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The Revelations of Repetitive Brain Imaging

Imaging the same brain again and again unearths new insights into connectivity and may bring MRI into the process of diagnosing neuropsychiatric illnesses.

Meeri Kim, Contributor
Thu, 11/30/2017


To much of the outside world, a neuroscientist scanning his own brain in the MRI about 150 times during an 18-month stretch may sound eccentric, narcissistic and maybe just a little bit nuts. But to pediatric neurologist Nico Dosenbach, it felt like accumulating this kind of neuroimaging data had snapped the black-and-white world of functional MRI into vibrant color. Dosenbach sees patients regularly and heads a research lab at Washington University in St. Louis that focuses on brain plasticity and improving MRI data quality. The glaring flaws of many functional neuroimaging studies had always bothered him — low signal-to-noise ratio, lack of replicability and loss of information due to group-level averaging.

“The signal-to-noise ratio was so bad that you couldn’t scan someone once and do anything with the data,” said Dosenbach. “The solution was to average many people and look at these central group tendencies, which a lot of people in cognitive psychology were happy with, but it always seemed messy and unsatisfying to me.”

Four years ago, he saw parts of a now-legendary massive MRI data set from Russell Poldrack — a leader in the field of neuroimaging and cognitive neuroscience who, for a year and a half between 2012 and 2014, had begun every Tuesday and Thursday morning with a 10-minute brain scan. The huge amount of sampling in a single person allowed for fine-grained mapping of human functional neuroanatomy without group-level averaging. His became the most studied brain in the world, and the ambitious experiment produced the most detailed connectivity map the field had ever seen.

Galvanized by Poldrack's radical efforts, Dosenbach immediately decided to pursue a similar research path. As a clinician, he felt strongly that characterizing individual brains with dense sampling could be the key to finally using functional MRI, known as fMRI, as a diagnostic tool for neuropsychiatric illness — something that had been promised since its invention but never realized.

Dosenbach is one of a growing number of researchers using fMRI who have begun to look more closely at connectivity features that vary from one person to the next. This novel approach moves away from the traditional search for common ground within a large group and hints at the beginning of a paradigm shift in the field.

Looking beyond the average

Focusing on individual differences has led to findings that wouldn't have been uncovered using the traditional approach. As an example, Dosenbach and nine of his colleagues — all junior faculty or graduate students — began to collect densely sampled fMRI data on themselves back in 2013. They measured functional connectivity, or the synchrony of activity across brain regions, during rest and task activation during motor/memory testing for a total of 10 hours of scan time over 12 sessions.

Their study showed that for a given individual, there is a clear alignment between activation patterns for a task and the resting state connectivity boundaries, which distinguish regions for which connectivity structure is particularly similar. However, these same task activation patterns did not stay within the boundaries of resting state connectivity for the group averaged data. The group published their results in Neuron earlier this year.

“Group averaging has told us a lot and been valuable, but fMRI is a pretty young technique, and I think we're outgrowing that way of doing studies,” said Emily Finn, a postdoctoral fellow at the National Institute of Mental Health in Bethesda, Maryland. “People are realizing that we haven't produced anything useful for the clinic and wondering how we can drill down deeper into individual data.”

Structural MRI of the brain, on the other hand, has proven invaluable for both research and clinical practice. High spatial resolution and signal-to-noise ratio contribute to excellent image quality from a single subject scan. Anatomical details and contrast between gray and white matter from T1- and T2-weighted sequences are used daily to make critical decisions in neurology and neurosurgery.

So why can't fMRI compete with its structural counterpart in this respect? The problem goes back to its woefully low signal-to-noise ratio. The majority of fMRI studies record changes in the blood oxygen level-dependent signal, which measures inhomogeneities in the magnetic field due to changes in blood oxygenation level and is used as a proxy for neural activity. In addition to only being a relative measure and an indirect marker of brain activity, they are a weak measure. Changes in the blood oxygen signal with a cognitive task can be less than 2 percent.

Averaging over many subjects and trials transformed the noisy, chaotic data into a cleaner response. However, this approach also meant that certain individual features would effectively get averaged out. Even though researchers were learning more about the overall brain, a single patient with an unknown psychiatric illness in need of a diagnosis was still out of luck when it came to fMRI.

