Locus Coeruleus Activity Strengthens Prioritized Memories Under Arousal

This Accepted Manuscript has not been copyedited and formatted. The final version may differ from this version.

Research Articles: Behavioral/Cognitive

Locus coeruleus activity strengthens prioritized memories under arousal

David Clewetta, Ringo Huangb, Rico Velascob, Tae-Ho Leec and Mara Mathera,b aNeuroscience Graduate Program, University of Southern California, USA 90089 bDavis School of Gerontology, University of Southern California, USA 90089 cDepartment of Psychology and Neuroscience, University of North Carolina at Chapel Hill, USA 27599

DOI: 10.1523/JNEUROSCI.2097-17.2017

Received: 25 July 2017

Revised: 16 November 2017

Accepted: 28 November 2017

Published: 4 January 2018

Author contributions: D.V.C. and M.M. designed research; D.V.C., R.H., and R.V. performed research; D.V.C. contributed unpublished reagents/analytic tools; D.V.C., R.H., R.V., T.-H.L., and M.M. analyzed data; D.V.C., R.H., R.V., T.-H.L., and M.M. wrote the paper.

Conflict of Interest: The authors declare no competing financial interests.

This project was funded by federal NIH grant R01AG025340. This project was also funded by a USC Endowed Fellowship (D.C.). The authors declare no competing financial interests. The authors thank Dr. Alexa Tompary for her helpful comments on the manuscript and Avi Chanales for his assistance editing one of the figures.

Correspondence should be addressed to To whom correspondence should be addressed: University of Southern California, 3715 McClintock Avenue, Room 351, Los Angeles, CA 90089, Email: [email protected], Phone: 213-740-9401, Fax: 213-821-5561

Cite as: J. Neurosci ; 10.1523/JNEUROSCI.2097-17.2017

Alerts: Sign up at www.jneurosci.org/cgi/alerts to receive customized email alerts when the fully formatted version of this article is published.

Accepted manuscripts are peer-reviewed but have not been through the copyediting, formatting, or proofreading process.

1 2 3 4 5 6 Locus coeruleus activity strengthens prioritized 7 memories under arousal 8 9 David Clewetta*, Ringo Huangb, Rico Velascob, Tae-Ho Leec, & Mara Mathera,b 10 11 12 aNeuroscience Graduate Program, University of Southern California, USA 90089 13 bDavis School of Gerontology, University of Southern California, USA 90089 14 cDepartment of Psychology and Neuroscience, University of North Carolina at Chapel Hill, USA 15 27599 16 17 *To whom correspondence should be addressed: 18 University of Southern California 19 3715 McClintock Avenue, Room 351 20 Los Angeles, CA 90089 21 Email: [email protected] 22 Phone: 213-740-9401 23 Fax: 213-821-5561 24 Pages: 53 25 Figures/Tables: 6 and 6 26 Abstract: 224 words 27 Introduction: 646 words 28 Discussion: 1,990 words 29

30 Acknowledgements: This project was funded by federal NIH grant R01AG025340. This project

31 was also funded by a USC Endowed Fellowship (D.C.). The authors declare no competing

32 financial interests. The authors thank Dr. Alexa Tompary for her helpful comments on the

33 manuscript and Avi Chanales for his assistance editing one of the figures.

1 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 2

34 Abstract

35 Recent models posit that bursts of locus coeruleus (LC) activity amplify neural gain, such that

36 limited attention and encoding resources focus even more on prioritized mental representations

37 under arousal. Here, we tested this hypothesis in human males and females using functional

38 magnetic resonance imaging (fMRI), neuromelanin MRI, and pupil dilation, a biomarker of

39 arousal and LC activity. During scanning, participants performed a monetary incentive encoding

40 task in which threat of punishment motivated participants to prioritize encoding of scene images

41 over superimposed objects. Threat of punishment elicited arousal and selectively enhanced

42 memory for goal-relevant scenes. Furthermore, trial-level pupil dilations predicted better scene

43 memory under threat, but were not related to object memory outcomes. FMRI analyses

44 revealed that greater threat-evoked pupil dilations were positively associated with greater scene

45 encoding activity in LC and parahippocampal cortex, a region specialized to process scene

46 information. Across participants, this pattern of LC engagement for goal-relevant encoding was

47 correlated with neuromelanin signal intensity, providing the first evidence that LC structure

48 relates to its activation pattern during cognitive processing. Threat also reduced dynamic

49 functional connectivity between high (parahippocampal place area) and lower priority (lateral

50 occipital cortex) category-selective visual cortex in ways that predicted increased memory

51 selectivity. Together these findings support the idea that, under arousal, LC activity selectively

52 strengthens prioritized memory representations by modulating local and functional network-level

53 patterns of information processing.

54 55

2 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 3

56 Significance Statement

57 Adaptive behavior relies on the ability to select and store important information amidst

58 distraction. Prioritizing encoding of task-relevant inputs is especially critical in threatening or

59 arousing situations, when forming these memories is essential for avoiding danger in the future.

60 However, little is known about the arousal mechanisms that support such memory selectivity.

61 Using fMRI, neuromelanin MRI, and pupil measures, we demonstrate that locus coeruleus (LC)

62 activity amplifies neural gain, such that limited encoding resources focus even more on

63 prioritized mental representations under arousal. For the first time, we also show that LC

64 structure relates to its involvement in threat-related encoding processes. These results shed

65 new light on the mechanisms by which we process important information when it is most

66 needed.

68 69

3 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 4

70 1. Introduction

71 Adaptive behavior relies on the efficient selection and storage of important information

72 among competing inputs, particularly under threatening or arousing conditions. Indeed,

73 emotionally arousing stimuli are preferentially attended and encoded, often at the cost of

74 processing surrounding neutral information (Anderson et al., 2006; Bisby & Burgess, 2014;

75 Hurlemann et al., 2005; Kensinger et al., 2007; Knight & Mather, 2009; LaBar & Cabeza, 2006;

76 Phelps et al., 2006; Strange et al., 2003). However, arousal’s influence on cognition isn’t limited

77 to enhancing emotional memoranda and suppressing neutral stimuli. Rather, arousal seems to

78 enhance processing prioritized (e.g., goal-relevant) representations regardless of their

79 emotionality, while also suppressing less salient representations (Easterbrook, 1959; Mather &

80 Sutherland, 2011; Sakaki et al., 2014; Sutherland & Mather, 2012).

81 For instance, emotional sounds or images enhance memory of goal-relevant neutral

82 stimuli seen just beforehand (Lee et al., 2015b; Sakaki et al., 2014) and impair memory of less

83 salient neutral information (Clewett et al., 2017a; Hurlemann et al., 2005; Sakaki et al., 2014;

84 Strange et al., 2003). Similar memory enhancements have been reported in studies pairing

85 shock with neutral images (Dunsmoor et al., 2012; Dunsmoor et al., 2015; Schwarze et al.,

86 2012). Moreover, memory for neutral information benefits from the receipt of reward or

87 punishment (Adcock et al., 2006; Murty et al., 2012; Weymar et al., 2013). An important open

88 question is whether, like emotional stimuli, threat of punishment enhances memory for salient

89 neutral stimuli by activating arousal-related brain mechanisms.

90 Although the dopaminergic system facilitates reward-motivated encoding (Adcock et al.,

91 2006; Shohamy & Adcock, 2010), recent findings suggest that dopamine does not support

92 encoding of neutral items incentivized by threat of punishment (Murty et al., 2012). Instead,

93 threat-motivated encoding appears to be driven by the amygdala, a region that enhances

94 emotional memories via NE modulation (Markovic et al., 2014; McGaugh, 2013; McGaugh &

95 Roozendaal, 2002; Strange & Dolan, 2004). Further, the LC coordinates processing of a wide

4 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 5

96 range of motivationally significant stimuli (Aston-Jones et al., 1999; Berridge & Waterhouse,

97 2003; Bouret & Richmond, 2015; Mohanty et al., 2008; Sara, 2009; Varazzani et al., 2015).

98 Thus, the LC is well equipped to amplify task-relevant encoding when it is most needed.

99 Theoretical models posit that phasic LC activity enhances neural gain, such that strong

100 inputs are further enhanced, while weaker inputs are further inhibited (Aston-Jones & Cohen,

101 2005; Eldar et al., 2013; Usher et al., 1999). According to the recent ‘glutamate amplifies

102 noradrenergic effects’ (GANE) model, neural gain is achieved via local interactions between NE

103 release and excitatory glutamate transmission, which signals the activation strength of a mental

104 representation (Mather et al., 2015). In this framework, local NE-glutamate interactions up-

105 regulate excitation in regions transmitting important information, leading to more selective

106 neuronal processing and memory outcomes. These local excitatory effects are also thought to

107 engage lateral inhibitory processes that suppress weaker, competing inputs.

108 In this fMRI study we tested GANE’s core prediction that arousal recruits LC activity to

109 enhance task-relevant memory representations and suppress task-irrelevant representations

110 (Mather et al., 2015). During scanning, participants performed a monetary incentive encoding

111 task in which they prioritized a background scene in attention and memory while ignoring a

112 transparent foreground object. Sympathetic arousal was induced on some trials by threatening

113 to deduct money from a preset account if participants later forgot loss-cued scenes. We

114 hypothesized that pupil dilations, a biomarker of phasic LC activity (Joshi et al., 2015; Murphy et

115 al., 2014; Reimer et al., 2016; Varazzani et al., 2015), would predict enhanced scene memory

116 and impaired object memory. We also hypothesized that pupil dilation would selectively

117 modulate successful scene encoding activity in LC and parahippocampal place area (PPA), a

118 region specialized to process scenes (Epstein & Kanwisher, 1998), and queried whether such

119 patterns were related to LC neuromelanin signal intensity (Keren et al., 2009). Finally, we

120 investigated whether threat-related LC activity amplifies competition within relevant functional

121 networks, yielding greater memory selectivity under arousal.

5 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 6

122 2. Methods

123 2.1 Participants. Thirty-two healthy young adults were recruited from the University of Southern

124 California’s Psychology Subject Pool and nearby community to participate in this experiment. All

125 participants provided written informed consent approved by the University of Southern California

126 Institutional Review Board and received monetary compensation for their participation and

127 performance on the task. All eligible individuals had normal or normal-to-corrected vision and

128 hearing and were not taking beta-blockers or psychoactive drugs. To limit the influence of sex

129 hormones (i.e., progesterone and estradiol) on the predicted memory and arousal effects,

130 female participants had to be: 1) using monophasic hormonal contraceptives and 2) using those

131 contraceptives for at least 3 months. Female participants only completed the experiment on

132 their active pill days (days 8-21).

133 Six participants were excluded from data analysis: two participants fell asleep during the

134 scan; one participant had excessive head movement during the scan (> 2mm); two participants

135 violated recruitment criteria on the day of their scan; and one participant failed to follow task

136 instructions. Because the focus of this paper was on examining LC activity, as indexed by pupil

137 dilation, an additional four participants were excluded due to poor eye-tracking quality (e.g.,

138 pupil values were unattainable for > 50% of the encoding trials). This resulted in 22 participants

139 (Mage = 22.18; SDage = 2.44; 10 female) for all analyses.

140 2.2 Monetary Incentive Encoding Task

141 2.2.1 Stimuli. The visual stimuli consisted of 96 neutral objects and 96 neutral scenes

142 images selected from previous datasets (Gabrieli et al., 1997; Kensinger et al., 2006) and the

143 Internet. Objects consisted of animals or kitchen utensils centered on a white background. Half

144 of the scene images depicted indoor scenes, while the other half depicted outdoor scenes. Each

145 of these images was resized to be 300 x 300 pixels and rendered in grayscale. From these

146 images, 96 “overlap” stimuli were created by overlaying one object image on top of one scene

147 image; the object image was rendered transparent using Adobe Photoshop 5.0, such that the

6 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 7

148 foreground object and background scene images were equally discernable. Each of the

149 grayscale, un-merged object and scene stimuli were yoked with a categorically similar but

150 perceptually different image that served as a foil during the recognition memory test. This

151 resulted in a total of 192 scene and object images.

152 To isolate cognitive-related changes in pupil dilation, image luminance was normed

153 across all of the task screens (i.e., overlap stimuli, fixation crosses, scrambled images, and

154 money symbols) using the SHINE toolbox in MATLAB (Willenbockel et al., 2010). In order to

155 model an “active baseline” (Stark & Squire, 2001) in the FMRI analyses, scrambled overlap

156 images were generated by shuffling a 0.27° x 0.27° box in a random fashion using MATLAB.

157 Threat cues were accompanied by a negative buzzer sound acquired from the

158 International Affective Digitized Sounds (Bradley & Lang, 2007); Sound ID #712; MArousal = 7.98,

159 SDArousal = 1.62; MValence = 2.42, SDValence = 1.62). No sound was paired with the neutral money

160 cues.

161 2.2.2 Task design. FMRI data was acquired during the monetary incentive encoding

162 task. The overall structure of the experiment was a 2 (Priority: high vs. low) x 2 (Arousal: threat

163 vs. neutral) within-subjects design. There was a total of 96 encoding trials in the task, which

164 were subdivided into four blocks of 24 trials each. Half of the trials in each block belonged to the

165 threat condition and the other half to the neutral condition. Stimulus order was randomized

166 across all blocks and the order of the arousal trials was randomized within each block.

167 2.2.3 Encoding phase. During the task, participants were presented with a series of

168 grayscale “overlap” images of a transparent object overlaid on a background scene (Figure 1).

169 At the beginning of each trial, participants were shown one of two gray monetary symbols for 1s.

170 In the threat condition, a “no symbol” cued participants that they would lose 50 cents if they

171 forgot the upcoming scene on the subsequent memory test. In the neutral condition, a gray

172 square symbol cued participants that, while they should still try to memorize the background

173 scene, they would not be punished for forgetting it later. To increase arousal on threat trials,

7 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 8

174 each threat cue was accompanied by a highly arousing buzzer sound. The monetary cue was

175 followed by a jittered central fixation cross (2, 4 or 6s). After this variable anticipation period, the

176 overlap image appeared for 2.5 seconds.

