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com/science/article/pii/S0165032722014124
Manuscript_15b946f40331c19eb556729a5b57fbfe

Attentional Control Deficits and Suicidal Ideation Variability: An Ecological Momentary

Assessment Study in Major Depression

Sarah Herzog1,a,b, John G. Keilpa,b, Hanga Galfalvy a,b, J. John Manna,b, Barbara Stanleya,b

a Department of Psychiatry, Columbia University Irving Medical Center, Columbia University,

New York, NY, USA
b Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute,
New York, NY, USA

1
Corresponding Author: Sarah Herzog, Ph.D.; 1051 Riverside Dr., Unit 42, New York, NY 10032;
sarah.herzog@nyspi.columbia.edu; Tel: 646-774-7518

Main Text Word Count: 3,649

Abstract: 201 words
Tables/Figures: 2 tables, 1 figure

Funding Statement: This work was supported by NIMH Grant #R01MH109326 (MPI: B. Stanley, M. Oquendo) and
NIMH Grant #5P50MH090964 (PI: J. Mann).

Declaration of Interests: Drs. Stanley and Mann receive royalties from the Research Foundation for Mental Hygiene
for the commercial use of the CSSRS. All other authors declare no conflicts of interest.

© 2022 published by Elsevier. This manuscript is made available under the Elsevier user license
https://www.elsevier.com/open-access/userlicense/1.0/
 1

Background
Neuropsychological studies in suicide attempters have sought to identify cognitive
mechanisms that contribute to suicide risk. Depressed individuals with a suicide attempt history
show impairments across multiple domains of executive functioning compared to depressed non-
attempters, including poorer selective attention, inhibitory control, and working memory
(Hoffman et al., 2022; Interian et al., 2020; J. Keilp et al., 2013; J. G. Keilp et al., 2014; J. G.
Keilp et al., 2001); decreased verbal fluency (Richard-Devantoy, Berlim, & Jollant, 2014),
impaired decision-making (Jollant et al., 2005; Malloy-Diniz, Neves, Abrantes, Fuentes, &
Corrêa, 2009) and more rigid problem-solving abilities (Pollock & Williams, 1998). Several of
these impairments appear to be related to behavioral factors that increase likelihood of suicidal
behavior. For example, inhibitory control deficits in suicide attempters are hypothesized to be
associated with a propensity for impulsivity or aggression that increases risk of acting on suicidal
ideation (Dougherty et al., 2004; Swann et al., 2005). Cognitive rigidity and impaired decision-
making (Jollant et al., 2010; Patsiokas, Clum, & Luscomb, 1979) have been linked to
diminished capacity to anticipate negative consequences and generate alternative solutions to
acute emotional crises (Grover et al., 2009; Schotte & Clum, 1987). Perhaps most consistently,
suicidal populations demonstrate impairment in attentional control (Jollant, Lawrence, Olié,
Guillaume, & Courtet, 2011; J. G. Keilp, Gorlyn, Oquendo, Burke, & Mann, 2008; Thompson &
Ong, 2018), a component of executive functioning that allows for flexible shifting of attentional
resources, maintained focus on task-relevant information, and inhibition of competing
distractors. These attentional deficits are thought to undermine the ability to shift focus away
from suicidal urges, thereby accelerating risk of suicidal behavior, particularly in the context of a
suicidal crisis (Wenzel & Beck, 2008).
In contrast to suicidal behavior, suicidal ideation has been less consistently linked to
executive dysfunction, with some studies indicating deficits in decision-making in individuals
with SI (Marzuk, Hartwell, Leon, & Portera, 2005; Westheide et al., 2008) and others showing
little or no association between SI severity and various neurocognitive functions (J. Keilp et al.,
2013). To date, however, attentional control deficits have not been studied in relation to
variability in suicidal ideation, i.e., the degree of fluctuation in ideation intensity and duration
across short periods of time (Oquendo et al., 2020). Highly variable SI has recently been posited
to be a potential phenotypic marker for a subgroup of suicidal individuals with distinct biological
 2