“There was a big promise when fMRI was invented that we would have more knowledge to diagnose neuropsychiatric disease, and that hasn't quite happened yet,” said Julien Dubois, a postdoctoral researcher in the Neurosurgery Department at Cedars-Sinai Medical Center in Los Angeles. “But this is really the goal — to build a diagnostic tool with fMRI. That is the main motivation for finding biomarkers and individual signatures of disease, or even variability in the neurotypical population.”

A unique morning routine

Before having the most studied brain in the world, Poldrack remembered what a collaborator in psychiatry told him once: People with mental health disorders have highly variable brain function over time. He realized that, for those patients, a simple one-and-done fMRI scan to characterize their brain activity wouldn't make sense. Perhaps even neurotypical individuals wouldn't have consistent data from month to month, or day to day.

As a first step, he aimed to understand the volatility of the healthy brain with resting state fMRI. Partially inspired by the quantified self movement, which is driven by technology-enabled gathering of reams of personal biometric data to better understand one's body, Poldrack embarked on a yearslong odyssey of exploring the depths of his own brain.

“I would be in the scanner Tuesday and Thursday mornings, and once I developed a routine, it wasn't bad,” said Poldrack. “I'm not sure a lot of my scientific colleagues really got it. Most probably thought it would tell us something interesting in the long run, but I'm sure some of them thought I was crazy.”

He admits it was a bit of a fishing expedition in that he didn't know what to expect as a result. But through the 84 resting state sessions, Poldrack and his colleagues learned that sources of within-subject variability in functional connectivity differ from sources of between-subject variability. Over the course of the study, Poldrack's visual, somatomotor, and some dorsal attention regions proved to be more volatile than other regions of his brain. On the other hand, the visual and somatomotor regions had less variability across 120 subjects than default mode, attentional, and control network regions.

The experiment in dense sampling also proved that measures of individual functional brain organization can be reliable with sufficient data. For instance, 9 minutes of scan time generated respectable reproducibility of correlation network estimates, but as scan time increased, quality continued to go up until it finally converged with the true correlation matrix at 100 minutes. Previous resting state studies commonly collected only 5 to 10 minutes of data.

“This work has pushed some people to think there’s no utility in doing studies that don’t do dense sampling. I haven’t gone that far, but I think it points us to where we can do a lot better,” said Poldrack. “All the studies we’re doing now involve dense sampling — at least twice as much data, and in some cases, several orders of magnitude more data than we used to collect.”

Carpe noctem -- seize the night

About midway through the project, Poldrack had given his collaborators at Washington University some of his data to work with — which is when Dosenbach became hooked on the concept of dense sampling. But as a new faculty member, he found himself lacking the necessary funds to book the dozens of hours needed for the study in the university's research scanner.

“I only had about $10,000 in funding, and the scanner cost $600 per hour — but we had a eureka moment when my friend told me that if we use the scanner after midnight, there’s a 90 percent discount,” Dosenbach said. “I slapped him on the back and said, 'We’re doing this.'”

Dosenbach and his research partner Steve Nelson, a former psychologist at Washington University, decided to start a nightly gathering called the Midnight Scan Club. A group of junior faculty and graduate students would meet at the campus beer hall, make their way to the lab, and take turns either running the scanner or getting scanned.

“I changed my daily routine by staying up later and later, and then I would nap at night from 9 to 11 p.m., so that midnight was a good time for me to be in the scanner,” said Dosenbach.


Image shows a 3-D image of a skull and jaw, which display folds so as to look as if the entire head is actually the brain, placed atop a black background with the caption "Midnight Scan Club."

Image courtesy of Nico Dosenbach

Soon others from the university's neuroscience community were offering their time to become part of the after-hours society. They even designed an official Midnight Scan Club logo (a brain in the shape of a skull), made T-shirts, and decided on a motto: “Carpe noctem,” or “Seize the night.”

In the end, they collected more than 10 hours of resting state and task fMRI on each of the 10 individuals in their mid-20s to mid-30s, all with advanced degrees. Even after performing co-registration on the group to match their different brain areas into a single coordinate system, pieces of the same brain network varied anatomically across individuals. These small, spatially variable network pieces — which may be functionally significant — would likely disappear with group averaging.

