Latent Constructs: Negative-Positive Valence Domains in Anxiety and Depression
Conditions: Anxiety, Depression
Sponsor: University of California, San Diego

Specific Aims 
The diagnostic system that is currently used for mental disorders is based on verbal report, observable 
behavior, and clinical phenomena that have been aggregated based on statistical approaches to provide 
reliable categories1.  However, the connection between psychiatric disorders and their underlying neurobiology 
has been difficult to establish.  The NIMH Research Domain Criteria (RDoC) framework is a heuristic approach 
to integrate neuroscience with psychopathology2.  Thus, RDoC is the attempt to develop constructs that (a) 
connect across units of analysis, (b) are applicable across established diagnostic criteria, and (c) are a point of 
departure to provide measurable constructs to gain a deeper understanding of psychiatric conditions3. 
We focus on the positive and negative valence system domains proposed by the RDoC4,5, using self-report, 
behavior, physiology, and neural circuit unit of analysis measures, apply them to a clinical population of 
individuals with anxiety or depression recruited from primary care clinics across two sites and apply latent 
variable analysis to derive latent constructs of positive and negative valence systems functioning that cut 
across units of analyses.  Our preliminary studies provide initial evidence for the relative independence of 
measures designed to assess each domain.  Building on these studies we will adopt a three stage approach: 
first, principal component analyses will be used for variable reduction within a task; second, confirmatory factor 
analyses will be used to determine whether the model of positive and negative valence systems fit with the 
measures obtained from multiple units of analyses; and third, latent variable analysis will be used to relate 
measures across different units of analysis.  The specific aims of the proposal are: 
(1) To recruit, assess, and analyze self-report, behavioral, physiological, and neural circuit unit of 
analysis data in an exploratory sample of n=100 treatment seeking anxious and/or depressed 
individuals presenting to primary care clinics at UCSD and UCLA. 
a. Hypothesis 1: Measures of reward sensitivity, attentional bias towards positive valenced stimuli, approach 
behavior in an approach/avoidance task, propensity to display a happy facial expression, increased 
respiratory sinus arrhythmia (vagal tone), and activation in the ventral striatum during a reward processing 
task will load together on a latent construct (“Positive Valence System”). 
b. Hypothesis 2: Measures of high harm avoidance, attentional bias towards negatively valenced stimuli, 
avoidance behavior in a approach/avoidance task, propensity to display a negative facial expression (e.g., 
sadness, fear), increased heart rate and skin conductance while viewing negative stimuli, and amygdala 
activation during a fear conditioning task will load together on a latent construct (“Negative Valence 
System”). 
c. Hypothesis 3: Latent variable scores of individuals with anxiety or depression will span a two-dimensional 
space according to these two latent constructs and will show significant variability across DSM IV (or 
DSM-5) diagnostic categories. 
(2) To obtain a confirmatory sample of n=100 subjects at both sites using the same measurement 
approaches to determine the reproducibility and robustness of the latent constructs obtained from 
the exploratory sample. 
a. Hypothesis 1: Latent constructs of positive and negative valence systems show good reproducibility in a 
confirmatory factor analysis. 
b. Hypothesis 2: Variable reduction can be used to obtain reliable projection onto the latent constructs using 
latent variable analyses. 
c. Hypothesis 3: Individuals with clinically severe and impairing anxiety or depression will fall into 
heterogeneous clusters of dysfunctions within the positive and negative valence systems. 
(3) To obtain a reliability sample of n=50 individuals at UCSD and UCLA that will be re-tested within a 
month to determine the reliability of the latent constructs. 
a. Hypothesis 1: Latent constructs of positive and negative valence systems have good test-retest reliability. 
b. Hypothesis 2: Latent constructs of positive and negative valence systems have better test-retest reliability 
than measures from individual units of analyses. 
This will be the first time neural systems measures of positive valence and negative valence systems are 
investigated within the same broadly defined sample of anxious or depressed patients.  Thus, this study has 
the potential to substantially improve our understanding of how the positive and negative valence systems 
underlie clinically severe and impairing anxiety and depression in terms of inter-relationships across units of 
analyses.  Upon completion, we will have robust and reliable dimensional measures that quantify the positive 
and negative valence systems.  The latent constructs will be the deliverables of this proposal.  We will create a 
web-based repository to make these latent constructs, i.e. the measures and the derived factors, available to 
other researchers.  These latent constructs will serve as the platform for subsequent work focused on 
treatment, which can be developed to remediate the dysfunction in the specified dimension(s). 

Research Strategy 
A. Significance 
The Research Domain Criteria (RDoC) framework is a heuristic to integrate neuroscience approaches 
with psychopathology2 that will guide the development of a dimensional classification of mental disorders 
based on genetics and neuroscience3, however, the lack of reliable, robust latent constructs derived from 
experimental measures is a critical barrier to organizing mental disorders along dimensions.  The RDoC 
initiative highlights two important goals that need to be accomplished to overcome this barrier.  First, one 
needs to determine the relationship between different units of analyses, that is, between self-report, behavior, 
physiology, and neural circuits and clinically relevant psychopathology.  Second, one needs to transcend 
traditional diagnostic groups to adequately capture the variation of domains in clinical populations that can be 
mapped across units of analyses.  The current application aims to address these needs. 
We propose to use a clinical population of primary care patients with identified problems of anxiety 
and/or depression for this investigation to maximize generalizability and external validity.  Anxiety and 
depression are the most common mental disorders, and primary care remains the de facto mental health 
service system in the US6, 7.  By studying patients in this setting, we gain access to the most generalizable 
sample of persons whose anxiety and/or depressive symptoms are troubling enough that they seek care, 
without the filtering and biasing that would be seen in general psychiatric or other even more specialized 
clinical settings (e.g., anxiety or mood disorder clinic).  Although we start with well-established measures that 
are heuristically aimed at quantifying positive and negative valence systems, we expect that the precise 
mapping onto latent constructs that form the axes for positive and negative valence domains will be more 
complex and possibly surprising than expected.  For example, variables of attentional bias for negative 
emotion, degree of avoidance behavior in an approach/avoidance task, propensity to display negatively 
valenced facial expressions, and degree of amygdala activation during a fear conditioning procedure are 
hypothesized to relate to the negative valence domain.  However, the precise relationship and the common 
variance shared among these units of analysis as well as the most sensitive variables contributing to the 
domain are fundamentally unknown at this point in time.  Once identified, however, this understanding will 
change the way we conceptualize the positive and negative valence domains, measure them, incorporate them 
into computational and neural process models, and eventually incorporate them into treatment models. 
Once the proposed aims are completed, we will have a robust and reliable dimensional set of variables, 
obtained from two clinical sites, that quantify the positive and negative valence domains based on a latent 
variable approach.  These variables will subsequently be used to determine whether (a) they predict response 
to pharmacological or behavioral treatment; (b) they can be differentially responsive to therapies that are 
specifically aimed at modifying the positive or negative valence systems, and (c) they can be used in 
subsequent computational and/or neural process models of anxiety and depression to gain a more 
fundamental understanding of the pathology. 
Overview: Positive and Negative Valence Systems 
Affect, or the tendency to experience a given emotion, often is subdivided into two domains8. 
Positive affect is the tendency to experience positive emotions, such as happiness, excitement, elation, 
and enthusiasm.  Negative affect is the tendency to experience negative emotions, such as anger, 
resentment, sadness, anxiety, and fear.  Humans exhibit a range of emotions that span across positive 
and negative affect domains with some individuals experiencing more of one type of affect than 
another.  Positive affect and negative affect systems represent dimensions of psychopathology 
identified by the RDoC work groups4,5.  High negative affect is common to anxiety and depression9-11 
and comorbid anxiety and depression is associated with more negative affect than each disorder 
alone12.  Low positive affect is relatively specific to depression, with some evidence for social anxiety as 
well9,13.  In addition, psychophysiological and neurobiological data indicate that the negative affect 
system is closely tied to threat sensitivity whereas the positive affect system is closely tied to reward 
sensitivity.  
Positive Valence System: A central construct representing the positive valence system is approach 
motivation, which can be defined as processes that regulate the direction and maintenance of approach 
behavior, albeit moderated by pre-existing tendencies, learning, memory, stimulus characteristics, and 
deprivation states.  The construct of reward seeking and reward sensitivity is one component of approach 
motivation.  Reward sensitivity refers to sensitivity to the anticipation and receipt of positive stimuli.  The neural 
mechanisms of reward sensitivity involve the ventral striatum (VS) and orbitofrontal cortex (OFC).  The 
structures are involved in the processing of primary rewards, such as pleasant tastes14, smells15 or sights16, as 
well as secondary (monetary) rewards16-18.  The VS plays an important role in the anticipation of reward19,20 as 
well as the receipt of reward17,21. The VS is part of a larger fronto-striatal circuit subserving reward-related 
processing that also includes the orbitofrontal cortex (OFC), a subregion of the prefrontal cortex22.  An 