177 To manipulate goal relevance (i.e., top-down priority), participants were instructed to

178 only focus on and memorize each scene in the overlap images while ignoring the foreground

179 object. To reduce the salience/priority of the object even further, participants were told that their

180 memory would not be tested for any of the objects and that they were only meant to be a

181 distraction. To facilitate encoding and ensure participants were prioritizing the scenes, they were

182 also instructed to categorize each scene as indoors or outdoors as quickly and accurately as

183 possible when the overlap image appeared. Regardless of the cue, participants were instructed

184 to categorize and memorize the exact scene image on each trial.

185 Following the overlap image, participants performed a brief scrambled-image detection

186 task, which was used to: (1) prevent memory rehearsal of recent scenes, and (2) provide an

187 active baseline period that could be modeled in the neuroimaging analyses. In this detection

188 task, participants saw three scrambled overlap images for 1s each, which were separated by 1s

189 fixation cross screens in between. Participants had to make a button response with their right

190 hand as quickly as possible when they saw each scrambled image. They pressed the left button

191 when they saw the first scrambled image, the right button when they saw the second scrambled

192 image, and the left button again for the third scrambled image. Each trial concluded with a 2-3s

193 jittered fixation cross, which served as the inter-trial-interval (ITI).

194 2.2.4 Memory test. Approximately 15 minutes after completing the monetary incentive

195 encoding task in the scanner, participants were administered a two-alternative forced choice

196 recognition memory test outside the scanner (Figure 1; top right). To examine the differential

197 effects of threat-related arousal on memory for high (scene) versus lower (object) priority

198 images, we tested memory for all scene and object images that appeared during the task. There

199 were 192 memory trials (96 of each visual category) in total. Prior to beginning the memory test,

8 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 9

200 participants were told that their memory would be tested for all objects and scenes and to try

201 their best on every trial.

202 On each memory trial, one object or scene image from the previous encoding task was

203 displayed alongside a semantically similar but perceptually different (e.g., different shape,

204 orientation etc.) foil image. The order of stimulus presentation was randomized and an equal

205 number of old items appeared on the left and right of the screen. Participants had to indicate

206 which of the two images they had seen in the scanner.

207 After making their response via button press, participants were prompted to rate their

208 confidence in their memory accuracy according to one of three options: extremely confident,

209 somewhat confident, or just guessing. Participants had to make their memory responses within

210 10 seconds and their confidence rating within 5 seconds. If they failed to respond within these

211 response deadlines, the screen automatically advanced to the next trial and memory accuracy

212 was recorded as a miss. To prevent fatigue, the memory test was divided into three blocks.

213 2.3 Procedure. Upon arriving for the experiment, participants provided written informed consent

214 and completed several pen-and-paper questionnaires. Next, participants were given instructions

215 for the monetary incentive encoding task on a computer. To familiarize participants with the

216 experiment, they completed six task practice trials and three memory test practice trials prior to

217 entering the scanner.

218 Scanning procedures took approximately 1 hour. Inside the MRI scanner, participants

219 first completed the LOC/PPA functional localizer task (see Section 4.2.2 for details), which was

220 followed by a T1-weighted high-resolution neuroanatomical scan. After the structural scan,

221 participants completed 4 runs of the monetary incentive encoding task, which lasted

222 approximately 24 minutes. Between each run, participants were reminded of the task

223 instructions and to keep their head as still as possible. Scanning concluded with a ~2 minute

224 neuromelanin-sensitive weighted scan, which was used to help localize and delineate the LC in

225 the brainstem in each participant (Clewett et al., 2016; Keren et al., 2009; Shibata et al., 2006).

9 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 10

226 After exiting the scanner and finishing the memory test, participants completed a custom

227 post-experiment questionnaire that how arousing and unpleasant they found the buzzer sounds

228 on threat trials, and how arousing they found the threat and neutral money cues. All ratings

229 were made on a 7-point scale ranging from 1 = not at all to 7 = very (see Table 1). To assess

230 punishment-related motivation, participants also indicated how motivated they were to

231 memorize scenes on threat trials and neutral trials, separately, by making a vertical hash mark

232 along a line spectrum ranging from not motivated on the left to extremely motivated on the right.

233 Motivation responses were converted to scores by measuring the distance of the hash mark

234 from the leftmost point (not motivated) in cm. In addition, to ensure that participants prioritized

235 the scenes during the task, they were asked whether they tried to memorize all the scenes and

236 objects.

237 2.4 MRI data acquisition. All neuroimaging data were acquired on a 3T Siemens PRISMA

238 scanner located at the University of Southern California Dana & David Dornsife Neuroimaging

239 Center. The visual stimuli were displayed on a liquid crystal display monitor (1024 x 768 pixels

240 at 60 Hz), which participants viewed via a mirror attached to a 32-channel matrix head coil. A

241 high-resolution T1-weighted anatomical image (MPRAGE) was acquired to aid with functional

242 image co-registration (slices = 176 axial; TR/TE/TI = 2300ms/2.26ms/1060ms; FOV = 256mm;

243 in-plane resolution = 1mm2; slice thickness = 1mm with no gap; bandwidth = 200Hz/Px;

244 GRAPPA with acceleration factor = 2; duration: 10 minutes and 42 seconds).

245 Functional images for one run (183 volumes) of the localizer task and four runs (187

246 volumes each) of the monetary incentive encoding task were acquired using one echo-planar

247 imaging sequence (TR/TE = 2000/25 ms, 41 interleaved slices with no gaps, FOV = 192 mm;

248 FA = 90°; 3mm isotropic voxel size). Following the monetary incentive encoding task, we

249 collected a neuromelanin-sensitive weighted MRI scan using a T1-weighted fast spin echo

250 (FSE) imaging sequence (TR/TE = 750/12 ms, FA = 120q, 1 average to increase SNR, 11 axial

10 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 11

251 slices, FOV = 220 mm, bandwidth = 220 Hz/Px, slice thickness = 2.5 mm, slice gap = 3.5 mm;

252 in-plane resolution = 0.429 x 0.429 mm2).

253 Experimental Design and Statistical Analyses

254 3. Memory Analyses

255 3.1 Mean task and memory performance. To probe top-down attention during the monetary

256 incentive encoding task, we performed paired t-tests on scene categorization accuracy and

257 reaction times (RT) comparing threat and neutral trials. To determine how threat differentially

258 influenced memory for high and lower priority information, we performed a 2 (Priority: high vs.

259 low) x 2 (Arousal: threat vs. neutral) repeated-measures ANOVA. Planned two-tailed follow-up

260 paired t-tests were also used to test whether, compared with neutral trials, threat enhanced

261 memory for scenes and impaired memory for objects. Memory performance was calculated as

262 the proportion of correctly remembered trials in each arousal trial type.

263 3.2 Memory codependency analysis. According to arousal-biased competition (ABC) theory,

264 arousal biases limited mental resources towards prioritized representations, such that goal-

265 relevant stimuli are even more likely to be remembered at the expense of remembering their

266 competing distracters (Mather & Sutherland, 2011). Thus, to examine how threat-induced

267 arousal influenced competitive memory tradeoffs at the trial-level, we performed a memory

268 codependency analysis (Clewett et al., 2017a; Clewett et al., 2017b). Specifically, we examined

269 how memory accuracy for each scene differed according to memory accuracy for its

270 corresponding object. Each of the 96 trials from the monetary incentive encoding task were

271 categorized as one of four possible outcomes: 1) remembered scene and forgot object, 2) forgot

272 scene and remembered object, 3) remembered both, or 4) forgot both. The frequencies of each

273 memory outcome were calculated for threat and neutral conditions, separately.

274 To test our main behavioral prediction that arousal enhances goal-relevant memory

275 selectivity rather than memory more globally (e.g., remembering both the scene and its

276 corresponding object), we performed a 2 (Arousal: threat vs. neutral) x 2 (Memory Outcome:

11 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 12

277 rememberedsceneforgotobject vs. remembered both) repeated-measures ANOVA. Follow-up two-

278 tailed paired t-tests were used to examine the main effects of Arousal on selective versus global

279 memory outcomes.

280 3.3 Eye-tracking

281 3.3.1 Pupil Dilation Analysis. During FMRI scanning, pupil size was measured

282 continuously at 60 Hz using an infrared ASL model 504 eye-tracker system (Applied Science

283 Laboratories, Bedford, MA). To remove noisy or unreliable samples, pupil data were

284 preprocessed using the following steps: (1) An upper and lower threshold for pupil diameter was

285 applied by measuring participants’ maximum pupil size while viewing a purely black screen and

286 minimum pupil size while viewing a purely white screen; (2A) eye-blinks and other artifacts were

287 removed using a linear interpolation algorithm that constrained interpolation to periods of data

288 loss < 1s; (2B) interpolation was applied 100ms before the first missing sample and ended

289 100ms after the last missing sample to account for rapid changes in pupil size during blinks; (3)

290 Trials with estimated pupil dilations that were three standard deviations above or below the

291 average pupil dilation were excluded from analysis.

292 Stimulus-evoked pupil dilation responses were examined for two events of interest: the

293 money cue and the overlap stimulus. For the money cue, average pupil diameter was estimated

294 from 1-2.5s after cue onset. For the overlap stimulus, average pupil diameter was estimated

295 across a 1s window centered upon the time-point of the maximum pupil size. The search

296 window for the maximum pupil size was constrained to 1-2.5 seconds after scene onset when

297 the stimulus-evoked pupil response was most apparent. To measure dilation, average pupil size

298 during these periods was baseline-normed by subtracting the average pupil size during the

299 500ms window prior to event onset. To examine the influence of threat-related arousal on pupil

300 dilation, we performed two-tailed paired t-tests comparing average pupil dilation responses

301 between threat and neutral trials during the money cue and overlap stimulus periods,

302 separately.

12 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 13

303 3.3.2 The relationship between threat-evoked pupil responses and memory

304 selectivity. To examine whether pupil dilation responses were associated with enhanced

305 memory selectivity under threat, we performed hierarchical generalized linear modeling (HGLM)

306 analyses using the glmer function in the lme4 library (Baayen et al., 2008). The parameters

307 were estimated with the maximum likelihood method in R (R Core Team, 2012). Each trial was

308 used as a Level 1 unit and each participant was used as a Level 2 unit.

309 Arousal condition (1 = threat, -1 = neutral) and mean pupil dilation responses to the

310 overlap image were group-centered and modeled as the level-1 predictors of trial-level memory

311 outcomes. In two separate HLMs, we examined how threat-induced pupil dilation responses to

312 the overlap stimulus influenced scene (high priority) and object memory (lower priority)

313 independently (dependent variables: 1 = Remembered Scene/Object; 0 = Forgot Scene/Object).

314 The main effects of Arousal and Overlap Stimulus Dilation - and their interaction - were included

315 in these models. Next, we performed two more HGLMs to query whether dilations to the money

316 cue interacted with dilations to the overlap stimulus to influence memory outcomes. These

317 HGLMs was the same as before, but included an additional group-centered level-1 predictor for

318 pupil dilation to the money cues. The main effects of Arousal, Overlap Stimulus Dilation, and

319 Money Cue Dilation, were included in these models along with all two- and three-way

320 interactions. Additional HGLMs were performed to rule out the potential influence of other task

321 or physiological predictors on memory, including baseline pupil size prior to the overlap image,

322 scene memory confidence ratings, and anticipation duration between the money cue and

323 overlap stimulus.

324 4. Neuroimaging Analyses

325 4.1 Image preprocessing. Image preprocessing was performed using FSL Version 5.0.4

326 (FMRIB’s Software Library, www.fmrib.ox.ac.uk/fsl). The functional volumes were preprocessed

327 according to the following steps: motion correction using MCFLIRT, removal of non-brain tissue

328 using BET, spatial smoothing using a Gaussian kernel of 6mm full-width-at-half-maximum

13 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 14

329 (FWHM), grand-mean intensity normalization of the entire 4D data set by a single multiplicative

330 factor and a high-pass temporal filter of 100s. Structured noise and physiological artifacts, such

331 as extreme head motion, white matter/CSF signal, and cardiac pulsation/respiratory artifacts

332 located across the whole brain – especially in the fourth ventricle - were identified and removed

333 from the dataset using a single-session independent component analysis (ICA; (Beckmann et

334 al., 2005). The criteria used for classifying noise components are described in more detail

335 elsewhere (Clewett et al., 2013).

336 Next, two image registration procedures were used to query the effects of arousal on

337 scene encoding at the whole-brain level. For the conventional alignment whole-brain analysis,

338 each participant’s denoised mean functional volume was co-registered to his or her T1-weighted

339 high-resolution anatomical image using brain-based registration (BBR). Anatomical images

340 were then co-registered to the 2mm isotropic MNI-152 standard-space brain using an affine

341 registration with 12 degrees of freedom. For the brainstem-specific analysis, we performed an

342 additional affine transformation with trilinear interpolation. In this step, registration was weighted

343 by a binarized brainstem mask thresholded at 50% tissue-type probability (Harvard-Oxford

344 Probabilistic Atlas). Prior research has shown that this technique is optimal for aligning voxels

345 within the brainstem (Napadow et al., 2006).

346 4.2 Pupil-parametric general linear models (GLMs) of subsequent memory effects

347 4.2.1 Whole brain analysis. Functional images were analyzed to examine how threat-

348 induced arousal affected perceptual and successful encoding-related activity across the whole

349 brain using a parametric general linear model (GLM). We created separate event-related

350 regressors by modeling the onset times of the monetary cues and the overlap stimuli with

351 durations of 1s and 2.5s, respectively. Each task regressor was convolved with a dual-gamma

352 canonical hemodynamic response function and their temporal derivatives were used to model

353 data. Importantly, FMRI analyses were focused on the period when participants viewed the

354 overlap image.

