Pattern Separation, Brain Derived Neurotrophic Factors, and Mechanisms of Vortioxetine
Phase: PHASE4
Conditions: Depressive Disorder, Major Depression, Depression, Depression, Unipolar
Sponsor: Massachusetts General Hospital

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Pattern Separation, BDNF, and the Mechanisms of Vortioxetine 
 
PI:   
Andrew Nierenberg, MD 
Version:  
1.4 
 
Date:  
April 2, 2018 
NCT: 02969876 
 
Background and rationale 
 
The pathophysiology of Major depressive disorder (MDD) is likely to involve multiple 
brain regions including the hippocampus, nucleus accumbens, prefrontal cortex and 
hypothalamus (Krishnan and Nestler, 2010). A fundamental challenge in developing new 
treatments is identifying circuit-specific contributions to pathophysiology of MDD and 
elucidating the circuit-based mechanisms underlying actions of novel antidepressants.  The 
identification of circuit-based endophenotypes that are objectively quantified and non-
invasively imaged will permit stratification of risk for disease, prediction and monitoring of 
treatment responses using circuit-based readouts as biomarkers and promote innovation 
of new antidepressants (Kheirbek et al., 2012). 
Antidepressants influence neural circuits in multiple ways including stimulation of adult 
neurogenesis in the dentate gyrus (DG)-CA3 circuit of the hippocampus in rodents and 
humans (Malberg et al., 2000; Boldrini et al., 2009; Boldrini et al., 2012; Boldrini et al., 
2013).   Blocking adult hippocampal neurogenesis in rodents abrogates some of the 
behavioral effects of antidepressants (Santarelli et al., 2003) (Li et al., 2008; Walker et al., 
2014).  Importantly, we have recently found that in a rodent model of depression, 
enhancing adult hippocampal neurogenesis is sufficient to confer resilience against chronic 
stress (Hill et al, submitted).  Since adult hippocampal neurogenesis generates new 
neurons in the DG throughout life in humans (Spalding et al., 2013), it may represent a 
mechanism by which antidepressants exert their therapeutic effects. Understanding how 
adult neurogenesis affects DG-CA3 structure and functions is critical to defining the 
actions of antidepressants. 
 
Work by us and others has demonstrated that adult hippocampal neurogenesis 
plays a critical role in fundamental encoding functions of the DG-CA3 circuit such as 
pattern separation, a mnemonic process by which similar inputs are made more distinct 
(Clelland et al., 2009; Sahay et al., 2011b; Sahay et al., 2011a; Nakashiba et al., 2012; 
Niibori et al., 2012).  Blockade of adult hippocampal neurogenesis impairs pattern 
separation, whereas enhancement of adult hippocampal neurogenesis is sufficient to 
improve pattern separation.  Pattern separation is essential for optimal encoding of 
contextual information so as to ensure that new experiences are stored without overlapping 
or interfering with those previously encoded.   Pattern separation also serves to constrain 
pattern completion, a process by which full memories are retrieved based on partial cues. 
Although this central function of adult hippocampal neurogenesis in pattern separation 
(minimization of interference) is critical for formation of new episodic memories, it may also 
play a critical role in regulation of affect.  This is possible in several different ways.  First, 

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optimal encoding of emotional cues gates adaptive activation of fear and stress circuits.  
Decreased levels of neurogenesis may result in inappropriate encoding of fearful stimuli 
and the excessive activation of hypothalamic circuits and production of stress hormones 
such as glucocorticoids.    
 