important functional coupling exists between the VS and OFC23.  Reward-processing also involves other neural 
regions, including the amygdala24-26, dorsal ACC27  and the hippocampus28.  
Relationship between reward sensitivity and the positive valence system:  There is evidence to show that 
individuals with deficits in positive affect (i.e., individuals with depressive disorders) show deficits in reward 
processing, at both behavioral29 and neural levels30.  At the behavioral level, individuals with major depression 
are less responsive to reward-relevant stimuli than are non-depressed individuals and deficits in reward 
responding are associated with deficits in positive affect or the ability to experience pleasure29,31.  At the neural 
level, depression is associated with attenuated activation in fronto-striatal circuits, namely the VS and caudate, 
during reward processing30.  Anhedonia32,33 (or, the inability to experience pleasure), and reward-related 
processing34 have been considered critical factors in the development of depression.  Reward sensitivity in 
anxiety disorders has been less well-studied. Similar to depression, evidence of attenuated striatal activation 
during reward processing has been found in individuals diagnosed with posttraumatic stress disorder 
(PTSD)35,36, particularly in relation to anhedonic features of PTSD (e.g., emotional numbing).  Other studies, 
however, find evidence of heightened striatal activation during reward anticipation in some anxiety disorders37. 
This heterogeneity underscores the potential value of moving towards a dimensional understanding of reward 
sensitivity and positive valence system functioning in anxiety and depression. 
Negative Valence System: Responses to acute threat (fear) and potential harm (anxiety) were considered by 
the RDoC workshop committee to be central constructs within the negative valence system.  One approach to 
measuring response to threat is via fear conditioning, which involves excitatory learning of CS-US 
associations38,39.  Research on fear learning uniquely adapts to translational neuroscience contexts because 
we understand with great precision the relevant neural processes in many species, including humans.  The 
brain regions that have most consistently been associated with fear conditioning are the amygdala40-44 (AMYG) 
and insular cortex45.  In healthy adults, increased activity in the AMYG and insula is typically observed in 
response to the CS during conditioning.  Response to loss was cited by the RDoC committee as another 
critical component process of the negative valence system, and may be particularly related to depression. 
Reward paradigms that include loss or punishment trials (e.g., losing money for incorrect responses46-48) can 
be used to measure behavioral and neural responses to loss anticipation and outcome.  Research in healthy 
adults suggests that the ventral and dorsal striatum (caudate) are associated with anticipation and receipt of 
loss or punishment using these paradigms46,47. 
Relationship between fear conditioning, loss, and the negative valence system: Individuals with 
disorders characterized by elevated negative affect (i.e., individuals with anxiety disorders), exhibit behavioral 
and neural perturbations during fear conditioning.  In terms of behavioral data, individuals with high trait anxiety 
or with anxiety disorders show elevated excitatory learning (during fear conditioning)49,50.  In terms of neural 
data, individuals with anxiety disorders show elevated AMYG activation during fear conditioning: this has been 
observed in individuals with posttraumatic stress disorder51,52, spider phobia and social anxiety disorder45. 
Although no studies have evaluated fear conditioning as a function of depression, other evidence shows that 
depression as a disorder is associated with dysregulation in the same neural regions that are central to fear 
conditioning.  For example, a number of studies implicate AMYG hyperactivity in major depression during 
emotion-related tasks such as when viewing fearful faces53,54, and in individuals with the short allele of the 
5HTTLPR serotonin transporter polymorphism55, which is associated with depression vulnerability56 and 
symptom severity57.  Studies using behavioral paradigms to measure response to loss find that individuals 
diagnosed with major depression differ from healthy controls in their neural responses to loss in the striatum58 
and anterior cingulate cortex48.  Some evidence suggests that anhedonic features of depression may be 
particularly associated with neural activation in the striatum in response to loss58. 
Positive and Negative Valence Systems: Physiological, Behavioral and Self-Report Units of Analysis 
To more completely characterize positive and negative valence system constructs, the RDoC initiative 
underscores the need to (1) identify measures at multiple units of analysis (e.g., self-report, behavior, 
physiology) that reliably capture the variation of a given domain, and (2) to establish the relationship between 
different units of analyses, with an emphasis upon linking underlying brain function (e.g., neural circuits) to 
behavior.  Thus, we will use well-established physiological, behavioral, and self-report measures as the units of 
analysis, and with which our group has prior experience (see Approach section below), to (1) examine latent 
constructs of positive and negative valence domains and (2) examine the relationships between those units of 
analysis and neural indices of reward sensitivity and fear conditioning.  Our choice of measures was guided by 
empirical data for specific measures heuristically aimed at quantifying each domain, and evidence suggesting 
that those measures relate to neural circuits governing positive and negative valence system functioning.  For 
example, attentional biases for emotional information are considered a hallmark of anxiety and depression59,60. 
Attentional bias for threat-relevant stimuli is reliably associated with negative affective states (e.g., anxiety60) 
and has been linked to greater activation in the AMYG during emotion processing61.  In contrast, attentional 
bias for positive stimuli is associated with positive affective states62 and neural activity reflecting approach-

