Just as a city cannot be understood by studying individual buildings without considering the roads connecting them, the brain cannot be fully comprehended by examining individual neurons without studying how they work together as a network.
This is how Rongsong Liu, a professor in the University of Wyoming Department of Mathematics and Statistics and the Department of Zoology and Physiology, views the work she and other researchers conducted using graph theory to better understand autism spectral disorder (ASD) in mice. Graph theory is a mathematical approach for studying networks.
The study found an altered prefrontal neuronal network in Shank3 mice, an autism mouse model. Compared to wild-type mice, Shank3 mice display reduced neural activity, and a less-integrated and a more rigid prefrontal network alongside impaired social behavior.
“This article represents our collaborative work and provides a proof-of-concept for employing graph theory-based metrics to quantitatively differentiate between normal and aberrant prefrontal circuitry,” Liu says. “We believe these findings may be especially interesting to a general population because they demonstrate how mathematical analysis can help scientists understand changes in the microcircuits of the brain.”
Liu was a co-lead author and a co-corresponding author of a paper titled “Graph theory identifies altered prefrontal microcircuit organization in Shank3 mice, a mouse model of autism” that was published Aug. 25 in Cell Reports, which publishes high-quality papers across the entire life sciences spectrum. The primary criterion for publication in the journal is new biological insight.
Liu conducted data analysis on calcium-imaging dataset and applied graph-theory for network analyses. Yan Zhang, a postdoctoral fellow in the National Institute on Drug Abuse Intramural Research Program at the National Institutes of Health (NIDA/NIH), was the paper’s other co-first author.
Yun Li, an associate professor of neuroscience in the UW Department of Zoology and Physiology, was the paper’s senior author and one of three co-corresponding authors. Li led the project from its conception, as well as designed and supervised the mouse behavior and calcium-imaging study. She also played a central role in bringing together the experimental and analytical components of the research.
Mallory Lai, Liu’s postdoctoral researcher, co-wrote the paper and designed and performed all of the machine-learning analyses. Lai showed that neural activity and network measurements could help predict the genotype and social behavior differences in mice.
Additionally, Viyaleta Davydzenka, a UW undergraduate student, and Nathaniel England, a UW master’s student—both since graduated—in Li’s lab performed analysis on the mice behavioral dataset.
Other contributors to the paper were from NIDA/NIH, the University of Maryland School of Medicine, and John Hopkins University School of Medicine.
ASD is a complex neurodevelopmental condition that occurs in all racial and ethnic groups. ASD occurs four times more commonly in boys than girls, according to the paper. People with ASD exhibit differences in social behavior, difficulty in communication, and repetitive behaviors.
Twenty to 30 percent of people with ASD carry genetic variants, such as gene mutations on Shank3. Shank3 is an important synaptic scaffolding protein to help organize connections—called synapses—between neurons, Li says. Shank3 mice are homozygous knockout mice displaying autistic-like behaviors, including impaired sociability.
Homozygous knockout mice are genetically altered laboratory mice that have both copies, or alleles, of a specific gene inactivated or removed in all of their cells.
Researchers used graph theory to identify the altered network pattern in Shank3 mice. Each active neuron is considered as a node, and the activity correlations between two active neurons are treated as connections between nodes.
“We applied this approach to neural calcium-imaging recordings obtained from the prefrontal cortex of freely moving wild-type and Shank3 mice during social-behavior tests,” Liu says. “This allowed us to measure and compare how neurons were organized and how their network changed between nonsocial and social situations in wild-type and Shank3 mice.”
During the study, which began in 2019, researchers conducted social behavior tests in mice to evaluate sociability and preference for social novelty across three 10-minutes stages: habituation, sociability, and social novelty stages.
During habituation, the mouse explores two empty containers, Li says. In the sociability stage, a novel target mouse is enclosed in one container while the other container remains empty. In the social novelty stage, a second novel target mouse is introduced into the previously empty container alongside the now-familiar mouse.
“Wild-type mice typically spend equal time exploring both empty containers during habituation but spend significantly more time investigating the social target than the empty container during the sociability stage, demonstrating a normal sociability,” Li says. “Furthermore, wild-type mice prefer exploring the novel social target over the familiar one during the social novelty stage. In sharp contrast, Shank3 knockout mice display no preference for social targets in the social behavior test, indicating deficits in both sociability and social novelty preference.”
Preference for social novelty is defined by whether a mouse spends more time investigating the new mouse compared to the familiar one, Li explains.
The paper also illustrates the value of collaboration between applied mathematics and empirical neuroscience, Liu says.
“Experimental imaging provides real-time measurements of neural activity changes, while graph theory and machine learning provide quantifiable tools to identify and compare patterns within those complex data,” she says. “It may contribute to a better understanding of how changes in brain-network organization are relevant to neurological and neurodevelopmental conditions.
The research was funded through several NIH and NIDA grants.
“This study does not directly provide a treatment or cure to autism in humans. However, it helps us understand how autism-linked gene mutations, such as Shank3, may alter brain-network organization and social-information processing,” Liu says. “This approach could eventually contribute to identifying new biomarkers and inspiring future circuit-level interventions.”
This story was originally published on UW News.
