International Journal of Cognitive Therapy,5(2),170-185,2012 @ 2012 International Association for Cognitive

Is the Looming Maladaptive Cognitive Style a Central Mechanism in the (Generalized) Anxiety-(Major) Comorbidity: An Intra-Individual, Time Series Study Dana Tzur-Bitan and Nachshon Meiran Ben Gurion University of the Negev

David M. Steinberg TelAviv University Golan Shahar Ben Gurion University of the Negev; School of Medicine

The authors examined the unfolding of anxiety-depression comorbidity while emphasizing its multifaceted and intra-individual nature. An intensive time se- ries design was employed, whereby three young adult patients with diagnosed comorbid Generalized Anxiety Disorder (GAD) and Major Depression Disorder (MDD) were followed daily for a period of 6 months. Daily reports included af- fective states, cognitive vulnerability, and symptoms of depression and anxiety.The Looming Maladaptive Style .(LMS), pertaining to the tendency to generate mental scenarios of potentially threatening situations as rapidly rising in risk, prospectively predicted anxiety, depression, and hopelessness, a dimension of depressive vulner- ability. Effects of anxiety and depression on cognitive vulnerability were also docu- mented. Findings suggest that LMS confers vulnerability to emotional disorders, broadly defined, and emphasize the need to include an intra-individual analysis for the purpose of elucidating the nature of psychopathological comorbidity.

Anxiety-Depression comorbidity is the rule rather than the exception in psychopa- thology (Kessler et aI., 1996; Maser & Cloninger, 1990; Mineka, Watson, & Clack, 1998; Zimmerman, Chelminski, & McDermut, 2002). Such comorbidity is impli-

Address correspondence to Dana Tzur-Bitan, Dept. of , Ben-Gurion University of the Negev, Beer Sheva 84105. E-mail: [email protected]@[email protected]. shaharg@bgu. ac.il.