oriented motivation63.  Moreover, diminished attentional processing of positive information is associated with 
depression64, and some anxiety disorders (e.g., social anxiety65). In the sections below, we present preliminary 
data using measures of observable behavior (approach/avoidance conflict, facial expression coding, attentional 
bias), physiology (e.g., heart rate, skin conductance), and self-report (reward sensitivity, harm avoidance) to 
demonstrate that (1) we can reliably measure the constructs of interest and (2) empirically derive two 
dimensions that reflect positive and negative valence domains using data reduction methods.  
Summary  
The RDOC has identified positive and negative valence systems as constructs that can be used to 
relate altered neural systems functioning to anxiety and depression.  Reward sensitivity, measured via reward-
related decision-making paradigms, is viewed as a marker of the positive valence system.  Threat sensitivity, 
measured via fear conditioning paradigms, is viewed as a marker of the negative valence system.  The neural 
underpinnings of reward-related decision-making and fear conditioning are well understood.  However, no 
studies to date have assessed the neural processes of both reward sensitivity and threat sensitivity as units of 
analysis among a larger set of measures (including cognitive, behavioral and physiological), within samples of 
anxious or depressed patients that is sufficiently large to generate a robust and reliable dimensional set of 
variables that quantify the positive and negative valence domains.  That is the goal of this proposal.  We 
hypothesize that measures of reward sensitivity, attentional bias towards positively valenced stimuli, approach 
behavior in an approach/avoidance task, propensity to display a happy facial expression while viewing positive 
stimuli, increased respiratory sinus arrhythmia (RSA; i.e., vagal tone) and activation in the ventral striatum 
during a reward processing task will load together on one latent construct (i.e., positive valence).  Also, we 
hypothesize that measures of harm avoidance, attentional bias towards negatively valenced stimuli, avoidance 
behavior in a approach/avoidance task, propensity to display negative facial expressions (e.g., sadness, fear), 
increased heart rate, skin conductance, and EMG while viewing negative stimuli, and amygdala activation 
during a fear conditioning task will load together on a second latent construct (i.e., negative valence).  Further, 
we hypothesize that latent factor scores of individuals with anxiety or depression will span this two-dimensional 
space.  We aim to replicate these latent factor scores in a confirmatory sample, and test the stability of these 
scores in a test-retest assessment in a smaller subset of the larger sample.  Our goal is to create a robust set 
of variables for measuring positive and negative valence that will become publically available for future 
research.  
B. Innovation 
The proposed project is innovative in the following ways:  
(a) Extant research on the neural underpinnings of anxiety and depression has been largely 
constrained by comparing DSM-IV diagnosed subjects to healthy control subjects.  Here, we propose a large 
sample of individuals seeking treatment for anxiety or depression, whose symptoms are such that they are 
impairing and driving treatment seeking, but may or may not meet criteria for a well-defined DSM-IV disorder. 
This approach is required to advance our understanding of the dimensions of psychopathology that are either 
shared across anxiety and depression (i.e., negative valence) or are more specific (although not exclusive) to 
depression (i.e., positive valence).  Furthermore, whereas studies of the neural correlates of positive and 
negative valence systems are beginning to emerge, we are unaware of any studies that have included 
paradigms that are directly designed to assess both valence systems within the same sample.  This will be the 
first time neural indices of positive valence and negative valence are investigated within the same broadly 
defined sample of anxious or depressed patients.  Extant studies of neural processes in emotional disorders 
are limited to relatively small sample sizes.  By combining resources across two sites, we will recruit a large 
enough sample to conduct more comprehensive analyses than ever before achieved. 
(b) Although researchers have begun to evaluate relationships between specific neural processes, such 
as fear conditioning, and other “units of analysis” such as attentional bias to threat, no study to date attempts 
the comprehensive approach proposed herein for measuring multiple units of analysis for each valence 
system.  That is, we propose to measure self-report, behavior, physiology, and neural circuits, and to assess 
their loading on constructs of positive and negative valence systems, and their relative contributions to each 
construct.  We are employing an empirically derived approach, involving latent variable analyses, for 
developing a “battery of measures” for positive valence and negative valence, thus avoiding the tautology of 
relying upon a predetermined measure of each valence system against which to validate additional measures.  
(c) The majority of the experimental approaches we propose for deriving variables (i.e., units of 
analysis) are well-established. However, we additionally include the automated facial action coding system 
(i.e., Computer Expression Recognition Toolbox; CERT) as a novel approach to measuring behavioral indices 
of affect on a millisecond scale in real time. Aside from its novelty and empirical grounding in a well-established 
literature of facial expression coding and emotion66, a very attractive feature of CERT is its potential clinical 
utility.  This measure can be easily incorporated into real world clinical settings, and provide the clinician with 
an objective index of positive or negative valence, that in turn eventually may guide treatment choice.  

(d) We are collaborating with a statistician who has developed sophisticated analytical approaches to 
arrive at individual level outcome predictions (see letter of support from Dr. Wes Thompson).  Specifically, we 
propose a three step approach consisting of (1) data reduction based on principal component analysis, (2) 
hypothesis-driven examination of the positive and negative valence domains using confirmatory factor 
analysis, and (3) latent variable analysis to examine the relationship between different units of analysis and 
underlying brain function to arrive at a comprehensive, robust, and reliable set of measures for quantifying 
negative and positive valence systems in clinically relevant populations. 
(e) We aim to have robust and reliable dimensional measures that quantify the positive and negative 
valence systems.  We will create a web-based repository to make these latent constructs (i.e. the measures 
and the derived factors), available to other researchers.  This will promote the validation of the positive and 
negative valence systems in other clinical populations, and thus are truly generic domains as envisioned by the 
RDoC initiative.  Finally, these latent constructs will serve as the platform for a subsequent proposal focused 
on treatment application; in other words, to what extent do treatments change measures of each construct, and 
to what extent do scores on each construct predict or moderate treatment outcomes? 
C. Approach 
Sample 
We considered various populations based on our prior studies.  We have conducted extensive studies in 
college populations67-69, non-treatment seeking70, and treatment seeking71 psychiatric populations presenting to 
an Anxiety Disorders Clinic.  We opted to identify individuals with anxiety or depression in a primary care 
setting, which we have used successfully to recruit a significant number of subjects72, to maximize 
generalizability and external validity.  Medication status was another important consideration.  On the one 
hand, including medicated individuals with the possibility of using statistical techniques to account for their 
effect would provide a larger subject pool and could be conceived as being more representative.  On the other 
hand, given our prior studies showing that antidepressants affect brain activation73-75, a medication-free group 
would provide a more scientifically compelling approach to delineate the underlying latent constructs reflecting 
the positive and negative valence systems.  Moreover, the former would require a much larger number of 
subjects and could not rule out a contribution of medication we chose the latter approach as detailed below. 
Study clinics and subjects. We will recruit two waves of n=100 individuals each from two clinics (see power 
analysis): UCSD Primary Care Clinic and the UCLA Family Health Center (n=50 per wave per clinic). The 
UCLA clinic has 10 faculty physicians and 36 residents, the UCSD clinic is staffed by 40 faculty physicians.  
Consistent with our prior work72, we expect to recruit a sample where ethnic minorities (approximately 40% in 
the UCLA clinic and 47% at the UCSD clinic) and low-income individuals (approximately 25% at both clinics) 
are well represented. Assuming equal non-refusal rates across sociodemographic groups, we project a final 
sample that is 50% female; 86% Caucasian, 6% African-American, 20% percent Hispanic, 6% Asian and 2% 
other; 25% low-income; and between 1-5% uninsured.  
Facilitated Referral. Using methods we successfully used in the CALM study72, subjects will be enrolled in this 
study through an approach we call “facilitated referral.”  First, we will allow providers to refer potential subjects 
directly to our intake evaluation.  Second, providers may screen patients in participating clinics using screening 
instruments described below, and if eligible, then offer the potential subject referral to our intake evaluation.  
This “facilitated screening and referral” is intended to capture subjects who might have anxiety or depression 
that are not known to the provider.  To optimize recruitment and minimize attrition we have added a liaison at 
each clinic site who will aid in recruitment and enrollment 
Subject inclusion and exclusion criteria. To enhance generalizability, and importantly, to enable selection of 
subjects in keeping with the RDoC intent, this study will feature broad inclusion criteria and narrow exclusion 
criteria.  This approach was selected to maximize the clinical diversity of the study population.  General 
Inclusion criteria are: (1) Referred for treatment of anxiety and/or depressive symptoms; (2) Score on the 
PHQ-9 is 10 or higher76 and/or score on the OASIS is 8 or higher77; (3) Between the ages of 18-55; (4) Able to 
provide informed, written consent.  Notably, in our prior CALM study, inclusion based on the OASIS plus a 
DSM-IV anxiety disorder yielded a highly comorbid anxious and depressed sample approximately 76% had 
comorbid depression).  Thus, we fully expect that inclusion based on elevated anxiety (OASIS) or depression 
(PHQ-9) scores will yield a sample with the full range of anxiety and depression symptoms and associated 
functional impairment.  Exclusion criteria include: The first 7 criteria below closely match procedures we 
successfully employed in our prior primary care CALM study (1) No telephone or easy access to telephone; (2) 
Alcohol or marijuana dependence (but not abuse) in the past year; (3) All other drug abuse or dependence in 
the past year. Subjects with alcohol or marijuana abuse in the past 12 months will be permitted in the study, 
but also will be given referrals for additional substance abuse treatment which may occur concurrently; (4) 
Bipolar I or Psychotic disorders; (5) Moderate to severe traumatic brain injury with evidence of neurological 
deficits, neurological disorders, or severe or unstable medical conditions that might be compromised by 
participation in the study (to be determined by primary care provider); (6) Active suicidal ideation; (7) Inability to 

