Large-Scale Network Coupling with the Fusiform Cortex Facilitates Future Social Motivation

This Accepted Manuscript has not been copyedited and formatted. The final version may differ from this version. Research Article: New Research Cognition and Behavior Large-Scale Network Coupling with the Fusiform Cortex Facilitates Future Social Motivation Network Coupling Facilitates Social Motivation Amanda V. Utevsky 1,2, David V. Smith 3, Jacob S. Young 4 and Scott A. Huettel 1,2 1 Center for Cognitive Neuroscience, Duke University, Durham, NC 27708 2 Department of Psychology and Neuroscience, Duke University, Durham, NC 27708 3 Department of Psychology, Temple University, Philadelphia, PA 19122 4 Pritzker School of Medicine, University of Chicago, Chicago, IL 60637 DOI: 10.1523/ENEURO.0084-17.2017 Received: 15 March 2017 Revised: 20 September 2017 Accepted: 26 September 2017 Published: 3 October 2017 Author Contributions: AVU, DVS, JSY, and SAH designed research. AVU, DVS, JSY performed research. AVU, DVS, JYS analyzed data. AVU, DVS, SAH wrote the paper. Funding: HHS NIH National Institute of Mental Health (NIMH) 100000025 F31-MH105137 Funding: HHS NIH National Institute of Mental Health (NIMH) 100000025 F32-MH107175 Funding: HHS National Institutes of Health (NIH) 100000002 NIMH RC1-88680 Funding: Duke Institute for Brain Sciences, Duke University (DIBS) 100005572 Conflict of Interest: Authors report no conflict of interest. This study was funded by a grant from the National Institutes of Health (NIMH RC1-88680), an Incubator Award from the Duke Institute for Brain Sciences (SAH), and by NIMH National Research Service Awards F31- MH105137 (AVU) and F32-MH107175 (DVS). Correspondence should be addressed to Address for correspondence: Scott A. Huettel, Box 90999, Duke University, Durham, NC 27708, USA. E-mail: scott.huettel@duke.edu phone: (919) 668-5286, fax: (919) 681-0815 Cite as: eneuro 2017; 10.1523/ENEURO.0084-17.2017 Alerts: Sign up at eneuro.org/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. Copyright 2017 Utevsky et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 Large-Scale Network Coupling with the Fusiform Cortex Facilitates Future Social Motivation Abbreviated Title: Network Coupling Facilitates Social Motivation Amanda V. Utevsky 1,2, David V. Smith 3, Jacob S. Young 4, Scott A. Huettel 1,2 * 1 Center for Cognitive Neuroscience, Duke University, Durham, NC, 27708 2 Department of Psychology and Neuroscience, Duke University, Durham, NC, 27708 3 Department of Psychology, Temple University, Philadelphia, PA, 19122 4 Pritzker School of Medicine, University of Chicago, Chicago, IL, 60637 AVU, DVS, JSY, and SAH designed research. AVU, DVS, JSY performed research. AVU, DVS, JYS analyzed data. AVU, DVS, SAH wrote the paper. *Address for correspondence: Scott A. Huettel Box 90999, Duke University Durham, NC 27708, USA scott.huettel@duke.edu phone: (919) 668-5286 fax: (919) 681-0815 Number of Figures: 4 Number of Tables: 2 Number of words for Abstract: 233 Number of words for Significance Statement: 116 Number of words for Introduction: 637 Number of words for Discussion: 1759 Acknowledgments: This study was funded by a grant from the National Institutes of Health (NIMH RC1-88680), an Incubator Award from the Duke Institute for Brain Sciences (SAH), and by NIMH National Research Service Awards F31-MH105137 (AVU) and F32-MH107175 (DVS). The authors thank Christopher Coutlee and Cary Politzer for their assistance in task design and data collection. Conflict of Interest: Authors report no conflict of interest. Funding sources: National Institutes of Health (NIMH RC1-88680), the Duke Institute for Brain Sciences (SAH), NIMH National Research Service Awards F31-MH105137, F32- MH107175 (DVS). Page 1 of 38

43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 Abstract Large-scale functional networks as identified through the coordinated activity of spatially distributed brain regions have become central objects of study in neuroscience because of their contributions to many processing domains. Yet, it remains unclear how these domaingeneral networks interact with focal brain regions to coordinate thought and action. Here, we investigated how the default-mode network (DMN) and executive control network (ECN) two networks associated with goal-directed behavior shape task performance through their coupling with other cortical regions several seconds in advance of behavior. We measured these networks connectivity during an adaptation of the monetary incentive delay response-time task in which human participants viewed social and non-social images (i.e., pictures of faces and landscapes, respectively) while brain activity was measured using fmri. We found that participants displayed slower response times (RTs) subsequent to social trials relative to nonsocial trials. To examine the neural mechanisms driving this subsequent-rt effect, we integrated independent components analysis (ICA) and a networkbased psychophysiological interaction (nppi) analysis; this allowed us to investigate taskrelated changes in network coupling that preceded the observed trial-to-trial variation in RT. Strikingly, when subjects viewed social rewards, an area of the fusiform gyrus consistent with the functionally-defined fusiform face area (FFA) exhibited increased coupling with the ECN (relative to the DMN) and the relative magnitude of coupling tracked the slowing of RT on the following trial. These results demonstrate how large-scale, domain-general networks can interact with focal, domain-specific cortical regions to orchestrate subsequent behavior. Page 2 of 38

65 66 67 68 Keywords: effective connectivity, executive control, default mode network, faces, goal- directed behavior 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 Page 3 of 38