Second, impaired pattern separation of cues in the environment may underlie the 
negative cognitive bias seen in MDD by increased pattern completion of strongly encoded 
aversive memories. Alternatively, negative response bias may arise from increased pattern 
separation of cues with a negative valence that can lead to excessive deliberation on 
aversive outcomes.  Third, impaired pattern separation may affect processing of cue-
contingency relationships and abnormal perception of salience and reward resulting in 
increased anxiety and anhedonia.   Recent studies in rodents suggest that genetic 
blockade of 
 adult hippocampal neurogenesis increases anhedonia (Snyder et al., 2011; Walker et 
al., 2014). Thus, enhancing adult hippocampal neurogenesis through augmentation 
of pattern separation may reverse abnormal activation of emotional circuits in the 
amygdala, hypothalamus, prefrontal cortex and nucleus accumbens to modulate 
expression of fear and stress reactivity, executive functions and anhedonia (Figure 
1). 
Human imaging studies have suggested that the DG-CA3 circuit’s functions in 
pattern separation are conserved between mice and men (Yassa and Stark, 2011). Using 
fMRI in combination with an incidental encoding task in which subjects are presented a 
series of objects of varying similarity, several groups have found that the DG-CA3 circuit is 
most likely to be engaged when object similarity is high (Yassa and Stark, 2011).   Thus, 
the DG may perform input-output transformations similar to that seen in rodents 
(Leutgeb et al., 2007).   Interestingly, using a behavioral proxy of the pattern separation 
imaging based task, preliminary studies suggest that pattern separation of neutral, but not 
negative items, is impaired in individuals with depressive symptoms (Dery et al., 2013; 
Shelton and Kirwan, 2013).  In contrast, discrimination of pattern separation-completion 
balance may represent a circuit-based endophenotype in MDD. However, despite a 
proposed role for pattern separation in antidepressant action (Sahay and Hen, 2007), it is 
not known whether antidepressants enhance pattern separation in individuals with MDD. 
Addressing this fundamental gap in our understanding of antidepressant action will enable 
stratification of risk for MDD, prediction and monitoring of treatment responses using 
pattern separation as a biomarker and promote development of new antidepressants. 
 
Antidepressants may influence pattern separation in DG-CA3 through direct 
stimulation of adult hippocampal neurogenesis and indirectly via regulation of growth 
factors such as brain derived neurotrophic factor or BDNF (Waterhouse et al., 2012). 
 
 
 

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Interestingly, BDNF levels are upregulated in the DG during encoding and contribute to 
pattern separation (Bekinschtein et al., 2013).   In addition to enhancing the survival of 
adult-born neurons, BDNF stimulates synaptogenesis and dendritic growth (Duman and 
Monteggia, 2006; Sen et al., 2008; Waterhouse et al., 2012).  Thus, at a structural level 
the increased integration of new neurons into the DG-CA3 circuit is accompanied 
changes in grey matter.  How changes in DG-CA3 structural properties link relate to 
change in pattern separation is poorly understood.   Furthermore, the impact of 
antidepressants on grey matter in DG-CA3 has been difficult to assess owing to a lack 
of studies employing high resolution structural imaging. 
Here, we propose to bridge these important gaps in our understanding of 
vortioxetine’s effects on cognition and mood by interrogating the impact of vortioxetine 
on pattern separation.  In addition, we will examine changes in peripheral BDNF levels.  
Together, these studies should generate unprecedented insights into mechanisms 
of action of vortioxetine and establish pattern separation and peripheral BDNF levels 
as biomarkers for antidepressant response. 
 
 
 
The central hypotheses are: 
1. MDD participants at baseline will show an imbalance in pattern separation-
completion that is reflected in behavioral tasks. Subjects’ pattern separation 
performance will statistically significantly improve with vortioxetine. 
2. MDD participants will have a statistically significant improvement in cognitive 
measures that will be mediated by changes in blood BDNF levels, and pattern 

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separation-completion tasks. 
 
Study Objectives 
We will implement a high throughout behavioral pattern separation task to probe the 
effects of vortioxetine on pattern separation in individuals with MDD.  This study will 
measure pattern separation in patients with MDD pre and post treatment. 
 
 
Specific Aims. 
Aim 1. 
To assess if vortioxetine improves pattern separation 
in patients with MDD. 
Hypothesis:  MDD participants at baseline will show an imbalance in pattern 
separation- completion that is reflected in behavioral tasks.  
Subjects’ pattern separation performance will statistically 
significantly improve with vortioxetine 
Aim 2.  
To determine how vortioxetine increases cognitive 
functioning. 
 
Hypothesis:  MDD participants will have a statistically significant 
improvement in cognitive measures that will be mediated by 
changes in blood BDNF levels, and behavioral pattern 
separation-completion 
Inclusion and Exclusion Criteria  
 
Inclusion criteria.  
1. Meets criteria for DSM-IV and V Major Depressive Disorder. 
2. QIDS-C ≥ 16 
3.  Men and women Age > 18 and < 65. 
 