speak English; (8) Current use of a medication that could affect brain functioning, e.g., anxiolytics, 
antipsychotics, antidepressants, or mood stabilizers; (9) MRI exclusions: cardiac pacemaker, metal fragments 
in eyes/skin/body (shrapnel), aortic/aneurysm clips, prosthesis, by-pass surgery/coronary artery clips, hearing 
aid, heart valve replacement, shunt (ventricular or spinal), electrodes, metal plates/pins/screws/wires, or 
neuro/bio-stimulators (TENS unit), persons who have ever been a metal worker/welder, history of eye 
surgery/eyes washed out because of metal, vision problems uncorrectable with lenses, inability to lie still on 
one’s back for 60 minutes; prior neurosurgery; tattoos with metal dyes, unwillingness to remove body piercings; 
(10) non-correctable vision or hearing problems, or color blindness, as some cognitive tests require intact 
sensory functioning.  Patients who are excluded from the study will be referred outside of the study for 
additional care. 
Further diagnostic assessment. Although, as noted above, entry into the study is not based on meeting 
diagnostic criteria for a particular mood or anxiety disorder, it will be important to characterize how our findings 
map onto DSM (currently DSM-IV, but we will adapt to using DSM-5 criteria, which will become available for 
the start date of this study).  Accordingly, patients will complete a diagnostic interview with study personnel, 
using an abbreviated version of the Mini International Neuropsychiatric Interview (MINI Version 5.0.0 or its 
DSM-5 successor, when available)78.  The MINI was chosen over other diagnostic interviews (e.g., SCID or 
CIDI) because of its relative brevity, good inter-rater reliability, and suitability for use by an interviewer with 
limited training, and because we used it in our previous primary care treatment study, CALM72, and so we have 
extensive experience in its training, quality control, use and interpretation.  We will include sections on PD, 
SAD, Posttraumatic Stress Disorder, GAD, OCD, and Major Depression and several modules to provide further 
clinical information or to determine ineligibility (Suicidality, Manic/Hypomanic Episode, Alcohol 
Abuse/Dependence, Drug Abuse/Dependence, and Psychotic Disorders).  
Procedures: Patients presenting to the UCSD and UCLA clinics and completed complete the screening 
measures (OASIS and PHQ-9) are eligible subjects and will be asked to participate via our facilitated referral 
system. Individuals who accept an invitation to take part in the study will undergo a MINI diagnostic interview.  
Subjects included in the study will return for a behavioral testing session and a neuroimaging testing session. 
During the behavioral session participants will complete the self-report assessments, modified probe detection 
task, approach/avoidance conflict task, and emotional reactivity assessment in which they will view blocks of 
emotional images while state affect and physiology will be assessed throughout the procedures. The fMRI 
session will be scheduled within 2 days of the behavioral session and will comprise the following sequence of 
events: Prior to scanning, each subject will complete (1) informed consent, (2), self-report emotion 
assessments, (3) review of fMRI procedures and MRI safety screen, (4) review of instructions to complete the 
fear conditioning and reward tasks, and completion of practice tasks to ensure comprehension of instructions. 
The scanning protocol will consist of (1) reward processing task, (2) high-resolution structural scan, (3) fear 
conditioning task.  At subset of participants (n=25 from each site) will be invited to return approximately one-
month later to complete the same battery of assessments to establish test-retest reliability of the latent 
constructs (see Specific Aim 3). 
Self-report Measures  
(1) Positive and Negative Affective Schedule - State/Trait (PANAS79): The PANAS is a widely used 
measure comprising 20-items assessing activated forms of PA and NA using 5-point scales (1 = very 
slightly/not at all, 5 = extremely).  To assess trait PA and NA, participants will be asked to respond according to 
how they have felt "during the past week". State PA and NA will be asked by asking participants to rate how 
they feel “right now (that is, at the present moment)” throughout the laboratory assessments following blocks of 
viewing positive, neutral, and negative IAPs images (see Emotional Reactivity section below).  The PANAS 
has high internal consistency and temporal stability (trait version).  Correlational data support its convergent 
and discriminant validity.  Confirmatory factor analyses support the construct validity of the PANAS80. (2) Mood 
and Anxiety Symptom Questionnaire - Short Form (MASQ9): The MASQ is a 62-item measure of mood and 
anxiety symptoms developed to evaluate predictions of the tripartite model of anxiety and depression. The 
MASQ has four subscales: (1) General Distress Anxious Symptoms (GDA: 11 items), (2) General Distress 
Depressive Symptoms (GDD: 12 items), (3) Anxious Arousal (AA: 17 items), and (4) Anhedonic Depression 
(AD: 22 items). Respondents indicate the extent to which they experienced each symptom during the past 
week from 1=not at all to 5=extremely. The MASQ subscales have been found to have adequate convergent 
and discriminant validity as well as good internal consistency81,82 in student, adult volunteer, and clinical 
samples. (3) Behavioral Inhibition and Activation Scales (BIS/BAS83): The behavioral inhibition and 
activation scales (BIS/BAS) include 20-items assessing dispositional BIS and BAS sensitivities (i.e. avoidance 
and approach motives), which are hypothesized to reflect the negative and positive valence systems, 
respectively. Items are rated on four-point scales (1 = strongly disagree; 4 = strongly agree).  The BAS has 
three subscales (Drive, Reward Responsiveness, and Fun Seeking); however, factor analyses support a single 
higher-order factor.  The BIS/BAS has good test-retest reliability.  Correlational data support the relative 
orthogonality and convergent, discriminant, and predictive validity of the subscales83. 