84 85 86 87 88 89 90 91 92 93 94 Significance A diverse set of behaviors from normal to pathological has been linked to the responses of large-scale functional networks. Yet, it remains unclear how these domain-general networks shape subsequent processing in cortical regions with domain-specific function. Here, we examine how two such networks the default-mode network (DMN) and executive control network (ECN) connect functionally with other cortical regions to alter performance in an incentive-compatible task. We found that differential coupling between a prototypical face processing region and DMN and ECN tracked subsequent improvements in performance to social stimuli. Our approach allowed us to examine direct coupling with functional networks to future behavior, providing a significant step forward in understanding how large-scale networks coordinate thought and action. 95 96 97 Page 4 of 38

98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 Introduction Since the discovery of functionally correlated brain regions (Biswal et al., 1995), large-scale functional networks have been considered fundamental features of brain activity (Beckmann et al., 2005; Behrens and Sporns, 2012; Gusnard and Raichle, 2001). These networks are highly reliable across large samples of participants (Biswal et al., 2010; Smith et al., 2013) and are thought to reflect intrinsic properties of brain organization. Many of these networks reflect sensory and perceptual processes instantiated within visual or auditory regions (Smith et al., 2009). Still others contribute broadly to cognitive processing across stimulus and task domains, including the default-mode (DMN), executive control (ECN), and frontoparietal (FPN) networks (Dosenbach et al., 2007; Smith et al., 2009). These functional networks have been linked to various aspects of behavior including demographic variables (Cowdrey et al., 2012; Filippini et al., 2009), traits (Kucyi et al., 2014; Shannon et al., 2011), and cognitive states (De Havas et al., 2012; Smith et al., 2009). Yet, for effective behavior in a particular task, these large-scale domain-general networks must alter ongoing task-specific processing in focal brain regions. To claim that such interactions (i.e., between large-scale networks and specific brain regions) are critical for behavior change, several conditions should be met. First, a large-scale network should be identifiable during task performance independently of other concurrent activation; that is, networks should be able to be extracted regardless of other processes occurring in focal brain regions that might contribute to that task (i.e., without relying on seed-based analyses; Cole et al., 2010; Smith et al., 2014b). Second, the coupling between a given network and a given focal brain region should systematically vary across task conditions according to the relative engagement of the task (Friston et al., 1997; O Reilly et al., 2012). Third, those Page 5 of 38

121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 changes in coupling (e.g., effective connectivity (Friston, 2011)) should predict the characteristics of subsequent behavior, to provide evidence that the coupling contributes to effective task performance. If these conditions are met, there would be strong evidence that coupling between a functional network and a focal brain region contributes to a specific cognitive process. In the current study, we examined effective connectivity between large-scale networks and focal brain regions while subjects played a response-time game to receive social and nonsocial rewards in a modified monetary incentive delay (MID) task (Knutson et al., 2000). We have previously used this task to produce meaningful variability in reaction time behavior that reflects relative motivation (Clithero et al., 2011). We focused on the DMN and ECN since both have been linked to task performance, engagement, and other markers of executive function and preparatory behavior. Behaviorally, we found that participants exhibited slower response times (RTs) subsequent to social relative to nonsocial trials, reflecting a change in motivation according to social stimulus type. We investigated whether coupling with DMN and ECN contributed to this subsequent-rt effect by combining independent components (ICA) and psychophysiological interactions (PPI) analyses. This network PPI (nppi) pipeline allowed us to examine how DMN and ECN contribute to other cortical regions to shape subsequent motivated behavior up to several seconds later. Strikingly, we found that DMN and ECN differentially coupled with a region in the fusiform gyrus (FG) when subjects viewed the social rewards, and that changes in this coupling tracked the effect of stimulus type on subsequent RT. This region of the FG is consistent with the functionally-defined fusiform face area (FFA) a region classically associated with face processing (Kanwisher et al., 1997; McCarthy et al., 1997). Page 6 of 38

144 145 146 147 148 149 150 These findings highlight functional network interactions that guide subsequent changes in reaction time behavior. While the cognitive mechanisms underlying the behavioral effect of previous stimulus type on subsequent reaction time require future research, we propose that this effect may be driven by increased attentional interference of social rewards relative to nonsocial rewards or a subsequent change in task motivation. Collectively, these results indicate that functional networks associated with goal-directed behavior can interact with focal brain regions to support future motivated behavior. 151 Materials and Methods 152 153 154 155 156 157 158 159 160 Participants A group of 50 self-reported heterosexual males completed the task (mean age: 23.8 years, range 18-32); this sample size was established prior to data collection. All participants were screened prior to data collection to rule out prior or current psychiatric or neurological illness. We excluded four participants because of data quality issues (see Preprocessing), and we excluded five participants because of a malfunctioning button box in the scanner. These exclusions left a final sample of 41 participants (mean age: 24.1 years, range: 18-32). All participants gave written informed consent as part of a protocol approved by the Institutional Review Board of Duke University Medical Center. 161 162 163 164 Stimuli and Tasks The experiment consisted of four components: 1) a training session outside the scanner; 2) a modified monetary incentive delay task (MID; Knutson et al., 2000, 2001); 3) a passive viewing task with results previously published (Young et al., 2015); 4) two Page 7 of 38

165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 additional post-scan rating tasks. All participants received variable cash payment depending on their performance. In advance of the neuroimaging session, participants memorized the associations of four fractal images with different potential rewards (see an example in Figure 1). On the day of the scan, subjects were tested on the fractal images associations to ensure they remembered the meaning of each image; if participants identified every association correctly on the first attempt (n = 31), they received an extra $5 in addition to their study compensation. All participants successfully learned the image associations before entering the scanner. Each trial of the modified MID task began with a fractal image presented as a cue for 1500 ms (Figure 1). The different cues indicated the different potential benefits of a fast response on that trial: [1] reducing the delay until the presentation of a face image of a variable attractiveness rating (Social Delay; SD), [2] increasing the attractiveness of a face image presented after a variable duration (Social Attractiveness; SA), [3] reducing the delay until the presentation of a landscape image of a variable attractiveness rating (Nonsocial Delay; ND), [4] increasing the attractiveness of a landscape image presented after a variable duration (Nonsocial Attractiveness; NA). Following the cue, a fixation cross appeared for a variable duration (interstimulus interval; 1500 4500 ms), followed by a target (a white triangle) that appeared on the screen for 750 ms. Participants then responded by pressing a button box with their right index finger before the target disappeared. A feedback shape then appeared for 750 ms; that shape was colored green if the participant s response was sufficiently fast (a win), and was colored blue if the response was too slow (a loss). After the feedback image disappeared, Page 8 of 38

