UNIVERSITY PARK, Pa. — To understand how different regions of the brain work together, researchers use a method called resting-state functional magnetic resonance imaging (rsfMRI). The method measures brain activity by observing changes in blood flow to different parts of the brain; however, rsfMRI does not explain how these blood flow changes to different brain regions relate to what is happening with the brain’s neurons — cells that send and receive messages in the form of electronic signals.
A team of researchers led by Nanyin Zhang, the Dorothy Foehr Huck and J. Lloyd Huck Chair in Brain Imaging and professor of biomedical engineering at Penn State, set out to answer this question. They recently published their findings, made in mice, in the journal eLife.
Penn State News sat down with Zhang, who is also affiliated with the electrical engineering and the engineering science and mechanics departments, as well as the Huck Institutes of the Life Sciences, to learn more about his findings.
Q: How does rsfMRI work? What can it tell researchers, and what are its limitations?
Zhang: Resting-state functional magnetic resonance imaging (rsfMRI) allows scientists to study how different parts of the brain work together. This method shows us when different parts of the brain are active together by looking at their spontaneous changes in blood flow. We call these coordinated patterns “resting-state brain networks” (RSNs). Even though we use these RSNs in many situations, we still don't fully understand how these blood flow changes are related to what's happening to neural activities in the brain. Lack of this knowledge highlights a significant gap in our understanding of functional brain networks.
Q: How did you address the limitations of rsfMRI?
Zhang: We aimed to address the question of how RSNs and rsfMRI relate to spontaneous neural activity. We used a technique that involves simultaneous recordings of rsfMRI and electrophysiology signals, which provides the measurement of neural activity and the rsfMRI signal from the same brain site at the same time. By measuring these two signals at the same time, we hoped to elucidate exactly how spontaneous blood flow changes in the brain are related to neural activities.
Q: What were your findings? What does this new understanding about “invisible” signaling tell us?
Zhang: We found a disparity in the spatial and temporal relationships between the electrophysiology signal, which directly measures neural activity, and the rsfMRI signal. The brain-wide RSN connectivity spatial patterns revealed by the rsfMRI signal can be recapitulated by the electrophysiology signal. However, these two types of signals do not align well over time.
These seemingly paradoxical findings imply that there are electrophysiology “invisible signals” contributing to the rsfMRI signal, which expands the conventional viewpoint on the relationship of neural activity and rsfMRI signal. The conventional view believes the electrophysiology signal underlies most of the rsfMRI signal, whereas our results suggest that a major source of the rsfMRI signal might actually originate from an electrophysiology-invisible component.
Q: What are the implications of these findings for studying brains and brain imaging?
Zhang: It is generally believed that the rsfMRI signal can be explained using the electrophysiology signal. The possibility that RSNs may arise from electrophysiology-invisible brain activities that play a significant role in regulating rsfMRI signals suggests our understanding of the neural basis of the rsfMRI signal, and hence the interpretation of RSNs, might not be adequate. Therefore, it is important to continue to investigate the neural basis of the rsfMRI signal to make sure we are accurately interpreting brain activity.
Q: How does rsfMRI in animals inform research in humans?
Zhang: The neural mechanism of rsfMRI in animals is very likely the same as that in humans. The findings in the current study might have high translational value to human rsfMRI studies.
The National Institute of Neurological Disorders and Stroke and the National Institute of Mental Health funded this work.