and psychosocial risk factors for suicidal behavior (Bernanke, Stanley, & Oquendo, 2017),
including greater affective lability, stress reactivity, and impulsive aggression—all factors
conceivably related to impairment in attentional control (Rueda, Posner, & Rothbart, 2004).
Indeed, efficient attentional control is integral to self-regulation and modulation of stress
reactivity (O'Bryan, Kraemer, Johnson, McLeish, & McLaughlin, 2017) since regulation of
attention in the early stages of emotion generation may stem the cascade of negative emotions
more effectively than later efforts at regulation (Gross, 2001). With regard to SI specifically,
attentional deficits might contribute to acute fluctuations in ideation by impairing the ability to
inhibit intrusive suicidal thoughts (Peers & Lawrence, 2009) and efficiently redirect attention
away from them (Becker, Strohbach, & Rinck, 1999), while also promoting general
distractibility (Stawarczyk, Majerus, Catale, & D'Argembeau, 2014).
Currently, suicidal ideation variability, and heterogeneity in the clinical phenomenology
of SI more generally, remain poorly understood. This is perhaps due to challenges in observing
suicidal thoughts as they occur in real-time (Kleiman et al., 2018). However, advances in
smartphone-based technology have led to a burgeoning body of research examining dynamic
changes and phenotypic differences in SI using ecological momentary assessment (EMA)
methods (Kleiman & Nock, 2017). EMA involves repeated, frequent sampling of participants’
thoughts and behaviors over a discrete time period, often using portable digital devices
(Shiffman, Stone, & Hufford, 2008), making it particularly suitable for studying spontaneous
fluctuations in SI over hours to days.
In the current study, we sought to assess deficits in attentional control in a sample of
depressed adults and examine the contribution of attentional control deficits to variability in SI.
We used EMA sampling over a period of seven consecutive days to characterize variability and
intensity of suicidal thinking. Two computer-based neuropsychological measures were used to
characterize attentional control capacities. We also examined the relationship between attentional
control deficits and variability in depressive affect over the course of the EMA period, since
variability in SI is often accompanied by simultaneous shifts in depressed mood (J. G. Keilp et
al., 2018; Kyron, Hooke, & Page, 2019). While prior research suggests that measures of
attentional control are not strongly associated with retrospective self-report measures of SI (J.
Keilp et al., 2013; J. G. Keilp et al., 2008; Saffer & Klonsky, 2018), we hypothesized that
attentional control measures are more likely to be associated with variability and severity of
 3

suicidal ideation when assessed hour-to-hour (Gratch et al., 2020) in the context of a depressive
episode.

Methods
Subjects
Ninety-five individuals (n=62 female, n=29 male, n=4 non-binary/other) with major
depressive disorder (MDD) were recruited from the New York Presbyterian Hospital emergency
room and through local and web-based advertising. The study was approved by the Institutional
Review Board of the New York State Psychiatric Institute. All participants gave written
informed consent. Clinical ratings were conducted one to two days prior to neuropsychological
testing. A detailed history of past suicidal behavior was obtained for all participants using
structured interviews (see Clinical Assessment below). All subjects were free of significant
physical illness including neurological disease, as determined by clinical history and
examination.

Clinical Assessment
The SCID-I was used to assess lifetime and current DSM-IV/DSM-5 major psychiatric
disorders (Spitzer, Williams, Gibbon, & First, 1990) and SCID-II was used to assess DSM-
IV/DSM-5 personality disorders (First, 2014). Participants completed a battery of baseline
clinical measures for assessment of depression severity (Beck Depression Scale; Beck, Steer, and
Brown (1996)) and suicidal ideation (Beck Scale for Suicidal Ideation; Beck, Kovacs, and
Weissman (1979)). To better characterize the sample, traits associated with suicidal behavior
were also assessed, including impulsivity (Barratt Impulsiveness Scale, 11th Revision; Patton,
Stanford, and Barratt (1995)), affective lability (Affective Lability Scale; Look, Flory, Harvey,
and Siever (2010)), aggression (Brown-Goodwin Lifetime History of Aggression Scale; Brown,
Goodwin, Ballenger, Goyer, and Major (1979)), and hostility (Buss Durkee Hostility Scale; Buss
and Durkee (1957)). All clinical assessments were completed by trained master’s level clinicians.

