Threat distractor and perceptual load modulate test-retest reliability of anterior cingulate cortex response

Accepted Manuscript Threat distractor and perceptual load modulate test-retest reliability of anterior cingulate cortex response

Nora Bunford, Kerry L. Kinney, Jamie Michael, Heide Klumpp PII: DOI: Reference:

S0278-5846(16)30163-4 doi: 10.1016/j.pnpbp.2017.04.007 PNP 9065

To appear in:

Progress in Neuropsychopharmacology & Biological Psychiatry

Received date: Revised date: Accepted date:

22 August 2016 28 February 2017 6 April 2017

Please cite this article as: Nora Bunford, Kerry L. Kinney, Jamie Michael, Heide Klumpp , Threat distractor and perceptual load modulate test-retest reliability of anterior cingulate cortex response. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Pnp(2017), doi: 10.1016/j.pnpbp.2017.04.007

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ACCEPTED MANUSCRIPT Running head: FMRI RACC RELIABILITY

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Threat Distractor and Perceptual Load Modulate Test-Retest Reliability of Anterior Cingulate Cortex Response

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Nora Bunford1 , Kerry L. Kinney2 , Jamie Michael1 , & Heide Klumpp1,2

Mood and Anxiety Disorders Research Program, Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60608, USA

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Department of Psychology, University of Illinois at Chicago, Chicago, IL 60607, USA

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Correspondence regarding this manuscript should be addressed to Nora Bunford c/o Heide Klumpp University of Illinois at Chicago, Department of Psychiatry 1747 W. Roosevelt Rd., Rm. 246 Chicago, IL 60608 E-mail: [email protected]

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Abstract Accumulating data from fMRI studies implicate the rostral anterior cingulate cortex (rACC) in inhibition of attention to threat distractors that compete with task-relevant goals for processing resources. However, little data is available on the reliability of rACC activation. Our aim in the

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current study was to examine test-retest reliability of rACC activation over a 12-week period, in

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the context of a validated emotional interference paradigm that varied in perceptual load. During functional MRI, 23 healthy volunteers completed a task involving a target letter in a string of

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identical letters (low load) or in a string of mixed letters (high load) superimposed on angry, fearful, and neutral face distractors. Intraclass correlation coefficients (ICCs) indicated that under

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low, but not high, perceptual load, rACC activation to fearful vs. neutral distractors was

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moderately reliable. Conversely, regardless of perceptual load, rACC activation to angry vs. neutral distractors was not reliable. Regarding behavioral performance, ICCs indicated that

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accuracy was not reliable regardless of distractor type or perceptual load. Although reaction time

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(RT) was similarly not reliable regardless of distractor type under low perceptual load, RT to angry vs. neutral distractors and to fearful vs. neutral distractors was reliable under high

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perceptual load. Together, results indicate the test-retest reliability of rACC activation and

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corresponding behavioral performance are context dependent; reliability of the former varies as a function of distractor type and level of cognitive demand, whereas reliability of the latter depends on behavioral index (accuracy vs. RT) and level of cognitive demand but not distractor type. Keywords: test-retest reliability, neuroimaging, fMRI, attentional control, emotional distractors

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Threat Distractor and Perceptual Load Modulate Test-Retest Reliability of Anterior Cingulate Cortex Response Negativity bias refers to preferential processing of threatening signals. As there are limits to perceptual capacity, prioritization of what is attended to is necessary and evidence indicating

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that threatening information is especially attention- grabbing suggests that there is an evolutionary

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advantage in the ability to selectively attend to threat (Ohman, Flykt, & Esteves, 2001; Ohman,

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Lundqvist, & Esteves, 2001; Yiend, 2010). There is a large body of work on this negativity bias, in particular on its relation to emotional disorders, given that excessive selective attention to

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threat is a hallmark of anxiety disorders, major depressive disorder, and vulnerability to

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developing such disorders (e.g., high trait anxious) (Bar-Haim, Lamy, Pergamin, & BakermansKranenburg, Van Ijzendoorn, 2007; Peckham, McHugh, & Otto, 2010).