188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 participants waited a delay period that varied according to the cue and whether the subject won or lost that trial. Lastly, the reward image (either a face or a landscape) was shown for 2000 ms, and there was an intertrial interval (ITI) that was jittered between 1500 and 4500 ms (with the 2000 ms reward image duration, the minimum and maximum time delays between reward onset and subsequent cue onset was 3500 and 6500 ms, respectively). Each run of the modified MID task consisted of 36 trials, nine images of each SD, SA, ND, and NA in randomized order. Across all runs included in the analyses, the four types of transitions (e.g., social followed by social, social followed by nonsocial, etc.) each occurred on approximately 25±1% of trials, reflecting effective randomization of stimulus types across trials. Social images were photographs of young adult women and were cropped to show only the face (images were drawn from Smith et al. 2010). Nonsocial images were photographs of landscapes, and did not contain any body or facial features. All images were resized to uniform dimensions. Attractiveness (high/medium/low) of images was determined by ratings by an independent group. Additionally, performance thresholds were determined independently for each subject by an algorithm that adapted the task difficulty so that accuracy would be approximately 60% (observed hit rate = 60.7%). After completing the four runs of the modified MID task, participants completed an unrelated passive-viewing task described elsewhere (Young et al., 2015). Following this second scanner task, participants completed two tasks outside the scanner. In the first task, participants rated the attractiveness of each image they viewed in the modified MID task on a scale of 1 to 7. Each participant s highest-rated face and landscape images were then used in a second task involving forced choices between a landscape image and a face image presented simultaneously. Page 9 of 38

211 212 All tasks were programmed and displayed using the Psychophysics Toolbox (Version 3; Brainard, 1997) for MATLAB (Mathworks Inc.). 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 Behavioral Analysis Behavioral data were analyzed using MATLAB and IBM SPSS Statistics 20. A response time (RT) difference was calculated for each participant by subtracting their mean RT on the trials subsequent to nonsocial trials from their mean RT on trials subsequent to social trials. Consequently, positive RT differences indicate shorter RTs subsequent to nonsocial trials (relative to social trials), whereas negative differences indicate shorter RTs subsequent to social trials (relative to nonsocial trials). To control for factors other than prior stimulus category such as attractiveness of the prior trial s reward image, the prior trial s RT, and the subsequent trial s stimulus type we incorporated these factors into a general linear model (GLM) for each subject. The GLM included the following as regressors: (1) stimulus type on previous (t-1) trial, (2) RT on previous trial, (3) attractiveness rating of the reward image of the previous trial, and (4) stimulus type on current trial t. This analysis indicates the specific influence of each regressor on the current trial s RT, and allowed us to examine the unique influence of the previous trial s stimulus category (social or nonsocial) on current RT. In our analyses, positive stimulus type beta weights reflect slower RTs on or following social trials, whereas negative stimulus type betas weights reflect faster RTs on or following social trials. 230 231 232 Image Acquisition Neuroimaging data were collected using a General Electric MR750 3.0 Tesla scanner equipped with an 8-channel parallel imaging system. We used a T 2*-weighted spiral-in Page 10 of 38

233 234 235 236 237 238 239 240 sensitivity encoding sequence (SENSE factor = 2), with slices parallel to the axial plane connecting the anterior and posterior commissures (repetition time (TR): 1580 ms; echo time (TE): 30 ms; matrix: 64 x 64; field of view (FOV): 243 mm; voxel size: 3.8 x 3.8 x 3.8 mm; 37 axial slices; flip angle: 70 degrees). The first eight volumes of each run were removed to allow for magnetic stabilization. We additionally acquired whole-brain highresolution anatomical scans (T1-weighted FSPGR sequence; TR: 7.58 ms; TE: 2.93 ms; matrix: 256 x 256; FOV: 256 mm; voxel size: 1 x 1 x 1 mm; 206 axial slices; flip angle: 12 degrees) to allow for coregistration and normalization. 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 Preprocessing Our preprocessing used tools from the FMRIB Software Library package (FSL Version 4.1.8; http://www.fmrib.ox.ac.uk/fsl/; Smith et al., 2004; Woolrich et al., 2009). We corrected for head motion by realigning the time series to the middle volume (Jenkinson and Smith, 2001), and then removed non-brain material using a brain extraction tool (Smith, 2002). We then corrected intravolume slice-timing differences using Fourier-space phase shifting to align to the middle slice (Sladky et al., 2011). After spatially smoothing the image using a 5-mm full-width-half-maximum isotropic Gaussian kernel, we applied a high-pass temporal filter with a 100-second cutoff, and we normalized each 4-dimensional dataset to the grand-mean intensity using a single multiplicative factor. Lastly, we spatially normalized the functional data to the Montreal Neurological Institute (MNI) Template avg152 T1- weighted template (3 mm isotropic resolution) using a 12-parameter affine transformation implemented in FLIRT (Jenkinson and Smith, 2001). As part of our preprocessing and quality control, we additionally examined three partially correlated measures of quality assurance: signal-to-fluctuation-noise ratio (SFNR; Page 11 of 38