Exclusion criteria. 
1. The following DSM-IV diagnoses (any current or past history, except substance 
abuse disorders):  
a. bipolar disorder 
b. schizophrenia 
c. schizoaffective disorder 
d. obsessive compulsive disorder 
e. organic mental disorder 
f. substance use disorders, including alcohol, active within the last 6 months 

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g. delusional disorder 
h. psychotic disorder not elsewhere classified 
i. patients with mood congruent or mood incongruent psychotic features 
2. Unable to follow instructions or otherwise unable to participate in the trial. 
3. Pregnant women or women of child bearing potential who are not using a 
medically accepted means of contraception (defined as oral contraceptive pill or 
implant, condom, diaphragm, spermicide, IUD, s/p tubal ligation, partner with 
vasectomy) 
4. Patients who, in the investigator’s judgment, pose a current, serious suicidal or 
homicidal risk. These individuals will be referred immediately to appropriate 
clinical treatment 
5. Serious or unstable medical illness including cardiovascular, hepatic, renal,  
respiratory, endocrine, neurologic, any history of seizure disorder or 
hematologic disease. 
6. History of multiple adverse drug reactions or allergy to the study drugs.  
7. Current use of other psychotropic drugs except benzodiazepines.  
8. Clinical or laboratory evidence of hypothyroidism. 
9. Patients who have had at least two failed trials of an antidepressant during their 
current major depressive episode. 
10. Patients who have ever had electroconvulsive therapy (ECT) 
11. Concomitant use of serotonergic agents 
12. Chronic MDD – current depressive episode lasting longer than 2 years 
 
Study Design and Schedule of Procedures 
 
Behavioral Pattern Separation Task.  
 
We will implement a rapid, high throughput behavioral task that captures the input-
output transformation function characteristic of pattern separation processes (Stark et al., 
2013).  In this task, participants are shown a series of every-day objects as described 
above and are asked to identify the objects as indoors or outdoors. Immediately following 
encoding of this task, the subjects are asked to call the objects as “old” if they have seen 
the objects before in the task, “new” or “similar”.   As previously done by Stark and 
colleagues (Stark et al., 2013), a third of the objects in the testing phase are “old”, “similar” 
and “new”.  Identifying a “similar’ object correctly conveys pattern separation, whereas, 
incorrectly identifying it as “old” conveys pattern completion. By plotting responses as a 
function of object similarity, we can generate an input-output transfer curve.    This task 
will serve the purpose of rapid assessment of putative changes in pattern separation-like 
mechanisms. 
Study Design. 
 
We propose a proof-of-concept 6-week open trial of vortioxetine for 20 patients 
with MDD. Each participant will use 10-20mg of vortioxetine daily. Because Abrupt 
discontinuation of this drug can lead to adverse side effects, the subjects will be 

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encouraged to consult a primary care physician, or a study clinician in follow-up care to 
discontinue the vortioxetine safely; if the subject chooses to discontinue the vortioxetine 
after the study drug phase, the study clinicians will follow the vortioxetine’s suggested 
titration guidelines.  
 
The rationale for an open trial is that we are particularly interested in the 
heterogeneity of effect of vortioxetine on the biological and behavioral parameters, 
especially comparing responders to nonresponders. The open trial will also facilitate 
recruitment and completion.  
 
 
Screening measures will include a diagnostic assessment and psychiatric history 
(MINI), depressive symptoms (QIDS), physical exam, vital signs, height, weight, urine 
toxicology, urine pregnancy (for females), and screening labs (11.5mL of blood will be 
drawn). Baseline assessment measures will include an assessment of adverse events, the 
QIDS, DSST, RAVLT learning and memory, pattern separation tasks and assessment of 
blood levels of BDNF (8mL of blood will be drawn). Patients will be assessed at weeks 2, 4, 
and 6 with the QIDS and will be asked if they have experienced any adverse events. 
Cognitive testing (DSST and RAVLT), and pattern separation tasks and assessment of 
blood levels of BDNF (8mL of blood will be drawn) will be done again at week 6 (see Table 
1: Study Schedule of Assessments).   
 
 
Primary endpoints. 
 
1. Pattern separation tasks 
2. Blood levels of BDNF 
3. QIDS 
4. DSST, RAVLT learning and memory 
 
Statistical Analysis Plan 
 
Target sample size is 20 on the basis of detecting a moderate effect size in all 
measures. Univariate comparisons for primary and secondary endpoints will be done with 
a paired T-test. Pearson correlation coefficients will be used to assess dyadic 
relationships. Logistic regression will be used to understand the relationship between 
differences in cognitive tests as the dependent variable with changes in BDNF, pattern 
separation tasks, QIDS, and cognitive tests.  
 
Data Management and Analysis  
 
Data Confidentiality and Security. Subjects will be identified by alphanumeric 
codes only; consistent with HIPPA procedures, names, initials and other possible 
identifiers will not be included in the electronic database. Hard copies of data that could 
identify subjects will be stored in locked file cabinets with restricted access; all subject-

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level data will be password protected. 
 