14 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 15

355 Trials were sorted by arousal type and scene memory outcome. A lower-level GLM was

356 then constructed for each participant using 4 task regressors: 1) threat scene remembered, 2)

357 threat scene forgotten, 3) neutral scene remembered, and 4) neutral scene forgotten. To

358 examine how putative LC activity modulated brain activity encoding, four complementary task

359 regressors were modeled to examine the parametric relationship between trial-level pupil

360 dilation and brain activity during encoding. These parametric regressors were scaled according

361 to the magnitude of the pupil dilation response to each overlap stimulus. As a result, the BOLD

362 response was weighted more strongly on trials with larger stimulus-evoked pupil responses than

363 on trials with smaller pupil responses, thereby providing an index of phasic noradrenergic

364 modulation of local brain activity (Murphy et al., 2011). Pupil dilation values were mean-centered

365 across each fMRI run and input into the “strength” column of the three-column format EV files in

366 FSL.

367 Eight additional regressors were included in the GLM: 2 task regressors modeled activity

368 during the money cue (threat and neutral) and 6 nuisance regressors derived from the motion

369 correction procedure accounted for residual head motion. Three separate contrasts were

370 created to test for main effects of Memory (remembered vs. forgot), Arousal (threat vs. neutral),

371 and an arousal-by-memory interaction effect: [threat (remembered > forgot) – neutral

372 (remembered – forgot)]. Whole-brain statistical parametric brain maps were acquired separately

373 for images registered using the conventional approach and brainstem-optimized approach.

374 From the lower-level statistical parametric maps, a second-level fixed-effects analysis

375 was performed across each participant’s functional runs. The resulting contrast images were

376 analyzed in higher-level mixed-effects analysis using FMRIB’s local analysis of mixed effects

377 (FLAME 1 + 2; (Beckmann et al., 2003). A single group average for each of the contrasts-of-

378 interest was calculated using a one-sample t-test. To correct for multiple comparisons, Z-

379 statistic images were thresholded using clusters determined by Z > 2.3 and a corrected cluster

380 significance threshold of P = .05 (Worsley, 2001). In addition, this entire procedure was

15 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 16

381 repeated for subsequent object memory outcomes to probe the specificity of arousal’s effects on

382 processing prioritized information.

383 4.2.2 PPA/LOC functional localizer. To examine how threat-induced arousal

384 differentially influenced encoding-related activity in visual category-selective cortex, we

385 performed a functional localizer task to delineate the parahippocampal place area (PPA; high

386 priority region) and lateral occipital cortex (LOC; lower priority region) in the left and right

387 hemisphere of each participant. The localizer scan consisted of 48 scrambled objects, 48

388 objects, and 48 scene images. Scrambled images of the intact stimuli were generated using the

389 shuffling procedure described in Section 2.2.1. All images were resized to 300 x 300 pixels,

390 gray-scaled, luminance-normed and displayed on a gray background. Image presentation was

391 divided into 6 blocks, which lasted 60 seconds each. These blocks were further subdivided into

392 3 mini-blocks lasting 20s each, in which a series of images from one of the three visual

393 categories was displayed on the screen (e.g., scrambled-only, object-only, or scene-only). Each

394 mini-block contained 8 images lasting 10s and were separated from each other by a 10-s

395 fixation cross. Each image was displayed for 1s, followed by 250ms fixation cross inter-stimulus-

396 interval (ISI). The order of mini-blocks was counterbalanced across participants. Participants

397 were instructed to passively view the images as if they were watching a movie. Importantly,

398 none of the stimuli from the localizer task were used in the monetary incentive encoding task.

399 4.3 Region-of-interest (ROI) analyses for pupil-parametric GLM

400 4.3.1 Univariate PPA/LOC ROI analyses. Region-of-interest (ROI) analyses were

401 performed to determine how threat modulated successful scene and object encoding-related

402 activity in high (PPA) and low priority (LOC) visual category-selective cortex. The ROI masks for

403 each LOC and PPA region were defined individually from the localizer session as 6mm-radius

404 spheres centered upon peak voxels in the ventral occipital and temporal cortex that were most

405 selective for objects (object block > scrambled objects + scene blocks; Z = 2.57, uncorrected)

406 and for scenes (scene block > scrambled objects + object blocks; Z = 2.57, uncorrected),

16 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 17

407 respectively. Using this approach, the ROIs were definable for all participants for both LOC

408 (mean peak MNI voxel coordinates: Left [-42 -71 -8]; Right [47 -69 -8]) and PPA (mean peak

409 MNI voxel coordinates: Left [-27 -47 -6]; Right [27 -44 -9]). These peak coordinates for the L/R

410 PPA and LOC are displayed in standard space in Figure 8.

411 These functionally-defined left/right PPA and left/right LOC masks were aligned to each

412 participant’s second-level statistical parametric maps and used to extract percent signal change

413 of task-related activity using FSL Featquery (fmrib.ox.ac.uk/fsl/feat5/featquery.html) To test our

414 main hypothesis that successful scene encoding relates to enhanced activity in scene-selective

415 visual cortex (PPA) and reduced activity in the lower priority object-selective visual cortex (LOC)

416 under arousal, we performed a 2 (Region: PPA vs. LOC) x 2 (Hemisphere: left vs. right) x 2

417 (Arousal: threat vs. neutral) x 2 (Memory: remember vs. forgot) repeated-measures ANOVA.

418 4.3.2 Univariate locus coeruleus and ventral tegmental area ROI analyses. A final

419 ROI analysis for the LC was conducted using the same statistical procedures as above. To

420 localize and delineate the LC in each individual, LC ROIs were hand-drawn on each

421 participant’s neuromelanin-sensitive weighted image by two independent drawers using

422 procedures described in a previous study (Clewett et al., 2016). The inter-rater reliability was

423 high (ICC range: 0.85-0.99). Bilateral LC ROIs were manually defined as a ~1.29 mm wide by

424 ~1.29 mm high (i.e., 3 x 3 voxels) in an axial slice located 7mm below the inferior boundary of

425 the inferior colliculus. These masks were centered on the left and right voxels with the highest

426 signal intensities neighboring the corners of the fourth ventricle. A dorsal pontine tegmentum

427 (PT) reference ROI was defined as a 10 x 10 voxel square located 6 voxels above the more

428 ventral of the two LCs and equidistantly between them.

429 We were unable to delineate LCs in 6 participants due to a scan not being collected (n =

430 2) or inter-space registration issues (n = 4). For those participants, we used a standard-space

431 2SD LC mask derived from a previous study (Keren et al., 2009). Each participant’s LC ROI

432 transformed into MNI 2mm standard-space using a slight variation of the transformation steps

17 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 18

433 used for the whole-brain brainstem-weighted alignment procedure, because the neuromelanin

434 images had higher resolution. Neuromelanin-to-standard-space transformation steps included:

435 1) Registering each participant’s neuromelanin scan to his/her high-resolution anatomical scan

436 using an affine transformation with 6 DOF; 2) Registering each participant’s high-resolution

437 anatomical scan to the MNI standard 1mm brain template using an affine transformation with 12

438 DOF; 3) Performing an additional registration by weighting the T1-to-MNI 1mm registration by a

439 binarized MNI 1mm standard-space mask; and 4) Registering the MNI 1mm standard-space

440 brain to the MNI 2mm standard-space brain to acquire a more sensitive, intermediate

441 registration prior to transforming the LC ROIs to standard space. LC percent signal changes

442 values were extracted and submitted to a 2 (Arousal: threat vs. neutral) x 2 (Memory: remember

443 vs. forgot) repeated-measures ANOVA to examine the effects of threat on successful scene and

444 object encoding activity.

445 Although the ventral tegmental area (VTA) does not appear to support threat-motivated

446 encoding processes (Murty et al., 2012), there are some indications it can be activated by threat

447 cues more broadly (Knutson et al., 2000; Shigemune et al., 2014). Thus, to determine whether

448 the dopaminergic system contributes to threat-enhanced memory selectivity, we also performed

449 a VTA ROI analysis on scene and object encoding activity. To define this ROI, we thresholded

450 and binarized a probabilistic VTA anatomical mask from a prior study (Murty et al., 2014) at 50%

451 and extracted percent signal change values. These values were then submitted to the same rm-

452 ANOVA analysis as the LC ROI.

453 4.4 Parahippocampal place area functional connectivity during encoding

454 4.4.1 Whole-brain analysis. To examine dynamic interactions between the PPA (high

455 priority visual cortex) and the rest of the brain during encoding, we performed a

456 Psychophysiological Interaction (PPI) analysis (Friston et al., 1997). The same lower-level GLM

457 was used as before, but included 9 additional regressors (1 physiological regressor and 8

458 interaction regressors). Whole-brain PPI analyses were performed for the left and right PPA for

18 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 19

459 object and scene subsequent memory effects, separately.

460 The PPA physiological regressor was created aligning each participant’s left and right

461 PPA to his/her preprocessed functional data and extracting their mean activity timeseries. Eight

462 interaction regressors were used to model the interaction between the PPA timeseries regressor

463 and the pupil-modulated (parametric) and non-pupil-modulated (uniform scale) task-related

464 regressors. As before, we tested for significant main and interaction effects of Arousal (threat

465 vs. neutral) and Memory (remember vs. forgot) on whole-brain left and right PPA functional

466 connectivity patterns modulated by trial-level changes in pupil dilation to the overlap stimulus.

467 We applied the same multiple comparison corrections as the whole-brain univariate GLM.

468 4.4.2 PPA functional connectivity ROI analysis. Based on our a priori prediction that

469 arousal amplifies competitive functional interactions between the PPA and LOC (Lee et al.,

470 2014), we also performed ROI analyses. Using the same protocol as the whole-brain univariate

471 GLM, each participant’s left and right LOC was used to extract parameter estimates of left and

472 right PPA connectivity from their conventionally registered whole-brain maps. The resulting

473 parameter estimates were submitted to a 2 (Priority: high vs. low) x 2 (PPA Hemisphere: left vs.

474 right) x 2 (LOC Hemisphere: left vs. right) x 2 (Arousal: threat vs. neutral) x 2 (Memory:

475 remember vs. forgot) repeated-measures ANOVA. To optimize PPA connectivity estimates with

476 the LC, each participant’s LC mask was used to extract parameter estimates from their

477 brainstem-aligned maps. These PPA-LC ROIs were then submitted to the same statistical

478 analyses as the LOC region but without an LC laterality factor.

479 4.5 Individual differences in threat-related memory performance, LC encoding activity,

480 and LC structure. In our final analyses, we queried the relationships between arousal’s effects

481 on local and network-level brain processing, LC structure (i.e., CNR), and threat-related memory

482 selectivity across all participants using a Theil-Sen estimator, a robust linear correlation

483 technique that is less sensitive to outliers than parametric regressions (Wilcox, 2004). To these

484 ends, we first computed threat-neutral difference scores for: 1) scene memory, 2) object

19 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 20

485 memory, and 3) the difference between the two (i.e., scene – neutral; gain effect in memory).

486 These three threat-related memory measures were then linearly correlated with LC contrast-to-

487 noise values, LC and VTA ROI estimates, and PPA-LOC functional connectivity estimates.

488 4.5.1 Relationship between LC functional activation and structure. First, we

489 examined whether LC signal contrast-to-noise ratio estimates, a putative biomarker for LC

490 neuronal density and structural integrity (Keren et al., 2009; Keren et al., 2015), was associated

491 with individual differences in LC-related scene and object encoding activity. LC contrast-to-noise

492 ratios were calculated based on the mean LC signal intensity relative to the reference PT signal

493 intensity using the following formula: LCCNR = (LCintensity – PTintensity)/PTintensity (Clewett et al., 2016;

494 Sasaki et al., 2006; Shibata et al., 2006). The two raters’ left and right side estimates of LC

495 contrast-to-noise ratios were all averaged together to increase the signal-to-noise ratio.

496 Four participants were excluded from this LC structure-function correlation analysis: Two

497 participants had striping artifacts in their neuromelanin images that overlapped with the LC,

498 while two other participants did not have neuromelanin scans due to time constraints. Of the

499 remaining 18 participants with valid LC signal intensities, the overall mean and standard

500 deviation for LC contrast-to-noise ratio (M = 0.19, SD = 0.045) was comparable to values in

501 prior work using this technique (Clewett et al., 2016). Two robust correlations were performed

502 between LC CNR values and threat-related LC scene encoding activity and object encoding

503 activity, separately.

504 4.5.2 Relationship between LC/VTA functional activation and memory. In the

505 second robust regression analysis, we performed three robust regressions between the threat-

506 related noradrenergic and dopaminergic encoding activity and memory: 1) threat-enhanced

507 scene memory, 2) threat-impaired object memory, and 3) the subtraction between the two (i.e.,

508 scene effect – object effect; “gain effect” in memory). LC and VTA ROI estimates were modeled

509 as predictors in the same robust regression to control for multiple comparisons (p = .05, two-

510 tailed).

20 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 21

511 4.5.3 Relationships between threat-related PPA functional connectivity during

512 successful encoding and memory. In the last regression analyses, we performed the same

513 three brain-behavior correlations as the LC/VTA ROI analysis but using the PPA-LOC functional

514 connectivity ROIs as predictors of memory performance. As before, to control for multiple

515 comparisons, all six PPA-LOC connectivity combinations (two PPA hemispheres, two LOC

516 hemispheres, and two item priority types) were modeled as brain predictors of threat-impaired

517 object memory, threat-enhanced scene memory, and their difference (scene - object

518 subtraction) in three separate robust regressions (significance level: p = .05, two-tailed).

519 5. Results

520 5.1 Task performance

521 5.1.1 Arousal/motivation ratings and encoding results. As expected, participants

522 rated the threat money cues as significantly more arousing than neutral money cues, t(21) =

523 7.25, p < .001. Participants also indicated being significantly more motivated to memorize

524 scenes on threat trials compared to neutral trials, t(21) = 4.02, p = .001 (Table 1). Other

525 statistical comparisons from paired t-tests for attention accuracy (scene categorization), reaction

526 times, and detection task performance are also displayed in Table 1.