Behavioral Measures 
(1) Approach/Avoidance Conflict Task84: Participants will complete a computer-based approach-avoidance 
conflict (ACC) task. For each trial of the AAC, participants are shown a runway with pictures on each side to 
represent two potential outcomes.  Each potential outcome includes an affective stimulus and a certain level of 
reward points.  The valence of the affective stimulus is represented via a sun indicating a positive affective 
stimulus or a cloud indicating a negative affective stimulus.  Level of reward points is represented by the red in 
the rectangles (more red = more points).  The subject is to move an avatar on the runway (by pressing keys on 
a keyboard) to indicate their relative preference for each potential outcome.  The location of the avatar at the 
end of the decision phase corresponds to the probability of each of the two outcomes occurring.  If the subject 
moves the avatar to the middle of the runway, there is a 50% chance of each outcome; if he moves all the way 
to one side, there is a 90% chance of the nearest outcome and a 10% chance of the further outcome; and so 
on.  Subjects therefore control the likelihood of the two outcomes but are unable to determine one or the other 
outcome for certain. The initial starting position of the avatar (at each of the nine possible positions, ranging 
from −4 to +4) is counterbalanced to enable characterization regarding the influence of effort on behavior.  The 
affective stimuli used in the AAC paradigm include image and sound combinations (International Affective 
Picture System (IAPS85); International Affective Digitized Sounds (IADS86) and other freely available audio 
files).  The “reward” includes 0, 2, 4, or 6 points presented along with a trumpet sound.  There are three types 
of trials in the AAC task: (1) ‘Avoidance-only’, in which 0 points are offered for both outcomes.  For these 
conditions, there is no explicit motivation to approach the negative affective outcome. (2) 'Approach-only', in 
which reward outcomes can be acquired in the absence of punishment, and hence there is no explicit 
motivation to avoid the reward outcome.  (3) Three levels of ‘Conflict’ in which 2, 4, or 6 points are offered for 
the outcome involving a negative affective stimulus while 0 points are offered for the outcome involving a 
positive affective stimulus. These conditions are designed to produce approach-avoidance conflict, as the 
same behavior is associated both with reward and punishment.  The main dependent measure will be the 
response latency from initial button press to end decision point, averaged across trials.  
Preliminary evidence for Approach/Avoidance Conflict as a Behavioral Marker of Positive and 
Negative Valence Domains: Background: Research in animals and humans suggests that 
approach/avoidance conflict is a basic phenotype that reflects the relative strength of reward-related vs. 
aversive behavioral tendencies.  Our group has developed a paradigm in which approach and avoidance 
drives can be measured behaviorally by manipulating the relative degree of reward and punishment associated 
with a given decision84.  We examined whether approach vs. avoidance behavioral tendencies, as measured 
through an individual's response latency to approach vs. avoid rewarding or aversive outcomes, reflects 
distinct positive and negative dimensions.  Methods: A cross-section of young adults recruited from 
UCSD/SDSU (N=184) completed a computerized approach-avoidance conﬂict (AAC) paradigm (described 
above). Response latency data were used to compute separate approach and avoidance bias scores for each 
level of conflict, yielding a total of 3 approach bias scores and 3 avoidance bias scores.  Results: A principal 
component analysis with Promax rotation indicated that the data were best described by a 2-factor solution. 
The first dimension accounted for 56.1% of the variance and comprised the three avoidance conflict indices 
(negative valence domain).  The second factor accounted for 25.0% of the variance and comprised the three 
approach conflict indices (positive valence domain).  The two components were moderately related, r(184) = 
.38, but sufficiently distinct dimensions.  Conclusion: Approach/avoidance conflict paradigms provide relatively 
independent indices of behavioral tendencies to approach rewarding outcomes vs. avoiding aversive 
outcomes, which may reflect positive vs. negative valence domains, respectively. 
(2) Facial Expressions: Advances in computer vision and machine learning over the past 15 years have led to 
the emergence of technology for automatic analysis of affective behavior87.  During this time, the Machine 
Perception Laboratory at UCSD (MPLab) has focused on development of systems for automatic analysis of 
facial behavior, including audio-visual speech recognition88-90 and recognition of facial expressions89-93.  The 
output of the face detector is scaled to 90x90 and fed directly to the facial expression analysis system.  First 
the face image is passed through a bank of Gabor filters at 8 orientations and 9 scales (2-32 pixels/cycle at 0.5 
octave steps).  The filterbank representations are then channeled to a classifier to code the image in terms of a 
set of expression dimensions.  Research at the MPLab has demonstrated that performing feature selection on 
the Gabor filters prior to classification enhances both speed and accuracy.  This approach combines feature 
selection based on Adaboost with feature integration using support vector machine.  Automatic Facial 
Expression Analysis: A video camera will record each participant during viewing IAPs pictures (see Emotional 
Reactivity section below) in order to permit coding of facial expressions. Automatic facial expression analysis 
will be conducted by the Computer Expression Recognition Toolbox (CERT94), software developed and 
validated by our collaborators at the Machine Perception Laboratory at UCSD (MPLab).  CERT analysis 
corresponds to the well-validated Facial Action Coding System (FACS95,96), a comprehensive method to 
objectively code facial expressions.  CERT automatically codes the intensity of 26 component facial 
movements referred to as action units (AUs).  

Preliminary evidence for Automatic Coding of Facial Expressions during Emotion Elicitation of 
Distinct Behavioral Indices of Positive and Negative Valence Domains: Background: Facial expressions 
are well-established as behavioral indices that reflect a person's emotions and internal states.  The Facial 
Action Coding System (FACS66) is a reliable, valid, and widely used method to objectively code facial 
expressions.  Previous studies suggest that particular component facial movements referred to as action units 
(AUs) reflect positive vs. negative emotional states97.  For example, AU 12 (movement of the zygomatic major 
muscle) is associated with positive affective states whereas AU 15 (“lip corner depressors”; sadness) and AUs 
1+4 (anxiety) are associated with negative affective states.  To provide proof of concept, we used the 
Computer Expression Recognition Toolbox 
(CERT89,94) to automatically code facial expressions 
of participants completing impromptu videotaped 
speeches98.  Methods: Videotaped speeches were 
obtained from N=59 participants from98 recruited for 
a study of public speaking fears. We used CERT to 
automatically code the intensity of 26 AUs from 
FACs. Drawing on prior research66,99, we examined 
the mean intensity rating for the following AUs 
averaged across the duration of the speech task: 
AUs 12 and 6 (positive emotion); AU 15 (sadness); 
AU 1+4 (anxiety).  Results: A principal component 
analysis (PCA) with Promax rotation indicated that 
the data were best described by a 2-factor solution.  
The first dimension accounted for 41.6% of the 
variance and comprised AU 12 and 6 (positive 
valence domain).  The second factor accounted for 
28.1% of the variance and comprised AU 15 
(sadness) and AU 1+4 (anxiety) (negative valence 
domain). The two components were orthogonal, r(59) = .004. See Figure for component plot (main figure) and 
scatterplot (inset) illustrating individual variability and independence of dimensions. Conclusion: Automatic 
coding of facial expressions may provide a validated and efficient method to measure independent dimensions 
of positive and negative behavioral domains.  
(3) Modified Probe Detection Task: Attentional bias for positive and negative information will be measured 
using a version of the modified probe detection task (MPDT100).  Emotional stimuli will comprise standardized 
male and female disgust, sad, happy, and neutral faces101.  For emotional trials, each face will be paired with a 
neutral face from the same person (e.g., disgust–neutral). For baseline trials, a neutral face will be paired with 
a neutral face from the same person.  Neutral-neutral trials serve as a baseline to compute facilitation and 
disengagement bias scores102,103.  Participants will be presented with 128 trials—comprising all combinations 
of Actor (two male, two female), Face Pairs (happy-neutral, disgust-neutral, sad-neutral, neutral-happy, 
neutral-disgust, neutral-sad, neutral(h)-neutral(h), neutral(d)-neutral(d), neutral(s)-neutral(s)), Probe Type (E or 
F), Probe Location (Top or Bottom): 4 (Actor) x 8 (Face Pairs) x 2 (Probe Type) × 2 (Probe Location).  
Evidence for the Independence of Attentional Bias Indices of Positive and Negative Valence 
Domains: Background: Attentional biases for emotional information are considered a hallmark of anxiety and 
depression59,60.  Whereas selective processing of positive stimuli is associated with positive affective states62, 
preferential attentional allocation for negative emotional information is associated with negative affective states 
(anxiety and depression).  Evidence also suggests that aberrant attentional processing of positive information 
is associated with depression64, and some anxiety disorders (e.g., social anxiety65).  We adopted a dimensional 
approach to examine whether attentional biases for positive vs. negative stimuli reflect at least partially distinct 
dimensions.  Method: A cross-section of healthy young adults (N = 194) recruited as part of prior studies65 
completed a well-established modified probe detection task (MPDT100) designed to measure attentional bias for 
positive and negative information (described above).  Trials comprised negative-neutral, positive-neutral, or 
neutral-neutral face pairs.  Neutral-neutral trials served as a baseline to compute facilitation and 
disengagement bias scores, yielding four attentional bias indices: (1) facilitation bias for positive; (2) facilitation 
bias for negative; (3) disengagement bias for positive; (4) disengagement bias for negative.  Results: A 
principal component analysis with Promax rotation indicated that the data were best described by a 2-factor 
solution.  The first dimension accounted for 41.9% of the variance and comprised the two negative bias indices 
(negative valence domain).  The second factor accounted for 36.0% of the variance and comprised the two 
positive bias indices (positive valence domain).  The two components were orthogonal, r(194) = -.07. 
Conclusion: Measures of attentional bias for positive and negative stimuli reflect distinct dimensions and 
therefore may serve as a cognitive marker of positive vs. negative valence domains, respectively. 