Neuropsychological Assessment
The Continuous Performance Task – Identical Pairs Version, 4-digits fast condition
(CPT-IP; Cornblatt, Risch, Faris, Friedman, and Erlenmeyer-Kimling (1988)) and a
 4

computerized adaptation of the standard color-word Stroop task (J. G. Keilp et al., 2008),
measures of complementary aspects of attention, were administered on a Macintosh laptop. Both
tasks are well-established paradigms that have been used extensively in the assessment of
attention in depressed and suicidal populations (J. Keilp et al., 2013; J. G. Keilp et al., 2001;
Keller, Leikauf, Holt-Gosselin, Staveland, & Williams, 2019; Saffer & Klonsky, 2018).
The CPT-IP presents four-digit strings in quick succession (50ms exposure, 950ms
intertrial interval), and participants respond when the number string presented is identical to the
string that preceded it. The main outcome score of the CPT-IP, detection sensitivity (d′, or d-
prime), reflects the ability to discriminate the signal (i.e., target stimuli, exact matches of
previous items) from noise (specific non-target stimuli that are similar but not identical to the
previous item) by taking the standardized difference of hit and false alarm rates. Higher scores
on d′ indicate greater detection sensitivity and sustained attention to the task. A secondary
measure of response bias (or beta) was also calculated, where higher scores indicate a more
conservative response style that emphasizes avoidance of non-targets, while lower scores reflect
a more liberal approach to target detection at the expense of a potential increase in non-target
responding.
The Stroop task was adapted from the paper-and-pencil version of the task and used
standard color/word stimuli (red, blue, and green) in a blocked presentation (J. G. Keilp et al.,
2008). In the first block, participants identified color names printed in black via keypress on an
external keypad. In the second block, participants identified the printed display color of a string
of X's. In the third block (i.e., interference condition), color names are presented in incongruous
display colors, and participants indicate the display color while ignoring the text. The primary
outcome score on the Stroop is the interference score, which was calculated as the percent
increase in reaction time to color names printed in incongruous colors, relative to colored X’s in
the previous block. Higher Stroop interference scores reflect poorer attentional control (and thus
greater susceptibility to interference effects). All test scores were compared to normative data,
based on age, education, and/or sex, that have been established in the lab and then converted to
standardized z-scores for analyses (see J. G. Keilp, Sackeim, and Mann (2005)).

Ecological Momentary Assessment

 5

Participants completed a consecutive seven-day ecological momentary assessment

(EMA) period, during which they reported on their depressive affect and suicidal ideation six
times daily, at random intervals within 2-hour epochs. The six EMA response periods were
distributed across a 12-hour wake period of participants’ choosing. Depressive affect was
assessed with seven items adapted from the Profile of Mood States (Curran, Andrykowski, &
Studts, 1995) and rated from 1 ("very slightly or not at all") to 5 ("extremely"). Items inquired
into the degree to which participants felt sad, guilty, ashamed, miserable, rejected, angry at
themselves, or lonely since the last epoch. SI was assessed with nine items adapted from the SSI
(Beck et al., 1979). Participants rated how strongly they experienced each of the following since
the last epoch on a 5-point (1 to 5) Likert scale: a wish to live; a wish to die; a wish to escape;
thoughts about dying; thoughts about suicide; urge to die by suicide; thoughts about hurting self;
urges to hurt self; and whether they had reasons for living.
The mean number of EMA epochs with responses was 34.3 (SD=11.46), reflecting an
overall response rate of 82%. Participants with fewer than 10 observations during the EMA
period were excluded from analyses. Total scores for SI and depressive affect were each
computed by summing items for the respective scales within the same epoch. Internal
consistency of items in both the EMA depressive affect and ideation scales was good
(Cronbach’s a >=.85), and reliability for estimating subject-specific mean values was excellent
(.99). Variability of SI and depressive affect over the seven-day period was estimated per subject
by taking the root mean square of successive differences (RMSSD) for the total scores across the
EMA period (Choo, Oquendo, Stanley, & Galfalvy, 2020). RMSSD is a summary measure that
combines amplitude and autocrrelation of the sucessive scores.