527 5.2 Memory Performance

528 5.2.1 Mean memory performance results. To determine whether threat-related arousal

529 differentially influences memory of high and lower priority information, we performed a 2

530 (Priority: high vs. low) x 2 (Arousal: threat vs. neutral) repeated-measures ANOVA. Participants

531 remembered significantly more goal-relevant scenes than distracting objects, F(1,21) = 62.33, p

2 532 < .001, ηp = .75 (Figure 2, top panel). Consistent with our main behavioral prediction, threat-

533 induced arousal had opposing effects on memory for high and lower priority stimuli, F(1,21) =

2 534 15.32, p = .001, ηp = .42, with threat selectively enhancing high priority memories. Planned

535 comparisons indicated that this arousal-by-priority interaction effect on memory was

536 predominantly driven by significant threat-induced enhanced memory of high priority scenes,

21 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 22

537 t(21) = 4.26, p < .001. By contrast, there was no significant threat-induced suppression of

538 memory of low priority objects, t(21) = -1.18, p = .25. However, separate one-tailed, one-sample

539 t-tests revealed that object memory was significantly above chance on neutral trials, t(21) =

540 3.31, p = .003, but not on threat trials, t(21) = 1.55, p = .14, suggesting that floor effects for

541 object memory in the threat condition may have limited the range for a meaningful statistical

542 comparison.

543 5.2.2 Memory codependency results. To determine how threat-related arousal

544 differentially influences memory selectivity at the trial-level, we performed a 2 (Memory

545 Outcome: rememberedsceneforgotobject vs. remembered both) x 2 (Arousal: threat vs. neutral)

546 repeated-measures ANOVA. Overall, participants were significantly more likely to remember

547 both stimuli rather than to remember the scene and forget its corresponding object, F(1,21) =

2 548 6.30, p = .02, ηp = .23 (Figure 2; bottom panel). There was no significant interaction effect on

549 trial frequency.

550 Planned paired t-tests revealed that threat significantly enhanced memory for scenes at

551 the cost of memory for competing objects, t(21) = 3.69, p = .006, whereas threat had no effect

552 on global successful memory for paired scene and object stimuli, t(21) = 0.90, p = .38. In

553 addition, the results indicated that participants were more likely to remember scene-object pairs

554 than to show a scene-biased memory tradeoff on neutral trials, t(21) = -2.70, p = .013. Thus,

555 greater memory selectivity under threat appeared to be driven by less narrowing of attentional

556 and encoding resources to neutral scenes.

557 6. Pupil Results

558 6.1 Event-related pupil dilation. Planned two-tailed paired t-tests revealed that pupil dilations

559 were significantly larger in response to money cues on threat compared to neutral trials, t(21) =

560 10.53, p < .001 (Figure 3C). Conversely, pupil dilations were significantly larger in response to

561 overlap images on neutral trials compared to threat trials, t(21) = -2.17, p = .041 (Figure 3C).

22 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 23

562 6.2 Relationship between encoding-related pupil dilation and memory. Next, we performed

563 hierarchical generalized linear regression (HGLM) analyses to query the relationship between

564 threat-related pupil dilation and high and lower priority stimulus memory outcomes. The HGLM

565 analyses on scene memory revealed a significant main effect of Arousal Condition, z = 4.57, p <

566 .001, on scene encoding, mirroring the results of the ANOVA. There was also a significant pupil

567 dilation-by-condition interaction effect on scene encoding, z = 2.20, p = .028 (Table 2). Plotting

568 this interaction effect revealed that threat-evoked pupil dilations during the overlap image

569 predicted better scene encoding (Figure 3D). There were no significant effects of pupil dilation

570 on object memory, indicating that the beneficial mnemonic effects of arousal are unique to the

571 high priority stimulus (p > .05).

572 Several additional HGLM’s were performed to rule out the potential influence of other

573 task predictors on scene/object memory. In the first HGLM, we found that the overlap dilation-

574 by-condition interaction effect on scene memory remained significant after including a trial-level

575 predictor for pupil dilations to the money cue and its interactions with other predictors (Arousal

576 Condition and Overlap Dilation; p < .05). There were no significant main or interaction effects of

577 money cue dilations on scene or object memory. Next, modeling trial-level covariate predictors

578 for cue-to-overlap anticipation duration and memory confidence rating did not alter the results in

579 both the model with money dilations and without it (scene interaction effect: p < .05; all object

580 effects: p > .05). There was, however, a significant main effect of scene memory confidence

581 rating on successful scene encoding (p < .001).

582 We also created an HGLM model that include a term and interactions for average

583 baseline pupil size prior to the onset of the overlap image based on suggestions that baseline

584 pupil diameter predicts gain effects in learning (Eldar et al., 2013). Including these terms slightly

585 diminished the overlap-by-condition interaction (p = .095), but there was no significant main

586 effect or interactions of baseline pupil diameter on scene memory (ps > .46). Finally, in the

587 object HGLM, there was significant main effect of anticipation duration on object memory, such

23 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 24

588 that longer waiting periods between the money cue and the overlap image was associated with

589 worse object memory irrespective of arousal condition, z = -2.21, p = .027.

590 7. Brain Results

591 The neuroimaging results are organized into two main sections: the pupil-parametric

592 GLM and the PPA-seeded functional connectivity analyses. Each of these sections are then

593 broken down into three subsections: ROI results, whole-brain results, and brain-behavior

594 correlations.

595 7.1 Pupil-parametric GLM

596 7.1.1 Catecholamine and visual category-selective cortex ROI results. First, we

597 performed parametric GLM analyses in which we scaled trial-level BOLD response to the

598 overlap image by the magnitude of pupil dilation. To examine how threat-induced arousal

599 affected scene and object encoding activity in the LC, VTA, and category-selective visual cortex

600 ROIs, we extracted percent signal change values from each participant’s LC mask, as well as

601 his/her left/right PPA and LOC that were identified by the functional localizer.

602 Consistent with a neural gain effect in memory, threat-enhanced LC encoding activity

603 significantly differed between trials with subsequently remembered scene and object stimuli

2 604 (three-way interaction: F(1,21) = 4.87, p = .039, ηp = .19). Follow-up Arousal x Memory

605 ANOVAs revealed that this three-way interaction effect was driven by LC activity being

606 significantly greater when participants encoded threat-motivated scenes compared to neutral-

2 607 trial scenes, F(1,21) = 6.56, p = .018, ηp = .24 (Figure 5, Table 3). By contrast, LC activity did

608 not differ between threat and neutral conditions when participants successfully encoded

609 distracter objects (p > .94). We did not find any significant main or interaction effects of Arousal,

610 Priority or Memory on VTA activity, thereby ruling out a significant dopaminergic influence on

611 competitive encoding processes under threat.

612 For the visual category-selective cortex ROIs, there was no significant main effect of

613 hemisphere on brain activity during encoding of scenes or objects (ps > .05). Further, collapsing

24 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 25

614 across hemispheres revealed no other main or interaction effects on LOC or PPA activity for

615 scenes or objects (ps > .05).

616 7.1.2 Whole-brain results. Analysis of pupil-modulated BOLD signal changes at the

617 whole-brain level indicated that there was no significant main effect of Arousal on brain activity

618 during the overlap image. For scene encoding, there was a significant main effect of Memory on

619 brain activity, with successful scene encoding being associated with reduced activity in left

620 frontal opercular cortex/insula and bilateral supramarginal gyrus around the temporo-parietal

621 junction (TPJ). Supporting our hypothesis, there was a significant arousal-by-memory

622 interaction effect on scene encoding activity, such that, compared to the neutral condition, threat

623 of punishment modulated successful scene encoding activity in the left posterior

624 parahippocampal cortex (PHC), left posterior hippocampus, and left lingual gyrus (Figure 4;

625 Table 3).

626 For object encoding, we identified a significant main effect of Memory on object

627 encoding activity, such that successfully encoding object stimuli corresponded with increased

628 activation of right occipital pole and right lateral prefrontal cortex, including inferior frontal gyrus

629 and middle frontal gyrus (Figure 4, Table 3). We also found a significant arousal-by-memory

630 interaction effect, such that threat led to enhanced object encoding activity in a right lateral

631 occipital cortex region (Figure 4; Table 3).

632 In the brainstem-aligned images, threat of punishment modulated successful scene

633 encoding activity in a brainstem/cerebellum cluster encompassing the known anatomical

634 location of the LC (Figure 5). There were no significant interaction or main effects on brain

635 activity for object memory, suggesting that brainstem neuromodulatory nuclei did not

636 significantly influence distracter encoding.

637 7.1.3 Relationship between individual differences in LC structure, LC function, and

638 threat-related memory outcomes. Next, we investigated whether individual differences in LC

639 structure, as indexed by neuromelanin contrast-to-noise ratio, were related to LC ROI estimates

25 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 26

640 of threat-enhanced scene encoding and threat-impaired object encoding from the prior analysis.

641 We also examined whether these LC structural and functional measures were associated with

642 memory outcomes. Robust regression analyses revealed that, across participants, the degree of

643 threat-enhanced LC scene encoding activity was significantly positively correlated with LC

644 contrast-to-noise ratio, β(21) = .79, p = .0067 (Figure 5). Threat-related LC activity for object

645 encoding was not significantly associated with LC contrast-to-noise ratio across participants,

646 β(21) = .60, p = .15. This finding suggests that participants with a greater lifetime of LC

647 engagement and concomitant NE metabolism (Mather et al., 2017), as signified by greater LC

648 neuromelanin signal intensity, also activated the LC to a larger degree when forming goal-

649 relevant memories under threat. There were no significant correlations between LC contrast-to-

650 noise ratio and either memory outcome under threat (ps > .05).

651 When examining the relationship between threat-related LC and VTA object/scene

652 encoding activity, we did not find any significant correlations with threat-related memory

653 outcomes (Table 4). However, there was a statistical trend towards a significant correlation

654 between LC gain-related encoding activity and enhanced memory selectivity under threat, β(20)

655 = .54, p = .11.

656 7.2 PPA functional connectivity results

657 7.2.1 ROI results. Psychophysiological Interaction (PPI) analyses were used to examine

658 whether arousal conditions and trial-level pupil dilations to the encoding stimulus modulated

659 dynamic functional connectivity patterns between the PPA and LC. We did not find a significant

660 main effect of Priority (p > .05) or main effect of LOC side on brain activity (ps > .05). Thus, we

661 performed follow-up 2 (Memory: remember vs. forgot) x 2 (Arousal: threat vs. neutral) ANOVAs

662 collapsed across LOC sides and for object and scene memory, separately.

663 For the scene encoding PPI analysis, we found that threat significantly reduced right

664 PPA-LOC functional connectivity when scenes were successfully encoded, F(1,21) = 4.50, p =

2 665 .046, ηp = 0.18 (Figure 6; Table 5). This finding suggests that greater arousal-biased

26 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 27

666 competition between high and lower priority visual cortical regions is associated with better goal-

667 relevant memory under threat.

668 For the object encoding PPI analysis, there were no significant main or interaction

669 effects of Arousal and Memory on PPA-LOC functional connectivity (ps > .10). In addition, there

670 were no significant results for scenes or objects when we examined the effects of threat and

671 encoding success on PPA-LC functional connectivity (ps > .05).

672 7.2.2 Whole-brain results. At the whole-brain level, we found that right PPA functional

673 connectivity with early visual processing regions, including the left occipital fusiform gyrus and

674 LOC, diminished significantly when participants successfully encoded scenes under threat

675 (Figure 6). This finding suggests that reduced PPA-LOC functional coupling – or potentially

676 more competitive network-level interactions (i.e., anti-correlated functional connectivity) -

677 between high and lower priority visual cortex was associated with arousal-enhanced encoding

678 of the goal-relevant scene stimulus. We did not find any other main nor interaction effects on

679 right or left PPA-seeded functional connectivity, nor did we find any significant results for PPA

680 functional connectivity associated with object encoding.

681 7.2.3 Relationship between individual differences in PPA functional connectivity

682 and threat-related memory outcomes. Robust correlation analyses revealed that, under

683 threat, greater right PPA-right LOC scene encoding functional connectivity was marginally anti-

684 correlated with threat-impaired object memory, β(13) = -.93, p = .06, and threat-enhanced

685 memory scene memory, β(13) = -.62, p = .11 (Table 6). Consistent with our main prediction,

686 right PPA-right LOC gain-related patterns in functional connectivity were also significantly

687 positively associated with greater gain effects in memory under threat, β(17) = -1.02, p = .01

688 (Figure 6). This finding indicates that, under threat, individuals who showed more competitive

689 (i.e., anti-correlated) PPA-LOC functional interactions when encoding high versus low priority

690 information also showed enhanced high priority scene memory and impaired lower priority

691 object memory under threat.

27 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 28

692 8. Discussion

693 During arousing moments, the release of catecholamines is essential for forming

694 adaptive memories of significant events (Harley, 2004). Yet unlike the well-established role of

695 the dopaminergic system in facilitating reward-motivated learning (Sara, 2009), considerably

696 less is known about the LC-NE system’s contributions to motivated encoding processes,

697 particularly when humans are faced with threat. Using both fMRI and pupil dilation measures, a

698 non-invasive biomarker of phasic LC activity (Joshi et al., 2015; Liu et al., 2017; Murphy et al.,

699 2014; Reimer et al., 2016; Varazzani et al., 2015), our results provide the first empirical support

700 for the idea that a momentary increase in arousal engages the LC to selectively process and

701 store prioritized information regardless of its intrinsic emotionality.

