The Brain Circuits and Dynamics of Curiosity-Driven Behavior in Naturally Curious Marmosets

Cerebral Cortex, September 2021;31: 4220–4232 https://doi.org/10.1093/cercor/bhab080 Advance Access Publication Date: 12 April 2021 Original Article ORIGINAL ARTICLE The Brain Circuits and Dynamics of Curiosity-Driven Behavior in Naturally Curious Marmosets Xiaoguang Tian1,2 , Afonso C. Silva1,2 and Cirong Liu1,2,3,4 1 Department of Neurobiology, University of Pittsburgh Brain Institute, University of Pittsburgh, Pittsburgh PA Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda MD 20892, USA, 3 Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China and 4 Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai 201210, China 15261, USA, 2 Cerebral Address correspondence to Dr Cirong Liu. Email: crliu@ion.ac.cn; Dr Afonso C. Silva. Email: afonso@pitt.edu. Abstract Curiosity is a fundamental nature of animals for adapting to changing environments, but its underlying brain circuits and mechanisms remain poorly understood. One main barrier is that existing studies use rewards to train animals and motivate their engagement in behavioral tasks. As such, the rewards become significant confounders in interpreting curiosity. Here, we overcame this problem by studying research-naïve and naturally curious marmosets that can proactively and persistently participate in a visual choice task without external rewards. When performing the task, the marmosets manifested a strong innate preference towards acquiring new information, associated with faster behavioral responses. Longitudinally functional magnetic resonance imaging revealed behavior-relevant brain states that ref lected choice preferences and engaged several brain regions, including the cerebellum, the hippocampus, and cortical areas 19DI, 25, and 46D, with the cerebellum being the most prominent. These results unveil the essential brain circuits and dynamics underlying curiosity-driven activity. Key words: fMRI, functional connectivity, natural viewing, nonhuman primates, novelty seeking Introduction Living organisms, including humans, learn to use external stimuli information to adapt to rapidly changing environments. A decision-making strategy is reinforced when the choice increases reward probability. However, organisms can also use an alternative decision-making strategy of exploration, in which the choice is not associated with any external rewards (Davis et al. 1950; Harlow et al. 1950; Kidd and Hayden 2015). One innate motivation for such a strategy is the brain’s information-seeking drive that intrinsically urges the organisms to explore their environments (Anselme 2010; Bromberg-Martin and Hikosaka 2011; Funamizu et al. 2012), similar to children playing and adult hobbies. Although these intrinsically motivated behaviors depend on internal factors that are difficult to characterize, one particular intrinsic motivation, curiosity, provides opportunities to investigate its underlying brain mechanisms. Although curiosity engages multiple brain regions and associated cognitive processes, it does not have a well-defined psychological definition. Curiosity is a natural and active exploratory process to gain new information. As one of our most basic natures, curiosity generally induces many essential behaviors to explore activities or stimuli that are novel, surprising, or intriguingly complex (Kidd and Hayden 2015). Yet, the neural mechanisms underlying curiosity remain poorly understood. Many studies about curiosity have been performed on human subjects. However, their curiosity measures are highly subjective (van Lieshout et al. 2018; Kobayashi et al. 2019; Schwartenbeck et al. 2019). Other studies on curiosity used animal models, for example, nonhuman primates (NHP) (Redgrave and Gurney © The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com Brain Circuits of Curiosity-Driven Behavior 2006; Bromberg-Martin and Hikosaka 2011). As animals cannot directly report internal mental activities, these experiments made use of external reward modulation (e.g., water, juice, treats) to elicit exploratory behaviors and characterize curiosity by measuring their willingness to sacrifice reward (Redgrave and Gurney 2006; Bromberg-Martin and Hikosaka 2011). As such, the rewards become significant confounders in interpreting curiosity. However, the use of external rewards is not necessary to elicit curiosity-driven behavior. For example, human infants naturally explore new environments regardless of physical rewards (Berlyne and Slater 1957; Kreitler et al. 1984). Macaques also exhibit a robust motivation to solve mechanical puzzles without extrinsic incentives (Davis et al. 1950; Harlow 1950). Therefore, NHP can produce curiosity-driven behaviors without external rewards. Our study aimed to probe curiosity-related behaviors and brain activities in NHP using functional magnetic resonance imaging (fMRI). We chose to use the common marmoset, one of the smallest but highly social NHP species. We purposely recruited a large cohort of marmosets to identify naturally curious monkeys that can actively engage in the experiments without reward-based training. Furthermore, marmosets are easy to be handled, constrained, and acclimatized for awake magnetic resonance imaging (MRI) scanning (Silva et al. 2011; Liu et al. 2019), which provides critical practical advantages to track their behavioral changes and fMRI responses from a researchnaïve state. To elicit curiosity-driven behaviors, we used a classic delayed free-choice task without the influence of external rewards (Fig. 1A), so that the choice depends entirely on the animals’ innate preferences. In this task, animals choose freely between two target images (novel and familiar) after watching a short movie clip. To minimize the influence of any reward-driven experience, we recruited research-naïve animals for our study and monitored their behavioral changes and fMRI responses longitudinally. Materials and Methods Animals All procedures were approved by the Animal Care and Use Committee of the National Institute of Neurological Disorders and Stroke, National Institutes of Health. A total of 15 healthy adult marmosets (nine males and six females, 3–8 years old) were recruited and screened for the study (the detailed information is shown in Supplementary Table S1, sheet 1). All marmosets had received a 3-week acclimatization training for MRI scans so that they could be familiar with MRI environments and endured long-term MRI scanning with minimum stress. The MRI acclimatization training involved no rewards, and the detailed