Statistical Analyses
Data processing
Ninety-three of 95 participants completed the Stroop task, and 89 completed the CPT
task. EMA and neuropsychological data were graphed, and their distribution inspected for shape
and outlying values. Three participants had extremely low accuracy scores (> 5SD from mean)
on the Stroop color/word condition, indicating careless performance, and were excluded from
analysis. Five participants with outlying Stroop interference scores were capped (winsorized) to
 6

the nearest non-outlier value. EMA SI RMSSD data were right-skewed, and extreme values were
winsorized to reduce the effect of potential outliers.
Descriptive analyses
For purposes of describing characteristics that may be associated with SI variability,
groups of high- and low-variability of SI were created by dichotomizing SI RMSSD along the
sample median. SI variability groups were then compared on demographic and clinical factors
using univariate analyses of variance (ANOVAs) and chi-square analyses, presented in Table 1.
Primary and sensitivity analyses
Bivariate correlations were used to assess associations between measures of attention and
EMA variables. Linear regression modeling was used to assess the relative contribution of
attentional interference to variability of EMA suicidal ideation while accounting for other mood-
related variables. All analyses were conducted in SPSS, Version 28. All linear regression models
were subject to diagnostic tests of normality, homoscedasticity, and absence of multicollinearity,
to ensure non-violation of assumptions.

Results
Group Characteristics
EMA-determined SI variability groups did not differ in age, gender, race, ethnicity,
education, medication status, history of trauma exposure, number of prior depressive episodes, or
comorbid personality disorder diagnoses (see Table 1). More than half of all participants reported
a prior suicide attempt (n=55), and groups did not differ in the proportion of individuals with a
prior attempt, or average number of attempts. Compared with the low SI variability group, the
high variability group reported greater baseline depression (BDI), baseline suicidal ideation
(SSI), trait affective lability (ALS), and trait hostility (Buss-Durkee). During the EMA period,
the high SI variability group reported greater severity and variability of depressive affect and
greater severity of SI (Table 1). When applying a Bonferroni correction to group-wise tests to
adjust for multiple comparisons, only group differences on severity and variability of EMA SI
and depressive affect were retained.

Attentional Control and EMA SI & Depressive Affect

 7

An a priori power analysis was conducted using G* Power (Faul, Erdfelder, Lang, &
Buchner, 2007) to ascertain the sample size required to detect a two-tailed bivariate correlation
of r=.28 between measures of attention and EMA SI (based on Becker et al. (1999)). Results
indicated that a total sample size of n=97 was required to achieve 80% power with an alpha of
.05.
Scatterplots of correlations between neuropsychological measures and EMA-assessed
mood and SI variables are depicted in Figure 1 and reported in Table 2. Stroop interference was
positively correlated with both EMA SI severity and SI variability, whereas CPT performance
and response bias were not associated with either. Stroop interference was also correlated with
severity, but not variability, of EMA depressive affect. CPT performance was not associated with
severity or variability of EMA depressive affect, but response bias was negatively correlated
with severity and variability of depressive affect, such that a conservative response style on the
CPT task was associated with lower severity and variability of depressive affect.
Since SI variability was moderately correlated with severity and variability of depressive
affect (see Table 2), and both were assessed simultaneously during the EMA period, we sought
to determine whether the relationship between SI variability and Stroop interference scores was a
function of depressive affect. We conducted a linear regression analysis with Stroop interference
as the predictor variable, depressive affect severity and variability entered as covariates, and SI
RMSSD (variability) as the dependent variable. The overall model was significant, F(3,86)=
23.816, p<.001, R2=.454. Stroop interference predicted SI variability (b= 0.165, p= .048, 95% CI
0.003 – 0.495) independent of depressive affect variability (b=0.537, p<.001, 95% CI 0.327 –
0.636) and severity (b= 0.195, p= .032, 95% CI 0.007 – 0.147), which were also predictive of SI
variability.
As an additional sensitivity analysis, EMA SI severity and baseline depression were
added to the model as covariates, as they both demonstrated low-level associations with Stroop
interference. The overall model was significant, F(5,83)=23.171, p<.001, R2= .583. Stroop
interference remained a significant predictor of SI variability (b=0.169, p=.041, 95% CI 0.010 –
0.451), independent of SI severity (b=0.162, p=.092, 95% CI -0.013 – 0.172) and depressive
affect variability (b= 0.584, p=<.001, 95% CI 0.130 – 0.301). Depressive affect severity was no
longer significant in the model (b=-0.151, p=.168, 95% CI -0.145 – 0.026) and neither was
baseline depression (b=-.078, p=.373, 95% CI -0.043 – 0.016).
 8