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In addition to psychological factors, perceptual load influences negativity bias (Desimore

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& Duncan, 1995). According to load theory, attention to salient but task-irrelevant stimuli such as threat cues depends on the availability of processing resources. That is, when perceptual load is

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low (e.g., task is easy to execute), task-relevant resources are available (i.e., ‘left over’) to

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covertly or overtly process task-irrelevant threat distractors (Lavie, Lin, Zokaei, & Thoma, 2009). In contrast, when task-relevant processing depletes capacity limits (e.g., task is difficult to

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execute), attention to threat distractors is inhibited as there are insufficient resources to process distractors. Hereafter, ‘attentional control’ refers to the inhibition of attention to threat distractors, particularly when demands on perceptual capacity are low and thus emotional interference incurred by task-irrelevant threat stimuli is high. Attentional control and factors that modulate it are commonly evaluated at the behavioral level. Converging results from affective dot probe, emotional Stroop, and exogenous cueing studies (Fox, Russo, Bowles, & Dutton, 2001; Koster, Crombez, Van Damme, Verschuere, & De

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Houwer, 2004; Koster, Crombez, Verschuere, & De Houwer, 2004; Koster, Crombez, Verschuere, Van Damme, & Wiersema, 2006; MacLeod, Mathews, & Tata, 1986) support convergent validity of the attentional control construct. Yet, behavioral indices of attentional control are generally unreliable. For example, although one-week reliability of response times

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(RTs) corresponding to color naming (of a presented word) has been shown to be high for

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congruent trials, they are low for emotional interference (i.e., congruent minus incongruent trials;

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Eide, Kemp, Silberstein, Nathan, & Stough, 2002; Strauss, Allen, Jorgensen, & Cramer, 2005). Likewise, poor test-retest reliability has been observed in healthy participants with dot-probe

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RTs to the dot (Schmukle, 2005; Staugaard, 2009).

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paradigms where attention allocation to threat (vs. non-threat) stimuli is determined covertly by

Together, RTs appear to be a problematic index of attentional control from the perspective

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of temporal stability. Given advances in neuroscience, it stands to reason that neural correlates of

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this construct exhibit more reliability than behavioral ones as the former are more proximal to mechanisms that sub-serve the attention system. In support, electroencephalograph (EEG) indices

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of neural activation to attentional control exhibit acceptable reliability. For example, excellent

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reliability of spontaneous theta/beta ratio – a neurophysiological index of attentional control (Putman, Arias-Garcia, Pantazi, & van Schie, 2012; Putman, van Peer, Maimari, & van der

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Werff, 2010) – has been found over the period of one week among healthy adults (Angelidis, van der Does, Schakel, & Putman, 2016). These data are consistent with evidence indicating neurophysiological markers of higher-order cognitive functions have acceptable test-retest reliability (Huffmeijer, Bakermans-Kranenburg, Alink, & van IJzendoorn, 2014). However, despite its good temporal resolution, EEG has poor spatial resolution. As such, it is unknown whether neural activation in brain regions associated with attentional control are temporally stable though findings across fMRI studies with various tasks suggest that brain

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activity is moderately stable (intraclass correlations [ICC] averaging 0.50) (Bennett & Miller, 2010). Yet, ICC values ranged from -0.08 to 0.99 and this wide range is likely due to various factors. For example, noise caused by differences between and within participants (e.g., arousal, non-task related cognitive processes), scanners (e.g., thermal noise), and task design (e.g.,

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number of runs) (Friedman & Glover, 2006) have the potential to influence reliability (Bennett &

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Miller, 2010). Thus, when examining the temporal stability of neurofunctional activity it is

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prudent to focus on activation in brain regions to phenomena for which there is evidence of construct validity and reproducibility.

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One such brain region is the rostral anterior cingulate cortex (rACC), which is implicated

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in attentional control. Specifically, studies of psychiatrically healthy individuals show rACC engagement is integral to modulating the conflict that occurs when cognitive goals compete with

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salient distractors (Etkin et al., 2011; Kanske & Kotz, 2010). In line with load theory (Lavie et

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al., 2009), Bishop and colleagues (Bishop, Jenkins, & Lawrence, 2007) showed healthy (i.e., low trait anxious) relative to high trait anxious individuals exhibited enhanced rACC activation to

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fearful (> neutral) face distractors under low, but not high, perceptual load. Similarly, we

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observed healthy participants had greater rACC activation to fearful (> neutral) face distractors compared to patients with social anxiety disorder when perceptual load was low but not high

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(Wheaton, Fitzgerald, Phan, & Klumpp, 2014). Interestingly, we found no evidence of differential ACC activation to angry (> neutral) face distractors regardless of perceptual load suggesting the threat conveyed by angry faces may have been less salient (i.e., distracting) than that conveyed by fearful faces. Indications of threat content having influenced findings is consistent with data obtained in other studies wherein expressions of anger and fear had differential effects on the attention system (Engen & Singer, 2014; Klumpp et al., 2011) along

