Brain Reveals New Relations Between Brain and Body

Stanford psychologist’s 18-month study of his own brain reveals new relations between brain and body

Russell Poldrack scanned his brain to create the most detailed map of brain connectivity ever. In the process, he and his colleagues revealed strong correlations between brain function and gene expression, and how the brain reorganizes itself when running low on caffeine.

Scientia — Every Tuesday and Thursday morning for a year and a half, Russell Poldrack started off his day by climbing into an MRI machine and scanning his brain for 10 minutes. The self-experimentation has made the Stanford psychologist’s brain the most studied in the world.

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Professor Poldrack, who began the work at the University of Texas and has continued it at Stanford, committed himself to the long-term study in order to expand the understanding of how different parts of the brain talk to each other, an organization known as the connectome, and how that behavior changes over time.

For 18 months, Stanford psychologist Russell Poldrack scanned his brain activity twice a week to understand how functional areas of his brain communicate.

To bolster the results, Poldrack also fasted and drew blood on Tuesdays, which was analyzed to draw connections between brain function and gene expression. This step also showed definitive – and surprising – evidence of just how the brain reorganizes itself when it’s low on coffee.

The results are published in Nature Communications.

Mapping brain communication

In any action that a person undertakes, many different regions of the brain communicate with each other, serving as a sort of check-and-balance system to make sure that the correct actions are taken to deal with the situation at hand. These messages are communicated over more than a dozen networks, sets of functional areas of the brain that preferentially talk to one another.

There are multiple networks for vision, a somatosensory/motor network, and there are others that are attributed to attention or task management. Collectively, these are known as the connectome. Because the strength or efficiency of these individual networks can affect behavior, they have become of greater interest to researchers in recent years.

To isolate these connections, researchers examine functional MRI data collected while the patient (including Poldrack) is at rest.

“I would get in the MRI and basically close my eyes and zone out while it took a picture of my brain every second for 10 minutes,” he said. “Once we had that data, we could get ideas of which regions of my brain are talking to each other by how correlated they are over time. This tells us how much connectivity there is within each network.”

Previously produced connectome maps have consisted of snapshots of individual brains, averaged together to identify general networks between different functional areas of the brain. Poldrack was interested in learning if these connections hold up for the same person over months.

Do they change depending on mood or other environmental influencers? And, if the connections do change, how much would his connectome look like someone else’s? Would his activity on a “sad” day look like that of a depressed person’s?

Poldrack’s connectivity was surprisingly consistent, but it did show some changes over the course of the 18 months which have never before been observed. While this result raises many new questions, the robust consistency showed that the long-term approach has promise for revealing differences between healthy brains and those of patients with neurological disorders that might suffer from disrupted connectivity, such as schizophrenia or bipolar disorder.

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The coffee effect

An obvious piggyback experiment to an 18-month-long study is to examine changes in gene expression. On Tuesdays, Poldrack fasted and gave a blood sample after his scanning session. The RNA from his white blood cells was sequenced to determine his gene expression, which was then compared to his brain function. The researchers found a strong correlation between brain activity and changes in the expression of many different families of genes. They also found that expression of genes related to inflammation and immune response matched Poldrack’s psoriasis flare-ups. Because the data set is so massive, there are still many questions to ask, which is why Poldrack is making all of the data publicly available.

Russell Poldrack
Lisa DeNeffe Photography

Fasting for the blood draw also revealed a surprising bonus result: “Easily the biggest factor we found in terms of affecting my brain connectivity was whether I had had breakfast and caffeine or not,” Poldrack said.

On Tuesdays, when he hadn’t drunk his morning joe before the scan, the connectivity within his brain looked very different from his caffeinated brain. Anecdotally, this makes sense. But such an influence had never been observed before. In particular, the connection between the somatosensory motor network and the systems responsible for higher vision grew significantly tighter without caffeine.

“That was totally unexpected, but it shows that being caffeinated radically changes the connectivity of your brain,” Poldrack said. “We don’t really know if it’s better or worse, but it’s interesting that these are relatively low-level areas. It may well be that I’m more fatigued on those days, and that drives the brain into this state that’s focused on integrating those basic processes more.”

The signal and the noise

The results are evidence that long-term analysis is a useful tool, Poldrack said, and hopefully make it attractive to a larger population of subjects. In many ways he was an ideal subject – he was available, committed to the long haul, and could sit still in the scanner – but diversity will ultimately lead to more interesting discoveries.

“I’m generally a pretty happy and even-keeled person,” he said. “My positive mood is almost always high, and my negative mood is almost always non-existent. It would be interesting to scan people with a wider emotional variation and see how their connections look over time.”

In the meantime, Poldrack and his colleagues have made the entire data set and the ready-built tools to analyze it available through this site: http://myconnectome.org/wp/data-sharing. The data set is so large and deep, Poldrack said, that he hopes people will approach it from innovative angles and uncover connections that will help advance the research.

Poldrack, meanwhile, plans to hone software to elucidate the interplay between brain function and gene expression.

“It’s a hard data set to know what to do with, because it’s hard to tell if something is noise or if it’s real with just one person. But there’s potentially some really interesting stuff here,” he said. “There are a ton of relationships between brain connectivity and gene expression in the blood, that are clearly there and seem to be strong, but we just don’t have a way to understand them based on current neuroscience.”

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– Credit and Resource –

Written by: By Bjorn Carey

Researched conducted by: Russell Poldrack

Provided by: Stanford University

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Brain Reveals New Relations Between Brain and Body

Personalized heart models for surgical planning

System can convert MRI scans into 3D-printed, physical models in a few hours.

