Research

The Neuroinformatics and Brain Connectivity (NBC) Laboratory focuses on characterizing activity within and between brain regions among healthy individuals and those diagnosed with neuropsychiatric disorders. With emphasis on development of data analysis algorithms, neuroscience informatics tools, and neuroimaging ontologies, the Lab’s mission is to enhance understanding of (dys)functional brain networks in health and disease. Improved understanding of such brain networks may impact educational trajectories for students (e.g. Exploring the Neural Mechanisms of Physics Learning), as well as treatment outcomes for patients (e.g. Impact of HIV and Cannabis on Brain Function). Through our various projects, the Lab aims to address real-world issues. If you would like to know more about specific projects, you are welcome to visit us on GitHub and the Open Science Framework.

Exploring the Neural Mechanisms of Physics Learning

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Funded by the National Science Foundation, this project aims to gather and assess evidence of learning and knowledge organization, as measured by functional magnetic resonance imaging (fMRI), throughout university-level physics environments. This project was designed to extend the theories and research-base behind Modeling Instruction, a well-established curriculum intervention in physics. With the use of neuroimaging, we can understand how learning-environments drive the functional organization of large scale brain networks in physics students. Our work provides deep insight into the ways in which students learn physics, with implications for other STEM disciplines.

 

Impact of HIV and Cannabis on Brain Function

Funded by the National Institute on Drug Abuse of the NIH, this project utilizes fMRI to clarify how HIV infection and cannabis use (both alone and in combination) impact brain function. Lagging behind rapid changes to state laws, societal views, and medical practice is scientific investigation of cannabis’s impact on brain function, especially in patients with HIV/AIDS. We are addressing this knowledge gap by using advanced fMRI techniques to rigorously assess brain activity at the regional, network, and global levels in a sample of adults stratified by HIV-serostatus and cannabis use. Clarifying the impact of HIV infection and cannabis use on the brain is critically important for developing treatments to improve patients’ mental functions, identifying poor candidates for medical marijuana, and providing patients, healthcare providers, and policymakers with scientific information allowing for informed decision-making regarding cannabis use.

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Investigation and Functional Characterization of Intrinsic Connectivity Networks

Independent component analysis (ICA) can be applied to resting-state fMRI data to identify functionally connected intrinsic connectivity networks (ICNs). Smith et al. (2009) demonstrated that similar ICNs could be extracted by applying ICA to data archived in the BrainMap database, suggesting that such networks represent fundamental components of the brain’s functional architecture. Laird et al. (2011) demonstrated that the BrainMap co-activation networks could be classified into 4 groups relevant to their associated mental processes: [1] emotional and interoceptive processes, [2] motor and visuospatial integration, coordination, and execution, [3] visual perception, and [4] higher cognitive processes. Our current projects focus on examining the functional organization and significance of these ICNs, with particular focus on their hierarchical modularity across model orders and how results can be integrated with knowledge gained from graph theory techniques.

Functional Parcellation Using Meta-Analytic Connectivity Modeling

Traditional functional neuroimaging meta-analyses focus on assessing convergence for a set of studies that examine similar paradigms or cognitive processes of interest. Meta-analytic connectivity modeling (MACM) refers to an application of the activation likelihood estimation (ALE) meta-analysis method that examines task-dependent functional connectivity for a user-defined region of interest. In MACM, BrainMap is searched for all experiments that feature at least one focus of activation within the seed region, and ALE is performed over all foci of the retrieved experiments to quantify their convergence. Significant convergence outside the seed indicates the above-chance recruitment of additional areas whenever the seed was active, i.e., significant co-activation. A recent extension of the MACM approach is its application to connectivity-based parcellation (CBP) as an approach to identify functionally homogenous sub-clusters of voxels within a seed region. MACM is performed individually for all voxels in the seed, and the resultant voxel-wise connectivity patterns are clustered to identify groups of voxels demonstrating similar co-activation connectivity. Voxels are clustered into distinct sub-regions of the original seed based on this information.

Development of a Semantic Framework and Ontology for Cognitive Paradigms

Data sharing efforts within the human neuroimaging community are rapidly growing, driven equally by NIH and NSF policies and a new generation of scientists who are committed to sharing to enable large-scale knowledge discovery in the brain. These data sharing efforts have highlighted the need for standardized terminologies within neuroimaging data descriptions. Standardized terminologies can serve as a common vocabulary for data sharing, but once the effort is begun to define the terms clearly, the next step is defining how the concepts those terms represent are related. The ability to describe the cognitive paradigms used during the behavioral portions of a neuroimaging study is critical for sharing data and integrating information across experiments. Cognitive paradigms are not standardized; they are infinitely flexible, and can vary by choice of stimuli, timing, the instructions given to the subject, and the responses the subject is expected to make. Current work is being carried out to develop the Cognitive Paradigm Ontology (CogPO), which is designed to promote automated annotation and reasoning across disparate data sources.