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with reports that emotional content of stimuli differentially impacts selective attention (Yiend, 2010). Taken together, attentional control is a well-established construct and the rACC has been observed across independent laboratories and repeatedly as playing a role in the neural activation

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associated with attentional control. The aim of the current study was to extend this body of work

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by evaluating the test–retest reliability of rACC activity separated by 12 weeks. Based on prior

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literature and theory, we hypothesized that participants would exhibit stable rACC activation to fearful face distractors under low, but not high, perceptual load. Angry face distractors were also

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included to examine whether the specificity of threat stimulus influenced the temporal stability of

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rACC activation. We also evaluated the test-retest reliability of behavioral performance though we did not expect accuracy or RT to be consistent across two measurements given previous

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reports of poor reliability in studies of emotional interference or attention to threat.

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Method

Participants

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Data for the current study were collected as part of an ongoing research project.

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Participants were recruited via community advertisements and had to be free of major medical or neurologic illness as confirmed by a Board Certified physician. Participants were required to

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have no current or history of Axis I diagnoses as confirmed by the Structured Clinical Interview for DSM-IV ( SCID-IV; First, Spitzer, Gibbon, & Williams, 1995) conducted by a master’s level clinician. Exclusion criteria included contraindications to fMRI (e.g., pregnancy, non-removable ferrous objects), positive test for illicit substances, or current cognitive dysfunction (e.g., dementia, pervasive developmental disorder, traumatic brain injury). This research was approved by the Institutional Review Board at the University of Illinois at Chicago (UIC) and all

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procedures complied with the Helsinki Declaration. All participants provided written informed consent and were compensated for their time. Participants were 23 healthy adults (34.78% male) who had an average age of 26.83 (SD = 7.44, range 18-50 years) and educational level of 16.52 years (SD = 2.25, range 12-

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21 years). Regarding race/ethnicity, 47.83% self-identified as Caucasian, 34.78% as Asian, 4% as

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African American, and 13.04% as Hispanic.

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fMRI Task

During fMRI, participants completed the Emotional Faces Interference Task (EFIT),

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based on paradigms previously used to probe emotional interference in healthy and patient

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cohorts in other (Bishop et al., 2007) as well as our own laboratories, (Klumpp et al., 2016; Wheaton et al., 2014). FMRI measurement was conducted within a week of completing clinical

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assessments (e.g., SCID-IV) (Week 0) and 12 weeks later at a second time-point (Week 12).

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In the EFIT, participants are presented with a string of six letters superimposed on taskirrelevant standardized angry, fearful, or neutral face distractors (Ekman & Friesen, 1976).

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Participants are asked to identify target letters, N or X, by button press and to do so as accurately

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and quickly as possible. In low perceptual load trials, the string of six letters comprises target letters only, whereas high perceptual load trials include a single target letter and five non-target

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letters (H, K, M, W, Z), arranged in randomized order (see Figure 1). The face distractors are pictures of 10 actors (6 female), with each actor being used once for each condition (e.g., angry low, angry high, fearful low, fearful high, neutral low, and neutral high) for each run. Thus, each condition occurs 10 times per run, and participants completed 2 runs of the task. The experimental paradigm comprised two image acquisition runs, each including 12 blocks of five trials. A mixed block/event-related design was used wherein perceptual load (low vs. high) varied across blocks and facial expression (angry, fearful, and neutral) varied within

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blocks on a trial-by-trial basis. Images were presented for 200 ms and followed by a fixation cross presented for 1,800 ms. Within blocks, trials were separated by a jittered interstimulus interval lasting 2-6 s. Blocks were separated by 4-8 s. fMRI Data Acquisition and Preprocessing

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Scanning was conducted on a 3 Tesla GE Signa System (General Electric Healthcare;

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Waukesha, WI) with an 8-channel head coil. Functional data were acquired using gradient-echo

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echoplanar imaging (EPI) sequence with the following parameters: TR = 2 s, TE = minFull [~25 ms], flip angle = 90º, FOV = 22 x 22 cm2 , acquisition matrix 64 x 64, 3-mm slice thickness, 44

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axial slices, 180 volumes per run. For anatomical localization, a high-resolution, T1-weighted

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volumetric anatomical scan was acquired.