702 Our behavioral results revealed that threat-induced arousal amplified the effects of top-

703 down priority such that memory for goal-relevant stimuli was selectively enhanced. Much work

704 shows that motivation enhances declarative memory encoding and can narrow the focus of

705 attention onto stimuli relevant to our goals (Kaplan et al., 2012; Levine & Edelstein, 2009;

706 Montagrin et al., 2013), at least when rewarding or punishment-related incentives precede

707 actual goal attainment (Kaplan et al., 2012). Consistent with this, prior studies show that cues

708 associated with the threat of shock (Murty et al., 2012) or monetary punishment (Shigemune et

709 al., 2014) lead to enhanced memory for neutral standalone scene and word images,

710 respectively. By demonstrating that, like emotional stimuli, threat of punishment impacts

711 memory of neutral representations differently depending on their goal relevance, our results are

712 an important extension of prior work (Lee et al., 2015a; Mather & Sutherland, 2011; Sakaki et

713 al., 2014). We also found a statistical trend towards the predicted threat-related impairment of

714 low priority stimuli. Low priority memory was significantly above chance in neutral condition,

715 suggesting that participants were able to encode some distracting inputs. Yet, because

716 performance in the threat condition was not significantly above chance, there may have been a

717 floor effect that limited the range for a meaningful object memory comparison. Future studies

28 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 29

718 could promote stronger encoding of both stimuli, such as increasing the image duration, to

719 improve the ability to detect reliable threat-related memory impairments for less salient

720 information.

721 Using trial-level pupil dilation as a proxy of LC activity (Murphy et al., 2014), we showed

722 that enhanced threat-related pupil dilations specifically predicted better memory for goal-

723 relevant images. In similar findings, previous work shows that greater pupil dilation to high-value

724 words versus low-value words is associated with reward-related memory enhancements (Ariel &

725 Castel, 2014). Likewise, pupil dilations evoked by events known to activate the LC-NE system,

726 including target detection (Hoffing & Seitz, 2015) or oddball sounds (Tona et al., 2016), lead to

727 enhanced encoding of concurrent scene images. However, to date, the notion that these effects

728 signify LC neuromodulation has been limited to speculation, because these studies did not

729 directly measure LC activity in the brain. By weighting the BOLD response by pupil dilation, our

730 imaging results support a link between pupil responses during memory encoding and human LC

731 activity. Interestingly, pupil dilation has been shown to predict subsequent perceptual stability of

732 one percept over another during perceptual rivalry (Einhäuser et al., 2008). From the

733 perspective that phasic LC activity amplifies neural gain (Aston-Jones & Cohen, 2005; Mather et

734 al., 2015), our results suggest that threat-related LC activity biases awareness to favor

735 prioritized neutral representations.

736 In accordance with this, we found that LC activity was associated with more selective

737 and localized neuronal processing under arousing conditions. Specifically, stimulus-evoked

738 pupil dilations modulated scene-encoding activity in the LC and parahippocampal cortex (PHC),

739 a predominantly scene-selective region (Köhler et al., 2002; Staresina et al., 2011). LC

740 neuromodulation did not relate to scene encoding activity in the LOC (the region representing

741 obects; Grill-Spector et al., 1999), indicating that such regulation is localized to prioritized

742 representational cortex. In fact, our whole-brain analysis revealed that threat-induced arousal

743 modulated scene-encoding activity in the LC/PHC and nowhere else. Unlike studies focused on

29 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 30

744 perception (Lee et al., 2014), we observed pupil-modulated encoding patterns further along the

745 ventral visual stream than PPA in the PHC and posterior hippocampus, which also show some

746 selectivity for scene encoding (Mundy et al., 2012; Preston et al., 2010). This finding is

747 consistent with recent imaging studies showing that aversive contexts amplify PHC responses

748 to surprising events (Murty et al., 2016) and during threat-motivated source memory binding

749 (Shigemune et al., 2014). In sum, our results support a recent model positing that LC

750 neuromodulation enhances processing important representations selectively (Mather et al.,

751 2015).

752 Our whole-brain fMRI analyses also revealed that lateral occipital cortex activity was

753 associated with successful object encoding under threat. It is important to stress that, at the

754 behavioral level, threat trended towards significantly suppressing distracter encoding. Thus,

755 even though distracter encoding was uncommon, this result indicates that threat modulated

756 visual cortex activity only when objects were successfully encoded. Although speculative, one

757 possibility is that object inputs sometimes triggered greater stimulus-driven attention and

758 reorienting, thereby becoming more prioritized. But this threat-induced strengthening of object

759 traces did not relate to LC activation, suggesting that other threat-related arousal mechanisms

760 facilitate encoding of low priority representations.

761 Another interesting finding was that, across arousal conditions, successful scene

762 encoding corresponded with reduced activity in the left frontal operculum/insula and left/right

763 temporoparietal junction (TPJ). Prior work implicates these regions in processing internal state

764 information (Craig, 2009) and bottom-up reorienting (Corbetta & Shulman, 2002). Further, other

765 neuroimaging work demonstrates that increased TPJ activity impairs episodic memory encoding

766 of goal-relevant stimuli (Uncapher et al., 2011). Thus, our findings are consistent with the idea

767 that successful top-down encoding processes rely on the effective suppression of task-irrelevant

768 internal and external information.

30 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 31

769 By simultaneously modeling pupil dilations to the monetary cue and overlap stimulus, we

770 were also able to link the mnemonic benefit of putative LC activity to the stimulus-encoding

771 period. Why would the LC’s mnemonic influence be evident during the encoding event but not

772 during the arousing cue? One possibility is that increased pupil dilation at encoding reflects

773 more effortful cognition and energy investment (Kahneman & Beatty, 1966; Varazzani et al.,

774 2015). Recent work in monkeys shows that – although pupil dilation occurs in responses to

775 reward cues – LC activity was more closely correlated with dilations during the onset of goal-

776 directed behavioral responses and the degree of effort requires to perform those actions

777 (Varazzani et al., 2015). Thus, our finding of a positive relationship between pupil-modulated LC

778 encoding activity may signify participants’ increased effort to encode target information,

779 consistent with evidence implicating the LC in effortful, goal-directed attention and behavior

780 (Alnæs et al., 2014; Aston-Jones & Cohen, 2005; Bouret et al., 2012; Floresco, 2015; Raizada &

781 Poldrack, 2008).

782 The notion that our pupil and LC activity results reflect increased attentional modulation

783 rather than emotional arousal, per se, aligns with extant neurobiological models of emotional

784 memory. Specifically, converging theoretical and empirical work demonstrate that the immediate

785 benefit of attention on emotional memory are dissociable from longer-term emotional memory

786 enhancements (Sharot & Phelps, 2004; Talmi, 2013; Talmi et al., 2008), with the latter involving

787 post-encoding noradrenergic modulation of the amygdala (LaLumiere et al., 2003; McGaugh &

788 Roozendaal, 2002). While we cannot dissociate whether LC activation in this study

789 corresponded with greater selective attention versus emotional arousal (or both), the GANE

790 model is agnostic to how NE levels become elevated: insofar as the LC is sufficiently activated

791 by a salient event, we would expect enhanced memory selectivity for information that is

792 prioritized at that moment (Mather et al., 2015). Interestingly, empirical work shows that the

793 facilitating effects of emotional arousal on prioritized neutral memoranda become even more

794 pronounced after a one-week delay (Anderson et al., 2006; Knight & Mather, 2009). Thus, one

31 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 32

795 avenue for future research would be to examine whether enhancing post-encoding

796 noradrenergic activity amplifies the priority-dependent effects of phasic LC activation at

797 encoding (Roozendaal & Hermans, 2017).

798 Prior research using neuromelanin sensitive imaging has linked LC signal intensity, a

799 structural marker of LC neuronal density (Keren et al., 2009; Keren et al., 2015), to trait

800 measures of executive function (Clewett et al., 2016) and parasympathetic control over the

801 heart (Mather et al., 2017). The present study expands upon these studies by demonstrating

802 that individuals with greater LC neuromelanin are also more likely to engage the LC to encode

803 task-relevant representations under arousal. To our knowledge, these data provide the first

804 human evidence of a direct link between task-related LC activity and its structure. These results

805 highlight the importance of measuring individual differences in LC structure, because it may

806 account for differences in cognitive abilities (Clewett et al., 2016), vulnerability to anxiety

807 (Bangasser et al., 2016), relative levels of tonic sympathetic activity (Mather et al., 2017), or

808 age-related neuronal pathology (Braak et al., 2011; Mather & Harley, 2016; Robertson, 2013).

809 Using functional connectivity analyses, we found that successful threat-motivated

810 encoding yielded dynamic coupling patterns consistent with increased neural gain. Specifically,

811 better high priority encoding under threat was associated with reduced functional coupling

812 between high priority cortex (PPA) and lower priority cortex (LOC). Further, an individual

813 differences analysis revealed that, under threat, individuals who showed less communication

814 between these competing visual regions when encoding high versus low priority information

815 showed more selective memory for goal-relevant information.. Thus, beyond influencing

816 perception (Lee et al., 2014), arousal also seems to bias network-level processes to support

817 “winner-take-more” and “loser-take-less” effects in memory. These results dovetail with prior

818 theoretical (Aston-Jones & Cohen, 2005; Mather et al., 2015) and neural network models (Eldar

819 et al., 2013; Eldar et al., 2016; Servan-Schreiber et al., 1990; Usher et al., 1999) of LC gain

820 modulation proposing that lateral inhibition between competing brain regions is critical for

32 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 33

821 biasing processing in favor of task-relevant over task-irrelevant stimuli. Supporting this idea, a

822 transcranial magnetic stimulation (TMS) study showed that inhibiting LOC activity improves

823 scene categorization accuracy via an increase in PPA activity (Mullin & Steeves, 2011, 2013).

824 Thus, effective distracter suppression may also liberate mental or energetic resources to focus

825 even more on encoding the most important representations.

826 There are several limitations in our study. First, imaging human brainstem activity is

827 often confounded by cardiac pulsation artifact, the LC’s small size, and low spatial resolution of

828 fMRI (Astafiev et al., 2010). Importantly, we used a rigorous methodological approach to

829 mitigate these issues. A brainstem-optimized alignment procedure was used for image

830 registration (Napadow et al., 2006) and we removed confounding signals in the fourth ventricle

831 using ICA (Beckmann & Smith, 2004; Clewett et al., 2013). We also analyzed the task data

832 using regressors modulated by trial-level pupil dilation as proxy for phasic LC activity. Finally,

833 we utilized neuromelanin-sensitive weighted imaging to acquire participant-specific LC structural

834 ROIs (Clewett et al., 2016; Sasaki et al., 2006; Shibata et al., 2006). The significant relationship

835 between LC BOLD signal and LC neuromelanin signal helps validate that our imaging results

836 signified true LC activity. Moreover, it is unlikely that our findings resulted from threat amplifying

837 physiological noise related to greater sympathetic arousal, because we did not observe a main

838 effect of threat on LC activity. Nonetheless, our results should be interpreted with caution given

839 current methodological constraints on imaging human brainstem activity. Another important

840 consideration is that we paired aversive sounds with the threat cues to amp up overall arousal.

841 Given that prior studies have reported greater VTA activity in response to only images of

842 threatening money cues (Shigemune et al., 2014), it is possible that our null dopaminergic

843 results may have been driven by a combination of both aversive motivational and unconditioned

844 stressors.

845 In conclusion, our findings support the idea that LC activity helps optimizes the selection

846 and formation of adaptive memories when it is most needed. Ultimately, this work will inform

33 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 34

847 future studies investigating the role of neuromodulators in motivated cognition, particularly with

848 respect to avoiding threat and energizing adaptive behavior. Furthermore, our results may

849 inform how arousal-related disorders characterized by the formation of maladaptive memories

850 under threat (e.g., PTSD) or inabilities to focus attention (e.g., ADHD) relate to altered LC-NE

851 system function.

34 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 35

852 References 853 854 Adcock, R. A., Thangavel, A., Whitfield-Gabrieli, S., Knutson, B., & Gabrieli, J. D. E. (2006). 855 Reward-motivated learning: Mesolimbic activation precedes memory formation. Neuron, 856 50, 507-517. 857 Alnæs, D., Sneve, M. H., Espeseth, T., Endestad, T., van de Pavert, S. H. P., & Laeng, B. 858 (2014). Pupil size signals mental effort deployed during multiple object tracking and 859 predicts brain activity in the dorsal attention network and the locus coeruleus. Journal of 860 Vision, 14(4). doi: 10.1167/14.4.1 861 Anderson, A. K., Wais, P. E., & Gabrieli, J. D. E. (2006). Emotion enhances remembrance of 862 neutral events past. Proceedings of the National Academy of Sciences of the United 863 States of America, 103(5), 1599-1604. doi: 10.1073/pnas.0506308103 864 Ariel, R., & Castel, A. (2014). Eyes wide open: enhanced pupil dilation when selectively studying 865 important information. Experimental Brain Research, 232(1), 337-344. doi: 866 10.1007/s00221-013-3744-5 867 Astafiev, S. V., Snyder, A. Z., Shulman, G. L., & Corbetta, M. (2010). Comment on “Modafinil 868 shifts human locus coeruleus to low-tonic, high-phasic activity during functional MRI” and 869 “Homeostatic sleep pressure and responses to sustained attention in the 870 suprachiasmatic area”. Science, 328(5976), 309-309. 871 Aston-Jones, G., & Cohen, J. D. (2005). An integrative theory of locus coeruleus-norepinephrine 872 function: Adaptive gain and optimal performance. Annual Review of Neuroscience, 28, 873 403-450. 874 Aston-Jones, G., Rajkowski, J., & Cohen, J. (1999). Role of locus coeruleus in attention and 875 behavioral flexibility. Biological Psychiatry, 46(9), 1309-1320. 876 Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed 877 random effects for subjects and items. Journal of memory and language, 59(4), 390-412. 878 doi: 10.1016/j.jml.2007.12.005 879 Bangasser, D. A., Wiersielis, K. R., & Khantsis, S. (2016). Sex differences in the locus 880 coeruleus-norepinephrine system and its regulation by stress. Brain Research, 1641, 881 177-188. 882 Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005). Investigations into resting- 883 state connectivity using independent component analysis. Philosophical Transactions of 884 the Royal Society B-Biological Sciences, 360(1457), 1001-1013. doi: 885 10.1098/rstb.2005.1634 886 Beckmann, C. F., Jenkinson, M., & Smith, S. M. (2003). General multilevel linear modeling for 887 group analysis in FMRI. Neuroimage, 20(2), 1052-1063. 888 Beckmann, C. F., & Smith, S. M. (2004). Probabilistic independent component analysis for 889 functional magnetic resonance imaging. IEEE Transactions on Medical Imaging, 23, 890 137-152. 891 Berridge, C. W., & Waterhouse, B. D. (2003). The locus coeruleus-noradrenergic system: 892 modulation of behavioral state and state-dependent cognitive processes. Brain 893 Research Reviews, 42(1), 33-84. doi: 10.1016/s0165-0173(03)00143-7 894 Bisby, J. A., & Burgess, N. (2014). Negative affect impairs associative memory but not item 895 memory. Learning and Memory, 21(1), 21-27. 896 Bouret, S., Ravel, S., & Richmond, B. J. (2012). Complementary neural correlates of motivation 897 in dopaminergic and noradrenergic neurons of monkeys. Front Behav Neurosci, 6, 40- 898 40. 899 Bouret, S., & Richmond, B. J. (2015). Sensitivity of Locus Ceruleus Neurons to Reward Value 900 for Goal-Directed Actions. The Journal of Neuroscience, 35(9), 4005-4014.

