Carnegie Mellon University has funded eight new neuroscience projects through its ProSEED grant program. The projects, which are part of CMU's BrainHub initiative, propose innovative solutions to answer some of the most pressing questions in brain science and represent the university's strengths in biology, computer science, psychology, statistics and engineering. These areas are the foundation for the global BrainHub initiative, which focuses on how the structure and activity of the brain give rise to complex behaviors.

ProSEED, a program initiated by Carnegie Mellon President Subra Suresh, provides startup funding for innovative, cross-disciplinary projects that span a number of disciplines. The ProSEED/BrainHub seed grants were created to help researchers develop novel approaches to the study of brain and behavior, and foster new collaborations within CMU and with BrainHub partner institutions. The ProSEED/BrainHub grants, which total $400,000, will allow researchers to complete the fundamental research needed to apply for further funding from governmental and other sources.

"Most revolutionary ideas in science are initially viewed as 'high risk.' As a university, we need to provide our researchers with an environment in which they are empowered to explore new ideas. By doing so, we increase the impact of our faculty and their work," said Interim Provost Nathan Urban, BrainHub director and the Dr. Frederick A Schwertz Distinguished Professor of Life Sciences.

The newly funded projects include creating new methods for discovering biomarkers for brain activity, investigating new ways to understand the mechanisms behind neural functioning, and researching the connection between the biome and the brain.

The projects are:

Measuring Brain Changes During Stress Management Training
It is well established that mindfulness training has many health benefits, including alleviating stress. To determine exactly how the brain changes during behavioral stress management training, Gustavo K. Rohde, associate professor of biomedical engineering, and J. David Creswell, associate professor of psychology, will develop and test a new computational method that will adapt Rohde's image analysis framework to effectively analyze three-dimensional structural magnetic resonance images of the brain. The new technology will be used in Creswell's mindfulness meditation stress management training studies to measure the training's effects on the brain's gray matter architecture.

Computational and Biological Approaches To Delineate the Neural Circuitry of Learning
As we learn, activity and connections between our neurons change. Understanding the cellular and molecular basis of these changes is essential to understanding how the neurons in the brain work together to perform a new task or acquire a new skill. Steve Chase, assistant professor of biomedical engineering and member of the Center for the Neural Basis of Cognition (CNBC), and Aryn Gittis and Sandra Kuhlman, assistant professors of biological sciences and CNBC members, will train mice to operate a brain computer interface, creating a model system for studying learning at the neuronal level.

Statistical Methods To Identify Early Biomarkers of Brain Dysfunction in Parkinson's Disease
While many advances have been made in identifying the cellular mechanisms that cause brain dysfunction in neurological diseases, little is known about how these mechanisms develop over time, especially in their early stages. Being able to identify what changes in the brain precede symptoms of disease can lead to early diagnosis and treatment. Gittis and Associate Professor of Statistics Valerie Ventura will study the activity of neuronal networks in a mouse model of Parkinson's disease to identify changes that could be used as early biomarkers of the disease.

Inferotemporal Cortex as an Adjustable Filter
Neurons in the inferotemporal cortex, the area of the brain responsible for recognizing objects, respond differently to novel and familiar images. CNBC Professor Carl Olson and Sripati Arun, assistant professor at the Centre for Neuroscience at the Indian Institute of Science, will see if this difference in neuron function helps the brain to pick out familiar images from a cluttered background.

Understanding the Topology of Neural Networks: An Information Processing Approach
In the early stages of development, synapses are generated at a rapid pace. As the brain ages, synapses are pruned and silenced, creating a more efficient and stable network of neuronal circuitry. Associate Professor of Computational Biology Ziv Bar-Joseph and Professor of Biological Sciences Alison Barth will use an experimental and computational approach to better understand and model how neuronal pruning leads to improved brain function and facilitates learning. Insights from these models will also be used to improve the design of computational communication networks.

"Mind Altering Bugs": Identifying Bacterial-Dependent Gene Expression Changes in the Drosophila Brain
The microbiome — the community of micro-organisms that live in and on our bodies — has been found to influence learning, memory, anxiety, depression and autism-associated behaviors, but the mechanisms by which this happens are poorly understood. Associate Professor of Biological Sciences Brooke M. McCartney, Assistant Professor of Biological Sciences N. Luisa Hiller, Associate Professor of Computational Biology Carleton Kingsford and Professor of Biological Sciences Aaron Mitchell will study how the fruit fly's microbiome impacts gene expression in the brain and, as a result, complex behaviors.

ConnPort: Creating a Standardized Interface To Access Human Connectome Data
Voluntary behaviors result from billions of neurons in the brain working together through trillions of synaptic connections across thousands of functionally distinct areas. While most connectome analysis methods focus on the architecture of these areas, Timothy Verstynen, assistant professor of psychology and member of the CNBC, and Barnabas Poczos and Aarti Singh, both assistant professors of machine learning, will work toward creating a complete neural wiring blueprint for the entire human brain. To this point, establishing a blueprint for the whole brain has not been possible because of problems obtaining standard datasets from current neuroimaging technologies. Neuroscientists acquire large datasets with particular scientific goals in mind, and machine learning theorists develop tools to analyze those specific sets of data. Verstynen, Poczos and Singh will work with a research programmer to create ConnPort, optimized, publicly available datasets that are converted into formats accessible to machine leaning and statistics students to explore computational problems of brain connectivity.

Interpreting the Outputs of Dimensionality Reduction Using Spikin Network Models
A major question in systems neuroscience is how to analyze the activity of many neurons recorded together by large-scale neural recording technologies, such as multi-electrode arrays and optical imaging. One approach, which has yielded tantalizing results in a handful of recent high-profile studies, is dimensionality reduction, which identifies patterns of activity co-fluctuation across the neurons. To interpret the outputs of dimensionality reduction, it is essential to understand how they depend on the underlying network structure (e.g., uniform vs. clustered connectivity), sampling of the network (e.g., excitatory vs. inhibitory neurons), and number of neurons and trials sampled. Current recording technologies do not typically provide information about neural connectivity, and neuron and trial counts are limited. CMU's Byron Yu, assistant professor of electrical and computer engineering and biomedical engineering, will work with the University of Pittsburgh's Brent Doiron, associate professor of mathematics, and Matthew Smith, assistant professor of ophthalmology, to study these questions in spiking network models, where the underlying network structure is known and a large number of neurons and trials can be sampled. Closely guided by applying the same methods to neural activity recorded in the brain, this work seeks to lay a foundation for understanding how many neurons work together to give rise to brain function.

As the birthplace of artificial intelligence and cognitive psychology, Carnegie Mellon has been a leader in the study of brain and behavior for more than 50 years. The university has created some of the first cognitive tutors, helped to develop the Jeopardy-winning Watson, founded a groundbreaking doctoral program in neural computation, and completed cutting-edge work in understanding the genetics of autism. Building on its strengths in biology, computer science, psychology, statistics and engineering, CMU recently launched BrainHub, a global initiative that focuses on how the structure and activity of the brain give rise to complex behaviors.