Data from all participants met criteria for quality with minimal motion correction

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(movements were < 3 mm and < 3 degrees rotation in any one direction) and the first 4 volumes

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from each run were discarded to allow for T1 equilibration effects. Conventional preprocessing steps were used in Statistical Parametric Mapping (SPM8) software package (Wellcome Trust

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Centre for Neuroimaging, London www.fil.ion.ucl.ac.uk/spm). Briefly, images were temporally

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corrected to account for differences in slice time collection, spatially realigned to the first image of the first run, normalized to a Montreal Neurological Institute (MNI) template, resampled to 2 x

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2 x 2 mm voxels, and smoothed with an 8mm isotropic Gaussian kernel. A general linear model was applied to the time series, convolved with the canonical hemodynamic response function and with a 128s high-pass filter. Blocks of low and high perceptual load were modeled separately based on task-irrelevant face type (angry, fearful, and neutral) resulting in six regressors (angry low, angry high, fearful low, fearful high, neutral low, neutral high), the effects of which were estimated for each voxel for each participant, and contrast images were generated for second-level analysis. Our primary contrasts of interest were angry

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low vs. neutral low, fearful low vs. neutral low, angry high vs. neutral high, and fearful high vs. neutral high. Analytic Plan To examine the stability of activation in our a priori region of interest, the Automatic

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Anatomical Labeling (AAL) system (Tzourio-Mazoyer et al., 2002) was used to generate an

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anatomically-based rACC mask (AAL ACC below the line z=0) (search volume = 4160 mm).

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In the Statistical Package for the Social Sciences (SPSS; Chicago, IL version 22), parameter estimates of activation in the rACC mask (β weights, arbitrary units [a.u.]) were

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subjected to descriptive statistics with corresponding 95% confidence intervals [CIs] and

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measures of accuracy (i.e., bias and standard errors [SE]) obtained with 1,000 bootstrap resamples obtained via random sampling with replacement) computed.

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Finally, intraclass correlation coefficients (ICC) with corresponding 95% CIs were computed.

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ICCs represent the ratio of between-subjects variance to total variance and are the appropriate metric (as opposed to Pearson’s r) for assessing test-retest reliability when observations are not

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independent (Shrout & Fleiss, 1979). In the current study, we used an average measures ICC as it

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corresponds to the reliability of the mean of repeated measures (e.g., the means of activation to threatening vs. neutral distractors over two scan sessions). This form of test-retest reliability is

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relevant to the current study wherein activation is measured longitudinally over more than one scan sessions (Johnstone et al., 2005). Specifically, in SPSS, the ICC model was a two-way mixed model with estimates for absolute agreement and 95% CIs. Absolute agreement between activations is an index of the degree to which scores are identical over time. The same processes (i.e., computation of descriptive statistics and ICC coefficients) were followed to analyze the testretest reliability of behavioral performance (accuracy and RT).

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In accordance with convention, ICCs, which range from -1 to 1 were interpreted as follows: 0-0.2 as poor, 0.3-0.4 as fair, 0.5-0.6 as moderate, 0.7-0.8 as strong, and >0.8 as almost perfect (Sundvall, Ingerslev, Knudsen, & Kirkegaard, 2013). Of note, it is possible for ICCs to be negative when the within- group variance exceeds the between-groups variance, suggesting a

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measure is not reliable.

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In addition to our a priori ICC approach, we performed a 2 (Threat Type: fearful >

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neutral, angry > neutral faces) x 2 (Load: low, high) x 2 (Time: Week 0, Week 12) repeated measures ANOVA to explore whether a whole-brain approach would reveal ACC effects.

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Significance was set at p < 0.005 uncorrected with a minimum of at least 20 contiguous voxels to

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strike a balance between Type I and Type II error rates (Lieberman & Cunningham, 2009). To illustrate the magnitude and direction of activation for any omnibus ACC effects, parameter

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estimates of peak activation for significant findings (β weights, arbitrary units [a.u.]) were

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extracted from a spherical (10-mm diameter) region of interest (ROI) from each participant and

Results

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Behavioral Results

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submitted to post-hoc analysis in SPSS.

To confirm that manipulation of low vs. high perceptual load was successful and explore

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whether behavioral measures interacted with distractor type or time we conducted a 3 (Distractor type: angry, fearful, neutral) x 2 (Perceptual load: low, high) x 2 (Time: Week 0, Week 12) repeated measures ANOVA. For accuracy, results revealed a main effect of Load, F(1, 22) = 185.82, p < .001. No other main effects or interactions were significant (all p’s >0.05). To evaluate the direction of load, a follow-up paired t-test was conducted. Results revealed that accuracy was higher in the low relative to high load condition t(22) = 13.63, p < .001. We conducted the same analysis for reaction time (RT) for accurate trials, and there was a main effect

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of perceptual load, F(1, 22) = 223.91, p < .001. A follow-up paired t-test to examine the direction of load showed RT was faster in the low relative to high load condition t(22) = -14.96, p < .001. For descriptive statistics, see Table 1. Test-Retest Reliability of Rostral Anterior Cingulate Cortex Activation

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Angry vs. neutral distractors. Forlow perceptual load, ICCs based on the rACC mask

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indicated poor stability of activation to angry vs. neutral distractors (ICC = .022; 95% CIs = [-

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1.306; .585], p = .480). Regarding high perceptual load, results were similar (ICC = -.005; 95% CIs = [-1.370; .574], p = .505). See Figure 2 for comparison of rACC activation to angry vs.