35 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 36

901 Braak, H., Thal, D. R., Ghebremedhin, E., & Del Tredici, K. (2011). Stages of the pathologic 902 process in Alzheimer disease: age categories from 1 to 100 years. Journal of 903 Neuropathology and Experimental Neurology, 70(11), 960-969. 904 Bradley, M. M., & Lang, P. J. (2007). The International Affective Digitized Sounds (2nd Edition; 905 IADS-2): Affective ratings of sounds and instruction manual. Technical report B-3. 906 University of Florida. Gainesville, Fl. 907 Clewett, D., Lee, T. H., Greening, S. G., Ponzio, A., Margalit, E., & Mather, M. (2016). 908 Neuromelanin marks the spot: Identifying a locus coeruleus biomarker of cognitive 909 reserve in healthy aging. Neurobiology of Aging, 37, 117-126. 910 Clewett, D., Sakaki, M., Huang, R., Nielsen, S. E., & Mather, M. (2017a). Arousal amplifies 911 biased competition between high and low priority memories more in women than in men: 912 The role of elevated noradrenergic activity. Psychoneuroendocrinology, 80, 80-91. 913 Clewett, D., Sakaki, M., Nielsen, S., Petzinger, G., & Mather, M. (2017b). Noradrenergic 914 mechanisms of arousal’s bidirectional effects on episodic memory. Neurobiology of 915 Learning and Memory, 137, 1-14. 916 Clewett, D., Schoeke, A., & Mather, M. (2013). Amygdala functional connectivity is reduced after 917 the cold pressor task. Cognitive, Affective, & Behavioral Neuroscience, 13(3), 501-518. 918 Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in 919 the brain. Nature Reviews Neuroscience, 3(3), 201-215. doi: 10.1038/nrn755 920 Craig, A. D. (2009). How do you feel - now? The anterior insula and human awareness. Nature 921 Reviews Neuroscience, 10(1), 59-70. doi: 10.1038/nrn2555 922 Dunsmoor, J. E., Martin, A., & LaBar, K. S. (2012). Role of conceptual knowledge in learning 923 and retention of conditioned fear. Biological Psychology, 89(2), 300-305. 924 Dunsmoor, J. E., Murty, V. P., Davachi, L., & Phelps, E. A. (2015). Emotional learning 925 selectively and retroactively strengthens memories for related events. Nature. 926 Easterbrook, J. A. (1959). The effect of emotion on cue utilization and the organization of 927 behavior. Psychological Review, 66(3), 183-201. doi: 10.1037/h0047707 928 Einhäuser, W., Stout, J., Koch, C., & Carter, O. (2008). Pupil dilation reflects perceptual 929 selection and predicts subsequent stability in perceptual rivalry. Proceedings of the 930 National Academy of Sciences of the United States of America, 105(5), 1704-1709. doi: 931 10.2307/25451346 932 Eldar, E., Cohen, J. D., & Niv, Y. (2013). The effects of neural gain on attention and learning. 933 Nature Neuroscience. 934 Eldar, E., Niv, Y., & Cohen, J. D. (2016). Do you see the forest or the tree? Neural gain and 935 breadth versus focus in perceptual processing. Psychological Science, 27(12), 1632- 936 1643. 937 Epstein, R., & Kanwisher, N. (1998). A cortical representation of the local visual environment. 938 Nature, 392(6676), 598-601. doi: 10.1038/33402 939 Floresco, S. B. (2015). Noradrenaline and dopamine: sharing the Workload. Trends in 940 Neurosciences, 38(8), 465-467. 941 Friston, K. J., Buechel, C., Fink, G. R., Morris, J., Rolls, E., & Dolan, R. J. (1997). 942 Psychophysiological and modulatory interactions in neuroimaging. Neuroimage, 6, 218- 943 229. 944 Gabrieli, J. D. E., Brewer, J. B., Desmond, J. E., & Glover, G. H. (1997). Seperate neural bases 945 of two fundamental memory processes in the human medial temporal lobe. Science, 946 276, 264-266. 947 Grill-Spector, K., Kushnir, T., Edelman, S., Avidan, G., Itzchak, Y., & Malach, R. (1999). 948 Differential processing of objects under various viewing conditions in the human lateral 949 occipital complex. Neuron, 24(1), 187-203. 950 Harley, C. W. (2004). Norepinephrine and dopamine as learning signals. Neural Plasticity, 11(3- 951 4), 191-204.

36 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 37

952 Hoffing, R. C., & Seitz, A. R. (2015). Pupillometry as a glimpse into the neurochemical basis of 953 human memory encoding. Journal of Cognitive Neuroscience. 954 Hurlemann, R., Hawellek, B., Matusch, A., Kolsch, H., Wollersen, H., Madea, B., . . . Dolan, R. 955 J. (2005). Noradrenergic modulation of emotion-induced forgetting and remembering. 956 Journal of Neuroscience, 25(27), 6343-6349. doi: 10.1523/JNEUROSCI.0228-05.2005 957 Joshi, S., Li, Y., Kalwani, Rishi M., & Gold, Joshua I. (2015). Relationships between pupil 958 diameter and neuronal activity in the locus coeruleus, colliculi, and cingulate cortex. 959 Neuron. doi: http://dx.doi.org/10.1016/j.neuron.2015.11.028 960 Kahneman, D., & Beatty, J. (1966). Pupil diameter and load on memory. Science, 154(3756), 961 1583-1585. 962 Kaplan, R. L., Van Damme, I., & Levine, L. J. (2012). Motivation matters: Differing effects of pre- 963 goal and post-goal emotions on attention and memory. Frontiers in psychology, 3, 404. 964 Kensinger, E. A., Garoff-Eaton, R. J., & Schacter, D. L. (2006). Memory for specific visual 965 details can be enhanced by negative arousing content. Journal of Memory and 966 Language, 54(1), 99-112. doi: 10.1016/j.jml.2005.05.005 967 Kensinger, E. A., Garoff-Eaton, R. J., & Schacter, D. L. (2007). How negative emotion enhances 968 the visual specificity of a memory. Journal of Cognitive Neuroscience, 19(11), 1872- 969 1887. 970 Keren, N. I., Lozar, C. T., Harris, K. C., Morgan, P. S., & Eckert, M. A. (2009). In vivo mapping 971 of the human locus coeruleus. Neuroimage, 47(4), 1261-1267. doi: 972 10.1016/j.neuroimage.2009.06.012 973 Keren, N. I., Taheri, S., Vazey, E. M., Morgan, P. S., Granholm, A.-C. E., Aston-Jones, G. S., & 974 Eckert, M. A. (2015). Histologic validation of locus coeruleus MRI contrast in post- 975 mortem tissue. Neuroimage, 113, 235-245. 976 Knight, M., & Mather, M. (2009). Reconciling findings of emotion-induced memory enhancement 977 and impairment of preceding items. Emotion, 9(6), 763-781. doi: 10.1037/a0017281 978 Knutson, B., Westdorp, A., Kaiser, E., & Hommer, D. (2000). FMRI visualization of brain activity 979 during a monetary incentive delay task. Neuroimage, 12(1), 20-27. 980 Köhler, S., Crane, J., & Milner, B. (2002). Differential contributions of the parahippocampal 981 place area and the anterior hippocampus to human memory for scenes. Hippocampus, 982 12(6), 718-723. 983 LaBar, K. S., & Cabeza, R. (2006). Cognitive neuroscience of emotional memory. Nature 984 Reviews Neuroscience, 7(1), 54-64. 985 LaLumiere, R. T., Buen, T. V., & McGaugh, J. L. (2003). Post-training intra-basolateral 986 amygdala infusions of norepinephrine enhance consolidation of memory for contextual 987 fear conditioning. Journal of Neuroscience, 23(17), 6754-6758. 988 Lee, T.-H., Greening, S. G., & Mather, M. (2015a). Encoding of goal-relevant stimuli is 989 strengthened by emotional arousal in memory. Frontiers in psychology, 6. 990 Lee, T. H., Greening, S. G., & Mather, M. (2015b). Encoding on goal-relevant stimuli is 991 strengthened by emotional stimuli in memory. Frontiers in psychology, 6:1173. 992 Lee, T. H., Sakaki, M., Cheng, R., Velasco, R., & Mather, M. (2014). Emotional arousal 993 amplifies the effects of biased competition in the brain. Social Cognitive and Affective 994 Neuroscience, 9(12), 2067-2077. 995 Levine, L. J., & Edelstein, R. S. (2009). Emotion and memory narrowing: A review and goal- 996 relevance approach. Cognition & Emotion, 23(5), 833-875. doi: 997 10.1080/02699930902738863 998 Liu, Y., Rodenkirch, C., Moskowitz, N., Schriver, B., & Wang, Q. (2017). Dynamic Lateralization 999 of Pupil Dilation Evoked by Locus Coeruleus Activation Results from Sympathetic, Not 1000 Parasympathetic, Contributions. Cell reports, 20(13), 3099-3112.

37 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 38

1001 Markovic, J., Anderson, A. K., & Todd, R. M. (2014). Tuning to the significant: Neural and 1002 genetic processes underlying affective enhancement of visual perception and memory. 1003 Behavioural Brain Research, 259, 229-241. 1004 Mather, M., Clewett, D., Sakaki, M., & Harley, C. W. (2015). Norepinephrine ignites local hot 1005 spots of neuronal excitation: how arousal amplifies selectivity in perception and memory. 1006 Behavioral and Brain Sciences, 1-100. 1007 Mather, M., & Harley, C. W. (2016). The locus coeruleus: Essential for maintaining cognitive 1008 function and the aging brain. Trends in Cognitive Sciences, 20(3), 214-226. 1009 Mather, M., & Sutherland, M. R. (2011). Arousal-biased competition in perception and memory. 1010 Perspectives on Psychological Science, 6, 114-133. 1011 Mather, M., Yoo, H. J., Clewett, D. V., Lee, T.-H., Greening, S. G., Ponzio, A., . . . Thayer, J. F. 1012 (2017). Higher locus coeruleus MRI contrast is associated with lower parasympathetic 1013 influence over heart rate variability. Neuroimage, 150, 329-335. 1014 McGaugh, J. L. (2013). Making lasting memories: Remembering the significant. Proceedings of 1015 the National Academy of Sciences, 110(Supplement 2), 10402-10407. 1016 McGaugh, J. L., & Roozendaal, B. (2002). Role of adrenal stress hormones in forming lasting 1017 memories in the brain. Current Opinion in Neurobiology, 12(2), 205-210. 1018 Mohanty, A., Gitelman, D. R., Small, D. M., & Mesulam, M. M. (2008). The spatial attention 1019 network interacts with limbic and monoaminergic systems to modulate motivation- 1020 induced attention shifts. Cerebral Cortex, 18(11), 2604-2613. doi: 1021 10.1093/cercor/bhn021 1022 Montagrin, A., Brosch, T., & Sander, D. (2013). Goal conduciveness as a key determinant of 1023 memory facilitation. Emotion, 13(4), 622. 1024 Mullin, C. R., & Steeves, J. K. (2011). TMS to the lateral occipital cortex disrupts object 1025 processing but facilitates scene processing. Journal of Cognitive Neuroscience, 23(12), 1026 4174-4184. 1027 Mullin, C. R., & Steeves, J. K. (2013). Consecutive TMS-fMRI reveals an inverse relationship in 1028 BOLD signal between object and scene processing. Journal of Neuroscience, 33(49), 1029 19243-19249. 1030 Mundy, M. E., Downing, P., & Graham, K. S. (2012). Extrastriate cortex and medial temporal 1031 lobe regions respond differentially to visual feature overlap within preferred stimulus 1032 category. Neuropsychologia, 50(13), 3053-3061. 1033 Murphy, P. R., O'Connell, R. G., O'Sullivan, M., Robertson, I. H., & Balsters, J. H. (2014). Pupil 1034 diameter covaries with BOLD activity in human locus coeruleus. Human Brain Mapping, 1035 35(8), 4140-4154. 1036 Murphy, P. R., Robertson, I. H., Balsters, J. H., & O'Connell, R. G. (2011). Pupillometry and P3 1037 index the locus coeruleus–noradrenergic arousal function in humans. Psychophysiology, 1038 48(11), 1532-1543. doi: 10.1111/j.1469-8986.2011.01226.x 1039 Murty, V. P., LaBar, K. S., & Adcock, R. A. (2012). Threat of punishment motivates memory 1040 encoding via amygdala, not midbrain, interactions with the medial temporal lobe. The 1041 Journal of Neuroscience, 32(26), 8969-8976. 1042 Murty, V. P., LaBar, K. S., & Adcock, R. A. (2016). Distinct medial temporal networks encode 1043 surprise during motivation by reward versus punishment. Neurobiology of Learning and 1044 Memory, 134, 55-64. 1045 Murty, V. P., Shermohammed, M., Smith, D. V., Carter, R. M., Huettel, S. A., & Adcock, R. A. 1046 (2014). Resting state networks distinguish human ventral tegmental area from substantia 1047 nigra. Neuroimage, 100, 580-589. 1048 Napadow, V., Dhond, R., Kennedy, D., Hui, K. K., & Makris, N. (2006). Automated brainstem co- 1049 registration (ABC) for MRI. Neuroimage, 32(3), 1113-1119.