Psychophysiological Measures: Research suggests that physiological responses to positive and negative 
imagery are moderated by trait affect.  For example, Oliveira and colleagues104 reported that individuals high in 
trait positive affect display attenuated autonomic reactions to distressing images when these images where 
preceded by a safety signal.  Hofmann105 reported that trait affect moderated the relationship between state 
affect and EEG responses during an imagery induction. In addition, individuals high in trait anger, enjoyment 
and surprise display inhibited startle responses to positive images106.  Thus, given the well-established link 
between imagery and physiology107 we will use the validated International Affective Picture System (IAPS85) to 
measure physiological responses to affective images. 
(1) Emotional Reactivity: a series of 20 positive, 20 neutral and 20 negative slides (from the IAPS85) each 
presented for 5 sec in blocks of 10 with a 1 sec inter-trial interval within blocks and 120 sec interval between 
blocks.  There will be two orders of trials to the block sequence.  Order of slides will be fixed at first 
assessment and then counterbalanced as subsequent assessments. Images will be presented on a computer 
screen and interspersed with a fixation cross to which subjects are asked to attend.  Heart rate (HR), 
respiratory sinus arrhythmia (RSA), skin conductance (SCR), and frontal electromyogram (EMG) will be 
recorded continuously using Biopac instrumentation (Lehigh, Pennsylvania).  Data acquisition and storage will 
be facilitated by an IBM Pentium II, Keithley-Metrabyte A-D board, and Labview software.  Raw data are 
continuously monitored via an oscilloscope.  Data are sampled at a rate of 20Hz, and averaged over 30 
second intervals corresponding to experimental periods.  Heart Rate: Heart rate is recorded using standard 
limb electrocardiogram leads connected to a BIOPAC bioamplifier (S75-01), that inputs to a tachometer (S77-
26) to plot beats per minute.  Vagal tone will be measured via respiratory sinus arrhythmia (RSA), a non-
invasive measure of cardiac vagal control characterized by increases in heart rate with inspiration and 
decreases in heart rate with expiration108.  RSA will be computed from the R-wave to R-wave interbeat interval 
series in the frequency range of spontaneous breathing (.12 Hz - .40 Hz)109.  Skin conductance: Skin 
conductance responses (SCRs) will be recorded with two Ag/AgCl electrodes filled with isotonic electrolyte gel. 
The electrodes will be attached to the thenar and hypothenar eminences of the left hand.  Throughout the 
entire experiment a constant voltage of 0.5 V will be applied across the electrodes that are connected to a 
BIOPAC SCR 100 C amplifier with a gain of 2 mS/V. SCRs will be recorded continually on AcqKnowledge373 
at a sampling rate of 1000 Hz during all study phases.  Startle Reflex Electromyogram: Electromyogram 
(EMG) responses will be measured from the orbicularis oculi using two electrodes (Ag/AgCl, 11 mm outer 
diameter, 4 mm inner diameter contact surface) filled with electrode gel and fitted with adhesive collars.  One 
electrode will be placed directly underneath the left eye, and another one approximately 1 cm to the left below 
the outside corner of the left eye. EMG activity will be amplified with a Biopac EMG amplifier and sampled 
(1000 Hz) by a Biopac MP150 workstation, filtered online with a passband of 28–500 Hz, rectified, and then 
smoothed (five-sample boxcar filter).  Responses will be recorded on AcqKnowledge373 at a sampling rate of 
1000 Hz. 
Preliminary Evidence for Distinct Physiological Correlates of Positive and Negative Valence 
Systems: Background: Positive and negative affect are well-established in their association with anxiety and 
depression.  However, it is less clear how positive and negative affect relate to psychophysiological measures 
in clinical samples of anxiety or depression.  As a first step toward addressing this issue, we examined 
psychophysiological responses obtained from a mixed anxiety disorder sample exposed to stress-provoking 
and relaxation-inducing experimental conditions.  Methods: Participants (N = 74) were a treatment-seeking 
sample from UCLA with a primary diagnosis of an anxiety disorder.  During a pre-treatment laboratory 
assessment, participants were evaluated using the Positive and Negative Affect Scale (PANAS) and 
psychophysiological measures (i.e., heart rate (HR) and heart rate variability (HRV)).  PANAS was measured 
at the beginning of baseline and end of a hyperventilation task and meditative relaxation task, and 
psychophysiology was measured throughout the tasks.  Results: During the 15-minute meditative relaxation 
task, PA was associated with, a) high-frequency heart rate variability (HF-HRV) throughout all 15 minutes of 
the task and, b) root mean square of the successive differences (RMSSD) during the last five minutes.  NA 
was associated with HR during the last five minutes of relaxation.  Furthermore, baseline NA was negatively 
correlated with HF-HRV and positively correlated with RMSSD throughout all 15 minutes of relaxation and 
positively correlated with low-frequency/high-frequency heart rate variability (LF/HF-HRV) at the first and last 
five minutes.  Conclusions: This study demonstrates that we can measure physiological responses under 
different experimental conditions, and that positive and negative affect systems may be associated with 
differential markers of physiological reactivity.  In the current proposal, we will extend this approach by 
measuring physiological responses during exposure to positive, neutral, and negative affective images. 
Neuroimaging 
(1) Reward Processing Task: We will employ a slow event-related fMRI card-guessing paradigm46,47,110 for 
three reasons: First, it is well validated and widely used for reward-related brain function.  Second, it 
specifically examines neural processes associated with basic reward-processing which most closely addresses 