170 DEPRESSION ANXIETY COMORBIDITY: A TSA STUDY 171 cated in (a) a more severe course of illness, (b) elevated social, marital, and occupa- tional impairment, (c) risk for suicide, and (d) a poor treatment response (Coryell et al., 1988; Cox, Direnfeld, Swinson, & Norton, 1994; Noyes et aI., 1993; Ormel, Oldehinkel, Brilman, & van den Brink, 1993; Weissman, Klerman, Markowitz, & Ouellette, 1989). Nevertheless, the underlying mechanisms of this comorbidity are stillpoorly understood. Two dominant groups of explanatory models of this comorbidity are the shared factorhypothesis and the causal relationships explanation. The shared factor explana- tion pertains to the possibility that a higher order psychopathological factor underlies both anxiety and depression, such as the negative affectability factor of the Tripartite Model (Clark & Watson, 1991), or neuroticism as a shared genetic predisposition (Gray& McNaughton, 2000). Conversely, the causal relationships explanation targets one disorder as the cause of the other, or, alternatively, suggests reciprocal causality. Suchcausal relations are usually established by the temporal precedence of one dis- order over the other, yet while most studies suggest that anxiety proceeds, and might evenbring about, depression (Breslau, Schultz, & Peterson, 1995; Hettema, Prescott, & Kendler, 2003; Kessler et al., 1996; Lewinsohn, Zinbarg, Seeley, Lewinsohn, & Sack,1997), other studies are consistent with the supposition that both disorders are equallylikely to be the first in a comorbidit)r sequence (Moffit et al., 2007). Thus, should such a relationship exist, standard longitudinal studies fail to clearly determine itsdirectionality. Two important considerations in comorbidity research, which, in our opinion, havenot been fully addressed, are the multifaceted nature of anxiety and depression and their dynamic, intra-individual, unfolding over time. With respect to their mul- tifaceted nature, both anxiety and depression are presumed to include mood states (e.g., fear, sadness), clinical symptoms (e.g., hypervigilance, anhedonia), and distinct cognitive vulnerability. Regarding the latter, our focus was on helplessness, hopeless- ness, rumination, and looming. Helplessnessis theorized to be a vulnerability dimen- sion shared by both anxiety and depression, whereas Hopelessnessappears to be a dis- tinct depressive vulnerability (Alloy,Kelly,Mineka, & Clements, 1990). Rumination, a focalcognitive vulnerability, pertains to the tendency for self-reflection and repetitive and passive focus on one's negative emotions (Morrow & Nolen-Hoeksema, 1990; Nolen-Hoeksema, 1991). While rumination was demonstrated to be implicated in depressivevulnerability, it was also shown to be involved in the co-occurrence of de- pression and anxiety (Cox, Enns, & Tylor, 2001; Rector, Antony, Laposa, Kocovski, & Swinson, 2008). Another important dimension of cognitive vulnerability is the Looming Maladap- tiveStyle(LMS), pertaining to individuals'tendency to generate mental scenariosof potentially threatening situations as rapidly rising in risk or intensifying in danger. This style was shown to be a distinct dimension of cognitive vulnerability to anxiety disorders (Riskind, Rector, & Cassin, 2011; Riskind, Tzur, Williams, Mann, & Sha- har,2007; Riskind & Williams, 2006; Riskind, Williams, Gessner, Chrosniak, & Cor- tina, 2000; Williams, Shahar, Riskind, & Joiner, 2005). LMS was also proposed as a depressivevulnerability dimension, mainly through its derailment of perceived ability to cope with life problems, thereby generating helplessness and hopelessness (Rector, Kamkar,& Riskind, 2008). A second consideration in comorbidity research is the dynamic, time variant, nature of each disorder (Tzur-Bitan, Meiran, & Shahar, 2010). Exploring the dynamic unfolding of depression and anxiety over time may enable the detection of causal re- 172 TZUR-BITAN ET AL. lationships, not only with respect to disorders,but also in terms of componentsof each disorder, for example, between a cognitive vulnerability dimension of anxiety and a mood component of depression. An analogy demonstrating the promise of this ap- proach comes from intelligence research. In this field, the dominant theory posits a general intelligence factor (Spearman, 1904). According to this theory, the abilities that are measured in individual tests indicate this general ability to a certain degree. However, new studies that emphasized the development of abilities suggest a drasti- cally different account. According to this alternative account, the correlations between abilities reflect the influence of one ability on the acquisition of another, rather than reflecting the fact that these abilities are determined by a general factor (Van der Mass et aI., 2006). Such dynamics are best captured using intra-individual research methodol- ogy (Molenaar, 2004; Molenaar & Camp bell, 2009), preferably relying on a time series (TS) design (Ford & Lerner, 1992; Gotlieb, 1992, 2003; Granic & Hollen- stein, 2003; Molenaar, Sinclair, Rovine, Ram, & Corneal, 2009; Wohlwill, 1973). Extensively used designs, such as cross-lagged Structural Equation Modeling (e.g., Shahar & Davidson, 2003) or Latent Difference Scores Modeling (LDS; McArdle & Hamagami, 2001), do allow for the investigation of dynamic unfolding of psychologi- cal conditions. However, these designs are limited in several important respects. First, they test hypothesized causal relationships across-rather than within-individuals. As such, they make the assumption that causal relationships are identical across all, or most, of the individuals, an assumption that might be (grossly) oversimplified (Granic & Hollenstein, 2003). Second, rank order designs such as cross-lagged SEM or LDS do not examine changes in absolute levels of a symptom (e.g., changes in depression symptoms) but rather estimate rank-order change, namely, change in the relative po- sitioning of participants in comparison to others. Finally,although rank order designs permit intense measurement over time, in practice such intensity is difficult to employ because of the need to assess a large sample of participants. The end result is comprised of studies employing up to five or so waves of measurement, as compared to approxi- mately 180 in this study. TS designs circumvent these shortcomings. Such designs consist of assessments employed at short intervals (e.g.; one day, in the present case). Data pertaining to each variable (e.g., depression) per each participant is then considered as a series. Each level of a variable within a series is related to the level predicted by previous fluctuations of that variable within the same individual, rather than to the sample mean or to other participants. Then, series are linked to each other intra-individually, allowing one to empirically examine the extent to which causal patterns are identical versus different across individuals. Integrating the two considerations, namely, the multifaceted nature of anxietyand depression and the distinction between rank-order and intra-individual changes, we examined the direction of relationships between affective, symptomatic, and cognitive components of anxiety and depression. Our participants were three patients diagnosed with comorbid Generalized Anxiety Disorder (GAD) and Major Depressive Disorder (MDD). Such comorbidity was selected because it is quite prevalent (Massion, War- shaw, & Keller, 1993; Wittchen, Beesdo, Bittner, & Goodwin, 2003). These patients were assessed daily over a 6-month period. TS analyses were conducted to disentangle directionality pertaining to the three disorder components, whereby each participant served as an entire study (e.g., Brossart, Willson, Patton, Kivilighan, & Multon, 1998; DEPRESSIONANXIETY COMORBIDITY: A TSA STUDY 173

Dunn,Jacob, Hummon, & Seilhamer, 1987; Pole, Ablon, & O'Connor, 2008). Our mainfocuswas on within-participant fmdings replicated across the three patients.

