New research from the University of Buffalo, using computational models of individual people’s connectomes, shed light into individual differences in brain activation patterns, as well as how those patterns may change over time. Since 2009, scientists around the globe have worked to create the Human Connectome, a structural blueprint of the various neural pathways and connections that underlie thought, reason, emotion, and behavior in the brain. Thanks to those pioneering efforts, we now understand that different regions of the brain work together in concert, forming specific networks that facilitate movement, or learning, or our interactions with others—the cognitive skills that allow us to survive and thrive in our daily lives. Yet despite these advances, it’s still not entirely clear how these networks may differ from person to person. Sarah Muldoon, a mathematician at the University of Buffalo, has long been interested in understanding individual differences in the brain.
Computers and chimera states
While human beings share similar brain networks that underlie skills like auditory functioning or how we pay attention to the world around us, brain scans show that these networks can be activated in different ways in different people. Muldoon and former post-doctoral fellow Kanika Bansal wondered if chimera states, a concept used in modern physics, might offer some insights into how individual connectomes show different patterns of activation. Physicists were intrigued to find that when they coupled several oscillators (devices that produce energy waves) together in the exact same fashion, they did not always produce the same kind of consistent, synchronized activity as expected."
Muldoon said that this kind of modeling might help better distinguish true signal from noise in neuroimaging data as scientists study individual differences, and in it may also help sharpen personalized medicine strategies as well.
“We are already doing brain stimulation treatments and neuromodulation for disorders like depression and Parkinson’s disease,” she said. “As we continue to develop these kinds of treatment strategies, it’s going to be really important to understand where there may be a lot of individual variability in activation patterns between people, so you can make sure you are stimulating the right areas.”
That said, Muldoon argued that the most important takeaway from this study is that there is a lot of diversity in patterns of brain activity and it’s important that scientists are able to develop methods and models to better quantify and understand such differences.
“Using this kind of framework, we can start to ask, ‘What regions are involved in this particular function? How might the activation pattern differ if I stimulate here versus there?’” she said. “That is the kind of information that can tell us something about how each of these networks interacts with the brain as a whole, as well as how their activity may differ from person to person, or even how it may differ in one person over time. We can really start to look at the different factors that contribute to these different connectivity patterns and understand how they work together to give rise to a particular ability in the brain.”
PhD in Physics, University of Michigan, Physics (2009)
Dr. Muldoon's group develops novel techniques and measures to investigate and quantify the role of network organization in brain function. This work is grounded in network theory, a field that draws upon tools from mathematics, physics, engineering, and computer science to understand, predict, and describe complex interactions in systems of connected elements. We use network analysis to understand the relationship between the underlying structural connections in the brain, observed brain signals, and functional interactions between neurons/brain regions. Additionally, we develop techniques to investigate how the spatial location of network elements relates to their role in overall network function and how this differs between healthy and pathological settings, with a specific interest in epilepsy research.
S.F. Muldoon, V. Villette, T. Tressard, A. Malvache, and R. Cossart, GABAergic inhibition shapes interictal dynamics in awake epileptic mice, Brain (2015)
A.J. Trevelyan, S.F. Muldoon, E.M. Merricks, C. Racca, K. Staley, The role of inhibition in epileptic networks, J Clin Neurophysiol 32 227-34 (2015)
U. Braun, S.F. Muldoon, and D.S. Bassett, On human brain networks in health and disease, eLS 1-9 (2015)
S. Feldt Muldoon, I. Soltesz, and R. Cossart, Spatially clustered neuronal assemblies comprise the microstructure of synchrony in chronically epileptic networks, PNAS 110 3567-3572 (2013)
S. Feldt, P. Bonifazi, and R. Cossart, Dissecting functional connectivity of cortical microcircuits: experimental and theoretical insights TINS 34 225-236 (2011)
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S. Feldt, J. Waddell, V.L. Hetrick, J.D. Berke and M. Zochowski, Functional clustering algorithm for the analysis of dynamic network data, Phys. Rev. E. 79, 056104 (2009)
S. Feldt, H. Osterhage, F. Mormann, K. Lehnertz, and M. Zochowski, Inter-network and intra-network communications during bursting dynamics: applications to seizure prediction, Phys. Rev. E. 76, 021920 (2007)