Scientia — Researchers at MIT and Boston Children’s Hospital have developed a system that can take MRI scans of a patient’s heart and, in a matter of hours, convert them into a tangible, physical model that surgeons can use to plan surgery.

New system from MIT and Boston Children’s Hospital researchers converts MRI scans into 3D-printed heart models (shown here).
Photo: Bryce Vickmark

The models could provide a more intuitive way for surgeons to assess and prepare for the anatomical idiosyncrasies of individual patients. “Our collaborators are convinced that this will make a difference,” says Polina Golland, a professor of electrical engineering and computer science at MIT, who led the project. “The phrase I heard is that ‘surgeons see with their hands,’ that the perception is in the touch.”

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This fall, seven cardiac surgeons at Boston Children’s Hospital will participate in a study intended to evaluate the models’ usefulness.

Golland and her colleagues will describe their new system at the International Conference on Medical Image Computing and Computer Assisted Intervention in October. Danielle Pace, an MIT graduate student in electrical engineering and computer science, is first author on the paper and spearheaded the development of the software that analyzes the MRI scans. Mehdi Moghari, a physicist at Boston Children’s Hospital, developed new procedures that increase the precision of MRI scans tenfold, and Andrew Powell, a cardiologist at the hospital, leads the project’s clinical work.

The work was funded by both Boston Children’s Hospital and by Harvard Catalyst, a consortium aimed at rapidly moving scientific innovation into the clinic.

MRI data consist of a series of cross sections of a three-dimensional object. Like a black-and-white photograph, each cross section has regions of dark and light, and the boundaries between those regions may indicate the edges of anatomical structures. Then again, they may not.

Determining the boundaries between distinct objects in an image is one of the central problems in computer vision, known as “image segmentation.” But general-purpose image-segmentation algorithms aren’t reliable enough to produce the very precise models that surgical planning requires.

New system from MIT and Boston Children’s Hospital researchers converts MRI scans into 3D-printed heart models (shown here).
Photo: Bryce Vickmark

Human factors

Typically, the way to make an image-segmentation algorithm more precise is to augment it with a generic model of the object to be segmented. Human hearts, for instance, have chambers and blood vessels that are usually in roughly the same places relative to each other. That anatomical consistency could give a segmentation algorithm a way to weed out improbable conclusions about object boundaries.

The problem with that approach is that many of the cardiac patients at Boston Children’s Hospital require surgery precisely because the anatomy of their hearts is irregular. Inferences from a generic model could obscure the very features that matter most to the surgeon.

In the past, researchers have produced printable models of the heart by manually indicating boundaries in MRI scans. But with the 200 or so cross sections in one of Moghari’s high-precision scans, that process can take eight to 10 hours.

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“They want to bring the kids in for scanning and spend probably a day or two doing planning of how exactly they’re going to operate,” Golland says. “If it takes another day just to process the images, it becomes unwieldy.”

Pace and Golland’s solution was to ask a human expert to identify boundaries in a few of the cross sections and allow algorithms to take over from there. Their strongest results came when they asked the expert to segment only a small patch —one-ninth of the total area — of each cross section.

New system from MIT and Boston Children’s Hospital researchers converts MRI scans into 3D-printed heart models (shown here).
Photo: Bryce Vickmark

In that case, segmenting just 14 patches and letting the algorithm infer the rest yielded 90 percent agreement with expert segmentation of the entire collection of 200 cross sections. Human segmentation of just three patches yielded 80 percent agreement.

“I think that if somebody told me that I could segment the whole heart from eight slices out of 200, I would not have believed them,” Golland says. “It was a surprise to us.”

Together, human segmentation of sample patches and the algorithmic generation of a digital, 3-D heart model takes about an hour. The 3-D-printing process takes a couple of hours more.

Prognosis

Currently, the algorithm examines patches of unsegmented cross sections and looks for similar features in the nearest segmented cross sections. But Golland believes that its performance might be improved if it also examined patches that ran obliquely across several cross sections. This and other variations on the algorithm are the subject of ongoing research.

The clinical study in the fall will involve MRIs from 10 patients who have already received treatment at Boston Children’s Hospital. Each of seven surgeons will be given data on all 10 patients — some, probably, more than once. That data will include the raw MRI scans and, on a randomized basis, either a physical model or a computerized 3-D model, based, again at random, on either human segmentations or algorithmic segmentations.

Using that data, the surgeons will draw up surgical plans, which will be compared with documentation of the interventions that were performed on each of the patients. The hope is that the study will shed light on whether 3-D-printed physical models can actually improve surgical outcomes.

“Absolutely, a 3-D model would indeed help,” says Sitaram Emani, a cardiac surgeon at Boston Children’s Hospital who is not a co-author on the new paper. “We have used this type of model in a few patients, and in fact performed ‘virtual surgery’ on the heart to simulate real conditions. Doing this really helped with the real surgery in terms of reducing the amount of time spent examining the heart and performing the repair.”

“I think having this will also reduce the incidence of residual lesions — imperfections in repair — by allowing us to simulate and plan the size and shape of patches to be used,” Emani adds. “Ultimately, 3D-printed patches based upon the model will allow us to tailor prosthesis to patient.”

“Finally, having this immensely simplifies discussions with families, who find the anatomy confusing,” Emani says. “This gives them a better visual, and many patients and families have commented on how this empowers them to understand their condition better.”

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– Credit and Resource –

Larry Hardesty | MIT News Office

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Personalized heart models for surgical planning