METHOD

Participants

Twohundred and twenty-six female undergraduate students were evaluated for anxiety anddepression using the Beck Depression Inventory (BDI; Beck, Steer, & Brown, 1996)and the Beck Anxiety Inventory (BA!; Beck, Epstein, Brown, & Steer, 1988). Studentshigh in anxiety and depression, as evident by a higher than cutoff score of 15on the BDI and BA!, were invited for a structured clinical interview (SCID; First, Spitzer,Gibbon, & Williams, 1996), and were assessed for the presence of current GADand MDD. Of this sample, four were invited to a clinical interview, and two werefound to meet SCID criteria for comorbid GAD and MDD. These participants wererecruitedto the followup study and completeda 6-month follow-up. Students treated at the counseling services of Ben Gurion University were also invitedto participate. An initial screening was conducted by a clinician from the coun- selingservices based on a clinical interview and MMPI data (Butcher, Dahlstrom, Graham,Tellegen, & Kaemmer, 1989). From this pool of candidates, eight students wereevaluated using the SCID. However, only one met criteria for a diagnosis of comorbidanxiety and depression. This student participated in the next stage. Thus, a totalof three participants with a SCID diagnosis of both MDD and GAD completed the6-month longitudinal daily evaluation.

Procedure

Uponsigning an informed consent form, the above three participants began a 6-month follow-up which included daily assessment via phone conversations with the study coordinator. Each phone conversation was documented. In cases where participants reported elevated distress, a risk assessment and crisis intervention was conducted by Golan Shahar, who is an experienced clinical psychologist. Due to repeated adminis- tration of the experience report form, it was necessary to shorten the form as much aspossible, while still including the necessary items for the evaluation of the different dimensions. Therefore, specific items were included in the experience report form.

Assessment

Participants' daily level of affect, symptoms, and cognitions were measured via a report form, developed for this study. The affective component of anxiety and depression was derived from the Positiveand Negative Affect Scale-ExpendedForm (PANAS-X; Watson & Clark, 1994). It is a 60-item adjective checklist designed to measure two higher order effects of negative and positive affect along with 11 specific affects such as guilt, self-assurance, fear, sadness, etc. For the purpose of the current study, 11 items ad- dressing two specific affects were included: six items related to fear and five related 174 TZUR-BITAN ET AL.

