Liang Zhan, assistant professor of electrical and computer engineering at Pitt's Swanson School of Engineering, received a $500,000 CAREER award from the National Science Foundation to develop computational tools that improve our understanding of the human brain.
In this project, he will leverage brain modular structure to study brain imaging genetics and develop new computational tools to illuminate how genetic factors impact brain structure and function. Researchers can use this technology to examine how specific genes, or their variants, affect neural systems and contribute to brain disorders. This work could ultimately advance the fields of biomedical informatics, neuroscience, and data science.
Zhan's team will specifically study Alzheimer's disease - a condition that currently affects 5.8 million Americans and is projected to nearly triple to 14 million people by 2060.
"There is no clear evidence to show how Alzheimer's disease develops," said Zhan. "Researchers are developing a variety of methods to uncover the mechanisms behind Alzheimer's onset and progression, but there is a lack of effective computational tools to study this disease."
Though this work focuses on Alzheimer's disease, the proposed tools can be applied to other brain research as well.
"Current brain imaging genetics studies assume a one-to-one linear relationship between genes and imaging features, but linearity is too simplistic and does not allow researchers to identify high-level patterns," explained Zhan. "Additionally, MRI research often focuses on small regions of the brain, which reduces the complexity of the imaging down to one-dimension and discards important information on brain dynamics. Instead, my group will focus on characterizing higher-level brain network features."
Connecting the Dots with the Human Connectome
In collaboration with the University of Illinois at Chicago (UIC), he will couple this CAREER award with two R01 grants from the National Institutes of Health to further investigate brain function in neurological disorders.
Maintaining essential brain function, such as learning and memory, requires synapses to pass electrical and chemical signals between neurons. Synaptic dysfunction is a hallmark of many neurological disorders - including Alzheimer's disease - and leads to hyperexcitation in neuronal circuits. However, neural network changes related to normal aging make it difficult for researchers to distinguish disease-specific alterations from normal changes.
Zhan and collaborators will develop innovative computational tools to characterize hyperexcitation patterns in aging and Alzheimer's Disease and validate their framework with longitudinal mouse models and human data from the Alzheimer's Disease Neuroimaging Initiative and the Human Connectome Project.
"The brain needs to have a balance between neural excitation and inhibition," said Zhan. "The synaptic dysfunction in Alzheimer's disease leads to hyperexcitation in neuronal circuits, and this abnormal balance may contribute to disease onset and progression. The hyperexcitation indicator (HI), defined using multimodal MRI data, will signal an imbalance between neural excitation and inhibition."
Adding to the complexity of this research, other psychiatric conditions may be significant contributors to accelerated cognitive decline and progression to dementia. Zhan will collaborate on another R01 at UIC to examine late-life depression and uncover its impact on neurodegeneration. They will apply a similar approach to this study and clarify the relationship between depression and neurodegenerative processes in late life.
A preliminary study demonstrated the effectiveness of the group's hyperexcitation.
"We matched cognitively normal individuals with a genetic predisposition to Alzheimer's disease with a group of individuals without a genetic predisposition, based on age and sex," said Zhan. "The results supported the idea that genetically predisposed women, who are four-times more likely to develop Alzheimer's disease than men, exhibited hyperexcitation at age 50, and our method was more sensitive at of detecting this difference."
The goal of this work is to accelerate the discovery of more robust, non-invasive imaging biomarkers of Alzheimer's disease and other neurological disorders.