protocol was described in our previous paper (Silva et al. 2011; Liu et al. 2019). After the adaptation period, marmosets underwent longitudinal MRI scanning experiments with calibration and delayed free-choice tasks. We did not implement any restriction of food or fluid intake for the marmosets before the experiments. Note that we attempted to deliver random liquid rewards to these marmosets on the first day after each successful trial regardless of their choices. However, since the marmosets never received any previous training, they seldomly consumed the rewards delivered. Therefore, we discontinued offering rewards after the first day. We excluded any animals that failed to get involved in the tasks from our experiments. Among the 15 marmosets, only three individuals were actively and persistently engaged in the Tian et al. 4221 task (see Supplementary Table S1, sheet 1 for detail). The three marmosets included one male (named “A”) and two females (“K” and “B”). Behavioral Tasks Marmosets wore customized jackets and helmets to stabilize their heads inside the MRI (Silva et al. 2011). A computer monitor was set in front of the animal with a resolution of 1920 × 1080. The maximum viewing angles were restricted within 10◦ on the horizontal axis and 8◦ on the vertical axis, and the eye movements were sampled at 1 kHz by the infrared eye tracker (Arrington Research, Inc.). Before the delayed free-choice task, marmosets underwent an eye-tracking calibration task to obtain accurate eye positions. In the calibration task, marmosets directed their gaze to the center of the screen when a center white dot (diameter 1◦ of visual angle) appeared. After the fixation within a 2◦ radius of visual angle for 100–200 ms, the central dot disappeared. A face image randomly selected from a prepared dataset (54 pieces in total) appeared on the screen in 21 random positions (each lasting 1 s). The image attracted animals to make a saccade to them, and the landing positions were manually verified and extracted for the offline calibration (an example in Supplementary Fig. S1). The number of daily trials varied between 105 and 147, depending on the animal’s performance. In general, the percentage of valid trials for the calibration was >90%. The delayed free-choice task involved four periods: the movie-watching, the delay, the decision-making, and the resting periods (Fig. 1A). Marmosets fixated on a central white circle within a 2◦ radius of visual angles for a random period of 1–1.6 s to initiate playing a 20 s video clip. There were 440 clips in our video database. Videos clips were chosen to minimize content repetition. Supplementary Table S1 (sheets 2–4) describes the number of video clips played in a random sequence for each marmoset. After the video clip ended, the central white circle reappeared for a randomly variable period of 1.5–2.5 s to allow marmosets to regain fixation. The marmoset had to maintain its gaze within a 2◦ visual angle radius around the fixation dot, during which (500 ms right after the circle appeared) two target images were randomly shown at the horizontal ±5◦ within the size of 6◦ × 5◦ . One of the targets was from the played video, and the other was a novel image unrelated to the video and never seen by the marmosets before. The whole delay process lasted 1.5–2.5 s. The choice consisted of the first response saccade after the central white fixation circle disappeared (fixation offset). After the decision period (6 s), a resting period started with a blank screen. An example trial of the delayed free-choice task is shown in Supplementary Movie S1. fMRI Data Acquisition MRI scans were conducted in a 7 T/300 mm magnet (Bruker Biospin, Inc.) with a 150 mm gradient set (450 mT/m with 150 μs; Resonance Research Inc.), a 16-rung high-pass birdcage radiofrequency transmission coil, a custom-built eight-channel phased-array receiver coil, and a ParaVision 6.0.1 system. The fMRI data were collected with a 2D gradient-echo Echo-planar imaging (EPI) sequence [repetition time (TR) = 2 s, echo time (TE) = 22.2 ms, flip angle = 70.4◦ , field of view (FOV) = 28 × 36 mm, matrix size = 56 × 72, 38 axis slices with a 0.5 mm slice thickness, voxel resolution = 0.5 mm isotropic, 180 time points and 6 min per run]. For each session, half of the runs were collected with 4222 Cerebral Cortex, 2021, Vol. 31, No. 9 Figure 1. The longitudinal performance in the delayed free-choice task. (A) Task design: Marmosets foveated to a central fixation dot for 1–1.6 s to initiate the playing of a 20 s video clip. After the video, the fixation dot reappeared for 1.5–2.5 s to allow marmosets to return fixation to the center, during which two target images were shown (one was from the played video, and the other was a novel image). The animals’ choice consisted of the first saccade made after the fixation dot disappeared. After the decision period (6 s), the marmosets rested for 16 s before the next trial. (B) The behavioral performances for each marmoset: The top-left panel shows the proportion of choices to the novel target (the black dashed line highlights the 50% probability). The top-right panel shows the percentage difference in viewing time between two targets (novel–familiar) during the decision period. The black dashed line highlights the same viewing time in two targets. The bottom-left panel depicts the average reaction time of the choice saccades (ms). The bottom-right panel shows the average peak velocities of the choice saccades (deg/s). (C) Comparison of the behavioral responses across days. Behavioral data were split into two halves by the median of days for each marmoset (Day 1–5 and 6–11 for the marmoset A; Day 1–6 and 7–13 for the marmoset K; Day 1–3 and 4–6 for marmoset B). The opaque colors highlight the pooled data of later days, and transparent colors represent early day’s data. The first column depicts the relationship between the reaction time (x-axis) and the amplitude (y-axis) of the choice saccades, and the second column shows the relationship between the peak velocities (x-axis) and the amplitude (y-axis). The later-training days show significant differences in these measures between the novel and the familiar target choices (Wilcoxon rank-sum test, see detailed statistics in Supplementary Table S4). All data are presented as mean ± standard error of the mean and the statistic test ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001, corrected. the L-to-R (blip-up) phase-encoding direction and the other half with the R-to-L (blip-down) phase-encoding direction to compensate for different EPI