256 257 258 259 260 261 262 263 Friedman and Glover, 2006), volume-to-volume head motion, and number of motion spikes within the time series (motion spikes were identified by evaluating the root-mean-squareerror of each volume relative to the middle time point). Measures on each metric were considered outliers if they exceeded the 75 th percentile plus the value of 150% of the interquartile range (i.e., a standard boxplot threshold); runs that were identified as outliers were excluded from further analyses. Additionally, any participant who had fewer than two good runs (out of four total runs) was excluded from further analyses. These criteria eliminated four participants. 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 Neuroimaging Analysis To best meet the conditions for identifying task-specific interactions between largescale networks and focal brain regions (see Introduction), our neuroimaging analyses proceeded in two phases, each described in a separate section below. First, we used independent components analysis (ICA; Beckmann & Smith, 2004) and spatial regression (Filippini et al., 2009) to identify the large-scale neural networks of interests (DMN and ECN) and to examine the networks levels of activation over the course of each run. Second, we used generalized network psychophysiological interaction (nppi) models (adapted from McLaren et al., 2012) to identify brain regions whose coupling with the ECN and DMN changed as a function of the effect of stimulus type on subsequent RT. Importantly, this ICAbased nppi approach follows the logic of ROI-based PPI analyses, with the critical difference of examining connectivity with data-driven large-scale neural networks instead of a specific seed region. Critically, we note that this nppi pipeline allowed us to test the three necessary conditions for inferences that functional networks alter task-specific processing in focal brain regions. Page 12 of 38

279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 Identifying Large-Scale Functional Networks We used FSL s Multivariate Exploratory Linear Decomposition into Independent Components (MELODIC) Version 3.10 to identify large-scale functional networks in the neuroimaging data (Beckmann and Smith, 2004). MELODIC ICA was implemented using temporal concatenation, which looks for common spatial patterns of components across participants data without assuming a specific or common time course across all participants; we note that previous research has also used temporal concatenation-based ICA on task-based fmri data (Utevsky et al., 2014; Young et al., 2015). The preprocessed data were whitened and projected into a 25-dimensional subspace (Ray et al., 2013; Utevsky et al., 2014). The whitened data were decomposed into sets of vectors describing the temporal and spatial signal variation, using a fixed-point iteration technique to optimize non-gaussian spatial source distribution (Hyvärinen, 1999). The estimated component maps were then thresholded by dividing the maps by the standard deviation of the residual noise, then fitting a Gaussian-Gamma mixture model to the histogram of the normalized intensity values (Beckmann and Smith, 2004). This first step provided a data-driven means to identify functional networks present during task performance; this allowed us to meet the first condition for identifying task-specific interactions between large-scale networks and specific brain regions. All unthresholded spatial maps from the ICA were then submitted to a spatial regression (part of FSL s dual regression analysis) to estimate the time courses of each network (Filippini et al., 2009; Leech et al., 2011). In this analysis, spatial maps are regressed onto each participant s functional data, resulting in a matrix of T (time points) x C Page 13 of 38

301 302 (components) beta coefficients that characterize each subject s time courses for each network. 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 Characterizing Reward-Related Network Connectivity and Activation We assessed task-dependent network coupling using a generalized nppi model. The generalized PPI, in contrast to a standard PPI, computes a separate PPI term for each task condition. This approach has been shown to yield more accurate estimates of how connectivity varies as a function of psychological context (McLaren et al., 2012). The generalized nppi analysis was carried out using FMRI Expert Analysis Tool (FEAT) Version 5.0.1. The run-level model included six task regressors: social cue (duration = 1.5 4.5 s), nonsocial cue (duration = 1.5 4.5 s), hits (duration = 0.75 s), misses (duration = 0.75 s), social reward outcome (duration = 2 s), and nonsocial reward outcome (duration = 2 s). We additionally included time courses of both the DMN and ECN that were produced by the spatial regression. Because of the minimum ITI of 1500 ms and the potential confound of the cue presentation occurring between the outcome and subsequent target phases of the task, we examined whether there was any collinearity between the outcomes and cues across the runs included in our analysis. For each run included in our analyses, we calculated the correlation values between the face and land outcome regressors and the face and land cue regressors and then averaged these correlation values across all runs. The mean correlation values between face or land cues and face or land outcomes ranged from r = - 0.25 to r = -0.17 (minimum r = -0.28; maximum r = -0.10), indicating that our run-level analyses were able to isolate and model cues and outcomes independently, with minimal collinearity between regressors. Page 14 of 38

324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 For our network interaction analysis, nppi regressors were formed by multiplying the DMN and ECN time courses (zeroed to the mean of the time course), separately, by the social outcome and nonsocial outcome regressors (zeroed to the minimum value of the task time course) (McLaren et al., 2012); this yielded four nppi regressors: (1) DMN*social, (2) DMN*nonsocial, (3) ECN*social, (4) ECN*nonsocial. To control for motion in the scanner, we additionally included motion spikes and motion parameters as regressors. Lastly, to control for the influence of other networks and potential artifacts on our generalized nppi, we included the time courses of the remaining 23 components from the ICA. The nppi analysis allowed us to examine task-specific coupling between ECN and DMN with other regions in the brain, fulfilling the second condition for identifying the task-specific interactions of interest. Subject-level analyses for the generalized nppi were run using FEAT and implementing FMRIB s Local Analysis of Mixed Effects (FLAME 1), and examined activation across runs within each participant. Group-level analyses included each subject s demeaned betas from the prior stimulus-category regressor (see Behavioral Analysis); this allowed us to examine whether network coupling predicts the characteristics of subsequent RT, and fulfill the third condition for identifying task-specific interactions of interest. The group-level analysis additionally included the main effect of group and three motion-related parameters (SFNR, volume-to-volume head motion, and number of motion spikes within the time series). All resulting z-statistic images were thresholded using a cluster-forming threshold of 2.3 and a corrected cluster-significance threshold of p < 0.05. Although this threshold combined with FSL s FLAME 1 protects against false positives, we note that all of our results also survived permutation-based testing (Eklund et al., 2016). In these supplemental tests, Page 15 of 38