to sadness. For all variables, participants were requested to report their feelings and experiences from last measurement. Specifically,they were asked, ((Sincelast report,to what degreehaveyou beenfeeling. . .)) Symptoms of depression and anxiety were assessed using items from the Beck DepressionInventory (BDI; Beck et al., 1996) and the BeckAnxiety Inventory (BM; Beck et al., 1988). Each consists of 21 self-report items each, designed to measure symptoms of depression (BDI) or anxiety (BM), respectively. Five items addressing depression symptoms were chosen from the BDI, and included: sadness, anhedonia, sleep disturbances, appetite, and energy. An additional four items addressing anxiety symptoms were chosen from the BM, and included: fear of the worst happening, feel- ings of panic, increased heartbeat, and difficulty breathing. Cognitive vulnerability to anxiety and depression was assessed by the items from the short versionof the HopelesmessHelplesmessQuestionnaire(Lester,2001), an eight- item Likert scale, measuring helplessness, hopelessness, and haplessness (the feeling of having bad luck or bad fortune). This questionnairewas adopted from the Pessimism Scale (Beck, Weissman, Lester, & Trexler, 1974), and was shortened by the aid of Principal Components Analysis to include four items of each subscale (Lester, 2001). In the current study, eight items of the short version subscales of helplessness and hopelessness were used, and included items such as "I can't think of reasonable ways to reach my current goal" for helplessness, and "I look forward to the future with hope and enthusiasm" for hopelessness. Cognitive vulnerability to anxiety was also assessed using items from the Looming Maladaptive StyleQuestionnaire (LMSQ, Riskind et aI., 2000), a measure of individu- als' tendency to generate mental scenarios of potentially threatening situations that are rapidly rising in risk or intensifying in danger. The LMSQ consists of six items describ- ing potentially stressful situations (such as hearing a strange noise from the engine of your car while driving on the highway) and participants are asked to complete four questions for each description using a 5-point Likert scale (such as: "To what extent do you feel worried or anxious due to imagining this scenario?"; '1\re your chances of having difficulties with your car increasing or decreasing with each moment?"). In the current study, the description was removed in order to fit the daily experience structure of our questionnaire. Therefore four items were redesigned to address increasing feel- ings of worry from a general daily event (e.g., "To what degree have you felt worried or anxious?"; '1\re your chances of having difficulties increasing or decreasing each moment?"). Cognitive vulnerability to depression was derived from the Ruminative Response Scale of the Response Style Questionnaire (RRS- RSQ; Nolen- Hoeksema & Morrow, 1991). The RRS-RSQ is a 22-item self-report measure aimed at assessing rumination in response to depressed mood such as focusing on either the meaning of rumination or the subjective feelings related to the depressed mood ("I think about how I feel"), on symptoms and on consequences and causes ("I think I won't be able to do my job if! won't snap out of it"). For the current study, three items were chosen to represent ruminative response style, and included Items 1, 17, and 26. In summary, the questionnaire that the participants filled in on a daily basis yield- ed the following scale scores: Fear, Sadness, Depression Symptoms, Anxiety Symp- toms, Hopelessness, Helplessness, Rumination, and Looming. DEPRESSION ANXIETY COMORBIDITY: A TSA STUDY 175

Data Analysis

Adetailed description of the TS analysis is provided in supplementary materials avail- able online.1 TS analysis evaluates a series of observations pertaining to a specific variable(e.g., Depression Symptoms). TS enables the examination of the influence of pastvalues of the series on its current value, referred as "auto-influences." These auto influencesare represented by the three components of what is known as the Autore- gressiveIntegrated Moving Average (ARIMA) model (Box, Jenkins, & Reinsel, 2008). The first component, the autoregressiveone, represents partial accumulation from past values(e.g., today's depression is likelyto spill over to tomorrow's). The second com- ponent is needed when the mean level of the variable changes over time (i.e., there is an increase or decrease, etc.). The third is the moving average component, which rep- resentsthe influences of random changes in the past (also called "random shocks") on thecurrent level.These changes or "shocks" are said to be random because they cannot bepredicted on the basis of the past behavior of the sequence. The underlying principle of TS is that the three auto components, or sources of influence, represented in ARIMA, should be ta~en into account prior to estimating the effect of X on Y This is done as follows. First, the second ARIMA component, representing the mean change pattern, is addressed by differencing, in which the pre- ceding value is subtracted from the current value. The other two components (i.e., autoregressive and moving average) are addressed via "pre-whitening," a procedure tantamount to residualization in regression. The procedure is somewhat analogous to hierarchical regression in which the first block of predictors includes the ARIMA components (representing Y influences upon itself) and the second block includes X. Importantly for our purposes, TS enables the examination of directional (i.e., causal) relations between two series. Assessment of causal relations is conducted by evaluatingan input-output 1tWdelconsisting of a predictor (X) and outcome (Y). Both are "cleaned" from their respective auto influences. Causal examination is conducted via Granger Causality Test (Granger, 1969), followed by fitting a ('1ransferFunction)) (TF). The Granger Causality Test examines whether past levelsof variable X determine current levels of variable Y Specifically,X is said to Granger-cause Y if previous levels of X are significantly correlated with the current level of Y, meeting the requirement for temporal precedence in determining causality. This test includes a comparison be- tween the residuals of two regression models, one containing only past values of the variableY,the other containing both past values of X and past values ofY A significant reduction in unexplained variance, as determined by the F-test difference, comparing the two residual series, means that residuals are significantly lower when adding past values of X into the regression model, thereby, X Granger-causes Y Once Granger causality is determined, it is important to determine and quantifY its directionality (e.g., whether an increase in depression causes subsequent increase or decrease in anxiety) using TF. In the TF, the current level of the variable Y is estimated as a function of the mean level of that variable (represented by an intercept term), and the influence of current and past levels of X. Additional influences of past values of the output series, after accounting for influences of variable X, are assessed by an additional ARMA procedure (also referred to as "noise function"), and are added to

1. https://docs.google.comlftle/d/OB70112h3HcwONmFjN2EzNlMtZIN1NiOOMDcSLTkxOGltYTYxNjFmZm UyN2Fm/edit 176 TZUR-BITAN ET AL.