Discussion
This study is the first to provide evidence of an association between attentional control
capacities and daily variability of suicidal ideation (SI), a potential marker of a subgroup of
suicidal individuals with specific neurobiological and clinical risk factors (Bernanke et al.,
2017). Highly variable SI over a seven-day EMA period in depressed subjects was associated
with poorer attentional control on the Stroop task. Notably, the association between SI variability
and attentional control deficits was not accounted for by severity or variability of depressive
affect, assessed concurrently with SI during the EMA period, even while both depressive affect
indices independently predicted SI variability. Moreover, the independent contribution of Stroop
interference to SI variability persisted in sensitivity analyses accounting for severity of EMA SI
and baseline depression. Our findings support prior work on SI variability as a marker of suicidal
individuals with greater affective lability (Rizk et al., 2019) and stress-responsive increases in SI
(Oquendo et al., 2020) that potentially reflect underlying impairment in cognitive control and
emotion regulation capacities. In the current sample, high SI variability was associated with
greater affective lability, more variable depressive affect, and greater trait hostility, all of which
potentially reflect cross-cutting deficits in the regulation of emotional reactivity.
In addition to SI variability, impaired attentional control as measured by the Stroop task
was correlated with severity of SI over the course of the EMA period, despite mixed findings in
the literature regarding the association between SI and neuropsychological deficits (J. Keilp et
al., 2013). This discrepancy with previous literature may be explained by the susceptibility of
traditional retrospective measures to incomplete or biased recall (e.g., difficulty remembering
past-week experiences that are incongruent with current mood), particularly for individuals with
transient or less severe SI. It should be noted, however, that our post-hoc follow-up analyses
indicated that the association between Stroop interference and EMA SI severity was no longer
significant when accounting for concurrently assessed depressive affect, likely due to the strong
correlation between the latter two variables (r=.753, see Table 2). This might suggest that
attentional interference is uniquely associated with temporal dynamics of SI and less directly
associated with the average magnitude of SI, perhaps further explaining inconsistent findings in
the literature regarding attentional control deficits and suicidal ideation. Our results thus
highlight the potential incremental utility of EMA sampling methods in characterizing clinically
relevant temporal patterns of suicidal ideation.
 9