347 348 349 350 351 352 353 statistical significance was assessed in a nonparametric fashion via FSL s randomise; this tool uses Monte Carlo permutation-based testing with 10,000 permutations and = 0.05, corrected for multiple comparisons across the whole brain (Nichols and Holmes, 2002; Winkler et al., 2014). Brain images and activations are displayed using MRIcroGL (http://www.mccauslandcenter.sc.edu/mricrogl/; Rorden et al., 2007). All coordinates are reported in Montreal Neurological Institute (MNI) space. 354 Results 355 356 357 358 359 360 361 362 363 364 365 366 Previous stimulus category influences current behavior Social rewards, such as images of individuals or interactions with others, provide a useful tool for examining the effects of both social context and non-consumable rewards on motivated behavior. However, relatively little is known about the effects of social and nonsocial rewards on future motivated behavior, and how the brain orchestrates future motivated action. To examine the effect of social rewards on subsequent motivated actions, we calculated a subsequent-rt effect by subtracting RTs following nonsocial trials from RTs following social trials. When averaging across value and delay trials, we found an overall effect of previous trial stimulus type on current trial RT: participants exhibited increased (slower) RTs subsequent to social trials (M = 0.313 s, SD = 0.008 s) compared to subsequent to nonsocial trials (M = 0.306 s, SD = 0.008 s; t (40) = 2.63, p = 0.01, d = 0.41; Figure 2A). This pattern replicated when examining hit trials only (social: M = 0.317 s, SD = 0.009 s; Page 16 of 38

367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 nonsocial: M = 0.309 s, SD = 0.008 s; t (40) = 2.63, p = 0.01, d = 0.41). Thus, participants were slower after performing a social trial compared to a nonsocial trial. We next ran a general linear model (GLM) to control for properties of the previous and current trials (see Behavioral Analysis). Across participants, current stimulus category was the strongest predictor of current RT (t (40) = -4.31, p <.0001, d = 0.67), reflecting that participants were faster to respond during a social trial than during a nonsocial trial. The next strongest predictor was the previous trial s stimulus category (t (40) = 2.46, p = 0.01, d = 0.38), reflecting that participants were slower to respond following a social trial than following a nonsocial trial. Neither previous trial s RT nor previous trial s reward attractiveness rating significantly predicted the current trial s RT (Figure 2B; Table 1). We note that the effect of previous stimulus type on current RT was still significant even after accounting for the large effect of current stimulus type, reflecting a distinct role of previous stimulus type unaccounted for by the other variables included in the GLM. These results indicate that of the measured properties of the previous trial, prior stimulus type had the strongest effect on current RT. 382 383 384 385 386 387 388 Network coupling with fusiform gyrus tracks effect of stimulus category on subsequent behavior Our behavioral results indicated that current-trial stimulus type influences motivated behavior on the subsequent trial (which occurred seven seconds or more later), such that participants were slower to respond following social trials compared to following nonsocial trials. However, it remains unclear how interactions in the brain during this previous stimulus outcome affect the subsequent RT. To investigate this, we examined how effective Page 17 of 38

389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 connectivity with the ECN a network implicated in cognitive control and goal-directed behavior and the DMN a network linked with task engagement orchestrate this change in future motivated behavior. We predicted that this subsequent-rt effect would be guided, in part, by changes in the coupling between these large-scale functional networks and domainspecific brain regions when viewing social images compared to when viewing nonsocial images. In particular, we predicted that the subsequent-rt effect would be driven by changes in the relative coupling of the ECN and the DMN during the reward outcome phase of the previous trial. Our analysis pipeline allowed us to meet the criteria to claim that interactions between large-scale networks and specific brain regions are critical for behavior changes (see Introduction). After running the ICA, we identified the DMN and ECN maps by running spatial correlations between the unthresholded maps from our ICA and the DMN and ECN maps from Smith and colleagues (2009). From our 25 components, we selected the maps that best matched the DMN (R = 0.776; other components: R mean = -0.006, R min = -0.179, R max = 0.124) and ECN (R = 0.64; other components: R mean = 0.019, R min = -0.092, R max = 0.296) maps from Smith and colleagues (2009) (see Table 2). For ease of visualization, thresholded maps (Z > 4) are shown in Figure 3. Following network identification, we ran a nppi analysis that used participantspecific DMN and ECN time courses (estimated by the spatial regression analysis) as the physiological regressors and presentation of social and nonsocial images as the psychological regressors. This nppi identified regions that are influenced by the ECN and DMN in a task-dependent manner. We then tested whether these influences on cortex predicted the effect of prior stimulus type on current RT. Our nppi analysis indicated that Page 18 of 38

412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 effective connectivity between the fusiform gyrus (FG; peak: x = 38, y = -64, z = -20, p < 0.0001, voxel extent = 383) and the ECN increased (compared to FG-DMN connectivity) when participants viewed social rewards (Figure 4A) and that the magnitude of this increase tracked the slowing of RT on the subsequent trial (Figure 4B). Strikingly, this peak voxel is consistent with the often functionally-defined FFA (Kanwisher and Yovel, 2006; McCarthy et al., 1997). Identification of the terms associated with this peak voxel using the meta-analytical tool Neurosynth (http://neurosynth.org) (Yarkoni et al., 2011) yielded faces, FFA, and fusiform face within its top four associations. These results suggest that functional networks associated with goal-directed and preparatory behavior can interact with focal brain regions to support task-relevant behavior. To ensure that this connectivity was associated with the previous trial s outcome rather than the current trial s cue, we ran an additional nppi to examine whole-brain connectivity during social cues, nonsocial cues, and nonsocial > social cues. Notably, there were no regions exhibiting changes in connectivity with the ECN relative to the DMN in any of the three contrasts tested. To further investigate this, we used the FG region showing changes in ECN-DMN connectivity during the previous outcome phase as a mask during the current cue phase, and examined whether the ECN-DMN connectivity from this region of interest (ROI) tracked the effect of prior stimulus on current RT. Across the three contrasts tested (social, nonsocial, nonsocial > social), there was no correlation between the ROI connectivity estimates and the effect of previous stimulus type on current RT (social: r = - 0.12, p = 0.45; nonsocial: r = 0.01, p = 0.94; nonsocial > social: r = 0.23, p = 0.15). Collectively, these results support the claim that changes in subsequent RT are associated with Page 19 of 38