TABLE 1. Summary of Collected Series in Each Data Set

Anxiety Depression Affeet Fear Sadness

Symptoms Anxiety symptoms Depression symptoms

Cognitions 1 Helplessness Hopelessness

Cognition2 Looming Rumination

the model. Thus, the modeling process in TS involves a sequential testing of various influences. When a given influence is found not to be significant, it is dropped from the model. Model fitting is done by evaluating different combinations of parameters for the series until the residuals show no additional information (such series is referred to as "white noise"). Missing values accounted for less than 1% of the overall data across all 3 partici- pants. Since Time Series Analysis cannot handle missing data, all missing values were replaced by the mean value of previous and following reports of the specific measured item.

RESULTS

Modeling Each Series Separately

Table 1 summarizes the data collected and analyzed for each of the three participants. Each of the variables in Table 1 was computed as the sum of its items in any given day. Table 2 summarizes results ofTS modeling the separate series. It presents the models selected for each of the eight series estimated in all three participants. Below we will describe patterns that are similar in at l~ast two out of the three participants. For Fear, there was an autoregressive component in two participants, indicating that levels of Fear accumulated. For Helplessness and Hopelessness, all three par- ticipants corresponded to a moving average pattern indicative of a local influence of past cognitions. Additionally, Rumination had a dominant auto regressive component, showing that its level persisted over time. Looming had a dominant moving average component in two out of three participants, indicative of local influence of past levels of Looming, rather than a persistent pattern.

Granger Causality Tests

All models were examined with a time lag of 1 (p = 1 autoregressive order), due to the fact that most ARIMA models pointed to the order of 1. In series showing high au- tocorrelation, indicative of a trend (non-stationary series), differencing was employed prior to the Granger causality test, and the differenced series as fitted in the ARIMA modeling stage was entered into the regression. Table 3 presents a summary of all pairs showing statistically significant Granger's causality (based on the F-test). Note that Table 3 presents the direction of causality. We restrict the description of causal rela- DEPRESSION ANXIETY COMORBIDITY: A TSA STUDY 177

TABLE 2. Summary of ARIMA Models Fitted to Eight Collected Series of Each Participant RS HO RG

fear (1,0,0) Lowvariance (4,1,0) Sadness (0,0,1) Low variance (4,1,0) Anxietysymptoms (0,0,1) (2,0,0) (1,1,1) Depressionsymptoms (0,1,1) (1,0,0) (1,1,1) elplessness (0,1,1) (0,1,1) (0,1,1) Hopelessness (0,1,1) (0,1,1) (0,1,1) looming (0,0,1) (1,0,0) (0,0,1)

Rumination Random (1,0,0) (2,0,0)

rIote.ARIMA(p, d, q) representation as follows: p is the order of the autoregressive component, such that (1,0,0) repre- sentsa pure autoregressive process by which variable X is determined by accumulation of its past values going back p times. d~ the order of differencing (in which time t is subtracted from t-I. When I-\. for example, this would mean that the previ- ouslevelof t is subtracted from its current level, Le., t - t-l), in case of series exhibiting a trend. q is the order of the moving averagecomponent, representing local influence/random shock going back q times. Series showing low variance cannot be analyzeddue to violation of normal distribution assumption.

nonsto those obtained for two or more participants. A schematic summary of these analysesis presented in Figures 1-3. Within Depression. Hopelessness Granger-caused Depression Symptoms in two of threeparticipants (HB, RG), while showing the reversed causal direction in the third (RS). Within Anxiety. Looming was Granger-related to Anxiety Symptoms in two ofthree participants, showing a unidirectional pattern in which Looming Granger- causedAnxiety Symptoms in one participant (RG), and indicating bidirectional causal relationsin the other (HB). BetweenDisorders.Looming Granger-causedsome aspectsof depression in all threeparticipants. Specifically, it caused Depression Symptoms in two out of three participantsand caused Hopelessness in another two of the three participants. In oneparticipant (HB) this causal relationship was bidirectional, showing a feedback loopinvolving Looming and Depression Symptoms. Helplessness was also found to Granger-causeDepression Symptoms in two participants and in one of them there wasalso a reverse causality, indiciative of a feedback loop involving Helplessness and DepressionSymptoms.