Executive control of attention is key to the ability to regulate emotions and manage
suicidal thoughts and urges; and also plays a role in interventions for suicide risk that emphasize
directing attention away from aversive internal states, e.g., Dialectical Behavioral Therapy
(DBT) and Safety Planning Intervention (SPI) (Stanley et al., 2018). Individuals with poor
cognitive control might therefore be less adept at managing negative affect or avoiding intrusive
suicidal thoughts. This is consistent with functional neuroimaging studies of Stroop interference
that demonstrate altered activation of regions important to emotion regulation in depressed
patients compared to healthy controls (Wagner et al., 2006) such as the rostral anterior cingulate
gyrus (rACG), left dorsolateral prefrontal cortex (DLPFC), and left supramarginal gyrus
(Chechko et al., 2013; Loeffler et al., 2019). Moreover, depressed and suicidal individuals
demonstrate deficits on Stroop tasks that do not involve any overt emotion elicitation (J. G. Keilp
et al., 2008) as is the case in the current study, suggesting that this inefficiency might affect a
broad array of adaptive cognitive functions in day-to-day activities. Still, broad deficits in
interference processing may contribute to variability in suicidal ideation by disrupting efforts to
focus attention on task-relevant stimuli, leaving such individuals more vulnerable to fluctuating
SI and labile mood in response to the daily flow of internal and external perturbations.
Attentional control deficits in individuals with highly variable SI may also have important
implications for their ability to benefit from crisis management interventions that make use of
distraction-based behavioral strategies or internal coping skills (Stanley & Brown, 2012; Stanley
et al., 2018). Potentially, distraction-based crisis intervention techniques for de-escalation of
suicidal ideation may capitalize on proneness to distractibility in individuals with high SI
variability, thereby compensating for deficits in attention control that hinder attempts at self-
regulation. However, it is also possible that deficits in sustained attention and interference
processing may render distraction-based techniques less effective. Continued research is
therefore necessary to determine whether suicidal individuals with attentional control deficits
benefit from distraction-based interventions for management of suicidal crises. Promisingly,
however, a randomized clinical trial of mindfulness-based cognitive therapy for suicide
prevention in high-risk U.S. military veterans yielded improvement in attentional control as
measured by an emotional Stroop task, suggesting a potential route for ameliorating cognitive
control deficits in suicidal populations (Chesin et al., 2021).
 10

Notably, our study did not find associations between EMA-assessed mood and SI
variables and attentional control performance as measured by the CPT, consistent with earlier
work comparing these attention tasks (J. G. Keilp et al., 2008). The CPT is a sustained attention
task that requires maintaining focus over time, in contrast to the Stroop task, which requires
distinguishing target from distractor information from moment to moment over discrete trials.
Few deficits have been found in past suicide attempters on sustained attention tasks; rather,
suicide attempters have been discriminated from other groups on the basis of their impaired
ability to efficiently distinguish targets from distractors (J. G. Keilp et al., 2008; J. G. Keilp et
al., 2001). However, poorer performance on the CPT primary measure (d prime) was related to
greater baseline depression, mirroring prior literature findings on deficits in effortful sustained
attention related to depression (Farrin, Hull, Unwin, Wykes, & David, 2003; Politis, Lykouras,
Mourtzouchou, & Christodoulou, 2004; Porter, Gallagher, Thompson, & Young, 2003).
The current study has several limitations. While EMA data are prospective in nature, our
cross-sectional analysis precludes the ability to draw causal inferences about the relationship
between attentional control deficits and EMA-assessed characteristics of SI. The sample was also
relatively young (ranging from 18 to 57 years of age) and predominantly female, which may
limit generalizability to other samples. Additionally, severity of suicidal behavior in past
attempters was relatively low compared to previous samples from our group, suggesting a
possible attenuation in range of risk indicators for suicidal behavior. These limitations
notwithstanding, the current study is the first to examine neuropsychological deficits in relation
to patterns of suicidal ideation measured using ecological-momentary assessment. We provide
evidence that highly variable SI is associated with deficits in attentional control and interference
processing. Such deficits in attentional control potentially play a role in in the experience of
sharp increases in suicidal ideation in response to stressors, which in turn may raise risk for
suicidal behavior. Follow-up studies are necessary to better elucidate neurocognitive
mechanisms causally related to suicidal subtypes and patterned experiences of suicidal ideation.
 11