434 435 436 437 438 439 440 441 442 443 444 445 446 differential network connectivity with the FG during the previous outcome phase, and cannot be attributed to other aspects of task performance. No regions showed increased coupling with ECN (relative to DMN) during nonsocial rewards that tracked the effect on subsequent RT, nor did any regions show increased coupling with DMN (relative to ECN) during social or nonsocial rewards that tracked the effect on subsequent RT. Additionally, there were no regions that exhibited changes in coupling with the DMN or ECN when comparing social and nonsocial rewards (e.g., DMNsocial > DMN-nonsocial; ECN-social > ECN-nonsocial; and the inverse contrasts) that tracked the effect on subsequent RT. Lastly, we ran a whole-brain GLM using the RT betas as a covariate to examine whether any regions activation tracked the effect of stimulus type on subsequent RT; we found that no regions tracked this effect using this traditional GLM. Thus, coupling between large-scale networks and the FG that tracked the effect on subsequent RT was only observed during the viewing of social rewards. 447 448 449 450 451 452 453 454 455 Discussion Recent neuroscience research has highlighted the relevance of large-scale functional networks to various aspects of behavior (Anticevic et al., 2010; Eichele et al., 2008; Kelly et al., 2008; Rosen et al., 2015). While many of these studies have linked network activation and connectivity to behavior, the contribution of these networks to motivated behaviors via focal cortical regions has been relatively understudied. For example, although previous work has found correlations between DMN and working memory (Piccoli et al., 2015; Sambataro et al., 2010) or sustained attention (Bonnelle et al., 2011; Gui et al., 2015), understanding how these distributed functional networks influence other cortical regions to influence Page 20 of 38

456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 behavior has remained a significant challenge. Here, we found that participants were slower in a response time task after having performed a trial for a social reward relative to a nonsocial reward (after accounting for the influence of the current trial cue type), reflecting a change in motivated behavior according to previous social stimulus type (Clithero et al., 2011). We then examined the neural mechanisms underlying this effect using nppi analysis an adaptation of generalized PPI analysis (i.e., effective connectivity) that examines effective connectivity with large-scale functional networks. This analysis pipeline allowed us to identify changes in coupling between large-scale networks and focal brain regions that predicted subsequent motivated behavior. Our results demonstrate that two goal-relevant networks, the DMN and ECN, interact with FG in a manner that predicts trial-to-trial adjustments in response time. Our analysis on our modified MID task identified that participants exhibit slower reaction times on trials subsequent to social outcomes, relative to trials subsequent to nonsocial outcomes. These results are consistent with a difference in motivational processes due to the social nature of the previous reward outcome, analogous to effects on motivation observed in similar tasks with nonsocial stimuli (Clithero et al., 2011). There are at least two potential explanations for this subsequent reaction time effect. The first hypothesis is that social images are more distracting than nonsocial images, and participants may still covertly attend to a prior social reward more so than they do a prior nonsocial reward. Previous research indicates that social images interfere with visual attention to a greater degree than nonsocial images (Ebitz et al., 2013); thus, it is possible that this differential interference may affect subsequent trials differently, as well. A second and related potential explanation is that participants decreased motivation can be attributed to the satisfaction of receiving a Page 21 of 38

479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 motivating reward on the previous trial, much akin to results seen in satisfaction of search (SOS) research. In SOS research, participants performing visual search tasks tend to discontinue their search after finding an initial item, and either miss or display slower reaction times for subsequent items (Berbaum et al., 1990; Fleck et al., 2010). As applied to our MID task, a more motivating reward outcome (social images) may hinder the performance on the subsequent trial. Future research will be needed to explore the cognitive mechanisms driving the observed subsequent-rt effect and to determine whether it is specific to social versus nonsocial stimuli, or may generalize to items that are more motivating versus less motivating. Our findings expand on recent research examining the relevance of DMN and ECN activation to behavior, potentially in task-relevant contexts. Specifically, we found that increased ECN (relative to DMN) coupling with the FG is associated with enhanced subsequent task performance. These results are consistent with previous studies demonstrating different relationships between the DMN and ECN with task behavior: while ECN activation is often associated with heightened task performance and behavior (Dosenbach et al., 2007; Seeley et al., 2007), DMN activation is frequently linked to decrements in behavior and engagement in both humans (Eichele et al., 2008; Weissman et al., 2006) and non-human primates (Hayden et al., 2009, 2010; Heilbronner and Platt, 2013). Importantly, however, our findings extend these previous results by demonstrating direct coupling of these networks with a prototypical social-perception processing region in a taskdependent manner. Our results additionally highlight that the opposing effects of DMN and ECN are not limited to concurrent behavior, but also affect subsequent behavior with effects observed seven or more seconds later. These findings support the idea that large-scale Page 22 of 38

