Because of dogs' prolonged evolution with humans, many of the canine cognitive skills are thought to represent a selection of traits that make dogs particularly sensitive to human cues. But how does the dog mind actually work? To develop a methodology to answer this question, we trained two dogs to remain motionless for the duration required to collect quality fMRI images by using positive reinforcement without sedation or physical restraints. The task was designed to determine which brain circuits differentially respond to human hand signals denoting the presence or absence of a food reward. Head motion within trials was less than 1 mm. Consistent with prior reinforcement learning literature, we observed caudate activation in both dogs in response to the hand signal denoting reward versus no-reward.

Competing interests: The authors have read the journal's policy and have the following conflicts: Mark Spivak is the president of Comprehensive Pet Therapy (CPT). He supervised all training procedures without compensation and contributed concepts to the design and performance of the experiment. This does not alter the authors' adherence to all the PLoS ONE policies on sharing data and materials.

Because of their prolonged evolution with humans, many of the canine cognitive skills are thought to represent a selection of traits that make dogs particularly sensitive to human cues [16] . For this reason, we selected a simple discrimination task with two human hand signals for initial study with canine fMRI. Although there is growing evidence that dogs do not need to be conditioned to learn human hand signals, for this first experiment we chose to associate the hand signals with primary rewards to provide a linkage with comparable imaging experiments in both humans and monkeys and to maximize the chance of observing a significant brain response. Importantly, the reward-prediction error hypothesis of the dopamine system provides a concrete prediction of activity in the ventral caudate of the dog. The task was designed to determine which brain circuits differentially respond to hand signals denoting the presence or absence of a food reward. Based on the reinforcement learning literature, we hypothesized that the transfer of reward association to a hand signal would manifest in the ventral striatum [17] , [18] , [19] , [20] , [21] .

The main challenge of fMRI in dogs comes from subject motion. Historically, the usual approach has been to either anesthetize the animal [8] , [9] or, as in rats and monkeys, immobilize them [10] , [11] , [12] , [13] , [14] , [15] . Clearly, if we wish to understand canine cognition, anesthesia is not an option. Immobilization is technically possible, although ethically objectionable for a dog, and, as we show, unnecessary to acquire useful fMRI data. Instead, because dogs so readily follow human commands, they can be trained to go into an MRI scanner and hold their head still enough for fMRI studies without any restraint. Moreover, they will do this happily with nothing more than positive reinforcement.

As the oldest domesticated species, with estimates ranging from 9,000–30,000 years BCE, the minds of dogs inevitably have been shaped by millennia of contact with humans [1] , [2] . As a result of this physical and social evolution, dogs, more than any other species, have acquired the ability to understand and communicate with humans. A resurgence of research in canine cognition has revealed the range (and variability) of skills such as following pointing and gaze cues [3] , [4] , [5] , fast mapping of novel words [6] , and the conjecture that dogs have emotions [7] . Although the growing list of canine cognitive skills is impressive, how does the dog mind actually work? We are left to infer canine brain function from behavior and ultimately guess at the inner workings of the dog brain. However, the widespread use of functional magnetic resonance imaging (fMRI) to study brain function in both humans and non-human primates has paved the way for potentially understanding how the dog brain works. Here, we report the development of behavioral and technical methods to acquire fMRI data in fully awake, unrestrained dogs.

Results

Subjects were two spayed, female, domesticated dogs. Callie was a 2 year-old feist of indeterminate pedigree, who had been adopted from a local shelter at the age of 9 months and weighed 12 kg. Apart from basic obedience, she had no specialized training. McKenzie was a 3 year-old border collie and was already well-trained in agility competition and weighed 16 kg. Training and handling for the following procedures were performed by each dog's owner under the supervision of a professional trainer. This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All procedures were approved by the Institutional Animal Care and Use Committee of Emory University (Protocol Number: DAR-2001274-120814).