 
Back-up and Storage. An archival record of all data that has passed through the 
above- noted quality checks will be maintained on a data entry hard disk and on a 
network drive and, for archival data for the study as a whole, by the database manager. 
Redundant copies of the CD-ROM archive disks will be stored in a separate offsite cabinet 
to ensure the survival of the data in case of fire or other disaster. Full network and CD 
ROM backups will be made weekly and monthly, respectively. 
 
 
Data Access for Investigators. The database manager and, when necessary, Dr. 
Nierenberg will participate in all conference calls in which data management and analysis 
are discussed. Masking treatment assignment, the data base manager will generate 
biannual reports for the investigative team covering recruitment, subject progress, data 
flow, treatment adherence and retention, acute and follow-up outcomes and adverse 
events. 
 
 
General Considerations for Data Analysis. We will perform descriptive statistics 
prior to testing our hypotheses. These statistics will include means (or frequencies and 
proportions for categorical variables), standard deviations, skewness and kurtosis, and 
odds-ratios. Variables with distributions violating assumptions of normality will be 
transformed. If this fails to improve the normality of the distribution, distribution-free 
statistics will be employed. 
 
 
The study has baseline and post-treatment creating clustered data due to repeated 
measures. Thus, we will use established methods for such study designs to examine 
treatment differences. The two most popular approaches for longitudinal data modeling are  
the  generalized  estimating  equations  (GEE)  and  linear  mixed-effects  model 
approach (LMM).  Both approaches accommodate missing data. While LMM explicitly 
models between- and within-subject variation using random effects, GEE ignores between-
subject variability by treating subjects as independent units and basing model estimation 
and inference on the marginal distribution of the response of such units. Although LMM 
has the advantage of being able to separate between-subject variability from the total 
variability of responses, it requires parametric assumptions on observed and latent 
variables, rendering inference vulnerable to departures from normal distribution. The GEE, 
although incapable of modeling between-subject variation, can provide robust estimates. 
Although LMM will be the main analytic tool, particularly for estimating between-subject 
variability, we will also use GEE to assess the robustness of the population parameters 
such as treatment effects. All analyses will be conducted using the latest version of SAS, 
which includes procedures for LMM and GEE.  We will apply these models for both the 
intent-to-treat and completer analyses. Inference based on GEE or LMM is valid provided 
that missing data are non-informative or “missingness” does not depend on the value of 
the unobserved outcome. Although missing data in many studies in mental health research 
are non-informative, we will perform sensitivity analyses to ensure that this is the case. In 
particular, we will entertain both parametric 

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and semi-parametric models that assume some missingness mechanism and compare 
estimates obtained from such models with those from GEE and LMM. Such analyses 
will inform us whether the non-informative missingness assumption is violated and if so, to 
what extent. If the non-informative missingness assumption is deemed to be severely 
violated, we will report treatment effects using the methods that account for informative 
missingness. 
 
 
Power Analysis and Sample Size Determination. The following sample size 
calculations pertain to the expected changes related the psychophysiological indices to 
be obtained. The sample size selected for this proposal is based on detection of 
between-group significant differences at an alpha of .05 and on published analysis of 
pattern separation-completion balance (Bakker et al., 2012). 
 
 

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Table 1: Schedule of Assessments 
* Baseline visits will occur within 4 days to 10 days of the screening visits.  
 
 
 
 
Week 
Screening 
Baseline 
Week 2 
Week 4 
Week 6  
Visit  
V1 
V2 
V3 
V4 
V5 
Procedure 
 
 
 
 
 
Consent 
X 
 
 
 
 
Inclusion/Exclusion 
X 
 
 
 
 
Adverse events 
 
X 
X 
X 
X 
MINI 
X 
 
 
 
 
QIDS-C 
X 
X 
X 
X 
X 
Physical Exam 
X 
 
 
 
 
Concomitant Medications 
X 
X 
X 
X 
X 
Progress Note 
X 
X 
X 
X 
X 
Vitals 
X 
 
 
 
 
Height & weight  
X 
 
 
 
 
Urine tests: toxicology  
X 
 
 
 
 
Urine tests: pregnancy  
X 
 
 
 
 
Blood tests: Screening Labs 
X 
 
 
 
 
Blood tests: BDNF 
 
X 
 
 
X 
Cognitive tests: DSST 
 
X 
 
 
X 
Cognitive tests: RAVLT 
 
X 
 
 
X 
Pattern separation tests  
 
X 
 
 
X 

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