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neutral distractors given load. See Table 1 for descriptive statistics.

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Fearful vs. neutral distractors. For low perceptual load, ICCs related to activation in the rACC mask indicated moderate stability to fearful vs. neutral distractors. In particular, the

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average measures ICC was .540 (95% CIs = [-.013; .796], p = .021) across the two scan sessions.

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Results indicate rACC activation measured twice across 12 weeks was moderately reliable. With regard to high perceptual load, ICCs indicated rACC response to fearful vs. neutral distractors

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changed over time (ICC = -.440; 95% CIs = [-.571; .256], p = .791). Therefore, rACC activity

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under high load was unreliable. See Figure 2 for comparison of rACC activation to fearful vs. neutral distractors given load. See Table 1 for descriptive statistics.

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Functional MRI

At a conventional threshold for significance (Lieberman & Cunningham, 2009), the whole brain repeated measures ANOVA failed to reveal a main effect of threat distractor type, load, time, or interactions for ACC activation. Test–retest reliability of behavioral performance Angry vs. neutral distractors. Regarding accuracy under low perceptual load, ICCs corresponding to accuracy indicated poor stability in the presence of angry vs. neutral distractors

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(ICC = -.829; 95% CIs = [-3.312; .224], p = .918). For high perceptual load, results suggested only fair stability of accuracy (ICC = .408; 95% CIs = [-.395; .749], p = .113). See Figure 3 for comparison of accuracy in the presence of angry vs. neutral distractors given load. With regard to RTs for accurate trials under low perceptual load, ICCs indicated poor

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stability of in the presence of angry vs. neutral distractors (ICC = .278; 95% CIs = [-.703; .694], p

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= .226). For high perceptual load, ICCs indicated strong stability of RT in the presence of angry

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vs. neutral distractors. The average measures ICC for accuracy under high perceptual load was .670 (95% CIs = [.222; .860], p = .006) across the two scan sessions, indicating that the mean

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RT in the presence of angry distractors given load.

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estimate of RT measured twice across 12 weeks is highly reliable. See Figure 4 for comparison of

Fearful vs. neutral distractors. For accuracy related to low perceptual load , ICCs

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results suggested only fair stability (ICC = .390; 95% CIs = [-.438; .741], p = .127). Accuracy

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related to high perceptual load was comparable in that stability was also fair (ICC = .311; 95%

distractors given load.

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CIs = [-.626; .708], p = .195). See Figure 3 for comparison of accuracy in the presence of fearful

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RTs for accurate trials under low perceptual load had poor stability in the presence of fearful vs. neutral distractors (ICC = .225; 95% CIs = [-.828; .671], p = .278). Yet, under high

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load ICCs revealed strong stability of RT. Specifically, the average measures ICC for accuracy under low perceptual load was .748 (95% CIs = [.406; .893], p = .001) across the two scan sessions, indicating that the mean estimate of RT measured twice across 12 weeks was highly reliable. See Figure 4 for comparison of accuracy in the presence of fearful distractors given load. Discussion To our knowledge this is the first study wherein the test-retest reliability of rostral anterior cingulate cortex (rACC) activation during attentional control was examined. The temporal

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stability of rACC activity was evaluated in the context of threatening face distractors (angry, fearful) under low and high perceptual load with a validated paradigm (Bishop et al., 2007; Wheaton et al., 2014). Behavioral findings confirmed the manipulation of perceptual load was successful as accuracy was higher and response times (RT) faster in the low relative to high

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perceptual load condition. As indexed by intraclass correlation coefficients (ICCs), we expected

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rACC activation to fearful face distractors would be stable over a 12-week interval under low

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perceptual load. Our hypotheses were supported.