our interest in reward sensitivity.  Third, it allows separate examination of reward-related brain function during 
anticipation and receipt of reward and loss.  Our group has already employed this task.  Trials will be 
presented in a pseudorandom order with predetermined outcomes.  During each 20-sec trial, participants have 
4-secs to guess, via button press, whether the value of a visually presented card with a possible value of 1-9 is 
higher or lower than 5.  After a choice is made, the trial type is presented visually for 6-secs indicating whether 
the trial is a reward-anticipation (upward arrow) or loss-anticipation (downward arrow) type.  In Reward-
Anticipation trials, participants can win money if their guess is correct with no-change in earnings if their guess 
is incorrect.  In Loss-Anticipation trials, participants lose money if their guess is incorrect with no-change in 
earnings if their guess is correct.  The anticipation-period is immediately followed by the outcome-period, 
where participants are presented with the “actual” numerical value of the card (500ms) and receive outcome 
feedback (additional 500ms): a green upward arrow for win (Reward-Outcome), a red downward arrow for loss 
(Loss-Outcome), or a yellow circle for no-change feedback (No-Change).  A crosshair is then presented for 9-
sec (inter-stimulus-interval, ISI).  The outcome-period is defined as a 7-sec period, from when participants are 
presented with the actual numerical value of the card, and the baseline-period comprises the last three secs of 
the ISI.  24 trials will be presented in 1 run with a balanced number of trial types within runs.  Participants will 
be told they will receive $1 for each win, lose 50 cents for each loss, and obtain no earnings change for no-
change-outcomes.  Outcome probabilities will, in fact, be fixed such that participants receive the same 
earnings, although participants are led to believe their performance would 
determine net monetary gain.  The task is programmed in E-prime 2.1 (Psychology 
Software Tools, Inc., Pittsburgh, PA) using a Windows-based operating system.  
Stimuli will be presented in the MR scanner using a screen at the foot of the 
scanner bed via back-projection from a head-coil-mounted mirror or high-resolution 
magnet-compatible goggles (Resonance Technology, Inc).  Button press 
responses will be collected using an MR-compatible button box and recorded in E-
prime. 
Preliminary evidence for ventral striatal activation during reward 
processing as part of the positive valence system: The UCLA site completed 
an fMRI pilot study of reward-related neural circuitry in healthy participants (n=7), 
using a task by Bar-Haim111, wherein participants pressed one of two buttons to 
obtain a monetary reward.  In guaranteed-reward trials, participants were told which button to press and got a 
reward for doing so; in non-guaranteed-reward trials, participants had to guess which button led to the reward 
(which was randomly determined).  Neutral control trials had participants passively view geometric shapes.  
Two participants were scanned at another site (NWU) and five were scanned at UCLA, and all data were 
analyzed at UCLA.  Initial analyses using a small-volume correction on a VS ROI showed significant activation 
in the striatum during anticipation of guaranteed rewards (-9, 9, -3, t=3.78, 20 voxels, see Figure 2), relative to 
neutral trials.  
(2) Fear Conditioning Task: We will use a slow event-related fMRI differential Pavlovian fear-
conditioning task, as in several prior fMRI studies of fear learning in healthy and anxious samples.  All phases 
of conditioning (i.e., Habituation, Conditioning, Extinction, and Extinction Recall) will be completed during fMRI 
scanning.  The US will consist of a shock applied to the fingers.  The CS will consist of 3 different neutral facial 
expressions as human faces lead to stronger conditioning than geometric figures.  One CS will be paired with 
the US during acquisition and subsequently extinguished (CS+E); one will be paired with the US during 
acquisition but never extinguished (CS+U); and one will never be paired with the US (CS-).  The specific 
neutral facial expressions assigned to CS+E, CS+U, and CS- will be counterbalanced across participants. 
Preliminary Evidence: The UCLA site completed a pilot study of an fMRI 
extinction recall paradigm in healthy Ps (n=10), aged 18-21.  Participants (Ps) 
first completed habituation, fear conditioning, and extinction in the laboratory 
using two different neutral facial expressions as the CS+ and CS- and an 
uncomfortable scream as the US.  Approximately two weeks later, Ps 
completed fMRI scanning while they were presented with both CSs again, in 
the form of morphed images that combined the CS+ and CS- faces in different 
percentages.  Five Ps were scanned at another site (Northwestern University 
(NU) and five were scanned at UCLA.  Data from NU were transferred to UCLA. 
All data were analyzed at UCLA and combined to examine group effects.  Using 
parametric modulation analyses in which the stimulus-morphing range for each 
trial was entered as a regressor at the trial level, Ps exhibited significantly 
greater activity in subACC (3, 30, -12, t=3.20) in response to the CS+ vs. CS- 
Figure 4. subACC activation of 
combined UCLA/NU Ps during 
extinction recall of CS+ vs CS-     
(3, 30, -12, t=3.20, p<.05). 

(see Figure 1).  Also, this subACC activation was negatively correlated with activity in the AMYG (-27, 0, -21, 
r=-.85, p<.05). 
fMRI Data Processing 
fMRI Image Acquisition: Neuroimaging data will be acquired using a Siemens Tim Trio 3 Tesla MRI 
scanners at the UCLA Staglin IMHRO Center for Cognitive Neuroscience and a GE Signa 3 Tesla MRI 
scanner at the Keck Center for Functional Neuroimaging at UCSD. Task parameters will be identical at both 
sites.  There will be 3 functional runs (Habituation, Conditioning and Reward)in one session. Functional runs 
will utilize a gradient echo EPI sequence covering 39 axial slices (3.1mm thick; TR=2000ms; TE=25ms; 
FOV=200x200mm; Matrix=64x64; Flip Angle=90°).  Structural 3D axial MPRAGE images will be acquired at 
both sessions to assist with image registration and normalization (1mm thick; TR=2200ms; TE=3.29ms; 
FOV=256x256; Matrix=256x256; Flip Angle=9°; 192 slices).  Within fear learning, Habituation scan will last 
4min12sec, and Conditioning will last 11min12sec.  The Reward Processing Task will last 13min.  To control 
random fluctuations in MRI field homogeneity and other factors (e.g., humidity and temperature) across the two 
scanning sessions, we will scan a standard phantom prior to each session and normalize data to the phantom 
for each P as necessary.  
Cross-site imaging consistency: To maximize reliability of data collection across sites and the 
transfer of data from UCLA to UCSD: (1) Initial phantom scans for site qualification will be used; (2) UCLA and 
UCSD scans will be transferred to the neuroimaging data server at UCSD within 48 hours of acquisition and 
kept in a separate ‘quarantine’ area until put through a standard quality control pipeline to examine for artifacts, 
movement, etc.  Scans that pass this quality assurance will be placed into the main database.  UCLA data will 
be transferred to the UCSD server via a File Transfer Protocol (FTP) between the server at UCSD’s Keck 
Center for Functional MRI (CFMRI), where UCSD data are collected, and the FUNC server at UCLA, (3) 
quality checks of data will be conducted and quality assurance scans conducted, and (4) analysis for site 
effects will be incorporated into the testing of primary study hypotheses.  A variety of metrics have been used 
to assess inter-scanner reliability, such as volume of activation or BOLD signal change, and the automated 
quality assurance routine, which gives one dispersion measure per volume (averaged across voxels) 
expressed as percent signal change relative to the mean of the time-series.  This measure will be incorporated 
in first-level analyses as a covariate of no interest to account for differences in data quality.  Intraclass 
correlation coefficients (ICC) commonly based on a measure of effect size will be computed from this quality 
measure to estimate within and between site reliability.  The data preprocessing and modeling pathway will 
also utilize field maps to correct for individual field in homogeneities of each scanner and several regressors of 
no interest will be included to account for reliability in between site variance (using realignment parameters, 
and the quality measure as regressors). 
fMRI Data Analysis. Image analysis pathway: The basic structural and functional image processing 
will be done with the Analysis of Functional Neuroimages (AFNI) software package112.  A multivariate regressor 
approach detailed below will be used to relate changes in EPI intensity to differences in task characteristics. 
EPI images will be co-registered using a 3D-coregistration algorithm that has been developed to minimize the 
amount of image translation and rotation relative to all other images. Six motion parameters will be obtained 
across the time series for each subject.  Based on the distribution of these average motion parameters, 
subjects will be excluded if the average in any one of these parameters exceeds 2 standard deviations from 
the mean or if movement exceeds the size of the voxel (4 mm).  This assures that differences between groups 
are not due to differences in movements during scanning.  Motion parameters will be used as regressors to 
adjust EPI intensity changes due to motion artifacts. This has been shown to increase power in detecting task-
related activation.  All slices of the EPI scans will be temporally aligned following registration to ensure different 
relationships with the regressors are not due to the acquisition of different slices at different times during the 
repetition interval.   
Multiple regressor analyses: Regressors of interest have been described above for each task 
separately. Specifically, a 0-1 reference function will be convolved with a gamma variate function to model a 
prototypical hemodynamic response (6-8 second delay) and to account for the temporal dynamics of the 
hemodynamic response (typically 12-16 seconds)113 . The convolved time series will be normalized and used 
as a regressor of interest.  A series of regressors of interest and the motion regressors will be entered into the 
AFNI program 3dDeconvolve to determine the height of each regressor for each subject.  The key measure is 
the voxel-wise normalized relative signal change (or % signal change for short), which is obtained by dividing 
the regressor coefficient by the sum of the zero-order regressor and the mean first-order regressor.  In 
previous studies, SPATIAL smoothing using a filter with full width half maximum of approximately 1-2 voxel-
size (i.e., 3-6 mm) has resulted in intra-group variability of EPI scans that parallel those observed for averages 
of the structural MR images and has been reported in the literature to yield the highest detection power114. 
Spatially smoothed % signal change data will be transformed into Talairach coordinates based on the 
anatomical MR image, which is transformed manually in AFNI.   