distortion and signal drop-out. Also, two sets of spin-echo EPI with opposite phase-encoding directions (LR and RL) were collected for the EPI-distortion correction (TR = 3 s, TE = 0.44 ms, flip angle = 90◦ , FOV = 28 × 36 mm, matrix size = 56 × 72, 38 axis slices with a 0.5 mm slice thickness, 8 volumes per set). In each session, a T2 -weighted Brain Circuits of Curiosity-Driven Behavior structural image was scanned for the co-registration purpose (TR = 6000 ms, TE = 9 ms, flip angle = 90◦ , FOV = 28 × 36 mm, matrix size = 112 × 144, 38 axis slices, resolution = 0.25 × 0.25 × 0.5 mm3 , number of averages = 8). A total of 11 MRI sessions (one session per day) were collected for marmoset A, 13 sessions for marmoset K, and six sessions for marmoset B. Note that the marmoset B was not available after 6-day experiments due to an accident in the marmoset housing facility. There were 6–14 runs in each session, varying daily depending on the animal’s performance (see Supplementary Table S1, sheets 2–4 for detail). Each run lasted for 6 min and contained seven trials. Data Analysis Preprocessing of Behavioral Data For the eye position calibration, eye landing positions for the 21 screen locations were manually extracted and then fitted with a polynomial method described in our previous studies (Tian et al. 2016, 2018). Supplementary Figure S1 shows an example of calibration. The delayed free-choice task’s eye movements were also verified in post hoc analyses to correct misses or false detections based on the eye movement velocity and accelerations. The updated eye movement data were used to characterize choice properties (the choices to novel or familiar images, reaction time (RT) times, peak velocities, and amplitudes) and to exclude invalid trials, in which the marmoset quitted the task, failed to maintain fixation during the delay period, or made saccadic choices too early (<50 ms) or too late (>700 ms). Preprocessing of fMRI Data Data preprocessing was performed using the Analysis of Functional NeuroImages (AFNI) (Cox 2012) and the FMRIB Software Library (FSL) (Jenkinson et al. 2012) software packages. For each run, the first four time points were removed for the magnetization to reach a steady state. The remaining time series was despiked by the “3dDespike”, slice timing corrected by the “3dTshift,” motion corrected by the “3dvolreg” of the AFNI, and corrected for EPI distortions by the “topup” of the FSL. Motion censoring, nuisance signals removal, and band passing were performed in one regression model by the “3dDeconvolve” of the AFNI. In detail, any TRs and the previous TRs were censored if the motion was >0.2 mm to minimize the influence of head movements. Nuisance signals were regressed, including demeaned and derivatives of motion parameters, the mean white matter signal, and the mean cerebrospinal fluid signal. No extra spatial smoothing was performed as our analyses were region of interest (ROI) based. To examine the effect of frequencies, we tested different band-passing filters, including 0.004–0.05 Hz (extremely low frequency), 0.01–0.1 Hz (low frequency), and 0.1–0.2 Hz (high frequency). By repeating the same analysis (see below “Quantification of behavior-most-correlated states”), the data with low-frequency filtering provided the most significant contributions [Supplementary Fig. S2, tested by oneway analysis of variance (ANOVA), see detailed statistics in Supplementary Table S4]. The results and conclusions from the data with low-frequency filtering were similar to the data without band-passing filtering. In the main figures, we only reported the results from the unfiltered data. Dynamic Connectivity Analysis We extracted the time series of 130 brain regions defined by the Paxinos parcellation of the Marmoset Brain Mapping Atlas Tian et al. 4223 (Liu et al. 2018, 2020) and used a sliding window method to calculate the dynamic connectivity (Allen et al. 2014). The sliding window was defined as a length of 3 TRs (6 s) and a step of 1 TR (2 s), which generated 130 × 130 covariance matrices Dr,s,w for each run r, each session s, and each time point w (Choe et al. 2017; Preti et al. 2017). In previous studies (Hutchison et al. 2013; Barttfeld et al. 2015), the covariance was estimated from the regularized inverse covariance matrix by the graphical LASSO (least absolute shrinkage and selection operator) method with an additional L1 norm constraint on the inverse covariance matrix to enforce sparsity. Here, we adopted l = 0.1 as most previous studies (Friedman et al. 2008; Barttfeld et al. 2015; Cai et al. 2018). The covariance matrix of each time window was Fisher’s ztransformed (Zr,s,w = tan−1 Dr,s,w ) to z-score for further analysis. In addition, the sliding window size did not affect results. Unsupervised Clustering and Brain States To identify the reoccurring connectivity patterns (brain states), we applied the K-means clustering algorithm (with K from 3 to 10) on the covariance matrices Zr,s,w using the cosine distance metric implemented in MATLAB 2018b. As the choice preference was used to select the K (see the next section), we excluded Zr,s,w matrices of the decision period (6 s) to avoid potential biases, except for the special analyses in Figures 4 and 5 and Supplementary Figure S5 (pooling all time periods). The resulting centroids (the median of clusters) of the K-mean algorithm are referred to as the brain state BSn (as shown in Fig. 2B) and each matrix Zr,s,w is assigned a BSn (we also tested other metrics for the clustering, the Pearson’s correlation, and the Euclidean distance, which resulted in similar results and conclusions). To select the number (K) of brain states (Fig. 2A), we calculated the occurrence rate of each state n along with the time dimension of each session s: BSs pns = n n s . i=1 BSn Then, we computed the Pearson’s correlation coefficient between the longitudinal change of the occurrence rate and the probability of choosing novel targets. The selected number of K and the behavior-most-correlated state were the ones with the highest correlation, respectively. Normalized Occurrence Rates in Different Periods We applied a running window of 1 s with a step of 100 ms to compute averaged occurrences rate (over all sessions) in the periods of movie-watching, resting, and decision (Figs 3–5). Due to different total lengths of movies, resting, delay, and decision periods, the occurrence rates were normalized by their length ratio in each session (we also tested different sizes of running windows, e.g., 2 s, and obtained similar patterns). Based on the same analysis, we further