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 Table 1. Demographic and Clinical Characteristics of Depressed Patients with Stable and Variable
17 Suicidal
Ideation.
Depressed Patients
Total Sample Low Variability High Variability Test of Difference
(N=95) SI SI
Variables (n=50) (n=45)
Mean/ Mean/ Mean/
SD/% SD/% SD/% F/X2 p
Freq Freq Freq
Demographic Information
Age (years) 30.59 10.71 31.40 11.28 29.69 10.10 766.664 .440
Gender: Female 62 65.3% 33 66.0% 29 64.4% 4.317 .115
Male 29 30.5% 13 26.0% 16 35.6%
Other/Not Reported 4 4.2% 4 8.0% 0 0.0%
Education (years) 15.49 2.40 15.82 2.32 15.13 2.12 2.255 .137
Race: African Amer/Black 13 13.7% 5 10.0% 8 17.8% 4.957 .292
Asian 11 11.6% 8 16.0% 3 6.7%
Indigenous/Native 2 2.1% 2 4.0% 0 0.0%
Multiracial/Unknown 18 18.9% 10 20.0% 8 17.8%
White 51 53.7% 25 50.0% 26 57.8%
Ethnicity: Hispanic 28 29.5% 14 28.0% 14 31.1% 0.042 .838
Non-Hispanic 65 68.4% 34 68.0% 31 68.9%
Highest occupational level 5.93 1.90 6.27 1.56 5.55 2.17 3.105 .082
Number of prior depressive
26.02 41.09 28.76 43.37 22.26 38.21 0.387 .536
episodes
Duration of current episode
220.59 398.69 219.30 317.79 221.97 474.82 0.001 .976
(weeks)
Psychiatric medication* 37 38.9% 22 44.0% 15 33.3% 0.947 .330
Non-psychiatric medication* 40 42.1% 17 34.0% 23 51.1% 2.654 .103

Comorbid Psychopathology
Borderline Personality
21 22.1% 9 18.0% 12 26.7% 1.033 .309
Disorder
 18

Other Personality Disorder 31 32.6% 14 28.0% 17 37.8% 1.030 .310

Past substance
30 31.6% 15 30.0% 15 33.3% 0.122 .727
abuse/dependence

Rating scale scores

Beck Depression Inventory 22.45 10.48 20.31 10.50 24.73 10.06 4.284 .041
Barratt Impulsivity Scale 67.11 11.12 65.21 10.93 69.24 11.07 2.972 .088
Affective Lability Scale 68.62 30.03 61.64 25.59 76.10 32.83 5.281 .024
Brown-Goodwin Aggression
16.46 4.56 16.11 4.31 16.84 4.83 0.575 .450
Scale
Buss Durkee Hostility
33.54 12.23 30.77 11.99 36.64 11.88 5.377 .023
Inventory

Suicidal Ideation and

Behavior
Suicidal Ideation: 2 weeks
9.31 9.82 9.71 11.76 9.00 8.23 0.061 .806
prior
Suicidal Ideation: Current 5.46 6.69 4.00 6.75 7.02 6.34 4.619 .034
Any suicide attempt 55 57.9% 26 52.0% 29 64.4% 1.015 .314
Number of previous attempts 1.28 1.60 1.10 1.53 1.47 1.67 1.189 .279

Ecological-Momentary
Assessment
Number of EMA epochs with
34.27 11.46 35.22 13.18 33.22 9.20 0.718 .399
responses
Suicidal ideation variability 3.01 2.22 1.45 0.54 4.75 2.09 116.168 <.001
Suicidal ideation severity 6.73 5.25 4.23 3.41 9.51 5.56 31.820 <.001
Depressive affect variability 3.35 2.14 2.48 1.44 4.32 2.37 21.466 <.001
Depressive affect severity 5.70 4.51 4.22 3.46 7.34 5.00 12.736 <.001
 19

Table 2. Correlations Between EMA and Neuropsychological Variables

Variables 1 2 3 4 5 6 7
1 EMA SI Variability _
2 EMA SI Severity .573*** _
3 EMA Depressive Affect Variability .653*** .282** _
4 EMA Depressive Affect Severity .429*** .732*** .343** _
5 Baseline Depression .255* .514*** .134 .454*** _
6 Stroop Interference .229* .298** -.004 .226* .254** _
7 CPT D Prime -.058 .106 -.039 .095 -.109 -.219* _
8 CPT Response Bias -.142 -.017 -.275** -.174* -.015 -.019 .118
†: p<.10, *: p<.05, **: p<.01, ***: p<.001
Figure 1.