I suspected the latter, but the proof was still hidden in the fMRI data.

Callie gave up whatever she was hunting. Long ago, she had quickly learned how to work door latches. Whether it was from luck or from watching humans, I don’t know, but now, she ran full tilt and jumped to push open the porch door, precisely timing her leap to hit the handle. She blasted into the kitchen with a burst of energy.

She immediately went over to Helen and rested her head on Helen’s thigh.

“Look!” Helen said. “She’s doing the ‘touch’ command.”

“She’s telling you something,” I said.

“She wants food?”

“Yup.”

Helen laughed and gave Callie a morsel from her plate. I am not sure who was more satisfied: Helen for understanding Callie’s intent, or Callie for making Helen do what she wanted.

“I have good news too,” Helen said.

“Really?”

Helen paused for dramatic effect.

“Come on,” Kat said. “Don’t keep us hanging.”

“I got an A on my science test.”

“Yay!”

“That’s awesome,” I said. “I’m very proud of you. You had to work really hard to do that.”

Helen beamed.

Sometimes playing hooky really does pay off.

20

Does My Dog Love Me?

THE FIRST PHASES OF THE Dog Project were coming to an end. Callie had been to the scanner four times and McKenzie three. We had proved that the dogs could hold still enough to obtain high-quality images of their brains. And even more impressively, we had shown that the reward systems of their brains activated in response to the appropriate hand signals. We had finished the first scientific paper and sent it off for publication, which meant that we had some time to reflect on what we found and what we wanted to do next. As far as we knew, we had the only two dogs in the world trained to go into an MRI scanner, and we had proven that the whole crazy idea wasn’t so crazy after all.

The excitement in the lab was electrifying. I had been scanning human brains since fMRI was discovered, but nothing I had experienced in my career matched this intensity. Even the dawn of human brain imaging didn’t match. Perhaps because scientists had been studying the human brain in various ways for over a century, we already knew a lot about how it worked. More often than not, brain imaging tended to confirm what we knew about the human brain and rarely resulted in a sea change in our understanding of the human mind.

But the Dog Project was entirely different.

I felt like Christopher Columbus discovering the New World. The dog’s brain was a great, unexplored continent. We had no idea how the canine brain worked, but we had the tools to figure it out and two subjects ready to assist. All we had to do was step into the unknown and start exploring.

The screensaver on Lisa’s computer was displaying a montage of Sheriff. Sheriff was almost two years old. Lisa had acquired him as a puppy when she graduated from Emory and started working in the lab. He was the first dog she could truly call her own, and she absolutely adored him.

“You really love Sheriff, don’t you?” I commented.

“Of course,” Lisa replied, “and he loves me too.”

Gavin, who had been observing with bemusement, couldn’t resist teasing Lisa about this.

“That depends on what you mean by love.”

Lisa, ever the pragmatist, replied, “Love? I would accept codependency.” She was dead serious. “Look, I think the best you can hope for with humans is to eventually have a relationship where both people are mutually dependent on each other. What’s wrong with that?”

She had caught Gavin uncharacteristically off guard and he had no response. Lisa continued. “So what if Sheriff’s love for me is based on food and belly rubs? He gives back affection and companionship. If most human relationships were that simple, more people would probably be happier.”

“What if we could prove that Sheriff loved you?” I asked.

“You mean more than food and belly rubs?”

Gavin rolled his eyes and said, “That’s impossible.”

Andrew, who had refrained from wading into the debate on love, had been staring intently at his computer screen. “Check this out.”

On the screen was the structural image of Callie’s brain. I had now seen this image a hundred times and knew it better than my own brain. Overlaid was an activation map. We had been looking at pictures like this for weeks and I had become accustomed to seeing the red, orange, and yellow hot spots superimposed on the caudate nucleus—the center of the reward system. But this image was different.

Andrew had digitally warped McKenzie’s brain to match Callie’s. This is a normal step in the analysis of human fMRI data. When we collect data on a large number of subjects, we need a way to compare activation in everyone’s brains. But because every person’s brain is physically different, we use a digital method that morphs each brain into the same size and shape. This allows scientists to average the activation patterns of many individuals and determine the commonalities of brain function.

In humans, brain sizes tend to vary by about only 1 or 2 percent. Some people have round heads while others are more oval-shaped. Even so, the basic anatomy is pretty much the same, and we need to stretch and twist the brains only a little bit to make them all match up.

Dogs are different. Of all the species on the planet, dogs have the largest variations in size. What other species can range in size from a 4-pound Chihuahua to a 150-pound Great Dane and still be considered the same animal? As you might expect, their brain sizes have a similarly large variation.

When we started analyzing the data from the Dog Project, we did it separately for Callie and McKenzie. McKenzie was about 50 percent larger than Callie, so we knew their brains were going to be different. Because of this large variation in size, we didn’t think the usual computer algorithms would work, so we hadn’t even attempted to digitally combine their brains.

Until now.

By carefully identifying key landmarks in the dogs’ brains, Andrew had been able to get them to line up. Once aligned, he was able to perform an analysis on the combined dataset. They say that two heads are better than one, and in this case that was absolutely true. Although both Callie and McKenzie had performed beyond our expectations, they still had their limits. They had each stayed in the MRI for ten minutes of continuous scanning. But ultimately, the noise and confinement wore them down, and they got tired of the task. In the end, Callie had sat through almost forty repetitions of the task and McKenzie about thirty. This was good enough to prove the feasibility of canine fMRI. But to go to the next step, and really start figuring out how the dog brain worked, we needed a lot more repetitions and, ideally, a lot more dogs. Combining the results from Callie and McKenzie was a first step in this direction.

More observations meant more power to detect faint signals in the brain. By merging the datasets of the two dogs, we were now staring at a result on the computer screen that we hadn’t seen when looking at the dogs individually.

Andrew pointed to an area of activation on the side of the brain. This region was about a centimeter higher than the reward system, and it was located in the middle of the cortex. Since the usual landmarks of the human brain didn’t apply, we were left guessing what part of the dog brain we were looking at.

Cross-referencing an atlas of dog brain anatomy, I asked, “Is that the motor cortex?”

Andrew shrugged and said, “It’s in the middle of the cortex, about where the human central sulcus would be.”