Specifically, rACC activation derived from an anatomy-based mask was moderately

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reliable to fearful (vs. neutral) face distractors under low, but not high perceptual load. However,

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rACC activation angry (vs. neutral) face distractors was unreliable regardless of load condition. These results indicating that the specific threat content of distractors influenced test-retest

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reliability is consistent with our previous findings related to attentional control where rACC

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activation differed between fearful and angry face distractors (Klumpp et al., 2011; Wheaton et al., 2014). Fearful and angry expressions are both motivationally relevant and thus more salient

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than neutral faces. However, when directed to the viewer as in our task, a fearful expression is

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more ambiguous than an angry expression as the source of threat communicated by the former is unknown (Biehl, Matsumoto, & Ekman, 1997; Ewbank et al., 2009; Fox, Calder, & Yiend, 2007;

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Whalen, 1998). Signals of ambiguous threat may be persistently salient at the neural level. Evidence of reliable test-retest rACC activation to fearful distractors under low perceptual load is consistent with the notion that attentional control interacts with perceptual load (Lavie et al., 2009; O’Connor, Fukui, Pinsk, & Kastner, 2002). Given that high emotional interference is associated with low load, findings of more stable rACC activation in this condition may reflect greater employment of control mechanisms. Extending this conceptualization to the high load condition, rACC activation to fearful distractors was potentially less stable as emotional

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interference was lower and thus demand for cognitive, top-down control was lower. In other words, individual differences in rACC response over time was greater when the need to inhibit a salient distractor was diminished. From a statistical perspective, a few additional considerations regarding differences in the

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reliability of rACC activation given perceptual load are worthy of note. The first consideration is

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in relation to the sample standard deviation (SD). Regardless of distractor type, there was a

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smaller change in the SD from Week 0 to Week 12 in the low compared to the high load condition. Also regardless of distractor type there was a smaller change in the SD from Week 0 to

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Week 12 in the low compared to the high load condition. As SD is an index of the degree to

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which members of a group differ from the average value for the group, these differences suggest greater change in the spread of rACC activation values from Week 0 to Week 12 for high relative

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to low load, which likely reduced reliability.

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The second consideration is in relation to indices of measurement accuracy related to the sample SD. (1) Bias is an index of the degree to which the bootstrap distribution (of any statistic,

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such as the mean or the SD) – obtained via random sampling with replacement – and the actual

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sample distribution systematically disagree. Thus, greater bias may reduce reliability. In our sample, there was an increase in bias related to the SD from Week 0 to Week 12 as indexed by

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the magnitude of the CIs around the SDs. 95% CIs indicate that if the current analyses were replicated across different experiments, in 95% of those experiments the observed values would fall somewhere within the lower and upper bound of the CI; narrower CIs correspond to greater confidence in the accuracy of the obtained values. The CIs around the sample SDs were better for Week 0 and Week 12 measurements for low load and Week 0 measurement for high load compared to Week 12 measurement for high load – better in the sense that they indicate greater “accuracy” or confidence in the obtained values.

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Taking the first and second consideration together, the relative difference between Week 0 and Week 12 measurements for low compared to high load as well as the absolute difference between indices of accuracy for Week 0 and Week 12 measurements for low load and Week 0 measurement for high load compared to Week 12 measurement for high load, all indicate less

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confidence or more potential error in Week 12 measurements for high perceptual load.

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In addition to our a priori approach, we performed a whole-brain ANOVA to explore

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whether there were main effects of distractor type, load, time, or interactions for ACC. Significant whole-brain ACC activity was not observed and may pertain to our modest sample

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size and relatively homogenous, psychiatrically healthy cohort. That is, ACC effects are

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commonly found when healthy individuals are contrasted with patients (Wheaton et al., 2014) or with individuals vulnerable to experiencing excessive anxiety (e.g., high trait anxious) (Bishop et

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al., 2007; Klumpp et al., 2011). Importantly, it is not possible to derive ICC values from data

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based on t-maps. Therefore, the null results do not negate reliability findings as magnitude of neural activity is not necessarily predictive of reliability. In other words, a region may have

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suboptimal activation according to whole-brain data yet that region can exhibit reliable activation

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(Caceres, Hall, Zelaya, Williams, & Mehta, 2009). Our results are a particularly important addition to the neuroimaging literature as although

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there are well-developed standards for using measures such as rating scales and questionnaires in psychiatric assessment and treatment (Fredrikson & Furmark, 2003), neuroimaging has not been held to the same standards (Frewen, Dozois, & Lanius, 2008). Since neuroimaging is considered a ‘hard’ science laboratory measure, it is presumed to be rigorous (Lilienfeld, 2014). This representative heuristic (Tversky & Kahneman, 1974), in addition to practical issues associated with repeating scans (e.g., costs, scheduling) may partly explain why little is known about the psychometric properties of measures of brain function (Siegle, 2011). This is a notable limitation

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as arguments for incorporating neuroimaging into psychiatric assessment and treatment imply that deficits in some brain function can be measured and, later, re-measured to determine whether change has occurred, so as to quantify phenomena such as disease course or treatment response (Forgeard et al., 2011).Determining change, however, is entirely predicated on the stability of the

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function and its measure – that, in the absence of changes in disease course or of treatment,

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individuals exhibit the same or similar brain function over time (Siegle, 2011). As such, to

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increase adoption into clinical practice, it is prudent to first examine the psychometric properties of measures of brain function.