502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 networks interact with lower-level perceptual regions to contribute to motivational processes (Clithero et al., 2011) and shape later behavior. Unlike previous studies examining large-scale networks (Brewer et al., 2011; Ossandon et al., 2011; Seeley et al., 2007; Utevsky et al., 2014), our experiment demonstrates network coupling that directly shapes subsequent response time. While prior studies report associations between functional connectivity and behavior, estimates of functional connectivity solely report on correlations in activation (not coupling) between regions, which can be the result of various phenomena (Friston, 2011). Specifically, reported changes in functional connectivity can arise from numerous causes, including: changes in connectivity with another region, changes in observation noise (or signal-to-noise ratios), and changes in the degree of neuronal fluctuations (for a review, see Friston, 2011). Thus, changes in functional connectivity may not reflect changes in coupling between cortical regions. In contrast to functional connectivity, we implemented an adaptation of a traditional PPI analysis to measure effective connectivity between functional networks and other cortical regions. Psychophysiological interaction analyses measure whether a psychological context (e.g., outcome stimulus category) influences how one brain region or network (the seed ) contributes to another (the target ) by examining whether an interaction between the psychological context and the seed is identified in the target (Smith et al., 2016). Thus, a PPI analysis can eschew the potential confounds of functional connectivity analyses and reflects a change in neural coupling (e.g., effective connectivity). We note that our novel approach of generalized nppi applying generalized PPI analyses to large-scale networks allows us to examine task-dependent contributions between networks and other cortical Page 23 of 38

524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 regions (Friston et al., 1997; Friston, 2011). In this way, our study extends prior work by demonstrating specific task-dependent coupling of the DMN/ECN. Although recent meta-analytic work has demonstrated that PPI produces consistent and specific patterns of connectivity (Smith et al., 2016; Smith and Delgado, 2017), it is important to note that PPI results can be interpreted in two ways (Friston et al., 1997). First, our effects could reflect a context-specific modulation of effective connectivity. In this case, face presentations modulate the degree to which the DMN and ECN contribute to FG. Our results focus on the difference between DMN and ECN contributions to FG which seem to facilitate social motivation (Clithero et al., 2011). Alternatively, our effects could reflect a modulation of stimulus-specific responses. In this case, the DMN and ECN influence how FG responds to the presentation of the face; under this interpretation, our results suggest that the degree to which DMN enhances face responses in FG is greater than that of ECN. In either interpretation, the resulting effect on FG connectivity predicts behavior. A better understanding of the mechanisms and causal relationship underlying our results may be facilitated by other analytical approaches, such as dynamic causal modeling (DCM) (Friston et al., 2003; Friston, 2011), although relatively less is understood regarding how the biophysical models implemented in DCM apply to distributed functional networks (Buxton et al., 1998; Friston et al., 2003). Additionally, other methodological approaches, such as transcranial magnetic stimulation (Fox et al., 2012; Luber and Lisanby, 2014; Mueller et al., 2014) or transcranial current stimulation (Hampstead et al., 2014; Keeser et al., 2011) may also better inform the specific interactions between FG and the ECN and DMN. One potential caveat to note is that our task design included a cue to indicate the current trial s stimulus type in between the previous trial s outcome phase and the current trial s Page 24 of 38

547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 response phase. Because the current trial s stimulus type is the strongest predictor of the current trial s RT, there may be concerns over whether the neural coupling we observe can be attributed to the cue, rather than the previous trial s outcome. This concern is ameliorated through both the randomization of trial types (i.e., the identity of the outcome on the previous trial is uncorrelated with the trial type on the subsequent trial), as well as the run-level models implemented in our PPI analysis that included separate regressors for both the outcome phase (social and nonsocial outcomes, separately) and the cue phase (social and nonsocial cues, separately). Including these separate regressors for each phase allowed us to distinguish effects of the cue and outcome phases on connectivity. This concern is additionally mitigated by the analyses examining connectivity during the cue phase. A nppi showed no changes in ECN-DMN connectivity during social cues, nonsocial cues, or social > nonsocial cues when looking across the whole brain. A supplementary ROI analysis using the FG region indicated that FG connectivity with the ECN (relative to the DMN) during these three contrasts did not track the effect of the previous trial stimulus type on subsequent RT (as illustrated by the lack of significant correlations between the connectivity estimates and the effect of previous stimulus type on subsequent RT). However, future research may benefit from a modified task design in which there are no informative stimuli presented between a reward image and subsequent response. As an additional caveat, we note that our paradigm and results leave room for interpretive challenges. Because participants exhibited faster response times to view social images compared to nonsocial images, we cannot discern whether our nppi results from the social-stimuli condition are due to the stimulus type itself (i.e. face images) or would generalize to other highly motivating stimuli. While prior work linking FG to face processing Page 25 of 38

570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 supports the interpretation that our results are indeed due to that specific stimulus type (Engell and McCarthy, 2014; Kanwisher et al., 1997; Kanwisher and Yovel, 2006; McCarthy et al., 1997), future studies could compare within-domain images of varying attractiveness to other categories of motivating stimuli (e.g., money). Such an analysis would speak to whether these behavioral and neural results are unique to face image rewards, or are due to participant differences in subjective value (Bos et al., 2013; Smith et al., 2014a). Our results demonstrate task-dependent contributions between the DMN and ECN and the FG that shape subsequent motivated behavior. These large-scale networks are known to be disrupted in a variety of psychopathologies marked by impairments in attention and reward processing, including autism spectrum disorder (Assaf et al., 2010; Young et al., 2015), obsessive compulsive disorder (Stern et al., 2011, 2012), and major depressive disorder (Grimm et al., 2011; Sheline et al., 2009), and so an improved understanding of how they influence moment-to-moment behavior could have clinical relevance and advance models of pathophysiology (Cuthbert and Insel, 2013; Insel et al., 2010). Thus, this study marks a significant step toward better understanding and treatment of disorders characterized by impaired social and reward processing. Page 26 of 38

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836 837 838 Young, J.S., Smith, D.V., Coutlee, C.G., Huettel, S.A., 2015. Synchrony between sensory and cognitive networks is associated with subclinical variation in autistic traits. Front. Hum. Neurosci. 146. doi:10.3389/fnhum.2015.00146 839 840 Page 34 of 38