Transfer Functions

Pairsof variables showing statistically significant Granger causality results were further analyzedusing TE Each pair was first pre-whitened using the filter of the input series (i.e.,the ARMA process fitted to the input series). The CrossCorrelation Function (CCF), which evaluates the similarity between each input-output series as a func- non of the time-lag, was evaluated to gain an appreciation of the lag structure to be fitted,as well as for re-examining directionality. Competing models were tested for theoptimal information criterion (AlCC, SBC; Akaike, 1979; Schwarz, 1978. For furtherelaboration see supplementary materials). Parameter estimates were evaluated ...... 'I 00

TABLE 3. Significant and Marginal Granger's Causality (F-tests)

Predictors

Anxiety Depression Dependant Fear Sadness Symptoms Symptoms Helplessness Hopelessness Looming Rumination Fear 4.01*(HO) Sadness 3.74 P -.052('0) Anxiety symptoms 3.16 P -.07(HO) 3.9S*(HO) 9.81**(HO) 4.17*('0)

Depression symptoms 3.51 P -.06(HO) 4.71*(HO) 4.82 *(HO) 43.46**(HO) 3.20 P -.07(RG) 3.25 P -.06(RG) Helplessness 4.20*(HO)

Hopelessness 7.57**(") 6.42 **(") 6.86**(05) 3.93*(00)

Looming 3.66 P -.OSS(HO) 12.99**(HO) 9.71**(HO) 8.96**(HO) 3.29 P -.06('0) Rumination 13.90**(HO) 22.21**(HO) 2.93 p-.08(RG)

Note. *p < .05, up < .001. Blank cellsindicate nonsignificance, for results approaching significance, p value is detailed. Case initials are reported in the parentheses. >-i N c:: ~ t;J:j

~ ~ ~ DEPRESSION ANXIETY COMORBIDITY: A TSA STUDY 179

Sadness

Anxiety Depn:ssion Symptoms SymptomS

FIGURES1-3.Visual representation of causal network as detected by Granger CausalityTestinparticipantsRS,HB,RG,respectively.Blackarrowsrepresent unidirectionalcausality, broken a"OWS represent marginal significance. 180 TZUR-BITAN ET AL.

TABLE4. Transfer and Noise Function Models of Pairs Showing Granger Causality within Each Disorder Construct

Noise Function

Case Output Intercept Transfer Function (Input) AR MA

RS ( 1-B)hopeless, -0.05 1.09 sad, - 1.07 sad,., (1-0.93B)e,

RS ( 1-B)hopeless, -0.30 0.47(1-B) dep.symp, (l-0.74B)e,

HB loom, 1.81 1.80anx.sym, + 0.99 anx.sym,., 1/(1-0.70B) (1-0.36B)e, + 0.51 anx.symp'.2 + 0.37anx. symp,.,+ 0.38anx.symp'4

HB anx.symp, -0.20 0.10helpless, 1/(l-0.83B) (1-0.45B)e,

HB loom, 3.60 O.27helpless, 1/(1-0.72B)

HB rumt 3.74 1.11 dep.symp, + 0.33dep.symp,., (1+0.26B)e,

HB dep.symp, -1.48 0.20hopeless,+0.17hopeless,.,

RG anx.symp, -0.98 0.22100m, + 0.07100m,., 1/(1-0.50B)

RG (l-B)sad, -0.05 0.12( 1-B)dep.symp, (1-0.71B)e,

RG dep.symp, -10.55 O.44hopeless, +0.40hopeless,., 1/(1-0.38B)

Note. Variables names has been shortened for formula simplification: Sadness-sad, Anxiety Symptoms-anx.symp, Depres- sion Symptoms=dep.symp, Helplessness~Helpless, Hopelessness=Hopeless, Looming-Loom, Rumination~ rum. The Backshift operator is commonly used as an abbreviation in TS literature, and represents an index for lagging the expression in parenthesis, e.g., (l-B) Output, represents a series that has undergone differencing procedure of Order 1, and is factored into: (l-B) Output, = Output, - Output,., (see supplementary materials for a detailed explanation). e, represents the residuals (noise series) of the output series after accounting for the input variance.