quantified the normalized occurrence rates under different choice conditions (choices to novel or familiar images) and the rate difference between two choice conditions (novel–familiar). In addition, the sliding window size did not affect results. The Similarity of Normalized Occurrence Rates (Coupling Periods) across Monkeys We repeated the above analysis for each marmoset to obtain the differences in occurrence rates between two choice conditions of different periods (novel–familiar). We then estimated the autocorrelation of such rate-difference curves across three 4224 Cerebral Cortex, 2021, Vol. 31, No. 9 Figure 2. Dynamic functional connectivity reveals behavior-most-correlated brain states. (A) The number of clusters (brain states) in K-mean clustering. We explored different numbers of clusters by the K-means algorithm (K = 3–10) to find brain states with a high correlation with choice preferences. Each gray dot represents a correlation value between each brain state’s occurrence rates across days and the longitudinal changes of the choice preference. Each colored star represents the average Pearson’s correlation value of all states for each K, and red arrowheads highlight the clusters with the highest mean correlation values. The data was presented as mean ± standard error of the mean. (B) Brain states for the selected K-mean cluster ranked by their Fisher’s z-transformed correlation values with the behavior data. For each animal, we selected the K-mean cluster with the highest mean correlation from above result A (K = 7 for marmoset A; K = 5 for marmosets K and B). Black outlines highlight the brain states with the highest correlation in each marmoset. (C) The behavior-most-correlated brain states across different marmosets share common components, indicated by their highest similarities. Black outlines highlight the correlation values of one marmoset’s behavior-most-correlated brain state to all brain states of another marmoset. Brain Circuits of Curiosity-Driven Behavior Tian et al. 4225 Figure 3. Occurrence rates of brain states during movie-watching and resting periods. For the behavior-most-correlated states (A) and the behavior-least-correlated states (B), the left panels present the normalized occurrence rates with choices to novel targets (red lines) and familiar targets (blue lines), as well as the difference between these two occurrence rates (novel–familiar, black lines), in the different periods (movie and resting). The right panels depict across-marmoset patterns of their occurrence rate differences. The first row shows the pooling results of the occurrence rate difference (novel–familiar) from the three marmosets for each period. The second row shows the autocorrelation spectrums of the occurrence rate differences across marmosets at time lag 0. The spectrums during the movie-watching uncover the coupling periods across marmosets (highlighted by orange arrowheads), including 2–7 s, 13–18 s after the movie onset (the first column), but not in the resting period (the second column) or any periods of the behavior-least-correlated state. Data are presented as mean ± standard error of the mean. monkeys at the lag 0 (implemented by the MATLAB 2018b). Given the rate-difference curves of three monkeys, Y = Y1 , Y2 , Y3 , at time T1 , T2 , . . . , TN , the spectrum of lag 0 autocorrelation function at time r1 , r2 , . . . , rN is   3 3  Yj=i − Y i=1 j=1 Yi − Y . r= 2 3  Y − Y i i=1 The resulting spectrum was smoothed with a small window (0.1 × 0.1) for visualization. Higher values in the spectrum mean higher similarity of the temporal pattern of rate-difference curves across monkeys. Quantification of Behavior-Most-Correlated States For each brain connection, we counted how many other states were significantly different from the behavior-mostcorrelated state in their correlation values (repeated oneway ANOVA measures, P < 0.05, see detailed statistics in 4226 Cerebral Cortex, 2021, Vol. 31, No. 9 Figure 4. Occurrence rates of brain states around the decision period. For the behavior-most-correlated states (A) and the behavior-least-correlated states (B), the left panels present the normalized occurrence rates with the choices to the novel targets (red lines) and the familiar targets (blue lines), as well as the difference between these two occurrence rates (novel–familiar, black lines), around the decision period (aligned on the fixation offset). The light gray areas are the range of response times of all trials. The right panels present the analysis of the occurrence rate difference across marmosets. The first row shows the pooling results of the occurrence rate difference (novel–familiar) from the three marmosets. The second row shows the autocorrelation spectrums of the occurrence rate difference at the time lag 0. The spectrums reveal a prominent coupling time across marmosets, which is 1 s around fixation offset (highlighted with the orange arrowheads), but not in the behavior-least-correlated states. Data are presented as mean ± standard error of the mean (SEM). (C) The longitudinal change of the occurrence rate pattern of the behavior-most-correlated states at the time of delay and decision periods. The left panels show the daily changes of the normalized occurrence rate of the states during the 1 s before the fixation offset. The right panels show the pooled results of the normalized occurrence rate and across-subject coupling during the early training days and late training days, respectively. (D) The longitudinal change of the occurrence rate pattern of the behavior-most-correlated states around 1 s of fixation offset. The left panels show the daily changes of the normalized occurrence rate of behavior-most-correlated states during the 1 s after the fixation offset. The right-top panel shows the pooled results of the normalized occurrence rate and across-subject coupling during the early training days and late training days, respectively. The bottom-right panels show the normalized probability histogram of the corresponding saccades for target selection. Data are presented as mean ± SEM. Brain Circuits of Curiosity-Driven Behavior Tian et al. 4227 Figure 5. The key brain regions in the behavior-most-correlated brain state. (A) The right histograms present the ranking indices of all brain regions in each marmoset (normalized number of significant functional connections; Supplementary Table S2 provides the detailed ranking information). The arrows mark the top 10 regions. The left pie charts summarize the proportion of the normalized values in the top 10 ranked regions in each