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The current findings expand on limited but pertinent neuroimaging data that suggest

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reliable neurofunctional activity to paradigms that probe higher-order cognitive functions. For example, in a classification learning study, Aron and colleagues (2006) found moderately stable

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activation across two sessions 1 year apart in regions that included lateral, medial, and orbital

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frontal cortex. Similarly, others have found moderately reliable activation to a probabilistic reversal learning task across two sessions 16 weeks apart in orbitofrontal–striatal anterior

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prefrontal/insular, dorsolateral prefrontal, and ACC regions (Freyer et al., 2009). Moreover,

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dorsolateral prefrontal cortex activation to a working memory task (i.e., Sternberg Item Recognition Paradigm) was shown to be highly reliable over the course of 9-13 weeks (interscan

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intervals varied across participants) in healthy participants but not in patients with schizophrenia (Manoach et al., 2001). Collectively, preliminary evidence suggests activation in a frontal region implicated in higher-order cognitive functions may be reliable over time. Regarding behavioral data, we did not expect accuracy or RT to be reliable over the 12 week interval. Nonetheless, there was evidence of reliability for RT, but not accuracy, in the high perceptual load condition, regardless of distractor type. Conversely, under low load, neither RT nor accuracy were reliable. Considered together with emotional Stroop and dot-probe

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paradigm studies (Eide et al., 2002; Schmukle, 2005; Staugaard, 2009; Strauss et al., 2005), indices of behavioral performance generally show poor test-retest reliability. Our stable RT finding under high load may relate to reduction in variance over time due to the challenging nature of the high load condition.

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In general, these data support the idea that brain activation and behavioral performance

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provide unique information on phenomena of interest (Morris & Cuthbert, 2012) and, in the

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current case, with regard to reliability. As such, our findings are part of an emerging literature that highlights the importance of multi- method measurement. Our findings emphasize the

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importance of accounting for task- and measurement-dependent changes over time as test-retest

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reliability of brain activation appears to vary as a function of distractor type and level of cognitive demand, whereas behavioral test-retest reliability appears to not vary as a function of

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distractor type, but rather of cognitive demand and behavioral index (accuracy vs. RT).

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In summary, rACC activation during attentional control has been observed across laboratories indicating it is reproducible. We found rACC activation derived from an anatomy-

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based mask was reliable to fearful but not angry face distractors under low perceptual load.

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Regarding behavioral performance, RT was relLavieiable under high perceptual load across fearful and angry face distractors. Findings emphasize the importance of accounting for task- and

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measurement-dependent changes over time in addition to the influence of stimulus specificity in the context of task-irrelevant stimuli and perceptual load. Moreover, evidence of temporal stability in rACC despite lack of omnibus/whole-brain ACC findings is in keeping with the observation that a region may be reliable in its activation even if it is low in magnitude of response (Caceres et al., 2009). This issue is important to consider as fMRI research has become increasingly conservative to protect against Type I error (Lieberman & Cunningham, 2009). As a

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result, activation that is theoretically-relevant (e.g., implicated in relation to a valid construct) and temporally stable may not be detected with conventional whole-brain thresholds. As noted above, the test-retest reliability of fMRI data is under-researched, however, it can be argued that assessment of temporal stability across domains of measurement (e.g., fMRI,

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EEG, psychophysiology, task-based behavior, self-report) has generally been a neglected area of

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reliable than others for attentional control and/or threat processing.

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study. Therefore, further research is necessary to determine whether certain measures are more

This study is not without important limitations. First, our paradigm was not designed to

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dissociate activation implicated in the detection of conflict and resolution of conflict; it will be

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important to examine test-retest reliability of ACC activation specific to these functions. Second, the same face distractors were used on both occasions, therefore, potential task-irrelevant effects

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related to novelty could not be examined. Third, we elected focus on rACC, a region strongly

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implicated in attentional control; with this approach, we cannot rule out the possibility that other regions associated with top-down control may have been reliable. Lastly, results are based on

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healthy individuals and, therefore, may not generalize to psychiatric populations.