38 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 39

1050 Phelps, E. A., Ling, S., & Carrasco, M. (2006). Emotion facilitates perception and potentiates the 1051 perceptual benefits of attention. Psychological Science, 17(4), 292-299. doi: 1052 10.1111/j.1467-9280.2006.01701.x 1053 Preston, A. R., Bornstein, A. M., Hutchinson, J. B., Gaare, M. E., Glover, G. H., & Wagner, A. D. 1054 (2010). High-resolution fMRI of content-sensitive subsequent memory responses in 1055 human medial temporal lobe. Journal of Cognitive Neuroscience, 22(1), 156-173. 1056 Raizada, R. D. S., & Poldrack, R. A. (2008). Challenge-driven attention: interacting frontal and 1057 brainstem systems. Frontiers in Human Neuroscience, 2. doi: 1058 10.3389/neuro.09.003.2007 1059 Reimer, J., McGinley, M. J., Liu, Y., Rodenkirch, C., Wang, Q., McCormick, D. A., & Tolias, A. 1060 S. (2016). Pupil fluctuations track rapid changes in adrenergic and cholinergic activity in 1061 cortex. Nature communications, 7. 1062 Robertson, I. H. (2013). A noradrenergic theory of cognitive reserve: implications for Alzheimer's 1063 disease. Neurobiology of Aging, 34(1), 298-308. 1064 Roozendaal, B., & Hermans, E. J. (2017). Norepinephrine effects on the encoding and 1065 consolidation of emotional memory: improving synergy between animal and human 1066 studies. Current Opinion in Behavioral Sciences, 14, 115-122. 1067 Sakaki, M., Fryer, K., & Mather, M. (2014). Emotion strengthens high priority memory traces but 1068 weakens low priority memory traces. Psychological Science, 25(387-395). 1069 Sara, S. J. (2009). The locus coeruleus and noradrenergic modulation of cognition. Nature 1070 Reviews Neuroscience, 10(3), 211-223. 1071 Sasaki, M., Shibata, E., Tohyama, K., Takahashi, J., Otsuka, K., Tsuchiya, K., . . . Sakai, A. 1072 (2006). Neuromelanin magnetic resonance imaging of locus ceruleus and substantia 1073 nigra in Parkinson's disease. NeuroReport, 17(11), 1215-1218. 1074 Schwarze, U., Bingel, U., & Sommer, T. (2012). Event-related nociceptive arousal enhances 1075 memory consolidation for neutral scenes. The Journal of Neuroscience, 32(4), 1481- 1076 1487. 1077 Servan-Schreiber, D., Printz, H., & Cohen, J. D. (1990). A network model of catecholamine 1078 effects: gain, signal-to-noise ratio, and behavior. Science, 249(4971), 892-895. 1079 Sharot, T., & Phelps, E. A. (2004). How arousal modulates memory: Disentangling the effects of 1080 attention and retention. Cognitive, Affective, & Behavioral Neuroscience, 4, 294-306. 1081 Shibata, E., Sasaki, M., Tohyama, K., Kanbara, Y., Otsuka, K., Ehara, S., & Sakai, A. (2006). 1082 Age-related changes in locus ceruleus on neuromelanin magnetic resonance imaging at 1083 3 Tesla. Magnetic Resonance in Medical Sciences, 5(4), 197-200. 1084 Shigemune, Y., Tsukiura, T., Kambara, T., & Kawashima, R. (2014). Remembering with gains 1085 and losses: effects of monetary reward and punishment on successful encoding 1086 activation of source memories. Cerebral Cortex, 24(5), 1319-1331. 1087 Shohamy, D., & Adcock, R. A. (2010). Dopamine and adaptive memory. Trends in Cognitive 1088 Sciences, 14(10), 464-472. doi: 10.1016/j.tics.2010.08.002 1089 Staresina, B. P., Duncan, K. D., & Davachi, L. (2011). Perirhinal and parahippocampal cortices 1090 differentially contribute to later recollection of object-and scene-related event details. The 1091 Journal of Neuroscience, 31(24), 8739-8747. 1092 Stark, C. E. L., & Squire, L. R. (2001). When zero is not zero: The problem of ambiguous 1093 baseline conditions in fMRI. Proceedings of the National Academy of Sciences of the 1094 United States of America, 98(22), 12760-12765. 1095 Strange, B. A., & Dolan, R. J. (2004). beta-Adrenergic modulation of emotional memory-evoked 1096 human amygdala and hippocampal responses. Proceedings of the National Academy of 1097 Sciences of the United States of America, 101(31), 11454-11458. 1098 Strange, B. A., Hurlemann, R., & Dolan, R. J. (2003). An emotion-induced retrograde amnesia 1099 in humans is amygdala- and beta-adrenergic-dependent. Proceedings of the National

39 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 40

1100 Academy of Sciences of the United States of America, 100(23), 13626-13631. doi: 1101 10.1073/pnas.1635116100 1102 Sutherland, M. R., & Mather, M. (2012). Negative arousal amplifies the effects of saliency in 1103 short-term memory. Emotion, 12, 1367-1372. doi: 10.1037/a0027860 1104 Talmi, D. (2013). Enhanced emotional memory: Cognitive and neural mechanisms. Current 1105 Directions in Psychological Science, 22(6), 430-436. 1106 Talmi, D., Anderson, A. K., Riggs, L., Caplan, J. B., & Moscovitch, M. (2008). Immediate 1107 memory consequences of the effect of emotion on attention to pictures. Learning and 1108 Memory, 15(3), 172-182. 1109 Tona, K. D., Murphy, P., Brown, S. B., & Nieuwenhuis, S. (2016). The accessory stimulus effect 1110 is mediated by phasic arousal: A pupillometry study. Psychophysiology, 53(7), 1108- 1111 1113. 1112 Uncapher, M. R., Hutchinson, J. B., & Wagner, A. D. (2011). Dissociable effects of top-down 1113 and bottom-up attention during episodic encoding. Journal of Neuroscience, 31(35), 1114 12613-12628. 1115 Usher, M., Cohen, J. D., Servan-Schreiber, D., Rajkowski, J., & Aston-Jones, G. (1999). The 1116 role of locus coeruleus in the regulation of cognitive performance. Science, 283(5401), 1117 549-554. 1118 Varazzani, C., San-Galli, A., Gilardeau, S., & Bouret, S. (2015). Noradrenaline and Dopamine 1119 Neurons in the Reward/Effort Trade-Off: A Direct Electrophysiological Comparison in 1120 Behaving Monkeys. The Journal of Neuroscience, 35(20), 7866-7877. 1121 Weymar, M., Bradley, M. M., Hamm, A. O., & Lang, P. J. (2013). When fear forms memories: 1122 Threat of shock and brain potentials during encoding and recognition. Cortex, 49(3), 1123 819-826. 1124 Wilcox, R. R. (2004). Some results on extensions and modifications of the Theil—Sen 1125 regression estimator. British Journal of Mathematical and Statistical Psychology, 57(2), 1126 265-280. 1127 Willenbockel, V., Sadr, J., Fiset, D., Horne, G. O., Gosselin, F., & Tanaka, J. W. (2010). 1128 Controlling low-level image properties: the SHINE toolbox. Behavior Research Methods, 1129 42(3), 671-684. 1130 Worsley, K. J. (2001). Statistical analysis of activation images. In P. Jezzard, P. M. Matthews, & 1131 S. M. Smith (Eds.), Functional MRI: An Introduction to Methods. New York: Oxford 1132 University Press. 1133 1134

40 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 41

1135 Figure Legends 1136 1137 Figure 1. Schematic trial from the monetary incentive encoding fMRI task. Each trial consisted

1138 of a money cue, a jittered ISI, an overlap image (scene/object combo), and a distracter task. In

1139 this task, participants were instructed to prioritize each background scene in attention and

1140 memory, while ignoring the transparent object. On half of the trials, participants were motivated

1141 to encode the scenes by a threat cue (“no symbol”) accompanied by an arousing buzzer noise

1142 (threat condition). This symbol cued participants that they would lose 50 cents from a preset

1143 account if they forgot the upcoming scene on a subsequent recognition memory test. On the

1144 other half of trials (neutral condition), participants saw an image of a square. On these trials,

1145 they were instructed to try to memorize the scene as best as they could, but they had no

1146 additional incentive to do so. The red square denotes the 2.5s stimulus-encoding period when

1147 brain activity was analyzed. The scrambled overlap images were used to model an “active

1148 baseline” in the fMRI analyses and to prevent participants from rehearsing recent scenes. The

1149 top right panel shows a schematic trial from the two-alternative forced choice memory test that

1150 was administered outside the scanner ~15 minutes after encoding. Note that images are not

1151 drawn to scale.

1152

1153 Figure 2. Memory performance on the monetary incentive encoding fMRI task. (A) Proportion of

1154 correctly remembered high priority scenes and lower priority objects by Arousal condition (threat

1155 of punishment versus neutral). Data are represented as means +/- SEM. Statistical chance-level

1156 performance is 0.5. (B; left) Proportion of trials where participants showed a selective memory

1157 trade-off in favor of the high priority scene (left bars) or remembered both the scene and object

1158 stimuli in the overlap images (right bars). (B; right) Schematic of memory codependency

1159 analysis. Displayed object/scene images represent successful memory. Light gray squares

1160 signify bins there were used to calculate memory selectivity. *p < .05, **p < .01, ***p < .001.

41 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 42

1161 Figure 3. Pupil results. (A) Mean timecourse of evoked pupil diameter changes to the money

1162 cue broken down by arousal type (threat vs. neutral). (B) Mean timecourse of evoked pupil

1163 diameter changes to the overlap stimulus broken down by memory type (scene vs. object),

1164 arousal type (threat vs. neutral), and memory outcome (remember vs. forgot). (A/B) Pupil

1165 dilations were calculated by subtracting window of peak dilation, the rightmost shaded

1166 rectangles, minus pupil size during the baseline period, the leftmost shaded rectangle. (C)

1167 Average pupil dilation was significantly larger in response to the threat money cue compared to

1168 the neutral money cue, but significantly lower in response to overlap images in the threat

1169 compared to the neutral condition. Data are represented as means +/- SEM. (D) Results from

1170 the hierarchical generalized linear modeling (HGLM) analyses indicated that threat-enhanced

1171 pupil dilations to the overlap images predicted better scene memory compared to the neutral

1172 condition. Lines and shading indicate regression fits and standard errors for the threat (red) and

1173 neutral (grey) conditions. The y-axis represents the probability of accurate scene recognition.

1174 Note that, while not displayed, there were no significant effects for object memory outcomes. *p

1175 < .05; ***p < .001. R = remember; F = forgot.

1176

1177 Figure 4. Whole-brain pupil-parametric GLM examining the effects of threat-induced arousal on

1178 scene and object encoding activity. Successful object encoding was associated with increased

1179 activity in the occipital pole and right inferior frontal gyrus (IFG) and middle frontal gyrus (MFG;

1180 top left). Successful scene encoding was associated with suppressed activity in left inferior and

1181 middle frontal gyrus (IFG/MFG), left insula, and bilateral temporo-parietal junction (TPJ)

1182 encompassing the supramarginal gyrus (SMG; top right). Compared to the neutral trials, threat

1183 of punishment significantly enhanced object-encoding activity in the lateral occipital cortex

1184 (LOC; bottom left). Compared to the neutral trials, threat of punishment significantly enhanced

1185 scene-encoding activity in parahippocampal cortex (PHC; bottom right panel). Note that the

1186 statistical results are not displayed, because the bar graphs are meant to illustrate the direction

42 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 43

1187 of the effects in each whole-brain cluster. Data are represented as mean percent signal change

1188 +/- SEM. R = remember; F = forgot. L = left; R = right.

1189

1190 Figure 5. Threat of punishment amplified scene-encoding activity in the locus coeruleus (LC). In

1191 this pupil-parametric GLM analysis, participant’s whole-brain statistical parametric maps were

1192 written into standard-space using a procedure optimized for brainstem alignment (Napadow et

1193 al., 2006). Whole-brain analyses revealed that threat-enhanced scene encoding was associated

1194 with higher activity in a brainstem region encompassing the LC. Qualitative comparisons

1195 revealed substantial overlap between the spatial location of this functional cluster (red) and both

1196 the Keren et al. (2009) 2SD 2mm standard-space LC mask (blue dots) and one participant’s

1197 neuromelanin-sensitive weighted image (bright dots; top panel). This result was confirmed to be

1198 the LC in a planned ROI analysis, in which participants’ hand-drawn neuromelanin LC masks

1199 were used to extract percent signal change (bottom left). Data are represented as means +/-

1200 SEM. The degree to which participants engaged the LC during threat-related scene encoding

1201 was also correlated with his/her LC neuromelanin contrast-to-noise ratio (CNR), a putative

1202 biomarker of LC structure (bottom right). *p < .05; R = scene remember; F = scene forgot.