“None of the 10 individual brain networks looked like the group average, which is like a super blurred-out version,” he said. “It's as if you took a bunch of photographs of faces, lined up people’s features, and averaged them all — it would look like a cartoon face. But if you look [at] each individual face, they're all different.”

Also, resting state functional connectivity measures remained highly subject-specific. Many more similarities were seen among sessions from the same person rather than across subjects, which is the idea behind a second approach to studying individual differences with fMRI.

The connectome fingerprint

Functional connectome fingerprinting takes advantage of each human brain's unique organization. A person's functional connectivity profile, assembled by measuring the correlation of activity across two or more regions, can be thought of as a fingerprint. But while the loops and arches of an actual fingerprint don't correspond to anything meaningful, the variable features of connectivity in the brain have been linked to behavioral traits like fluid intelligence and sustained attention.

A 2015 study published in Nature Neuroscience confirmed that this fingerprint could indeed pick out an individual from a crowd. The researchers used data from 126 healthy individuals scanned for 6 sessions each for the Human Connectome Project, a project to map the neural pathways that underlie human brain function. Connectivity profiles were created for every subject — basically, a large 268-by-268 matrix with correlations between the timecourses of each possible pair of 268 nodes that cover the brain.

This connectome fingerprint could successfully identify subjects from the group across scan sessions and even during resting and task-based conditions. The researchers achieved an average identification accuracy of 93 percent between the pair of resting-state scans, while task-task comparison was a bit lower at 87 percent.

“We found that the thing that accounts for the most variance in functional brain activation is who you are, not what you are doing while you're being scanned,” said study author Finn, from the National Institute of Mental Health. “We get this gestalt signature for how the brain is organized, which does change a little bit during a task, but it doesn't change so much that you don't look like yourself.”

From this model, Finn and her colleagues could also predict fluid intelligence scores and the ability to sustain attention in a previously unseen group of subjects. They also applied the model to children with attention deficit hyperactivity disorder, finding that those predicted to have a higher ability to sustain attention tended to have fewer symptoms of attention dysfunction.

Moving towards fMRI for the clinic

“In the past couple of years, there have been good studies with large enough sample sizes that have shown that there is some promise to using resting state for diagnosis,” said Cedars-Sinai's Dubois. “But one thing is still missing: If you want to establish the diagnostic value of a measure, you need to look at both the sensitivity and specificity.”

Dubois, who investigates the neural basis of IQ and personality, notes that there are significant challenges to face before fMRI can be truly used as a diagnostic tool. Collecting longitudinal data sets on a single person is expensive, time-consuming and not always feasible for those with neuropsychiatric diseases.

“For my psychiatric patients, I’ll order no lab tests or neuroimaging,” said Theodore Satterthwaite, a psychiatrist at the University of Pennsylvania. “But this method of dense sampling probably isn’t good for clinical populations because it requires you to get a lot of data. Researchers can sit in the scanner and get these large data sets, but my patient with bipolar disorder isn’t going to do it.”

Satterthwaite believes the connectome fingerprinting approach might work better for his patients, at least until fMRI hardware and software get to the point where today's definition of densely sampled data can be condensed into an everyday 30-minute scan. And although the technology continues to improve, experts in the field believe it will take many years of research before fMRI becomes a standard tool to guide treatment.

“There’s a ton of interest in precision psychiatry and using imaging to tailor drug treatment for psychiatric disorders, but I think we’re really far from being in the clinic,” said Poldrack. “We have a long way to go.”

The movement to look for individual differences with fMRI that Poldrack helped pioneer remains in early stages — and appears to be gaining momentum. Dosenbach says more groups have started to dip their toes in the water, including those at Harvard, New York University, INSERM in France, and the Max Planck Institute in Germany. Researchers from other institutions have even contacted him about creating their own local chapter of the Midnight Scan Club.

“Most MRI machines just sit idle at night. It’s this giant resource that's available, so I told people to go for it,” Dosenbach said. “If we’re ever going to help patients, the only way is for us to get more and better data.”