for significance and white noise diagnostics were applied to ensure that all the relevant variation in the series was modeled. Note that the models selected were those best fitting the data. TF results are presented in T;;I.bles4 and 5. These results are presented as regres- sion models so as to enable simple and accessible format for readers more familiar with multiple regression. Full diagnostics, information criterion, model fitting procedure and pre-transformations are detailed in the supplementary material. Note that pairs with Granger test results approaching significance and random cross-correlation func- tions were not further analyzed (see Table 5). The following sections are subdivided into betWeen disorders, within depression and within anxiety.Each subsection consists of tWo parts: the causality pattern and the influences of a series on itself (indicating accumulation over time vs. local influences) once causality was considered (Noise Functions). Within Disorders.Table 4 represents the TFM and Noise Functions for all pairs showing significant causality within each disorder.

Within Depression Causal Relations Between Series.In Participants HB and RG, Hopelessness posi- tively caused Depression Symptoms at Times t and t-l) ffi(hopelesst) = .20, P < .001, ffi(hopelesst) = .17, P < .05; ffi(hopeless,) = .44, P < .001, ffi(hopelesst) = .40, P < .01, respectively. For participant RS, the inverse temporal-causal direction prevailed, showing that changes in Depression Symptoms from day before yesterday to yesterday positively caused changes in Hopelessness, ffi(hopeless,hopelesst)= .47,p < .01. DEPRESSION ANXIETY COMORBIDITY: A TSA STUDY 181

TABLES. Transfer and Noise Function Models of Pairs Showing Granger causality Between Anxiety Components and Depression Components

Noise Function

Case Output Intercept TF (Input) AR MA

RS hopeless, 16.77 0.27Ioom,+0.10Ioom, 1/(1-0.41 B)

RG (1-B)hopeless, 0.05(1-B)loom, (1-o.79B)e,

RG (1-B)dep.symp, 0.12(1-B)loom, 1/(1-0.31B)

HB dep.symp, 1.88 0.1 Ohelpless,+0.13helpless'.5 + 1/(1-0.728)

0.09helpless,~

HB dep.symp, -1.10 0.28Ioom,+0.13loom,.,

NoiseFunction.The inclusion of Hopelessnessin the models (reported in the abovesection) revealed additional influences of series on themselves. In Participant RS,an MA component showed that Depression Symptoms were influenced by local pastinfluences, 9(dep.symp,)= .93,p < .001. In ParticipantRG, Depressionsymp- toms accumulated as indicated by an AR component,

Within Anxiety

Causal &lations Between Series. Looming positively caused Anxiety Symptoms at Times t and t-l in Participant RG, ro(loom,) = .22,p < .01, ro(loom,)= .07,p < .05.In ParticipantHB AnxietySymptomspositivelycausedLooming going back t-4 days,ro(loom,)= 1.80,p < .01, ro(loom,) = .99,p < .01, ro(loom,) = .51,p < .01, ro(loom,)= .37,p < .05, ro(loom,) = .38,p < .05. NoiseFunction. When the inter-series causal relations (reported in the preceding section)were included in the model, we identified additional influences of series on themselves.In Participant HB, LMS both accumulated and was influenced by local pastinfluencesas indicated by an ARMA (1,1) model,

BetweenDisorders

Causal&lations Between Series.Results presented in Table 5 confirm those yield- edbythe Granger causality test. In two participants (RG, HB), Looming positively Granger-causedDepression Symptoms, with the most apparent effect seen for t-l andt. These results mean that yesterday's and today's looming predicts today's de- pressionsymptoms in 2 participants. In participant RG, the output series represents differencedvalues (i.e., level of change from yesterday to today) rather than absolute \'alues,making the interpretation slightly more complex. Nonetheless, the essentially positivecausaldirection remained. Specifically,changes in Looming from yesterday to todaypositively caused changes in Depression Symptoms from today to tomorrow, ro(loom,-loom,)= .12, P < .001. In Participant HB, the same trajectory prevails, in whichchanges in Looming from day-before-yesterday to yesterday positively caused changesin DepressionSymptomsfrom yesterdayto today,ro(loom,)= .28,ro(loom,) 182 TZUR-BITAN ET AL.