marmoset, highlighting five overlapped brain regions across marmosets. (B) The left panel displays the five top-ranked regions across all marmosets; the pie charts show the normalized ranking indices for the five regions (from inner to outside represent the data of marmosets A, K, and B, respectively). The matrices on the right show the normalized number of significant states among these five brain regions, demonstrating the cerebellum’s core role (tested by repeated-measures one-way ANOVA; see detailed statistics in Supplementary Table S4). For verification, Supplementary Figure S5A displays extended results for the top 10 regions across all marmosets, and Supplementary Figure S5B shows the top five areas for each marmoset. (C) The most significant areas in the cerebellum. The first panel shows the lobule parcellations of the cerebellum. The second panels show the 48 ROIs defined in the cerebellum (24 per hemisphere). The red nodes represent the ROIs that are more significant than the entire cerebellum in all three marmosets (see detailed statistics in Supplementary Table S4). The orange nodes show the ROIs that are more significant in any two marmosets. The gray nodes show less significant ROIs than the entire cerebellum in at least two marmosets. Supplementary Table S4). The numbers were then normalized to a range of 0–1 (ranking index) by scaling normalization. To identify the contributions of brain areas to the behaviormost-correlated states, we summed these numbers of total connections in each brain region and sorted them in descending order. The most important brain area had the highest value [Fig. 5A and Supplementary Tables S2 (sheets 2–4) and S3]. We also summed the numbers from three monkeys and sorted them in descending order to examine the contribution of each brain area at the population level (Fig. 5B and Supplementary Table S2, sheet 1). Seed-Based Analysis of the Cerebellum A total of 24 ROIs (sphere, diameter = 2 mm) were evenly distributed across each cerebellum hemisphere (48 in total). The mean time series of each ROI was extracted from the preprocessed fMRI data and replaced the whole cerebellum’s mean time series to calculate dynamic functional connectivity (FC) (see “Dynamic connectivity analysis”). The resulting dynamic FC then replaced the corresponding data of the cerebellum in each brain state. With the replaced data, we repeated the analysis in “Quantification of behavior-mostcorrelated states” and obtained the contributions of each ROI 4228 Cerebral Cortex, 2021, Vol. 31, No. 9 to the behavior-most-correlated states. The number of total significant states of each ROI was then compared with the whole cerebellum’s previous corresponding results to identify the ROIs that contributed more significantly than the whole cerebellum (tested by repeated-measures ANOVA, see detailed statistics in Supplementary Table S4). Results The Behavioral Performance Reveals an Innate Preference Towards the Novel Information We monitored the behavioral performance of research-naïve marmosets in a delayed free-choice task. The task required marmosets to watch a 20 s video clip, which had minimal repeats of the videos played each day across monkeys (Supplementary Table S1, sheets 2–3). The marmosets then freely choose between two target images: One selected from the watched video or a novel image they had never seen before (Fig. 1A). We recruited 15 research-naïve marmosets (details are in Supplementary Table S1, sheet 1). The only training these marmosets took was the necessary MRI acclimatization procedures before the experiment (Liu et al. 2019). As these marmosets had not experienced any prior training for behavioral experiments, nine animals showed no interest in engaging in any visual tasks, even on the first day. Three of them participated in the eye-calibration session and a few trials of the delayed free-choice task. However, their engagement only lasted 1 or 2 days, after which they quit most of the trials due to a lack of motivation. The three remaining marmosets (A, K, and B) showed active and persistent involvement in the task. We recorded their behavioral performance and fMRI responses longitudinally (11 days for the marmoset A, 13 days for K, and 6 days for B). As the experiments included no external rewards, the longitudinal changes of choice saccades and viewing time reflected the marmosets’ innate preference. In the beginning, their decision strategy formed under uncertainty, and their choices were random (Fig. 1B, top left). As they became more familiar with the experiments, they reinforced the strategy of selecting novel images, which provided more information than familiar images. The choice preference was not affected by the stimuli spatial location because the novel images were shown randomly on either the left or the right side of the central fixation dot (Supplementary Fig. S3B, Wilcoxon rank-sum test, P > 0.05, see detailed statistics in Supplementary Table S4). More importantly, the animals’ viewing time of unknown images increased, reflecting their clear preference for novel information (Fig. 1B, top right, and Supplementary Fig. S3A). The three marmosets also showed other consistent longitudinal behavioral changes, including a significant decrease in faster responses (Fig. 1B, bottom panels) and invalid trials (Supplementary Fig. S3C). In the beginning, about half of the trials were invalid, in which the marmosets either quit the trials, failed to maintain fixation during the delay period, or made the saccadic choice responses too early (<50 ms) or too late (>700 ms). As the marmosets became more familiar with the task, the number of invalid trials dropped dramatically (Supplementary Fig. S3C). As marmosets became more familiar with the task, they also responded faster during the decisionmaking period, quantified by the average RT (Fig. 1B, bottom left) and the average peak velocity (Fig. 1B, bottom right) of the response saccades. For marmosets A, K, and B, the average RT dropped significantly from 355, 403, and 381 ms (first day) to 239, 207, and 214 ms (last day), respectively. Correspondingly, the average peak velocity increased significantly from 214, 212, and 212.3 deg/s to 328, 298.5, and 309.2 deg/s, respectively. As previous studies suggested, we further examined whether the novelty modulated the saccadic metrics (the response speed) (Reppert et al. 2015; Ebitz et al. 2018). By comparing the average RT and peak velocities of the two conditions across days directly, we found no significant differences between the choices of familiar targets and those of novel