841 Figure Captions 842 843 844 845 846 847 848 849 850 851 852 853 854 855 Figure 1: Task Design Fifty heterosexual men performed a modified MID task which examined timed responses to view social and nonsocial images as rewards (faces and landscapes, respectively). Each trial of the task began with an abstract image presented as a cue for 1500 ms indicating the potential reward for that trial. Following the cue, a fixation cross appeared for a variable duration (interstimulus interval; 1500 4500 ms), followed by a target (a white triangle) that appeared on the screen for 750 ms; participants responded by pressing a button box with their right index finger before the target disappeared. After the target, a feedback image (a colored triangle) appeared for 750 ms; the feedback image was colored green if the participants response was sufficiently fast on the trial (a win), and was blue if the response was too slow (a loss). After the feedback image disappeared, the participants waited a delay period that varied according to the cue and whether the subject won or lost that trial. Lastly, the reward image (either a face or a landscape) was shown for 2000 ms. Consent was provided for the use of the image in this figure. 856 857 858 859 860 861 862 Figure 2: Response Times are Slower Following Face Trials Compared to Landscape Trials (A) Distribution of response time (RT) differences according to previous trial s stimulus type, calculated by subtracting RTs following nonsocial trials from RTs following social trials. Response times on trials following social rewards were greater than those on trials following nonsocial rewards, indicating an effect of previous reward stimulus type on subsequent behavior. (B) Average beta weights (with S.E.M. plotted) across subjects from a behavioral Page 35 of 38

863 864 865 866 867 868 869 870 871 872 regression predicting current RT. We regressed current trial RT on a model including the following regressors: stimulus category (social or nonsocial) on previous trial, RT on previous trial, attractiveness rating of the previous trial s reward, and stimulus category on current trial. Of these four regressors, current stimulus category most strongly predicted current RT; the negative beta weight indicates that participants are faster to respond during a social trial compared to during a nonsocial trial. The next most predictive regressor was previous trial s stimulus category; the positive beta weight indicates that participants were slower to respond following social trials compared to following nonsocial trials. Neither previous trial s RT nor previous trial s reward attractiveness significantly predicted the current trial s RT. 873 874 875 876 877 878 879 Figure 3: Networks Identified from Independent Components Analysis Using independent components analysis across our task runs, we identified large-scale functional networks. This analysis produced 25 components. Our analyses focused on the components that best matched default-mode network (highest-correlating component illustrated on top) and the executive control network (highest-correlating component illustrated on bottom) from Smith and colleagues (2009). For visualization purposes, maps are thresholded at Z > 4. 880 881 882 883 884 Figure 4: Fusiform Gyrus Connectivity Tracks Effect of Stimulus Type on Subsequent Behavior (A) A network PPI indicated that an area in the fusiform gyrus (FG) exhibits heightened effective connectivity with ECN (compared to DMN) during social reward outcomes; thresholded at p < 0.05. We note that this result also held with permutation-based testing Page 36 of 38

885 886 887 888 889 (Eklund et al., 2016). (B) Parameters estimates extracted from the FG connectivity track the effect of stimulus type on subsequent behavior (stimulus-type beta weights estimated from our behavioral GLM; see Behavioral Analysis): as FG-DMN connectivity increases relative to FG-ECN connectivity, RTs are further slowed following social trials compared to following nonsocial trials. 890 Page 37 of 38

891 Table Captions 892 893 894 895 896 897 898 Table 1. Behavioral Regression Results To examine the effects of other trial characteristics on current trial RT, we regressed current trial RT on a model including: stimulus category (social or nonsocial) on previous trial, RT on previous trial, attractiveness rating of the previous trial s reward, and stimulus category on current trial. Our analysis indicated that current stimulus type had the strongest effect on current RT; however, of all the characteristics from the prior trial, only prior stimulus type had a significant effect on current RT. Regressor Parameter Estimate (s.e.m) t-stat p-value Current trial (t) stimulus type -0.055 (0.013) -4.31 < 0.0001 Prior trial (t-1) stimulus type 0.033 (0.013) 2.46 0.01 Prior trial (t-1) attractiveness 0.019 (0.011) 1.74 0.09 Prior trial (t-1) response time < 0.0001 (0.013) 0.005 0.99 899 900 901 902 903 Table 2. Spatial Correspondence with Canonical Networks To identify the DMN and ECN, we ran spatial correlations between canonical neural networks (Smith et al., 2009) and the 25 components from our ICA. The highest-correlating ICA component numbers for each network map and the correlation values are listed. Canonical Network Independent Component No. Spatial Correlation (r) Visual 1 IC10 0.82 Visual 2 IC01 0.66 Visual 3 IC01 0.45 Default-Mode IC06 0.78 Cerebellar IC18 0.32 Sensorimotor IC13 0.59 Auditory IC14 0.66 Executive Control IC04 0.64 R Frontoparietal IC03 0.64 L Frontoparietal IC07 0.77 Page 38 of 38

Outcome Cue ISI Target Feedback Delay + + 2000ms ITI 1500ms 1500-4500ms 750ms 750ms 5000-11000ms 1500-4500ms 2000ms

A 8 Number of Participants 7 6 5 4 3 2 Faster following face trials Faster following landscape trials 1 0-0.03-0.02-0.01 0 0.01 0.02 0.03 0.04 0.05 Face-Land RT Difference (s) B 0.04 * Previous trial attractiveness Previous trial stimulus type Previous trial reaction time Current trial stimulus type Parameter Estimates (β) 0.02 0-0.02-0.04-0.06 ** p < 0.001 * p < 0.05 **

Z = 21 Y = -61 X = 0 Default-Mode Network Z = 12 Y = 27 X = 0 Executive Control Network

A B Increased FG-ECN coupling Increased FG-DMN coupling Network Coupling with FG (DMN - ECN PPI β) Z = -17 Y = -65 X = 42 15 10 5 0-5 -10-0.1 0 0.1 0.2 Change in Behavior (Face-Scene (Current Stimulus RT Effect β) β) Faster after social trials Slower after social trials