1203

1204 Figure 6. Dynamic functional connectivity between the parahippocampal place area (PPA) and

1205 rest of the brain during scene encoding. At the whole-brain level, enhanced scene encoding

1206 activity in the threat vs. neutral condition was associated with reduced right PPA functional

1207 coupling with early visual processing cortical regions, including the left lateral occipital cortex

1208 (LOC; blue cluster; top left). ROI analyses using each participant’s functionally defined LOC

1209 masks confirmed this result (top right). Data are represented as means +/- SEM. Right PPA

1210 functional connectivity with the LOC was associated with different memory outcomes under

1211 threat-induced arousal (bottom panel). Specifically, those who showed less right PPA- right LOC

1212 functional coupling during successful scene versus object encoding under threat also exhibited

43 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 44

1213 greater threat-related gain effects in declarative memory. Note that the statistic reflects the

1214 residual correlation coefficient after controlling for the relationship between gain effects in

1215 memory and the other PPA-LOC functional connectivity predictors. FC = functional connectivity;

1216 *p < .05.

1217

44 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 45

1218 Table 1. Monetary incentive encoding task performance and post-task ratings of money cue 1219 arousal and motivation. Threat Trials Neutral Trials Significance

Arousal rating for money cue 3.95 (1.36) 1.59 (.091) P < .001*** Motivation to memorize scenes 5.05 (1.99) 2.52 (2.16) P = .001** Scene Categorization Accuracy .94 (.08) .94 (.05) n.s. Scene Categorization RT 1181.56 (206.41) 1136 (139.08) n.s. Scrambled Image #1 Accuracy .91 (.09) .90 (.08) n.s. Scrambled Image #2 Accuracy .96 (.04) .96 (.05) n.s. Scrambled Image #3 Accuracy .95 (.05) .97 (.04) n.s. Scrambled Image #1 RT 557.18 (77.87) 542.86 (81.29) n.s. Scrambled Image #2 RT 325.02 (52.19) 318.05 (46.54) n.s. Scrambled Image #3 RT 324.2 (48.48) 319.98 (48.25) n.s. 1220 Notes: RT = Reaction Time; Motivation ratings for memorizing the scenes were measured in 1221 centimeters from 0 (not motivated) to 7 (extremely motivated); Cue arousal ratings were 1222 measured on a scale from 1 (not at all) to 7 (very). P-values from paired t-tests are displayed in 1223 the rightmost column comparing threat vs. neutral trials.

45 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 46

1224 Table 2. Hierarchical generalized linear modeling (HGLM) results examining the relationship 1225 between pupil dilation responses to the overlap stimulus and arousal condition (threat or 1226 neutral) on scene/object memory. Predictors Estimate SE z p

Scene Memory

Intercept 0.77741 0.08815 8.819 < 2.00E-16***

Arousal Condition 0.22457 0.04914 4.57 4.87E-06***

Overlap Pupil Dilation 0.01611 0.01401 1.149 0.2505

Overlap Dilation x Condition 0.03092 0.01405 2.2 0.0278* Object Memory

Intercept 0.14405 0.04493 3.206 0.00135**

Arousal Condition -0.04403 0.04493 -0.98 0.32719

Overlap Pupil Dilation -0.01391 0.01276 -1.09 0.27592

Overlap Dilation x Condition 0.01076 0.01276 0.843 0.39929 1227 Notes: Arousal Condition is coded as Threat = 1 and Neutral = -1. Scene/Object memory were 1228 coded as Remember = 1 and Forgot = 0. *p < .05; **p < .01; ***p < .001.

46 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 47

1229 Table 3. Effects of threat and object/scene memory encoding on whole-brain activity, as 1230 parametrically modulated by pupil dilation responses to the overlap image. MNI Peak

Coordinates (mm) Brain Region Cluster Z-stat X Y Z Voxels Scene Memory Conventional Cortical Alignment Threat > Neutral ------Forgot > Remember L Frontal Operculum 3 3.67 -40 16 2 1450 L Insula 3 3.54 -38 8 -6 L Central Opercular Cortex 3 3.54 -38 8 8 L Frontal Operculum 3 3.38 -38 18 8 L Inferior Frontal Gyrus 3 3.29 -62 26 12 L Central Opercular Cortex 3 3.29 -44 -6 8 L Angular Gyrus/Supramarginal Gyrus 2 3.55 -56 -50 18 954 L Anterior Supramarginal Gyrus 2 3.34 -62 -28 34 L Middle Temporal Gyrus 2 3.26 -56 -52 8 L Posterior Supramarginal Gyrus 2 3.21 -60 -50 10 L Posterior Supramarginal Gyrus 2 3.21 -50 -48 12 L Posterior Supramarginal Gyrus 2 3.2 -58 -44 26 R Posterior Supramarginal Gyrus 1 3.6 62 -36 42 613 R Posterior Supramarginal Gyrus 1 3.36 62 -44 20 R Anterior Supramarginal Gyrus 1 3.28 60 -32 40 R Anterior Supramarginal Gyrus 1 3.22 66 -28 34 R Anterior Supramarginal Gyrus 1 3.2 62 -28 46 R Anterior Supramarginal Gyrus 1 3.2 62 -32 46

Threat (R > F) – Neutral (R > F) L Lingual Gyrus/PHG 1 3.98 -14 -42 -6 1127 L Parahippocampal Gyrus 1 3.84 -14 -40 -10 R Lingual Gyrus/PHG 1 3.81 14 -40 -10 L Posterior Hippocampus 1 3.39 -22 -34 -8 R Temporal Occipital Fusiform Cortex 1 3.34 32 -56 -12

47 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 48

R Temporal Occipital Fusiform Cortex 1 3.29 24 -58 -14

Brainstem Alignment Threat > Neutral ------Remember > Forgot ------Threat (R > F) – Neutral (R > F) L Cerebellum 1 3.51 -14 -58 -28 718 L Cerebellum 1 3.46 4 -62 -30 R Cerebellum 1 3.4 8 -54 -28 Brainstem 1 3.39 -8 -34 -30 R Cerebellum 1 3.32 4 -70 -34 Brainstem 1 3.24 -12 -30 -30 Object Memory Conventional Cortical Alignment Remember > Forgot R Frontal Pole 2 3.35 26 46 20 938 R Precentral Gyrus 2 3.21 54 8 42 R Frontal Pole 2 3.21 34 46 16 R Inferior Frontal Gyrus 2 3.18 56 14 32 R Frontal Pole 2 3.16 30 46 14 R Frontal Pole 2 3.08 32 50 24 R Occipital Pole 1 3.5 10 -94 16 843 R Occipital Pole 1 3.25 10 -98 18 R Precuneus Cortex 1 3.1 8 -58 8 L Intracalcarine Cortex 1 3.09 -6 -66 8 R Precuneus Corrtex 1 3.08 10 -58 16 R Superior Lateral Occipital Cortex 1 3.06 16 -82 20

Threat (R > F) – Neutral (R > F) R Inferior Lateral Occipital Cortex 1 3.39 36 -70 8 851 R Cerebellum 1 3.23 38 -60 -24 R Cerebellum 1 3.2 36 -58 -28 Superior Lateral Occipital Cortex 1 3.15 40 -88 22 R Cerebellum 1 3.08 38 -64 -24

48 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 49

R Occipital Fusiform Gyrus 1 3.03 34 -62 -12

Brainstem Alignment Remember > Forgot ------Threat (R > F) – Neutral (R > F) ------1231 Notes: H = hemisphere; L = left; R = right; PHG = Parahippocampal Gyrus; TO = Temporal 1232 Occipital; R = Remember F = Forgot; for Brainstem Alignment, results are only reported if 1233 clusters were centered upon voxels in brainstem or midbrain, because this alignment procedure 1234 may lead to misalignment of higher subcortical and cortical structures.

49 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 50

1235 Table 4. Robust multiple regression results showing relationship between threat-related 1236 encoding activity in noradrenergic and dopaminergic ROIs for scenes/objects, separately, and 1237 threat-related memory outcomes. Threat (R > F) > Neutral (R > F) ROI Activity Estimate SE p Relationship with Threat-Related Suppression of Low Priority Object Memory Intercept -0.21 0.38 0.76 Locus Coeruleus: Scene Encoding 0.07 0.38 0.93 Ventral Tegmental Area: Scene Encoding 0.41 0.32 0.18 Locus Coeruleus: Object Encoding -0.20 0.32 0.69 Ventral Tegmental Area: Object Encoding -0.10 0.34 0.64 Relationship with Threat-Related Enhancement of High Priority Scene Memory Intercept 0.04 0.33 0.82 Locus Coeruleus: Scene Encoding 0.26 0.42 0.41 Ventral Tegmental Area: Scene Encoding 0.09 0.28 0.82 Locus Coeruleus: Object Encoding -0.65 0.29 0.08~ Ventral Tegmental Area: Object Encoding 0.16 0.35 0.82 Relationship with Threat-Enhanced Gain Effects in Memory Intercept 0.05 0.36 0.89 Locus Coeruleus: Gain Effect 0.54 0.32 0.11 Ventral Tegmental Area: Gain Effect 0.17 0.28 0.39 1238 Notes: “Gain Effect” refers to a two-way interaction score between Priority, Memory, and 1239 Arousal: Scene [Threat (R>F) – Neutral (R>F) – Object [Threat (R>F) – Neutral (R>F)]; R = 1240 remember and F = forgot. ~p < .10.

50 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 51

1241 Table 5. Effects of threat of punishment on whole-brain parahippocampal place area (PPA) 1242 functional connectivity during successful scene encoding, as parametrically modulated by pupil 1243 dilation responses to the overlap image. MNI Peak

Coordinates (mm) Brain Region Cluster Z-stat X Y Z Voxels

Scene Memory Right Parahippocampal Place Area Threat > Neutral ------Remember > Forgot ------Neutral (R > F) – Threat (R > F) R Occipital Pole 2 3.62 10 -92 -20 652 L Occipital Pole 2 3.14 -2 -90 -24 L Occipital Fusiform Gyrus 2 3.14 -12 -86 -26 L Occipital Fusiform Cortex 2 3.12 -12 -84 -22 L Lingual Gyrus 2 3.05 -4 -90 -20 L Occipital Fusiform Gyrus 2 3.04 -12 -90 -20 L Occipital Fusiform Gyrus 1 3.16 -42 -70 -24 397 L Occipital Fusiform Gyrus 1 2.98 -38 -76 -24 L Lateral Occipital Cortex 1 2.95 -34 -82 -26 L Occipital Fusiform Gyrus/LOC 1 2.88 -40 -68 -16 L Occipital Fusiform Gyrus 1 2.84 -32 -76 -24 L Temporal Occipital Fusiform Cortex 1 2.83 -34 -66 -24

Left Parahippocampal Place Area Threat > Neutral ------Remember > Forgot ------Threat (R > F) – Neutral (R > F) ------

Object Memory Right Parahippocampal Place Area Threat > Neutral ------Remember > Forgot ------Neutral (R > F) – Threat (R > F) ------

51 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 52

Left Parahippocampal Place Area Threat > Neutral ------Remember > Forgot ------Threat (R > F) – Neutral (R > F) ------1244 Notes: H = hemisphere; L = left; R = right; PHG = Parahippocampal Gyrus; TO = Temporal 1245 Occipital; R > F = Remember vs. Forgot; for Brainstem Alignment, results are only reported if 1246 clusters were centered upon voxels in brainstem or midbrain, because this alignment procedure 1247 may lead to misalignment of higher subcortical and cortical structures.

52 RUNNING HEAD: LOCUS COERULEUS MOTIVATED MEMORY 53

1248 Table 6. Robust multiple regression results showing relationship between functional connectivity 1249 analyses seeded in the parahippocampal place area (high priority category-selective cortex) and 1250 lateral occipital cortex (low priority category-selective cortex) for scenes/objects encoding, 1251 separately, and threat-related memory outcomes. Threat (R > F) > Neutral (R > F) ROI Connectivity# Estimate SE p Relationship with Threat-Related Suppression of Low Priority Object Memory Intercept -0.12 0.37 0.93 Right PPA-Right LOC: Scene Encoding -0.93 0.41 0.06~ Right PPA-Left LOC: Scene Encoding 0.70 0.55 0.51 Left PPA-LOC: Scene Encoding 0.28 0.40 0.60 Left PPA-LOC: Scene Encoding -0.54 0.41 0.49 Right PPA-Right LOC: Object Encoding 0.34 0.36 0.37 Right PPA-Left LOC: Object Encoding 0.04 0.37 0.85 Left PPA-Right LOC: Object Encoding -0.17 0.32 0.75 Left PPA-Left LOC: Object Encoding 0.26 0.38 0.41 Relationship with Threat-Related Enhancement of High Priority Scene Memory Intercept -0.06 0.27 0.62 Right PPA-Right LOC: Scene Encoding -0.62 0.33 0.07~ Right PPA-Left LOC: Scene Encoding 0.10 0.55 0.79 Left PPA-Right LOC: Scene Encoding -0.08 0.36 0.73 Left PPA-Left LOC: Scene Encoding 0.67 0.44 0.17 Right PPA-Right LOC: Object Encoding 0.23 0.35 0.47 Right PPA-Left LOC: Object Encoding 0.10 0.39 0.70 Left PPA-Right LOC: Object Encoding -0.02 0.31 0.88 Left PPA-Left LOC: Object Encoding -0.39 0.38 0.44 Relationship with Threat-Enhanced Gain Effects in Memory Intercept 0.01 0.26 0.98 Right PPA-Right LOC: Gain Effect -1.02 0.33 0.01* Right PPA-Left LOC: Gain Effect 0.12 0.36 0.72 Left PPA-Right LOC: Gain Effect 0.36 0.29 0.27 Left PPA-Left LOC: Gain Effect 0.02 0.31 0.73 1252 Notes: PPA = Parahippocampal Place Area; LOC = Lateral Occipital Cortex; “Gain” refers to a 1253 three-way interaction score between Priority, Memory and Arousal: Scene [Threat (R>F) – 1254 Neutral (R>F) – Object [Threat (R>F) – Neutral (R>F)]; R = remember and F = forgot. # refers to 1255 functional connectivity estimate between PPA and LOC during encoding; ~p < .10. *p < .05.