Three fMRI scanning sessions were performed over a period of 6 weeks. Callie participated in all sessions, while McKenzie participated in the last two. The goal of the first session was to familiarize the Callie with the scanner environment and determine the feasibility of acquiring both structural and fMRI data. The goal of the second session was to optimize the scan parameters and to obtain enough fMRI data to evaluate its quality for movement-related artifacts. It was observed that the onset of each imaging sequence tended to startle the dogs, causing them to move or exit the scanner. This was effectively mitigated in the final session by playing recordings of the scanner noise through the intercom while the dog got settled into the chin rest. The preceding protocol encouraged habituation to the scanner noise and eliminated startle reactions. In the third and final session, the onset was not startling and the dogs didn't move severely when the actual sequence started. This approach allowed us to obtain functional runs long enough for fMRI analysis as well as a high quality structural image.

For the final scanning session, we used a simple instrumental conditioning task in which the required behavior was to place the head on the chin rest and not move (Fig. 1). After a variable interval of approximately 5 s, a hand signal was given that indicated the presence or absence of a food reward that would be received. The left hand up indicated a hot dog reward, while both hands pointing toward each other horizontally indicated no reward. The hand signals were chosen to be easily distinguishable and were maintained for approximately 10 s. The dog had to continue holding still during this period. Dogs had been amply trained on these hand signals in the simulator prior to the final scan session. Because the dogs had been trained to go into the head coil in a “sphinx” position (Fig. 1), the handler gave the hand signals from the head end of the scanner, facing the dog. Trial types were approximately random and alternating (but not predictably) such that we had an approximately equal number of both trial types.

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larger image TIFF original image Download: Figure 1. Training and task for dogs in the MRI scanner. (A) Callie in the training apparatus, which consisted of a replica of the head coil inside a tube of the approximate diameter of the MRI bore. Consistent positioning of the head was achieved by training the dog to place her head in a chin rest molded to the lower jaw from mid-snout to behind the mandible. The chin rest was affixed to a wood shelf that spanned the head coil but allowed enough space for the paws underneath. No restraints were used. The training procedure gradually shaped the desired behavior of placing the head in the rest and not moving through positive reinforcement only. Dogs were free to exit the apparatus at any time. (B) McKenzie inside the real head coil in the MRI. Her handler is giving a hand signal that denotes upcoming “reward.” We used a simple instrumental conditioning task in which the required behavior was to place the head on the chin rest and not move. After a variable interval of approximately 5 s, a hand signal was given that indicated whether a reward would be delivered. The dog had to continue holding still during this period to get the reward. The left hand up indicated a hot dog reward, while both hands pointing toward each other horizontally indicated no-reward. The hand signals were maintained for approximately 10 s. Reward-trials ended by the handler reaching in with the food to the dog. Person in the photograph has given written informed consent for publication. https://doi.org/10.1371/journal.pone.0038027.g001

FMRI data were acquired on a Siemens 3 T Trio. We used a single-channel transmit-receive head coil because of its large size and ability to accommodate the dog in the sphinx position. The chin rest was constructed to fit inside the coil. First, a single sagittal plane image was acquired as a localizer, which lasted 3 s. For functional scans, we used single-shot echo-planar imaging (EPI) to acquire volumes of 28 sequential 3 mm slices with a 10% gap (TE = 28 ms, TR = 1610 ms, flip angle = 70°, 64×64 matrix, FOV = 192 mm). Slices were oriented dorsally to the dog's brain (coronal to the magnet because the dog was positioned 90° from the usual human orientation) with the phase-encoding direction left-to-right (Fig. S1). For each dog, two runs of 190 volumes were acquired, each lasting 5 minutes, during which the reward/no-reward task was performed. For Callie, this yielded a total of 19 reward trials and 20 no-reward, and 16 reward and 11 no-reward trials for McKenzie (but of longer duration). After the functional runs, a T2-weighted structural image was acquired with a turbo spin-echo sequence (30 3 mm slices, TR = 3710, TE = 8.3, 26 echo trains), which lasted 24 s. This sequence was optimized to yield good contrast between grey and white matter in the fastest time possible. The dog was required to hold still for the entire 24 s, after which she was rewarded.