Second level analyses: The voxel-wise Talairach-transformed % signal change data is the main 
dependent measure. For fear learning, contrasts include CS+E + CS+U vs. CS- to assess activity during 
Conditioning.  For reward, contrasts of interest include Reward-Anticipation vs. baseline and Reward-Outcome 
vs. baseline, indexing the expectation and receipt of reward, respectively.  Similar contrasts will be used for 
Loss-Anticipation and Loss-Outcome.  Movement parameters from the realignment stage will be entered as 
covariates of no interest in these analyses to control for participant movement. The dependent measures will 
be entered into a mixed effects model.  Mixed effects models offer a flexible framework to model the sources of 
variation and correlation115.  In particular, correlation among the data occurs when a number of observations 
are taken on the same observational unit over time.  In addition mixed effects models are particularly robust for 
the analysis of unbalanced data when compared to similar analyses within the general linear model framework.  
We will use the implementation of the linear mixed effects models in R116, which estimates the parameters of 
the mixed model using Maximum Likelihood Estimation (MLE) or Restricted Maximum Likelihood Estimation 
(RMLE) procedure.  These calculations will be done within the R computing environment using routines that 
read in AFNI data sets. 
General Statistical Approach: The goal of this proposal is to derive latent variables that adequately quantify 
the positive and negative valence domain across different units of analyses.  The analysis of the variables that 
are extracted from each unit (as shown in Figure below) will consist of three steps.  First, a principal 
component analysis will be conducted for each unit of analysis to determine the number of independent 
degrees of freedom contributing to the variance observed in each unit.  For example, we will utilize all facial 
action units, which have been recorded during viewing of IAPS pictures in a principal components analysis.  
We expect to extract at least two independent components (as shown in the pilot data sections above).  The 
action units that show the highest correlation with the components will be used for subsequent analyses.  
Second, we will conduct a confirmatory factor analysis with the variables from each unit of analysis that 
showed the highest correlation with the principal components and propose two factors – positive valence 
system and negative valence system.  We will subsequently test the statistical significance of the coefficients 
contributing to the factors.  Finally, we will conduct a latent variable analysis as detailed below to relate one 
unit directly to another unit of analysis. 
 
Power and sample size considerations: Currently there is no estimation for sample size for factor 
analysis that is based on statistical theory.  The traditional perspective is that there should be at least 10 cases 
for each item in the instrument being used117, which has been found to often yield inadequate results118.  
However, one can use several approaches to determine whether the sample size is adequate for the proposed 
study.  First, sample size depends on the ratio of the number of variables to the number of factors, and the 
proportion of the total variance in the data that is extracted by the factor analysis119. With 10 variables and 2 

proposed factors even given a high common variance a sample size of 50 subjects should suffice. 
Alternatively, one can take a multiple regression approach to approximate the variance that needs to be 
accounted for by a given variable.  For example, given a sample size of n=100, and 10 explanatory variables in 
the CFA, the minimum detectable r2 = 0.156 with an alpha of 0.05 and a beta of 1–0.8120.  In other words, our 
approach will be successful if the measurement captures at least 15% of the variance observed in the 
population.  Our prior single regressor analyses revealed correlation coefficients of 0.4 and above, which 
translates to explaining at least 16% and is therefore consistent with our proposed sample size.  Moreover, 
according to Westland118, several considerations should be given when examining sample sizes for structural 
equation models.  Among these is the notion that latent variables impose an a-priori belief system and re-
attribute the variance found among the measures across different levels of analyses.  Given our sample size of 
n=100, we will be able to examine a ratio of indicators to latent variables of > 3. In other words, we will be able 
to examine at least three latent constructs at one time. 
Linear latent variable approach: There is no uniformly agreed upon definition of latent variable 
analysis 121.  However, one approach is to define a latent variable as a variable for which there is no sample 
realization for at least some observations in a given sample.  In this context, the positive or negative valence 
domain can be considered a latent variable.  More formally, the latent variable model consists of a 
measurement part describing the responses Yi = (Yi1, . . . , Yip)′: 
and a structural part describing the latent variables ηi = (ηi1, . . . , ηil)′: where i = 1, . . . , n is the index of the 
sampling unit (e.g. individuals), j = 1, . . . , p is the index of the observed variables (measurements or within 
cluster observations) and s = 1, . . . , l is the index of the l distinct latent variables.  Fundamentally, latent 
variable approaches can be divided into posteriori latent variables, which are latent variables derived from the 
data analysis and a priori latent variables are hypothesized prior to an examination of the data.  The approach 
proposed here is to utilize a-posteriori approaches with various latent variable models for the exploratory 
sample and to transition to the a-priori approach for the confirmatory sample.  The goal of this proposal is to 
develop a heuristic framework that will enable one to combine the different units of analysis in a hypothesis-
driven manner.  Practically, this translates to establishing different structures that are superimposed on the 
measurement part and examining statistically whether certain structures 
provide better explanation of the observed variation than others. 
Implementation of the latent variable approach: We will use the 
implementation of the Linear Latent Variable model provided by the lava-
package122 as part of the R116.  This package provides a very general 
modeling framework, which includes structural equation models and 
mixed models as important special cases. This model class also allows 
for non-linear effects of covariates and non-linear parameter constraints.  
The models are specified independently of data using commands that are 
similar to standard regression modeling in R.  The lava-package also 
includes sophisticated inferential methods such as multi-group analyses, 
robust standard errors for clustered correlated data, maximum likelihood 
based inference with data missing at random and inference for indirect 
and total effects. 
Example of latent variable approach relating 
approach/avoidance task to activation of brain areas during reward 
paradigm and fear conditioning paradigm:  This latent model was 
created with the lava package in R.  We assumed that three units of 
analysis (attentional biases, facial expressions and approach/avoidance 
behavior) contribute differentially to positive and negative value systems 
(domains).  These latent factors – in turn – determine the degree of 
activation in ventral striatum (VS) orbitofrontal cortex (OFC) and amygdala (AMY) during reward processing 
and insula (IN) and amygdala (AMY) during fear conditioning (FC).  The regression coefficients can 
subsequently be used to calculate for each subject the degree of positive and negative valence system “state”, 
which provides the measure that we termed the deliverable of this project. 
Test/Re-test Reliability: We will utilize standard test/re-test approaches using the omega function 
implemented in the psych package that is available in R.  This tool calculates standard ICCs but also 
Cronbach's coefficient and Guttman’s estimates.  
Path diagram of an example model with 
correlation between residuals of the latent 
variables. Observed variables are framed with 
rectangles whereas latent variables are framed 
with ellipses. Regression associations are 
depicted as one-headed arrows and covariance 
or correlations are shown as (dashed) double-
headed arrows; color-codes: exogenous:=light 
blue, endogenous:=orange, latent:=green. 
posDomain
Attbiaspos
Appbias
HappyFace
negDomain
Attbiasneg
Avoidbias NegativeFace
VS_Rew
OFC_Rew
Amy_Rew
Ins_FC
Amy_FC

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