  two  elements:  (a)  retention  and  (b)  demonstrating  cocaine-free  status  using  the 
SRPHK1 criteria. The Treatment Effectiveness Score (TES) provides a metric for this composite outcome. TES 
is the sum of all cocaine-negative  tests (according to SRPHK1 rules). Higher TES indicates that participants 
both continue to participate  in the study and are negative for cocaine. For each group, BMA is conducted 
across all DCMs in the model space. Group difference in the gating effect by the dorsal striatum is assessed 
using Bayesian confidence probability. In addition, BMA is conducted across all DCMs in the model space for 
each subject in the medication group and in the placebo group. Evaluation of differential treatment response, 
measured by the TES, as a function of the averaged (across the DCM model space) gating effect, will use 
Poisson  regression.  Violations  of  assumptions  of  dispersion  will  result  in  the  use  of  more  appropriate 
approaches such as zero-inflated Poisson, negative binomial, or zero-inflated negative binomial models. 
Regression of treatment, averaged gating, and their interaction will permit evaluation of differential treatment 
response as function of gating. The critical element in doing so is the evaluation of the interaction term. Given 
that the current study is powered to detect group differences, it is likely underpowered to detect an interaction. 
Brookes, et al. demonstrate that for studies with 80% power to detect a between-groups effect, power to detect 
an interaction effect of the same magnitude as the main effect is approximately 29%. One solution is to adopt a 
Bayesian  subgroup  analysis  approach.  This  approach  utilizes  the  posterior  distribution  of  the  interaction 
parameter to estimate the probability that it exceeds some number (e.g., for Poisson regression a Risk Ratio of 
1.0). Specification of vague neutral priors will reflect uncertainty regarding parameter values, utilizing prior 
distributions for the lower order effects that are neutral and diffuse with specification ~N(0, 1 x 10-6) on the log 
scale (i.e., centered at the null hypothesis  with a 95% Credible Interval of ± 1960) for all coefficients,  and 
~U(0,100)  for any required dispersion  terms. Examination  of the interaction  of treatment and the subgroup 
index forms the crux of the evaluation of the subgroup effect. For evaluating the interaction representing the 
differential subgroup effect, analysis will be conducted in two ways: (a) utilizing diffuse, indifferent priors (~N(0, 
1 x 10-6) on the log scale for the interaction  coefficients,  and (b) using informative  skeptical  priors for the 
coefficients  will evaluate sensitivity  of statistical conclusions  to specification  of priors. Informative,  skeptical 
priors will be derived via the method first proposed by Dixon and Simon. Evaluation of resulting posterior 
distributions will permit conclusions regarding the probability that subgroup/interaction effects of varying 
magnitudes  obtain. Examples  of this use of the posterior distribution  may be found in Green et al. (2009). 
Bayesian modeling will use SAS v9.2 (Proc Genmod and Proc Monte-Carlo Markov chain [MCMC]), and in 
WinBugs 1.4.3 (http://www.mrc-bsu.cam.ac.uk/bugs). Convergence of Bayesian analyses on the posterior 
distributions  via  MCMC  will  be  assessed  via  graphical  (Trace  Plot,  Autocorrelation  Plot)  and  quantitative 
(Geweke Diagnostics, Gelman-Rubin Diagnostics, and Heidelberger-Welsh Diagnostics) evidence. Evaluation 
of posterior  distributions  will permit statements  regarding  the probability  that effects of varying magnitudes 
exist, given the data. 


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While the use of urn randomization will likely result in comparable groups, substantial heterogeneity may still 
exist within conditions on baseline variables such as severity, duration of use, and impulsivity. We propose 
subgroup analyses following the procedures described above. This will determine the degree to which baseline 
variables moderate the interaction of treatment and snDCM.