Data were processed with AFNI. Because the dogs exited the scanner between runs, head positioning was slightly different. Using fiducial markers on the brain, we roughly aligned the second run to the first. Next, we used a two-pass motion correction to complete the alignment and generate measurements of movement within each run (Fig. 2A). Because many trials ended with a food reward, the dog moved her head while consuming the treat, but once consumed, she placed her head back in the chin rest. Movement and loss-of-shim artifacts were expected during this period. A large field of view guaranteed that the entire brain was captured regardless of the exact trial-to-trial position. Activation time series were examined and censored for artifacts through a multistep process [22]. Volumes with obvious movement were excluded and the remaining volumes used to calculate percent signal change on a voxelwise basis. We then excluded any volume in which the signal, averaged over the whole brain, changed by more than 1% from the previous scan. Finally, the sequence of scans was examined in a movie loop and any remaining scans that exhibited sudden movements were excluded. This resulted in the retention of 236 out 380 volumes for Callie (62%) and 222 volumes for McKenzie (58%). Although the inter-trial movements were large compared to humans, once set in the chin rest, the dogs were comparable, if not better, than humans. The average total translation within each trial was less than 1 mm (Fig. 2B). The results of motion correction were checked by scrolling through time in AFNI to confirm that the brain remained in the same position throughout the retained scans (see Movies S2 and S3). Despite the fact that the dogs went in and out of the field, after motion correction the brain was observed to stay in the same position within a voxel. To account for any remaining variance due to misalignment and to improve the signal to noise ratio, scans were then smoothed with a 6 mm gaussian kernel.

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larger image TIFF original image Download: Figure 2. Motion during canine fMRI. (A) Timeseries of translations required to correct for motion during the scan sessions. Volume 32 was the target for Callie, and volume 1 was the target for McKenzie. The plots therefore represent the total movement from the target volume. The spikes and breaks occurred when the dog moved its head out of the field of view, which typically happened following a reward. The volumes with artifacts were excluded from further analysis, leaving 62% of the volumes for Callie and 58% for McKenzie. Although the dogs did not place their heads back in exactly the same position, once they did, very little motion was observed. McKenzie exhibited a slow anterior-posterior drift during the second run, but this was sufficiently slow as to not cause movement artifacts during trials. (B) Average motion during a trial, separated by reward and no-reward conditions and after exclusion of volumes with artifacts. Scan volumes are 1610 ms apart. Notably, within-trial motion was less than 1 mm in all directions for both dogs, and no difference between the reward and no-reward conditions was observed. https://doi.org/10.1371/journal.pone.0038027.g002

Key events of each trial were marked by an observer with button presses and logged to a computer capturing scanner pulses. These events were used to formulate a GLM for analysis of the fMRI data: 1) reward hand signal; 2) no-reward hand signal; 3) and reward. The hand signals were specified as variable duration events, while the reward was specified as an impulse. All events were convolved with a standard hemodynamic response function. The design matrix also included constants and linear drifts for each run, and the six motion parameters. A censor file specified the volumes to be excluded from the regression.

Because of the weight of evidence implicating the ventral striatum in reward-prediction error learning, we focused our analysis on the head of the caudate. In the dog, the caudate is located ventral to the genu of the corpus callosum, between the olfactory peduncle and anterior limb of the internal capsule [23], [24], [25]. The latter is easily identified on T2-weighted images as two dark diagonal lines (Fig. S2, S3, S4, S5). The contrast of reward versus no-reward hand signals revealed a significant cluster of activation in the region of the right caudate of both dogs (Fig. 3). With the entire extent of activation displayed in all slices, and referenced to the corresponding slices of the T2 image, it is clear that these clusters are very close to, if not exactly on, the caudate (Figs. S2, S3, S4, S5). Although the statistical significance of the caudate cluster was modest in each dog individually (p<0.01 in Callie, and p<0.001 in McKenzie), the observation of the same location in the same condition in both dogs, and in the hypothesized region, strongly suggests that these were not spurious findings. The average trial responses to the hand signals showed a distinct hemodynamic response to the reward signal but not the no-reward signal, which would be expected for the association of reward to one signal but not the other (Fig. 3). When the datasets of both dogs were combined by spatial warping, activation of the caudate cluster was significant at p<0.05 after correcting for FDR over the search volume of the ventral brain from olfactory bulb to internal capsule (p<0.01 height and cluster extent>6).