Behind the paper: neuronal activity can drive cerebrospinal fluid flow in the human brain

In this ‘behind the paper’ post, Stephanie Williams discusses how the new equipment, techniques and methods developed in her lab helped them find out what drives cerebrospinal fluid flow in the human brain.

I am a 3^rd year Psychology PhD student in the Lewis Lab, which is a human brain imaging lab in the departments of Electrical Engineering and Computer Science at Boston University. One of the great advantages to doing psychology research within an engineering environment is having access to the latest brain imaging methods! My labmates are always working on new hardware and software projects that increase the quality of brain imaging data we can collect, which in turn broadens the type of questions we can ask using concepts from cognitive psychology. Some of the tools that Lewis lab members are developing help with post-processing of brain imaging data, while other hardware projects actually improve incoming data as it is collected. For example, some of my labmates have built devices to improve the way electroencephalography data is collected inside the scanner, and are collaborating with scientists at the Athinoula A. Martinos Center to test new equipment to help us collect cleaner signals.

One of these novel imaging methods, developed by Dr. Lewis in 2019, actually served as the inspiration for the current project! Dr. Lewis and her team developed a new, non-invasive way to measure cerebrospinal fluid (CSF) flow in the human brain. Previously, it had been possible to measure fluid flow in the human brain but it was typically done with a special type of brain scan that was designed to only measure flow signals, which couldn’t capture hemodynamic signals from blood vessels simultaneously. Lewis lab members realized that they could take advantage of what is traditionally viewed as an artifact in MRI scans, called the “inflow effect” or “time of flight” artifact. Instead of treating the information as an artifact, they could extract information from the signals to track the inflow of CSF at the bottom edges of their images, while simultaneously collecting traditional hemodynamic signals using the traditional blood oxygen level dependent (BOLD) contrast.

Lewis lab members then applied this imaging technique to a sleep study to measure how CSF moved through the brain. Volunteers arrived to the lab around midnight, had an electroencephalography cap placed on their heads to measure their neural activity, and then slept inside an MRI machine for a couple of hours. Lewis lab members then tested whether the CSF flow in the brain was locked to neural activity and blood vessel changes during sleep to better understand what was driving the CSF flow. Ultimately, they observed a really tight relationship between neural activity and waves of fluid flow. Waves of neural activity seemed to be driving pulses of CSF upwards into the brain. This was a really intriguing finding, as it suggests that neurons themselves might be controlling fluid flow. This 2019 work pioneered a new technique to capture flow signals in humans, provided a new way to track fluid flow in the brain non-invasively, and suggested that neurons were drivers of a neurovascular mechanism that regulates fluid flow in the human brain.

There were of course many interesting questions we wanted to ask next using this new methodology. The most basic question we were interested in answering first was whether it would be possible to drive waves of CSF flow during wakefulness, using the neurovascular coupling mechanism that Dr. Lewis identified. That’s the question we addressed in our new paper: can neurons drive CSF flow during wakefulness?

A typical day for this project involved meeting a research volunteer and scanning them inside of an MRI machine for 2 hours. Our volunteers played an intense flashing 4-minute game, repeating the game 12 to 16 times in a row and staying very still while we took images of their brain. We attached sensors to their hands to measure their pulse, and put a belt around their waist to measure their breath during the intense visual stimulation. We then downloaded the images from the MRI scanner, preprocessed them, manually traced each individual’s fourth ventricle, and extracted the CSF time series from those data. We were pleasantly surprised at how large the evoked CSF flow pulses were during wakefulness — although it didn’t match the scale of the flow that Dr. Lewis had observed during sleep, it was a significant amount of flow that was larger than what we observed when subjects were just resting quietly while awake.

We were curious next about whether the amount of neural activity could regulate the magnitude of CSF flow on a finer-grain scale. The neurovascular coupling that we observed during both sleep and wakefulness suggested that large, fast changes in neural activity — periods of intense activity followed by periods of low activity — should drive larger CSF inflow than tonic or constant levels of neural activity. To test whether larger changes in neural and hemodynamic signals could drive larger CSF waves, we changed the length of the intense periods of neural activity by making our intense visual stimulus longer. When we kept the intense flashing stimuli on the screen for longer periods of time, we observed larger pulses of CSF inflow than we did for shorter periods. This suggests that the magnitude of neural activity may tightly control the amount of fluid that circulates in the brain. The implication of this finding is that neurons may change activity patterns specifically to drive the circulation of fluids in the brain.                 

Looking forward, we are planning a new set of experiments in an older cohort to test how neurally driven flow might change across the lifespan. Previous imaging work in aging populations has shown that there are distinct neural and hemodynamic changes in older brains, like blood vessel stiffening. It’s possible that these age-related differences could lead to reduced inflow of CSF into large fluid filled cavities in the brain, which we would be able to detect as reduced neurally driven flow during wakefulness. We are interested in knowing whether driving neural activity and blood vessel dilations in older adults can also drive the same flow patterns that we observed in our young cohort, or whether there might be reduced or altered flow patterns. Unobstructed CSF circulation is critical for brain health, so identifying age-related changes in flow could provide insight about why brain health may deteriorate with brain age.

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