= .14,p< .001. In Participant RS, elevated Looming today and yesterday predicted high current level of Hopelessness, ro(loomt)= .26,p < .001, ro(loomt) = .012,p < .005. In Participant RG, changes in Looming from day-before-yesterday to yesterday positively caused changes in Hopelessness, ro(loomt-loomt) = .05,p < .001. In addi- tion, changes in Helplessness 5 and 6 days ago positively caused changes in today's DepressionSymptomsin ParticipantHB, ro(helplesst)= .13,ro(helplesst.o)= .09,p< .001. NoiseFunction.The Noise Functions reveal influencesof a series on itself that are seen only after causal relations from other variables are entered into the model in the TFM. In Participant RS, there was an AR component indicating accumulating effects of Hopelessness; after the influence of Looming was included in the model, ~(hopelesst) = .41,p < .001. A similar accumulating effect (AR component) for De- pression Symptoms was apparent in Participants RG and HB; after Looming was entered into the model, ~(hopelesst) = .31, .72 correspondently,p < .001. In Partici- pant RG, local leaps in either Depression Symptoms, 8(dep.symPt) = .90,p < .001, or Hopelessness, 8(hopelesst) = .79,p < .001, caused the reverse effect the following day (an MA component).

DISCUSSION

Espousing a multidimensional, within-individual, TS study of the associations be- tween aspects of anxiety and depression in three participants with comorbid MDD and GAD, we found evidence consistent with causal relations in each of our three participants. Focusing on patterns emerging for at least two participants, we found that LMS was predictive of Depression Symptoms in two participants. We also found that LMS predicted Hopelessness, a cognitive vulnerability marker of depression, in another two participants. This pattern of results provides a very strong test for the vulnerability status of the LMS, not only in the context of anxiety, but also-consistent with Rector, Kamkar, & Riskind's (2008) clinical case illustration-in the context of depressive symptoms and cognitive vulnerability to depression. Within each disorder component', we identified directional (i.e., Granger-causal) relationships involving Hopelessness and Depression Symptoms across all three par- ticipants. In two participants, these relationships were unidirectional, leading from Hopelessness to Depression Symptoms. In the third participant, an opposite pattern, leading from Depression Symptoms to Hopelessness, was found. The importance of these findings is twofold. First, they provide, to the best of our knowledge for the first time, support for the Hopelessness Theory for Depression using an intra-individual, TS design. Second, our findings show that hopelessness, as a cognitive vulnerability factor, may also be influenced by depression symptoms, an effect that is consistent with the higWy debated scarring hypothesis (Lewinsohn, Steinmetz, Larson, & Franklin, 1981; Shahar & Davidson, 2003; Shahar & Henrich, 2010). The fact that each individual had her own specific causal network, with specific trajectories not always congruent with known literature, highlights the limitations inherent in cross-sectional and "snap shot" longitudinal designs in terms of captur- ing clinical dynamics. As such, the present findings are .in line with theoretical mod- els suggesting the need for more complex, novel tools of analysis (Essex & Smythe, DEPRESSION ANXIETY COMORBIDITY: A TSA STUDY 183

1999;Grace 2001; Molenaar, 2004, 2007; Molenaar & Valsiner 2005; Sohn 1999; Toomela,2007). The most salient limitation of this study should be acknowledged. Because we focusedon an intra-individual network of causal relations, each participant was treated asa separate study, leading to a sample of N = 3. While this is common in Time Se- riesstudies (Brossart et aI., 1998; Dunn et aI, 1987; Pole et aI, 2008), replications with additional participants are needed. As well, our exclusive reliance on self-report measuresmight have inflated shared method variance, inadvertently boosting the as- sociationsbetween the study variables. Third, whereas the time scale employed in the presentstudy was based on days, important changes in depression, anxiety, and their interrelations might have occurred on other time scales (hours, weeks, months), and our fmdings could not be generalized to these time scales. Within the context of these limitations,however, the present study provides a detailed examination of the anxiety- depressioncomorbidity by differentiating between symptoms, affective, and cognitive vulnerability dimensions that characterize each of the disorders, using-for the first time to our knowledge-a sophisticated, intra-individual TSA.

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