ones (Supplementary Fig. S3D, Wilcoxon rank-sum test, P > 0.05; see detailed statistics in Supplementary Table S4). As the saccadic peak velocity and RT depend on the amplitude, the considerable amplitude variation of the two conditions in each day may account for the insignificance. To minimize the influences of amplitude variation, we pooled the longitudinal dataset into two halves (early training days and late training days) by the median recording day for each marmoset (Day 1– 5 and 6–11 for the marmoset A; Day 1–6 and 7–13 for the marmoset K; Day 1–3 and 4–6 for marmoset B). We measured the amplitude–RT relationship (Fig. 1C, left column) and the amplitude–peak–velocity relationship (Fig. 1C, right column). We found a significant difference for each marmoset in the RT and the average peak velocities between the two conditions in the late training days (Fig. 1C, opaque color, Wilcoxon rank-sum test: P < 0.01, corrected, see detailed statistics in Supplementary Table S4), but not in the early training days (Fig. 1C, transparent color, Wilcoxon rank-sum test: P > 0.05, corrected, see detailed statistics in Supplementary Table S4). Thus, when the animals became familiar with the task (late training days), they preferred the novel images with faster response times and higher peak velocities. The longitudinal improvement also supports the idea of the novel image selection and preference. Behavior-Most-Correlated Brain States Reveal the Mechanism of Choice Preference After finding novel image selection and preference performance, we then examined the temporal dynamics of brain connectivity underlying innate preferences. From the fMRI data of each trial, we extracted the time series of 130 brain regions defined by the Paxinos parcellation of the Marmoset Brain Mapping Atlas (Liu et al. 2018, 2020). We then applied a sliding window approach to calculate dynamic FC patterns associated with each time point in 130 × 130 correlation matrices. After clustering these matrices into several brain states (K), we examined the Pearson’s correlation between the longitudinal changes of the choice preference (shown in Fig. 1B, top left) and the occurrence rate changes in brain states. The analysis revealed brain states in multiple K solutions that were strongly correlated with the behavior (Fig. 2A). These behavior highly correlated brain states provided similar results in our following analysis. Here, we present brain states from K = 7, 5, and 5 for marmosets A, K, and B as examples, since these K solutions showed the highest mean correlations (Fig. 2A). We ranked these selected brain states according to their correlation levels with behavioral changes to identify the behavior-most-correlated brain states (Fig. 2B). The behaviormost-correlated brain states shared the highest cross-subject similarities, suggesting that these states were not random but had consistent temporal and spatial features across marmosets (Fig. 2C). Brain Circuits of Curiosity-Driven Behavior To characterize the behavior-most-correlated states’ temporal features, we pooled all trials of the two choice conditions. We then plotted the normalized occurrence rates of the behaviormost-correlated state during the periods (movie-watching before the decision period and the resting period after the decision period). The temporal variations of the normalized occurrence rates presented a similar pattern for the three marmosets during the movie-watching periods, which was prominent in the curve of occurrence rate difference (novel– familiar) between the two choice conditions (Fig. 3A, left panel first and second rows). The autocorrelation spectrum of the curves at time lag 0 further highlighted a similar pattern by revealing prominent coupling periods across marmosets at 2– 7 s and 13–18 s after the movie’s start (Fig 3A, right panel, column 1). The pattern only appeared during the movie-watching period. We did not observe any consistent patterns across marmosets during the resting period (Fig. 3A, left panel, 3–4 rows, and right panel, column 2) or in the other states with low correlations, for example, the behavior-least-correlated states (Fig. 3B). Therefore, the temporal pattern was specific for the behavior highly correlated states and the movie-watching period. The coupling pattern of the behavior-most-correlated states was unrelated to either eye movement patterns or movieshowing sequence, as saccade occurrence showed no significant difference between the two conditions (Supplementary Fig. S4A, Wilcoxon rank-sum test, P > 0.05, see detailed statistics in Supplementary Table S4) and the movie-showing sequences are random for different marmosets (Supplementary Fig. S4B; see detailed statistics in Supplementary Table S4). The movie contents did not affect the brain states, as the normalized occurrences of different movie contents showed no significant bias (Supplementary Fig. S4D). By splitting the brain states’ occurrence rate data into two halves by the median of days, we found that the coupling pattern did not appear at the early training days but became prominent at the later period (Supplementary Fig. S4C). These results suggest that the brain states’ temporal pattern may reflect information accumulation during the movie-watching period. Next, we examined the temporal pattern of the behaviormost-correlated brain states during the decision period, when the animals expressed their innate preference. We first reclustered the brain states by including all time points of delay and decision periods and identified highly similar patterns of the behavior-most-correlated states as above (Supplementary Fig. S5, Pearson’s correlation, ranging from r = 0.70 to r = 0.94 value, P < 0.05, see detailed statistics in Supplementary Table S4). We found similar temporal dynamics for the three marmosets by plotting their occurrence rates around the decision period (Fig. 4A, left panel). The autocorrelation spectrums revealed a prominent time coupling at 1 s before the decision period onset (Fig. 4A, right panel). We did not find any similar patterns in the other states with low correlations, such as the behavior-least-correlated states (Fig. 4B). The pattern was also robust in the behaviormost-correlated states of other clustering K solutions. Still, it became weaker when the correlation with behaviors decreased, suggesting that the pattern does not depend on the choice of the clustering K (Supplementary Fig. S6). Before the decision, the coupling pattern also presented a longitudinal training effect, as it was weaker in the early training days but became stronger in