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Notwithstanding limitations, to the best of our knowledge this is the first study wherein the test-retest reliability of rACC activation and corresponding behavioral performance was

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examined with a well-validated attentional control paradigm (Wheaton et al., 2014). We observed moderate test-retest reliability of rACC activation in the context of fearful face distractors when processing resources were available to ‘attend’ to such distractors. At the behavioral level, reliability of reaction time in the presence of angry and fearful distractors was high when taskrelevant processing resources were maximized. These findings are encouraging for a number of reasons. Attentional control is an important feature in day-to-day intra- and interpersonal functioning. Given the crucial role of the rACC in this process, understanding stability of

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activation in this region could contribute to frameworks of adaptive behavior. Furthermore, as noted, prior to using neuroimaging to track disease progress in patients or response to treatment, it is necessary to establish that healthy volunteers exhibit stable brain function over time. As such, the current results represent a step toward incorporating neuroimaging into psychiatric

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assessment and treatment and the establishment of standards for their use.

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Research reported in this publication was supported in part by the National Institute of Mental Health of the National Institutes of Health under Award Number K23MH093679 to HK and the

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Center for Clinical and Translational Science (CCTS) UL1RR029879.

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Figure 1. Diagram of experimental design and paradigm.

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Table 1

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Descriptive statistics and indices of measurement error for rACC activation and behavioral performance depending on load at week 0 and week 12 M M 95% CIs SD SD 95% CIs Accuracy in the presence of angry distracters (% of all responses) Week 0 low load 91.95 85.43;96.95 14.12 6.37;19.92 Week 12 low 91.08 84.34;96.30 14.37 6.27;20.88 load Week 0 high 64.68 59.56;69.99 13.01 9.27;15.64 load Week 12 high 64.13 57.82;70.43 16.00 11.63;19.24 load Accuracy in the presence of fearful distracters (% of all responses) Week 0 low load 90.650 82.397; 96.304 17.076 5.824;25.647 Week 12 low 90.434 81.962;96.734 17.574 4.757;26.183 load Week 0 high 60.652 53.696;67.174 16.943 12.507;20.147 load Week 12 high 60.652 53.918;66.951 15.249 10.599;18.431 load Accuracy in the presence of neutral distracters (% of all responses) Week 0 low load 91.73 85.21;96.73 14.11 5.98;19.84 Week 12 low 88.69 81.30;94.34 15.96 7.26;23.39 load Week 0 high 64.56 58.04;70.86 16.09 11.14;20.03 load Week 12 high 65.21 59.56;71.30 14.49 10.86;17.32 load Reaction time in the presence of angry distracters (in milliseconds) Week 0 low load 817.05 718.22;905.92 241.79 100.62;358.75 Week 12 low 820.82 764,48;874.89 158.29 89.06;217.53 load Week 0 high 1142.82 1006.70;1244.67 297.43 131.198;457.70 load Week 12 high 1140.01 1079.00;1208.08 165.14 118.83;199.16 load Reaction time in the presence of fearful distracters (in milliseconds) Week 0 low load 808.872 707.623;559.892 223.618 93.633;342.757 Week 12 low 798.045 752.082;844.176 123.008 75.855;156.649 load Week 0 high 1178.965 1041.112;1269.751 283.746 95.614;456.115 load Week 12 high 1423.78 1106.66;1214.724 132.409 95.620;159.708 load

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Reaction time in the presence of neutral distracters (in milliseconds) Week 0 low load 791.19 697.90;860.95 206.20 Week 12 low load 813.20 764.47;874.88 139.04 Week 0 high load 1135.82 1005.47;1224.34 276.94 Week 12 high load 1158.09 1096.97;1223.84 158.26 rACC activation to angry vs. neutral distracters Week 0 low load .42 -2.21;2.89 6.27 Week 12 low load -.24 -2.51;2.31 6.05 Week 0 high load -.011 -1.687;1.755 4.37 Week 12 high load -2.08 -4.25;-.09 5.29 rACC activation to fearful vs. neutral distracters Week 0 low load .811 -.593; 1.989 3.141 Week 12 low load -1.302 -3.029; .145 4.052 Week 0 high load .641 -.898; 2.174 3.757 Week 12 high load 1.319 -.709; 3.871 5.594 Note. rACC = rostral anterior cingulate cortex

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Highlights

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23 adults completed an emotional interference task twice across 12 weeks during fMRI rACC activation to fearful distractors was reliable under low perceptual load rACC activation to angry distractors was not reliable regardless of perceptual load task accuracy was not reliable regardless of distractor type or perceptual load task reaction time was reliable under high but not low perceptual load

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