the later training days (Fig. 4C). Therefore, the longitudinal change of the behavior-most-correlated brain states reveal Tian et al. 4229 an enhancement of target identification and facilitate choice making in favor of the novel target (Fig. 1B,C). Besides the prominent effect before the choice making, the coupling patterns also occurred at the time of choice making (1 s after fixation offset), although they were relatively weak (Fig. 4A). Repeating the same analysis as for Figure 4C, we found an opposite effect that was more prominent in early training days than in later training days (Fig. 4D). The coupling patterns at this time reflected the motivation of information-seeking driven by the uncertainty of the selected target. When the animals were unfamiliar with the task on early training days, the uncertainty of choice outcomes drives the information seeking, resulting in a more prominent coupling. On later training days, the animals were familiar with the task, and the information seeking for novel targets became goal directed. Thus, the coupling patterns of the behavior-most-correlated brain states became weaker. In summary, the temporal patterns of the behavior-mostcorrelated brain states reveal numerous components of curiosity: the information accumulation during the movie-watching period (Fig. 3 and Supplementary Fig. S4C), the identification of novel target before fixation offset (Fig. 4A–C), and the motivation of information seeking for the selected target during the decision period (Fig. 4A,B,D). The longitudinal change of such temporal patterns is also strongly associated with behavior improvement (Fig. 1B,C). Thus, the behavior-most-correlated brain states indeed reflect an intact, curiosity-driven information-seeking process. The Cerebellum is a Central Region in the Behavior-Most-Correlated Brain States After characterizing the temporal dynamics of the behaviormost-correlated brain states, we investigated which brain areas contributed most to it. For each brain connection, we counted how many other states were significantly different from the behavior-most-correlated state (tested by repeated-measures one-way ANOVA, see detailed statistics in Supplementary Table S4), and then summed these numbers to associate with each brain region. We normalized the added numbers to a range from 0 to 1 and ranked all brain regions according to their numbers. Since the ranking results per brain hemisphere (Supplementary Table S3) were similar to the pooled results of the two hemispheres (Supplementary Table S2), we presented the pooled results. Figure 5A shows brain region rankings (right histograms) and the top 10 ranked regions of each marmoset (left pie charts), highlighting five overlapped brain regions. These five brain regions are top ranked and share high similarity in all marmosets (Fig. 5A,B), including the cerebellum (ranked first), the hippocampus (second), the peristriate visual cortex (A19DI, third), the subgenual cingulate cortex (A25, fourth), and the dorsolateral prefrontal cortex (A46D, fifth). Among these top-ranked regions, connections involving the cerebellum contributed significantly to the other brain regions (Fig. 5B, repeated one-way ANOVA measures, see detailed statistics in Supplementary Table S4). In contrast, connections involving other brain regions did not show the same level of contributions (Fig. 5B). We also observed similar results in the top 10 brain regions’ contribution matrices across marmosets (Supplementary Fig. S7A, repeated one-way ANOVA measures, see detailed statistics in Supplementary Table S4). In other words, the cerebellum is not only the most prominent region but also contributes most 4230 Cerebral Cortex, 2021, Vol. 31, No. 9 to the other top-ranked regions, indicating its central role in the behavior-most-correlated states. For the top-ranked brain regions of each marmoset, since the cerebellum is one of them, we also found that it has relatively high correlation with other brain areas (Supplementary Fig. S7B). The cerebellum is a large brain region consisting of multiple functionally distinct areas (Fig. 5C). As there was no digital atlas of the marmoset cerebellum, we defined 48 (24 per hemisphere) ROIs to examine which parts of the cerebellum contributed the most to the behavior-most-correlated states (Fig. 5C). Repeating the same brain state analysis for each ROI, we found that multiple areas of the cerebellum contributed more significantly to the behavior-most-correlated states than the whole cerebellum (by repeated-measures one-way ANOVA, see detailed statistics in Supplementary Table S4). There is no clear lateralization, as the significant ROIs are distributed closely within the two hemispheres. For example, ROI 20 on the left and ROI 22 on the right belong to the same area (Crus I/II). Although significant ROIs were widespread, a few ROIs were more prominent and consistent across all marmosets, including the Crus I/II (ROIs 20, 22), lobule IX (ROI 10), and lobule IV (ROI 2). These regions may have unique roles in task learning and innate choice preference. Discussion Curiosity is an intrinsic desire that motivates information seeking and maximizes knowledge or opportunities (Loewenstein 1994). However, its accurate psychological concept is still not well defined, and therefore its underlying brain circuits and mechanisms remain poorly understood. Here, we successfully induced the curiosity-related information-seeking behavior in the research-naïve marmosets. The marmosets showed an innate preference for novel images over familiar ones without any influences of extrinsic rewards, reflected by both saccadic metrics of choices and the viewing time (Fig. 1 and Supplementary Fig. S3). This preference is consistent with the description of curiosity as a typical information-seeking behavior (Berlyne 1978; Baranes et al. 2015; Oudeyer and Smith 2016) that is intrinsically rewarding (Loewenstein 1994; Kang et al. 2009). Using longitudinal fMRI and dynamic FC analysis, we further revealed the brain states that were closely correlated with behavioral performance and potential curiosity functions. During the movie-playing period, the temporal patterns of the brain states reflected the process of information accumulation, which is consistent with the description of curiosity functions, the improvement of knowledge (Kidd and Hayden 2015; Gruber and Ranganath 2019; Kobayashi and Hsu 2019). Before the decision, the temporal patterns indicated an enhancement for the novel target selection, consistent with previous findings that curiosity needs executive control (Gottlieb et al. 2014; Foley et al. 2017; Lau et al. 2020). At the time of target selection, the uncertainty of selected target information decreases with training, reflected by the weak coupling patterns. Since curiosity improves learning and optimizes learning experience (Berlyne 1954, 1960, 1978; Berlyne and Slater 1957; Loewenstein 1994; Kidd and Hayden 2015; Oudeyer and Smith 2016), it is reasonable to consider that the novel target and its related information seeking became goal directed when animals were familiar with the task. Such possibility is also supported by many studies, which might benefit the learner by enhancing the coding and retention of new information (Kinney and Kagan 1976; Kang et al. 2009; Kidd and Hayden 2015; Oudeyer and Smith 2016). By examining the brain state, we identified several key brain regions supporting the potential cognitive processes underlying curiosity. The hippocampus has been regarded as an indispensable component of curiosity in previous human and theoretical studies (Kang et al. 2009; Gruber et al. 2014; Gruber and Ranganath 2019). Curiosity also recruited the dorsolateral prefrontal cortex (A46D) and the peristriate visual cortex (A19DI), two central regions for attention (Gottlieb and Oudeyer 2018; Kobayashi et al. 2019) that ensure efficient information seeking (Kinney and Kagan 1976). Curiosity is also linked with the emotional state (Loewenstein 1994; Kang et al. 2009). Consistently, we found that the subgenual cingulate cortex (A25), an area involving autonomic and emotional control (Joyce and Barbas 2018), contributed significantly to the brain state. Finally, the most prominent area associated with curiosity-driven behavior is the cerebellum. By employing ROI-based analysis, we further revealed the subregions of cerebellum that contribute more to the behavior-most-correlated states, especially the Crus I/II and lobules IX and IV. Since there are few studies on the marmoset cerebellum, we explored the subregions functions based on comparative evidence from humans and macaques. In these species, Crus I, Crus II, and the lobule IX correspond to the first, second, and third nonmotor representative areas of the cerebellum, respectively (Guell et al. 2018; King et al. 2019), and anatomically communicate with forebrain structures, including the areas we found here (Strick 1985; Dum and Strick 2003; Strick et al. 2009; Bernard et al. 2014; Proville et al. 2014). In addition, recent studies even revealed that the inactivation of the Crus I only disrupted the proportion of correct choices without disrupting the ability to make a choice (Deverett et al. 2018), as well as the loss of prior knowledge (Deverett et al. 2019). Besides these high cognitive functions, the lobule IV of the anterior cerebellum is mainly associated with sensorimotor control and learning (Kelly and Strick 2003). The involvement of these subregions indicates that multiple nonmotor functions in the cerebellum are involved in curiosity-related behaviors. Although we have identified temporal and spatial patterns of curiosity-related brain states, caution should be exercised when making direct inferences about how curiosity works. First, similar to the information-seeking neuronal circuit (White et al. 2019; Cervera et al. 2020), curiosity might also involve the regions for the information evaluation and the demand control (effector) to accomplish its functions. The lobule IV of the cerebellum is more likely related to the effector regions due to its associated sensorimotor functions (Kelly and Strick 2003), whereas the other subregions of the cerebellum (Crus I, Crus II, and the lobule IX), the hippocampus, and the cortical regions identified here might relate to information evaluation because of their involvement in multiple high-cognitive functions. Second, the improvement of saccadic metrics suggests that sensorimotor learning may also be involved in our study, as curiosity optimizes the learning experience. However, how curiosity influences the sensorimotor learning is unclear and worthy of future investigations. Finally, we did not detect several other curiosity-related brain areas reported in previous studies, including the subcortical dopamine system, the lateral intraparietal cortex, the orbitofrontal cortex, and the anterior insular regions (Jepma et al. 2012; Gruber et al. 2014; Blanchard et al. 2015; van Lieshout et al. 2018). This discrepancy may be explained by the experimental design, in which we removed the influence of external rewards so that some brain areas may not be strongly activated. Also, our FC analysis only detected the most significant brain regions based on FC. As curiosity enrolls many cognitive processes, our Brain Circuits of Curiosity-Driven Behavior analysis might miss some curiosity-related brain areas working independently. In conclusion, we induced curiosity-relevant behaviors in research-naïve marmosets and examined the associated brain areas by analyzing noninvasive fMRI data and dynamic FC brain states. The brain states’ temporal dynamics and their related key regions unveil an important evidence on the role of curiosity. Although facing several limitations, the current study provides new insights on the brain regions involved in curiosity-driven behavior and warrants further investigations combining neuroimaging, electrophysiology, and pharmacological inactivation approaches to further elucidate the underpinning mechanisms of curiosity. Supplementary Material Supplementary material can be found at Cerebral Cortex online. Funding This research was supported, in part, by the Intramural Research Program of the National Institutes of Health, National Institute of Neurological Disorders and Stroke (ZIA NS003041); the PA Department of Health (SAP #4100083102 to A.C.S.); and Shanghai Municipal Science and Technology Major Project (Grant No. 2018SHZDZX05 to C.L.). Authors’ Contributions X.T. and C.L. designed the research, performed the experiments, analyzed the data, and interpreted the results. X.T., C.L., and A.C.S. wrote and revised the paper. C.L. and A.C.S. supervised the project. Notes The authors thank D. Szczupak and X. (Lisa) Zhang for their assistance in animal handling; C. C. Yen for his assistance in MRI scanning; R. Krauzlis and D. Leopold for the experimental equipment support; Z. Hafed, H. Jiang, L. Wang, and C. Xue for the scientific discussions and suggestions for draft preparation and revision; and the National Institutes of Health Fellows Editorial Board for the editorial assistance. 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